diff --git a/BLcourse4/bayesian_neural_networks_wine.ipynb b/BLcourse4/bayesian_neural_networks_wine.ipynb
index 47b78eac5434d3613788ffe9088a3ad7202b141e..25119f1faf14731fc2517611613acc0757803d2b 100644
--- a/BLcourse4/bayesian_neural_networks_wine.ipynb
+++ b/BLcourse4/bayesian_neural_networks_wine.ipynb
@@ -109,7 +109,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 2,
"metadata": {
"id": "47HHMmwvJZX6"
},
@@ -145,7 +145,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {
"id": "cz1h4N0jJZX6"
},
@@ -164,7 +164,7 @@
" )\n",
"\n",
" print(\"Start training the model...\")\n",
- " model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)\n",
+ " model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset,verbose=0)\n",
" print(\"Model training finished.\")\n",
" _, rmse = model.evaluate(train_dataset, verbose=0)\n",
" print(f\"Train RMSE: {round(rmse, 3)}\")\n",
@@ -185,7 +185,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 4,
"metadata": {
"id": "4gjSUoRRJZX7"
},
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 5,
"metadata": {
"id": "NmMnI_LzJZX7"
},
@@ -263,11 +263,20 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 6,
"metadata": {
"id": "j71HZHqEJZX7"
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:absl:You use TensorFlow DType <dtype: 'float32'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to float32.\n",
+ "WARNING:absl:You use TensorFlow DType <dtype: 'float64'> in tfds.features This will soon be deprecated in favor of NumPy DTypes. In the meantime it was converted to float64.\n",
+ "2023-03-24 10:23:39.453411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14650 MB memory: -> device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:01:00.0, compute capability: 7.0\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -281,7 +290,6 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2023-03-24 00:48:26.870482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14628 MB memory: -> device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:01:00.0, compute capability: 7.0\n",
"WARNING:tensorflow:From /p/software/jusuf/stages/2023/software/TensorFlow/2.11.0-foss-2022a-CUDA-11.7/lib/python3.10/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
"Instructions for updating:\n",
"Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
@@ -307,7 +315,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 7,
"metadata": {
"id": "pAMJafENJZX8"
},
@@ -316,229 +324,29 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Start training the model...\n",
- "Epoch 1/100\n"
+ "Start training the model...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2023-03-24 00:48:44.574518: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n",
- "2023-03-24 00:48:44.576634: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n",
- "2023-03-24 00:48:45.665693: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x1d48f450 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
- "2023-03-24 00:48:45.665736: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Tesla V100-PCIE-16GB, Compute Capability 7.0\n",
- "2023-03-24 00:48:45.672561: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
- "2023-03-24 00:48:45.864279: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ "2023-03-24 10:23:41.483290: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n",
+ "2023-03-24 10:23:41.485760: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n",
+ "2023-03-24 10:23:45.555278: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x1cd1d830 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2023-03-24 10:23:45.555318: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Tesla V100-PCIE-16GB, Compute Capability 7.0\n",
+ "2023-03-24 10:23:45.562298: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2023-03-24 10:23:45.816637: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "17/17 [==============================] - 3s 29ms/step - loss: 28.1717 - root_mean_squared_error: 5.3077 - val_loss: 30.2676 - val_root_mean_squared_error: 5.5016\n",
- "Epoch 2/100\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 26.5429 - root_mean_squared_error: 5.1520 - val_loss: 28.8875 - val_root_mean_squared_error: 5.3747\n",
- "Epoch 3/100\n",
- "17/17 [==============================] - 0s 8ms/step - loss: 25.1419 - root_mean_squared_error: 5.0142 - val_loss: 27.3569 - val_root_mean_squared_error: 5.2304\n",
- "Epoch 4/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 23.7807 - root_mean_squared_error: 4.8765 - val_loss: 25.7095 - val_root_mean_squared_error: 5.0705\n",
- "Epoch 5/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 22.4434 - root_mean_squared_error: 4.7374 - val_loss: 23.9920 - val_root_mean_squared_error: 4.8982\n",
- "Epoch 6/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 21.1341 - root_mean_squared_error: 4.5972 - val_loss: 22.2725 - val_root_mean_squared_error: 4.7194\n",
- "Epoch 7/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 19.8476 - root_mean_squared_error: 4.4551 - val_loss: 20.5857 - val_root_mean_squared_error: 4.5371\n",
- "Epoch 8/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 18.5882 - root_mean_squared_error: 4.3114 - val_loss: 18.9616 - val_root_mean_squared_error: 4.3545\n",
- "Epoch 9/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 17.3534 - root_mean_squared_error: 4.1657 - val_loss: 17.4257 - val_root_mean_squared_error: 4.1744\n",
- "Epoch 10/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 16.1507 - root_mean_squared_error: 4.0188 - val_loss: 15.9671 - val_root_mean_squared_error: 3.9959\n",
- "Epoch 11/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 14.9810 - root_mean_squared_error: 3.8705 - val_loss: 14.6045 - val_root_mean_squared_error: 3.8216\n",
- "Epoch 12/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 13.8507 - root_mean_squared_error: 3.7217 - val_loss: 13.3276 - val_root_mean_squared_error: 3.6507\n",
- "Epoch 13/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 12.7603 - root_mean_squared_error: 3.5722 - val_loss: 12.1438 - val_root_mean_squared_error: 3.4848\n",
- "Epoch 14/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 11.7146 - root_mean_squared_error: 3.4227 - val_loss: 11.0377 - val_root_mean_squared_error: 3.3223\n",
- "Epoch 15/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 10.7145 - root_mean_squared_error: 3.2733 - val_loss: 10.0098 - val_root_mean_squared_error: 3.1638\n",
- "Epoch 16/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 9.7619 - root_mean_squared_error: 3.1244 - val_loss: 9.0556 - val_root_mean_squared_error: 3.0092\n",
- "Epoch 17/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 8.8589 - root_mean_squared_error: 2.9764 - val_loss: 8.1651 - val_root_mean_squared_error: 2.8575\n",
- "Epoch 18/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 8.0045 - root_mean_squared_error: 2.8292 - val_loss: 7.3380 - val_root_mean_squared_error: 2.7089\n",
- "Epoch 19/100\n",
- "17/17 [==============================] - 0s 8ms/step - loss: 7.1984 - root_mean_squared_error: 2.6830 - val_loss: 6.5688 - val_root_mean_squared_error: 2.5630\n",
- "Epoch 20/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 6.4430 - root_mean_squared_error: 2.5383 - val_loss: 5.8499 - val_root_mean_squared_error: 2.4186\n",
- "Epoch 21/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 5.7350 - root_mean_squared_error: 2.3948 - val_loss: 5.1847 - val_root_mean_squared_error: 2.2770\n",
- "Epoch 22/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 5.0756 - root_mean_squared_error: 2.2529 - val_loss: 4.5714 - val_root_mean_squared_error: 2.1381\n",
- "Epoch 23/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 4.4679 - root_mean_squared_error: 2.1137 - val_loss: 4.0112 - val_root_mean_squared_error: 2.0028\n",
- "Epoch 24/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 3.9115 - root_mean_squared_error: 1.9778 - val_loss: 3.4982 - val_root_mean_squared_error: 1.8704\n",
- "Epoch 25/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 3.4032 - root_mean_squared_error: 1.8448 - val_loss: 3.0336 - val_root_mean_squared_error: 1.7417\n",
- "Epoch 26/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 2.9429 - root_mean_squared_error: 1.7155 - val_loss: 2.6135 - val_root_mean_squared_error: 1.6166\n",
- "Epoch 27/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 2.5300 - root_mean_squared_error: 1.5906 - val_loss: 2.2460 - val_root_mean_squared_error: 1.4987\n",
- "Epoch 28/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 2.1674 - root_mean_squared_error: 1.4722 - val_loss: 1.9191 - val_root_mean_squared_error: 1.3853\n",
- "Epoch 29/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.8475 - root_mean_squared_error: 1.3592 - val_loss: 1.6381 - val_root_mean_squared_error: 1.2799\n",
- "Epoch 30/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.5732 - root_mean_squared_error: 1.2543 - val_loss: 1.3985 - val_root_mean_squared_error: 1.1826\n",
- "Epoch 31/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.3427 - root_mean_squared_error: 1.1588 - val_loss: 1.2057 - val_root_mean_squared_error: 1.0981\n",
- "Epoch 32/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.1577 - root_mean_squared_error: 1.0760 - val_loss: 1.0539 - val_root_mean_squared_error: 1.0266\n",
- "Epoch 33/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.0113 - root_mean_squared_error: 1.0057 - val_loss: 0.9381 - val_root_mean_squared_error: 0.9685\n",
- "Epoch 34/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.9044 - root_mean_squared_error: 0.9510 - val_loss: 0.8645 - val_root_mean_squared_error: 0.9298\n",
- "Epoch 35/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.8344 - root_mean_squared_error: 0.9134 - val_loss: 0.8181 - val_root_mean_squared_error: 0.9045\n",
- "Epoch 36/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7923 - root_mean_squared_error: 0.8901 - val_loss: 0.7994 - val_root_mean_squared_error: 0.8941\n",
- "Epoch 37/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7762 - root_mean_squared_error: 0.8810 - val_loss: 0.7969 - val_root_mean_squared_error: 0.8927\n",
- "Epoch 38/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7717 - root_mean_squared_error: 0.8784 - val_loss: 0.7953 - val_root_mean_squared_error: 0.8918\n",
- "Epoch 39/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7702 - root_mean_squared_error: 0.8776 - val_loss: 0.7948 - val_root_mean_squared_error: 0.8915\n",
- "Epoch 40/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7679 - root_mean_squared_error: 0.8763 - val_loss: 0.7910 - val_root_mean_squared_error: 0.8894\n",
- "Epoch 41/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7649 - root_mean_squared_error: 0.8746 - val_loss: 0.7904 - val_root_mean_squared_error: 0.8890\n",
- "Epoch 42/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7621 - root_mean_squared_error: 0.8730 - val_loss: 0.7867 - val_root_mean_squared_error: 0.8870\n",
- "Epoch 43/100\n",
- "17/17 [==============================] - 0s 8ms/step - loss: 0.7593 - root_mean_squared_error: 0.8714 - val_loss: 0.7836 - val_root_mean_squared_error: 0.8852\n",
- "Epoch 44/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7553 - root_mean_squared_error: 0.8691 - val_loss: 0.7801 - val_root_mean_squared_error: 0.8832\n",
- "Epoch 45/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7516 - root_mean_squared_error: 0.8669 - val_loss: 0.7784 - val_root_mean_squared_error: 0.8822\n",
- "Epoch 46/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7488 - root_mean_squared_error: 0.8653 - val_loss: 0.7707 - val_root_mean_squared_error: 0.8779\n",
- "Epoch 47/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7445 - root_mean_squared_error: 0.8628 - val_loss: 0.7671 - val_root_mean_squared_error: 0.8758\n",
- "Epoch 48/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7420 - root_mean_squared_error: 0.8614 - val_loss: 0.7647 - val_root_mean_squared_error: 0.8745\n",
- "Epoch 49/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7396 - root_mean_squared_error: 0.8600 - val_loss: 0.7624 - val_root_mean_squared_error: 0.8732\n",
- "Epoch 50/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7374 - root_mean_squared_error: 0.8587 - val_loss: 0.7605 - val_root_mean_squared_error: 0.8721\n",
- "Epoch 51/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7349 - root_mean_squared_error: 0.8572 - val_loss: 0.7573 - val_root_mean_squared_error: 0.8702\n",
- "Epoch 52/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7331 - root_mean_squared_error: 0.8562 - val_loss: 0.7552 - val_root_mean_squared_error: 0.8690\n",
- "Epoch 53/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7315 - root_mean_squared_error: 0.8553 - val_loss: 0.7511 - val_root_mean_squared_error: 0.8667\n",
- "Epoch 54/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7291 - root_mean_squared_error: 0.8539 - val_loss: 0.7499 - val_root_mean_squared_error: 0.8660\n",
- "Epoch 55/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7284 - root_mean_squared_error: 0.8535 - val_loss: 0.7483 - val_root_mean_squared_error: 0.8651\n",
- "Epoch 56/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7268 - root_mean_squared_error: 0.8525 - val_loss: 0.7456 - val_root_mean_squared_error: 0.8635\n",
- "Epoch 57/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7241 - root_mean_squared_error: 0.8509 - val_loss: 0.7421 - val_root_mean_squared_error: 0.8615\n",
- "Epoch 58/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7226 - root_mean_squared_error: 0.8500 - val_loss: 0.7405 - val_root_mean_squared_error: 0.8605\n",
- "Epoch 59/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7200 - root_mean_squared_error: 0.8485 - val_loss: 0.7403 - val_root_mean_squared_error: 0.8604\n",
- "Epoch 60/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7177 - root_mean_squared_error: 0.8472 - val_loss: 0.7338 - val_root_mean_squared_error: 0.8566\n",
- "Epoch 61/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7151 - root_mean_squared_error: 0.8457 - val_loss: 0.7341 - val_root_mean_squared_error: 0.8568\n",
- "Epoch 62/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7126 - root_mean_squared_error: 0.8442 - val_loss: 0.7275 - val_root_mean_squared_error: 0.8529\n",
- "Epoch 63/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7112 - root_mean_squared_error: 0.8433 - val_loss: 0.7248 - val_root_mean_squared_error: 0.8513\n",
- "Epoch 64/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7079 - root_mean_squared_error: 0.8414 - val_loss: 0.7239 - val_root_mean_squared_error: 0.8508\n",
- "Epoch 65/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7049 - root_mean_squared_error: 0.8396 - val_loss: 0.7182 - val_root_mean_squared_error: 0.8475\n",
- "Epoch 66/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.7020 - root_mean_squared_error: 0.8379 - val_loss: 0.7133 - val_root_mean_squared_error: 0.8446\n",
- "Epoch 67/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6991 - root_mean_squared_error: 0.8361 - val_loss: 0.7102 - val_root_mean_squared_error: 0.8427\n",
- "Epoch 68/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6944 - root_mean_squared_error: 0.8333 - val_loss: 0.7045 - val_root_mean_squared_error: 0.8394\n",
- "Epoch 69/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6918 - root_mean_squared_error: 0.8317 - val_loss: 0.7033 - val_root_mean_squared_error: 0.8386\n",
- "Epoch 70/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6864 - root_mean_squared_error: 0.8285 - val_loss: 0.6954 - val_root_mean_squared_error: 0.8339\n",
- "Epoch 71/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6830 - root_mean_squared_error: 0.8264 - val_loss: 0.6929 - val_root_mean_squared_error: 0.8324\n",
- "Epoch 72/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6788 - root_mean_squared_error: 0.8239 - val_loss: 0.6858 - val_root_mean_squared_error: 0.8281\n",
- "Epoch 73/100\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6736 - root_mean_squared_error: 0.8207 - val_loss: 0.6806 - val_root_mean_squared_error: 0.8250\n",
- "Epoch 74/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6693 - root_mean_squared_error: 0.8181 - val_loss: 0.6732 - val_root_mean_squared_error: 0.8205\n",
- "Epoch 75/100\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6649 - root_mean_squared_error: 0.8154 - val_loss: 0.6676 - val_root_mean_squared_error: 0.8171\n",
- "Epoch 76/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6605 - root_mean_squared_error: 0.8127 - val_loss: 0.6627 - val_root_mean_squared_error: 0.8141\n",
- "Epoch 77/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6545 - root_mean_squared_error: 0.8090 - val_loss: 0.6585 - val_root_mean_squared_error: 0.8115\n",
- "Epoch 78/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6487 - root_mean_squared_error: 0.8054 - val_loss: 0.6527 - val_root_mean_squared_error: 0.8079\n",
- "Epoch 79/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6446 - root_mean_squared_error: 0.8029 - val_loss: 0.6437 - val_root_mean_squared_error: 0.8023\n",
- "Epoch 80/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6378 - root_mean_squared_error: 0.7986 - val_loss: 0.6394 - val_root_mean_squared_error: 0.7996\n",
- "Epoch 81/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6327 - root_mean_squared_error: 0.7955 - val_loss: 0.6323 - val_root_mean_squared_error: 0.7952\n",
- "Epoch 82/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6278 - root_mean_squared_error: 0.7923 - val_loss: 0.6248 - val_root_mean_squared_error: 0.7904\n",
- "Epoch 83/100\n",
- "17/17 [==============================] - 0s 8ms/step - loss: 0.6205 - root_mean_squared_error: 0.7877 - val_loss: 0.6181 - val_root_mean_squared_error: 0.7862\n",
- "Epoch 84/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6162 - root_mean_squared_error: 0.7850 - val_loss: 0.6121 - val_root_mean_squared_error: 0.7824\n",
- "Epoch 85/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6099 - root_mean_squared_error: 0.7810 - val_loss: 0.6088 - val_root_mean_squared_error: 0.7803\n",
- "Epoch 86/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6077 - root_mean_squared_error: 0.7796 - val_loss: 0.6015 - val_root_mean_squared_error: 0.7756\n",
- "Epoch 87/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.6020 - root_mean_squared_error: 0.7759 - val_loss: 0.5970 - val_root_mean_squared_error: 0.7727\n",
- "Epoch 88/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5985 - root_mean_squared_error: 0.7736 - val_loss: 0.5913 - val_root_mean_squared_error: 0.7690\n",
- "Epoch 89/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5933 - root_mean_squared_error: 0.7703 - val_loss: 0.5885 - val_root_mean_squared_error: 0.7671\n",
- "Epoch 90/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5915 - root_mean_squared_error: 0.7691 - val_loss: 0.5858 - val_root_mean_squared_error: 0.7654\n",
- "Epoch 91/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5857 - root_mean_squared_error: 0.7653 - val_loss: 0.5801 - val_root_mean_squared_error: 0.7617\n",
- "Epoch 92/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5840 - root_mean_squared_error: 0.7642 - val_loss: 0.5754 - val_root_mean_squared_error: 0.7585\n",
- "Epoch 93/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5811 - root_mean_squared_error: 0.7623 - val_loss: 0.5724 - val_root_mean_squared_error: 0.7566\n",
- "Epoch 94/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5767 - root_mean_squared_error: 0.7594 - val_loss: 0.5700 - val_root_mean_squared_error: 0.7550\n",
- "Epoch 95/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5750 - root_mean_squared_error: 0.7583 - val_loss: 0.5679 - val_root_mean_squared_error: 0.7536\n",
- "Epoch 96/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5744 - root_mean_squared_error: 0.7579 - val_loss: 0.5652 - val_root_mean_squared_error: 0.7518\n",
- "Epoch 97/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5725 - root_mean_squared_error: 0.7566 - val_loss: 0.5637 - val_root_mean_squared_error: 0.7508\n",
- "Epoch 98/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5717 - root_mean_squared_error: 0.7561 - val_loss: 0.5623 - val_root_mean_squared_error: 0.7499\n",
- "Epoch 99/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5719 - root_mean_squared_error: 0.7562 - val_loss: 0.5606 - val_root_mean_squared_error: 0.7487\n",
- "Epoch 100/100\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 0.5697 - root_mean_squared_error: 0.7548 - val_loss: 0.5593 - val_root_mean_squared_error: 0.7479\n",
"Model training finished.\n",
- "Train RMSE: 0.754\n",
+ "Train RMSE: 0.746\n",
"Evaluating model performance...\n",
- "Test RMSE: 0.748\n"
+ "Test RMSE: 0.738\n"
]
}
],
@@ -563,7 +371,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 8,
"metadata": {
"id": "sBt9xVWJJZX8"
},
@@ -572,16 +380,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Predicted: 5.7 - Actual: 6.0\n",
- "Predicted: 6.2 - Actual: 7.0\n",
- "Predicted: 6.0 - Actual: 7.0\n",
- "Predicted: 6.5 - Actual: 6.0\n",
- "Predicted: 6.4 - Actual: 5.0\n",
- "Predicted: 6.5 - Actual: 7.0\n",
+ "Predicted: 6.2 - Actual: 5.0\n",
+ "Predicted: 5.9 - Actual: 6.0\n",
+ "Predicted: 5.4 - Actual: 5.0\n",
+ "Predicted: 5.7 - Actual: 5.0\n",
"Predicted: 6.0 - Actual: 6.0\n",
- "Predicted: 6.0 - Actual: 6.0\n",
- "Predicted: 6.6 - Actual: 8.0\n",
- "Predicted: 5.6 - Actual: 6.0\n"
+ "Predicted: 5.3 - Actual: 6.0\n",
+ "Predicted: 5.4 - Actual: 5.0\n",
+ "Predicted: 5.5 - Actual: 5.0\n",
+ "Predicted: 6.1 - Actual: 6.0\n",
+ "Predicted: 5.8 - Actual: 5.0\n"
]
}
],
@@ -619,7 +427,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {
"id": "eRZ-K9-yJZX8"
},
@@ -670,7 +478,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 10,
"metadata": {
"id": "EtdF-vuJJZX9"
},
@@ -722,7 +530,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 11,
"metadata": {
"id": "_FTuHpudJZX9"
},
@@ -750,1010 +558,10 @@
"output_type": "stream",
"text": [
"Start training the model...\n",
- "Epoch 1/500\n",
- "5/5 [==============================] - 4s 557ms/step - loss: 57.0129 - root_mean_squared_error: 7.5497 - val_loss: 56.7286 - val_root_mean_squared_error: 7.5309\n",
- "Epoch 2/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 52.7787 - root_mean_squared_error: 7.2640 - val_loss: 49.4205 - val_root_mean_squared_error: 7.0291\n",
- "Epoch 3/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 55.1672 - root_mean_squared_error: 7.4266 - val_loss: 53.8305 - val_root_mean_squared_error: 7.3360\n",
- "Epoch 4/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 54.0287 - root_mean_squared_error: 7.3497 - val_loss: 50.6124 - val_root_mean_squared_error: 7.1131\n",
- "Epoch 5/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 50.9889 - root_mean_squared_error: 7.1395 - val_loss: 51.2843 - val_root_mean_squared_error: 7.1604\n",
- "Epoch 6/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 51.8222 - root_mean_squared_error: 7.1976 - val_loss: 53.2796 - val_root_mean_squared_error: 7.2985\n",
- "Epoch 7/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 52.6715 - root_mean_squared_error: 7.2567 - val_loss: 51.2377 - val_root_mean_squared_error: 7.1574\n",
- "Epoch 8/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 52.9132 - root_mean_squared_error: 7.2730 - val_loss: 55.1586 - val_root_mean_squared_error: 7.4260\n",
- "Epoch 9/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 52.2086 - root_mean_squared_error: 7.2245 - val_loss: 46.4330 - val_root_mean_squared_error: 6.8134\n",
- "Epoch 10/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 50.9335 - root_mean_squared_error: 7.1358 - val_loss: 46.8694 - val_root_mean_squared_error: 6.8452\n",
- "Epoch 11/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 50.2360 - root_mean_squared_error: 7.0867 - val_loss: 54.9606 - val_root_mean_squared_error: 7.4126\n",
- "Epoch 12/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 48.2649 - root_mean_squared_error: 6.9463 - val_loss: 45.6966 - val_root_mean_squared_error: 6.7591\n",
- "Epoch 13/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 49.0749 - root_mean_squared_error: 7.0043 - val_loss: 49.5114 - val_root_mean_squared_error: 7.0354\n",
- "Epoch 14/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 46.5035 - root_mean_squared_error: 6.8183 - val_loss: 47.7520 - val_root_mean_squared_error: 6.9091\n",
- "Epoch 15/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 53.5044 - root_mean_squared_error: 7.3137 - val_loss: 49.9759 - val_root_mean_squared_error: 7.0682\n",
- "Epoch 16/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 48.7602 - root_mean_squared_error: 6.9820 - val_loss: 49.8098 - val_root_mean_squared_error: 7.0565\n",
- "Epoch 17/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 48.0525 - root_mean_squared_error: 6.9308 - val_loss: 47.2923 - val_root_mean_squared_error: 6.8759\n",
- "Epoch 18/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 48.7001 - root_mean_squared_error: 6.9774 - val_loss: 45.3507 - val_root_mean_squared_error: 6.7332\n",
- "Epoch 19/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 45.9967 - root_mean_squared_error: 6.7809 - val_loss: 51.5288 - val_root_mean_squared_error: 7.1776\n",
- "Epoch 20/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 45.0668 - root_mean_squared_error: 6.7121 - val_loss: 44.2659 - val_root_mean_squared_error: 6.6521\n",
- "Epoch 21/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 45.8024 - root_mean_squared_error: 6.7665 - val_loss: 42.4139 - val_root_mean_squared_error: 6.5113\n",
- "Epoch 22/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 46.9955 - root_mean_squared_error: 6.8542 - val_loss: 47.0784 - val_root_mean_squared_error: 6.8600\n",
- "Epoch 23/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 44.1119 - root_mean_squared_error: 6.6405 - val_loss: 45.3969 - val_root_mean_squared_error: 6.7364\n",
- "Epoch 24/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 44.0670 - root_mean_squared_error: 6.6372 - val_loss: 45.3408 - val_root_mean_squared_error: 6.7327\n",
- "Epoch 25/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 45.0380 - root_mean_squared_error: 6.7099 - val_loss: 45.7874 - val_root_mean_squared_error: 6.7656\n",
- "Epoch 26/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 43.8603 - root_mean_squared_error: 6.6216 - val_loss: 42.9501 - val_root_mean_squared_error: 6.5527\n",
- "Epoch 27/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 44.7173 - root_mean_squared_error: 6.6860 - val_loss: 45.5102 - val_root_mean_squared_error: 6.7452\n",
- "Epoch 28/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 40.9454 - root_mean_squared_error: 6.3974 - val_loss: 43.2894 - val_root_mean_squared_error: 6.5785\n",
- "Epoch 29/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 42.2195 - root_mean_squared_error: 6.4966 - val_loss: 40.0281 - val_root_mean_squared_error: 6.3254\n",
- "Epoch 30/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 39.2515 - root_mean_squared_error: 6.2636 - val_loss: 42.5028 - val_root_mean_squared_error: 6.5181\n",
- "Epoch 31/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 42.7703 - root_mean_squared_error: 6.5386 - val_loss: 41.6595 - val_root_mean_squared_error: 6.4531\n",
- "Epoch 32/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 40.3649 - root_mean_squared_error: 6.3522 - val_loss: 39.7245 - val_root_mean_squared_error: 6.3015\n",
- "Epoch 33/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 38.1933 - root_mean_squared_error: 6.1788 - val_loss: 36.7037 - val_root_mean_squared_error: 6.0567\n",
- "Epoch 34/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 40.1268 - root_mean_squared_error: 6.3335 - val_loss: 40.8225 - val_root_mean_squared_error: 6.3880\n",
- "Epoch 35/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 39.1419 - root_mean_squared_error: 6.2552 - val_loss: 36.6122 - val_root_mean_squared_error: 6.0494\n",
- "Epoch 36/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 39.2029 - root_mean_squared_error: 6.2600 - val_loss: 36.6162 - val_root_mean_squared_error: 6.0498\n",
- "Epoch 37/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 38.5928 - root_mean_squared_error: 6.2109 - val_loss: 37.6816 - val_root_mean_squared_error: 6.1375\n",
- "Epoch 38/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 40.8756 - root_mean_squared_error: 6.3922 - val_loss: 39.3902 - val_root_mean_squared_error: 6.2753\n",
- "Epoch 39/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 38.9052 - root_mean_squared_error: 6.2360 - val_loss: 35.4052 - val_root_mean_squared_error: 5.9486\n",
- "Epoch 40/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 36.0730 - root_mean_squared_error: 6.0048 - val_loss: 39.9429 - val_root_mean_squared_error: 6.3188\n",
- "Epoch 41/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 37.7098 - root_mean_squared_error: 6.1394 - val_loss: 36.7212 - val_root_mean_squared_error: 6.0588\n",
- "Epoch 42/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 34.4501 - root_mean_squared_error: 5.8679 - val_loss: 35.2537 - val_root_mean_squared_error: 5.9361\n",
- "Epoch 43/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 36.3017 - root_mean_squared_error: 6.0235 - val_loss: 33.8819 - val_root_mean_squared_error: 5.8196\n",
- "Epoch 44/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 35.6835 - root_mean_squared_error: 5.9722 - val_loss: 38.2949 - val_root_mean_squared_error: 6.1871\n",
- "Epoch 45/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 33.1830 - root_mean_squared_error: 5.7592 - val_loss: 35.2678 - val_root_mean_squared_error: 5.9375\n",
- "Epoch 46/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 34.6403 - root_mean_squared_error: 5.8841 - val_loss: 34.4320 - val_root_mean_squared_error: 5.8666\n",
- "Epoch 47/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 35.9900 - root_mean_squared_error: 5.9980 - val_loss: 36.1400 - val_root_mean_squared_error: 6.0101\n",
- "Epoch 48/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 33.9624 - root_mean_squared_error: 5.8263 - val_loss: 34.1770 - val_root_mean_squared_error: 5.8447\n",
- "Epoch 49/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 33.8744 - root_mean_squared_error: 5.8188 - val_loss: 32.8798 - val_root_mean_squared_error: 5.7328\n",
- "Epoch 50/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 33.8414 - root_mean_squared_error: 5.8161 - val_loss: 35.2764 - val_root_mean_squared_error: 5.9379\n",
- "Epoch 51/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 33.2538 - root_mean_squared_error: 5.7651 - val_loss: 36.0869 - val_root_mean_squared_error: 6.0056\n",
- "Epoch 52/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 32.6954 - root_mean_squared_error: 5.7171 - val_loss: 34.1802 - val_root_mean_squared_error: 5.8448\n",
- "Epoch 53/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 31.4900 - root_mean_squared_error: 5.6101 - val_loss: 32.2507 - val_root_mean_squared_error: 5.6775\n",
- "Epoch 54/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 32.2751 - root_mean_squared_error: 5.6796 - val_loss: 33.6289 - val_root_mean_squared_error: 5.7975\n",
- "Epoch 55/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 30.5764 - root_mean_squared_error: 5.5276 - val_loss: 30.3345 - val_root_mean_squared_error: 5.5058\n",
- "Epoch 56/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 34.0280 - root_mean_squared_error: 5.8317 - val_loss: 30.1419 - val_root_mean_squared_error: 5.4883\n",
- "Epoch 57/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 31.7069 - root_mean_squared_error: 5.6292 - val_loss: 31.1548 - val_root_mean_squared_error: 5.5800\n",
- "Epoch 58/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 31.4960 - root_mean_squared_error: 5.6106 - val_loss: 29.9524 - val_root_mean_squared_error: 5.4720\n",
- "Epoch 59/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 33.5438 - root_mean_squared_error: 5.7904 - val_loss: 30.6013 - val_root_mean_squared_error: 5.5303\n",
- "Epoch 60/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 33.0082 - root_mean_squared_error: 5.7437 - val_loss: 29.7980 - val_root_mean_squared_error: 5.4571\n",
- "Epoch 61/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 28.9597 - root_mean_squared_error: 5.3796 - val_loss: 32.6550 - val_root_mean_squared_error: 5.7129\n",
- "Epoch 62/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 30.9942 - root_mean_squared_error: 5.5654 - val_loss: 29.8465 - val_root_mean_squared_error: 5.4615\n",
- "Epoch 63/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 29.6922 - root_mean_squared_error: 5.4475 - val_loss: 27.8239 - val_root_mean_squared_error: 5.2726\n",
- "Epoch 64/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 28.8519 - root_mean_squared_error: 5.3692 - val_loss: 29.0606 - val_root_mean_squared_error: 5.3892\n",
- "Epoch 65/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 28.9408 - root_mean_squared_error: 5.3777 - val_loss: 29.2193 - val_root_mean_squared_error: 5.4037\n",
- "Epoch 66/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 28.5027 - root_mean_squared_error: 5.3371 - val_loss: 30.2509 - val_root_mean_squared_error: 5.4986\n",
- "Epoch 67/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 28.6993 - root_mean_squared_error: 5.3552 - val_loss: 28.1582 - val_root_mean_squared_error: 5.3044\n",
- "Epoch 68/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 29.2733 - root_mean_squared_error: 5.4089 - val_loss: 27.6948 - val_root_mean_squared_error: 5.2611\n",
- "Epoch 69/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 28.0797 - root_mean_squared_error: 5.2971 - val_loss: 27.7246 - val_root_mean_squared_error: 5.2630\n",
- "Epoch 70/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 29.7607 - root_mean_squared_error: 5.4538 - val_loss: 27.6516 - val_root_mean_squared_error: 5.2566\n",
- "Epoch 71/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 28.9980 - root_mean_squared_error: 5.3831 - val_loss: 26.7979 - val_root_mean_squared_error: 5.1743\n",
- "Epoch 72/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 28.8477 - root_mean_squared_error: 5.3694 - val_loss: 28.0050 - val_root_mean_squared_error: 5.2903\n",
- "Epoch 73/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 28.7916 - root_mean_squared_error: 5.3644 - val_loss: 25.0189 - val_root_mean_squared_error: 4.9997\n",
- "Epoch 74/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 26.0490 - root_mean_squared_error: 5.1014 - val_loss: 25.2151 - val_root_mean_squared_error: 5.0195\n",
- "Epoch 75/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 26.4882 - root_mean_squared_error: 5.1449 - val_loss: 24.9189 - val_root_mean_squared_error: 4.9894\n",
- "Epoch 76/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 27.0735 - root_mean_squared_error: 5.2015 - val_loss: 26.9598 - val_root_mean_squared_error: 5.1903\n",
- "Epoch 77/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 26.0508 - root_mean_squared_error: 5.1021 - val_loss: 24.9614 - val_root_mean_squared_error: 4.9939\n",
- "Epoch 78/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 27.2053 - root_mean_squared_error: 5.2141 - val_loss: 23.9003 - val_root_mean_squared_error: 4.8863\n",
- "Epoch 79/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 27.3039 - root_mean_squared_error: 5.2236 - val_loss: 24.3818 - val_root_mean_squared_error: 4.9356\n",
- "Epoch 80/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 25.1867 - root_mean_squared_error: 5.0165 - val_loss: 24.0422 - val_root_mean_squared_error: 4.9009\n",
- "Epoch 81/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 24.2412 - root_mean_squared_error: 4.9212 - val_loss: 27.4497 - val_root_mean_squared_error: 5.2374\n",
- "Epoch 82/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 25.6320 - root_mean_squared_error: 5.0606 - val_loss: 25.4330 - val_root_mean_squared_error: 5.0411\n",
- "Epoch 83/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 25.0061 - root_mean_squared_error: 4.9987 - val_loss: 24.0740 - val_root_mean_squared_error: 4.9044\n",
- "Epoch 84/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 24.5057 - root_mean_squared_error: 4.9485 - val_loss: 23.7218 - val_root_mean_squared_error: 4.8681\n",
- "Epoch 85/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 23.6273 - root_mean_squared_error: 4.8586 - val_loss: 24.4322 - val_root_mean_squared_error: 4.9407\n",
- "Epoch 86/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 24.5021 - root_mean_squared_error: 4.9479 - val_loss: 23.0119 - val_root_mean_squared_error: 4.7947\n",
- "Epoch 87/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 24.7429 - root_mean_squared_error: 4.9721 - val_loss: 23.7462 - val_root_mean_squared_error: 4.8704\n",
- "Epoch 88/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 23.7419 - root_mean_squared_error: 4.8701 - val_loss: 23.0700 - val_root_mean_squared_error: 4.8007\n",
- "Epoch 89/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 22.6764 - root_mean_squared_error: 4.7592 - val_loss: 22.5206 - val_root_mean_squared_error: 4.7431\n",
- "Epoch 90/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 23.2829 - root_mean_squared_error: 4.8232 - val_loss: 23.0072 - val_root_mean_squared_error: 4.7943\n",
- "Epoch 91/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 24.4655 - root_mean_squared_error: 4.9441 - val_loss: 24.7697 - val_root_mean_squared_error: 4.9753\n",
- "Epoch 92/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 23.7739 - root_mean_squared_error: 4.8735 - val_loss: 23.2210 - val_root_mean_squared_error: 4.8169\n",
- "Epoch 93/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 22.4935 - root_mean_squared_error: 4.7403 - val_loss: 23.1276 - val_root_mean_squared_error: 4.8071\n",
- "Epoch 94/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 21.5418 - root_mean_squared_error: 4.6387 - val_loss: 22.3647 - val_root_mean_squared_error: 4.7268\n",
- "Epoch 95/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 22.2177 - root_mean_squared_error: 4.7109 - val_loss: 20.8299 - val_root_mean_squared_error: 4.5613\n",
- "Epoch 96/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 22.9174 - root_mean_squared_error: 4.7850 - val_loss: 21.4278 - val_root_mean_squared_error: 4.6270\n",
- "Epoch 97/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 22.0471 - root_mean_squared_error: 4.6931 - val_loss: 22.4457 - val_root_mean_squared_error: 4.7352\n",
- "Epoch 98/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 21.5958 - root_mean_squared_error: 4.6447 - val_loss: 21.9172 - val_root_mean_squared_error: 4.6791\n",
- "Epoch 99/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 22.6420 - root_mean_squared_error: 4.7566 - val_loss: 22.5990 - val_root_mean_squared_error: 4.7521\n",
- "Epoch 100/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 21.5387 - root_mean_squared_error: 4.6384 - val_loss: 21.2408 - val_root_mean_squared_error: 4.6060\n",
- "Epoch 101/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 21.7356 - root_mean_squared_error: 4.6600 - val_loss: 20.8517 - val_root_mean_squared_error: 4.5639\n",
- "Epoch 102/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 19.9914 - root_mean_squared_error: 4.4685 - val_loss: 20.5966 - val_root_mean_squared_error: 4.5356\n",
- "Epoch 103/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 20.1531 - root_mean_squared_error: 4.4862 - val_loss: 20.0918 - val_root_mean_squared_error: 4.4798\n",
- "Epoch 104/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 20.7306 - root_mean_squared_error: 4.5506 - val_loss: 19.8531 - val_root_mean_squared_error: 4.4528\n",
- "Epoch 105/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 20.1664 - root_mean_squared_error: 4.4880 - val_loss: 19.9779 - val_root_mean_squared_error: 4.4673\n",
- "Epoch 106/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 20.5011 - root_mean_squared_error: 4.5253 - val_loss: 20.2513 - val_root_mean_squared_error: 4.4973\n",
- "Epoch 107/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 19.6751 - root_mean_squared_error: 4.4332 - val_loss: 19.1514 - val_root_mean_squared_error: 4.3732\n",
- "Epoch 108/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 19.2913 - root_mean_squared_error: 4.3895 - val_loss: 18.8102 - val_root_mean_squared_error: 4.3338\n",
- "Epoch 109/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 19.0559 - root_mean_squared_error: 4.3627 - val_loss: 19.0571 - val_root_mean_squared_error: 4.3626\n",
- "Epoch 110/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 19.8701 - root_mean_squared_error: 4.4549 - val_loss: 19.8402 - val_root_mean_squared_error: 4.4514\n",
- "Epoch 111/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 18.4038 - root_mean_squared_error: 4.2875 - val_loss: 18.4694 - val_root_mean_squared_error: 4.2948\n",
- "Epoch 112/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 19.8099 - root_mean_squared_error: 4.4485 - val_loss: 18.8064 - val_root_mean_squared_error: 4.3337\n",
- "Epoch 113/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 18.3808 - root_mean_squared_error: 4.2843 - val_loss: 18.3303 - val_root_mean_squared_error: 4.2785\n",
- "Epoch 114/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 19.2153 - root_mean_squared_error: 4.3805 - val_loss: 18.1571 - val_root_mean_squared_error: 4.2578\n",
- "Epoch 115/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 17.9376 - root_mean_squared_error: 4.2323 - val_loss: 17.4677 - val_root_mean_squared_error: 4.1762\n",
- "Epoch 116/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 19.2671 - root_mean_squared_error: 4.3868 - val_loss: 17.7120 - val_root_mean_squared_error: 4.2057\n",
- "Epoch 117/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 17.5023 - root_mean_squared_error: 4.1802 - val_loss: 17.5487 - val_root_mean_squared_error: 4.1860\n",
- "Epoch 118/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 17.5560 - root_mean_squared_error: 4.1871 - val_loss: 17.6304 - val_root_mean_squared_error: 4.1958\n",
- "Epoch 119/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 17.5586 - root_mean_squared_error: 4.1871 - val_loss: 16.7192 - val_root_mean_squared_error: 4.0862\n",
- "Epoch 120/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 18.1624 - root_mean_squared_error: 4.2585 - val_loss: 17.1333 - val_root_mean_squared_error: 4.1365\n",
- "Epoch 121/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 17.1096 - root_mean_squared_error: 4.1332 - val_loss: 16.7204 - val_root_mean_squared_error: 4.0858\n",
- "Epoch 122/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 16.6286 - root_mean_squared_error: 4.0742 - val_loss: 16.8656 - val_root_mean_squared_error: 4.1036\n",
- "Epoch 123/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 16.7034 - root_mean_squared_error: 4.0836 - val_loss: 16.6020 - val_root_mean_squared_error: 4.0720\n",
- "Epoch 124/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 17.4135 - root_mean_squared_error: 4.1699 - val_loss: 16.2530 - val_root_mean_squared_error: 4.0280\n",
- "Epoch 125/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 17.1698 - root_mean_squared_error: 4.1406 - val_loss: 16.1303 - val_root_mean_squared_error: 4.0130\n",
- "Epoch 126/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 16.4671 - root_mean_squared_error: 4.0544 - val_loss: 15.7126 - val_root_mean_squared_error: 3.9600\n",
- "Epoch 127/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 16.3764 - root_mean_squared_error: 4.0436 - val_loss: 16.4962 - val_root_mean_squared_error: 4.0584\n",
- "Epoch 128/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 16.1884 - root_mean_squared_error: 4.0201 - val_loss: 16.2926 - val_root_mean_squared_error: 4.0335\n",
- "Epoch 129/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 15.5219 - root_mean_squared_error: 3.9356 - val_loss: 15.7980 - val_root_mean_squared_error: 3.9715\n",
- "Epoch 130/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 16.6318 - root_mean_squared_error: 4.0753 - val_loss: 15.9729 - val_root_mean_squared_error: 3.9935\n",
- "Epoch 131/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 16.0311 - root_mean_squared_error: 4.0012 - val_loss: 15.0711 - val_root_mean_squared_error: 3.8788\n",
- "Epoch 132/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 15.8952 - root_mean_squared_error: 3.9838 - val_loss: 15.0009 - val_root_mean_squared_error: 3.8695\n",
- "Epoch 133/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 15.3139 - root_mean_squared_error: 3.9104 - val_loss: 15.0381 - val_root_mean_squared_error: 3.8742\n",
- "Epoch 134/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 15.2270 - root_mean_squared_error: 3.8985 - val_loss: 14.6033 - val_root_mean_squared_error: 3.8183\n",
- "Epoch 135/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 15.5290 - root_mean_squared_error: 3.9374 - val_loss: 14.5799 - val_root_mean_squared_error: 3.8144\n",
- "Epoch 136/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 15.5088 - root_mean_squared_error: 3.9350 - val_loss: 15.2540 - val_root_mean_squared_error: 3.9025\n",
- "Epoch 137/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 15.0281 - root_mean_squared_error: 3.8734 - val_loss: 13.8088 - val_root_mean_squared_error: 3.7117\n",
- "Epoch 138/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 15.2251 - root_mean_squared_error: 3.8984 - val_loss: 14.1852 - val_root_mean_squared_error: 3.7625\n",
- "Epoch 139/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 14.2683 - root_mean_squared_error: 3.7737 - val_loss: 15.5110 - val_root_mean_squared_error: 3.9352\n",
- "Epoch 140/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 13.8089 - root_mean_squared_error: 3.7124 - val_loss: 13.6880 - val_root_mean_squared_error: 3.6964\n",
- "Epoch 141/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 14.0246 - root_mean_squared_error: 3.7416 - val_loss: 13.2767 - val_root_mean_squared_error: 3.6396\n",
- "Epoch 142/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 13.9703 - root_mean_squared_error: 3.7342 - val_loss: 13.3109 - val_root_mean_squared_error: 3.6448\n",
- "Epoch 143/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 13.8884 - root_mean_squared_error: 3.7231 - val_loss: 13.2664 - val_root_mean_squared_error: 3.6395\n",
- "Epoch 144/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 13.6581 - root_mean_squared_error: 3.6919 - val_loss: 14.3135 - val_root_mean_squared_error: 3.7802\n",
- "Epoch 145/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 13.4983 - root_mean_squared_error: 3.6704 - val_loss: 12.6536 - val_root_mean_squared_error: 3.5536\n",
- "Epoch 146/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 13.5602 - root_mean_squared_error: 3.6792 - val_loss: 13.4458 - val_root_mean_squared_error: 3.6625\n",
- "Epoch 147/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 12.5726 - root_mean_squared_error: 3.5417 - val_loss: 13.4134 - val_root_mean_squared_error: 3.6589\n",
- "Epoch 148/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 13.2480 - root_mean_squared_error: 3.6360 - val_loss: 12.6870 - val_root_mean_squared_error: 3.5586\n",
- "Epoch 149/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 12.8816 - root_mean_squared_error: 3.5854 - val_loss: 12.3010 - val_root_mean_squared_error: 3.5031\n",
- "Epoch 150/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 13.0819 - root_mean_squared_error: 3.6130 - val_loss: 12.4213 - val_root_mean_squared_error: 3.5199\n",
- "Epoch 151/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 12.6296 - root_mean_squared_error: 3.5498 - val_loss: 12.7055 - val_root_mean_squared_error: 3.5607\n",
- "Epoch 152/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 12.0153 - root_mean_squared_error: 3.4627 - val_loss: 11.9677 - val_root_mean_squared_error: 3.4553\n",
- "Epoch 153/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 11.9949 - root_mean_squared_error: 3.4595 - val_loss: 12.3309 - val_root_mean_squared_error: 3.5073\n",
- "Epoch 154/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 12.2917 - root_mean_squared_error: 3.5027 - val_loss: 11.8275 - val_root_mean_squared_error: 3.4345\n",
- "Epoch 155/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 12.5561 - root_mean_squared_error: 3.5394 - val_loss: 11.0467 - val_root_mean_squared_error: 3.3191\n",
- "Epoch 156/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 12.5082 - root_mean_squared_error: 3.5327 - val_loss: 11.6160 - val_root_mean_squared_error: 3.4045\n",
- "Epoch 157/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 11.1876 - root_mean_squared_error: 3.3405 - val_loss: 11.4853 - val_root_mean_squared_error: 3.3845\n",
- "Epoch 158/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 11.6028 - root_mean_squared_error: 3.4027 - val_loss: 11.5544 - val_root_mean_squared_error: 3.3947\n",
- "Epoch 159/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 12.0451 - root_mean_squared_error: 3.4672 - val_loss: 11.1705 - val_root_mean_squared_error: 3.3385\n",
- "Epoch 160/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 11.5716 - root_mean_squared_error: 3.3977 - val_loss: 10.2429 - val_root_mean_squared_error: 3.1956\n",
- "Epoch 161/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 11.1465 - root_mean_squared_error: 3.3342 - val_loss: 10.9106 - val_root_mean_squared_error: 3.2992\n",
- "Epoch 162/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 11.1903 - root_mean_squared_error: 3.3410 - val_loss: 9.6892 - val_root_mean_squared_error: 3.1080\n",
- "Epoch 163/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 10.5706 - root_mean_squared_error: 3.2465 - val_loss: 10.9300 - val_root_mean_squared_error: 3.3021\n",
- "Epoch 164/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 10.9836 - root_mean_squared_error: 3.3099 - val_loss: 11.2225 - val_root_mean_squared_error: 3.3446\n",
- "Epoch 165/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 10.1484 - root_mean_squared_error: 3.1813 - val_loss: 10.2744 - val_root_mean_squared_error: 3.2003\n",
- "Epoch 166/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 10.2213 - root_mean_squared_error: 3.1923 - val_loss: 10.5869 - val_root_mean_squared_error: 3.2498\n",
- "Epoch 167/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 10.4147 - root_mean_squared_error: 3.2222 - val_loss: 9.7714 - val_root_mean_squared_error: 3.1218\n",
- "Epoch 168/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 10.9415 - root_mean_squared_error: 3.3028 - val_loss: 10.0797 - val_root_mean_squared_error: 3.1711\n",
- "Epoch 169/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 10.2099 - root_mean_squared_error: 3.1904 - val_loss: 9.9081 - val_root_mean_squared_error: 3.1429\n",
- "Epoch 170/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 10.2230 - root_mean_squared_error: 3.1922 - val_loss: 10.0554 - val_root_mean_squared_error: 3.1654\n",
- "Epoch 171/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 10.0789 - root_mean_squared_error: 3.1705 - val_loss: 9.2104 - val_root_mean_squared_error: 3.0299\n",
- "Epoch 172/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 9.2213 - root_mean_squared_error: 3.0318 - val_loss: 8.8115 - val_root_mean_squared_error: 2.9644\n",
- "Epoch 173/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 9.4275 - root_mean_squared_error: 3.0656 - val_loss: 9.2080 - val_root_mean_squared_error: 3.0290\n",
- "Epoch 174/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 9.0314 - root_mean_squared_error: 3.0003 - val_loss: 9.2009 - val_root_mean_squared_error: 3.0281\n",
- "Epoch 175/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 9.5477 - root_mean_squared_error: 3.0854 - val_loss: 8.6904 - val_root_mean_squared_error: 2.9423\n",
- "Epoch 176/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 9.2999 - root_mean_squared_error: 3.0450 - val_loss: 8.1755 - val_root_mean_squared_error: 2.8547\n",
- "Epoch 177/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 9.3138 - root_mean_squared_error: 3.0468 - val_loss: 8.6634 - val_root_mean_squared_error: 2.9386\n",
- "Epoch 178/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 8.4299 - root_mean_squared_error: 2.8979 - val_loss: 8.0855 - val_root_mean_squared_error: 2.8388\n",
- "Epoch 179/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 9.2482 - root_mean_squared_error: 3.0361 - val_loss: 8.7413 - val_root_mean_squared_error: 2.9514\n",
- "Epoch 180/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 9.4816 - root_mean_squared_error: 3.0742 - val_loss: 7.9207 - val_root_mean_squared_error: 2.8096\n",
- "Epoch 181/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 8.3382 - root_mean_squared_error: 2.8830 - val_loss: 7.3657 - val_root_mean_squared_error: 2.7083\n",
- "Epoch 182/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 9.2579 - root_mean_squared_error: 3.0381 - val_loss: 8.4433 - val_root_mean_squared_error: 2.9002\n",
- "Epoch 183/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 8.7381 - root_mean_squared_error: 2.9511 - val_loss: 7.6804 - val_root_mean_squared_error: 2.7672\n",
- "Epoch 184/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 8.1768 - root_mean_squared_error: 2.8543 - val_loss: 8.1322 - val_root_mean_squared_error: 2.8463\n",
- "Epoch 185/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 7.7178 - root_mean_squared_error: 2.7733 - val_loss: 8.6524 - val_root_mean_squared_error: 2.9359\n",
- "Epoch 186/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 7.2317 - root_mean_squared_error: 2.6836 - val_loss: 7.4022 - val_root_mean_squared_error: 2.7155\n",
- "Epoch 187/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 8.0545 - root_mean_squared_error: 2.8329 - val_loss: 6.4824 - val_root_mean_squared_error: 2.5394\n",
- "Epoch 188/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 7.5644 - root_mean_squared_error: 2.7453 - val_loss: 7.4433 - val_root_mean_squared_error: 2.7221\n",
- "Epoch 189/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 6.7420 - root_mean_squared_error: 2.5909 - val_loss: 6.7884 - val_root_mean_squared_error: 2.6004\n",
- "Epoch 190/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 6.6862 - root_mean_squared_error: 2.5798 - val_loss: 6.8858 - val_root_mean_squared_error: 2.6186\n",
- "Epoch 191/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 7.6462 - root_mean_squared_error: 2.7598 - val_loss: 6.5038 - val_root_mean_squared_error: 2.5437\n",
- "Epoch 192/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 6.6282 - root_mean_squared_error: 2.5682 - val_loss: 7.1302 - val_root_mean_squared_error: 2.6633\n",
- "Epoch 193/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 6.6691 - root_mean_squared_error: 2.5767 - val_loss: 7.1483 - val_root_mean_squared_error: 2.6680\n",
- "Epoch 194/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 6.7153 - root_mean_squared_error: 2.5859 - val_loss: 6.5858 - val_root_mean_squared_error: 2.5603\n",
- "Epoch 195/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 6.2824 - root_mean_squared_error: 2.5006 - val_loss: 6.5737 - val_root_mean_squared_error: 2.5581\n",
- "Epoch 196/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 6.4096 - root_mean_squared_error: 2.5252 - val_loss: 6.7345 - val_root_mean_squared_error: 2.5892\n",
- "Epoch 197/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 7.0609 - root_mean_squared_error: 2.6521 - val_loss: 5.9300 - val_root_mean_squared_error: 2.4287\n",
- "Epoch 198/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 6.3157 - root_mean_squared_error: 2.5072 - val_loss: 5.8065 - val_root_mean_squared_error: 2.4043\n",
- "Epoch 199/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 5.8763 - root_mean_squared_error: 2.4176 - val_loss: 5.4865 - val_root_mean_squared_error: 2.3365\n",
- "Epoch 200/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 5.7583 - root_mean_squared_error: 2.3942 - val_loss: 5.7027 - val_root_mean_squared_error: 2.3826\n",
- "Epoch 201/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 5.3550 - root_mean_squared_error: 2.3077 - val_loss: 5.9821 - val_root_mean_squared_error: 2.4396\n",
- "Epoch 202/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 5.8802 - root_mean_squared_error: 2.4186 - val_loss: 5.5093 - val_root_mean_squared_error: 2.3404\n",
- "Epoch 203/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 5.9334 - root_mean_squared_error: 2.4301 - val_loss: 4.1049 - val_root_mean_squared_error: 2.0198\n",
- "Epoch 204/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 5.6824 - root_mean_squared_error: 2.3778 - val_loss: 5.2165 - val_root_mean_squared_error: 2.2775\n",
- "Epoch 205/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 5.5183 - root_mean_squared_error: 2.3421 - val_loss: 5.3573 - val_root_mean_squared_error: 2.3073\n",
- "Epoch 206/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 4.9171 - root_mean_squared_error: 2.2098 - val_loss: 4.7854 - val_root_mean_squared_error: 2.1814\n",
- "Epoch 207/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 4.8806 - root_mean_squared_error: 2.2027 - val_loss: 4.9441 - val_root_mean_squared_error: 2.2176\n",
- "Epoch 208/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.4776 - root_mean_squared_error: 2.1089 - val_loss: 4.5645 - val_root_mean_squared_error: 2.1297\n",
- "Epoch 209/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.5509 - root_mean_squared_error: 2.1263 - val_loss: 3.9549 - val_root_mean_squared_error: 1.9806\n",
- "Epoch 210/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 4.5692 - root_mean_squared_error: 2.1308 - val_loss: 3.6825 - val_root_mean_squared_error: 1.9107\n",
- "Epoch 211/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.5901 - root_mean_squared_error: 2.1360 - val_loss: 4.7644 - val_root_mean_squared_error: 2.1756\n",
- "Epoch 212/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.7254 - root_mean_squared_error: 1.9226 - val_loss: 4.6476 - val_root_mean_squared_error: 2.1484\n",
- "Epoch 213/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.1601 - root_mean_squared_error: 2.0323 - val_loss: 3.9025 - val_root_mean_squared_error: 1.9675\n",
- "Epoch 214/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 4.0721 - root_mean_squared_error: 2.0104 - val_loss: 4.5698 - val_root_mean_squared_error: 2.1318\n",
- "Epoch 215/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.4908 - root_mean_squared_error: 2.1123 - val_loss: 4.2339 - val_root_mean_squared_error: 2.0498\n",
- "Epoch 216/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 3.7456 - root_mean_squared_error: 1.9271 - val_loss: 3.0880 - val_root_mean_squared_error: 1.7467\n",
- "Epoch 217/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.8544 - root_mean_squared_error: 1.9558 - val_loss: 3.7808 - val_root_mean_squared_error: 1.9365\n",
- "Epoch 218/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 4.3509 - root_mean_squared_error: 2.0782 - val_loss: 3.1646 - val_root_mean_squared_error: 1.7707\n",
- "Epoch 219/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 4.2766 - root_mean_squared_error: 2.0614 - val_loss: 3.3612 - val_root_mean_squared_error: 1.8249\n",
- "Epoch 220/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.1580 - root_mean_squared_error: 1.7681 - val_loss: 3.6604 - val_root_mean_squared_error: 1.9053\n",
- "Epoch 221/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 4.0203 - root_mean_squared_error: 1.9978 - val_loss: 2.9766 - val_root_mean_squared_error: 1.7172\n",
- "Epoch 222/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 3.4552 - root_mean_squared_error: 1.8514 - val_loss: 3.5258 - val_root_mean_squared_error: 1.8685\n",
- "Epoch 223/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.1628 - root_mean_squared_error: 1.7699 - val_loss: 2.8967 - val_root_mean_squared_error: 1.6941\n",
- "Epoch 224/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 2.9762 - root_mean_squared_error: 1.7163 - val_loss: 2.4124 - val_root_mean_squared_error: 1.5434\n",
- "Epoch 225/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.2710 - root_mean_squared_error: 1.8005 - val_loss: 3.1914 - val_root_mean_squared_error: 1.7778\n",
- "Epoch 226/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 2.7753 - root_mean_squared_error: 1.6564 - val_loss: 2.5063 - val_root_mean_squared_error: 1.5745\n",
- "Epoch 227/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 2.5760 - root_mean_squared_error: 1.5949 - val_loss: 2.4899 - val_root_mean_squared_error: 1.5679\n",
- "Epoch 228/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 2.9668 - root_mean_squared_error: 1.7139 - val_loss: 2.2793 - val_root_mean_squared_error: 1.5005\n",
- "Epoch 229/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 3.4415 - root_mean_squared_error: 1.8462 - val_loss: 2.6341 - val_root_mean_squared_error: 1.6140\n",
- "Epoch 230/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 2.4956 - root_mean_squared_error: 1.5697 - val_loss: 2.3406 - val_root_mean_squared_error: 1.5202\n",
- "Epoch 231/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 2.7327 - root_mean_squared_error: 1.6446 - val_loss: 2.3558 - val_root_mean_squared_error: 1.5268\n",
- "Epoch 232/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 1.9456 - root_mean_squared_error: 1.3834 - val_loss: 1.9990 - val_root_mean_squared_error: 1.4028\n",
- "Epoch 233/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 2.4681 - root_mean_squared_error: 1.5619 - val_loss: 1.7766 - val_root_mean_squared_error: 1.3222\n",
- "Epoch 234/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.9332 - root_mean_squared_error: 1.3789 - val_loss: 2.2641 - val_root_mean_squared_error: 1.4941\n",
- "Epoch 235/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 2.6213 - root_mean_squared_error: 1.6087 - val_loss: 1.5658 - val_root_mean_squared_error: 1.2385\n",
- "Epoch 236/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.9243 - root_mean_squared_error: 1.3777 - val_loss: 2.0133 - val_root_mean_squared_error: 1.4086\n",
- "Epoch 237/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 1.7400 - root_mean_squared_error: 1.3077 - val_loss: 2.5270 - val_root_mean_squared_error: 1.5791\n",
- "Epoch 238/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.7342 - root_mean_squared_error: 1.3059 - val_loss: 1.7905 - val_root_mean_squared_error: 1.3261\n",
- "Epoch 239/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 2.2728 - root_mean_squared_error: 1.4968 - val_loss: 1.8633 - val_root_mean_squared_error: 1.3519\n",
- "Epoch 240/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 1.9659 - root_mean_squared_error: 1.3924 - val_loss: 2.1322 - val_root_mean_squared_error: 1.4487\n",
- "Epoch 241/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.8993 - root_mean_squared_error: 1.3671 - val_loss: 1.7111 - val_root_mean_squared_error: 1.2963\n",
- "Epoch 242/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.3633 - root_mean_squared_error: 1.1537 - val_loss: 1.1117 - val_root_mean_squared_error: 1.0385\n",
- "Epoch 243/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.8904 - root_mean_squared_error: 1.3645 - val_loss: 1.4145 - val_root_mean_squared_error: 1.1752\n",
- "Epoch 244/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 2.0334 - root_mean_squared_error: 1.4155 - val_loss: 1.3338 - val_root_mean_squared_error: 1.1411\n",
- "Epoch 245/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 1.6040 - root_mean_squared_error: 1.2535 - val_loss: 1.9589 - val_root_mean_squared_error: 1.3881\n",
- "Epoch 246/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.5015 - root_mean_squared_error: 1.2124 - val_loss: 1.5129 - val_root_mean_squared_error: 1.2170\n",
- "Epoch 247/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.7780 - root_mean_squared_error: 1.3221 - val_loss: 1.1408 - val_root_mean_squared_error: 1.0529\n",
- "Epoch 248/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.4319 - root_mean_squared_error: 1.1834 - val_loss: 1.7733 - val_root_mean_squared_error: 1.3191\n",
- "Epoch 249/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.3746 - root_mean_squared_error: 1.1600 - val_loss: 1.2148 - val_root_mean_squared_error: 1.0880\n",
- "Epoch 250/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 1.2237 - root_mean_squared_error: 1.0927 - val_loss: 1.2998 - val_root_mean_squared_error: 1.1265\n",
- "Epoch 251/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.2686 - root_mean_squared_error: 1.1119 - val_loss: 1.3742 - val_root_mean_squared_error: 1.1589\n",
- "Epoch 252/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.2953 - root_mean_squared_error: 1.1245 - val_loss: 1.4705 - val_root_mean_squared_error: 1.1994\n",
- "Epoch 253/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.0202 - root_mean_squared_error: 0.9941 - val_loss: 1.5974 - val_root_mean_squared_error: 1.2527\n",
- "Epoch 254/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.4037 - root_mean_squared_error: 1.1727 - val_loss: 1.6144 - val_root_mean_squared_error: 1.2592\n",
- "Epoch 255/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 1.4677 - root_mean_squared_error: 1.1999 - val_loss: 1.0819 - val_root_mean_squared_error: 1.0247\n",
- "Epoch 256/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.9700 - root_mean_squared_error: 0.9684 - val_loss: 0.9931 - val_root_mean_squared_error: 0.9794\n",
- "Epoch 257/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 1.1138 - root_mean_squared_error: 1.0403 - val_loss: 1.1167 - val_root_mean_squared_error: 1.0436\n",
- "Epoch 258/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.3040 - root_mean_squared_error: 1.1270 - val_loss: 1.2793 - val_root_mean_squared_error: 1.1182\n",
- "Epoch 259/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 1.2131 - root_mean_squared_error: 1.0872 - val_loss: 0.8645 - val_root_mean_squared_error: 0.9134\n",
- "Epoch 260/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.0912 - root_mean_squared_error: 1.0290 - val_loss: 0.8498 - val_root_mean_squared_error: 0.9060\n",
- "Epoch 261/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9726 - root_mean_squared_error: 0.9696 - val_loss: 0.9099 - val_root_mean_squared_error: 0.9377\n",
- "Epoch 262/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.9301 - root_mean_squared_error: 0.9474 - val_loss: 1.7797 - val_root_mean_squared_error: 1.3216\n",
- "Epoch 263/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9814 - root_mean_squared_error: 0.9755 - val_loss: 0.8853 - val_root_mean_squared_error: 0.9245\n",
- "Epoch 264/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.1899 - root_mean_squared_error: 1.0776 - val_loss: 1.1311 - val_root_mean_squared_error: 1.0479\n",
- "Epoch 265/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.1768 - root_mean_squared_error: 1.0721 - val_loss: 0.9160 - val_root_mean_squared_error: 0.9419\n",
- "Epoch 266/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 1.0009 - root_mean_squared_error: 0.9846 - val_loss: 0.9238 - val_root_mean_squared_error: 0.9451\n",
- "Epoch 267/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9160 - root_mean_squared_error: 0.9409 - val_loss: 0.8561 - val_root_mean_squared_error: 0.9079\n",
- "Epoch 268/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9431 - root_mean_squared_error: 0.9548 - val_loss: 0.9663 - val_root_mean_squared_error: 0.9675\n",
- "Epoch 269/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8485 - root_mean_squared_error: 0.9037 - val_loss: 1.0307 - val_root_mean_squared_error: 1.0027\n",
- "Epoch 270/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 1.0311 - root_mean_squared_error: 1.0009 - val_loss: 0.9047 - val_root_mean_squared_error: 0.9373\n",
- "Epoch 271/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8807 - root_mean_squared_error: 0.9211 - val_loss: 0.8334 - val_root_mean_squared_error: 0.8958\n",
- "Epoch 272/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8829 - root_mean_squared_error: 0.9226 - val_loss: 0.9294 - val_root_mean_squared_error: 0.9483\n",
- "Epoch 273/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8981 - root_mean_squared_error: 0.9330 - val_loss: 0.9881 - val_root_mean_squared_error: 0.9783\n",
- "Epoch 274/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9361 - root_mean_squared_error: 0.9520 - val_loss: 0.8773 - val_root_mean_squared_error: 0.9202\n",
- "Epoch 275/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8934 - root_mean_squared_error: 0.9294 - val_loss: 0.9074 - val_root_mean_squared_error: 0.9350\n",
- "Epoch 276/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8466 - root_mean_squared_error: 0.9030 - val_loss: 0.8607 - val_root_mean_squared_error: 0.9118\n",
- "Epoch 277/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8603 - root_mean_squared_error: 0.9112 - val_loss: 0.9530 - val_root_mean_squared_error: 0.9592\n",
- "Epoch 278/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8542 - root_mean_squared_error: 0.9061 - val_loss: 1.0390 - val_root_mean_squared_error: 1.0045\n",
- "Epoch 279/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.9364 - root_mean_squared_error: 0.9523 - val_loss: 0.8574 - val_root_mean_squared_error: 0.9091\n",
- "Epoch 280/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7772 - root_mean_squared_error: 0.8657 - val_loss: 0.8227 - val_root_mean_squared_error: 0.8896\n",
- "Epoch 281/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8404 - root_mean_squared_error: 0.9009 - val_loss: 0.8694 - val_root_mean_squared_error: 0.9161\n",
- "Epoch 282/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.9451 - root_mean_squared_error: 0.9578 - val_loss: 0.8427 - val_root_mean_squared_error: 0.8996\n",
- "Epoch 283/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8735 - root_mean_squared_error: 0.9207 - val_loss: 0.8784 - val_root_mean_squared_error: 0.9207\n",
- "Epoch 284/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8795 - root_mean_squared_error: 0.9230 - val_loss: 0.8963 - val_root_mean_squared_error: 0.9289\n",
- "Epoch 285/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.9196 - root_mean_squared_error: 0.9443 - val_loss: 0.8696 - val_root_mean_squared_error: 0.9160\n",
- "Epoch 286/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8769 - root_mean_squared_error: 0.9192 - val_loss: 0.8439 - val_root_mean_squared_error: 0.9005\n",
- "Epoch 287/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.9885 - root_mean_squared_error: 0.9803 - val_loss: 0.8304 - val_root_mean_squared_error: 0.8941\n",
- "Epoch 288/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8943 - root_mean_squared_error: 0.9303 - val_loss: 0.8848 - val_root_mean_squared_error: 0.9233\n",
- "Epoch 289/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8559 - root_mean_squared_error: 0.9085 - val_loss: 0.8719 - val_root_mean_squared_error: 0.9174\n",
- "Epoch 290/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8717 - root_mean_squared_error: 0.9202 - val_loss: 0.8596 - val_root_mean_squared_error: 0.9096\n",
- "Epoch 291/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8160 - root_mean_squared_error: 0.8869 - val_loss: 0.8449 - val_root_mean_squared_error: 0.9024\n",
- "Epoch 292/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8907 - root_mean_squared_error: 0.9287 - val_loss: 0.8087 - val_root_mean_squared_error: 0.8841\n",
- "Epoch 293/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7982 - root_mean_squared_error: 0.8753 - val_loss: 0.7915 - val_root_mean_squared_error: 0.8732\n",
- "Epoch 294/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8432 - root_mean_squared_error: 0.9006 - val_loss: 0.8336 - val_root_mean_squared_error: 0.8959\n",
- "Epoch 295/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8847 - root_mean_squared_error: 0.9259 - val_loss: 0.8626 - val_root_mean_squared_error: 0.9113\n",
- "Epoch 296/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8218 - root_mean_squared_error: 0.8915 - val_loss: 0.8999 - val_root_mean_squared_error: 0.9327\n",
- "Epoch 297/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.8519 - root_mean_squared_error: 0.9076 - val_loss: 0.8292 - val_root_mean_squared_error: 0.8956\n",
- "Epoch 298/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8427 - root_mean_squared_error: 0.9011 - val_loss: 0.8571 - val_root_mean_squared_error: 0.9092\n",
- "Epoch 299/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7611 - root_mean_squared_error: 0.8552 - val_loss: 0.8657 - val_root_mean_squared_error: 0.9137\n",
- "Epoch 300/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8126 - root_mean_squared_error: 0.8876 - val_loss: 0.8427 - val_root_mean_squared_error: 0.9054\n",
- "Epoch 301/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8605 - root_mean_squared_error: 0.9128 - val_loss: 0.8255 - val_root_mean_squared_error: 0.8938\n",
- "Epoch 302/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7714 - root_mean_squared_error: 0.8601 - val_loss: 0.8563 - val_root_mean_squared_error: 0.9112\n",
- "Epoch 303/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7957 - root_mean_squared_error: 0.8761 - val_loss: 0.9085 - val_root_mean_squared_error: 0.9375\n",
- "Epoch 304/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7844 - root_mean_squared_error: 0.8712 - val_loss: 0.8425 - val_root_mean_squared_error: 0.9030\n",
- "Epoch 305/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7842 - root_mean_squared_error: 0.8677 - val_loss: 0.9684 - val_root_mean_squared_error: 0.9689\n",
- "Epoch 306/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8297 - root_mean_squared_error: 0.8958 - val_loss: 0.8300 - val_root_mean_squared_error: 0.8966\n",
- "Epoch 307/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8642 - root_mean_squared_error: 0.9154 - val_loss: 0.8095 - val_root_mean_squared_error: 0.8850\n",
- "Epoch 308/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7821 - root_mean_squared_error: 0.8680 - val_loss: 0.8482 - val_root_mean_squared_error: 0.9025\n",
- "Epoch 309/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8451 - root_mean_squared_error: 0.9028 - val_loss: 0.8435 - val_root_mean_squared_error: 0.9028\n",
- "Epoch 310/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8444 - root_mean_squared_error: 0.9037 - val_loss: 0.9656 - val_root_mean_squared_error: 0.9663\n",
- "Epoch 311/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8242 - root_mean_squared_error: 0.8916 - val_loss: 0.8069 - val_root_mean_squared_error: 0.8841\n",
- "Epoch 312/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8191 - root_mean_squared_error: 0.8903 - val_loss: 0.8780 - val_root_mean_squared_error: 0.9213\n",
- "Epoch 313/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7556 - root_mean_squared_error: 0.8556 - val_loss: 0.9358 - val_root_mean_squared_error: 0.9521\n",
- "Epoch 314/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8293 - root_mean_squared_error: 0.8942 - val_loss: 0.8641 - val_root_mean_squared_error: 0.9132\n",
- "Epoch 315/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8204 - root_mean_squared_error: 0.8866 - val_loss: 0.8725 - val_root_mean_squared_error: 0.9171\n",
- "Epoch 316/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7977 - root_mean_squared_error: 0.8785 - val_loss: 0.8018 - val_root_mean_squared_error: 0.8809\n",
- "Epoch 317/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8145 - root_mean_squared_error: 0.8848 - val_loss: 0.8642 - val_root_mean_squared_error: 0.9146\n",
- "Epoch 318/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7636 - root_mean_squared_error: 0.8587 - val_loss: 0.8679 - val_root_mean_squared_error: 0.9153\n",
- "Epoch 319/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7634 - root_mean_squared_error: 0.8587 - val_loss: 0.8585 - val_root_mean_squared_error: 0.9104\n",
- "Epoch 320/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7867 - root_mean_squared_error: 0.8713 - val_loss: 0.8654 - val_root_mean_squared_error: 0.9139\n",
- "Epoch 321/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7826 - root_mean_squared_error: 0.8708 - val_loss: 0.9020 - val_root_mean_squared_error: 0.9398\n",
- "Epoch 322/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7988 - root_mean_squared_error: 0.8779 - val_loss: 0.8505 - val_root_mean_squared_error: 0.9057\n",
- "Epoch 323/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8049 - root_mean_squared_error: 0.8802 - val_loss: 0.8888 - val_root_mean_squared_error: 0.9291\n",
- "Epoch 324/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8355 - root_mean_squared_error: 0.8995 - val_loss: 0.8538 - val_root_mean_squared_error: 0.9084\n",
- "Epoch 325/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8064 - root_mean_squared_error: 0.8816 - val_loss: 0.8332 - val_root_mean_squared_error: 0.8968\n",
- "Epoch 326/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8232 - root_mean_squared_error: 0.8943 - val_loss: 0.8273 - val_root_mean_squared_error: 0.8933\n",
- "Epoch 327/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7938 - root_mean_squared_error: 0.8760 - val_loss: 0.8022 - val_root_mean_squared_error: 0.8778\n",
- "Epoch 328/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8227 - root_mean_squared_error: 0.8932 - val_loss: 0.8291 - val_root_mean_squared_error: 0.8995\n",
- "Epoch 329/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8229 - root_mean_squared_error: 0.8928 - val_loss: 0.8054 - val_root_mean_squared_error: 0.8829\n",
- "Epoch 330/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7957 - root_mean_squared_error: 0.8758 - val_loss: 0.8101 - val_root_mean_squared_error: 0.8828\n",
- "Epoch 331/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8638 - root_mean_squared_error: 0.9140 - val_loss: 0.8627 - val_root_mean_squared_error: 0.9137\n",
- "Epoch 332/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8050 - root_mean_squared_error: 0.8823 - val_loss: 0.8326 - val_root_mean_squared_error: 0.8969\n",
- "Epoch 333/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7702 - root_mean_squared_error: 0.8631 - val_loss: 0.8225 - val_root_mean_squared_error: 0.8900\n",
- "Epoch 334/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8861 - root_mean_squared_error: 0.9259 - val_loss: 0.9168 - val_root_mean_squared_error: 0.9442\n",
- "Epoch 335/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8093 - root_mean_squared_error: 0.8851 - val_loss: 0.8255 - val_root_mean_squared_error: 0.8948\n",
- "Epoch 336/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8370 - root_mean_squared_error: 0.8983 - val_loss: 0.8110 - val_root_mean_squared_error: 0.8856\n",
- "Epoch 337/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7753 - root_mean_squared_error: 0.8653 - val_loss: 0.7939 - val_root_mean_squared_error: 0.8760\n",
- "Epoch 338/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8356 - root_mean_squared_error: 0.8985 - val_loss: 0.8574 - val_root_mean_squared_error: 0.9119\n",
- "Epoch 339/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8365 - root_mean_squared_error: 0.9017 - val_loss: 0.8560 - val_root_mean_squared_error: 0.9095\n",
- "Epoch 340/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8059 - root_mean_squared_error: 0.8823 - val_loss: 0.8184 - val_root_mean_squared_error: 0.8885\n",
- "Epoch 341/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8240 - root_mean_squared_error: 0.8910 - val_loss: 0.7526 - val_root_mean_squared_error: 0.8532\n",
- "Epoch 342/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8019 - root_mean_squared_error: 0.8818 - val_loss: 0.8179 - val_root_mean_squared_error: 0.8909\n",
- "Epoch 343/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7642 - root_mean_squared_error: 0.8587 - val_loss: 0.8273 - val_root_mean_squared_error: 0.8940\n",
- "Epoch 344/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8141 - root_mean_squared_error: 0.8884 - val_loss: 0.8006 - val_root_mean_squared_error: 0.8808\n",
- "Epoch 345/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7686 - root_mean_squared_error: 0.8603 - val_loss: 0.7803 - val_root_mean_squared_error: 0.8691\n",
- "Epoch 346/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.9464 - root_mean_squared_error: 0.9597 - val_loss: 0.7958 - val_root_mean_squared_error: 0.8756\n",
- "Epoch 347/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8453 - root_mean_squared_error: 0.9054 - val_loss: 0.8060 - val_root_mean_squared_error: 0.8816\n",
- "Epoch 348/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 0.7978 - root_mean_squared_error: 0.8778 - val_loss: 0.8319 - val_root_mean_squared_error: 0.8973\n",
- "Epoch 349/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7762 - root_mean_squared_error: 0.8649 - val_loss: 0.8201 - val_root_mean_squared_error: 0.8882\n",
- "Epoch 350/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7811 - root_mean_squared_error: 0.8686 - val_loss: 0.8433 - val_root_mean_squared_error: 0.9006\n",
- "Epoch 351/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7815 - root_mean_squared_error: 0.8691 - val_loss: 0.8250 - val_root_mean_squared_error: 0.8974\n",
- "Epoch 352/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8195 - root_mean_squared_error: 0.8913 - val_loss: 0.7830 - val_root_mean_squared_error: 0.8675\n",
- "Epoch 353/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7549 - root_mean_squared_error: 0.8537 - val_loss: 0.8058 - val_root_mean_squared_error: 0.8827\n",
- "Epoch 354/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8039 - root_mean_squared_error: 0.8821 - val_loss: 0.8344 - val_root_mean_squared_error: 0.8998\n",
- "Epoch 355/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7958 - root_mean_squared_error: 0.8759 - val_loss: 0.7928 - val_root_mean_squared_error: 0.8747\n",
- "Epoch 356/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7770 - root_mean_squared_error: 0.8648 - val_loss: 0.8218 - val_root_mean_squared_error: 0.8909\n",
- "Epoch 357/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7814 - root_mean_squared_error: 0.8691 - val_loss: 0.7876 - val_root_mean_squared_error: 0.8725\n",
- "Epoch 358/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7551 - root_mean_squared_error: 0.8551 - val_loss: 0.8051 - val_root_mean_squared_error: 0.8836\n",
- "Epoch 359/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8334 - root_mean_squared_error: 0.8967 - val_loss: 0.8573 - val_root_mean_squared_error: 0.9122\n",
- "Epoch 360/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7956 - root_mean_squared_error: 0.8769 - val_loss: 0.8637 - val_root_mean_squared_error: 0.9155\n",
- "Epoch 361/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7380 - root_mean_squared_error: 0.8429 - val_loss: 0.8381 - val_root_mean_squared_error: 0.9007\n",
- "Epoch 362/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8270 - root_mean_squared_error: 0.8942 - val_loss: 0.8321 - val_root_mean_squared_error: 0.8980\n",
- "Epoch 363/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7946 - root_mean_squared_error: 0.8780 - val_loss: 0.8302 - val_root_mean_squared_error: 0.8948\n",
- "Epoch 364/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8029 - root_mean_squared_error: 0.8816 - val_loss: 0.7778 - val_root_mean_squared_error: 0.8675\n",
- "Epoch 365/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7732 - root_mean_squared_error: 0.8644 - val_loss: 0.8464 - val_root_mean_squared_error: 0.9059\n",
- "Epoch 366/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8335 - root_mean_squared_error: 0.8984 - val_loss: 0.8341 - val_root_mean_squared_error: 0.8970\n",
- "Epoch 367/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.8165 - root_mean_squared_error: 0.8884 - val_loss: 0.8166 - val_root_mean_squared_error: 0.8876\n",
- "Epoch 368/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7938 - root_mean_squared_error: 0.8756 - val_loss: 0.8025 - val_root_mean_squared_error: 0.8813\n",
- "Epoch 369/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7805 - root_mean_squared_error: 0.8693 - val_loss: 0.7811 - val_root_mean_squared_error: 0.8666\n",
- "Epoch 370/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7623 - root_mean_squared_error: 0.8585 - val_loss: 0.7603 - val_root_mean_squared_error: 0.8543\n",
- "Epoch 371/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8636 - root_mean_squared_error: 0.9157 - val_loss: 0.8131 - val_root_mean_squared_error: 0.8855\n",
- "Epoch 372/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7517 - root_mean_squared_error: 0.8521 - val_loss: 0.7903 - val_root_mean_squared_error: 0.8754\n",
- "Epoch 373/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8239 - root_mean_squared_error: 0.8927 - val_loss: 0.7908 - val_root_mean_squared_error: 0.8763\n",
- "Epoch 374/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8482 - root_mean_squared_error: 0.9055 - val_loss: 0.7890 - val_root_mean_squared_error: 0.8740\n",
- "Epoch 375/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7743 - root_mean_squared_error: 0.8651 - val_loss: 0.7905 - val_root_mean_squared_error: 0.8748\n",
- "Epoch 376/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8366 - root_mean_squared_error: 0.8995 - val_loss: 0.7512 - val_root_mean_squared_error: 0.8529\n",
- "Epoch 377/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.7907 - root_mean_squared_error: 0.8737 - val_loss: 0.7654 - val_root_mean_squared_error: 0.8597\n",
- "Epoch 378/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7412 - root_mean_squared_error: 0.8438 - val_loss: 0.7772 - val_root_mean_squared_error: 0.8686\n",
- "Epoch 379/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 0.7975 - root_mean_squared_error: 0.8777 - val_loss: 0.7919 - val_root_mean_squared_error: 0.8729\n",
- "Epoch 380/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8022 - root_mean_squared_error: 0.8808 - val_loss: 0.7692 - val_root_mean_squared_error: 0.8625\n",
- "Epoch 381/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.8045 - root_mean_squared_error: 0.8816 - val_loss: 0.7767 - val_root_mean_squared_error: 0.8652\n",
- "Epoch 382/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7206 - root_mean_squared_error: 0.8338 - val_loss: 0.7482 - val_root_mean_squared_error: 0.8494\n",
- "Epoch 383/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7976 - root_mean_squared_error: 0.8801 - val_loss: 0.7686 - val_root_mean_squared_error: 0.8612\n",
- "Epoch 384/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7643 - root_mean_squared_error: 0.8580 - val_loss: 0.7632 - val_root_mean_squared_error: 0.8590\n",
- "Epoch 385/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7427 - root_mean_squared_error: 0.8477 - val_loss: 0.7812 - val_root_mean_squared_error: 0.8699\n",
- "Epoch 386/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7780 - root_mean_squared_error: 0.8666 - val_loss: 0.8091 - val_root_mean_squared_error: 0.8863\n",
- "Epoch 387/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8136 - root_mean_squared_error: 0.8862 - val_loss: 0.7614 - val_root_mean_squared_error: 0.8556\n",
- "Epoch 388/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7549 - root_mean_squared_error: 0.8534 - val_loss: 0.7648 - val_root_mean_squared_error: 0.8582\n",
- "Epoch 389/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7507 - root_mean_squared_error: 0.8496 - val_loss: 0.7481 - val_root_mean_squared_error: 0.8518\n",
- "Epoch 390/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 0.7831 - root_mean_squared_error: 0.8714 - val_loss: 0.7516 - val_root_mean_squared_error: 0.8515\n",
- "Epoch 391/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8101 - root_mean_squared_error: 0.8840 - val_loss: 0.7700 - val_root_mean_squared_error: 0.8642\n",
- "Epoch 392/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7828 - root_mean_squared_error: 0.8699 - val_loss: 0.7782 - val_root_mean_squared_error: 0.8692\n",
- "Epoch 393/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7754 - root_mean_squared_error: 0.8654 - val_loss: 0.7783 - val_root_mean_squared_error: 0.8671\n",
- "Epoch 394/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6976 - root_mean_squared_error: 0.8207 - val_loss: 0.7357 - val_root_mean_squared_error: 0.8391\n",
- "Epoch 395/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7639 - root_mean_squared_error: 0.8584 - val_loss: 0.8244 - val_root_mean_squared_error: 0.8937\n",
- "Epoch 396/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7241 - root_mean_squared_error: 0.8336 - val_loss: 0.7628 - val_root_mean_squared_error: 0.8562\n",
- "Epoch 397/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7493 - root_mean_squared_error: 0.8508 - val_loss: 0.7743 - val_root_mean_squared_error: 0.8677\n",
- "Epoch 398/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7429 - root_mean_squared_error: 0.8471 - val_loss: 0.7921 - val_root_mean_squared_error: 0.8729\n",
- "Epoch 399/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7505 - root_mean_squared_error: 0.8507 - val_loss: 0.9588 - val_root_mean_squared_error: 0.9676\n",
- "Epoch 400/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7425 - root_mean_squared_error: 0.8478 - val_loss: 0.8502 - val_root_mean_squared_error: 0.9094\n",
- "Epoch 401/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7389 - root_mean_squared_error: 0.8437 - val_loss: 0.7949 - val_root_mean_squared_error: 0.8759\n",
- "Epoch 402/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7479 - root_mean_squared_error: 0.8535 - val_loss: 0.7907 - val_root_mean_squared_error: 0.8771\n",
- "Epoch 403/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7445 - root_mean_squared_error: 0.8482 - val_loss: 0.7599 - val_root_mean_squared_error: 0.8553\n",
- "Epoch 404/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7453 - root_mean_squared_error: 0.8484 - val_loss: 0.8323 - val_root_mean_squared_error: 0.8994\n",
- "Epoch 405/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7652 - root_mean_squared_error: 0.8604 - val_loss: 0.7581 - val_root_mean_squared_error: 0.8564\n",
- "Epoch 406/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7527 - root_mean_squared_error: 0.8512 - val_loss: 0.7761 - val_root_mean_squared_error: 0.8663\n",
- "Epoch 407/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8044 - root_mean_squared_error: 0.8810 - val_loss: 0.7412 - val_root_mean_squared_error: 0.8460\n",
- "Epoch 408/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7275 - root_mean_squared_error: 0.8385 - val_loss: 0.7300 - val_root_mean_squared_error: 0.8388\n",
- "Epoch 409/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7792 - root_mean_squared_error: 0.8681 - val_loss: 0.7162 - val_root_mean_squared_error: 0.8285\n",
- "Epoch 410/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7079 - root_mean_squared_error: 0.8241 - val_loss: 0.7341 - val_root_mean_squared_error: 0.8399\n",
- "Epoch 411/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7654 - root_mean_squared_error: 0.8613 - val_loss: 0.7662 - val_root_mean_squared_error: 0.8598\n",
- "Epoch 412/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7122 - root_mean_squared_error: 0.8308 - val_loss: 0.7708 - val_root_mean_squared_error: 0.8618\n",
- "Epoch 413/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.8058 - root_mean_squared_error: 0.8814 - val_loss: 0.7567 - val_root_mean_squared_error: 0.8545\n",
- "Epoch 414/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7657 - root_mean_squared_error: 0.8596 - val_loss: 0.7682 - val_root_mean_squared_error: 0.8618\n",
- "Epoch 415/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7407 - root_mean_squared_error: 0.8469 - val_loss: 0.8288 - val_root_mean_squared_error: 0.8955\n",
- "Epoch 416/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7746 - root_mean_squared_error: 0.8645 - val_loss: 0.7498 - val_root_mean_squared_error: 0.8532\n",
- "Epoch 417/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7307 - root_mean_squared_error: 0.8402 - val_loss: 0.7358 - val_root_mean_squared_error: 0.8437\n",
- "Epoch 418/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7624 - root_mean_squared_error: 0.8591 - val_loss: 0.7737 - val_root_mean_squared_error: 0.8651\n",
- "Epoch 419/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7474 - root_mean_squared_error: 0.8512 - val_loss: 0.7960 - val_root_mean_squared_error: 0.8762\n",
- "Epoch 420/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6921 - root_mean_squared_error: 0.8148 - val_loss: 0.7599 - val_root_mean_squared_error: 0.8568\n",
- "Epoch 421/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7265 - root_mean_squared_error: 0.8362 - val_loss: 0.8229 - val_root_mean_squared_error: 0.8924\n",
- "Epoch 422/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7629 - root_mean_squared_error: 0.8572 - val_loss: 0.7627 - val_root_mean_squared_error: 0.8572\n",
- "Epoch 423/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7308 - root_mean_squared_error: 0.8405 - val_loss: 0.7685 - val_root_mean_squared_error: 0.8634\n",
- "Epoch 424/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.8174 - root_mean_squared_error: 0.8899 - val_loss: 0.7959 - val_root_mean_squared_error: 0.8773\n",
- "Epoch 425/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7645 - root_mean_squared_error: 0.8586 - val_loss: 0.7177 - val_root_mean_squared_error: 0.8335\n",
- "Epoch 426/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7355 - root_mean_squared_error: 0.8428 - val_loss: 0.8012 - val_root_mean_squared_error: 0.8832\n",
- "Epoch 427/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7742 - root_mean_squared_error: 0.8641 - val_loss: 0.6973 - val_root_mean_squared_error: 0.8205\n",
- "Epoch 428/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7364 - root_mean_squared_error: 0.8433 - val_loss: 0.7549 - val_root_mean_squared_error: 0.8547\n",
- "Epoch 429/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7565 - root_mean_squared_error: 0.8556 - val_loss: 0.7335 - val_root_mean_squared_error: 0.8410\n",
- "Epoch 430/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7132 - root_mean_squared_error: 0.8286 - val_loss: 0.7741 - val_root_mean_squared_error: 0.8641\n",
- "Epoch 431/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7265 - root_mean_squared_error: 0.8365 - val_loss: 0.7497 - val_root_mean_squared_error: 0.8494\n",
- "Epoch 432/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7734 - root_mean_squared_error: 0.8635 - val_loss: 0.7308 - val_root_mean_squared_error: 0.8393\n",
- "Epoch 433/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7017 - root_mean_squared_error: 0.8211 - val_loss: 0.8711 - val_root_mean_squared_error: 0.9197\n",
- "Epoch 434/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7643 - root_mean_squared_error: 0.8597 - val_loss: 0.7315 - val_root_mean_squared_error: 0.8402\n",
- "Epoch 435/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7076 - root_mean_squared_error: 0.8245 - val_loss: 0.7836 - val_root_mean_squared_error: 0.8706\n",
- "Epoch 436/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.6618 - root_mean_squared_error: 0.7967 - val_loss: 0.7725 - val_root_mean_squared_error: 0.8634\n",
- "Epoch 437/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7410 - root_mean_squared_error: 0.8439 - val_loss: 0.7092 - val_root_mean_squared_error: 0.8266\n",
- "Epoch 438/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6737 - root_mean_squared_error: 0.8034 - val_loss: 0.7741 - val_root_mean_squared_error: 0.8645\n",
- "Epoch 439/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7576 - root_mean_squared_error: 0.8559 - val_loss: 0.7706 - val_root_mean_squared_error: 0.8638\n",
- "Epoch 440/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 0.6968 - root_mean_squared_error: 0.8187 - val_loss: 0.8011 - val_root_mean_squared_error: 0.8797\n",
- "Epoch 441/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7431 - root_mean_squared_error: 0.8499 - val_loss: 0.7224 - val_root_mean_squared_error: 0.8342\n",
- "Epoch 442/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7131 - root_mean_squared_error: 0.8285 - val_loss: 0.7628 - val_root_mean_squared_error: 0.8579\n",
- "Epoch 443/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7243 - root_mean_squared_error: 0.8362 - val_loss: 0.7491 - val_root_mean_squared_error: 0.8512\n",
- "Epoch 444/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7039 - root_mean_squared_error: 0.8225 - val_loss: 0.7340 - val_root_mean_squared_error: 0.8404\n",
- "Epoch 445/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6880 - root_mean_squared_error: 0.8140 - val_loss: 0.7309 - val_root_mean_squared_error: 0.8378\n",
- "Epoch 446/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.7294 - root_mean_squared_error: 0.8397 - val_loss: 0.7872 - val_root_mean_squared_error: 0.8736\n",
- "Epoch 447/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7333 - root_mean_squared_error: 0.8408 - val_loss: 0.7372 - val_root_mean_squared_error: 0.8448\n",
- "Epoch 448/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7214 - root_mean_squared_error: 0.8355 - val_loss: 0.7831 - val_root_mean_squared_error: 0.8723\n",
- "Epoch 449/500\n",
- "5/5 [==============================] - 0s 28ms/step - loss: 0.6966 - root_mean_squared_error: 0.8212 - val_loss: 0.7574 - val_root_mean_squared_error: 0.8567\n",
- "Epoch 450/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7110 - root_mean_squared_error: 0.8270 - val_loss: 0.7232 - val_root_mean_squared_error: 0.8352\n",
- "Epoch 451/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7144 - root_mean_squared_error: 0.8294 - val_loss: 0.7323 - val_root_mean_squared_error: 0.8424\n",
- "Epoch 452/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7804 - root_mean_squared_error: 0.8681 - val_loss: 0.7405 - val_root_mean_squared_error: 0.8469\n",
- "Epoch 453/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7456 - root_mean_squared_error: 0.8479 - val_loss: 0.7313 - val_root_mean_squared_error: 0.8390\n",
- "Epoch 454/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6589 - root_mean_squared_error: 0.7969 - val_loss: 0.7293 - val_root_mean_squared_error: 0.8384\n",
- "Epoch 455/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7256 - root_mean_squared_error: 0.8369 - val_loss: 0.7270 - val_root_mean_squared_error: 0.8350\n",
- "Epoch 456/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7633 - root_mean_squared_error: 0.8564 - val_loss: 0.6895 - val_root_mean_squared_error: 0.8145\n",
- "Epoch 457/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7487 - root_mean_squared_error: 0.8503 - val_loss: 0.7474 - val_root_mean_squared_error: 0.8491\n",
- "Epoch 458/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6566 - root_mean_squared_error: 0.7934 - val_loss: 0.7253 - val_root_mean_squared_error: 0.8374\n",
- "Epoch 459/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7292 - root_mean_squared_error: 0.8379 - val_loss: 0.6837 - val_root_mean_squared_error: 0.8111\n",
- "Epoch 460/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6782 - root_mean_squared_error: 0.8091 - val_loss: 0.7685 - val_root_mean_squared_error: 0.8608\n",
- "Epoch 461/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7348 - root_mean_squared_error: 0.8419 - val_loss: 0.7494 - val_root_mean_squared_error: 0.8503\n",
- "Epoch 462/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6886 - root_mean_squared_error: 0.8140 - val_loss: 0.7291 - val_root_mean_squared_error: 0.8385\n",
- "Epoch 463/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7805 - root_mean_squared_error: 0.8687 - val_loss: 0.7260 - val_root_mean_squared_error: 0.8380\n",
- "Epoch 464/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7480 - root_mean_squared_error: 0.8505 - val_loss: 0.7243 - val_root_mean_squared_error: 0.8356\n",
- "Epoch 465/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7482 - root_mean_squared_error: 0.8505 - val_loss: 0.7415 - val_root_mean_squared_error: 0.8480\n",
- "Epoch 466/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7471 - root_mean_squared_error: 0.8492 - val_loss: 0.7026 - val_root_mean_squared_error: 0.8282\n",
- "Epoch 467/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7071 - root_mean_squared_error: 0.8262 - val_loss: 0.7448 - val_root_mean_squared_error: 0.8489\n",
- "Epoch 468/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7759 - root_mean_squared_error: 0.8665 - val_loss: 0.7197 - val_root_mean_squared_error: 0.8320\n",
- "Epoch 469/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7428 - root_mean_squared_error: 0.8454 - val_loss: 0.7367 - val_root_mean_squared_error: 0.8443\n",
- "Epoch 470/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.6782 - root_mean_squared_error: 0.8072 - val_loss: 0.7085 - val_root_mean_squared_error: 0.8260\n",
- "Epoch 471/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7049 - root_mean_squared_error: 0.8225 - val_loss: 0.7204 - val_root_mean_squared_error: 0.8349\n",
- "Epoch 472/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.6491 - root_mean_squared_error: 0.7883 - val_loss: 0.7155 - val_root_mean_squared_error: 0.8292\n",
- "Epoch 473/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6897 - root_mean_squared_error: 0.8150 - val_loss: 0.6887 - val_root_mean_squared_error: 0.8135\n",
- "Epoch 474/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.6964 - root_mean_squared_error: 0.8196 - val_loss: 0.7141 - val_root_mean_squared_error: 0.8310\n",
- "Epoch 475/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7585 - root_mean_squared_error: 0.8556 - val_loss: 0.7135 - val_root_mean_squared_error: 0.8275\n",
- "Epoch 476/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7021 - root_mean_squared_error: 0.8225 - val_loss: 0.7421 - val_root_mean_squared_error: 0.8456\n",
- "Epoch 477/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.6938 - root_mean_squared_error: 0.8173 - val_loss: 0.7160 - val_root_mean_squared_error: 0.8315\n",
- "Epoch 478/500\n",
- "5/5 [==============================] - 0s 29ms/step - loss: 0.7404 - root_mean_squared_error: 0.8442 - val_loss: 0.7335 - val_root_mean_squared_error: 0.8408\n",
- "Epoch 479/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7401 - root_mean_squared_error: 0.8469 - val_loss: 0.6884 - val_root_mean_squared_error: 0.8150\n",
- "Epoch 480/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7076 - root_mean_squared_error: 0.8242 - val_loss: 0.7114 - val_root_mean_squared_error: 0.8268\n",
- "Epoch 481/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7180 - root_mean_squared_error: 0.8313 - val_loss: 0.7051 - val_root_mean_squared_error: 0.8240\n",
- "Epoch 482/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7148 - root_mean_squared_error: 0.8312 - val_loss: 0.7308 - val_root_mean_squared_error: 0.8386\n",
- "Epoch 483/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.7098 - root_mean_squared_error: 0.8251 - val_loss: 0.6752 - val_root_mean_squared_error: 0.8055\n",
- "Epoch 484/500\n",
- "5/5 [==============================] - 0s 30ms/step - loss: 0.6497 - root_mean_squared_error: 0.7903 - val_loss: 0.7359 - val_root_mean_squared_error: 0.8441\n",
- "Epoch 485/500\n",
- "5/5 [==============================] - 0s 36ms/step - loss: 0.6704 - root_mean_squared_error: 0.8047 - val_loss: 0.6974 - val_root_mean_squared_error: 0.8177\n",
- "Epoch 486/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.7476 - root_mean_squared_error: 0.8502 - val_loss: 0.7195 - val_root_mean_squared_error: 0.8328\n",
- "Epoch 487/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.7330 - root_mean_squared_error: 0.8407 - val_loss: 0.6829 - val_root_mean_squared_error: 0.8082\n",
- "Epoch 488/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.6270 - root_mean_squared_error: 0.7761 - val_loss: 0.7291 - val_root_mean_squared_error: 0.8374\n",
- "Epoch 489/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.7055 - root_mean_squared_error: 0.8251 - val_loss: 0.6826 - val_root_mean_squared_error: 0.8109\n",
- "Epoch 490/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6910 - root_mean_squared_error: 0.8147 - val_loss: 0.7298 - val_root_mean_squared_error: 0.8388\n",
- "Epoch 491/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.7084 - root_mean_squared_error: 0.8262 - val_loss: 0.7792 - val_root_mean_squared_error: 0.8680\n",
- "Epoch 492/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6939 - root_mean_squared_error: 0.8176 - val_loss: 0.7059 - val_root_mean_squared_error: 0.8249\n",
- "Epoch 493/500\n",
- "5/5 [==============================] - 0s 36ms/step - loss: 0.6629 - root_mean_squared_error: 0.7994 - val_loss: 0.7607 - val_root_mean_squared_error: 0.8549\n",
- "Epoch 494/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.7003 - root_mean_squared_error: 0.8204 - val_loss: 0.6696 - val_root_mean_squared_error: 0.8023\n",
- "Epoch 495/500\n",
- "5/5 [==============================] - 0s 31ms/step - loss: 0.6613 - root_mean_squared_error: 0.7970 - val_loss: 0.7362 - val_root_mean_squared_error: 0.8450\n",
- "Epoch 496/500\n",
- "5/5 [==============================] - 0s 32ms/step - loss: 0.6838 - root_mean_squared_error: 0.8071 - val_loss: 0.7198 - val_root_mean_squared_error: 0.8309\n",
- "Epoch 497/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.7360 - root_mean_squared_error: 0.8416 - val_loss: 0.7094 - val_root_mean_squared_error: 0.8279\n",
- "Epoch 498/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.7539 - root_mean_squared_error: 0.8541 - val_loss: 0.6967 - val_root_mean_squared_error: 0.8191\n",
- "Epoch 499/500\n",
- "5/5 [==============================] - 0s 33ms/step - loss: 0.7041 - root_mean_squared_error: 0.8237 - val_loss: 0.7500 - val_root_mean_squared_error: 0.8498\n",
- "Epoch 500/500\n",
- "5/5 [==============================] - 0s 34ms/step - loss: 0.7026 - root_mean_squared_error: 0.8223 - val_loss: 0.7973 - val_root_mean_squared_error: 0.8806\n",
"Model training finished.\n",
- "Train RMSE: 0.851\n",
+ "Train RMSE: 0.82\n",
"Evaluating model performance...\n",
- "Test RMSE: 0.849\n"
+ "Test RMSE: 0.778\n"
]
}
],
@@ -1781,7 +589,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 12,
"metadata": {
"id": "oDLpALs7JZX-"
},
@@ -1790,16 +598,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Predictions mean: 5.74, min: 5.14, max: 6.27, range: 1.13 - Actual: 6.0\n",
- "Predictions mean: 6.1, min: 5.56, max: 6.33, range: 0.77 - Actual: 7.0\n",
- "Predictions mean: 5.84, min: 5.34, max: 6.34, range: 1.0 - Actual: 7.0\n",
- "Predictions mean: 6.15, min: 5.61, max: 6.39, range: 0.78 - Actual: 6.0\n",
- "Predictions mean: 6.11, min: 5.58, max: 6.37, range: 0.79 - Actual: 5.0\n",
- "Predictions mean: 6.01, min: 5.37, max: 6.36, range: 0.99 - Actual: 7.0\n",
- "Predictions mean: 6.04, min: 5.51, max: 6.41, range: 0.91 - Actual: 6.0\n",
- "Predictions mean: 5.96, min: 5.42, max: 6.32, range: 0.9 - Actual: 6.0\n",
- "Predictions mean: 6.14, min: 5.65, max: 6.43, range: 0.78 - Actual: 8.0\n",
- "Predictions mean: 5.66, min: 4.99, max: 6.2, range: 1.21 - Actual: 6.0\n"
+ "Predictions mean: 6.22, min: 5.7, max: 6.53, range: 0.83 - Actual: 5.0\n",
+ "Predictions mean: 5.93, min: 5.45, max: 6.37, range: 0.91 - Actual: 6.0\n",
+ "Predictions mean: 5.67, min: 4.97, max: 6.32, range: 1.35 - Actual: 5.0\n",
+ "Predictions mean: 5.87, min: 4.87, max: 6.38, range: 1.51 - Actual: 5.0\n",
+ "Predictions mean: 5.91, min: 5.41, max: 6.37, range: 0.96 - Actual: 6.0\n",
+ "Predictions mean: 5.44, min: 4.62, max: 6.12, range: 1.5 - Actual: 6.0\n",
+ "Predictions mean: 5.32, min: 4.63, max: 6.03, range: 1.4 - Actual: 5.0\n",
+ "Predictions mean: 5.55, min: 4.81, max: 6.22, range: 1.41 - Actual: 5.0\n",
+ "Predictions mean: 6.14, min: 5.69, max: 6.51, range: 0.82 - Actual: 6.0\n",
+ "Predictions mean: 5.96, min: 5.41, max: 6.35, range: 0.94 - Actual: 5.0\n"
]
}
],
@@ -1840,7 +648,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 13,
"metadata": {
"id": "_e8b8DCJJZX-"
},
@@ -1850,1020 +658,20 @@
"output_type": "stream",
"text": [
"Start training the model...\n",
- "Epoch 1/500\n",
- "17/17 [==============================] - 2s 35ms/step - loss: 40.0905 - root_mean_squared_error: 6.3314 - val_loss: 35.8348 - val_root_mean_squared_error: 5.9858\n",
- "Epoch 2/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 37.6488 - root_mean_squared_error: 6.1355 - val_loss: 37.0272 - val_root_mean_squared_error: 6.0846\n",
- "Epoch 3/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 36.3662 - root_mean_squared_error: 6.0301 - val_loss: 37.7250 - val_root_mean_squared_error: 6.1417\n",
- "Epoch 4/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 34.3783 - root_mean_squared_error: 5.8630 - val_loss: 31.5982 - val_root_mean_squared_error: 5.6208\n",
- "Epoch 5/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 35.5805 - root_mean_squared_error: 5.9646 - val_loss: 30.3964 - val_root_mean_squared_error: 5.5129\n",
- "Epoch 6/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 32.8945 - root_mean_squared_error: 5.7350 - val_loss: 30.7139 - val_root_mean_squared_error: 5.5416\n",
- "Epoch 7/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 32.4754 - root_mean_squared_error: 5.6983 - val_loss: 30.3086 - val_root_mean_squared_error: 5.5048\n",
- "Epoch 8/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 31.0565 - root_mean_squared_error: 5.5724 - val_loss: 31.1866 - val_root_mean_squared_error: 5.5840\n",
- "Epoch 9/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 27.9851 - root_mean_squared_error: 5.2897 - val_loss: 28.6839 - val_root_mean_squared_error: 5.3552\n",
- "Epoch 10/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 28.2430 - root_mean_squared_error: 5.3141 - val_loss: 30.2928 - val_root_mean_squared_error: 5.5035\n",
- "Epoch 11/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 26.6376 - root_mean_squared_error: 5.1607 - val_loss: 25.9014 - val_root_mean_squared_error: 5.0888\n",
- "Epoch 12/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 25.2912 - root_mean_squared_error: 5.0285 - val_loss: 23.2093 - val_root_mean_squared_error: 4.8172\n",
- "Epoch 13/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 24.8960 - root_mean_squared_error: 4.9891 - val_loss: 25.5103 - val_root_mean_squared_error: 5.0504\n",
- "Epoch 14/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 24.3532 - root_mean_squared_error: 4.9344 - val_loss: 22.8375 - val_root_mean_squared_error: 4.7782\n",
- "Epoch 15/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 22.4276 - root_mean_squared_error: 4.7352 - val_loss: 20.9428 - val_root_mean_squared_error: 4.5758\n",
- "Epoch 16/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 21.1903 - root_mean_squared_error: 4.6028 - val_loss: 19.6403 - val_root_mean_squared_error: 4.4311\n",
- "Epoch 17/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 20.1841 - root_mean_squared_error: 4.4921 - val_loss: 18.8207 - val_root_mean_squared_error: 4.3375\n",
- "Epoch 18/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 18.6136 - root_mean_squared_error: 4.3137 - val_loss: 18.9530 - val_root_mean_squared_error: 4.3529\n",
- "Epoch 19/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 18.0773 - root_mean_squared_error: 4.2511 - val_loss: 16.2860 - val_root_mean_squared_error: 4.0348\n",
- "Epoch 20/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 16.7276 - root_mean_squared_error: 4.0893 - val_loss: 16.4384 - val_root_mean_squared_error: 4.0537\n",
- "Epoch 21/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 16.1143 - root_mean_squared_error: 4.0135 - val_loss: 13.6043 - val_root_mean_squared_error: 3.6876\n",
- "Epoch 22/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 15.5216 - root_mean_squared_error: 3.9390 - val_loss: 14.0286 - val_root_mean_squared_error: 3.7447\n",
- "Epoch 23/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 14.7735 - root_mean_squared_error: 3.8429 - val_loss: 13.6101 - val_root_mean_squared_error: 3.6885\n",
- "Epoch 24/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 14.6718 - root_mean_squared_error: 3.8297 - val_loss: 13.0984 - val_root_mean_squared_error: 3.6185\n",
- "Epoch 25/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 12.2290 - root_mean_squared_error: 3.4960 - val_loss: 12.7084 - val_root_mean_squared_error: 3.5642\n",
- "Epoch 26/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 11.5957 - root_mean_squared_error: 3.4043 - val_loss: 10.6381 - val_root_mean_squared_error: 3.2604\n",
- "Epoch 27/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 11.4028 - root_mean_squared_error: 3.3759 - val_loss: 10.7183 - val_root_mean_squared_error: 3.2729\n",
- "Epoch 28/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 9.9945 - root_mean_squared_error: 3.1604 - val_loss: 9.1325 - val_root_mean_squared_error: 3.0211\n",
- "Epoch 29/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 9.3494 - root_mean_squared_error: 3.0565 - val_loss: 8.5997 - val_root_mean_squared_error: 2.9314\n",
- "Epoch 30/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 8.7994 - root_mean_squared_error: 2.9653 - val_loss: 8.8903 - val_root_mean_squared_error: 2.9805\n",
- "Epoch 31/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 8.2323 - root_mean_squared_error: 2.8680 - val_loss: 7.1276 - val_root_mean_squared_error: 2.6684\n",
- "Epoch 32/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 7.7751 - root_mean_squared_error: 2.7870 - val_loss: 8.6531 - val_root_mean_squared_error: 2.9405\n",
- "Epoch 33/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 6.5677 - root_mean_squared_error: 2.5613 - val_loss: 6.9584 - val_root_mean_squared_error: 2.6366\n",
- "Epoch 34/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 6.4013 - root_mean_squared_error: 2.5287 - val_loss: 5.7653 - val_root_mean_squared_error: 2.3997\n",
- "Epoch 35/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 5.6532 - root_mean_squared_error: 2.3760 - val_loss: 5.1107 - val_root_mean_squared_error: 2.2591\n",
- "Epoch 36/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 5.3516 - root_mean_squared_error: 2.3118 - val_loss: 4.6135 - val_root_mean_squared_error: 2.1461\n",
- "Epoch 37/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 4.3631 - root_mean_squared_error: 2.0869 - val_loss: 4.0978 - val_root_mean_squared_error: 2.0225\n",
- "Epoch 38/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 4.2535 - root_mean_squared_error: 2.0606 - val_loss: 3.8108 - val_root_mean_squared_error: 1.9502\n",
- "Epoch 39/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.7561 - root_mean_squared_error: 1.9362 - val_loss: 3.3581 - val_root_mean_squared_error: 1.8305\n",
- "Epoch 40/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.4173 - root_mean_squared_error: 1.8466 - val_loss: 4.4214 - val_root_mean_squared_error: 2.1009\n",
- "Epoch 41/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.9929 - root_mean_squared_error: 1.7276 - val_loss: 2.6899 - val_root_mean_squared_error: 1.6373\n",
- "Epoch 42/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.6413 - root_mean_squared_error: 1.6226 - val_loss: 2.7743 - val_root_mean_squared_error: 1.6630\n",
- "Epoch 43/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.3123 - root_mean_squared_error: 1.5178 - val_loss: 1.9119 - val_root_mean_squared_error: 1.3792\n",
- "Epoch 44/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.9747 - root_mean_squared_error: 1.4020 - val_loss: 1.6839 - val_root_mean_squared_error: 1.2938\n",
- "Epoch 45/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.7826 - root_mean_squared_error: 1.3317 - val_loss: 1.9360 - val_root_mean_squared_error: 1.3882\n",
- "Epoch 46/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.5861 - root_mean_squared_error: 1.2557 - val_loss: 1.2034 - val_root_mean_squared_error: 1.0923\n",
- "Epoch 47/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.4216 - root_mean_squared_error: 1.1884 - val_loss: 1.2900 - val_root_mean_squared_error: 1.1311\n",
- "Epoch 48/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3748 - root_mean_squared_error: 1.1686 - val_loss: 1.0753 - val_root_mean_squared_error: 1.0327\n",
- "Epoch 49/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1092 - root_mean_squared_error: 1.0487 - val_loss: 0.9962 - val_root_mean_squared_error: 0.9933\n",
- "Epoch 50/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.0277 - root_mean_squared_error: 1.0096 - val_loss: 0.8349 - val_root_mean_squared_error: 0.9079\n",
- "Epoch 51/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.8958 - root_mean_squared_error: 0.9413 - val_loss: 0.7852 - val_root_mean_squared_error: 0.8797\n",
- "Epoch 52/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.8349 - root_mean_squared_error: 0.9081 - val_loss: 0.7919 - val_root_mean_squared_error: 0.8840\n",
- "Epoch 53/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7924 - root_mean_squared_error: 0.8849 - val_loss: 0.7981 - val_root_mean_squared_error: 0.8879\n",
- "Epoch 54/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.8176 - root_mean_squared_error: 0.8987 - val_loss: 0.7778 - val_root_mean_squared_error: 0.8759\n",
- "Epoch 55/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7833 - root_mean_squared_error: 0.8795 - val_loss: 0.7974 - val_root_mean_squared_error: 0.8873\n",
- "Epoch 56/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7784 - root_mean_squared_error: 0.8765 - val_loss: 0.7856 - val_root_mean_squared_error: 0.8804\n",
- "Epoch 57/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7977 - root_mean_squared_error: 0.8873 - val_loss: 0.7858 - val_root_mean_squared_error: 0.8799\n",
- "Epoch 58/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7723 - root_mean_squared_error: 0.8732 - val_loss: 0.7984 - val_root_mean_squared_error: 0.8882\n",
- "Epoch 59/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7682 - root_mean_squared_error: 0.8707 - val_loss: 0.8088 - val_root_mean_squared_error: 0.8933\n",
- "Epoch 60/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7653 - root_mean_squared_error: 0.8695 - val_loss: 0.7920 - val_root_mean_squared_error: 0.8846\n",
- "Epoch 61/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7327 - root_mean_squared_error: 0.8500 - val_loss: 0.8002 - val_root_mean_squared_error: 0.8885\n",
- "Epoch 62/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7430 - root_mean_squared_error: 0.8560 - val_loss: 0.7864 - val_root_mean_squared_error: 0.8806\n",
- "Epoch 63/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7344 - root_mean_squared_error: 0.8509 - val_loss: 0.7464 - val_root_mean_squared_error: 0.8574\n",
- "Epoch 64/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7413 - root_mean_squared_error: 0.8551 - val_loss: 0.7623 - val_root_mean_squared_error: 0.8674\n",
- "Epoch 65/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7648 - root_mean_squared_error: 0.8690 - val_loss: 0.7748 - val_root_mean_squared_error: 0.8751\n",
- "Epoch 66/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7379 - root_mean_squared_error: 0.8532 - val_loss: 0.7547 - val_root_mean_squared_error: 0.8637\n",
- "Epoch 67/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7404 - root_mean_squared_error: 0.8547 - val_loss: 0.7129 - val_root_mean_squared_error: 0.8385\n",
- "Epoch 68/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7299 - root_mean_squared_error: 0.8486 - val_loss: 0.7736 - val_root_mean_squared_error: 0.8734\n",
- "Epoch 69/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7297 - root_mean_squared_error: 0.8483 - val_loss: 0.7134 - val_root_mean_squared_error: 0.8381\n",
- "Epoch 70/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7320 - root_mean_squared_error: 0.8498 - val_loss: 0.7225 - val_root_mean_squared_error: 0.8441\n",
- "Epoch 71/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7264 - root_mean_squared_error: 0.8465 - val_loss: 0.7558 - val_root_mean_squared_error: 0.8633\n",
- "Epoch 72/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7204 - root_mean_squared_error: 0.8426 - val_loss: 0.7297 - val_root_mean_squared_error: 0.8478\n",
- "Epoch 73/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7168 - root_mean_squared_error: 0.8408 - val_loss: 0.7133 - val_root_mean_squared_error: 0.8379\n",
- "Epoch 74/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7172 - root_mean_squared_error: 0.8410 - val_loss: 0.6978 - val_root_mean_squared_error: 0.8286\n",
- "Epoch 75/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7218 - root_mean_squared_error: 0.8438 - val_loss: 0.7328 - val_root_mean_squared_error: 0.8500\n",
- "Epoch 76/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7195 - root_mean_squared_error: 0.8423 - val_loss: 0.7435 - val_root_mean_squared_error: 0.8556\n",
- "Epoch 77/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7103 - root_mean_squared_error: 0.8370 - val_loss: 0.7232 - val_root_mean_squared_error: 0.8444\n",
- "Epoch 78/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7142 - root_mean_squared_error: 0.8393 - val_loss: 0.6810 - val_root_mean_squared_error: 0.8193\n",
- "Epoch 79/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7055 - root_mean_squared_error: 0.8343 - val_loss: 0.7465 - val_root_mean_squared_error: 0.8581\n",
- "Epoch 80/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7222 - root_mean_squared_error: 0.8437 - val_loss: 0.7288 - val_root_mean_squared_error: 0.8471\n",
- "Epoch 81/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6885 - root_mean_squared_error: 0.8235 - val_loss: 0.7576 - val_root_mean_squared_error: 0.8640\n",
- "Epoch 82/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.7015 - root_mean_squared_error: 0.8310 - val_loss: 0.6959 - val_root_mean_squared_error: 0.8280\n",
- "Epoch 83/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6836 - root_mean_squared_error: 0.8202 - val_loss: 0.6933 - val_root_mean_squared_error: 0.8261\n",
- "Epoch 84/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7103 - root_mean_squared_error: 0.8369 - val_loss: 0.7498 - val_root_mean_squared_error: 0.8604\n",
- "Epoch 85/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6946 - root_mean_squared_error: 0.8272 - val_loss: 0.6811 - val_root_mean_squared_error: 0.8198\n",
- "Epoch 86/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6860 - root_mean_squared_error: 0.8223 - val_loss: 0.6788 - val_root_mean_squared_error: 0.8175\n",
- "Epoch 87/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6896 - root_mean_squared_error: 0.8242 - val_loss: 0.7371 - val_root_mean_squared_error: 0.8527\n",
- "Epoch 88/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6928 - root_mean_squared_error: 0.8261 - val_loss: 0.6733 - val_root_mean_squared_error: 0.8136\n",
- "Epoch 89/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6757 - root_mean_squared_error: 0.8159 - val_loss: 0.6769 - val_root_mean_squared_error: 0.8159\n",
- "Epoch 90/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.7093 - root_mean_squared_error: 0.8362 - val_loss: 0.7251 - val_root_mean_squared_error: 0.8456\n",
- "Epoch 91/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6809 - root_mean_squared_error: 0.8188 - val_loss: 0.7372 - val_root_mean_squared_error: 0.8525\n",
- "Epoch 92/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6952 - root_mean_squared_error: 0.8278 - val_loss: 0.6925 - val_root_mean_squared_error: 0.8256\n",
- "Epoch 93/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6842 - root_mean_squared_error: 0.8208 - val_loss: 0.6772 - val_root_mean_squared_error: 0.8166\n",
- "Epoch 94/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6940 - root_mean_squared_error: 0.8267 - val_loss: 0.6893 - val_root_mean_squared_error: 0.8231\n",
- "Epoch 95/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6815 - root_mean_squared_error: 0.8192 - val_loss: 0.6660 - val_root_mean_squared_error: 0.8102\n",
- "Epoch 96/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6785 - root_mean_squared_error: 0.8174 - val_loss: 0.6472 - val_root_mean_squared_error: 0.7979\n",
- "Epoch 97/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6784 - root_mean_squared_error: 0.8171 - val_loss: 0.6699 - val_root_mean_squared_error: 0.8113\n",
- "Epoch 98/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6850 - root_mean_squared_error: 0.8214 - val_loss: 0.7340 - val_root_mean_squared_error: 0.8498\n",
- "Epoch 99/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6689 - root_mean_squared_error: 0.8117 - val_loss: 0.6540 - val_root_mean_squared_error: 0.8029\n",
- "Epoch 100/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6590 - root_mean_squared_error: 0.8052 - val_loss: 0.6731 - val_root_mean_squared_error: 0.8142\n",
- "Epoch 101/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6725 - root_mean_squared_error: 0.8129 - val_loss: 0.6356 - val_root_mean_squared_error: 0.7909\n",
- "Epoch 102/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6864 - root_mean_squared_error: 0.8223 - val_loss: 0.6675 - val_root_mean_squared_error: 0.8114\n",
- "Epoch 103/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6541 - root_mean_squared_error: 0.8022 - val_loss: 0.6522 - val_root_mean_squared_error: 0.8008\n",
- "Epoch 104/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6557 - root_mean_squared_error: 0.8030 - val_loss: 0.7328 - val_root_mean_squared_error: 0.8498\n",
- "Epoch 105/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6714 - root_mean_squared_error: 0.8128 - val_loss: 0.6436 - val_root_mean_squared_error: 0.7951\n",
- "Epoch 106/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6510 - root_mean_squared_error: 0.8005 - val_loss: 0.6327 - val_root_mean_squared_error: 0.7881\n",
- "Epoch 107/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6779 - root_mean_squared_error: 0.8167 - val_loss: 0.6784 - val_root_mean_squared_error: 0.8183\n",
- "Epoch 108/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6606 - root_mean_squared_error: 0.8062 - val_loss: 0.6541 - val_root_mean_squared_error: 0.8016\n",
- "Epoch 109/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6551 - root_mean_squared_error: 0.8029 - val_loss: 0.6597 - val_root_mean_squared_error: 0.8048\n",
- "Epoch 110/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6641 - root_mean_squared_error: 0.8082 - val_loss: 0.6723 - val_root_mean_squared_error: 0.8127\n",
- "Epoch 111/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6512 - root_mean_squared_error: 0.8000 - val_loss: 0.6601 - val_root_mean_squared_error: 0.8052\n",
- "Epoch 112/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6576 - root_mean_squared_error: 0.8040 - val_loss: 0.6180 - val_root_mean_squared_error: 0.7786\n",
- "Epoch 113/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6459 - root_mean_squared_error: 0.7972 - val_loss: 0.6315 - val_root_mean_squared_error: 0.7876\n",
- "Epoch 114/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6574 - root_mean_squared_error: 0.8041 - val_loss: 0.6554 - val_root_mean_squared_error: 0.8028\n",
- "Epoch 115/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6632 - root_mean_squared_error: 0.8077 - val_loss: 0.6444 - val_root_mean_squared_error: 0.7963\n",
- "Epoch 116/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6509 - root_mean_squared_error: 0.8000 - val_loss: 0.6670 - val_root_mean_squared_error: 0.8099\n",
- "Epoch 117/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6706 - root_mean_squared_error: 0.8122 - val_loss: 0.6167 - val_root_mean_squared_error: 0.7788\n",
- "Epoch 118/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6500 - root_mean_squared_error: 0.7994 - val_loss: 0.6699 - val_root_mean_squared_error: 0.8121\n",
- "Epoch 119/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6423 - root_mean_squared_error: 0.7945 - val_loss: 0.6709 - val_root_mean_squared_error: 0.8122\n",
- "Epoch 120/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6441 - root_mean_squared_error: 0.7958 - val_loss: 0.6749 - val_root_mean_squared_error: 0.8148\n",
- "Epoch 121/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6335 - root_mean_squared_error: 0.7889 - val_loss: 0.6620 - val_root_mean_squared_error: 0.8080\n",
- "Epoch 122/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6433 - root_mean_squared_error: 0.7952 - val_loss: 0.6356 - val_root_mean_squared_error: 0.7896\n",
- "Epoch 123/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6346 - root_mean_squared_error: 0.7896 - val_loss: 0.6454 - val_root_mean_squared_error: 0.7963\n",
- "Epoch 124/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6613 - root_mean_squared_error: 0.8068 - val_loss: 0.6316 - val_root_mean_squared_error: 0.7876\n",
- "Epoch 125/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6347 - root_mean_squared_error: 0.7898 - val_loss: 0.6229 - val_root_mean_squared_error: 0.7822\n",
- "Epoch 126/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6565 - root_mean_squared_error: 0.8036 - val_loss: 0.6353 - val_root_mean_squared_error: 0.7903\n",
- "Epoch 127/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6380 - root_mean_squared_error: 0.7915 - val_loss: 0.6073 - val_root_mean_squared_error: 0.7712\n",
- "Epoch 128/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6282 - root_mean_squared_error: 0.7853 - val_loss: 0.6753 - val_root_mean_squared_error: 0.8152\n",
- "Epoch 129/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6351 - root_mean_squared_error: 0.7899 - val_loss: 0.6560 - val_root_mean_squared_error: 0.8028\n",
- "Epoch 130/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6255 - root_mean_squared_error: 0.7837 - val_loss: 0.6456 - val_root_mean_squared_error: 0.7968\n",
- "Epoch 131/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6413 - root_mean_squared_error: 0.7937 - val_loss: 0.6177 - val_root_mean_squared_error: 0.7789\n",
- "Epoch 132/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6243 - root_mean_squared_error: 0.7829 - val_loss: 0.6131 - val_root_mean_squared_error: 0.7752\n",
- "Epoch 133/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6407 - root_mean_squared_error: 0.7936 - val_loss: 0.6725 - val_root_mean_squared_error: 0.8125\n",
- "Epoch 134/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6414 - root_mean_squared_error: 0.7943 - val_loss: 0.6329 - val_root_mean_squared_error: 0.7885\n",
- "Epoch 135/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6362 - root_mean_squared_error: 0.7904 - val_loss: 0.6183 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 136/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6454 - root_mean_squared_error: 0.7966 - val_loss: 0.6360 - val_root_mean_squared_error: 0.7898\n",
- "Epoch 137/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6254 - root_mean_squared_error: 0.7838 - val_loss: 0.6453 - val_root_mean_squared_error: 0.7958\n",
- "Epoch 138/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6510 - root_mean_squared_error: 0.8002 - val_loss: 0.6138 - val_root_mean_squared_error: 0.7765\n",
- "Epoch 139/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6270 - root_mean_squared_error: 0.7846 - val_loss: 0.6286 - val_root_mean_squared_error: 0.7858\n",
- "Epoch 140/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6285 - root_mean_squared_error: 0.7853 - val_loss: 0.6319 - val_root_mean_squared_error: 0.7880\n",
- "Epoch 141/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6334 - root_mean_squared_error: 0.7883 - val_loss: 0.6409 - val_root_mean_squared_error: 0.7935\n",
- "Epoch 142/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6468 - root_mean_squared_error: 0.7974 - val_loss: 0.6535 - val_root_mean_squared_error: 0.8020\n",
- "Epoch 143/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6257 - root_mean_squared_error: 0.7834 - val_loss: 0.6293 - val_root_mean_squared_error: 0.7870\n",
- "Epoch 144/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6448 - root_mean_squared_error: 0.7958 - val_loss: 0.6359 - val_root_mean_squared_error: 0.7900\n",
- "Epoch 145/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6309 - root_mean_squared_error: 0.7869 - val_loss: 0.6649 - val_root_mean_squared_error: 0.8078\n",
- "Epoch 146/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6202 - root_mean_squared_error: 0.7804 - val_loss: 0.6253 - val_root_mean_squared_error: 0.7840\n",
- "Epoch 147/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6309 - root_mean_squared_error: 0.7867 - val_loss: 0.6408 - val_root_mean_squared_error: 0.7949\n",
- "Epoch 148/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6538 - root_mean_squared_error: 0.8018 - val_loss: 0.6251 - val_root_mean_squared_error: 0.7833\n",
- "Epoch 149/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6287 - root_mean_squared_error: 0.7859 - val_loss: 0.6172 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 150/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6430 - root_mean_squared_error: 0.7944 - val_loss: 0.6174 - val_root_mean_squared_error: 0.7778\n",
- "Epoch 151/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6242 - root_mean_squared_error: 0.7829 - val_loss: 0.6273 - val_root_mean_squared_error: 0.7856\n",
- "Epoch 152/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6305 - root_mean_squared_error: 0.7868 - val_loss: 0.6293 - val_root_mean_squared_error: 0.7860\n",
- "Epoch 153/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6368 - root_mean_squared_error: 0.7908 - val_loss: 0.6316 - val_root_mean_squared_error: 0.7875\n",
- "Epoch 154/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6538 - root_mean_squared_error: 0.8013 - val_loss: 0.6363 - val_root_mean_squared_error: 0.7902\n",
- "Epoch 155/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6380 - root_mean_squared_error: 0.7916 - val_loss: 0.6193 - val_root_mean_squared_error: 0.7792\n",
- "Epoch 156/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6361 - root_mean_squared_error: 0.7905 - val_loss: 0.6198 - val_root_mean_squared_error: 0.7803\n",
- "Epoch 157/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6249 - root_mean_squared_error: 0.7829 - val_loss: 0.6306 - val_root_mean_squared_error: 0.7867\n",
- "Epoch 158/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6341 - root_mean_squared_error: 0.7891 - val_loss: 0.6101 - val_root_mean_squared_error: 0.7737\n",
- "Epoch 159/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6191 - root_mean_squared_error: 0.7793 - val_loss: 0.6225 - val_root_mean_squared_error: 0.7823\n",
- "Epoch 160/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6303 - root_mean_squared_error: 0.7866 - val_loss: 0.6174 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 161/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6233 - root_mean_squared_error: 0.7825 - val_loss: 0.6164 - val_root_mean_squared_error: 0.7772\n",
- "Epoch 162/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6171 - root_mean_squared_error: 0.7779 - val_loss: 0.6148 - val_root_mean_squared_error: 0.7761\n",
- "Epoch 163/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6298 - root_mean_squared_error: 0.7860 - val_loss: 0.6183 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 164/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6436 - root_mean_squared_error: 0.7952 - val_loss: 0.6155 - val_root_mean_squared_error: 0.7760\n",
- "Epoch 165/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6283 - root_mean_squared_error: 0.7854 - val_loss: 0.6547 - val_root_mean_squared_error: 0.8017\n",
- "Epoch 166/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6247 - root_mean_squared_error: 0.7828 - val_loss: 0.6404 - val_root_mean_squared_error: 0.7929\n",
- "Epoch 167/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6230 - root_mean_squared_error: 0.7816 - val_loss: 0.6111 - val_root_mean_squared_error: 0.7749\n",
- "Epoch 168/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6295 - root_mean_squared_error: 0.7859 - val_loss: 0.5989 - val_root_mean_squared_error: 0.7648\n",
- "Epoch 169/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6377 - root_mean_squared_error: 0.7912 - val_loss: 0.6127 - val_root_mean_squared_error: 0.7752\n",
- "Epoch 170/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6264 - root_mean_squared_error: 0.7838 - val_loss: 0.6186 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 171/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6328 - root_mean_squared_error: 0.7877 - val_loss: 0.6280 - val_root_mean_squared_error: 0.7851\n",
- "Epoch 172/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6366 - root_mean_squared_error: 0.7902 - val_loss: 0.6232 - val_root_mean_squared_error: 0.7813\n",
- "Epoch 173/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6327 - root_mean_squared_error: 0.7879 - val_loss: 0.6155 - val_root_mean_squared_error: 0.7775\n",
- "Epoch 174/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6185 - root_mean_squared_error: 0.7786 - val_loss: 0.6130 - val_root_mean_squared_error: 0.7745\n",
- "Epoch 175/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6270 - root_mean_squared_error: 0.7842 - val_loss: 0.6335 - val_root_mean_squared_error: 0.7882\n",
- "Epoch 176/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6190 - root_mean_squared_error: 0.7792 - val_loss: 0.6283 - val_root_mean_squared_error: 0.7849\n",
- "Epoch 177/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6340 - root_mean_squared_error: 0.7886 - val_loss: 0.6016 - val_root_mean_squared_error: 0.7679\n",
- "Epoch 178/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6209 - root_mean_squared_error: 0.7804 - val_loss: 0.6259 - val_root_mean_squared_error: 0.7840\n",
- "Epoch 179/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6278 - root_mean_squared_error: 0.7848 - val_loss: 0.6519 - val_root_mean_squared_error: 0.8005\n",
- "Epoch 180/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6182 - root_mean_squared_error: 0.7785 - val_loss: 0.6094 - val_root_mean_squared_error: 0.7732\n",
- "Epoch 181/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6242 - root_mean_squared_error: 0.7823 - val_loss: 0.6422 - val_root_mean_squared_error: 0.7930\n",
- "Epoch 182/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6099 - root_mean_squared_error: 0.7729 - val_loss: 0.6315 - val_root_mean_squared_error: 0.7876\n",
- "Epoch 183/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6106 - root_mean_squared_error: 0.7734 - val_loss: 0.6259 - val_root_mean_squared_error: 0.7836\n",
- "Epoch 184/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6246 - root_mean_squared_error: 0.7826 - val_loss: 0.6501 - val_root_mean_squared_error: 0.7982\n",
- "Epoch 185/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6371 - root_mean_squared_error: 0.7906 - val_loss: 0.6247 - val_root_mean_squared_error: 0.7824\n",
- "Epoch 186/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6139 - root_mean_squared_error: 0.7757 - val_loss: 0.6108 - val_root_mean_squared_error: 0.7729\n",
- "Epoch 187/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6216 - root_mean_squared_error: 0.7811 - val_loss: 0.6034 - val_root_mean_squared_error: 0.7686\n",
- "Epoch 188/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6278 - root_mean_squared_error: 0.7847 - val_loss: 0.6175 - val_root_mean_squared_error: 0.7775\n",
- "Epoch 189/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6152 - root_mean_squared_error: 0.7765 - val_loss: 0.5986 - val_root_mean_squared_error: 0.7653\n",
- "Epoch 190/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6149 - root_mean_squared_error: 0.7766 - val_loss: 0.5915 - val_root_mean_squared_error: 0.7611\n",
- "Epoch 191/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6160 - root_mean_squared_error: 0.7772 - val_loss: 0.6220 - val_root_mean_squared_error: 0.7806\n",
- "Epoch 192/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6178 - root_mean_squared_error: 0.7785 - val_loss: 0.6133 - val_root_mean_squared_error: 0.7755\n",
- "Epoch 193/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6196 - root_mean_squared_error: 0.7797 - val_loss: 0.6121 - val_root_mean_squared_error: 0.7744\n",
- "Epoch 194/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6260 - root_mean_squared_error: 0.7838 - val_loss: 0.6043 - val_root_mean_squared_error: 0.7690\n",
- "Epoch 195/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6237 - root_mean_squared_error: 0.7818 - val_loss: 0.5896 - val_root_mean_squared_error: 0.7595\n",
- "Epoch 196/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6129 - root_mean_squared_error: 0.7751 - val_loss: 0.6037 - val_root_mean_squared_error: 0.7684\n",
- "Epoch 197/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6079 - root_mean_squared_error: 0.7717 - val_loss: 0.6243 - val_root_mean_squared_error: 0.7820\n",
- "Epoch 198/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6228 - root_mean_squared_error: 0.7812 - val_loss: 0.6170 - val_root_mean_squared_error: 0.7778\n",
- "Epoch 199/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6292 - root_mean_squared_error: 0.7851 - val_loss: 0.6019 - val_root_mean_squared_error: 0.7697\n",
- "Epoch 200/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6254 - root_mean_squared_error: 0.7828 - val_loss: 0.6176 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 201/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6240 - root_mean_squared_error: 0.7822 - val_loss: 0.6444 - val_root_mean_squared_error: 0.7945\n",
- "Epoch 202/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6249 - root_mean_squared_error: 0.7822 - val_loss: 0.6267 - val_root_mean_squared_error: 0.7844\n",
- "Epoch 203/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6121 - root_mean_squared_error: 0.7745 - val_loss: 0.6211 - val_root_mean_squared_error: 0.7805\n",
- "Epoch 204/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6132 - root_mean_squared_error: 0.7751 - val_loss: 0.6050 - val_root_mean_squared_error: 0.7699\n",
- "Epoch 205/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6221 - root_mean_squared_error: 0.7808 - val_loss: 0.6202 - val_root_mean_squared_error: 0.7809\n",
- "Epoch 206/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6202 - root_mean_squared_error: 0.7800 - val_loss: 0.6130 - val_root_mean_squared_error: 0.7758\n",
- "Epoch 207/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6191 - root_mean_squared_error: 0.7789 - val_loss: 0.6030 - val_root_mean_squared_error: 0.7694\n",
- "Epoch 208/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6086 - root_mean_squared_error: 0.7721 - val_loss: 0.5941 - val_root_mean_squared_error: 0.7631\n",
- "Epoch 209/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6078 - root_mean_squared_error: 0.7716 - val_loss: 0.6019 - val_root_mean_squared_error: 0.7677\n",
- "Epoch 210/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6178 - root_mean_squared_error: 0.7783 - val_loss: 0.6327 - val_root_mean_squared_error: 0.7879\n",
- "Epoch 211/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6261 - root_mean_squared_error: 0.7831 - val_loss: 0.6458 - val_root_mean_squared_error: 0.7962\n",
- "Epoch 212/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6392 - root_mean_squared_error: 0.7920 - val_loss: 0.6051 - val_root_mean_squared_error: 0.7698\n",
- "Epoch 213/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6103 - root_mean_squared_error: 0.7730 - val_loss: 0.6241 - val_root_mean_squared_error: 0.7819\n",
- "Epoch 214/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6146 - root_mean_squared_error: 0.7757 - val_loss: 0.6144 - val_root_mean_squared_error: 0.7754\n",
- "Epoch 215/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6124 - root_mean_squared_error: 0.7744 - val_loss: 0.6328 - val_root_mean_squared_error: 0.7879\n",
- "Epoch 216/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6089 - root_mean_squared_error: 0.7722 - val_loss: 0.6447 - val_root_mean_squared_error: 0.7950\n",
- "Epoch 217/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6082 - root_mean_squared_error: 0.7719 - val_loss: 0.6186 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 218/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6033 - root_mean_squared_error: 0.7695 - val_loss: 0.6124 - val_root_mean_squared_error: 0.7747\n",
- "Epoch 219/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6052 - root_mean_squared_error: 0.7700 - val_loss: 0.6248 - val_root_mean_squared_error: 0.7828\n",
- "Epoch 220/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6232 - root_mean_squared_error: 0.7816 - val_loss: 0.6246 - val_root_mean_squared_error: 0.7817\n",
- "Epoch 221/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6087 - root_mean_squared_error: 0.7720 - val_loss: 0.6211 - val_root_mean_squared_error: 0.7807\n",
- "Epoch 222/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6237 - root_mean_squared_error: 0.7818 - val_loss: 0.6280 - val_root_mean_squared_error: 0.7837\n",
- "Epoch 223/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6061 - root_mean_squared_error: 0.7704 - val_loss: 0.6298 - val_root_mean_squared_error: 0.7861\n",
- "Epoch 224/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6324 - root_mean_squared_error: 0.7876 - val_loss: 0.6057 - val_root_mean_squared_error: 0.7697\n",
- "Epoch 225/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6220 - root_mean_squared_error: 0.7806 - val_loss: 0.6157 - val_root_mean_squared_error: 0.7765\n",
- "Epoch 226/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6224 - root_mean_squared_error: 0.7810 - val_loss: 0.6458 - val_root_mean_squared_error: 0.7961\n",
- "Epoch 227/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6082 - root_mean_squared_error: 0.7717 - val_loss: 0.6249 - val_root_mean_squared_error: 0.7826\n",
- "Epoch 228/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6186 - root_mean_squared_error: 0.7784 - val_loss: 0.6111 - val_root_mean_squared_error: 0.7736\n",
- "Epoch 229/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6212 - root_mean_squared_error: 0.7800 - val_loss: 0.6161 - val_root_mean_squared_error: 0.7777\n",
- "Epoch 230/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6158 - root_mean_squared_error: 0.7769 - val_loss: 0.6092 - val_root_mean_squared_error: 0.7715\n",
- "Epoch 231/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6089 - root_mean_squared_error: 0.7723 - val_loss: 0.6109 - val_root_mean_squared_error: 0.7747\n",
- "Epoch 232/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6198 - root_mean_squared_error: 0.7793 - val_loss: 0.5931 - val_root_mean_squared_error: 0.7625\n",
- "Epoch 233/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6107 - root_mean_squared_error: 0.7733 - val_loss: 0.6446 - val_root_mean_squared_error: 0.7945\n",
- "Epoch 234/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6248 - root_mean_squared_error: 0.7826 - val_loss: 0.5931 - val_root_mean_squared_error: 0.7622\n",
- "Epoch 235/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6186 - root_mean_squared_error: 0.7781 - val_loss: 0.6535 - val_root_mean_squared_error: 0.8011\n",
- "Epoch 236/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6246 - root_mean_squared_error: 0.7821 - val_loss: 0.5929 - val_root_mean_squared_error: 0.7621\n",
- "Epoch 237/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6198 - root_mean_squared_error: 0.7790 - val_loss: 0.6115 - val_root_mean_squared_error: 0.7738\n",
- "Epoch 238/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6149 - root_mean_squared_error: 0.7762 - val_loss: 0.6072 - val_root_mean_squared_error: 0.7718\n",
- "Epoch 239/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6010 - root_mean_squared_error: 0.7670 - val_loss: 0.6032 - val_root_mean_squared_error: 0.7677\n",
- "Epoch 240/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5993 - root_mean_squared_error: 0.7656 - val_loss: 0.5866 - val_root_mean_squared_error: 0.7581\n",
- "Epoch 241/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6046 - root_mean_squared_error: 0.7694 - val_loss: 0.5988 - val_root_mean_squared_error: 0.7656\n",
- "Epoch 242/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6241 - root_mean_squared_error: 0.7821 - val_loss: 0.6067 - val_root_mean_squared_error: 0.7705\n",
- "Epoch 243/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6263 - root_mean_squared_error: 0.7831 - val_loss: 0.6044 - val_root_mean_squared_error: 0.7693\n",
- "Epoch 244/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6069 - root_mean_squared_error: 0.7709 - val_loss: 0.5945 - val_root_mean_squared_error: 0.7621\n",
- "Epoch 245/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6208 - root_mean_squared_error: 0.7797 - val_loss: 0.6179 - val_root_mean_squared_error: 0.7780\n",
- "Epoch 246/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6215 - root_mean_squared_error: 0.7803 - val_loss: 0.6072 - val_root_mean_squared_error: 0.7719\n",
- "Epoch 247/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6343 - root_mean_squared_error: 0.7884 - val_loss: 0.5996 - val_root_mean_squared_error: 0.7661\n",
- "Epoch 248/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6119 - root_mean_squared_error: 0.7743 - val_loss: 0.6259 - val_root_mean_squared_error: 0.7828\n",
- "Epoch 249/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6124 - root_mean_squared_error: 0.7742 - val_loss: 0.6367 - val_root_mean_squared_error: 0.7908\n",
- "Epoch 250/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6144 - root_mean_squared_error: 0.7757 - val_loss: 0.6139 - val_root_mean_squared_error: 0.7741\n",
- "Epoch 251/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6109 - root_mean_squared_error: 0.7735 - val_loss: 0.5904 - val_root_mean_squared_error: 0.7596\n",
- "Epoch 252/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6128 - root_mean_squared_error: 0.7744 - val_loss: 0.6337 - val_root_mean_squared_error: 0.7876\n",
- "Epoch 253/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6133 - root_mean_squared_error: 0.7745 - val_loss: 0.6500 - val_root_mean_squared_error: 0.7985\n",
- "Epoch 254/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6157 - root_mean_squared_error: 0.7761 - val_loss: 0.5994 - val_root_mean_squared_error: 0.7666\n",
- "Epoch 255/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5992 - root_mean_squared_error: 0.7656 - val_loss: 0.6389 - val_root_mean_squared_error: 0.7915\n",
- "Epoch 256/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6231 - root_mean_squared_error: 0.7812 - val_loss: 0.5971 - val_root_mean_squared_error: 0.7637\n",
- "Epoch 257/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6166 - root_mean_squared_error: 0.7768 - val_loss: 0.6054 - val_root_mean_squared_error: 0.7701\n",
- "Epoch 258/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6035 - root_mean_squared_error: 0.7686 - val_loss: 0.5935 - val_root_mean_squared_error: 0.7617\n",
- "Epoch 259/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6177 - root_mean_squared_error: 0.7776 - val_loss: 0.6155 - val_root_mean_squared_error: 0.7771\n",
- "Epoch 260/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6233 - root_mean_squared_error: 0.7813 - val_loss: 0.6246 - val_root_mean_squared_error: 0.7826\n",
- "Epoch 261/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6152 - root_mean_squared_error: 0.7759 - val_loss: 0.6074 - val_root_mean_squared_error: 0.7716\n",
- "Epoch 262/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6083 - root_mean_squared_error: 0.7712 - val_loss: 0.6176 - val_root_mean_squared_error: 0.7770\n",
- "Epoch 263/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6006 - root_mean_squared_error: 0.7667 - val_loss: 0.6158 - val_root_mean_squared_error: 0.7765\n",
- "Epoch 264/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6229 - root_mean_squared_error: 0.7807 - val_loss: 0.6189 - val_root_mean_squared_error: 0.7792\n",
- "Epoch 265/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6056 - root_mean_squared_error: 0.7695 - val_loss: 0.5977 - val_root_mean_squared_error: 0.7649\n",
- "Epoch 266/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6105 - root_mean_squared_error: 0.7731 - val_loss: 0.6329 - val_root_mean_squared_error: 0.7876\n",
- "Epoch 267/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6191 - root_mean_squared_error: 0.7783 - val_loss: 0.5993 - val_root_mean_squared_error: 0.7657\n",
- "Epoch 268/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6237 - root_mean_squared_error: 0.7818 - val_loss: 0.5856 - val_root_mean_squared_error: 0.7562\n",
- "Epoch 269/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6014 - root_mean_squared_error: 0.7671 - val_loss: 0.6008 - val_root_mean_squared_error: 0.7666\n",
- "Epoch 270/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6040 - root_mean_squared_error: 0.7688 - val_loss: 0.6047 - val_root_mean_squared_error: 0.7692\n",
- "Epoch 271/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5910 - root_mean_squared_error: 0.7604 - val_loss: 0.6058 - val_root_mean_squared_error: 0.7704\n",
- "Epoch 272/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6049 - root_mean_squared_error: 0.7694 - val_loss: 0.6012 - val_root_mean_squared_error: 0.7669\n",
- "Epoch 273/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6143 - root_mean_squared_error: 0.7750 - val_loss: 0.5990 - val_root_mean_squared_error: 0.7656\n",
- "Epoch 274/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6143 - root_mean_squared_error: 0.7754 - val_loss: 0.6109 - val_root_mean_squared_error: 0.7733\n",
- "Epoch 275/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6083 - root_mean_squared_error: 0.7712 - val_loss: 0.5930 - val_root_mean_squared_error: 0.7612\n",
- "Epoch 276/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6161 - root_mean_squared_error: 0.7768 - val_loss: 0.6183 - val_root_mean_squared_error: 0.7772\n",
- "Epoch 277/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6120 - root_mean_squared_error: 0.7740 - val_loss: 0.6157 - val_root_mean_squared_error: 0.7758\n",
- "Epoch 278/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6034 - root_mean_squared_error: 0.7677 - val_loss: 0.6090 - val_root_mean_squared_error: 0.7715\n",
- "Epoch 279/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6198 - root_mean_squared_error: 0.7789 - val_loss: 0.5872 - val_root_mean_squared_error: 0.7573\n",
- "Epoch 280/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6089 - root_mean_squared_error: 0.7718 - val_loss: 0.6231 - val_root_mean_squared_error: 0.7812\n",
- "Epoch 281/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6115 - root_mean_squared_error: 0.7738 - val_loss: 0.5920 - val_root_mean_squared_error: 0.7609\n",
- "Epoch 282/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6164 - root_mean_squared_error: 0.7768 - val_loss: 0.6025 - val_root_mean_squared_error: 0.7675\n",
- "Epoch 283/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6100 - root_mean_squared_error: 0.7725 - val_loss: 0.5931 - val_root_mean_squared_error: 0.7626\n",
- "Epoch 284/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6061 - root_mean_squared_error: 0.7704 - val_loss: 0.6282 - val_root_mean_squared_error: 0.7841\n",
- "Epoch 285/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6024 - root_mean_squared_error: 0.7679 - val_loss: 0.6013 - val_root_mean_squared_error: 0.7667\n",
- "Epoch 286/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6118 - root_mean_squared_error: 0.7739 - val_loss: 0.6005 - val_root_mean_squared_error: 0.7660\n",
- "Epoch 287/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6082 - root_mean_squared_error: 0.7717 - val_loss: 0.6110 - val_root_mean_squared_error: 0.7736\n",
- "Epoch 288/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6039 - root_mean_squared_error: 0.7683 - val_loss: 0.6300 - val_root_mean_squared_error: 0.7855\n",
- "Epoch 289/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6129 - root_mean_squared_error: 0.7743 - val_loss: 0.5945 - val_root_mean_squared_error: 0.7619\n",
- "Epoch 290/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6082 - root_mean_squared_error: 0.7715 - val_loss: 0.6012 - val_root_mean_squared_error: 0.7677\n",
- "Epoch 291/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6094 - root_mean_squared_error: 0.7727 - val_loss: 0.6037 - val_root_mean_squared_error: 0.7684\n",
- "Epoch 292/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6069 - root_mean_squared_error: 0.7706 - val_loss: 0.6193 - val_root_mean_squared_error: 0.7790\n",
- "Epoch 293/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6112 - root_mean_squared_error: 0.7734 - val_loss: 0.6064 - val_root_mean_squared_error: 0.7705\n",
- "Epoch 294/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6106 - root_mean_squared_error: 0.7729 - val_loss: 0.6066 - val_root_mean_squared_error: 0.7698\n",
- "Epoch 295/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6236 - root_mean_squared_error: 0.7814 - val_loss: 0.6156 - val_root_mean_squared_error: 0.7762\n",
- "Epoch 296/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6217 - root_mean_squared_error: 0.7797 - val_loss: 0.6119 - val_root_mean_squared_error: 0.7736\n",
- "Epoch 297/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6062 - root_mean_squared_error: 0.7703 - val_loss: 0.6054 - val_root_mean_squared_error: 0.7695\n",
- "Epoch 298/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6107 - root_mean_squared_error: 0.7726 - val_loss: 0.6136 - val_root_mean_squared_error: 0.7749\n",
- "Epoch 299/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6285 - root_mean_squared_error: 0.7844 - val_loss: 0.5835 - val_root_mean_squared_error: 0.7548\n",
- "Epoch 300/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6031 - root_mean_squared_error: 0.7679 - val_loss: 0.5896 - val_root_mean_squared_error: 0.7594\n",
- "Epoch 301/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6076 - root_mean_squared_error: 0.7706 - val_loss: 0.5964 - val_root_mean_squared_error: 0.7630\n",
- "Epoch 302/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6160 - root_mean_squared_error: 0.7759 - val_loss: 0.6156 - val_root_mean_squared_error: 0.7761\n",
- "Epoch 303/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6089 - root_mean_squared_error: 0.7720 - val_loss: 0.6008 - val_root_mean_squared_error: 0.7668\n",
- "Epoch 304/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6153 - root_mean_squared_error: 0.7758 - val_loss: 0.5969 - val_root_mean_squared_error: 0.7650\n",
- "Epoch 305/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5977 - root_mean_squared_error: 0.7644 - val_loss: 0.6282 - val_root_mean_squared_error: 0.7846\n",
- "Epoch 306/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6074 - root_mean_squared_error: 0.7711 - val_loss: 0.6018 - val_root_mean_squared_error: 0.7670\n",
- "Epoch 307/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6073 - root_mean_squared_error: 0.7705 - val_loss: 0.6219 - val_root_mean_squared_error: 0.7796\n",
- "Epoch 308/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6004 - root_mean_squared_error: 0.7663 - val_loss: 0.6140 - val_root_mean_squared_error: 0.7741\n",
- "Epoch 309/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6199 - root_mean_squared_error: 0.7787 - val_loss: 0.6346 - val_root_mean_squared_error: 0.7885\n",
- "Epoch 310/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6157 - root_mean_squared_error: 0.7759 - val_loss: 0.6078 - val_root_mean_squared_error: 0.7716\n",
- "Epoch 311/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6085 - root_mean_squared_error: 0.7714 - val_loss: 0.6082 - val_root_mean_squared_error: 0.7723\n",
- "Epoch 312/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5999 - root_mean_squared_error: 0.7659 - val_loss: 0.5895 - val_root_mean_squared_error: 0.7595\n",
- "Epoch 313/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6055 - root_mean_squared_error: 0.7693 - val_loss: 0.5959 - val_root_mean_squared_error: 0.7625\n",
- "Epoch 314/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6133 - root_mean_squared_error: 0.7742 - val_loss: 0.5851 - val_root_mean_squared_error: 0.7560\n",
- "Epoch 315/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6058 - root_mean_squared_error: 0.7695 - val_loss: 0.6100 - val_root_mean_squared_error: 0.7724\n",
- "Epoch 316/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6029 - root_mean_squared_error: 0.7674 - val_loss: 0.6042 - val_root_mean_squared_error: 0.7686\n",
- "Epoch 317/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5990 - root_mean_squared_error: 0.7655 - val_loss: 0.6118 - val_root_mean_squared_error: 0.7737\n",
- "Epoch 318/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6078 - root_mean_squared_error: 0.7712 - val_loss: 0.6200 - val_root_mean_squared_error: 0.7786\n",
- "Epoch 319/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6117 - root_mean_squared_error: 0.7734 - val_loss: 0.6076 - val_root_mean_squared_error: 0.7710\n",
- "Epoch 320/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6160 - root_mean_squared_error: 0.7766 - val_loss: 0.6059 - val_root_mean_squared_error: 0.7701\n",
- "Epoch 321/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6045 - root_mean_squared_error: 0.7688 - val_loss: 0.6065 - val_root_mean_squared_error: 0.7702\n",
- "Epoch 322/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6198 - root_mean_squared_error: 0.7783 - val_loss: 0.5969 - val_root_mean_squared_error: 0.7634\n",
- "Epoch 323/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6069 - root_mean_squared_error: 0.7704 - val_loss: 0.6192 - val_root_mean_squared_error: 0.7783\n",
- "Epoch 324/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5978 - root_mean_squared_error: 0.7643 - val_loss: 0.6034 - val_root_mean_squared_error: 0.7678\n",
- "Epoch 325/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6080 - root_mean_squared_error: 0.7709 - val_loss: 0.5874 - val_root_mean_squared_error: 0.7577\n",
- "Epoch 326/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6072 - root_mean_squared_error: 0.7706 - val_loss: 0.5869 - val_root_mean_squared_error: 0.7571\n",
- "Epoch 327/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6022 - root_mean_squared_error: 0.7669 - val_loss: 0.6110 - val_root_mean_squared_error: 0.7720\n",
- "Epoch 328/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6127 - root_mean_squared_error: 0.7742 - val_loss: 0.6033 - val_root_mean_squared_error: 0.7678\n",
- "Epoch 329/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6200 - root_mean_squared_error: 0.7784 - val_loss: 0.5943 - val_root_mean_squared_error: 0.7618\n",
- "Epoch 330/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6117 - root_mean_squared_error: 0.7737 - val_loss: 0.6379 - val_root_mean_squared_error: 0.7900\n",
- "Epoch 331/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6095 - root_mean_squared_error: 0.7721 - val_loss: 0.6168 - val_root_mean_squared_error: 0.7773\n",
- "Epoch 332/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6127 - root_mean_squared_error: 0.7739 - val_loss: 0.6001 - val_root_mean_squared_error: 0.7661\n",
- "Epoch 333/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6002 - root_mean_squared_error: 0.7659 - val_loss: 0.6029 - val_root_mean_squared_error: 0.7674\n",
- "Epoch 334/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6075 - root_mean_squared_error: 0.7709 - val_loss: 0.6149 - val_root_mean_squared_error: 0.7754\n",
- "Epoch 335/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6043 - root_mean_squared_error: 0.7688 - val_loss: 0.5903 - val_root_mean_squared_error: 0.7601\n",
- "Epoch 336/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6089 - root_mean_squared_error: 0.7715 - val_loss: 0.6202 - val_root_mean_squared_error: 0.7793\n",
- "Epoch 337/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5981 - root_mean_squared_error: 0.7646 - val_loss: 0.6002 - val_root_mean_squared_error: 0.7663\n",
- "Epoch 338/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6008 - root_mean_squared_error: 0.7664 - val_loss: 0.6187 - val_root_mean_squared_error: 0.7775\n",
- "Epoch 339/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6238 - root_mean_squared_error: 0.7814 - val_loss: 0.6011 - val_root_mean_squared_error: 0.7666\n",
- "Epoch 340/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5990 - root_mean_squared_error: 0.7648 - val_loss: 0.6086 - val_root_mean_squared_error: 0.7714\n",
- "Epoch 341/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6147 - root_mean_squared_error: 0.7752 - val_loss: 0.6017 - val_root_mean_squared_error: 0.7666\n",
- "Epoch 342/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6031 - root_mean_squared_error: 0.7676 - val_loss: 0.6086 - val_root_mean_squared_error: 0.7711\n",
- "Epoch 343/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6010 - root_mean_squared_error: 0.7666 - val_loss: 0.6205 - val_root_mean_squared_error: 0.7786\n",
- "Epoch 344/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6039 - root_mean_squared_error: 0.7679 - val_loss: 0.5987 - val_root_mean_squared_error: 0.7644\n",
- "Epoch 345/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5976 - root_mean_squared_error: 0.7636 - val_loss: 0.5957 - val_root_mean_squared_error: 0.7637\n",
- "Epoch 346/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6030 - root_mean_squared_error: 0.7676 - val_loss: 0.5989 - val_root_mean_squared_error: 0.7650\n",
- "Epoch 347/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6088 - root_mean_squared_error: 0.7714 - val_loss: 0.6070 - val_root_mean_squared_error: 0.7706\n",
- "Epoch 348/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6051 - root_mean_squared_error: 0.7691 - val_loss: 0.5846 - val_root_mean_squared_error: 0.7550\n",
- "Epoch 349/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6131 - root_mean_squared_error: 0.7740 - val_loss: 0.5942 - val_root_mean_squared_error: 0.7615\n",
- "Epoch 350/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6026 - root_mean_squared_error: 0.7671 - val_loss: 0.5993 - val_root_mean_squared_error: 0.7650\n",
- "Epoch 351/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6118 - root_mean_squared_error: 0.7730 - val_loss: 0.6113 - val_root_mean_squared_error: 0.7734\n",
- "Epoch 352/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6004 - root_mean_squared_error: 0.7660 - val_loss: 0.5842 - val_root_mean_squared_error: 0.7547\n",
- "Epoch 353/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6104 - root_mean_squared_error: 0.7724 - val_loss: 0.6276 - val_root_mean_squared_error: 0.7830\n",
- "Epoch 354/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6054 - root_mean_squared_error: 0.7690 - val_loss: 0.5951 - val_root_mean_squared_error: 0.7625\n",
- "Epoch 355/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6132 - root_mean_squared_error: 0.7743 - val_loss: 0.6059 - val_root_mean_squared_error: 0.7701\n",
- "Epoch 356/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6159 - root_mean_squared_error: 0.7754 - val_loss: 0.6036 - val_root_mean_squared_error: 0.7682\n",
- "Epoch 357/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6068 - root_mean_squared_error: 0.7700 - val_loss: 0.5966 - val_root_mean_squared_error: 0.7636\n",
- "Epoch 358/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6055 - root_mean_squared_error: 0.7691 - val_loss: 0.5908 - val_root_mean_squared_error: 0.7604\n",
- "Epoch 359/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5985 - root_mean_squared_error: 0.7644 - val_loss: 0.6057 - val_root_mean_squared_error: 0.7694\n",
- "Epoch 360/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6061 - root_mean_squared_error: 0.7700 - val_loss: 0.6253 - val_root_mean_squared_error: 0.7820\n",
- "Epoch 361/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6107 - root_mean_squared_error: 0.7727 - val_loss: 0.5946 - val_root_mean_squared_error: 0.7620\n",
- "Epoch 362/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5993 - root_mean_squared_error: 0.7653 - val_loss: 0.6020 - val_root_mean_squared_error: 0.7676\n",
- "Epoch 363/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6092 - root_mean_squared_error: 0.7716 - val_loss: 0.6007 - val_root_mean_squared_error: 0.7660\n",
- "Epoch 364/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6093 - root_mean_squared_error: 0.7717 - val_loss: 0.6002 - val_root_mean_squared_error: 0.7665\n",
- "Epoch 365/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6039 - root_mean_squared_error: 0.7682 - val_loss: 0.5976 - val_root_mean_squared_error: 0.7644\n",
- "Epoch 366/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6158 - root_mean_squared_error: 0.7763 - val_loss: 0.6506 - val_root_mean_squared_error: 0.7976\n",
- "Epoch 367/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6181 - root_mean_squared_error: 0.7770 - val_loss: 0.6022 - val_root_mean_squared_error: 0.7673\n",
- "Epoch 368/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6177 - root_mean_squared_error: 0.7769 - val_loss: 0.6054 - val_root_mean_squared_error: 0.7691\n",
- "Epoch 369/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5951 - root_mean_squared_error: 0.7622 - val_loss: 0.6127 - val_root_mean_squared_error: 0.7750\n",
- "Epoch 370/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5987 - root_mean_squared_error: 0.7648 - val_loss: 0.6192 - val_root_mean_squared_error: 0.7778\n",
- "Epoch 371/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6058 - root_mean_squared_error: 0.7692 - val_loss: 0.5930 - val_root_mean_squared_error: 0.7609\n",
- "Epoch 372/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6061 - root_mean_squared_error: 0.7698 - val_loss: 0.6082 - val_root_mean_squared_error: 0.7705\n",
- "Epoch 373/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6153 - root_mean_squared_error: 0.7759 - val_loss: 0.5898 - val_root_mean_squared_error: 0.7594\n",
- "Epoch 374/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6041 - root_mean_squared_error: 0.7682 - val_loss: 0.6071 - val_root_mean_squared_error: 0.7700\n",
- "Epoch 375/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6064 - root_mean_squared_error: 0.7699 - val_loss: 0.5995 - val_root_mean_squared_error: 0.7658\n",
- "Epoch 376/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5993 - root_mean_squared_error: 0.7657 - val_loss: 0.6180 - val_root_mean_squared_error: 0.7769\n",
- "Epoch 377/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6081 - root_mean_squared_error: 0.7712 - val_loss: 0.6074 - val_root_mean_squared_error: 0.7714\n",
- "Epoch 378/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6125 - root_mean_squared_error: 0.7734 - val_loss: 0.6092 - val_root_mean_squared_error: 0.7713\n",
- "Epoch 379/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6073 - root_mean_squared_error: 0.7707 - val_loss: 0.5841 - val_root_mean_squared_error: 0.7553\n",
- "Epoch 380/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6095 - root_mean_squared_error: 0.7718 - val_loss: 0.5785 - val_root_mean_squared_error: 0.7510\n",
- "Epoch 381/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6058 - root_mean_squared_error: 0.7694 - val_loss: 0.6216 - val_root_mean_squared_error: 0.7797\n",
- "Epoch 382/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6057 - root_mean_squared_error: 0.7692 - val_loss: 0.6089 - val_root_mean_squared_error: 0.7717\n",
- "Epoch 383/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6095 - root_mean_squared_error: 0.7720 - val_loss: 0.6071 - val_root_mean_squared_error: 0.7698\n",
- "Epoch 384/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5963 - root_mean_squared_error: 0.7630 - val_loss: 0.6205 - val_root_mean_squared_error: 0.7784\n",
- "Epoch 385/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5917 - root_mean_squared_error: 0.7601 - val_loss: 0.6022 - val_root_mean_squared_error: 0.7669\n",
- "Epoch 386/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6215 - root_mean_squared_error: 0.7796 - val_loss: 0.6245 - val_root_mean_squared_error: 0.7818\n",
- "Epoch 387/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6043 - root_mean_squared_error: 0.7681 - val_loss: 0.5981 - val_root_mean_squared_error: 0.7641\n",
- "Epoch 388/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5974 - root_mean_squared_error: 0.7640 - val_loss: 0.6051 - val_root_mean_squared_error: 0.7683\n",
- "Epoch 389/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6012 - root_mean_squared_error: 0.7664 - val_loss: 0.5977 - val_root_mean_squared_error: 0.7636\n",
- "Epoch 390/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6030 - root_mean_squared_error: 0.7675 - val_loss: 0.5977 - val_root_mean_squared_error: 0.7643\n",
- "Epoch 391/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5993 - root_mean_squared_error: 0.7648 - val_loss: 0.6225 - val_root_mean_squared_error: 0.7802\n",
- "Epoch 392/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6086 - root_mean_squared_error: 0.7712 - val_loss: 0.5826 - val_root_mean_squared_error: 0.7543\n",
- "Epoch 393/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6071 - root_mean_squared_error: 0.7698 - val_loss: 0.6022 - val_root_mean_squared_error: 0.7659\n",
- "Epoch 394/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6076 - root_mean_squared_error: 0.7703 - val_loss: 0.5958 - val_root_mean_squared_error: 0.7626\n",
- "Epoch 395/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6064 - root_mean_squared_error: 0.7693 - val_loss: 0.5982 - val_root_mean_squared_error: 0.7639\n",
- "Epoch 396/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6185 - root_mean_squared_error: 0.7774 - val_loss: 0.6004 - val_root_mean_squared_error: 0.7668\n",
- "Epoch 397/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6048 - root_mean_squared_error: 0.7685 - val_loss: 0.6336 - val_root_mean_squared_error: 0.7870\n",
- "Epoch 398/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6013 - root_mean_squared_error: 0.7661 - val_loss: 0.6033 - val_root_mean_squared_error: 0.7672\n",
- "Epoch 399/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6050 - root_mean_squared_error: 0.7685 - val_loss: 0.6296 - val_root_mean_squared_error: 0.7844\n",
- "Epoch 400/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5957 - root_mean_squared_error: 0.7625 - val_loss: 0.6341 - val_root_mean_squared_error: 0.7886\n",
- "Epoch 401/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5985 - root_mean_squared_error: 0.7650 - val_loss: 0.6014 - val_root_mean_squared_error: 0.7660\n",
- "Epoch 402/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6039 - root_mean_squared_error: 0.7679 - val_loss: 0.6088 - val_root_mean_squared_error: 0.7715\n",
- "Epoch 403/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5972 - root_mean_squared_error: 0.7640 - val_loss: 0.5884 - val_root_mean_squared_error: 0.7585\n",
- "Epoch 404/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6047 - root_mean_squared_error: 0.7687 - val_loss: 0.6100 - val_root_mean_squared_error: 0.7721\n",
- "Epoch 405/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5990 - root_mean_squared_error: 0.7643 - val_loss: 0.5989 - val_root_mean_squared_error: 0.7641\n",
- "Epoch 406/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6114 - root_mean_squared_error: 0.7726 - val_loss: 0.6069 - val_root_mean_squared_error: 0.7700\n",
- "Epoch 407/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6080 - root_mean_squared_error: 0.7706 - val_loss: 0.5884 - val_root_mean_squared_error: 0.7591\n",
- "Epoch 408/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6026 - root_mean_squared_error: 0.7672 - val_loss: 0.6055 - val_root_mean_squared_error: 0.7695\n",
- "Epoch 409/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6074 - root_mean_squared_error: 0.7704 - val_loss: 0.5968 - val_root_mean_squared_error: 0.7628\n",
- "Epoch 410/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6088 - root_mean_squared_error: 0.7711 - val_loss: 0.5814 - val_root_mean_squared_error: 0.7523\n",
- "Epoch 411/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6117 - root_mean_squared_error: 0.7729 - val_loss: 0.6238 - val_root_mean_squared_error: 0.7797\n",
- "Epoch 412/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5967 - root_mean_squared_error: 0.7634 - val_loss: 0.5913 - val_root_mean_squared_error: 0.7592\n",
- "Epoch 413/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6112 - root_mean_squared_error: 0.7727 - val_loss: 0.6035 - val_root_mean_squared_error: 0.7666\n",
- "Epoch 414/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6071 - root_mean_squared_error: 0.7699 - val_loss: 0.6227 - val_root_mean_squared_error: 0.7797\n",
- "Epoch 415/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6091 - root_mean_squared_error: 0.7713 - val_loss: 0.6041 - val_root_mean_squared_error: 0.7675\n",
- "Epoch 416/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6055 - root_mean_squared_error: 0.7691 - val_loss: 0.6089 - val_root_mean_squared_error: 0.7711\n",
- "Epoch 417/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6058 - root_mean_squared_error: 0.7696 - val_loss: 0.5916 - val_root_mean_squared_error: 0.7597\n",
- "Epoch 418/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5928 - root_mean_squared_error: 0.7607 - val_loss: 0.5833 - val_root_mean_squared_error: 0.7550\n",
- "Epoch 419/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5965 - root_mean_squared_error: 0.7631 - val_loss: 0.5861 - val_root_mean_squared_error: 0.7550\n",
- "Epoch 420/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6066 - root_mean_squared_error: 0.7694 - val_loss: 0.6149 - val_root_mean_squared_error: 0.7755\n",
- "Epoch 421/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6052 - root_mean_squared_error: 0.7688 - val_loss: 0.5987 - val_root_mean_squared_error: 0.7641\n",
- "Epoch 422/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5982 - root_mean_squared_error: 0.7644 - val_loss: 0.6369 - val_root_mean_squared_error: 0.7893\n",
- "Epoch 423/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5907 - root_mean_squared_error: 0.7595 - val_loss: 0.6039 - val_root_mean_squared_error: 0.7683\n",
- "Epoch 424/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6018 - root_mean_squared_error: 0.7665 - val_loss: 0.5959 - val_root_mean_squared_error: 0.7624\n",
- "Epoch 425/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5964 - root_mean_squared_error: 0.7633 - val_loss: 0.6098 - val_root_mean_squared_error: 0.7711\n",
- "Epoch 426/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6091 - root_mean_squared_error: 0.7710 - val_loss: 0.6124 - val_root_mean_squared_error: 0.7725\n",
- "Epoch 427/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6032 - root_mean_squared_error: 0.7674 - val_loss: 0.5915 - val_root_mean_squared_error: 0.7598\n",
- "Epoch 428/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6047 - root_mean_squared_error: 0.7687 - val_loss: 0.6030 - val_root_mean_squared_error: 0.7674\n",
- "Epoch 429/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6013 - root_mean_squared_error: 0.7662 - val_loss: 0.6091 - val_root_mean_squared_error: 0.7720\n",
- "Epoch 430/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5925 - root_mean_squared_error: 0.7605 - val_loss: 0.6025 - val_root_mean_squared_error: 0.7659\n",
- "Epoch 431/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6014 - root_mean_squared_error: 0.7659 - val_loss: 0.5879 - val_root_mean_squared_error: 0.7565\n",
- "Epoch 432/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6101 - root_mean_squared_error: 0.7717 - val_loss: 0.5789 - val_root_mean_squared_error: 0.7512\n",
- "Epoch 433/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6000 - root_mean_squared_error: 0.7650 - val_loss: 0.5961 - val_root_mean_squared_error: 0.7633\n",
- "Epoch 434/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6015 - root_mean_squared_error: 0.7661 - val_loss: 0.6059 - val_root_mean_squared_error: 0.7692\n",
- "Epoch 435/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6022 - root_mean_squared_error: 0.7662 - val_loss: 0.5894 - val_root_mean_squared_error: 0.7587\n",
- "Epoch 436/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6009 - root_mean_squared_error: 0.7656 - val_loss: 0.5948 - val_root_mean_squared_error: 0.7614\n",
- "Epoch 437/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5943 - root_mean_squared_error: 0.7614 - val_loss: 0.5980 - val_root_mean_squared_error: 0.7645\n",
- "Epoch 438/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5984 - root_mean_squared_error: 0.7639 - val_loss: 0.5967 - val_root_mean_squared_error: 0.7637\n",
- "Epoch 439/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5943 - root_mean_squared_error: 0.7614 - val_loss: 0.5959 - val_root_mean_squared_error: 0.7633\n",
- "Epoch 440/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5969 - root_mean_squared_error: 0.7632 - val_loss: 0.6065 - val_root_mean_squared_error: 0.7694\n",
- "Epoch 441/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6065 - root_mean_squared_error: 0.7694 - val_loss: 0.6003 - val_root_mean_squared_error: 0.7650\n",
- "Epoch 442/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5988 - root_mean_squared_error: 0.7644 - val_loss: 0.6035 - val_root_mean_squared_error: 0.7681\n",
- "Epoch 443/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5955 - root_mean_squared_error: 0.7620 - val_loss: 0.6097 - val_root_mean_squared_error: 0.7711\n",
- "Epoch 444/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6046 - root_mean_squared_error: 0.7685 - val_loss: 0.6135 - val_root_mean_squared_error: 0.7727\n",
- "Epoch 445/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5960 - root_mean_squared_error: 0.7630 - val_loss: 0.6084 - val_root_mean_squared_error: 0.7705\n",
- "Epoch 446/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6062 - root_mean_squared_error: 0.7692 - val_loss: 0.5952 - val_root_mean_squared_error: 0.7622\n",
- "Epoch 447/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6018 - root_mean_squared_error: 0.7665 - val_loss: 0.6144 - val_root_mean_squared_error: 0.7740\n",
- "Epoch 448/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6192 - root_mean_squared_error: 0.7776 - val_loss: 0.6194 - val_root_mean_squared_error: 0.7785\n",
- "Epoch 449/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6018 - root_mean_squared_error: 0.7664 - val_loss: 0.5825 - val_root_mean_squared_error: 0.7534\n",
- "Epoch 450/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6003 - root_mean_squared_error: 0.7653 - val_loss: 0.5851 - val_root_mean_squared_error: 0.7553\n",
- "Epoch 451/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6002 - root_mean_squared_error: 0.7653 - val_loss: 0.5984 - val_root_mean_squared_error: 0.7645\n",
- "Epoch 452/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6059 - root_mean_squared_error: 0.7695 - val_loss: 0.5861 - val_root_mean_squared_error: 0.7558\n",
- "Epoch 453/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6108 - root_mean_squared_error: 0.7723 - val_loss: 0.6044 - val_root_mean_squared_error: 0.7689\n",
- "Epoch 454/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6039 - root_mean_squared_error: 0.7678 - val_loss: 0.5888 - val_root_mean_squared_error: 0.7588\n",
- "Epoch 455/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5928 - root_mean_squared_error: 0.7602 - val_loss: 0.6128 - val_root_mean_squared_error: 0.7732\n",
- "Epoch 456/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6099 - root_mean_squared_error: 0.7718 - val_loss: 0.5932 - val_root_mean_squared_error: 0.7604\n",
- "Epoch 457/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6058 - root_mean_squared_error: 0.7687 - val_loss: 0.6141 - val_root_mean_squared_error: 0.7742\n",
- "Epoch 458/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5931 - root_mean_squared_error: 0.7608 - val_loss: 0.6086 - val_root_mean_squared_error: 0.7698\n",
- "Epoch 459/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6062 - root_mean_squared_error: 0.7694 - val_loss: 0.5917 - val_root_mean_squared_error: 0.7596\n",
- "Epoch 460/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5960 - root_mean_squared_error: 0.7627 - val_loss: 0.5878 - val_root_mean_squared_error: 0.7570\n",
- "Epoch 461/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5941 - root_mean_squared_error: 0.7613 - val_loss: 0.6085 - val_root_mean_squared_error: 0.7707\n",
- "Epoch 462/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6018 - root_mean_squared_error: 0.7663 - val_loss: 0.5911 - val_root_mean_squared_error: 0.7597\n",
- "Epoch 463/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6093 - root_mean_squared_error: 0.7713 - val_loss: 0.6088 - val_root_mean_squared_error: 0.7712\n",
- "Epoch 464/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5958 - root_mean_squared_error: 0.7621 - val_loss: 0.5931 - val_root_mean_squared_error: 0.7614\n",
- "Epoch 465/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5933 - root_mean_squared_error: 0.7609 - val_loss: 0.6055 - val_root_mean_squared_error: 0.7695\n",
- "Epoch 466/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6021 - root_mean_squared_error: 0.7662 - val_loss: 0.5812 - val_root_mean_squared_error: 0.7527\n",
- "Epoch 467/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6056 - root_mean_squared_error: 0.7687 - val_loss: 0.5885 - val_root_mean_squared_error: 0.7578\n",
- "Epoch 468/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5996 - root_mean_squared_error: 0.7652 - val_loss: 0.5939 - val_root_mean_squared_error: 0.7610\n",
- "Epoch 469/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6086 - root_mean_squared_error: 0.7706 - val_loss: 0.5928 - val_root_mean_squared_error: 0.7599\n",
- "Epoch 470/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6121 - root_mean_squared_error: 0.7731 - val_loss: 0.6056 - val_root_mean_squared_error: 0.7706\n",
- "Epoch 471/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6000 - root_mean_squared_error: 0.7654 - val_loss: 0.6039 - val_root_mean_squared_error: 0.7694\n",
- "Epoch 472/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6000 - root_mean_squared_error: 0.7654 - val_loss: 0.5965 - val_root_mean_squared_error: 0.7637\n",
- "Epoch 473/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5985 - root_mean_squared_error: 0.7641 - val_loss: 0.5940 - val_root_mean_squared_error: 0.7618\n",
- "Epoch 474/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5947 - root_mean_squared_error: 0.7623 - val_loss: 0.6021 - val_root_mean_squared_error: 0.7674\n",
- "Epoch 475/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6104 - root_mean_squared_error: 0.7719 - val_loss: 0.5842 - val_root_mean_squared_error: 0.7558\n",
- "Epoch 476/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6012 - root_mean_squared_error: 0.7661 - val_loss: 0.5945 - val_root_mean_squared_error: 0.7606\n",
- "Epoch 477/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5993 - root_mean_squared_error: 0.7644 - val_loss: 0.5845 - val_root_mean_squared_error: 0.7549\n",
- "Epoch 478/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6030 - root_mean_squared_error: 0.7674 - val_loss: 0.5942 - val_root_mean_squared_error: 0.7604\n",
- "Epoch 479/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5937 - root_mean_squared_error: 0.7611 - val_loss: 0.6035 - val_root_mean_squared_error: 0.7676\n",
- "Epoch 480/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5932 - root_mean_squared_error: 0.7605 - val_loss: 0.6074 - val_root_mean_squared_error: 0.7708\n",
- "Epoch 481/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6053 - root_mean_squared_error: 0.7682 - val_loss: 0.5996 - val_root_mean_squared_error: 0.7654\n",
- "Epoch 482/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5897 - root_mean_squared_error: 0.7586 - val_loss: 0.6033 - val_root_mean_squared_error: 0.7671\n",
- "Epoch 483/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6057 - root_mean_squared_error: 0.7688 - val_loss: 0.5989 - val_root_mean_squared_error: 0.7643\n",
- "Epoch 484/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5934 - root_mean_squared_error: 0.7611 - val_loss: 0.5921 - val_root_mean_squared_error: 0.7605\n",
- "Epoch 485/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.6002 - root_mean_squared_error: 0.7653 - val_loss: 0.5965 - val_root_mean_squared_error: 0.7625\n",
- "Epoch 486/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5941 - root_mean_squared_error: 0.7612 - val_loss: 0.5825 - val_root_mean_squared_error: 0.7541\n",
- "Epoch 487/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6036 - root_mean_squared_error: 0.7678 - val_loss: 0.5812 - val_root_mean_squared_error: 0.7527\n",
- "Epoch 488/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5981 - root_mean_squared_error: 0.7642 - val_loss: 0.6232 - val_root_mean_squared_error: 0.7803\n",
- "Epoch 489/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5991 - root_mean_squared_error: 0.7643 - val_loss: 0.6011 - val_root_mean_squared_error: 0.7665\n",
- "Epoch 490/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5951 - root_mean_squared_error: 0.7620 - val_loss: 0.6077 - val_root_mean_squared_error: 0.7692\n",
- "Epoch 491/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5976 - root_mean_squared_error: 0.7636 - val_loss: 0.5999 - val_root_mean_squared_error: 0.7646\n",
- "Epoch 492/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6045 - root_mean_squared_error: 0.7682 - val_loss: 0.6030 - val_root_mean_squared_error: 0.7673\n",
- "Epoch 493/500\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 0.5950 - root_mean_squared_error: 0.7614 - val_loss: 0.5833 - val_root_mean_squared_error: 0.7542\n",
- "Epoch 494/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5940 - root_mean_squared_error: 0.7611 - val_loss: 0.5883 - val_root_mean_squared_error: 0.7570\n",
- "Epoch 495/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6024 - root_mean_squared_error: 0.7666 - val_loss: 0.5930 - val_root_mean_squared_error: 0.7608\n",
- "Epoch 496/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6024 - root_mean_squared_error: 0.7666 - val_loss: 0.5829 - val_root_mean_squared_error: 0.7534\n",
- "Epoch 497/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6062 - root_mean_squared_error: 0.7695 - val_loss: 0.5980 - val_root_mean_squared_error: 0.7637\n",
- "Epoch 498/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6116 - root_mean_squared_error: 0.7725 - val_loss: 0.5880 - val_root_mean_squared_error: 0.7570\n",
- "Epoch 499/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.6067 - root_mean_squared_error: 0.7692 - val_loss: 0.6026 - val_root_mean_squared_error: 0.7668\n",
- "Epoch 500/500\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 0.5948 - root_mean_squared_error: 0.7613 - val_loss: 0.5912 - val_root_mean_squared_error: 0.7592\n",
"Model training finished.\n",
- "Train RMSE: 0.768\n",
+ "Train RMSE: 0.766\n",
"Evaluating model performance...\n",
- "Test RMSE: 0.753\n",
- "Predictions mean: 5.72, min: 5.15, max: 6.12, range: 0.97 - Actual: 6.0\n",
- "Predictions mean: 6.18, min: 5.93, max: 6.37, range: 0.44 - Actual: 7.0\n",
- "Predictions mean: 6.02, min: 5.7, max: 6.21, range: 0.51 - Actual: 7.0\n",
- "Predictions mean: 6.52, min: 6.32, max: 6.61, range: 0.29 - Actual: 6.0\n",
- "Predictions mean: 6.37, min: 6.15, max: 6.53, range: 0.39 - Actual: 5.0\n",
- "Predictions mean: 6.42, min: 6.21, max: 6.56, range: 0.35 - Actual: 7.0\n",
- "Predictions mean: 5.89, min: 5.55, max: 6.18, range: 0.62 - Actual: 6.0\n",
- "Predictions mean: 5.77, min: 5.37, max: 6.12, range: 0.74 - Actual: 6.0\n",
- "Predictions mean: 6.58, min: 6.43, max: 6.64, range: 0.21 - Actual: 8.0\n",
- "Predictions mean: 5.52, min: 5.09, max: 5.82, range: 0.72 - Actual: 6.0\n"
+ "Test RMSE: 0.766\n",
+ "Predictions mean: 6.38, min: 6.15, max: 6.57, range: 0.42 - Actual: 5.0\n",
+ "Predictions mean: 5.71, min: 5.4, max: 6.07, range: 0.67 - Actual: 6.0\n",
+ "Predictions mean: 5.6, min: 5.21, max: 5.98, range: 0.77 - Actual: 5.0\n",
+ "Predictions mean: 6.01, min: 5.32, max: 6.4, range: 1.08 - Actual: 5.0\n",
+ "Predictions mean: 5.86, min: 5.47, max: 6.14, range: 0.67 - Actual: 6.0\n",
+ "Predictions mean: 5.36, min: 5.01, max: 5.86, range: 0.85 - Actual: 6.0\n",
+ "Predictions mean: 5.27, min: 4.88, max: 5.75, range: 0.86 - Actual: 5.0\n",
+ "Predictions mean: 5.49, min: 5.05, max: 5.96, range: 0.91 - Actual: 5.0\n",
+ "Predictions mean: 6.18, min: 5.9, max: 6.37, range: 0.48 - Actual: 6.0\n",
+ "Predictions mean: 5.8, min: 5.45, max: 6.09, range: 0.63 - Actual: 5.0\n"
]
}
],
@@ -2909,7 +717,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 14,
"metadata": {
"id": "9nzDZQedJZX_"
},
@@ -2955,7 +763,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 15,
"metadata": {
"id": "5SdzlMuvJZYA"
},
@@ -2965,2010 +773,10 @@
"output_type": "stream",
"text": [
"Start training the model...\n",
- "Epoch 1/1000\n",
- "17/17 [==============================] - 3s 55ms/step - loss: 49.4791 - root_mean_squared_error: 6.0334 - val_loss: 47.2343 - val_root_mean_squared_error: 6.0839\n",
- "Epoch 2/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 43.4683 - root_mean_squared_error: 5.8431 - val_loss: 41.7599 - val_root_mean_squared_error: 5.6779\n",
- "Epoch 3/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 40.1974 - root_mean_squared_error: 5.8785 - val_loss: 26.8484 - val_root_mean_squared_error: 5.5150\n",
- "Epoch 4/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 26.8967 - root_mean_squared_error: 5.8974 - val_loss: 19.8879 - val_root_mean_squared_error: 5.5704\n",
- "Epoch 5/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 26.5412 - root_mean_squared_error: 5.6827 - val_loss: 20.3914 - val_root_mean_squared_error: 5.5372\n",
- "Epoch 6/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 27.7963 - root_mean_squared_error: 5.8320 - val_loss: 29.7995 - val_root_mean_squared_error: 5.7415\n",
- "Epoch 7/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 18.9358 - root_mean_squared_error: 5.4316 - val_loss: 18.4710 - val_root_mean_squared_error: 5.7641\n",
- "Epoch 8/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 16.4066 - root_mean_squared_error: 5.5915 - val_loss: 14.1811 - val_root_mean_squared_error: 5.4152\n",
- "Epoch 9/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 16.0122 - root_mean_squared_error: 5.4757 - val_loss: 11.5630 - val_root_mean_squared_error: 5.3809\n",
- "Epoch 10/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 12.9110 - root_mean_squared_error: 5.2266 - val_loss: 15.7345 - val_root_mean_squared_error: 5.2081\n",
- "Epoch 11/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 9.9049 - root_mean_squared_error: 5.2066 - val_loss: 8.7508 - val_root_mean_squared_error: 5.4685\n",
- "Epoch 12/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 12.1668 - root_mean_squared_error: 5.2535 - val_loss: 9.3646 - val_root_mean_squared_error: 4.9539\n",
- "Epoch 13/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 9.1198 - root_mean_squared_error: 5.1449 - val_loss: 8.0804 - val_root_mean_squared_error: 5.3199\n",
- "Epoch 14/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 8.3575 - root_mean_squared_error: 5.2122 - val_loss: 7.5787 - val_root_mean_squared_error: 4.9549\n",
- "Epoch 15/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 7.8478 - root_mean_squared_error: 5.0685 - val_loss: 7.3819 - val_root_mean_squared_error: 4.5496\n",
- "Epoch 16/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 7.2835 - root_mean_squared_error: 5.0197 - val_loss: 5.5337 - val_root_mean_squared_error: 4.6588\n",
- "Epoch 17/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 5.8864 - root_mean_squared_error: 4.8764 - val_loss: 5.2507 - val_root_mean_squared_error: 4.9555\n",
- "Epoch 18/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 5.4269 - root_mean_squared_error: 4.7535 - val_loss: 5.1980 - val_root_mean_squared_error: 4.7492\n",
- "Epoch 19/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 4.7675 - root_mean_squared_error: 4.6357 - val_loss: 4.4826 - val_root_mean_squared_error: 4.6135\n",
- "Epoch 20/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 4.7597 - root_mean_squared_error: 4.6966 - val_loss: 4.1850 - val_root_mean_squared_error: 4.6720\n",
- "Epoch 21/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 4.0850 - root_mean_squared_error: 4.4740 - val_loss: 4.3002 - val_root_mean_squared_error: 4.4141\n",
- "Epoch 22/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 4.1690 - root_mean_squared_error: 4.5988 - val_loss: 3.4871 - val_root_mean_squared_error: 4.7396\n",
- "Epoch 23/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.8374 - root_mean_squared_error: 4.6603 - val_loss: 4.0281 - val_root_mean_squared_error: 4.7955\n",
- "Epoch 24/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.9443 - root_mean_squared_error: 4.4598 - val_loss: 4.1184 - val_root_mean_squared_error: 4.7453\n",
- "Epoch 25/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.3186 - root_mean_squared_error: 4.3831 - val_loss: 3.2485 - val_root_mean_squared_error: 4.6034\n",
- "Epoch 26/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 3.1653 - root_mean_squared_error: 4.2772 - val_loss: 3.4270 - val_root_mean_squared_error: 4.2072\n",
- "Epoch 27/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 3.0534 - root_mean_squared_error: 4.2967 - val_loss: 3.1200 - val_root_mean_squared_error: 4.0117\n",
- "Epoch 28/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 2.8901 - root_mean_squared_error: 4.1737 - val_loss: 3.0920 - val_root_mean_squared_error: 4.0898\n",
- "Epoch 29/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 2.8258 - root_mean_squared_error: 4.1288 - val_loss: 2.7379 - val_root_mean_squared_error: 4.0551\n",
- "Epoch 30/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.6813 - root_mean_squared_error: 3.9338 - val_loss: 2.5755 - val_root_mean_squared_error: 3.7008\n",
- "Epoch 31/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.7103 - root_mean_squared_error: 4.1127 - val_loss: 2.6971 - val_root_mean_squared_error: 4.2257\n",
- "Epoch 32/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 2.6148 - root_mean_squared_error: 4.0079 - val_loss: 2.5960 - val_root_mean_squared_error: 3.7971\n",
- "Epoch 33/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.4886 - root_mean_squared_error: 3.8210 - val_loss: 2.6642 - val_root_mean_squared_error: 4.1584\n",
- "Epoch 34/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.5031 - root_mean_squared_error: 3.8983 - val_loss: 2.4248 - val_root_mean_squared_error: 4.0141\n",
- "Epoch 35/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.4476 - root_mean_squared_error: 3.8152 - val_loss: 2.4816 - val_root_mean_squared_error: 3.9046\n",
- "Epoch 36/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.3846 - root_mean_squared_error: 3.6896 - val_loss: 2.3603 - val_root_mean_squared_error: 3.8712\n",
- "Epoch 37/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.4445 - root_mean_squared_error: 3.7208 - val_loss: 2.3956 - val_root_mean_squared_error: 3.9012\n",
- "Epoch 38/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.3448 - root_mean_squared_error: 3.6869 - val_loss: 2.3151 - val_root_mean_squared_error: 3.4838\n",
- "Epoch 39/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 2.2703 - root_mean_squared_error: 3.4491 - val_loss: 2.1790 - val_root_mean_squared_error: 3.2574\n",
- "Epoch 40/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.2453 - root_mean_squared_error: 3.3830 - val_loss: 2.2611 - val_root_mean_squared_error: 3.5903\n",
- "Epoch 41/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 2.1681 - root_mean_squared_error: 3.1830 - val_loss: 2.1688 - val_root_mean_squared_error: 3.2343\n",
- "Epoch 42/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.1464 - root_mean_squared_error: 3.1410 - val_loss: 2.0790 - val_root_mean_squared_error: 2.9768\n",
- "Epoch 43/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.0773 - root_mean_squared_error: 2.9002 - val_loss: 2.1042 - val_root_mean_squared_error: 3.1225\n",
- "Epoch 44/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.0558 - root_mean_squared_error: 2.8647 - val_loss: 2.0106 - val_root_mean_squared_error: 2.7401\n",
- "Epoch 45/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 2.0222 - root_mean_squared_error: 2.8982 - val_loss: 2.0344 - val_root_mean_squared_error: 2.7835\n",
- "Epoch 46/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.9585 - root_mean_squared_error: 2.6959 - val_loss: 1.8660 - val_root_mean_squared_error: 2.5219\n",
- "Epoch 47/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.9363 - root_mean_squared_error: 2.6122 - val_loss: 1.8248 - val_root_mean_squared_error: 2.2971\n",
- "Epoch 48/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.8718 - root_mean_squared_error: 2.4994 - val_loss: 1.8952 - val_root_mean_squared_error: 2.4322\n",
- "Epoch 49/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.7604 - root_mean_squared_error: 2.2322 - val_loss: 1.7443 - val_root_mean_squared_error: 2.0142\n",
- "Epoch 50/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.7532 - root_mean_squared_error: 2.2011 - val_loss: 1.7838 - val_root_mean_squared_error: 2.3775\n",
- "Epoch 51/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.7096 - root_mean_squared_error: 2.1454 - val_loss: 1.6807 - val_root_mean_squared_error: 1.9999\n",
- "Epoch 52/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.5657 - root_mean_squared_error: 1.8387 - val_loss: 1.5407 - val_root_mean_squared_error: 1.7301\n",
- "Epoch 53/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.5655 - root_mean_squared_error: 1.8442 - val_loss: 1.5510 - val_root_mean_squared_error: 1.8079\n",
- "Epoch 54/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.5263 - root_mean_squared_error: 1.7567 - val_loss: 1.5527 - val_root_mean_squared_error: 1.7713\n",
- "Epoch 55/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.5102 - root_mean_squared_error: 1.7039 - val_loss: 1.4269 - val_root_mean_squared_error: 1.5772\n",
- "Epoch 56/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.4842 - root_mean_squared_error: 1.6800 - val_loss: 1.4029 - val_root_mean_squared_error: 1.4576\n",
- "Epoch 57/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.4071 - root_mean_squared_error: 1.4799 - val_loss: 1.4325 - val_root_mean_squared_error: 1.4910\n",
- "Epoch 58/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.3981 - root_mean_squared_error: 1.5214 - val_loss: 1.4305 - val_root_mean_squared_error: 1.6696\n",
- "Epoch 59/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.4090 - root_mean_squared_error: 1.4474 - val_loss: 1.3516 - val_root_mean_squared_error: 1.3792\n",
- "Epoch 60/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.3999 - root_mean_squared_error: 1.4882 - val_loss: 1.3528 - val_root_mean_squared_error: 1.2550\n",
- "Epoch 61/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3593 - root_mean_squared_error: 1.3733 - val_loss: 1.2630 - val_root_mean_squared_error: 1.2267\n",
- "Epoch 62/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3897 - root_mean_squared_error: 1.3679 - val_loss: 1.2686 - val_root_mean_squared_error: 1.3015\n",
- "Epoch 63/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3372 - root_mean_squared_error: 1.3199 - val_loss: 1.3184 - val_root_mean_squared_error: 1.3445\n",
- "Epoch 64/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3327 - root_mean_squared_error: 1.3463 - val_loss: 1.3474 - val_root_mean_squared_error: 1.3826\n",
- "Epoch 65/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3314 - root_mean_squared_error: 1.3077 - val_loss: 1.3613 - val_root_mean_squared_error: 1.3066\n",
- "Epoch 66/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.3267 - root_mean_squared_error: 1.2709 - val_loss: 1.2979 - val_root_mean_squared_error: 1.1779\n",
- "Epoch 67/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3340 - root_mean_squared_error: 1.3261 - val_loss: 1.3534 - val_root_mean_squared_error: 1.1973\n",
- "Epoch 68/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2930 - root_mean_squared_error: 1.2444 - val_loss: 1.3576 - val_root_mean_squared_error: 1.3215\n",
- "Epoch 69/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3195 - root_mean_squared_error: 1.2489 - val_loss: 1.3498 - val_root_mean_squared_error: 1.2839\n",
- "Epoch 70/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3049 - root_mean_squared_error: 1.2541 - val_loss: 1.3202 - val_root_mean_squared_error: 1.1756\n",
- "Epoch 71/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3159 - root_mean_squared_error: 1.2633 - val_loss: 1.3334 - val_root_mean_squared_error: 1.2451\n",
- "Epoch 72/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3132 - root_mean_squared_error: 1.2740 - val_loss: 1.3323 - val_root_mean_squared_error: 1.0962\n",
- "Epoch 73/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3110 - root_mean_squared_error: 1.2432 - val_loss: 1.3192 - val_root_mean_squared_error: 1.3352\n",
- "Epoch 74/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2994 - root_mean_squared_error: 1.2210 - val_loss: 1.2992 - val_root_mean_squared_error: 1.2611\n",
- "Epoch 75/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2884 - root_mean_squared_error: 1.2601 - val_loss: 1.3147 - val_root_mean_squared_error: 1.1548\n",
- "Epoch 76/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2681 - root_mean_squared_error: 1.2061 - val_loss: 1.3288 - val_root_mean_squared_error: 1.1821\n",
- "Epoch 77/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3250 - root_mean_squared_error: 1.2457 - val_loss: 1.3163 - val_root_mean_squared_error: 1.3217\n",
- "Epoch 78/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2892 - root_mean_squared_error: 1.2262 - val_loss: 1.2869 - val_root_mean_squared_error: 1.2102\n",
- "Epoch 79/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3471 - root_mean_squared_error: 1.2687 - val_loss: 1.2676 - val_root_mean_squared_error: 1.2697\n",
- "Epoch 80/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.3072 - root_mean_squared_error: 1.2520 - val_loss: 1.2947 - val_root_mean_squared_error: 1.2536\n",
- "Epoch 81/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2659 - root_mean_squared_error: 1.1922 - val_loss: 1.2640 - val_root_mean_squared_error: 1.1866\n",
- "Epoch 82/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2763 - root_mean_squared_error: 1.2246 - val_loss: 1.3086 - val_root_mean_squared_error: 1.1779\n",
- "Epoch 83/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2802 - root_mean_squared_error: 1.2484 - val_loss: 1.2653 - val_root_mean_squared_error: 1.2458\n",
- "Epoch 84/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2759 - root_mean_squared_error: 1.2011 - val_loss: 1.2514 - val_root_mean_squared_error: 1.2203\n",
- "Epoch 85/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2810 - root_mean_squared_error: 1.2443 - val_loss: 1.2476 - val_root_mean_squared_error: 1.1833\n",
- "Epoch 86/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2695 - root_mean_squared_error: 1.1964 - val_loss: 1.2856 - val_root_mean_squared_error: 1.2245\n",
- "Epoch 87/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2716 - root_mean_squared_error: 1.2282 - val_loss: 1.2865 - val_root_mean_squared_error: 1.1881\n",
- "Epoch 88/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2640 - root_mean_squared_error: 1.2045 - val_loss: 1.3174 - val_root_mean_squared_error: 1.2492\n",
- "Epoch 89/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2707 - root_mean_squared_error: 1.1940 - val_loss: 1.2369 - val_root_mean_squared_error: 1.1885\n",
- "Epoch 90/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2684 - root_mean_squared_error: 1.2222 - val_loss: 1.2394 - val_root_mean_squared_error: 1.1854\n",
- "Epoch 91/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2693 - root_mean_squared_error: 1.1987 - val_loss: 1.2374 - val_root_mean_squared_error: 1.2061\n",
- "Epoch 92/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2684 - root_mean_squared_error: 1.1897 - val_loss: 1.2313 - val_root_mean_squared_error: 1.1366\n",
- "Epoch 93/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2632 - root_mean_squared_error: 1.2303 - val_loss: 1.2459 - val_root_mean_squared_error: 1.2218\n",
- "Epoch 94/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2378 - root_mean_squared_error: 1.1726 - val_loss: 1.3026 - val_root_mean_squared_error: 1.1871\n",
- "Epoch 95/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2422 - root_mean_squared_error: 1.1629 - val_loss: 1.2598 - val_root_mean_squared_error: 1.2099\n",
- "Epoch 96/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2530 - root_mean_squared_error: 1.1729 - val_loss: 1.2735 - val_root_mean_squared_error: 1.1526\n",
- "Epoch 97/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2354 - root_mean_squared_error: 1.1864 - val_loss: 1.2383 - val_root_mean_squared_error: 1.1757\n",
- "Epoch 98/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2634 - root_mean_squared_error: 1.1810 - val_loss: 1.2321 - val_root_mean_squared_error: 1.2313\n",
- "Epoch 99/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2459 - root_mean_squared_error: 1.1912 - val_loss: 1.2358 - val_root_mean_squared_error: 1.1996\n",
- "Epoch 100/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2546 - root_mean_squared_error: 1.1995 - val_loss: 1.2306 - val_root_mean_squared_error: 1.1915\n",
- "Epoch 101/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2463 - root_mean_squared_error: 1.1990 - val_loss: 1.2091 - val_root_mean_squared_error: 1.1948\n",
- "Epoch 102/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2359 - root_mean_squared_error: 1.1922 - val_loss: 1.2461 - val_root_mean_squared_error: 1.1776\n",
- "Epoch 103/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2384 - root_mean_squared_error: 1.1686 - val_loss: 1.2511 - val_root_mean_squared_error: 1.2230\n",
- "Epoch 104/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2512 - root_mean_squared_error: 1.1776 - val_loss: 1.2456 - val_root_mean_squared_error: 1.1916\n",
- "Epoch 105/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2488 - root_mean_squared_error: 1.1655 - val_loss: 1.2081 - val_root_mean_squared_error: 1.1427\n",
- "Epoch 106/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2272 - root_mean_squared_error: 1.1978 - val_loss: 1.2451 - val_root_mean_squared_error: 1.1563\n",
- "Epoch 107/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2243 - root_mean_squared_error: 1.1549 - val_loss: 1.2384 - val_root_mean_squared_error: 1.1053\n",
- "Epoch 108/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2400 - root_mean_squared_error: 1.1624 - val_loss: 1.1971 - val_root_mean_squared_error: 1.1448\n",
- "Epoch 109/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2178 - root_mean_squared_error: 1.1521 - val_loss: 1.2323 - val_root_mean_squared_error: 1.1580\n",
- "Epoch 110/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2308 - root_mean_squared_error: 1.1490 - val_loss: 1.2220 - val_root_mean_squared_error: 1.1602\n",
- "Epoch 111/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2245 - root_mean_squared_error: 1.1660 - val_loss: 1.2184 - val_root_mean_squared_error: 1.1655\n",
- "Epoch 112/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2313 - root_mean_squared_error: 1.1536 - val_loss: 1.2299 - val_root_mean_squared_error: 1.1700\n",
- "Epoch 113/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2097 - root_mean_squared_error: 1.1543 - val_loss: 1.2055 - val_root_mean_squared_error: 1.1187\n",
- "Epoch 114/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2263 - root_mean_squared_error: 1.1647 - val_loss: 1.2424 - val_root_mean_squared_error: 1.2083\n",
- "Epoch 115/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2432 - root_mean_squared_error: 1.1450 - val_loss: 1.2150 - val_root_mean_squared_error: 1.1525\n",
- "Epoch 116/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2279 - root_mean_squared_error: 1.1592 - val_loss: 1.2287 - val_root_mean_squared_error: 1.1360\n",
- "Epoch 117/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2369 - root_mean_squared_error: 1.1444 - val_loss: 1.2080 - val_root_mean_squared_error: 1.1718\n",
- "Epoch 118/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2325 - root_mean_squared_error: 1.1640 - val_loss: 1.2231 - val_root_mean_squared_error: 1.1656\n",
- "Epoch 119/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2084 - root_mean_squared_error: 1.1373 - val_loss: 1.2237 - val_root_mean_squared_error: 1.1586\n",
- "Epoch 120/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2258 - root_mean_squared_error: 1.1462 - val_loss: 1.2104 - val_root_mean_squared_error: 1.1480\n",
- "Epoch 121/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2262 - root_mean_squared_error: 1.1687 - val_loss: 1.2350 - val_root_mean_squared_error: 1.1346\n",
- "Epoch 122/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2277 - root_mean_squared_error: 1.1622 - val_loss: 1.1852 - val_root_mean_squared_error: 1.1470\n",
- "Epoch 123/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2127 - root_mean_squared_error: 1.1596 - val_loss: 1.2250 - val_root_mean_squared_error: 1.1541\n",
- "Epoch 124/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2058 - root_mean_squared_error: 1.1271 - val_loss: 1.2022 - val_root_mean_squared_error: 1.1989\n",
- "Epoch 125/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2157 - root_mean_squared_error: 1.1667 - val_loss: 1.2109 - val_root_mean_squared_error: 1.0976\n",
- "Epoch 126/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2070 - root_mean_squared_error: 1.1464 - val_loss: 1.2010 - val_root_mean_squared_error: 1.1606\n",
- "Epoch 127/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2121 - root_mean_squared_error: 1.1448 - val_loss: 1.1947 - val_root_mean_squared_error: 1.1438\n",
- "Epoch 128/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2316 - root_mean_squared_error: 1.1858 - val_loss: 1.2310 - val_root_mean_squared_error: 1.1150\n",
- "Epoch 129/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2073 - root_mean_squared_error: 1.1584 - val_loss: 1.1832 - val_root_mean_squared_error: 1.0845\n",
- "Epoch 130/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2129 - root_mean_squared_error: 1.1442 - val_loss: 1.1806 - val_root_mean_squared_error: 1.1193\n",
- "Epoch 131/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2094 - root_mean_squared_error: 1.1404 - val_loss: 1.2245 - val_root_mean_squared_error: 1.1459\n",
- "Epoch 132/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2096 - root_mean_squared_error: 1.1124 - val_loss: 1.1952 - val_root_mean_squared_error: 1.1283\n",
- "Epoch 133/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2136 - root_mean_squared_error: 1.1442 - val_loss: 1.2135 - val_root_mean_squared_error: 1.1296\n",
- "Epoch 134/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2162 - root_mean_squared_error: 1.1520 - val_loss: 1.2023 - val_root_mean_squared_error: 1.1253\n",
- "Epoch 135/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2084 - root_mean_squared_error: 1.1399 - val_loss: 1.2253 - val_root_mean_squared_error: 1.1238\n",
- "Epoch 136/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2067 - root_mean_squared_error: 1.1213 - val_loss: 1.1823 - val_root_mean_squared_error: 1.1564\n",
- "Epoch 137/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2155 - root_mean_squared_error: 1.1623 - val_loss: 1.2044 - val_root_mean_squared_error: 1.0579\n",
- "Epoch 138/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2118 - root_mean_squared_error: 1.1336 - val_loss: 1.1835 - val_root_mean_squared_error: 1.1689\n",
- "Epoch 139/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2081 - root_mean_squared_error: 1.1062 - val_loss: 1.2060 - val_root_mean_squared_error: 1.1103\n",
- "Epoch 140/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2030 - root_mean_squared_error: 1.1400 - val_loss: 1.2258 - val_root_mean_squared_error: 1.1392\n",
- "Epoch 141/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2068 - root_mean_squared_error: 1.1296 - val_loss: 1.2109 - val_root_mean_squared_error: 1.1720\n",
- "Epoch 142/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2204 - root_mean_squared_error: 1.1383 - val_loss: 1.2119 - val_root_mean_squared_error: 1.1423\n",
- "Epoch 143/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2239 - root_mean_squared_error: 1.1335 - val_loss: 1.1849 - val_root_mean_squared_error: 1.1230\n",
- "Epoch 144/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2023 - root_mean_squared_error: 1.1353 - val_loss: 1.1841 - val_root_mean_squared_error: 1.0775\n",
- "Epoch 145/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2235 - root_mean_squared_error: 1.1427 - val_loss: 1.1954 - val_root_mean_squared_error: 1.1936\n",
- "Epoch 146/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2198 - root_mean_squared_error: 1.1436 - val_loss: 1.2075 - val_root_mean_squared_error: 1.1604\n",
- "Epoch 147/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1913 - root_mean_squared_error: 1.1203 - val_loss: 1.2001 - val_root_mean_squared_error: 1.1086\n",
- "Epoch 148/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1986 - root_mean_squared_error: 1.1317 - val_loss: 1.2273 - val_root_mean_squared_error: 1.1061\n",
- "Epoch 149/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2034 - root_mean_squared_error: 1.1290 - val_loss: 1.1827 - val_root_mean_squared_error: 1.1081\n",
- "Epoch 150/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1907 - root_mean_squared_error: 1.1319 - val_loss: 1.1592 - val_root_mean_squared_error: 1.1205\n",
- "Epoch 151/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2164 - root_mean_squared_error: 1.1208 - val_loss: 1.1742 - val_root_mean_squared_error: 1.1139\n",
- "Epoch 152/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2034 - root_mean_squared_error: 1.1413 - val_loss: 1.2134 - val_root_mean_squared_error: 1.1101\n",
- "Epoch 153/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2181 - root_mean_squared_error: 1.1359 - val_loss: 1.2168 - val_root_mean_squared_error: 1.1827\n",
- "Epoch 154/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1948 - root_mean_squared_error: 1.1220 - val_loss: 1.2341 - val_root_mean_squared_error: 1.1373\n",
- "Epoch 155/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2302 - root_mean_squared_error: 1.1181 - val_loss: 1.2092 - val_root_mean_squared_error: 1.2125\n",
- "Epoch 156/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2046 - root_mean_squared_error: 1.1507 - val_loss: 1.2227 - val_root_mean_squared_error: 1.1695\n",
- "Epoch 157/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2060 - root_mean_squared_error: 1.1659 - val_loss: 1.1801 - val_root_mean_squared_error: 1.0554\n",
- "Epoch 158/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1885 - root_mean_squared_error: 1.1229 - val_loss: 1.2039 - val_root_mean_squared_error: 1.1068\n",
- "Epoch 159/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2089 - root_mean_squared_error: 1.1165 - val_loss: 1.1850 - val_root_mean_squared_error: 1.1714\n",
- "Epoch 160/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1930 - root_mean_squared_error: 1.0992 - val_loss: 1.2189 - val_root_mean_squared_error: 1.0974\n",
- "Epoch 161/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1922 - root_mean_squared_error: 1.1013 - val_loss: 1.1908 - val_root_mean_squared_error: 1.1607\n",
- "Epoch 162/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1882 - root_mean_squared_error: 1.1603 - val_loss: 1.1877 - val_root_mean_squared_error: 1.1408\n",
- "Epoch 163/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1974 - root_mean_squared_error: 1.1437 - val_loss: 1.1777 - val_root_mean_squared_error: 1.1516\n",
- "Epoch 164/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1976 - root_mean_squared_error: 1.1135 - val_loss: 1.1963 - val_root_mean_squared_error: 1.1311\n",
- "Epoch 165/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1992 - root_mean_squared_error: 1.1014 - val_loss: 1.2034 - val_root_mean_squared_error: 1.1244\n",
- "Epoch 166/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1944 - root_mean_squared_error: 1.1278 - val_loss: 1.1985 - val_root_mean_squared_error: 1.1509\n",
- "Epoch 167/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1873 - root_mean_squared_error: 1.1057 - val_loss: 1.2090 - val_root_mean_squared_error: 1.1225\n",
- "Epoch 168/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1921 - root_mean_squared_error: 1.1212 - val_loss: 1.2009 - val_root_mean_squared_error: 1.0578\n",
- "Epoch 169/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2019 - root_mean_squared_error: 1.1216 - val_loss: 1.1953 - val_root_mean_squared_error: 1.1160\n",
- "Epoch 170/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1909 - root_mean_squared_error: 1.1164 - val_loss: 1.2071 - val_root_mean_squared_error: 1.1517\n",
- "Epoch 171/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1956 - root_mean_squared_error: 1.1059 - val_loss: 1.1841 - val_root_mean_squared_error: 1.1115\n",
- "Epoch 172/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1840 - root_mean_squared_error: 1.1164 - val_loss: 1.2000 - val_root_mean_squared_error: 1.1125\n",
- "Epoch 173/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1970 - root_mean_squared_error: 1.1168 - val_loss: 1.1861 - val_root_mean_squared_error: 1.1363\n",
- "Epoch 174/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2013 - root_mean_squared_error: 1.1144 - val_loss: 1.1914 - val_root_mean_squared_error: 1.1642\n",
- "Epoch 175/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1943 - root_mean_squared_error: 1.1144 - val_loss: 1.1764 - val_root_mean_squared_error: 1.0702\n",
- "Epoch 176/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1918 - root_mean_squared_error: 1.1267 - val_loss: 1.1820 - val_root_mean_squared_error: 1.1426\n",
- "Epoch 177/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1960 - root_mean_squared_error: 1.1254 - val_loss: 1.1902 - val_root_mean_squared_error: 1.1146\n",
- "Epoch 178/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1851 - root_mean_squared_error: 1.1044 - val_loss: 1.1797 - val_root_mean_squared_error: 1.1398\n",
- "Epoch 179/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1982 - root_mean_squared_error: 1.1420 - val_loss: 1.1875 - val_root_mean_squared_error: 1.0962\n",
- "Epoch 180/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1859 - root_mean_squared_error: 1.1051 - val_loss: 1.1930 - val_root_mean_squared_error: 1.1487\n",
- "Epoch 181/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1854 - root_mean_squared_error: 1.1078 - val_loss: 1.2124 - val_root_mean_squared_error: 1.1381\n",
- "Epoch 182/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1888 - root_mean_squared_error: 1.1301 - val_loss: 1.2062 - val_root_mean_squared_error: 1.1431\n",
- "Epoch 183/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1854 - root_mean_squared_error: 1.1168 - val_loss: 1.2014 - val_root_mean_squared_error: 1.0789\n",
- "Epoch 184/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1944 - root_mean_squared_error: 1.1265 - val_loss: 1.1764 - val_root_mean_squared_error: 1.0568\n",
- "Epoch 185/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1952 - root_mean_squared_error: 1.0824 - val_loss: 1.1812 - val_root_mean_squared_error: 1.1614\n",
- "Epoch 186/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1933 - root_mean_squared_error: 1.1276 - val_loss: 1.1977 - val_root_mean_squared_error: 1.1059\n",
- "Epoch 187/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1826 - root_mean_squared_error: 1.1147 - val_loss: 1.1983 - val_root_mean_squared_error: 1.0683\n",
- "Epoch 188/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2211 - root_mean_squared_error: 1.1411 - val_loss: 1.2042 - val_root_mean_squared_error: 1.1878\n",
- "Epoch 189/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1933 - root_mean_squared_error: 1.1095 - val_loss: 1.1822 - val_root_mean_squared_error: 1.1439\n",
- "Epoch 190/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2058 - root_mean_squared_error: 1.1336 - val_loss: 1.1848 - val_root_mean_squared_error: 1.1157\n",
- "Epoch 191/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1932 - root_mean_squared_error: 1.1280 - val_loss: 1.2083 - val_root_mean_squared_error: 1.1368\n",
- "Epoch 192/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1878 - root_mean_squared_error: 1.1082 - val_loss: 1.1921 - val_root_mean_squared_error: 1.1090\n",
- "Epoch 193/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1815 - root_mean_squared_error: 1.1210 - val_loss: 1.1944 - val_root_mean_squared_error: 1.1072\n",
- "Epoch 194/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2004 - root_mean_squared_error: 1.1141 - val_loss: 1.1911 - val_root_mean_squared_error: 1.1529\n",
- "Epoch 195/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2008 - root_mean_squared_error: 1.1186 - val_loss: 1.2018 - val_root_mean_squared_error: 1.1702\n",
- "Epoch 196/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1952 - root_mean_squared_error: 1.1062 - val_loss: 1.1851 - val_root_mean_squared_error: 1.0483\n",
- "Epoch 197/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1933 - root_mean_squared_error: 1.1235 - val_loss: 1.1928 - val_root_mean_squared_error: 1.1171\n",
- "Epoch 198/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1908 - root_mean_squared_error: 1.1276 - val_loss: 1.1781 - val_root_mean_squared_error: 1.1241\n",
- "Epoch 199/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1875 - root_mean_squared_error: 1.1236 - val_loss: 1.2027 - val_root_mean_squared_error: 1.1211\n",
- "Epoch 200/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1775 - root_mean_squared_error: 1.0999 - val_loss: 1.1738 - val_root_mean_squared_error: 1.0956\n",
- "Epoch 201/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1811 - root_mean_squared_error: 1.1134 - val_loss: 1.1927 - val_root_mean_squared_error: 1.1208\n",
- "Epoch 202/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1812 - root_mean_squared_error: 1.0942 - val_loss: 1.1840 - val_root_mean_squared_error: 1.1067\n",
- "Epoch 203/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2106 - root_mean_squared_error: 1.1393 - val_loss: 1.1782 - val_root_mean_squared_error: 1.0711\n",
- "Epoch 204/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1898 - root_mean_squared_error: 1.1323 - val_loss: 1.1989 - val_root_mean_squared_error: 1.0981\n",
- "Epoch 205/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1823 - root_mean_squared_error: 1.1045 - val_loss: 1.1784 - val_root_mean_squared_error: 1.1053\n",
- "Epoch 206/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1744 - root_mean_squared_error: 1.0884 - val_loss: 1.1758 - val_root_mean_squared_error: 1.0866\n",
- "Epoch 207/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1791 - root_mean_squared_error: 1.1049 - val_loss: 1.1802 - val_root_mean_squared_error: 1.1169\n",
- "Epoch 208/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1870 - root_mean_squared_error: 1.0868 - val_loss: 1.2096 - val_root_mean_squared_error: 1.1027\n",
- "Epoch 209/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1929 - root_mean_squared_error: 1.1418 - val_loss: 1.1594 - val_root_mean_squared_error: 1.0801\n",
- "Epoch 210/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1880 - root_mean_squared_error: 1.0929 - val_loss: 1.2016 - val_root_mean_squared_error: 1.1052\n",
- "Epoch 211/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1883 - root_mean_squared_error: 1.1050 - val_loss: 1.1827 - val_root_mean_squared_error: 1.1161\n",
- "Epoch 212/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1989 - root_mean_squared_error: 1.1125 - val_loss: 1.1948 - val_root_mean_squared_error: 1.0822\n",
- "Epoch 213/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1820 - root_mean_squared_error: 1.0987 - val_loss: 1.1814 - val_root_mean_squared_error: 1.0877\n",
- "Epoch 214/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1801 - root_mean_squared_error: 1.0952 - val_loss: 1.1617 - val_root_mean_squared_error: 1.1302\n",
- "Epoch 215/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1756 - root_mean_squared_error: 1.0984 - val_loss: 1.1907 - val_root_mean_squared_error: 1.1271\n",
- "Epoch 216/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1871 - root_mean_squared_error: 1.1008 - val_loss: 1.1581 - val_root_mean_squared_error: 1.1008\n",
- "Epoch 217/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1798 - root_mean_squared_error: 1.1097 - val_loss: 1.1599 - val_root_mean_squared_error: 1.0878\n",
- "Epoch 218/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1852 - root_mean_squared_error: 1.0912 - val_loss: 1.1644 - val_root_mean_squared_error: 1.1282\n",
- "Epoch 219/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1912 - root_mean_squared_error: 1.1297 - val_loss: 1.1814 - val_root_mean_squared_error: 1.0750\n",
- "Epoch 220/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1946 - root_mean_squared_error: 1.1170 - val_loss: 1.1914 - val_root_mean_squared_error: 1.1267\n",
- "Epoch 221/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1872 - root_mean_squared_error: 1.1253 - val_loss: 1.1883 - val_root_mean_squared_error: 1.1335\n",
- "Epoch 222/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1850 - root_mean_squared_error: 1.1283 - val_loss: 1.1557 - val_root_mean_squared_error: 1.0604\n",
- "Epoch 223/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1809 - root_mean_squared_error: 1.0841 - val_loss: 1.2018 - val_root_mean_squared_error: 1.0606\n",
- "Epoch 224/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1798 - root_mean_squared_error: 1.1016 - val_loss: 1.1630 - val_root_mean_squared_error: 1.1225\n",
- "Epoch 225/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1861 - root_mean_squared_error: 1.1009 - val_loss: 1.1671 - val_root_mean_squared_error: 1.0454\n",
- "Epoch 226/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1936 - root_mean_squared_error: 1.1153 - val_loss: 1.1800 - val_root_mean_squared_error: 1.1330\n",
- "Epoch 227/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1821 - root_mean_squared_error: 1.1324 - val_loss: 1.1642 - val_root_mean_squared_error: 1.1110\n",
- "Epoch 228/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1895 - root_mean_squared_error: 1.1092 - val_loss: 1.1684 - val_root_mean_squared_error: 1.0900\n",
- "Epoch 229/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1989 - root_mean_squared_error: 1.1055 - val_loss: 1.1872 - val_root_mean_squared_error: 1.1435\n",
- "Epoch 230/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1695 - root_mean_squared_error: 1.0956 - val_loss: 1.1912 - val_root_mean_squared_error: 1.1097\n",
- "Epoch 231/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1793 - root_mean_squared_error: 1.0943 - val_loss: 1.1833 - val_root_mean_squared_error: 1.0755\n",
- "Epoch 232/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1787 - root_mean_squared_error: 1.0887 - val_loss: 1.1841 - val_root_mean_squared_error: 1.1092\n",
- "Epoch 233/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1902 - root_mean_squared_error: 1.1247 - val_loss: 1.1691 - val_root_mean_squared_error: 1.0884\n",
- "Epoch 234/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1813 - root_mean_squared_error: 1.0740 - val_loss: 1.2042 - val_root_mean_squared_error: 1.1572\n",
- "Epoch 235/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1783 - root_mean_squared_error: 1.1131 - val_loss: 1.1915 - val_root_mean_squared_error: 1.0818\n",
- "Epoch 236/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.2017 - root_mean_squared_error: 1.1137 - val_loss: 1.1807 - val_root_mean_squared_error: 1.1136\n",
- "Epoch 237/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1902 - root_mean_squared_error: 1.1213 - val_loss: 1.1942 - val_root_mean_squared_error: 1.0907\n",
- "Epoch 238/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1781 - root_mean_squared_error: 1.1146 - val_loss: 1.1973 - val_root_mean_squared_error: 1.1590\n",
- "Epoch 239/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1908 - root_mean_squared_error: 1.1133 - val_loss: 1.1825 - val_root_mean_squared_error: 1.0850\n",
- "Epoch 240/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.2001 - root_mean_squared_error: 1.1152 - val_loss: 1.1788 - val_root_mean_squared_error: 1.1508\n",
- "Epoch 241/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1935 - root_mean_squared_error: 1.1132 - val_loss: 1.2114 - val_root_mean_squared_error: 1.1310\n",
- "Epoch 242/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1837 - root_mean_squared_error: 1.1124 - val_loss: 1.1718 - val_root_mean_squared_error: 1.1131\n",
- "Epoch 243/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1814 - root_mean_squared_error: 1.1023 - val_loss: 1.1837 - val_root_mean_squared_error: 1.1339\n",
- "Epoch 244/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1810 - root_mean_squared_error: 1.0887 - val_loss: 1.1625 - val_root_mean_squared_error: 1.1192\n",
- "Epoch 245/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1797 - root_mean_squared_error: 1.1091 - val_loss: 1.1853 - val_root_mean_squared_error: 1.1762\n",
- "Epoch 246/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1928 - root_mean_squared_error: 1.1219 - val_loss: 1.1792 - val_root_mean_squared_error: 1.1165\n",
- "Epoch 247/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1821 - root_mean_squared_error: 1.0873 - val_loss: 1.1917 - val_root_mean_squared_error: 1.1037\n",
- "Epoch 248/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1871 - root_mean_squared_error: 1.1118 - val_loss: 1.1943 - val_root_mean_squared_error: 1.0770\n",
- "Epoch 249/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1763 - root_mean_squared_error: 1.1166 - val_loss: 1.1829 - val_root_mean_squared_error: 1.1057\n",
- "Epoch 250/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1834 - root_mean_squared_error: 1.1057 - val_loss: 1.1722 - val_root_mean_squared_error: 1.1041\n",
- "Epoch 251/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1784 - root_mean_squared_error: 1.1018 - val_loss: 1.1916 - val_root_mean_squared_error: 1.0827\n",
- "Epoch 252/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1764 - root_mean_squared_error: 1.0829 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0720\n",
- "Epoch 253/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1823 - root_mean_squared_error: 1.0987 - val_loss: 1.1649 - val_root_mean_squared_error: 1.1399\n",
- "Epoch 254/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1771 - root_mean_squared_error: 1.1063 - val_loss: 1.1628 - val_root_mean_squared_error: 1.0863\n",
- "Epoch 255/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1753 - root_mean_squared_error: 1.1261 - val_loss: 1.1718 - val_root_mean_squared_error: 1.1181\n",
- "Epoch 256/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1685 - root_mean_squared_error: 1.0730 - val_loss: 1.1788 - val_root_mean_squared_error: 1.0882\n",
- "Epoch 257/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1907 - root_mean_squared_error: 1.0988 - val_loss: 1.1710 - val_root_mean_squared_error: 1.0730\n",
- "Epoch 258/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1689 - root_mean_squared_error: 1.0859 - val_loss: 1.1987 - val_root_mean_squared_error: 1.0730\n",
- "Epoch 259/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1871 - root_mean_squared_error: 1.0998 - val_loss: 1.1572 - val_root_mean_squared_error: 1.1101\n",
- "Epoch 260/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1780 - root_mean_squared_error: 1.0908 - val_loss: 1.1728 - val_root_mean_squared_error: 1.0936\n",
- "Epoch 261/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1824 - root_mean_squared_error: 1.1102 - val_loss: 1.1765 - val_root_mean_squared_error: 1.1021\n",
- "Epoch 262/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1852 - root_mean_squared_error: 1.1049 - val_loss: 1.1777 - val_root_mean_squared_error: 1.0981\n",
- "Epoch 263/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1753 - root_mean_squared_error: 1.1064 - val_loss: 1.1805 - val_root_mean_squared_error: 1.1038\n",
- "Epoch 264/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1783 - root_mean_squared_error: 1.0966 - val_loss: 1.1725 - val_root_mean_squared_error: 1.1359\n",
- "Epoch 265/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1712 - root_mean_squared_error: 1.0887 - val_loss: 1.1721 - val_root_mean_squared_error: 1.0962\n",
- "Epoch 266/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1793 - root_mean_squared_error: 1.1142 - val_loss: 1.1757 - val_root_mean_squared_error: 1.0847\n",
- "Epoch 267/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1636 - root_mean_squared_error: 1.0959 - val_loss: 1.1633 - val_root_mean_squared_error: 1.0590\n",
- "Epoch 268/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1770 - root_mean_squared_error: 1.1208 - val_loss: 1.1706 - val_root_mean_squared_error: 1.1157\n",
- "Epoch 269/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1670 - root_mean_squared_error: 1.0923 - val_loss: 1.1750 - val_root_mean_squared_error: 1.1057\n",
- "Epoch 270/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1746 - root_mean_squared_error: 1.1025 - val_loss: 1.1597 - val_root_mean_squared_error: 1.0692\n",
- "Epoch 271/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1773 - root_mean_squared_error: 1.0919 - val_loss: 1.1550 - val_root_mean_squared_error: 1.0526\n",
- "Epoch 272/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1708 - root_mean_squared_error: 1.1092 - val_loss: 1.2143 - val_root_mean_squared_error: 1.0846\n",
- "Epoch 273/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1775 - root_mean_squared_error: 1.0903 - val_loss: 1.1733 - val_root_mean_squared_error: 1.0970\n",
- "Epoch 274/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1764 - root_mean_squared_error: 1.0898 - val_loss: 1.1654 - val_root_mean_squared_error: 1.0788\n",
- "Epoch 275/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1835 - root_mean_squared_error: 1.1193 - val_loss: 1.1737 - val_root_mean_squared_error: 1.0875\n",
- "Epoch 276/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1814 - root_mean_squared_error: 1.0842 - val_loss: 1.1481 - val_root_mean_squared_error: 1.0741\n",
- "Epoch 277/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1743 - root_mean_squared_error: 1.1173 - val_loss: 1.1717 - val_root_mean_squared_error: 1.0776\n",
- "Epoch 278/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1888 - root_mean_squared_error: 1.0920 - val_loss: 1.1697 - val_root_mean_squared_error: 1.1046\n",
- "Epoch 279/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1810 - root_mean_squared_error: 1.1012 - val_loss: 1.1544 - val_root_mean_squared_error: 1.0986\n",
- "Epoch 280/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1906 - root_mean_squared_error: 1.1010 - val_loss: 1.1923 - val_root_mean_squared_error: 1.1708\n",
- "Epoch 281/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1759 - root_mean_squared_error: 1.1098 - val_loss: 1.1757 - val_root_mean_squared_error: 1.0905\n",
- "Epoch 282/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1816 - root_mean_squared_error: 1.0949 - val_loss: 1.2022 - val_root_mean_squared_error: 1.1559\n",
- "Epoch 283/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1675 - root_mean_squared_error: 1.0967 - val_loss: 1.1748 - val_root_mean_squared_error: 1.0909\n",
- "Epoch 284/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1782 - root_mean_squared_error: 1.0952 - val_loss: 1.1833 - val_root_mean_squared_error: 1.0877\n",
- "Epoch 285/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1850 - root_mean_squared_error: 1.1261 - val_loss: 1.1595 - val_root_mean_squared_error: 1.0474\n",
- "Epoch 286/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1744 - root_mean_squared_error: 1.1012 - val_loss: 1.1642 - val_root_mean_squared_error: 1.0813\n",
- "Epoch 287/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1760 - root_mean_squared_error: 1.0822 - val_loss: 1.1642 - val_root_mean_squared_error: 1.1394\n",
- "Epoch 288/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1748 - root_mean_squared_error: 1.1051 - val_loss: 1.1630 - val_root_mean_squared_error: 1.0608\n",
- "Epoch 289/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1773 - root_mean_squared_error: 1.1037 - val_loss: 1.1553 - val_root_mean_squared_error: 1.1545\n",
- "Epoch 290/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1835 - root_mean_squared_error: 1.0870 - val_loss: 1.1754 - val_root_mean_squared_error: 1.1455\n",
- "Epoch 291/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1784 - root_mean_squared_error: 1.1078 - val_loss: 1.1650 - val_root_mean_squared_error: 1.1261\n",
- "Epoch 292/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1779 - root_mean_squared_error: 1.0905 - val_loss: 1.1509 - val_root_mean_squared_error: 1.0687\n",
- "Epoch 293/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1757 - root_mean_squared_error: 1.0994 - val_loss: 1.1603 - val_root_mean_squared_error: 1.1427\n",
- "Epoch 294/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1710 - root_mean_squared_error: 1.1132 - val_loss: 1.1524 - val_root_mean_squared_error: 1.0694\n",
- "Epoch 295/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1776 - root_mean_squared_error: 1.1105 - val_loss: 1.1970 - val_root_mean_squared_error: 1.1538\n",
- "Epoch 296/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1755 - root_mean_squared_error: 1.1085 - val_loss: 1.1700 - val_root_mean_squared_error: 1.1319\n",
- "Epoch 297/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1733 - root_mean_squared_error: 1.0901 - val_loss: 1.1905 - val_root_mean_squared_error: 1.0399\n",
- "Epoch 298/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1813 - root_mean_squared_error: 1.1075 - val_loss: 1.1658 - val_root_mean_squared_error: 1.1121\n",
- "Epoch 299/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1659 - root_mean_squared_error: 1.0934 - val_loss: 1.1749 - val_root_mean_squared_error: 1.1276\n",
- "Epoch 300/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1721 - root_mean_squared_error: 1.0843 - val_loss: 1.1681 - val_root_mean_squared_error: 1.1141\n",
- "Epoch 301/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1810 - root_mean_squared_error: 1.1024 - val_loss: 1.1635 - val_root_mean_squared_error: 1.0973\n",
- "Epoch 302/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1694 - root_mean_squared_error: 1.0966 - val_loss: 1.1676 - val_root_mean_squared_error: 1.0296\n",
- "Epoch 303/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1751 - root_mean_squared_error: 1.0986 - val_loss: 1.1609 - val_root_mean_squared_error: 1.0818\n",
- "Epoch 304/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1835 - root_mean_squared_error: 1.1010 - val_loss: 1.1913 - val_root_mean_squared_error: 1.0870\n",
- "Epoch 305/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1838 - root_mean_squared_error: 1.1131 - val_loss: 1.1725 - val_root_mean_squared_error: 1.1260\n",
- "Epoch 306/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1659 - root_mean_squared_error: 1.1083 - val_loss: 1.1610 - val_root_mean_squared_error: 1.0476\n",
- "Epoch 307/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1790 - root_mean_squared_error: 1.1039 - val_loss: 1.1579 - val_root_mean_squared_error: 1.0666\n",
- "Epoch 308/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1683 - root_mean_squared_error: 1.1111 - val_loss: 1.1778 - val_root_mean_squared_error: 1.1338\n",
- "Epoch 309/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1716 - root_mean_squared_error: 1.0949 - val_loss: 1.1638 - val_root_mean_squared_error: 1.1131\n",
- "Epoch 310/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1733 - root_mean_squared_error: 1.0914 - val_loss: 1.1650 - val_root_mean_squared_error: 1.0995\n",
- "Epoch 311/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1666 - root_mean_squared_error: 1.0820 - val_loss: 1.1879 - val_root_mean_squared_error: 1.0796\n",
- "Epoch 312/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1724 - root_mean_squared_error: 1.0870 - val_loss: 1.1722 - val_root_mean_squared_error: 1.1335\n",
- "Epoch 313/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1818 - root_mean_squared_error: 1.0872 - val_loss: 1.1748 - val_root_mean_squared_error: 1.1180\n",
- "Epoch 314/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1657 - root_mean_squared_error: 1.1082 - val_loss: 1.1744 - val_root_mean_squared_error: 1.1022\n",
- "Epoch 315/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1754 - root_mean_squared_error: 1.0757 - val_loss: 1.1508 - val_root_mean_squared_error: 1.0767\n",
- "Epoch 316/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1745 - root_mean_squared_error: 1.1189 - val_loss: 1.1729 - val_root_mean_squared_error: 1.1327\n",
- "Epoch 317/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1607 - root_mean_squared_error: 1.0917 - val_loss: 1.1758 - val_root_mean_squared_error: 1.0690\n",
- "Epoch 318/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1705 - root_mean_squared_error: 1.1138 - val_loss: 1.1764 - val_root_mean_squared_error: 1.1094\n",
- "Epoch 319/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1684 - root_mean_squared_error: 1.0920 - val_loss: 1.1974 - val_root_mean_squared_error: 1.1454\n",
- "Epoch 320/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1737 - root_mean_squared_error: 1.1050 - val_loss: 1.2253 - val_root_mean_squared_error: 1.1171\n",
- "Epoch 321/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1743 - root_mean_squared_error: 1.1123 - val_loss: 1.1740 - val_root_mean_squared_error: 1.1561\n",
- "Epoch 322/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1747 - root_mean_squared_error: 1.0984 - val_loss: 1.1840 - val_root_mean_squared_error: 1.1319\n",
- "Epoch 323/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1661 - root_mean_squared_error: 1.0961 - val_loss: 1.1639 - val_root_mean_squared_error: 1.1270\n",
- "Epoch 324/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1612 - root_mean_squared_error: 1.0898 - val_loss: 1.1739 - val_root_mean_squared_error: 1.1052\n",
- "Epoch 325/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1706 - root_mean_squared_error: 1.0879 - val_loss: 1.1480 - val_root_mean_squared_error: 1.0402\n",
- "Epoch 326/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1764 - root_mean_squared_error: 1.1048 - val_loss: 1.1641 - val_root_mean_squared_error: 1.1125\n",
- "Epoch 327/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1812 - root_mean_squared_error: 1.1151 - val_loss: 1.1772 - val_root_mean_squared_error: 1.0707\n",
- "Epoch 328/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1679 - root_mean_squared_error: 1.0996 - val_loss: 1.1527 - val_root_mean_squared_error: 1.0592\n",
- "Epoch 329/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1670 - root_mean_squared_error: 1.0985 - val_loss: 1.1717 - val_root_mean_squared_error: 1.1010\n",
- "Epoch 330/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1793 - root_mean_squared_error: 1.0967 - val_loss: 1.1526 - val_root_mean_squared_error: 1.1291\n",
- "Epoch 331/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1805 - root_mean_squared_error: 1.1058 - val_loss: 1.1585 - val_root_mean_squared_error: 1.0970\n",
- "Epoch 332/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1754 - root_mean_squared_error: 1.0903 - val_loss: 1.1577 - val_root_mean_squared_error: 1.1092\n",
- "Epoch 333/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1738 - root_mean_squared_error: 1.0906 - val_loss: 1.1656 - val_root_mean_squared_error: 1.0954\n",
- "Epoch 334/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1628 - root_mean_squared_error: 1.0877 - val_loss: 1.1533 - val_root_mean_squared_error: 1.0999\n",
- "Epoch 335/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1694 - root_mean_squared_error: 1.0779 - val_loss: 1.1591 - val_root_mean_squared_error: 1.0850\n",
- "Epoch 336/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1807 - root_mean_squared_error: 1.1147 - val_loss: 1.2272 - val_root_mean_squared_error: 1.1827\n",
- "Epoch 337/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1701 - root_mean_squared_error: 1.0865 - val_loss: 1.1616 - val_root_mean_squared_error: 1.1028\n",
- "Epoch 338/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1680 - root_mean_squared_error: 1.0873 - val_loss: 1.1711 - val_root_mean_squared_error: 1.0898\n",
- "Epoch 339/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1804 - root_mean_squared_error: 1.1023 - val_loss: 1.1641 - val_root_mean_squared_error: 1.0451\n",
- "Epoch 340/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1644 - root_mean_squared_error: 1.0893 - val_loss: 1.1629 - val_root_mean_squared_error: 1.1030\n",
- "Epoch 341/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1767 - root_mean_squared_error: 1.1122 - val_loss: 1.1766 - val_root_mean_squared_error: 1.0824\n",
- "Epoch 342/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1706 - root_mean_squared_error: 1.0906 - val_loss: 1.1682 - val_root_mean_squared_error: 1.0956\n",
- "Epoch 343/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1718 - root_mean_squared_error: 1.1045 - val_loss: 1.1602 - val_root_mean_squared_error: 1.0723\n",
- "Epoch 344/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1704 - root_mean_squared_error: 1.0955 - val_loss: 1.1450 - val_root_mean_squared_error: 1.0957\n",
- "Epoch 345/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1661 - root_mean_squared_error: 1.0975 - val_loss: 1.1693 - val_root_mean_squared_error: 1.1012\n",
- "Epoch 346/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1744 - root_mean_squared_error: 1.0858 - val_loss: 1.1580 - val_root_mean_squared_error: 1.1231\n",
- "Epoch 347/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1750 - root_mean_squared_error: 1.0907 - val_loss: 1.1763 - val_root_mean_squared_error: 1.1144\n",
- "Epoch 348/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1682 - root_mean_squared_error: 1.1104 - val_loss: 1.1925 - val_root_mean_squared_error: 1.0859\n",
- "Epoch 349/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1690 - root_mean_squared_error: 1.0892 - val_loss: 1.1861 - val_root_mean_squared_error: 1.1038\n",
- "Epoch 350/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1693 - root_mean_squared_error: 1.1088 - val_loss: 1.1673 - val_root_mean_squared_error: 1.0590\n",
- "Epoch 351/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1701 - root_mean_squared_error: 1.0824 - val_loss: 1.1818 - val_root_mean_squared_error: 1.1086\n",
- "Epoch 352/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1723 - root_mean_squared_error: 1.0754 - val_loss: 1.1485 - val_root_mean_squared_error: 1.0705\n",
- "Epoch 353/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1639 - root_mean_squared_error: 1.0908 - val_loss: 1.1645 - val_root_mean_squared_error: 1.0712\n",
- "Epoch 354/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1684 - root_mean_squared_error: 1.0880 - val_loss: 1.1560 - val_root_mean_squared_error: 1.1012\n",
- "Epoch 355/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1706 - root_mean_squared_error: 1.0812 - val_loss: 1.1541 - val_root_mean_squared_error: 1.1048\n",
- "Epoch 356/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1752 - root_mean_squared_error: 1.0939 - val_loss: 1.1823 - val_root_mean_squared_error: 1.1099\n",
- "Epoch 357/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1713 - root_mean_squared_error: 1.0902 - val_loss: 1.1756 - val_root_mean_squared_error: 1.1049\n",
- "Epoch 358/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1659 - root_mean_squared_error: 1.1003 - val_loss: 1.1686 - val_root_mean_squared_error: 1.0608\n",
- "Epoch 359/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1710 - root_mean_squared_error: 1.0972 - val_loss: 1.1749 - val_root_mean_squared_error: 1.0863\n",
- "Epoch 360/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1716 - root_mean_squared_error: 1.0863 - val_loss: 1.1798 - val_root_mean_squared_error: 1.1173\n",
- "Epoch 361/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1672 - root_mean_squared_error: 1.0931 - val_loss: 1.1951 - val_root_mean_squared_error: 1.1276\n",
- "Epoch 362/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1663 - root_mean_squared_error: 1.0978 - val_loss: 1.1738 - val_root_mean_squared_error: 1.0941\n",
- "Epoch 363/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1761 - root_mean_squared_error: 1.0992 - val_loss: 1.1486 - val_root_mean_squared_error: 1.0913\n",
- "Epoch 364/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1643 - root_mean_squared_error: 1.0851 - val_loss: 1.1557 - val_root_mean_squared_error: 1.0759\n",
- "Epoch 365/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1602 - root_mean_squared_error: 1.0806 - val_loss: 1.1634 - val_root_mean_squared_error: 1.0977\n",
- "Epoch 366/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1675 - root_mean_squared_error: 1.0830 - val_loss: 1.1800 - val_root_mean_squared_error: 1.1498\n",
- "Epoch 367/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1662 - root_mean_squared_error: 1.0769 - val_loss: 1.1580 - val_root_mean_squared_error: 1.0609\n",
- "Epoch 368/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1686 - root_mean_squared_error: 1.0848 - val_loss: 1.1530 - val_root_mean_squared_error: 1.1113\n",
- "Epoch 369/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1615 - root_mean_squared_error: 1.0944 - val_loss: 1.1777 - val_root_mean_squared_error: 1.0814\n",
- "Epoch 370/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1681 - root_mean_squared_error: 1.0827 - val_loss: 1.1689 - val_root_mean_squared_error: 1.0782\n",
- "Epoch 371/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1640 - root_mean_squared_error: 1.1089 - val_loss: 1.1818 - val_root_mean_squared_error: 1.0902\n",
- "Epoch 372/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1696 - root_mean_squared_error: 1.0932 - val_loss: 1.1847 - val_root_mean_squared_error: 1.0768\n",
- "Epoch 373/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1690 - root_mean_squared_error: 1.0996 - val_loss: 1.1756 - val_root_mean_squared_error: 1.0760\n",
- "Epoch 374/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1676 - root_mean_squared_error: 1.0737 - val_loss: 1.1377 - val_root_mean_squared_error: 1.1017\n",
- "Epoch 375/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1686 - root_mean_squared_error: 1.0830 - val_loss: 1.1768 - val_root_mean_squared_error: 1.1312\n",
- "Epoch 376/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1649 - root_mean_squared_error: 1.0877 - val_loss: 1.1725 - val_root_mean_squared_error: 1.1236\n",
- "Epoch 377/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1846 - root_mean_squared_error: 1.0813 - val_loss: 1.1570 - val_root_mean_squared_error: 1.0769\n",
- "Epoch 378/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1672 - root_mean_squared_error: 1.0842 - val_loss: 1.1695 - val_root_mean_squared_error: 1.0713\n",
- "Epoch 379/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1629 - root_mean_squared_error: 1.0575 - val_loss: 1.1490 - val_root_mean_squared_error: 1.0835\n",
- "Epoch 380/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1636 - root_mean_squared_error: 1.0910 - val_loss: 1.1738 - val_root_mean_squared_error: 1.0299\n",
- "Epoch 381/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1733 - root_mean_squared_error: 1.0862 - val_loss: 1.1566 - val_root_mean_squared_error: 1.0909\n",
- "Epoch 382/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1773 - root_mean_squared_error: 1.0944 - val_loss: 1.1665 - val_root_mean_squared_error: 1.0639\n",
- "Epoch 383/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1705 - root_mean_squared_error: 1.0819 - val_loss: 1.1586 - val_root_mean_squared_error: 1.0924\n",
- "Epoch 384/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1828 - root_mean_squared_error: 1.1027 - val_loss: 1.1642 - val_root_mean_squared_error: 1.0758\n",
- "Epoch 385/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1699 - root_mean_squared_error: 1.0974 - val_loss: 1.1746 - val_root_mean_squared_error: 1.0597\n",
- "Epoch 386/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1663 - root_mean_squared_error: 1.1103 - val_loss: 1.1829 - val_root_mean_squared_error: 1.1275\n",
- "Epoch 387/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1732 - root_mean_squared_error: 1.0967 - val_loss: 1.1796 - val_root_mean_squared_error: 1.0975\n",
- "Epoch 388/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1678 - root_mean_squared_error: 1.0653 - val_loss: 1.1515 - val_root_mean_squared_error: 1.0676\n",
- "Epoch 389/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1639 - root_mean_squared_error: 1.1016 - val_loss: 1.1680 - val_root_mean_squared_error: 1.0487\n",
- "Epoch 390/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1698 - root_mean_squared_error: 1.0786 - val_loss: 1.1961 - val_root_mean_squared_error: 1.0935\n",
- "Epoch 391/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1755 - root_mean_squared_error: 1.0867 - val_loss: 1.1742 - val_root_mean_squared_error: 1.0876\n",
- "Epoch 392/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1671 - root_mean_squared_error: 1.0934 - val_loss: 1.1625 - val_root_mean_squared_error: 1.1110\n",
- "Epoch 393/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1685 - root_mean_squared_error: 1.0865 - val_loss: 1.1544 - val_root_mean_squared_error: 1.1083\n",
- "Epoch 394/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1728 - root_mean_squared_error: 1.0911 - val_loss: 1.1548 - val_root_mean_squared_error: 1.1117\n",
- "Epoch 395/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1587 - root_mean_squared_error: 1.0882 - val_loss: 1.1523 - val_root_mean_squared_error: 1.0723\n",
- "Epoch 396/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1620 - root_mean_squared_error: 1.0899 - val_loss: 1.1969 - val_root_mean_squared_error: 1.1401\n",
- "Epoch 397/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1635 - root_mean_squared_error: 1.0754 - val_loss: 1.1490 - val_root_mean_squared_error: 1.0133\n",
- "Epoch 398/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1740 - root_mean_squared_error: 1.1029 - val_loss: 1.1692 - val_root_mean_squared_error: 1.0812\n",
- "Epoch 399/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1640 - root_mean_squared_error: 1.0750 - val_loss: 1.1691 - val_root_mean_squared_error: 1.0565\n",
- "Epoch 400/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1670 - root_mean_squared_error: 1.0845 - val_loss: 1.1621 - val_root_mean_squared_error: 1.0536\n",
- "Epoch 401/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1674 - root_mean_squared_error: 1.0930 - val_loss: 1.1717 - val_root_mean_squared_error: 1.0662\n",
- "Epoch 402/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1737 - root_mean_squared_error: 1.1074 - val_loss: 1.1724 - val_root_mean_squared_error: 1.0402\n",
- "Epoch 403/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1745 - root_mean_squared_error: 1.0928 - val_loss: 1.1489 - val_root_mean_squared_error: 1.0671\n",
- "Epoch 404/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1634 - root_mean_squared_error: 1.0787 - val_loss: 1.1636 - val_root_mean_squared_error: 1.0467\n",
- "Epoch 405/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1600 - root_mean_squared_error: 1.0717 - val_loss: 1.1558 - val_root_mean_squared_error: 1.0909\n",
- "Epoch 406/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1691 - root_mean_squared_error: 1.0631 - val_loss: 1.1651 - val_root_mean_squared_error: 1.1063\n",
- "Epoch 407/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1674 - root_mean_squared_error: 1.0783 - val_loss: 1.1643 - val_root_mean_squared_error: 1.1224\n",
- "Epoch 408/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1810 - root_mean_squared_error: 1.1094 - val_loss: 1.1834 - val_root_mean_squared_error: 1.1124\n",
- "Epoch 409/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1654 - root_mean_squared_error: 1.0810 - val_loss: 1.1611 - val_root_mean_squared_error: 1.0532\n",
- "Epoch 410/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1658 - root_mean_squared_error: 1.1057 - val_loss: 1.1933 - val_root_mean_squared_error: 1.0905\n",
- "Epoch 411/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1672 - root_mean_squared_error: 1.0965 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0754\n",
- "Epoch 412/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1599 - root_mean_squared_error: 1.0710 - val_loss: 1.1484 - val_root_mean_squared_error: 1.0975\n",
- "Epoch 413/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1743 - root_mean_squared_error: 1.1044 - val_loss: 1.1604 - val_root_mean_squared_error: 1.1014\n",
- "Epoch 414/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1569 - root_mean_squared_error: 1.0781 - val_loss: 1.1680 - val_root_mean_squared_error: 1.0504\n",
- "Epoch 415/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1724 - root_mean_squared_error: 1.1017 - val_loss: 1.1670 - val_root_mean_squared_error: 1.0489\n",
- "Epoch 416/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1658 - root_mean_squared_error: 1.0932 - val_loss: 1.1651 - val_root_mean_squared_error: 1.0422\n",
- "Epoch 417/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1734 - root_mean_squared_error: 1.0944 - val_loss: 1.1738 - val_root_mean_squared_error: 1.1122\n",
- "Epoch 418/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1633 - root_mean_squared_error: 1.1025 - val_loss: 1.1677 - val_root_mean_squared_error: 1.0591\n",
- "Epoch 419/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1683 - root_mean_squared_error: 1.0850 - val_loss: 1.1543 - val_root_mean_squared_error: 1.0725\n",
- "Epoch 420/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1779 - root_mean_squared_error: 1.0999 - val_loss: 1.1661 - val_root_mean_squared_error: 1.0775\n",
- "Epoch 421/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1641 - root_mean_squared_error: 1.1102 - val_loss: 1.1578 - val_root_mean_squared_error: 1.0298\n",
- "Epoch 422/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1619 - root_mean_squared_error: 1.0956 - val_loss: 1.1614 - val_root_mean_squared_error: 1.0886\n",
- "Epoch 423/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1631 - root_mean_squared_error: 1.0826 - val_loss: 1.1649 - val_root_mean_squared_error: 1.0768\n",
- "Epoch 424/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1650 - root_mean_squared_error: 1.0913 - val_loss: 1.1870 - val_root_mean_squared_error: 1.0932\n",
- "Epoch 425/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1687 - root_mean_squared_error: 1.0835 - val_loss: 1.2007 - val_root_mean_squared_error: 1.0963\n",
- "Epoch 426/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1639 - root_mean_squared_error: 1.1121 - val_loss: 1.1643 - val_root_mean_squared_error: 1.0113\n",
- "Epoch 427/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1630 - root_mean_squared_error: 1.0726 - val_loss: 1.1506 - val_root_mean_squared_error: 1.0226\n",
- "Epoch 428/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1628 - root_mean_squared_error: 1.0543 - val_loss: 1.1696 - val_root_mean_squared_error: 1.0702\n",
- "Epoch 429/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1794 - root_mean_squared_error: 1.0939 - val_loss: 1.1428 - val_root_mean_squared_error: 1.0545\n",
- "Epoch 430/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1663 - root_mean_squared_error: 1.0878 - val_loss: 1.1644 - val_root_mean_squared_error: 1.1079\n",
- "Epoch 431/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1655 - root_mean_squared_error: 1.0995 - val_loss: 1.1520 - val_root_mean_squared_error: 1.0712\n",
- "Epoch 432/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1738 - root_mean_squared_error: 1.0925 - val_loss: 1.1539 - val_root_mean_squared_error: 1.0906\n",
- "Epoch 433/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1665 - root_mean_squared_error: 1.0892 - val_loss: 1.1605 - val_root_mean_squared_error: 1.1270\n",
- "Epoch 434/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1701 - root_mean_squared_error: 1.0928 - val_loss: 1.1606 - val_root_mean_squared_error: 1.0144\n",
- "Epoch 435/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1705 - root_mean_squared_error: 1.0851 - val_loss: 1.1839 - val_root_mean_squared_error: 1.0902\n",
- "Epoch 436/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1721 - root_mean_squared_error: 1.0853 - val_loss: 1.1678 - val_root_mean_squared_error: 1.0623\n",
- "Epoch 437/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1566 - root_mean_squared_error: 1.0811 - val_loss: 1.1603 - val_root_mean_squared_error: 1.0661\n",
- "Epoch 438/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1647 - root_mean_squared_error: 1.0788 - val_loss: 1.1704 - val_root_mean_squared_error: 1.1135\n",
- "Epoch 439/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1673 - root_mean_squared_error: 1.0842 - val_loss: 1.1647 - val_root_mean_squared_error: 1.1072\n",
- "Epoch 440/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1649 - root_mean_squared_error: 1.0650 - val_loss: 1.1566 - val_root_mean_squared_error: 1.0896\n",
- "Epoch 441/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1640 - root_mean_squared_error: 1.0770 - val_loss: 1.1780 - val_root_mean_squared_error: 1.0574\n",
- "Epoch 442/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1662 - root_mean_squared_error: 1.0780 - val_loss: 1.1729 - val_root_mean_squared_error: 1.0739\n",
- "Epoch 443/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1708 - root_mean_squared_error: 1.0931 - val_loss: 1.1489 - val_root_mean_squared_error: 1.0832\n",
- "Epoch 444/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1597 - root_mean_squared_error: 1.0756 - val_loss: 1.1715 - val_root_mean_squared_error: 1.0578\n",
- "Epoch 445/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1679 - root_mean_squared_error: 1.0957 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0926\n",
- "Epoch 446/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1648 - root_mean_squared_error: 1.0944 - val_loss: 1.1519 - val_root_mean_squared_error: 1.0520\n",
- "Epoch 447/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1585 - root_mean_squared_error: 1.0607 - val_loss: 1.1609 - val_root_mean_squared_error: 1.0529\n",
- "Epoch 448/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1578 - root_mean_squared_error: 1.0852 - val_loss: 1.1672 - val_root_mean_squared_error: 1.0587\n",
- "Epoch 449/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1596 - root_mean_squared_error: 1.0831 - val_loss: 1.1641 - val_root_mean_squared_error: 1.0725\n",
- "Epoch 450/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1598 - root_mean_squared_error: 1.0623 - val_loss: 1.1958 - val_root_mean_squared_error: 1.0994\n",
- "Epoch 451/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1622 - root_mean_squared_error: 1.0949 - val_loss: 1.1546 - val_root_mean_squared_error: 1.0990\n",
- "Epoch 452/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1620 - root_mean_squared_error: 1.0843 - val_loss: 1.1697 - val_root_mean_squared_error: 1.0614\n",
- "Epoch 453/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1657 - root_mean_squared_error: 1.0796 - val_loss: 1.1623 - val_root_mean_squared_error: 1.0683\n",
- "Epoch 454/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1612 - root_mean_squared_error: 1.0956 - val_loss: 1.1795 - val_root_mean_squared_error: 1.0980\n",
- "Epoch 455/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1623 - root_mean_squared_error: 1.0651 - val_loss: 1.1562 - val_root_mean_squared_error: 1.1370\n",
- "Epoch 456/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1666 - root_mean_squared_error: 1.0940 - val_loss: 1.1493 - val_root_mean_squared_error: 1.0755\n",
- "Epoch 457/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1609 - root_mean_squared_error: 1.0823 - val_loss: 1.1584 - val_root_mean_squared_error: 1.0985\n",
- "Epoch 458/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1676 - root_mean_squared_error: 1.0987 - val_loss: 1.1502 - val_root_mean_squared_error: 1.0958\n",
- "Epoch 459/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1654 - root_mean_squared_error: 1.0963 - val_loss: 1.1636 - val_root_mean_squared_error: 1.0613\n",
- "Epoch 460/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1682 - root_mean_squared_error: 1.0889 - val_loss: 1.1555 - val_root_mean_squared_error: 1.0507\n",
- "Epoch 461/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1566 - root_mean_squared_error: 1.0871 - val_loss: 1.1875 - val_root_mean_squared_error: 1.1143\n",
- "Epoch 462/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1691 - root_mean_squared_error: 1.0926 - val_loss: 1.1926 - val_root_mean_squared_error: 1.1470\n",
- "Epoch 463/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1580 - root_mean_squared_error: 1.0815 - val_loss: 1.1689 - val_root_mean_squared_error: 1.1151\n",
- "Epoch 464/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1625 - root_mean_squared_error: 1.0883 - val_loss: 1.1668 - val_root_mean_squared_error: 1.0881\n",
- "Epoch 465/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1651 - root_mean_squared_error: 1.0812 - val_loss: 1.1561 - val_root_mean_squared_error: 1.0960\n",
- "Epoch 466/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1720 - root_mean_squared_error: 1.0953 - val_loss: 1.1705 - val_root_mean_squared_error: 1.0467\n",
- "Epoch 467/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1648 - root_mean_squared_error: 1.0735 - val_loss: 1.1609 - val_root_mean_squared_error: 1.0683\n",
- "Epoch 468/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1633 - root_mean_squared_error: 1.0946 - val_loss: 1.1790 - val_root_mean_squared_error: 1.0887\n",
- "Epoch 469/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1662 - root_mean_squared_error: 1.0943 - val_loss: 1.1657 - val_root_mean_squared_error: 1.0825\n",
- "Epoch 470/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1670 - root_mean_squared_error: 1.0905 - val_loss: 1.1667 - val_root_mean_squared_error: 1.0892\n",
- "Epoch 471/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1634 - root_mean_squared_error: 1.0915 - val_loss: 1.1662 - val_root_mean_squared_error: 1.0956\n",
- "Epoch 472/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1674 - root_mean_squared_error: 1.0770 - val_loss: 1.1524 - val_root_mean_squared_error: 1.0521\n",
- "Epoch 473/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0616 - val_loss: 1.1887 - val_root_mean_squared_error: 1.0815\n",
- "Epoch 474/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1714 - root_mean_squared_error: 1.0703 - val_loss: 1.1622 - val_root_mean_squared_error: 1.0760\n",
- "Epoch 475/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1623 - root_mean_squared_error: 1.0783 - val_loss: 1.1757 - val_root_mean_squared_error: 1.0876\n",
- "Epoch 476/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1598 - root_mean_squared_error: 1.0871 - val_loss: 1.1558 - val_root_mean_squared_error: 1.0893\n",
- "Epoch 477/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1701 - root_mean_squared_error: 1.0887 - val_loss: 1.1657 - val_root_mean_squared_error: 1.0804\n",
- "Epoch 478/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1610 - root_mean_squared_error: 1.0699 - val_loss: 1.1640 - val_root_mean_squared_error: 1.1044\n",
- "Epoch 479/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1693 - root_mean_squared_error: 1.1131 - val_loss: 1.1551 - val_root_mean_squared_error: 1.0661\n",
- "Epoch 480/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1652 - root_mean_squared_error: 1.0780 - val_loss: 1.1784 - val_root_mean_squared_error: 1.0890\n",
- "Epoch 481/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1665 - root_mean_squared_error: 1.0868 - val_loss: 1.1673 - val_root_mean_squared_error: 1.0974\n",
- "Epoch 482/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0757 - val_loss: 1.1672 - val_root_mean_squared_error: 1.0645\n",
- "Epoch 483/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1748 - root_mean_squared_error: 1.0684 - val_loss: 1.1654 - val_root_mean_squared_error: 1.0787\n",
- "Epoch 484/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1660 - root_mean_squared_error: 1.0886 - val_loss: 1.1966 - val_root_mean_squared_error: 1.0748\n",
- "Epoch 485/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0850 - val_loss: 1.1705 - val_root_mean_squared_error: 1.0734\n",
- "Epoch 486/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1680 - root_mean_squared_error: 1.0805 - val_loss: 1.1714 - val_root_mean_squared_error: 1.0726\n",
- "Epoch 487/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1624 - root_mean_squared_error: 1.0916 - val_loss: 1.1532 - val_root_mean_squared_error: 1.0743\n",
- "Epoch 488/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1694 - root_mean_squared_error: 1.1050 - val_loss: 1.1563 - val_root_mean_squared_error: 1.1053\n",
- "Epoch 489/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1566 - root_mean_squared_error: 1.0682 - val_loss: 1.1667 - val_root_mean_squared_error: 1.0838\n",
- "Epoch 490/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1600 - root_mean_squared_error: 1.0807 - val_loss: 1.1627 - val_root_mean_squared_error: 1.1198\n",
- "Epoch 491/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1578 - root_mean_squared_error: 1.0827 - val_loss: 1.1707 - val_root_mean_squared_error: 1.0678\n",
- "Epoch 492/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1710 - root_mean_squared_error: 1.0701 - val_loss: 1.1707 - val_root_mean_squared_error: 1.0445\n",
- "Epoch 493/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1639 - root_mean_squared_error: 1.0914 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0918\n",
- "Epoch 494/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1623 - root_mean_squared_error: 1.0948 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0688\n",
- "Epoch 495/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0801 - val_loss: 1.1792 - val_root_mean_squared_error: 1.1031\n",
- "Epoch 496/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1623 - root_mean_squared_error: 1.0862 - val_loss: 1.1671 - val_root_mean_squared_error: 1.1219\n",
- "Epoch 497/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1615 - root_mean_squared_error: 1.0739 - val_loss: 1.1659 - val_root_mean_squared_error: 1.0773\n",
- "Epoch 498/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1679 - root_mean_squared_error: 1.0886 - val_loss: 1.1564 - val_root_mean_squared_error: 1.0725\n",
- "Epoch 499/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1556 - root_mean_squared_error: 1.0971 - val_loss: 1.1580 - val_root_mean_squared_error: 1.0555\n",
- "Epoch 500/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1694 - root_mean_squared_error: 1.0899 - val_loss: 1.1647 - val_root_mean_squared_error: 1.1243\n",
- "Epoch 501/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1601 - root_mean_squared_error: 1.0823 - val_loss: 1.1635 - val_root_mean_squared_error: 1.0634\n",
- "Epoch 502/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1593 - root_mean_squared_error: 1.0695 - val_loss: 1.1550 - val_root_mean_squared_error: 1.0966\n",
- "Epoch 503/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0714 - val_loss: 1.1572 - val_root_mean_squared_error: 1.1005\n",
- "Epoch 504/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1615 - root_mean_squared_error: 1.0599 - val_loss: 1.1588 - val_root_mean_squared_error: 1.0677\n",
- "Epoch 505/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1561 - root_mean_squared_error: 1.0793 - val_loss: 1.1731 - val_root_mean_squared_error: 1.1111\n",
- "Epoch 506/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1664 - root_mean_squared_error: 1.0933 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0468\n",
- "Epoch 507/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1630 - root_mean_squared_error: 1.0989 - val_loss: 1.1569 - val_root_mean_squared_error: 1.0894\n",
- "Epoch 508/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1682 - root_mean_squared_error: 1.0888 - val_loss: 1.1510 - val_root_mean_squared_error: 1.1122\n",
- "Epoch 509/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1651 - root_mean_squared_error: 1.0630 - val_loss: 1.1753 - val_root_mean_squared_error: 1.0957\n",
- "Epoch 510/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1603 - root_mean_squared_error: 1.0969 - val_loss: 1.1489 - val_root_mean_squared_error: 1.0804\n",
- "Epoch 511/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1723 - root_mean_squared_error: 1.0951 - val_loss: 1.1640 - val_root_mean_squared_error: 1.0593\n",
- "Epoch 512/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1599 - root_mean_squared_error: 1.0953 - val_loss: 1.1481 - val_root_mean_squared_error: 1.0105\n",
- "Epoch 513/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1594 - root_mean_squared_error: 1.0591 - val_loss: 1.1826 - val_root_mean_squared_error: 1.0528\n",
- "Epoch 514/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1670 - root_mean_squared_error: 1.0913 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0715\n",
- "Epoch 515/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1620 - root_mean_squared_error: 1.0845 - val_loss: 1.1672 - val_root_mean_squared_error: 1.1216\n",
- "Epoch 516/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1627 - root_mean_squared_error: 1.0731 - val_loss: 1.1564 - val_root_mean_squared_error: 1.0803\n",
- "Epoch 517/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1587 - root_mean_squared_error: 1.0832 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0779\n",
- "Epoch 518/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1555 - root_mean_squared_error: 1.0839 - val_loss: 1.1482 - val_root_mean_squared_error: 1.0693\n",
- "Epoch 519/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0826 - val_loss: 1.1521 - val_root_mean_squared_error: 1.0596\n",
- "Epoch 520/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1597 - root_mean_squared_error: 1.0847 - val_loss: 1.1579 - val_root_mean_squared_error: 1.0731\n",
- "Epoch 521/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1606 - root_mean_squared_error: 1.0668 - val_loss: 1.1657 - val_root_mean_squared_error: 1.0874\n",
- "Epoch 522/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1652 - root_mean_squared_error: 1.0876 - val_loss: 1.1598 - val_root_mean_squared_error: 1.0735\n",
- "Epoch 523/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1656 - root_mean_squared_error: 1.0813 - val_loss: 1.1735 - val_root_mean_squared_error: 1.1153\n",
- "Epoch 524/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1604 - root_mean_squared_error: 1.0770 - val_loss: 1.1767 - val_root_mean_squared_error: 1.0771\n",
- "Epoch 525/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1578 - root_mean_squared_error: 1.0886 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0693\n",
- "Epoch 526/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1653 - root_mean_squared_error: 1.0806 - val_loss: 1.1727 - val_root_mean_squared_error: 1.0715\n",
- "Epoch 527/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1555 - root_mean_squared_error: 1.0772 - val_loss: 1.1745 - val_root_mean_squared_error: 1.0405\n",
- "Epoch 528/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1585 - root_mean_squared_error: 1.0688 - val_loss: 1.1528 - val_root_mean_squared_error: 1.0850\n",
- "Epoch 529/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1707 - root_mean_squared_error: 1.0797 - val_loss: 1.1642 - val_root_mean_squared_error: 1.1010\n",
- "Epoch 530/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1528 - root_mean_squared_error: 1.0613 - val_loss: 1.1666 - val_root_mean_squared_error: 1.0957\n",
- "Epoch 531/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1616 - root_mean_squared_error: 1.0873 - val_loss: 1.1734 - val_root_mean_squared_error: 1.0489\n",
- "Epoch 532/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1652 - root_mean_squared_error: 1.0865 - val_loss: 1.1807 - val_root_mean_squared_error: 1.1281\n",
- "Epoch 533/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1602 - root_mean_squared_error: 1.0554 - val_loss: 1.1623 - val_root_mean_squared_error: 1.0259\n",
- "Epoch 534/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1572 - root_mean_squared_error: 1.0781 - val_loss: 1.1671 - val_root_mean_squared_error: 1.0891\n",
- "Epoch 535/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1705 - root_mean_squared_error: 1.0794 - val_loss: 1.1708 - val_root_mean_squared_error: 1.0622\n",
- "Epoch 536/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1607 - root_mean_squared_error: 1.0836 - val_loss: 1.1627 - val_root_mean_squared_error: 1.0867\n",
- "Epoch 537/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1546 - root_mean_squared_error: 1.0977 - val_loss: 1.1588 - val_root_mean_squared_error: 1.0565\n",
- "Epoch 538/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1637 - root_mean_squared_error: 1.0670 - val_loss: 1.1633 - val_root_mean_squared_error: 1.0638\n",
- "Epoch 539/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1665 - root_mean_squared_error: 1.0641 - val_loss: 1.1551 - val_root_mean_squared_error: 1.0361\n",
- "Epoch 540/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1709 - root_mean_squared_error: 1.0765 - val_loss: 1.1607 - val_root_mean_squared_error: 1.1138\n",
- "Epoch 541/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0820 - val_loss: 1.1606 - val_root_mean_squared_error: 1.0833\n",
- "Epoch 542/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1660 - root_mean_squared_error: 1.0898 - val_loss: 1.1646 - val_root_mean_squared_error: 1.0779\n",
- "Epoch 543/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1580 - root_mean_squared_error: 1.0879 - val_loss: 1.1760 - val_root_mean_squared_error: 1.0709\n",
- "Epoch 544/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1732 - root_mean_squared_error: 1.0739 - val_loss: 1.1481 - val_root_mean_squared_error: 1.0456\n",
- "Epoch 545/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1651 - root_mean_squared_error: 1.0873 - val_loss: 1.1628 - val_root_mean_squared_error: 1.0657\n",
- "Epoch 546/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1578 - root_mean_squared_error: 1.0780 - val_loss: 1.1620 - val_root_mean_squared_error: 1.0965\n",
- "Epoch 547/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1690 - root_mean_squared_error: 1.0880 - val_loss: 1.1514 - val_root_mean_squared_error: 1.0893\n",
- "Epoch 548/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1560 - root_mean_squared_error: 1.0918 - val_loss: 1.1462 - val_root_mean_squared_error: 1.0672\n",
- "Epoch 549/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1596 - root_mean_squared_error: 1.0684 - val_loss: 1.1778 - val_root_mean_squared_error: 1.0988\n",
- "Epoch 550/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1612 - root_mean_squared_error: 1.0876 - val_loss: 1.1717 - val_root_mean_squared_error: 1.1014\n",
- "Epoch 551/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1571 - root_mean_squared_error: 1.0694 - val_loss: 1.1473 - val_root_mean_squared_error: 1.1035\n",
- "Epoch 552/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1602 - root_mean_squared_error: 1.0545 - val_loss: 1.1512 - val_root_mean_squared_error: 1.0871\n",
- "Epoch 553/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1566 - root_mean_squared_error: 1.0853 - val_loss: 1.1663 - val_root_mean_squared_error: 1.0507\n",
- "Epoch 554/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1635 - root_mean_squared_error: 1.0900 - val_loss: 1.1638 - val_root_mean_squared_error: 1.1135\n",
- "Epoch 555/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1580 - root_mean_squared_error: 1.0784 - val_loss: 1.1656 - val_root_mean_squared_error: 1.1171\n",
- "Epoch 556/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1534 - root_mean_squared_error: 1.0676 - val_loss: 1.1631 - val_root_mean_squared_error: 1.0846\n",
- "Epoch 557/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1632 - root_mean_squared_error: 1.0819 - val_loss: 1.1542 - val_root_mean_squared_error: 1.1208\n",
- "Epoch 558/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1600 - root_mean_squared_error: 1.0726 - val_loss: 1.1580 - val_root_mean_squared_error: 1.1077\n",
- "Epoch 559/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1556 - root_mean_squared_error: 1.0737 - val_loss: 1.1683 - val_root_mean_squared_error: 1.1152\n",
- "Epoch 560/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0643 - val_loss: 1.1491 - val_root_mean_squared_error: 1.0473\n",
- "Epoch 561/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1582 - root_mean_squared_error: 1.0816 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0545\n",
- "Epoch 562/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1685 - root_mean_squared_error: 1.0961 - val_loss: 1.1422 - val_root_mean_squared_error: 1.0735\n",
- "Epoch 563/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1655 - root_mean_squared_error: 1.0781 - val_loss: 1.1745 - val_root_mean_squared_error: 1.0599\n",
- "Epoch 564/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1552 - root_mean_squared_error: 1.1041 - val_loss: 1.1788 - val_root_mean_squared_error: 1.0620\n",
- "Epoch 565/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1594 - root_mean_squared_error: 1.0665 - val_loss: 1.1717 - val_root_mean_squared_error: 1.0517\n",
- "Epoch 566/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1605 - root_mean_squared_error: 1.0719 - val_loss: 1.1689 - val_root_mean_squared_error: 1.0853\n",
- "Epoch 567/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1636 - root_mean_squared_error: 1.0895 - val_loss: 1.1573 - val_root_mean_squared_error: 1.0557\n",
- "Epoch 568/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1669 - root_mean_squared_error: 1.0832 - val_loss: 1.1434 - val_root_mean_squared_error: 1.1346\n",
- "Epoch 569/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1568 - root_mean_squared_error: 1.0810 - val_loss: 1.1721 - val_root_mean_squared_error: 1.1271\n",
- "Epoch 570/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1548 - root_mean_squared_error: 1.0859 - val_loss: 1.1405 - val_root_mean_squared_error: 1.0838\n",
- "Epoch 571/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1634 - root_mean_squared_error: 1.0806 - val_loss: 1.1558 - val_root_mean_squared_error: 1.0724\n",
- "Epoch 572/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1600 - root_mean_squared_error: 1.0865 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0891\n",
- "Epoch 573/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1597 - root_mean_squared_error: 1.1015 - val_loss: 1.1587 - val_root_mean_squared_error: 1.0854\n",
- "Epoch 574/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1602 - root_mean_squared_error: 1.0948 - val_loss: 1.1557 - val_root_mean_squared_error: 1.0843\n",
- "Epoch 575/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1546 - root_mean_squared_error: 1.0790 - val_loss: 1.1484 - val_root_mean_squared_error: 1.0559\n",
- "Epoch 576/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1595 - root_mean_squared_error: 1.0922 - val_loss: 1.1682 - val_root_mean_squared_error: 1.1132\n",
- "Epoch 577/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1561 - root_mean_squared_error: 1.0903 - val_loss: 1.1451 - val_root_mean_squared_error: 1.0783\n",
- "Epoch 578/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1599 - root_mean_squared_error: 1.0858 - val_loss: 1.1595 - val_root_mean_squared_error: 1.0641\n",
- "Epoch 579/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1597 - root_mean_squared_error: 1.0522 - val_loss: 1.1794 - val_root_mean_squared_error: 1.1094\n",
- "Epoch 580/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1609 - root_mean_squared_error: 1.0713 - val_loss: 1.1611 - val_root_mean_squared_error: 1.1173\n",
- "Epoch 581/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1614 - root_mean_squared_error: 1.0934 - val_loss: 1.1607 - val_root_mean_squared_error: 1.1073\n",
- "Epoch 582/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1532 - root_mean_squared_error: 1.0918 - val_loss: 1.1563 - val_root_mean_squared_error: 1.0832\n",
- "Epoch 583/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1711 - root_mean_squared_error: 1.0713 - val_loss: 1.1500 - val_root_mean_squared_error: 1.0636\n",
- "Epoch 584/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1506 - root_mean_squared_error: 1.0792 - val_loss: 1.1456 - val_root_mean_squared_error: 1.0688\n",
- "Epoch 585/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1625 - root_mean_squared_error: 1.0897 - val_loss: 1.1663 - val_root_mean_squared_error: 1.1221\n",
- "Epoch 586/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1589 - root_mean_squared_error: 1.0927 - val_loss: 1.1666 - val_root_mean_squared_error: 1.0804\n",
- "Epoch 587/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0765 - val_loss: 1.1751 - val_root_mean_squared_error: 1.0710\n",
- "Epoch 588/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1660 - root_mean_squared_error: 1.0771 - val_loss: 1.1510 - val_root_mean_squared_error: 1.0701\n",
- "Epoch 589/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1641 - root_mean_squared_error: 1.0761 - val_loss: 1.1729 - val_root_mean_squared_error: 1.1052\n",
- "Epoch 590/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1587 - root_mean_squared_error: 1.0887 - val_loss: 1.1561 - val_root_mean_squared_error: 1.0983\n",
- "Epoch 591/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1676 - root_mean_squared_error: 1.0930 - val_loss: 1.1603 - val_root_mean_squared_error: 1.0546\n",
- "Epoch 592/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1660 - root_mean_squared_error: 1.1001 - val_loss: 1.1661 - val_root_mean_squared_error: 1.0704\n",
- "Epoch 593/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1596 - root_mean_squared_error: 1.0890 - val_loss: 1.1597 - val_root_mean_squared_error: 1.0350\n",
- "Epoch 594/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0745 - val_loss: 1.1639 - val_root_mean_squared_error: 1.0932\n",
- "Epoch 595/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1574 - root_mean_squared_error: 1.0931 - val_loss: 1.1647 - val_root_mean_squared_error: 1.0579\n",
- "Epoch 596/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1627 - root_mean_squared_error: 1.0878 - val_loss: 1.1702 - val_root_mean_squared_error: 1.1109\n",
- "Epoch 597/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1563 - root_mean_squared_error: 1.0786 - val_loss: 1.1536 - val_root_mean_squared_error: 1.1091\n",
- "Epoch 598/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1601 - root_mean_squared_error: 1.0639 - val_loss: 1.1454 - val_root_mean_squared_error: 1.0933\n",
- "Epoch 599/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1571 - root_mean_squared_error: 1.0745 - val_loss: 1.1545 - val_root_mean_squared_error: 1.0784\n",
- "Epoch 600/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1656 - root_mean_squared_error: 1.0740 - val_loss: 1.1686 - val_root_mean_squared_error: 1.1170\n",
- "Epoch 601/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1584 - root_mean_squared_error: 1.0983 - val_loss: 1.1719 - val_root_mean_squared_error: 1.0628\n",
- "Epoch 602/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0751 - val_loss: 1.1712 - val_root_mean_squared_error: 1.0864\n",
- "Epoch 603/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0872 - val_loss: 1.1463 - val_root_mean_squared_error: 1.0829\n",
- "Epoch 604/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1640 - root_mean_squared_error: 1.1084 - val_loss: 1.1701 - val_root_mean_squared_error: 1.0941\n",
- "Epoch 605/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1588 - root_mean_squared_error: 1.0718 - val_loss: 1.1782 - val_root_mean_squared_error: 1.1237\n",
- "Epoch 606/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1631 - root_mean_squared_error: 1.0892 - val_loss: 1.1624 - val_root_mean_squared_error: 1.0823\n",
- "Epoch 607/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1586 - root_mean_squared_error: 1.0772 - val_loss: 1.1613 - val_root_mean_squared_error: 1.1258\n",
- "Epoch 608/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1584 - root_mean_squared_error: 1.0901 - val_loss: 1.1523 - val_root_mean_squared_error: 1.0829\n",
- "Epoch 609/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1600 - root_mean_squared_error: 1.0919 - val_loss: 1.1677 - val_root_mean_squared_error: 1.0809\n",
- "Epoch 610/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0788 - val_loss: 1.1556 - val_root_mean_squared_error: 1.0344\n",
- "Epoch 611/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1602 - root_mean_squared_error: 1.0736 - val_loss: 1.1601 - val_root_mean_squared_error: 1.0611\n",
- "Epoch 612/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1642 - root_mean_squared_error: 1.0807 - val_loss: 1.1568 - val_root_mean_squared_error: 1.0514\n",
- "Epoch 613/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1606 - root_mean_squared_error: 1.0730 - val_loss: 1.1775 - val_root_mean_squared_error: 1.0962\n",
- "Epoch 614/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1547 - root_mean_squared_error: 1.0761 - val_loss: 1.1575 - val_root_mean_squared_error: 1.0986\n",
- "Epoch 615/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1635 - root_mean_squared_error: 1.0887 - val_loss: 1.1677 - val_root_mean_squared_error: 1.0893\n",
- "Epoch 616/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1539 - root_mean_squared_error: 1.0783 - val_loss: 1.1626 - val_root_mean_squared_error: 1.0739\n",
- "Epoch 617/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1617 - root_mean_squared_error: 1.0933 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0814\n",
- "Epoch 618/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1658 - root_mean_squared_error: 1.0977 - val_loss: 1.1564 - val_root_mean_squared_error: 1.0838\n",
- "Epoch 619/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1584 - root_mean_squared_error: 1.0750 - val_loss: 1.1622 - val_root_mean_squared_error: 1.1273\n",
- "Epoch 620/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1519 - root_mean_squared_error: 1.0791 - val_loss: 1.1692 - val_root_mean_squared_error: 1.0446\n",
- "Epoch 621/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1614 - root_mean_squared_error: 1.0646 - val_loss: 1.1713 - val_root_mean_squared_error: 1.0720\n",
- "Epoch 622/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1657 - root_mean_squared_error: 1.0726 - val_loss: 1.1639 - val_root_mean_squared_error: 1.0001\n",
- "Epoch 623/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1567 - root_mean_squared_error: 1.0891 - val_loss: 1.1420 - val_root_mean_squared_error: 1.0607\n",
- "Epoch 624/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1675 - root_mean_squared_error: 1.0964 - val_loss: 1.1560 - val_root_mean_squared_error: 1.0931\n",
- "Epoch 625/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0852 - val_loss: 1.1597 - val_root_mean_squared_error: 1.0425\n",
- "Epoch 626/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1569 - root_mean_squared_error: 1.0747 - val_loss: 1.1596 - val_root_mean_squared_error: 1.1053\n",
- "Epoch 627/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1595 - root_mean_squared_error: 1.0583 - val_loss: 1.1614 - val_root_mean_squared_error: 1.0592\n",
- "Epoch 628/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1550 - root_mean_squared_error: 1.0923 - val_loss: 1.1852 - val_root_mean_squared_error: 1.0854\n",
- "Epoch 629/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1600 - root_mean_squared_error: 1.0838 - val_loss: 1.1386 - val_root_mean_squared_error: 1.0419\n",
- "Epoch 630/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1554 - root_mean_squared_error: 1.0741 - val_loss: 1.1554 - val_root_mean_squared_error: 1.0676\n",
- "Epoch 631/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1661 - root_mean_squared_error: 1.0891 - val_loss: 1.1598 - val_root_mean_squared_error: 1.1025\n",
- "Epoch 632/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1650 - root_mean_squared_error: 1.0839 - val_loss: 1.1651 - val_root_mean_squared_error: 1.0885\n",
- "Epoch 633/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1539 - root_mean_squared_error: 1.0990 - val_loss: 1.1494 - val_root_mean_squared_error: 1.0609\n",
- "Epoch 634/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1603 - root_mean_squared_error: 1.1106 - val_loss: 1.1494 - val_root_mean_squared_error: 1.0507\n",
- "Epoch 635/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1579 - root_mean_squared_error: 1.0728 - val_loss: 1.1655 - val_root_mean_squared_error: 1.1007\n",
- "Epoch 636/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1629 - root_mean_squared_error: 1.0821 - val_loss: 1.1605 - val_root_mean_squared_error: 1.0823\n",
- "Epoch 637/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1648 - root_mean_squared_error: 1.0926 - val_loss: 1.1586 - val_root_mean_squared_error: 1.0754\n",
- "Epoch 638/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1531 - root_mean_squared_error: 1.0829 - val_loss: 1.1659 - val_root_mean_squared_error: 1.0785\n",
- "Epoch 639/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1626 - root_mean_squared_error: 1.0927 - val_loss: 1.1580 - val_root_mean_squared_error: 1.0443\n",
- "Epoch 640/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1610 - root_mean_squared_error: 1.1027 - val_loss: 1.1467 - val_root_mean_squared_error: 1.0720\n",
- "Epoch 641/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1583 - root_mean_squared_error: 1.0856 - val_loss: 1.1516 - val_root_mean_squared_error: 1.0546\n",
- "Epoch 642/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0676 - val_loss: 1.1464 - val_root_mean_squared_error: 1.0917\n",
- "Epoch 643/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1561 - root_mean_squared_error: 1.0632 - val_loss: 1.1707 - val_root_mean_squared_error: 1.1238\n",
- "Epoch 644/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1548 - root_mean_squared_error: 1.0729 - val_loss: 1.1663 - val_root_mean_squared_error: 1.0619\n",
- "Epoch 645/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1644 - root_mean_squared_error: 1.0570 - val_loss: 1.1595 - val_root_mean_squared_error: 1.0658\n",
- "Epoch 646/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1631 - root_mean_squared_error: 1.0901 - val_loss: 1.1662 - val_root_mean_squared_error: 1.0725\n",
- "Epoch 647/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0837 - val_loss: 1.1442 - val_root_mean_squared_error: 1.0990\n",
- "Epoch 648/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1546 - root_mean_squared_error: 1.0826 - val_loss: 1.1569 - val_root_mean_squared_error: 1.0523\n",
- "Epoch 649/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1616 - root_mean_squared_error: 1.0619 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0919\n",
- "Epoch 650/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1551 - root_mean_squared_error: 1.0713 - val_loss: 1.1588 - val_root_mean_squared_error: 1.1394\n",
- "Epoch 651/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1586 - root_mean_squared_error: 1.0851 - val_loss: 1.1582 - val_root_mean_squared_error: 1.1153\n",
- "Epoch 652/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1498 - root_mean_squared_error: 1.0406 - val_loss: 1.1498 - val_root_mean_squared_error: 1.0551\n",
- "Epoch 653/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1595 - root_mean_squared_error: 1.0822 - val_loss: 1.1459 - val_root_mean_squared_error: 1.0344\n",
- "Epoch 654/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1605 - root_mean_squared_error: 1.0608 - val_loss: 1.1573 - val_root_mean_squared_error: 1.0935\n",
- "Epoch 655/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1595 - root_mean_squared_error: 1.1004 - val_loss: 1.1373 - val_root_mean_squared_error: 1.0718\n",
- "Epoch 656/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1538 - root_mean_squared_error: 1.0791 - val_loss: 1.1543 - val_root_mean_squared_error: 1.0576\n",
- "Epoch 657/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1567 - root_mean_squared_error: 1.0638 - val_loss: 1.1594 - val_root_mean_squared_error: 1.1031\n",
- "Epoch 658/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1621 - root_mean_squared_error: 1.0886 - val_loss: 1.1780 - val_root_mean_squared_error: 1.0982\n",
- "Epoch 659/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1624 - root_mean_squared_error: 1.0934 - val_loss: 1.1464 - val_root_mean_squared_error: 1.0760\n",
- "Epoch 660/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1594 - root_mean_squared_error: 1.0877 - val_loss: 1.1596 - val_root_mean_squared_error: 1.0505\n",
- "Epoch 661/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1559 - root_mean_squared_error: 1.0696 - val_loss: 1.1506 - val_root_mean_squared_error: 1.1318\n",
- "Epoch 662/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0784 - val_loss: 1.1507 - val_root_mean_squared_error: 1.0255\n",
- "Epoch 663/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1573 - root_mean_squared_error: 1.0825 - val_loss: 1.1622 - val_root_mean_squared_error: 1.0516\n",
- "Epoch 664/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1520 - root_mean_squared_error: 1.0754 - val_loss: 1.1793 - val_root_mean_squared_error: 1.0720\n",
- "Epoch 665/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1539 - root_mean_squared_error: 1.0805 - val_loss: 1.1645 - val_root_mean_squared_error: 1.0848\n",
- "Epoch 666/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1610 - root_mean_squared_error: 1.0765 - val_loss: 1.1723 - val_root_mean_squared_error: 1.0881\n",
- "Epoch 667/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1611 - root_mean_squared_error: 1.0971 - val_loss: 1.1663 - val_root_mean_squared_error: 1.1083\n",
- "Epoch 668/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0516 - val_loss: 1.1538 - val_root_mean_squared_error: 1.0830\n",
- "Epoch 669/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1623 - root_mean_squared_error: 1.0959 - val_loss: 1.1464 - val_root_mean_squared_error: 1.0698\n",
- "Epoch 670/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1700 - root_mean_squared_error: 1.0838 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0175\n",
- "Epoch 671/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1582 - root_mean_squared_error: 1.0908 - val_loss: 1.1521 - val_root_mean_squared_error: 1.0876\n",
- "Epoch 672/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1545 - root_mean_squared_error: 1.0835 - val_loss: 1.1493 - val_root_mean_squared_error: 1.1087\n",
- "Epoch 673/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1530 - root_mean_squared_error: 1.0538 - val_loss: 1.1644 - val_root_mean_squared_error: 1.1060\n",
- "Epoch 674/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1565 - root_mean_squared_error: 1.0744 - val_loss: 1.1460 - val_root_mean_squared_error: 1.0380\n",
- "Epoch 675/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0771 - val_loss: 1.1584 - val_root_mean_squared_error: 1.1080\n",
- "Epoch 676/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1573 - root_mean_squared_error: 1.0719 - val_loss: 1.1606 - val_root_mean_squared_error: 1.0900\n",
- "Epoch 677/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1601 - root_mean_squared_error: 1.0848 - val_loss: 1.1642 - val_root_mean_squared_error: 1.1267\n",
- "Epoch 678/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1523 - root_mean_squared_error: 1.0891 - val_loss: 1.1517 - val_root_mean_squared_error: 1.0389\n",
- "Epoch 679/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1654 - root_mean_squared_error: 1.0672 - val_loss: 1.1614 - val_root_mean_squared_error: 1.0804\n",
- "Epoch 680/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0640 - val_loss: 1.1660 - val_root_mean_squared_error: 1.0533\n",
- "Epoch 681/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1588 - root_mean_squared_error: 1.0907 - val_loss: 1.1611 - val_root_mean_squared_error: 1.0769\n",
- "Epoch 682/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1540 - root_mean_squared_error: 1.0741 - val_loss: 1.1568 - val_root_mean_squared_error: 1.0704\n",
- "Epoch 683/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1567 - root_mean_squared_error: 1.0804 - val_loss: 1.1713 - val_root_mean_squared_error: 1.0647\n",
- "Epoch 684/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1596 - root_mean_squared_error: 1.0740 - val_loss: 1.1723 - val_root_mean_squared_error: 1.0500\n",
- "Epoch 685/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1560 - root_mean_squared_error: 1.0927 - val_loss: 1.1627 - val_root_mean_squared_error: 1.0663\n",
- "Epoch 686/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1532 - root_mean_squared_error: 1.0843 - val_loss: 1.1583 - val_root_mean_squared_error: 1.0502\n",
- "Epoch 687/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1518 - root_mean_squared_error: 1.0700 - val_loss: 1.1461 - val_root_mean_squared_error: 1.0955\n",
- "Epoch 688/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1551 - root_mean_squared_error: 1.0784 - val_loss: 1.1621 - val_root_mean_squared_error: 1.1132\n",
- "Epoch 689/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1579 - root_mean_squared_error: 1.0777 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0781\n",
- "Epoch 690/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1568 - root_mean_squared_error: 1.0776 - val_loss: 1.1648 - val_root_mean_squared_error: 1.0887\n",
- "Epoch 691/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1573 - root_mean_squared_error: 1.0821 - val_loss: 1.1562 - val_root_mean_squared_error: 1.0817\n",
- "Epoch 692/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1612 - root_mean_squared_error: 1.1057 - val_loss: 1.1773 - val_root_mean_squared_error: 1.0851\n",
- "Epoch 693/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1533 - root_mean_squared_error: 1.0875 - val_loss: 1.1609 - val_root_mean_squared_error: 1.1034\n",
- "Epoch 694/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1650 - root_mean_squared_error: 1.0724 - val_loss: 1.1611 - val_root_mean_squared_error: 1.0918\n",
- "Epoch 695/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1530 - root_mean_squared_error: 1.0861 - val_loss: 1.1670 - val_root_mean_squared_error: 1.1127\n",
- "Epoch 696/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1551 - root_mean_squared_error: 1.0790 - val_loss: 1.1439 - val_root_mean_squared_error: 1.1343\n",
- "Epoch 697/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1664 - root_mean_squared_error: 1.0799 - val_loss: 1.1374 - val_root_mean_squared_error: 1.0266\n",
- "Epoch 698/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1556 - root_mean_squared_error: 1.0645 - val_loss: 1.1470 - val_root_mean_squared_error: 1.0958\n",
- "Epoch 699/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1486 - root_mean_squared_error: 1.0771 - val_loss: 1.1621 - val_root_mean_squared_error: 1.0921\n",
- "Epoch 700/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1536 - root_mean_squared_error: 1.0716 - val_loss: 1.1569 - val_root_mean_squared_error: 1.0902\n",
- "Epoch 701/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1577 - root_mean_squared_error: 1.0749 - val_loss: 1.1443 - val_root_mean_squared_error: 1.0437\n",
- "Epoch 702/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1503 - root_mean_squared_error: 1.0648 - val_loss: 1.1746 - val_root_mean_squared_error: 1.0363\n",
- "Epoch 703/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1557 - root_mean_squared_error: 1.0931 - val_loss: 1.1757 - val_root_mean_squared_error: 1.0661\n",
- "Epoch 704/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0732 - val_loss: 1.1668 - val_root_mean_squared_error: 1.0516\n",
- "Epoch 705/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0714 - val_loss: 1.1642 - val_root_mean_squared_error: 1.0478\n",
- "Epoch 706/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1611 - root_mean_squared_error: 1.0884 - val_loss: 1.1471 - val_root_mean_squared_error: 1.0897\n",
- "Epoch 707/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1532 - root_mean_squared_error: 1.0735 - val_loss: 1.1546 - val_root_mean_squared_error: 1.0824\n",
- "Epoch 708/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1549 - root_mean_squared_error: 1.0689 - val_loss: 1.1595 - val_root_mean_squared_error: 1.0933\n",
- "Epoch 709/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0766 - val_loss: 1.1540 - val_root_mean_squared_error: 1.1284\n",
- "Epoch 710/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1528 - root_mean_squared_error: 1.0818 - val_loss: 1.1651 - val_root_mean_squared_error: 1.1171\n",
- "Epoch 711/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1611 - root_mean_squared_error: 1.0658 - val_loss: 1.1491 - val_root_mean_squared_error: 1.0224\n",
- "Epoch 712/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1681 - root_mean_squared_error: 1.0823 - val_loss: 1.1469 - val_root_mean_squared_error: 1.0984\n",
- "Epoch 713/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0890 - val_loss: 1.1602 - val_root_mean_squared_error: 1.0711\n",
- "Epoch 714/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1568 - root_mean_squared_error: 1.0835 - val_loss: 1.1551 - val_root_mean_squared_error: 1.0567\n",
- "Epoch 715/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0653 - val_loss: 1.1648 - val_root_mean_squared_error: 1.0970\n",
- "Epoch 716/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1614 - root_mean_squared_error: 1.1001 - val_loss: 1.1571 - val_root_mean_squared_error: 1.0709\n",
- "Epoch 717/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1512 - root_mean_squared_error: 1.0760 - val_loss: 1.1554 - val_root_mean_squared_error: 1.0990\n",
- "Epoch 718/1000\n",
- "17/17 [==============================] - 0s 9ms/step - loss: 1.1547 - root_mean_squared_error: 1.1029 - val_loss: 1.1571 - val_root_mean_squared_error: 1.0357\n",
- "Epoch 719/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1487 - root_mean_squared_error: 1.0758 - val_loss: 1.1442 - val_root_mean_squared_error: 1.0829\n",
- "Epoch 720/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1596 - root_mean_squared_error: 1.0875 - val_loss: 1.1583 - val_root_mean_squared_error: 1.1084\n",
- "Epoch 721/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1592 - root_mean_squared_error: 1.0611 - val_loss: 1.1552 - val_root_mean_squared_error: 1.0535\n",
- "Epoch 722/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1549 - root_mean_squared_error: 1.0589 - val_loss: 1.1450 - val_root_mean_squared_error: 1.0832\n",
- "Epoch 723/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1581 - root_mean_squared_error: 1.0731 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0878\n",
- "Epoch 724/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1495 - root_mean_squared_error: 1.0643 - val_loss: 1.1495 - val_root_mean_squared_error: 0.9855\n",
- "Epoch 725/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1585 - root_mean_squared_error: 1.0699 - val_loss: 1.1744 - val_root_mean_squared_error: 1.0407\n",
- "Epoch 726/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1618 - root_mean_squared_error: 1.0700 - val_loss: 1.1572 - val_root_mean_squared_error: 1.0518\n",
- "Epoch 727/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1592 - root_mean_squared_error: 1.0655 - val_loss: 1.1523 - val_root_mean_squared_error: 1.0968\n",
- "Epoch 728/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1591 - root_mean_squared_error: 1.0875 - val_loss: 1.1696 - val_root_mean_squared_error: 1.0379\n",
- "Epoch 729/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1620 - root_mean_squared_error: 1.0766 - val_loss: 1.1666 - val_root_mean_squared_error: 1.0949\n",
- "Epoch 730/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1594 - root_mean_squared_error: 1.0878 - val_loss: 1.1529 - val_root_mean_squared_error: 1.0909\n",
- "Epoch 731/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1540 - root_mean_squared_error: 1.0914 - val_loss: 1.1552 - val_root_mean_squared_error: 1.0710\n",
- "Epoch 732/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1472 - root_mean_squared_error: 1.0673 - val_loss: 1.1623 - val_root_mean_squared_error: 1.1203\n",
- "Epoch 733/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1574 - root_mean_squared_error: 1.0764 - val_loss: 1.1490 - val_root_mean_squared_error: 1.0823\n",
- "Epoch 734/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1502 - root_mean_squared_error: 1.0770 - val_loss: 1.1820 - val_root_mean_squared_error: 1.1021\n",
- "Epoch 735/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1725 - root_mean_squared_error: 1.1077 - val_loss: 1.1526 - val_root_mean_squared_error: 1.0736\n",
- "Epoch 736/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1522 - root_mean_squared_error: 1.0748 - val_loss: 1.1607 - val_root_mean_squared_error: 1.0448\n",
- "Epoch 737/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0734 - val_loss: 1.1695 - val_root_mean_squared_error: 1.0716\n",
- "Epoch 738/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1447 - root_mean_squared_error: 1.0602 - val_loss: 1.1587 - val_root_mean_squared_error: 1.0674\n",
- "Epoch 739/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1497 - root_mean_squared_error: 1.0834 - val_loss: 1.1594 - val_root_mean_squared_error: 1.0777\n",
- "Epoch 740/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1498 - root_mean_squared_error: 1.0664 - val_loss: 1.1542 - val_root_mean_squared_error: 1.0914\n",
- "Epoch 741/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0624 - val_loss: 1.1487 - val_root_mean_squared_error: 1.0863\n",
- "Epoch 742/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1557 - root_mean_squared_error: 1.0778 - val_loss: 1.1468 - val_root_mean_squared_error: 1.0729\n",
- "Epoch 743/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1497 - root_mean_squared_error: 1.0615 - val_loss: 1.1535 - val_root_mean_squared_error: 1.0493\n",
- "Epoch 744/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1612 - root_mean_squared_error: 1.0752 - val_loss: 1.1485 - val_root_mean_squared_error: 1.0833\n",
- "Epoch 745/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1503 - root_mean_squared_error: 1.0764 - val_loss: 1.1648 - val_root_mean_squared_error: 1.0824\n",
- "Epoch 746/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1591 - root_mean_squared_error: 1.0630 - val_loss: 1.1766 - val_root_mean_squared_error: 1.0848\n",
- "Epoch 747/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1587 - root_mean_squared_error: 1.0906 - val_loss: 1.1732 - val_root_mean_squared_error: 1.0573\n",
- "Epoch 748/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1518 - root_mean_squared_error: 1.0650 - val_loss: 1.1634 - val_root_mean_squared_error: 1.0289\n",
- "Epoch 749/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1488 - root_mean_squared_error: 1.0748 - val_loss: 1.1436 - val_root_mean_squared_error: 1.0396\n",
- "Epoch 750/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1581 - root_mean_squared_error: 1.0890 - val_loss: 1.1378 - val_root_mean_squared_error: 1.0584\n",
- "Epoch 751/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1497 - root_mean_squared_error: 1.0765 - val_loss: 1.1676 - val_root_mean_squared_error: 1.0532\n",
- "Epoch 752/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1595 - root_mean_squared_error: 1.0994 - val_loss: 1.1580 - val_root_mean_squared_error: 1.0479\n",
- "Epoch 753/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0810 - val_loss: 1.1438 - val_root_mean_squared_error: 1.0578\n",
- "Epoch 754/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1491 - root_mean_squared_error: 1.0721 - val_loss: 1.1608 - val_root_mean_squared_error: 1.1034\n",
- "Epoch 755/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1525 - root_mean_squared_error: 1.0782 - val_loss: 1.1838 - val_root_mean_squared_error: 1.0669\n",
- "Epoch 756/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1443 - root_mean_squared_error: 1.0593 - val_loss: 1.1484 - val_root_mean_squared_error: 1.0261\n",
- "Epoch 757/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1551 - root_mean_squared_error: 1.0538 - val_loss: 1.1620 - val_root_mean_squared_error: 1.0414\n",
- "Epoch 758/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1541 - root_mean_squared_error: 1.0623 - val_loss: 1.1490 - val_root_mean_squared_error: 1.0953\n",
- "Epoch 759/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1535 - root_mean_squared_error: 1.0755 - val_loss: 1.1763 - val_root_mean_squared_error: 1.0592\n",
- "Epoch 760/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1566 - root_mean_squared_error: 1.0855 - val_loss: 1.1418 - val_root_mean_squared_error: 1.0730\n",
- "Epoch 761/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0865 - val_loss: 1.1489 - val_root_mean_squared_error: 1.0685\n",
- "Epoch 762/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1569 - root_mean_squared_error: 1.0797 - val_loss: 1.1534 - val_root_mean_squared_error: 1.0711\n",
- "Epoch 763/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1553 - root_mean_squared_error: 1.0596 - val_loss: 1.1425 - val_root_mean_squared_error: 1.0886\n",
- "Epoch 764/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1597 - root_mean_squared_error: 1.0863 - val_loss: 1.1590 - val_root_mean_squared_error: 1.0850\n",
- "Epoch 765/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1501 - root_mean_squared_error: 1.0665 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0904\n",
- "Epoch 766/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1623 - root_mean_squared_error: 1.0849 - val_loss: 1.1738 - val_root_mean_squared_error: 1.0581\n",
- "Epoch 767/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1584 - root_mean_squared_error: 1.0706 - val_loss: 1.1511 - val_root_mean_squared_error: 1.0996\n",
- "Epoch 768/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1513 - root_mean_squared_error: 1.0825 - val_loss: 1.1687 - val_root_mean_squared_error: 1.0788\n",
- "Epoch 769/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1476 - root_mean_squared_error: 1.0634 - val_loss: 1.1562 - val_root_mean_squared_error: 1.0943\n",
- "Epoch 770/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1580 - root_mean_squared_error: 1.0815 - val_loss: 1.1562 - val_root_mean_squared_error: 1.0771\n",
- "Epoch 771/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1567 - root_mean_squared_error: 1.0865 - val_loss: 1.1609 - val_root_mean_squared_error: 1.0454\n",
- "Epoch 772/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0776 - val_loss: 1.1607 - val_root_mean_squared_error: 1.0315\n",
- "Epoch 773/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1489 - root_mean_squared_error: 1.0657 - val_loss: 1.1416 - val_root_mean_squared_error: 1.0884\n",
- "Epoch 774/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1505 - root_mean_squared_error: 1.0698 - val_loss: 1.1592 - val_root_mean_squared_error: 1.0538\n",
- "Epoch 775/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1509 - root_mean_squared_error: 1.0840 - val_loss: 1.1623 - val_root_mean_squared_error: 1.0733\n",
- "Epoch 776/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1501 - root_mean_squared_error: 1.0794 - val_loss: 1.1540 - val_root_mean_squared_error: 1.0360\n",
- "Epoch 777/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1525 - root_mean_squared_error: 1.0910 - val_loss: 1.1716 - val_root_mean_squared_error: 1.1052\n",
- "Epoch 778/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1543 - root_mean_squared_error: 1.0916 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0513\n",
- "Epoch 779/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1555 - root_mean_squared_error: 1.0677 - val_loss: 1.1735 - val_root_mean_squared_error: 1.1195\n",
- "Epoch 780/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1540 - root_mean_squared_error: 1.0719 - val_loss: 1.1526 - val_root_mean_squared_error: 1.0512\n",
- "Epoch 781/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1518 - root_mean_squared_error: 1.0673 - val_loss: 1.1560 - val_root_mean_squared_error: 1.0605\n",
- "Epoch 782/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1557 - root_mean_squared_error: 1.0870 - val_loss: 1.1502 - val_root_mean_squared_error: 1.0673\n",
- "Epoch 783/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0584 - val_loss: 1.1658 - val_root_mean_squared_error: 1.1114\n",
- "Epoch 784/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1519 - root_mean_squared_error: 1.0676 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0975\n",
- "Epoch 785/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1529 - root_mean_squared_error: 1.0806 - val_loss: 1.1553 - val_root_mean_squared_error: 1.0925\n",
- "Epoch 786/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1520 - root_mean_squared_error: 1.0582 - val_loss: 1.1468 - val_root_mean_squared_error: 1.0568\n",
- "Epoch 787/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1581 - root_mean_squared_error: 1.0896 - val_loss: 1.1596 - val_root_mean_squared_error: 1.0762\n",
- "Epoch 788/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1530 - root_mean_squared_error: 1.0720 - val_loss: 1.1644 - val_root_mean_squared_error: 1.0461\n",
- "Epoch 789/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1575 - root_mean_squared_error: 1.0777 - val_loss: 1.1452 - val_root_mean_squared_error: 1.0415\n",
- "Epoch 790/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1537 - root_mean_squared_error: 1.0600 - val_loss: 1.1716 - val_root_mean_squared_error: 1.0558\n",
- "Epoch 791/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1563 - root_mean_squared_error: 1.0823 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0830\n",
- "Epoch 792/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1550 - root_mean_squared_error: 1.0735 - val_loss: 1.1591 - val_root_mean_squared_error: 1.1041\n",
- "Epoch 793/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1607 - root_mean_squared_error: 1.0715 - val_loss: 1.1584 - val_root_mean_squared_error: 1.0552\n",
- "Epoch 794/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1598 - root_mean_squared_error: 1.0681 - val_loss: 1.1714 - val_root_mean_squared_error: 1.1171\n",
- "Epoch 795/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1535 - root_mean_squared_error: 1.0757 - val_loss: 1.1512 - val_root_mean_squared_error: 1.0579\n",
- "Epoch 796/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1495 - root_mean_squared_error: 1.0649 - val_loss: 1.1603 - val_root_mean_squared_error: 1.0619\n",
- "Epoch 797/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1507 - root_mean_squared_error: 1.0861 - val_loss: 1.1751 - val_root_mean_squared_error: 1.0662\n",
- "Epoch 798/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1487 - root_mean_squared_error: 1.0572 - val_loss: 1.1579 - val_root_mean_squared_error: 1.0705\n",
- "Epoch 799/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1554 - root_mean_squared_error: 1.0698 - val_loss: 1.1534 - val_root_mean_squared_error: 1.0480\n",
- "Epoch 800/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1467 - root_mean_squared_error: 1.0684 - val_loss: 1.1646 - val_root_mean_squared_error: 1.0789\n",
- "Epoch 801/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1525 - root_mean_squared_error: 1.0775 - val_loss: 1.1672 - val_root_mean_squared_error: 1.1225\n",
- "Epoch 802/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1502 - root_mean_squared_error: 1.0799 - val_loss: 1.1587 - val_root_mean_squared_error: 1.0938\n",
- "Epoch 803/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1474 - root_mean_squared_error: 1.0924 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0875\n",
- "Epoch 804/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1552 - root_mean_squared_error: 1.0822 - val_loss: 1.1761 - val_root_mean_squared_error: 1.0720\n",
- "Epoch 805/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1626 - root_mean_squared_error: 1.0824 - val_loss: 1.1578 - val_root_mean_squared_error: 1.0955\n",
- "Epoch 806/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1613 - root_mean_squared_error: 1.0931 - val_loss: 1.1700 - val_root_mean_squared_error: 1.1097\n",
- "Epoch 807/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1569 - root_mean_squared_error: 1.0678 - val_loss: 1.1755 - val_root_mean_squared_error: 1.0606\n",
- "Epoch 808/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1458 - root_mean_squared_error: 1.0760 - val_loss: 1.1530 - val_root_mean_squared_error: 1.0464\n",
- "Epoch 809/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0776 - val_loss: 1.1552 - val_root_mean_squared_error: 1.0568\n",
- "Epoch 810/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1483 - root_mean_squared_error: 1.0745 - val_loss: 1.1490 - val_root_mean_squared_error: 1.0714\n",
- "Epoch 811/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1508 - root_mean_squared_error: 1.0707 - val_loss: 1.1776 - val_root_mean_squared_error: 1.0803\n",
- "Epoch 812/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1515 - root_mean_squared_error: 1.0577 - val_loss: 1.1508 - val_root_mean_squared_error: 1.0574\n",
- "Epoch 813/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1483 - root_mean_squared_error: 1.0622 - val_loss: 1.1435 - val_root_mean_squared_error: 1.0670\n",
- "Epoch 814/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1509 - root_mean_squared_error: 1.0838 - val_loss: 1.1729 - val_root_mean_squared_error: 1.1051\n",
- "Epoch 815/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1541 - root_mean_squared_error: 1.0632 - val_loss: 1.1479 - val_root_mean_squared_error: 1.0972\n",
- "Epoch 816/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1534 - root_mean_squared_error: 1.0768 - val_loss: 1.1702 - val_root_mean_squared_error: 1.1074\n",
- "Epoch 817/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1447 - root_mean_squared_error: 1.0666 - val_loss: 1.1452 - val_root_mean_squared_error: 1.0358\n",
- "Epoch 818/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1547 - root_mean_squared_error: 1.0819 - val_loss: 1.1465 - val_root_mean_squared_error: 1.0857\n",
- "Epoch 819/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1475 - root_mean_squared_error: 1.0721 - val_loss: 1.1698 - val_root_mean_squared_error: 1.0620\n",
- "Epoch 820/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1486 - root_mean_squared_error: 1.0704 - val_loss: 1.1707 - val_root_mean_squared_error: 1.0865\n",
- "Epoch 821/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1541 - root_mean_squared_error: 1.0774 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0515\n",
- "Epoch 822/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1469 - root_mean_squared_error: 1.0691 - val_loss: 1.1596 - val_root_mean_squared_error: 1.0432\n",
- "Epoch 823/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1477 - root_mean_squared_error: 1.0497 - val_loss: 1.1520 - val_root_mean_squared_error: 1.0791\n",
- "Epoch 824/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1540 - root_mean_squared_error: 1.0768 - val_loss: 1.1455 - val_root_mean_squared_error: 1.0369\n",
- "Epoch 825/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1495 - root_mean_squared_error: 1.0621 - val_loss: 1.1702 - val_root_mean_squared_error: 1.0605\n",
- "Epoch 826/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1537 - root_mean_squared_error: 1.0556 - val_loss: 1.1537 - val_root_mean_squared_error: 1.0474\n",
- "Epoch 827/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1473 - root_mean_squared_error: 1.0567 - val_loss: 1.1512 - val_root_mean_squared_error: 1.0840\n",
- "Epoch 828/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1537 - root_mean_squared_error: 1.0621 - val_loss: 1.1505 - val_root_mean_squared_error: 1.0565\n",
- "Epoch 829/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1547 - root_mean_squared_error: 1.0456 - val_loss: 1.1785 - val_root_mean_squared_error: 1.1291\n",
- "Epoch 830/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1614 - root_mean_squared_error: 1.0711 - val_loss: 1.1458 - val_root_mean_squared_error: 1.0426\n",
- "Epoch 831/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1468 - root_mean_squared_error: 1.0571 - val_loss: 1.1676 - val_root_mean_squared_error: 1.0938\n",
- "Epoch 832/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1581 - root_mean_squared_error: 1.0839 - val_loss: 1.1471 - val_root_mean_squared_error: 1.0758\n",
- "Epoch 833/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1534 - root_mean_squared_error: 1.0579 - val_loss: 1.1543 - val_root_mean_squared_error: 1.0906\n",
- "Epoch 834/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1573 - root_mean_squared_error: 1.0854 - val_loss: 1.1529 - val_root_mean_squared_error: 1.0524\n",
- "Epoch 835/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0605 - val_loss: 1.1638 - val_root_mean_squared_error: 1.1072\n",
- "Epoch 836/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1465 - root_mean_squared_error: 1.0785 - val_loss: 1.1448 - val_root_mean_squared_error: 1.0799\n",
- "Epoch 837/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1492 - root_mean_squared_error: 1.0781 - val_loss: 1.1607 - val_root_mean_squared_error: 1.0275\n",
- "Epoch 838/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0721 - val_loss: 1.1455 - val_root_mean_squared_error: 1.0812\n",
- "Epoch 839/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1552 - root_mean_squared_error: 1.0676 - val_loss: 1.1534 - val_root_mean_squared_error: 1.0939\n",
- "Epoch 840/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1519 - root_mean_squared_error: 1.0791 - val_loss: 1.1621 - val_root_mean_squared_error: 1.0633\n",
- "Epoch 841/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1483 - root_mean_squared_error: 1.0474 - val_loss: 1.1609 - val_root_mean_squared_error: 1.0873\n",
- "Epoch 842/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1544 - root_mean_squared_error: 1.0762 - val_loss: 1.1660 - val_root_mean_squared_error: 1.0722\n",
- "Epoch 843/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1557 - root_mean_squared_error: 1.0778 - val_loss: 1.1527 - val_root_mean_squared_error: 1.0805\n",
- "Epoch 844/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1513 - root_mean_squared_error: 1.0834 - val_loss: 1.1617 - val_root_mean_squared_error: 1.0340\n",
- "Epoch 845/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1496 - root_mean_squared_error: 1.0771 - val_loss: 1.1527 - val_root_mean_squared_error: 1.0453\n",
- "Epoch 846/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1518 - root_mean_squared_error: 1.0968 - val_loss: 1.1535 - val_root_mean_squared_error: 1.1012\n",
- "Epoch 847/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1536 - root_mean_squared_error: 1.0827 - val_loss: 1.1477 - val_root_mean_squared_error: 1.0254\n",
- "Epoch 848/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1488 - root_mean_squared_error: 1.0729 - val_loss: 1.1583 - val_root_mean_squared_error: 1.0892\n",
- "Epoch 849/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1637 - root_mean_squared_error: 1.0871 - val_loss: 1.1532 - val_root_mean_squared_error: 1.0968\n",
- "Epoch 850/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1517 - root_mean_squared_error: 1.0692 - val_loss: 1.1598 - val_root_mean_squared_error: 1.0880\n",
- "Epoch 851/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1588 - root_mean_squared_error: 1.0785 - val_loss: 1.1590 - val_root_mean_squared_error: 1.0805\n",
- "Epoch 852/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1483 - root_mean_squared_error: 1.0960 - val_loss: 1.1519 - val_root_mean_squared_error: 1.0780\n",
- "Epoch 853/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1479 - root_mean_squared_error: 1.0593 - val_loss: 1.1429 - val_root_mean_squared_error: 1.0310\n",
- "Epoch 854/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1545 - root_mean_squared_error: 1.0740 - val_loss: 1.1464 - val_root_mean_squared_error: 1.0722\n",
- "Epoch 855/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1543 - root_mean_squared_error: 1.0530 - val_loss: 1.1476 - val_root_mean_squared_error: 1.0845\n",
- "Epoch 856/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1550 - root_mean_squared_error: 1.0617 - val_loss: 1.1571 - val_root_mean_squared_error: 1.0403\n",
- "Epoch 857/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1534 - root_mean_squared_error: 1.0706 - val_loss: 1.1598 - val_root_mean_squared_error: 1.1306\n",
- "Epoch 858/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1468 - root_mean_squared_error: 1.0814 - val_loss: 1.1543 - val_root_mean_squared_error: 1.0952\n",
- "Epoch 859/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1542 - root_mean_squared_error: 1.0596 - val_loss: 1.1571 - val_root_mean_squared_error: 1.1005\n",
- "Epoch 860/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1512 - root_mean_squared_error: 1.0657 - val_loss: 1.1535 - val_root_mean_squared_error: 1.0772\n",
- "Epoch 861/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1500 - root_mean_squared_error: 1.0430 - val_loss: 1.1427 - val_root_mean_squared_error: 1.0633\n",
- "Epoch 862/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1533 - root_mean_squared_error: 1.0585 - val_loss: 1.1499 - val_root_mean_squared_error: 1.0538\n",
- "Epoch 863/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1529 - root_mean_squared_error: 1.0557 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0621\n",
- "Epoch 864/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1533 - root_mean_squared_error: 1.0733 - val_loss: 1.1528 - val_root_mean_squared_error: 1.0913\n",
- "Epoch 865/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1470 - root_mean_squared_error: 1.0601 - val_loss: 1.1582 - val_root_mean_squared_error: 1.1139\n",
- "Epoch 866/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1479 - root_mean_squared_error: 1.0604 - val_loss: 1.1585 - val_root_mean_squared_error: 1.0982\n",
- "Epoch 867/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1527 - root_mean_squared_error: 1.0938 - val_loss: 1.1758 - val_root_mean_squared_error: 1.0906\n",
- "Epoch 868/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1542 - root_mean_squared_error: 1.0797 - val_loss: 1.1519 - val_root_mean_squared_error: 1.0581\n",
- "Epoch 869/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0697 - val_loss: 1.1655 - val_root_mean_squared_error: 1.0789\n",
- "Epoch 870/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1466 - root_mean_squared_error: 1.0721 - val_loss: 1.1685 - val_root_mean_squared_error: 1.0658\n",
- "Epoch 871/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1479 - root_mean_squared_error: 1.0527 - val_loss: 1.1447 - val_root_mean_squared_error: 1.0718\n",
- "Epoch 872/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1524 - root_mean_squared_error: 1.0541 - val_loss: 1.1465 - val_root_mean_squared_error: 1.0818\n",
- "Epoch 873/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1486 - root_mean_squared_error: 1.0738 - val_loss: 1.1647 - val_root_mean_squared_error: 1.0660\n",
- "Epoch 874/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1519 - root_mean_squared_error: 1.0743 - val_loss: 1.1621 - val_root_mean_squared_error: 1.0894\n",
- "Epoch 875/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1503 - root_mean_squared_error: 1.0684 - val_loss: 1.1718 - val_root_mean_squared_error: 1.1284\n",
- "Epoch 876/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1479 - root_mean_squared_error: 1.0898 - val_loss: 1.1588 - val_root_mean_squared_error: 1.0486\n",
- "Epoch 877/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0608 - val_loss: 1.1452 - val_root_mean_squared_error: 1.0559\n",
- "Epoch 878/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1565 - root_mean_squared_error: 1.0775 - val_loss: 1.1515 - val_root_mean_squared_error: 1.0985\n",
- "Epoch 879/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1546 - root_mean_squared_error: 1.0839 - val_loss: 1.1580 - val_root_mean_squared_error: 1.0261\n",
- "Epoch 880/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1426 - root_mean_squared_error: 1.0581 - val_loss: 1.1683 - val_root_mean_squared_error: 1.1202\n",
- "Epoch 881/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1532 - root_mean_squared_error: 1.0688 - val_loss: 1.1537 - val_root_mean_squared_error: 1.0960\n",
- "Epoch 882/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1589 - root_mean_squared_error: 1.0818 - val_loss: 1.1541 - val_root_mean_squared_error: 1.0726\n",
- "Epoch 883/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1539 - root_mean_squared_error: 1.0732 - val_loss: 1.1513 - val_root_mean_squared_error: 1.0318\n",
- "Epoch 884/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1588 - root_mean_squared_error: 1.0679 - val_loss: 1.1449 - val_root_mean_squared_error: 1.1030\n",
- "Epoch 885/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1511 - root_mean_squared_error: 1.0757 - val_loss: 1.1589 - val_root_mean_squared_error: 1.0615\n",
- "Epoch 886/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1489 - root_mean_squared_error: 1.0784 - val_loss: 1.1434 - val_root_mean_squared_error: 1.0544\n",
- "Epoch 887/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1469 - root_mean_squared_error: 1.0736 - val_loss: 1.1555 - val_root_mean_squared_error: 1.1109\n",
- "Epoch 888/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1512 - root_mean_squared_error: 1.0657 - val_loss: 1.1590 - val_root_mean_squared_error: 1.0565\n",
- "Epoch 889/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1520 - root_mean_squared_error: 1.0732 - val_loss: 1.1519 - val_root_mean_squared_error: 1.0727\n",
- "Epoch 890/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1507 - root_mean_squared_error: 1.0841 - val_loss: 1.1537 - val_root_mean_squared_error: 1.0920\n",
- "Epoch 891/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1469 - root_mean_squared_error: 1.0753 - val_loss: 1.1656 - val_root_mean_squared_error: 1.0887\n",
- "Epoch 892/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1561 - root_mean_squared_error: 1.0882 - val_loss: 1.1579 - val_root_mean_squared_error: 1.0572\n",
- "Epoch 893/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1512 - root_mean_squared_error: 1.0744 - val_loss: 1.1506 - val_root_mean_squared_error: 1.0516\n",
- "Epoch 894/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1497 - root_mean_squared_error: 1.0763 - val_loss: 1.1534 - val_root_mean_squared_error: 1.0454\n",
- "Epoch 895/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1506 - root_mean_squared_error: 1.0711 - val_loss: 1.1455 - val_root_mean_squared_error: 1.0380\n",
- "Epoch 896/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1485 - root_mean_squared_error: 1.0675 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0671\n",
- "Epoch 897/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1472 - root_mean_squared_error: 1.0810 - val_loss: 1.1486 - val_root_mean_squared_error: 1.0970\n",
- "Epoch 898/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1464 - root_mean_squared_error: 1.0791 - val_loss: 1.1479 - val_root_mean_squared_error: 1.0778\n",
- "Epoch 899/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1510 - root_mean_squared_error: 1.0651 - val_loss: 1.1505 - val_root_mean_squared_error: 1.0505\n",
- "Epoch 900/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1451 - root_mean_squared_error: 1.0713 - val_loss: 1.1611 - val_root_mean_squared_error: 1.0806\n",
- "Epoch 901/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1475 - root_mean_squared_error: 1.0651 - val_loss: 1.1599 - val_root_mean_squared_error: 1.0719\n",
- "Epoch 902/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1590 - root_mean_squared_error: 1.0711 - val_loss: 1.1576 - val_root_mean_squared_error: 1.0881\n",
- "Epoch 903/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1505 - root_mean_squared_error: 1.0731 - val_loss: 1.1777 - val_root_mean_squared_error: 1.0583\n",
- "Epoch 904/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1460 - root_mean_squared_error: 1.0558 - val_loss: 1.1446 - val_root_mean_squared_error: 1.0579\n",
- "Epoch 905/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1590 - root_mean_squared_error: 1.0685 - val_loss: 1.1598 - val_root_mean_squared_error: 1.0478\n",
- "Epoch 906/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1546 - root_mean_squared_error: 1.0695 - val_loss: 1.1457 - val_root_mean_squared_error: 1.1054\n",
- "Epoch 907/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1477 - root_mean_squared_error: 1.0796 - val_loss: 1.1704 - val_root_mean_squared_error: 1.0432\n",
- "Epoch 908/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1534 - root_mean_squared_error: 1.0523 - val_loss: 1.1467 - val_root_mean_squared_error: 1.0645\n",
- "Epoch 909/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1534 - root_mean_squared_error: 1.0757 - val_loss: 1.1512 - val_root_mean_squared_error: 1.0563\n",
- "Epoch 910/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1527 - root_mean_squared_error: 1.0979 - val_loss: 1.1734 - val_root_mean_squared_error: 1.1126\n",
- "Epoch 911/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1516 - root_mean_squared_error: 1.0615 - val_loss: 1.1456 - val_root_mean_squared_error: 1.0961\n",
- "Epoch 912/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1509 - root_mean_squared_error: 1.0708 - val_loss: 1.1542 - val_root_mean_squared_error: 1.0554\n",
- "Epoch 913/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1558 - root_mean_squared_error: 1.0593 - val_loss: 1.1472 - val_root_mean_squared_error: 1.0778\n",
- "Epoch 914/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1442 - root_mean_squared_error: 1.0677 - val_loss: 1.1453 - val_root_mean_squared_error: 1.0627\n",
- "Epoch 915/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1492 - root_mean_squared_error: 1.0620 - val_loss: 1.1560 - val_root_mean_squared_error: 1.0605\n",
- "Epoch 916/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0745 - val_loss: 1.1494 - val_root_mean_squared_error: 1.1136\n",
- "Epoch 917/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1547 - root_mean_squared_error: 1.0571 - val_loss: 1.1588 - val_root_mean_squared_error: 1.0802\n",
- "Epoch 918/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1467 - root_mean_squared_error: 1.0846 - val_loss: 1.1660 - val_root_mean_squared_error: 1.0534\n",
- "Epoch 919/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1515 - root_mean_squared_error: 1.0585 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0747\n",
- "Epoch 920/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1481 - root_mean_squared_error: 1.0754 - val_loss: 1.1548 - val_root_mean_squared_error: 1.0574\n",
- "Epoch 921/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1512 - root_mean_squared_error: 1.0799 - val_loss: 1.1706 - val_root_mean_squared_error: 1.0471\n",
- "Epoch 922/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1531 - root_mean_squared_error: 1.0578 - val_loss: 1.1356 - val_root_mean_squared_error: 1.0099\n",
- "Epoch 923/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1508 - root_mean_squared_error: 1.0768 - val_loss: 1.1604 - val_root_mean_squared_error: 1.0529\n",
- "Epoch 924/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1510 - root_mean_squared_error: 1.0659 - val_loss: 1.1565 - val_root_mean_squared_error: 1.0588\n",
- "Epoch 925/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1546 - root_mean_squared_error: 1.0821 - val_loss: 1.1621 - val_root_mean_squared_error: 1.1188\n",
- "Epoch 926/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.0744 - val_loss: 1.1440 - val_root_mean_squared_error: 1.0390\n",
- "Epoch 927/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1500 - root_mean_squared_error: 1.0973 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0754\n",
- "Epoch 928/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1543 - root_mean_squared_error: 1.0604 - val_loss: 1.1606 - val_root_mean_squared_error: 1.1325\n",
- "Epoch 929/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1492 - root_mean_squared_error: 1.0670 - val_loss: 1.1495 - val_root_mean_squared_error: 1.0766\n",
- "Epoch 930/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1461 - root_mean_squared_error: 1.0628 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0757\n",
- "Epoch 931/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1455 - root_mean_squared_error: 1.0763 - val_loss: 1.1683 - val_root_mean_squared_error: 1.0727\n",
- "Epoch 932/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1516 - root_mean_squared_error: 1.0621 - val_loss: 1.1554 - val_root_mean_squared_error: 1.0568\n",
- "Epoch 933/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1553 - root_mean_squared_error: 1.0580 - val_loss: 1.1453 - val_root_mean_squared_error: 1.1282\n",
- "Epoch 934/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1443 - root_mean_squared_error: 1.0661 - val_loss: 1.1492 - val_root_mean_squared_error: 1.0753\n",
- "Epoch 935/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1520 - root_mean_squared_error: 1.0479 - val_loss: 1.1617 - val_root_mean_squared_error: 1.0633\n",
- "Epoch 936/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1538 - root_mean_squared_error: 1.0802 - val_loss: 1.1570 - val_root_mean_squared_error: 1.0607\n",
- "Epoch 937/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1453 - root_mean_squared_error: 1.0510 - val_loss: 1.1388 - val_root_mean_squared_error: 1.0623\n",
- "Epoch 938/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1461 - root_mean_squared_error: 1.0557 - val_loss: 1.1559 - val_root_mean_squared_error: 1.0457\n",
- "Epoch 939/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1499 - root_mean_squared_error: 1.0808 - val_loss: 1.1489 - val_root_mean_squared_error: 1.0595\n",
- "Epoch 940/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1462 - root_mean_squared_error: 1.0769 - val_loss: 1.1443 - val_root_mean_squared_error: 1.0726\n",
- "Epoch 941/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1508 - root_mean_squared_error: 1.0611 - val_loss: 1.1553 - val_root_mean_squared_error: 1.0577\n",
- "Epoch 942/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1485 - root_mean_squared_error: 1.0707 - val_loss: 1.1697 - val_root_mean_squared_error: 1.0928\n",
- "Epoch 943/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0630 - val_loss: 1.1523 - val_root_mean_squared_error: 1.0634\n",
- "Epoch 944/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1497 - root_mean_squared_error: 1.0902 - val_loss: 1.1602 - val_root_mean_squared_error: 1.0773\n",
- "Epoch 945/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1478 - root_mean_squared_error: 1.0724 - val_loss: 1.1521 - val_root_mean_squared_error: 1.1038\n",
- "Epoch 946/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1512 - root_mean_squared_error: 1.0721 - val_loss: 1.1712 - val_root_mean_squared_error: 1.1051\n",
- "Epoch 947/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1483 - root_mean_squared_error: 1.0866 - val_loss: 1.1566 - val_root_mean_squared_error: 1.0565\n",
- "Epoch 948/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1463 - root_mean_squared_error: 1.0590 - val_loss: 1.1393 - val_root_mean_squared_error: 1.1257\n",
- "Epoch 949/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1502 - root_mean_squared_error: 1.0619 - val_loss: 1.1318 - val_root_mean_squared_error: 1.0559\n",
- "Epoch 950/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1532 - root_mean_squared_error: 1.0804 - val_loss: 1.1631 - val_root_mean_squared_error: 1.1003\n",
- "Epoch 951/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1493 - root_mean_squared_error: 1.0583 - val_loss: 1.1466 - val_root_mean_squared_error: 1.1310\n",
- "Epoch 952/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1481 - root_mean_squared_error: 1.0689 - val_loss: 1.1725 - val_root_mean_squared_error: 1.0865\n",
- "Epoch 953/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1533 - root_mean_squared_error: 1.0796 - val_loss: 1.1650 - val_root_mean_squared_error: 1.1011\n",
- "Epoch 954/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1440 - root_mean_squared_error: 1.0720 - val_loss: 1.1518 - val_root_mean_squared_error: 1.0850\n",
- "Epoch 955/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1545 - root_mean_squared_error: 1.0670 - val_loss: 1.1437 - val_root_mean_squared_error: 1.0817\n",
- "Epoch 956/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1407 - root_mean_squared_error: 1.0622 - val_loss: 1.1535 - val_root_mean_squared_error: 1.0658\n",
- "Epoch 957/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1456 - root_mean_squared_error: 1.0623 - val_loss: 1.1396 - val_root_mean_squared_error: 1.0663\n",
- "Epoch 958/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1511 - root_mean_squared_error: 1.0595 - val_loss: 1.1648 - val_root_mean_squared_error: 1.0439\n",
- "Epoch 959/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1439 - root_mean_squared_error: 1.0596 - val_loss: 1.1743 - val_root_mean_squared_error: 1.0477\n",
- "Epoch 960/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1442 - root_mean_squared_error: 1.0780 - val_loss: 1.1526 - val_root_mean_squared_error: 1.0536\n",
- "Epoch 961/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1522 - root_mean_squared_error: 1.0525 - val_loss: 1.1467 - val_root_mean_squared_error: 1.0698\n",
- "Epoch 962/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1462 - root_mean_squared_error: 1.0574 - val_loss: 1.1556 - val_root_mean_squared_error: 1.1146\n",
- "Epoch 963/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1542 - root_mean_squared_error: 1.0709 - val_loss: 1.1477 - val_root_mean_squared_error: 1.0528\n",
- "Epoch 964/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1470 - root_mean_squared_error: 1.0508 - val_loss: 1.1493 - val_root_mean_squared_error: 1.0856\n",
- "Epoch 965/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1457 - root_mean_squared_error: 1.0654 - val_loss: 1.1529 - val_root_mean_squared_error: 1.0817\n",
- "Epoch 966/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1513 - root_mean_squared_error: 1.0860 - val_loss: 1.1431 - val_root_mean_squared_error: 1.0883\n",
- "Epoch 967/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1498 - root_mean_squared_error: 1.0676 - val_loss: 1.1594 - val_root_mean_squared_error: 1.0449\n",
- "Epoch 968/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1458 - root_mean_squared_error: 1.0735 - val_loss: 1.1475 - val_root_mean_squared_error: 1.0656\n",
- "Epoch 969/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1521 - root_mean_squared_error: 1.0779 - val_loss: 1.1454 - val_root_mean_squared_error: 1.0747\n",
- "Epoch 970/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1516 - root_mean_squared_error: 1.0534 - val_loss: 1.1370 - val_root_mean_squared_error: 1.0716\n",
- "Epoch 971/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1525 - root_mean_squared_error: 1.0731 - val_loss: 1.1543 - val_root_mean_squared_error: 1.0773\n",
- "Epoch 972/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1469 - root_mean_squared_error: 1.0810 - val_loss: 1.1636 - val_root_mean_squared_error: 1.0518\n",
- "Epoch 973/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1514 - root_mean_squared_error: 1.1038 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0699\n",
- "Epoch 974/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1522 - root_mean_squared_error: 1.0844 - val_loss: 1.1650 - val_root_mean_squared_error: 1.0160\n",
- "Epoch 975/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1481 - root_mean_squared_error: 1.0601 - val_loss: 1.1465 - val_root_mean_squared_error: 1.1098\n",
- "Epoch 976/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1465 - root_mean_squared_error: 1.0581 - val_loss: 1.1566 - val_root_mean_squared_error: 1.0972\n",
- "Epoch 977/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1485 - root_mean_squared_error: 1.0659 - val_loss: 1.1502 - val_root_mean_squared_error: 1.1010\n",
- "Epoch 978/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1520 - root_mean_squared_error: 1.0884 - val_loss: 1.1578 - val_root_mean_squared_error: 1.0469\n",
- "Epoch 979/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1481 - root_mean_squared_error: 1.0848 - val_loss: 1.1685 - val_root_mean_squared_error: 1.0593\n",
- "Epoch 980/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1483 - root_mean_squared_error: 1.0659 - val_loss: 1.1509 - val_root_mean_squared_error: 1.0320\n",
- "Epoch 981/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1489 - root_mean_squared_error: 1.0513 - val_loss: 1.1522 - val_root_mean_squared_error: 1.0920\n",
- "Epoch 982/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1492 - root_mean_squared_error: 1.0659 - val_loss: 1.1607 - val_root_mean_squared_error: 1.1042\n",
- "Epoch 983/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1500 - root_mean_squared_error: 1.0673 - val_loss: 1.1584 - val_root_mean_squared_error: 1.0993\n",
- "Epoch 984/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1532 - root_mean_squared_error: 1.0899 - val_loss: 1.1539 - val_root_mean_squared_error: 1.0684\n",
- "Epoch 985/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1542 - root_mean_squared_error: 1.0604 - val_loss: 1.1634 - val_root_mean_squared_error: 1.0304\n",
- "Epoch 986/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1543 - root_mean_squared_error: 1.0853 - val_loss: 1.1463 - val_root_mean_squared_error: 1.0750\n",
- "Epoch 987/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1518 - root_mean_squared_error: 1.0734 - val_loss: 1.1495 - val_root_mean_squared_error: 1.0874\n",
- "Epoch 988/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1447 - root_mean_squared_error: 1.0682 - val_loss: 1.1549 - val_root_mean_squared_error: 1.0584\n",
- "Epoch 989/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1383 - root_mean_squared_error: 1.0431 - val_loss: 1.1450 - val_root_mean_squared_error: 1.0868\n",
- "Epoch 990/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1458 - root_mean_squared_error: 1.0664 - val_loss: 1.1688 - val_root_mean_squared_error: 1.0477\n",
- "Epoch 991/1000\n",
- "17/17 [==============================] - 0s 11ms/step - loss: 1.1513 - root_mean_squared_error: 1.0619 - val_loss: 1.1478 - val_root_mean_squared_error: 1.0577\n",
- "Epoch 992/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1460 - root_mean_squared_error: 1.0573 - val_loss: 1.1404 - val_root_mean_squared_error: 1.0833\n",
- "Epoch 993/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1480 - root_mean_squared_error: 1.0705 - val_loss: 1.1724 - val_root_mean_squared_error: 1.0904\n",
- "Epoch 994/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1533 - root_mean_squared_error: 1.0837 - val_loss: 1.1587 - val_root_mean_squared_error: 1.1279\n",
- "Epoch 995/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1484 - root_mean_squared_error: 1.0537 - val_loss: 1.1562 - val_root_mean_squared_error: 1.0265\n",
- "Epoch 996/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1478 - root_mean_squared_error: 1.0612 - val_loss: 1.1727 - val_root_mean_squared_error: 1.0600\n",
- "Epoch 997/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1469 - root_mean_squared_error: 1.0691 - val_loss: 1.1450 - val_root_mean_squared_error: 1.0739\n",
- "Epoch 998/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1478 - root_mean_squared_error: 1.0760 - val_loss: 1.1716 - val_root_mean_squared_error: 1.0452\n",
- "Epoch 999/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1435 - root_mean_squared_error: 1.0705 - val_loss: 1.1503 - val_root_mean_squared_error: 1.0454\n",
- "Epoch 1000/1000\n",
- "17/17 [==============================] - 0s 10ms/step - loss: 1.1476 - root_mean_squared_error: 1.0630 - val_loss: 1.1508 - val_root_mean_squared_error: 1.0186\n",
"Model training finished.\n",
- "Train RMSE: 1.064\n",
+ "Train RMSE: 1.055\n",
"Evaluating model performance...\n",
- "Test RMSE: 1.058\n"
+ "Test RMSE: 1.033\n"
]
}
],
@@ -4996,21 +804,25 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 16,
"metadata": {
"id": "Dv_RwLyFJZYA"
},
"outputs": [
{
- "ename": "AttributeError",
- "evalue": "'Independent' object has no attribute 'sddev'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn [25], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m prediction_distribution \u001b[38;5;241m=\u001b[39m prob_bnn_model(examples)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mprediction_distribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msddev\u001b[49m())\n\u001b[1;32m 3\u001b[0m prediction_mean \u001b[38;5;241m=\u001b[39m prediction_distribution\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m 4\u001b[0m prediction_stdv \u001b[38;5;241m=\u001b[39m prediction_distribution\u001b[38;5;241m.\u001b[39mstddev()\u001b[38;5;241m.\u001b[39mnumpy()\n",
- "File \u001b[0;32m/p/project/training2305/sc_venv_template-bl/venv/lib/python3.10/site-packages/tensorflow_probability/python/layers/internal/distribution_tensor_coercible.py:87\u001b[0m, in \u001b[0;36m_TensorCoercible.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;66;03m# Look for the attribute in `tensor_distribution`, unless it's a `_tracking`\u001b[39;00m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# attribute accessed directly by `getattr` in the `Trackable` base class, in\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# which case the default passed to `getattr` should be returned.\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtensor_distribution\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_tracking\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m name:\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtensor_distribution\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m# Otherwise invoke `__getattribute__`, which will return the default passed\u001b[39;00m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# to `getattr` if the attribute was not found.\u001b[39;00m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(name)\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'Independent' object has no attribute 'sddev'"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prediction mean: 6.49, stddev: 0.82, 95% CI: [8.09 - 4.88] - Actual: 5.0\n",
+ "Prediction mean: 5.46, stddev: 0.69, 95% CI: [6.82 - 4.1] - Actual: 6.0\n",
+ "Prediction mean: 5.74, stddev: 0.72, 95% CI: [7.14 - 4.34] - Actual: 5.0\n",
+ "Prediction mean: 6.05, stddev: 0.77, 95% CI: [7.56 - 4.54] - Actual: 5.0\n",
+ "Prediction mean: 5.68, stddev: 0.74, 95% CI: [7.12 - 4.24] - Actual: 6.0\n",
+ "Prediction mean: 5.55, stddev: 0.69, 95% CI: [6.91 - 4.19] - Actual: 6.0\n",
+ "Prediction mean: 5.2, stddev: 0.66, 95% CI: [6.48 - 3.91] - Actual: 5.0\n",
+ "Prediction mean: 5.44, stddev: 0.68, 95% CI: [6.79 - 4.1] - Actual: 5.0\n",
+ "Prediction mean: 6.22, stddev: 0.79, 95% CI: [7.76 - 4.68] - Actual: 6.0\n",
+ "Prediction mean: 5.63, stddev: 0.7, 95% CI: [7.01 - 4.25] - Actual: 5.0\n"
]
}
],
diff --git a/BLcourse4/flows.ipynb b/BLcourse4/flows.ipynb
index 68e0dd9821397fbc3dd79d620c19e78ec4779f29..35e02c0a51f7c8d63e51933859a53e856a7c51e0 100644
--- a/BLcourse4/flows.ipynb
+++ b/BLcourse4/flows.ipynb
@@ -40,7 +40,7 @@
"\n",
"## Bijectors\n",
"\n",
- "A bijector is a function that is [injective](https://en.wikipedia.org/wiki/Injective_function) (1 to 1) and [surjective](https://en.wikipedia.org/wiki/Surjective_function) (onto). An equivalent way to view a bijective function is if it has an inverse. For example, a sum reduction has no inverse and is thus not bijective. $\\sum [1,0] = 1$ and $\\sum [-1, 2] = 1$. Multiplying by a matrix which has an inverse is bijective. $y = x^2$ is not bijective, since $y = 4$ has two solutions. \n",
+ "A bijector is a function that is [injective](https://en.wikipedia.org/wiki/Injective_function) (1 to 1) and [surjective](https://en.wikipedia.org/wiki/Surjective_function) (onto). An equivalent way to view a bijective function is if it has an inverse. Multiplying by a matrix which has an inverse is bijective. $y = x^2$ is not bijective, since $y = 4$ has two solutions. \n",
"\n",
"Remember that we must compute the determinant of the bijector Jacobian. If the Jacobian is dense (all output elements depend on all input elements), computing this quantity will be $O\\left(|x|_0^3\\right)$ where $|x|_0$ is the number of dimensions of $x$ because a determinant scales by $O(n^3)$. This would make computing normalizing flows impractical in high-dimensions. However, in practice we restrict ourselves to bijectors that have easy to calculate Jacobians. For example, if the bijector is $x_i = \\cos z_i$ then the Jacobian will be diagonal. Such a diagonal Jacobian means that each dimension is independent of the other though. \n",
"\n",
@@ -86,7 +86,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"vscode": {
"languageId": "python"
@@ -105,7 +105,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"vscode": {
"languageId": "python"
@@ -138,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {
"vscode": {
"languageId": "python"
@@ -153,13 +153,36 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x151b29a1f760>]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.plot(data[:, 0], data[:, 1], \".\", alpha=0.8)"
]
@@ -175,13 +198,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-03-24 12:01:23.730156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 377 MB memory: -> device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:01:00.0, compute capability: 7.0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<tfp.distributions.MultivariateNormalDiag 'MultivariateNormalDiag' batch_shape=[] event_shape=[2] dtype=float32>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"zdist = tfd.MultivariateNormalDiag(loc=[0.0] * ndim)\n",
"zdist"
@@ -196,13 +237,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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v/dudPRzt7AWmKLMVbnhJz67z8hiWo9x91/XwJIGX8PipAWzXQwKJqE7M0MkWHUYyioxzui81jV0IirTzvj2b+UHX1WBBFh2PiaxVs5PQciWW42G5eXQhECVXHaBge0F14njXIMPpYvB+kimugSbgvTdt4sWLo9ie+uNE3mbnhgSpgkPRdtncFOW2Xa3cc9tObmhPBoYR4MiLlyk6Hq6r3g9YlEBpvV29nhcxk3exVkKE0ABUYSbXrlFWvVacKZDEI0bQj9+WjJArcdjLSTimrvPChTHSBZUNb4oZ/NFH3wrA5x85zUTeDt5HE5BFHffemzZhux6dlyeU4fAqTYDjSTYmorTEdCbyDp6EnO3xn7/3BhuSEVrjJvfctp3JXBHbg2zBIR7RcTz41UO7eOzUQLDQNaHKiaf7Jjl7NV13SES26HKydxLbU8ZDKwsFtrbEuGtfOye6h3m9P0VX/xlgyjA+cHgfngTX9RSXwZH87ndeJRk1SESMBakV79+mBpueODvEXXs7FrVgG0UdX2qEBqCE8kVZz7VrhFWvF2dKBBdGskipSDR5K4+mCSayFv/9qTcDEs6NmxOVLrwrA1JOwXEr3iuiCzY1xchbNi9eHEMTkMrbQQzuozlmEDc1NjdH0DQt2MlB7dgTOQvH9Xj50hhjuakyXNZS7/f3nT382b98G/90up/vvTaIRJLK20xkLZ4+N1L3u/DzAboQOFIiJWxujnLfu3cHns9T3UPTREKH0gVOnB3inrdt51udvehCkC7aIMFyJbYrK3osZjIG1TLkfl6j89J4xVTj+aJR1PGlRiNUgXcB/xPYirqnX5VS/tfFnnepMNeyW62OuEZY9Xpx5l372vmHn/YRMzQm8jYtMRNNSI6fuUrOcmiOmYymC1wczVWcz3I9jrxwGZhyrUHtpDHTQCDRNA1dEzTHTFJ5W+3QTCXj4qYqJx4+sJU9Hc38u6MnKZSdy5OQKTq8eGm85mdKF12++ZPLbG2L0xY3MHSdvO3SeXlsmqdRC34uwNAFb9nSRO9EnovD2cAwXhrNlq5TZyhdYCRd5NipAQq2S2vcwHI9mqI6BUfiOB5ZHJIRjQ3JOFdT+boNV7XYkY102xtBHV9qNMIDcIB/L6V8RQjRDLwshHhCSvl6A87dUMyn7FYv5lusVa8n0XWiexjL8SjaHpoQaEKSKrhcGM4wmbMZSRcBwWShUpPP8STf+MkV4qaO8BNxqNDgTz5+kIzl0BQx+MN/7OLCsJrgpglBS0xH0zQ+c+eNtCUjFay8594c4ciLVypc9x0b4oymi3U/1zPnR0lGdHK2i5QOGtB5aZyiPeWVaEIZEx8CtfhLKQcsR/L0+VE0AY+80sfv/vw+bNcjW2IVAly/KcFIukjB9hSpSNcxDXUeQ/OYcD1ihk6q4GCk8ngSdE3UvLfV9x1YE257I9EIVeABYKD0/2khxBlgB7DqDMB8ym71MF+rXu1x1DIiRzt7Kphsv3jrdoqux9Nnh+lojmG7GWxXYuoa2aJDMqIrd1+qBeVJKNguiKlYOm4qcY2BiTxnr6ZJ5W2kVLtsc8zk0HVtHNjeGgiNlA/2uO/26zjRPUyqYIEUxCM60pM0xUw8aVd4B+XwQwJQrmDOctA0gQY4riRu6uQst8KweFKilY73oWsCz1Pej+24aAKckhfiU5g1DZCQtx02N8WC2N3/zobSBd62s5UtzXGe7B6qeW+rG5wOH9gahB6r2W1vJBqaAxBCXI+SCJ82G3A1jAabKcu7kJ19LpJUtRh9fjdfdU05lVdlvffetJkb2pN0XhrnaiqvYtuSdyAhqNOXLxpPQjyiA5KmqEm2aPNbf/9THFdWLDjXk+SKDs+/OUrn5Qn+7oXL3PO27Tz6aj+6JoLhHX4nYlPE4NxQmnNXM/zz+REihsByK3fyeig4lQftaIvTO5GnaHu4pXFj1ddH6XdCwG07WhmYKARej0CFPKDCl5aYwW/87A3s6WgmYzkVZUJD0+jqT/M6KWp1B840enw9LHwfDTMAQogm4DvAb0spp42gXQ2jwWZa6PPd2f1eep+IU6siUEse62hnr+rmgwrm2wOH9/HFx15D1wQPHu/mK/e9I/id48ppC678RxXv6/z8LVtKPQH1XXVPQsHxKDhAUe3YX3v20tR5DIeHnr3IXXs7GJjIc7SzF9t1Gckoco8mREWScD64MpbD8SRu6QR6wPLTEEDB9TA1ge1KmqI6P+oe5gt37+efTvdz7LXB4DvQhTJ+mhDs6WiuUCfyE34DE3m+3dkTfPd+d2D1vVmJ0eOriSDUqOnAJmrxH5FS/kMjzrlUaERi5sxAKuil94k45eKa/o2tpXenuvkkSEgV7CAMyVgOMUObRv/1PG+ay129/pRykOQHr1+ddS67qYtpnXnl58k7Lo+/2s8jr/QRj+jkLZeWmBGEFt4cVr8/lWj6kSrM6R3LUnRkQAmOGBoxUyefLuL4OQEE2RIRaWMiqrhJpXOWpAsoOi5//oM3SBUcdrTFg+rAp+64gYM7WiukxarDupUq0602glAjqgAC+BvgjJTyLxZ/Sasfp/sm0TWBrmsBEadaXNO/sdUsuiMvXsHJq90mV3Q43aP+5sejPtX34I5Wnjk7zHjOmbaQIvqUgIfPrsuXwoOZIJjbAnY9ietJiraL40nGcopb4EmVW9i9Mc4bVzN1Xy+pHE0WvL8QjGUKmIaOpkmkJ9F0gaEpRh9MkYnSBYe8JTg7kOJbnVeC85a/R972uFSqiriuJGs5/Lh7iM5L4xVqyb76EFDh5q9EmW61EYQa4QHcAfwacFoIcbL0u89LKY814NxLjoW4Ywd3tAaz6/z224zlzEne6r537+Zv/vkihiaYzNs8frqfJ7uH1FRf20XXNH7jjl0c7xrka89cnLaoDV1w+Jat/OTSGKmCHeQF6iFR6vPPFl1cqZR6ZoNXis+tKk9BLTqX80P1F3/5sdXQNcFEXmX0ZVnCciLnsWtDPFATgilv42+fu0QdhyWAADYkTaKmCNiSfp5lOF3kRPdwTTHSWt7gUrvnq40g1IgqwD9T2+CveizUHau1exw7NTBtBy9/H//Ywwe28ujJfiayRRCCzU1R+iby5C0XXRNI6fKVp87jSkgXp8elnif5yaUxAHQhZlz8AjAMjWKppXaumOlYicrIzwfJiOIFmLqGRGX1yxe6I+HyeD64Zv9vc71mIeCT79rNw89fDtSIj7x4hdGspUarIbixfXZ14OVwz1cbQWhdMwEX4475xx3vGuQbL1zhye6hoFmnPKNcSwb8ntu2c/ZqmmfPjzKaKVCwVWnM8VT225WSuGmQLjhB7VzXwPNgYyJCpujgScXFnwkSyBQcmmIGluvU3paprM+rMGFOX8GcoYg+AiFUSbB8R/c9kuprMDSw3OozVUKgEogf3N/Bzg2J0i+FajSSMjA0npRzUgeuVk3ypdgbjdVEEFrXBmAx7li5Pp8nlSTFDe1NwUitcrWa8hZWP9M/mrFoiRtYjiLt5C0VbydMXVFjXRdDU+27oBJelpSMZK1phJrZYGgaugZOnQVVfq5Gl2cEsG9LMzs3xtmYiPLdV/tIlZGZdE31+isWowyqGcdfv4rl1rcAW1qiTOYsTEPj1Z4JXrw4hq4Jdm5IMJQuULS9Un5EsLkpEtCLZ1p45ZRsvx36WhcWXdcGYDHumK/PJwRoJZGN0XSBRNTkdM8k//WH58oGYqhJPD4rzdA1ZGmXN3XVFmtEIW9LooaGaWi4nuR9ezbwypVxUiXFHlEKtHxCDcy8YA1dLfqxqv785YJeIiS91p/idP8kEV2bVtFwPBCux83tSS6P5TB0wfe7BinY9b0bo2RAXSmxXIe4qavkpjclQf4Hv7SvptLQTNi/bfp8xJVO0i01rhkDsNDkTTkttPzn8vPWkuE+uKOVRNQkVUpctcVN7j64jRPdwzx+up90weH6zUnSBZu37WzlwPbWoGadsxyEENiumrjr9/ebukeq9LqByTxPdg9XXIufIa9m0/lklmrU2/GXC66ETJkfX49BaLsyqCrkZ1j4PlTfgEZLzGAwVWQoXUQT8Kn3Xs/eUvlV3afZ1ZKr4edoqucjXqsQcqGsjkXg0KFDsrOzs2HnW0zyZrYZc76YhQC2tsYD4g4oMtDnHzmFJ6E5ZvKBfR08frqf5phJ30SemKGRt13a4gatiWiFPFdTxAg61E73TfKXPzyL48HVVIGoqc24AwKUmLDETZ2i486aKZ8Nm5Imo1l79gMbiFq6BDP93v+brz0Qj2jkLS+I9TeWRFEWm9lfTUSdhUII8bKU8tBsx10THsBiknn1XntmIMWXn+hmOFMsq03bFefOWA5NUSMY8/XoyV6ylstk3qYtbqqdWqpuOUO3g86/WtdmuZKhUrNNwfYwdbBn2MElaif85Xfs4PxwhpNXxomaOpP5hTHalnvxA+zaGOfKWH7GY4xSX4BhCCxnqnHIFyNvihnkSsrBluNNS9zNxNisbgWu7tlYD7gmDMBiknn1uvM+e+QVRsoWP6gFVz4Gyx9DNZ4tUrRdcpaLJpQ7nojoTORtJMrFzdte3bl7+7e10Bo3AgMAMy9+mNohd29McKJ7GNuFoqMWf3VzDaisetTQgi661QC7ytAJVFKwPFJwPJVFsRxZ4RnoulCJ100J3hzOkrNc0q6SFPNDtXqMTf9vlROTaw8unQvWssdwTRiAxSTz6nXn2Y7L1pYY2WKGiC6ImTp/8vFbg4fn0w93kivamIbOfe/ezUja4hsvTTHWhtJTST9PKurvw89f5v975iKf+8DNfObnbgqu4dipAc4PZ2teX734HlSM/fedV8iUsupB/bzGsY4HmustmMe/FBioai9WpdDpx1Vfsi5KzD/X5bX+VEUVYyxnc+SFy/ynjx+sydj0DfxDz14kZzl1B5c2IoRcC7gmDAAsrrZa/Vq/HDSSLrC5KTqthFQ+uluWGmo+eftu1Xaas5C2y8ZklKupAhqUJLg80lm1UP/0+28EopkAJ84O1Q16ZxPUeHM4N+PfyzFbXX21w/8mTEOjaHuqKajG1/PTnnGOnRpgYCKPoWlsSJgBYxOUrmG2aDOWtbFdpUQUWaAHOVMIuZAZjsuNa8YALAmEwNS1mjfQ57v7z9/+bS184e79PHqyl85L4wxniiDALj2hfgOOQMWwf/3UuWB4x572phnLefFSUrCWlt96gf9dx0yNXRsSnB/K1A1lescKfO5bP2Vj0sTUdT75rikD7nt3W1riOK6H5bgkIjqmrvErC5gkXC+E/PTDnQymCgGfoDx5vJqw4gZgNcZPvkjnzg2Jis68cjpv9ehuv7/cdlw8KUlG9EDeuroDTwJn+tN0D2Y42tnLB/Z1zEju8Utja5Jv3SCYuqAtEcHUNUazxQqD6RsHTShSVVPMIF2wsRwJKLfndN8kF4ezFXkbTdNIGlPDRba1xQECYZRaycJa+ZvqpqOBibwSQyndsFzRXrV8ghU1AKs1fvKt+lC6gOtJJrLWtOusHt1dPofOKsWbfuedkqsS/Pp7ruNk3yRn+lOBsEWuaHN+OD1rbK6hEmILxUyltaVGU0QnW8VdmC+2tsb4l+/YSdzU+cZPrjCeswPasr/42+Im8YhB3nLUBOKCOuZrz1zENDRSeTvwCn7l0K6Al+Hv3hNZi1/7mxcrKgbArM+o/3N5UtHUtaBTMxE1Vy2fYEUNwGprjfRRLtChCfjqMxcqxlCVl458lLuCyagZCFNMZC3ODWe4a28HN7QnKTqS7oF0wI83DZ3uwfrS2T7munjK9fkX8vqlQMFZ3OIHuDKW5388dZ6CI4mZSrm4/JwbEia6JjiwvRmAFy+M4yHJFBw1YNT2R6IJ0gWbrv5JDh/YWrF7l1cMHE/y0LMX2dkWn9MzWv0s3/fuKdWrMAdQB6utNbIc5QIdV1N5XE/SN57FciVnB1L896ferNv7X07yKScN3fs/nidTdBBAMmrwsdKQi2A+3gyY6wLyF/9K7vjVmKVnac7Il9oQa5GkRrM2hiZ4tXeyxL2wqxqcVLLPn13wozNDdPWn+dr9hzi4o5WHnr2oqja6huN6TOQ8ftw9FEwgmu0ZrX6WV/OiL8eKGoDV0BpZjwxSvaN/cF87Dz13CYCHnrtEW9xgx4Zk0Hvuv87fScr19QB+7zungvZeWfr3rbvaGJjIN7z77lqDrlXqGNT6uiK6crsNXWOkVIItP96fMRgkY4UKv/zR6tmiTSpv0xI3sRyPiE6gLVBr8Gg1VsOzvBCseBJwJVlXs5FBym/oQ89eLM21U9Rby5XBTL+jnb1B448akmmjaYK85QaloEx1b78QNJVERSKGoDjfJvtZcK3YlGRE56b2JKf6pslMEtVLkmK6aqDKFB3yMxhUpywR60kVmw+ni0xki2xqjuG4Hje2Jzm8f4vSFhjPBUneuTYTrZWF72PFDcBKojxu6x3LghBB5v9412CF1b9rbwePvNKH5ShF3p/b287eLSre9MUne8dzuJ4q19muxPWcIMtfHZt7nuTff/tV4hFt3uSc1eTeLzU0IRA16h8CKJYWdN7xyM8hzvBJWTFT55ffvoP33rSZLx07Q6rokiqoZqSesRwPP38Z2/VqJ1OuMaxrA1Du5ieiJkDNXd0fif2Xv/p2/u6FS7zWn6Lz8jivD6R54PC+qXOUdnTyFo7nsnNDHM+TbG6OsLkpyli2iOsp+aqxEvc+PxvntwbuvHkTT5eNB7sWUM+opYsOr5bKsOWY69LUhHL9lbKypCVm8icfP8jdt27jv/7wLDlLCYqOZIoYpURvNTOwPMRbazv8bGiUKvBDwC8BQ1LKtzbinMuBWqKdfh33yIuXAcFwusB/f+o8f/vsRT77/ps5N5QhV3SCMVy1xmf78t+ep8KJPR3NQXzqeB6TucU13jx/cWxRr1+NWMq9tjVu0hKPlIaubCNjORw7NcCRF68wkbOZyNlsSKgSYrkhn2kzuFYMQaM8gK8Df4WaEbgsWCyBqPz15XMAfX2/sawd1N0LjqTgOHz5h2eJR/SAW+56clpW2I8DyyfMnO6bJG87QfZ6sc049WS9Q0yHuoWCVN6i6Ej+98u9HHttAM+TTJaJkNqux3+6e39QvYGpzeDbnT3EIwYXRrJ8/dmLPHqyf9VwVhaLhhgAKeXTpalAy4LFEojqTezxF+y5oXSFDr0P15N4nqQtbuBJKrjl1ddSLjQykbVIF5a/3Xa9YmPCYCyn5hNKAVtbYlwcyZIrhVsFx8PQqnIyUnlz1ZvBmYEUj57sDzQhrjWloGXLATRyNNhiCUS1JvY8erI/WMR33LQJIVTXWXluyS6Rd+5793XTuOXVAz38MMB2XUYzVljqW0b41GkPQMLrA5PTvv/qnKGpi5o1fj9M9O9nPaWg1UhpnwuWzQA0cjTYYglEtSb2lC/i9uYoW1piQbvv1pYop/tT6GJqOm+5NFj5uSayFp/46gsUHBfL8UhGlLcghK9mIzDEVAY7xBJAVqoMz1Qg0AUkIgZ/8vFbgel9AFA7rCv/+2qltM8Fa7IKsBjShW+pfaqubzzKx0j5U2L9Gv65qxle608hSwMzhsv62KsZgJ9/5DQT+Sl3PyD/lB5GHYnwR9uGaDg0oVz8uX67pq7RElcVIL9N2A/v/HZtmHmHX62U9rlgTRoAWBjpYiZLXW1Q/NgvZzmMpJUykId6wE50D3Ps1EAF3dcPB9yq2nGiNF9PonabiK4FjUAhGouIoQaXOnP0rgTQljDJFW0eOdlLtmgzkXdwXY8vPvYaUJJ9ixg1B5D6m0d5h+F8PNLVEDY0qgz4TeD9wGYhRC/wB1LKv2nEuRuJmSx1uUHxFWOypRDAn2BbdDw2N0VxPPWAxAytwpAc3NFK3NQDhR6BWvRCKG0715Xz2p1CzB2GpmYAOJ4XkK7Kk7j+/wsI2nRdCYOpIroQvNozqQaKuB66ruFJGdzjgqOER6OmzsBEjt/7zikSUZ1c0QWhjLyUcNuuVu65beechUdXQ9jQqCrAJxpxnqXGXHIH/o2ZzFtM5GziER0PKJYWrj/bXhOSqKGTLfV6A0HI0BRTwz3+1Tt20jWQUg0qnsQT08d8tyUMJmsMAA0xP7geRA0oOpXTiYVQ8xHfe/NmIrrG02dHpo1ci5oaQsAdN2/mx2dHiBgamlDMQV/wNWe5DKXVfIWC45CznKCcmy+Jv750cYxzQ1luaE/OuphXS9iwZkOAhWAuuYPTfZNqLHVJ6stxJa0xk6a4Epnwqb6pvEuqkEcXMJG1+PTDnYznLIq2yw3tTaTyFn//ci+ZwsyLO1tYfKtsiNIIME/pJrQkTLJFG9tVnkDW8njndRtpS0Z47s3RaeXdnOVStF1eujROIqL6+D9z541886WegBh04+YkL1wcw9QEliurxpupkW7zKRGulk7YVWkAljI2mi13cHBHK54E1/UwdI1kVMd2peoTL43qKm9HdSU8fqqfwckCErXDD6UKaELMuvhhSjIsxOKRdzwEitRTzbA+N5zhU3tvKA0onQ5PQt5yuH5zE+PZIm3JSFD+AzV/4aVL49NEWQRKhShu6hQsB4ngVM8EAxP5GRmDq6V7cNUZgJWOjfZva+GPPvrWoKVXSpjMqfZSTYCpmRWz7QAGU4WK5F9HSxQpJZP5kPyz3IibOsUa/RV72psAlfVPlhSKdKEMuCIFCXRNm7Yjl/NDfvfn93H8zFUuDGfYVBJ9vf3GjfzOh/cBKgQ88uIVvvVSD5LZtQD9zejMQKpm+XE5sOoMwGqIjW5oT/Jrt18HQFf/JD86M0TEUNn7DckI6YJTsXPHTB2tVOd3Jbw5lA3d+hVCrsbij5sabclIoPW4rS3OheEMmhB4SAxNlQL/8CMHKio71SSvtmSEP/7YW/nskVfIWw6bmqL8zoenJkH7z67P+cjNIRxY6Q1v1RmAlY6Nqm/IJ965iyffGA6af8azFrGIjl3mBYznbDY3Rcnb7pzc/hDLC8vxmMha5EvDWwZLgiFTLdoSU9emJe+aIgYFx2MoXSARMQLDUM91959dt/Rs+K+ZCSu94a06A7DSsZF/Q2IRg9F0gbzt8qvv3MU/vNLLpuYo0pO8bVcb/3R6MHiNhuC2Xa1I4MWLY6QWOJ4rxNLAk/DfnjyPJz2y1lT+xit5cYmIgUBWLL4zAym+dOwMRcdDF4LfuGNXxQDZes+lqWulacVw/3uum1VZeKU3vFVnAGBllVX8oSCXRpQbf+TFKxy+ZQtNMZNM3iYRNdmQiFRQTScLNs+eH1UkFC8k+aw2SKgo/flVAN9Tm8zbxEu79bFTA5w4O8TViTwDk3kVJkjJV556k6aoPqObfrpvEtt1sT2J63p89ZkL3Lm3HaivLFxvw1suktCqNAArif3bSjPin7sUTPl97NV+cpZLPKL6BgQi4PT7s+uya33szhqHn9CbC6oP0zWN23a1cuSFy3zrpR48OcXX8KQMREVnc9PLK0i6rqFrIvAaZnLzqze85cwLaEty1jUIPxN7ZiDF4QNbaUtESJd05WOmjiypyQiUws/WlhjNMSMYABJiZbGY3irL9fj+a4Mc7byiDLqolCCTqGegevqP/7z48CtIG5IRNiTMIAcwXze/PC9gO25gRJYCa9IDqOUeVf9uPi5ULYtb3gKq6KWCiZyFJmAkbfGFu/dzbijNw89dCnf/NY5YqcLj0zv88WuqJVzQEjc5fMsWiq5Ha9TgL57o5tWeSaVEXLVD333rNm5oT0579uaT11rOvICQKyB8eOjQIdnZ2bmg19YT8yj/3QOH91U0b9RzoXwj4au++C7a5z60NxCG8Ic8PvzcJcbzNlKW2GZxk4/cup3HT/WRLrq4ngw9gTUGPxfQGjNJF+2pjk0BG5si5C2PRFTH0JSjnLddxrJW8LrdGxN4nlfxvDQKi80BCCFellIemu24NecB1CqbQGWMdeLs0KyllemS4GKai+ffgG1tcTV+CjVpzgMm8jbffvkKIHClrDuRJ8Tqhd8cJITkY2/bzndf7VeJXAkjGQtNCD5223bam6N8u7MHf7PUNaUxMJIpsiERYWAiz5mB1Lw9z5mwXInwNWcA6rlH5b+7a28HnZfGa7pQ5bt+uZEoH/4AyqPIWQ6uJ/nMnTcGs97KUXCmcsmaUISTouOF6j9rCH6DV89EnraEScH2yFmukhD3JEXX4/CBrTx6sh+voEI911Newl372nnp0jhff/YiRzt7+cLd+wPPc60IiK65EAAWngOoNQjEdt1pAhBHO3v4f453M56zcRyPqKlquznbpVgKFOfzrUV0gaEJcjVGWoVYHWiNmWQtNck5b6ueAl0T/OWvvp27b93GsVMDfP6R04pmLAS/86E95G2Xv/zROUB5f2/Z3sxoxqIlZnJhJEtLVKctGV2RVt9rNgSo52JVu0y1XKjq8OH9+zr4ftcguiZ48Hg3MCUAUbSVpBeoWXRFx2Pnhjh9EwUimpjTIAofricxdUHC1EIjsEoRNTVcTydjTY1ve9+ezcGmcG4oTbqoRn57rqTz8himrlVUHy4MZXA81R48m4DoahADgTVmABZbH60e+y0QwQDQoXQhEICQiNJEGvUg+E1B6bzN1pYYt2xv5uVLY+Rsb9qgyqgupun9uaWWVACjNKQixOrCWNYKsv8+nuwe5j/9Yxd7t7UwnFaqwFKCKyXPXxhDFyIQF/EktMQjgOS2XW109adnFBBdDWIgsMYMwGJ50/u3VY79frJ7CD/5p8Z1UzEmbEtLjKupAlFDoy0R4a597bQ3R4mbOi9cGKs5pXY2sU8vTBCsOGolbH29x2r8zbOX2JCM4HmSlphBwXbxpGRrS4zJvHL3PSnJWS4gSUbNoDtwLWgIrikD0Ij6aPnY7/Lkn6/75o8Js12P4UyxNFpK4/73XMc3X+ohZzmMZiy0OhSq2eb2het/5RDRBbomsBwPKSrvRb37IoFU3kZKSXPM5JffsZMT3cPkLYdk1OQPP7IvCBurR8LXW9Qrzf8vR6M0AX8B+K+ADnxNSvmnjThvNRrRKDTTHPdyAseRFy7zDz/tY3OTsv7nhjPYjouha0gpaYpGGHOs4LxRXcNF4obu/aqFoWnc9ZZ2nj43UqHbWN4XAJVjxEHRgA1dI2IIbt3Vxn23XxcIhcxF/qsaMz3Hy50bWLQBEELowF8DHwZ6gZeEEI9JKV9f7LlrYbH10Zm+/HKBhhPdw1iOR+94npaYyZ72JjovjZOzHIQQ2I5LS8xg58Y4771hEz/qHiZnOWSLLpqQSCmmac+FWFkUHJeXLo1hlwmzVouGNscM3nPjRl64OIYmBLarKgKJiE4yak4TClnomLBaz/FK5AYa4QG8CzgvpbwAIIT4FvAxYEkMQCMwmxE53jVIrmizqSnCcLqIJyXffKmHBw7vC+i/OcvFdl1G0kV+1D0cSEU3RQy+dOwMg5P5ZfxEIeYCT0Iq71CoquBUqwfv3pDgh2eGADWe/Hd/fh9tyUhdoZBGxfArkRtohAHYAfSU/dwLvLv6oEaOBlsK+K5XU8RQU2MLDuQdtFIyMG85QYkoVXBK2WAlGjqUyvNXT53jnrftAOAD+zp4/HQ/Bdtb0PjvEI2Hv8irF7+hgalpQVk3XXA48pMrpdcIooZG3nb5TBnVd6li+JXIDTTCAIgav5sWCDdyNFijUL7ofQZXpuiQLjjoQlF8TV0wmlVNQGcHUjx/cRRPqhmBSBgrjfp+vT/N6/1vsDFpEjcNTF0jFWoCLitaYjqpQm2DW++Be++NmzjVl6rgdfiSXq6UZC2Xo5290xh999y2HaChTL+VEMNphAHoBco7IXYC/Q0477yx0A5Af/DDlpY44zm7JABaKgsJmMhZxE2Nrz17KdhJDE2QjGgUHbdi9lzB9pDS4S1bm5nIW7jOqrB16wLpgjtrFcaHLgStcYPmuMnujXGsIQ9dF8RNHcf1yJUpB43n1ABZPz9UHqcfPrC1oYm75RbDaYQBeAnYI4S4AegDfhX4ZAPOOy/MlEApv0HAtF4Anxh0NZWnYCtRR89T/P6WmMlopognlaPj15Btz8PQdaKGjlPWDuxrzHf1p7DCxb9saIkZ09Saa8Hf3Q9sb+b8UDaQdtMFfOIdu7nv9uv48hPdPPH6UGBIcpbH3z57iT0dzWQsZ8bJ0mtpMCg0wABIKR0hxG8CP0CVAR+SUnYt+srmiXoJlFr8f4Gs6ABMRAweOLyPE2eHePrsMM0xs6TtrzLHngTPU4vcrxdLqRa7oU2PgBJRA8sJY//lRH4WTQb/LiVMjaihc344W6Eg7AFF12P/thbuuW0nT74xHLR4awLSBZsvPvYaf/TRt844WXotDQaFxo0GOwYca8S5Fop6CZRyw9A7ngMp2bkxOa0DcP+2Fm5oT9J5aZxU3iJiaNx582a++2o/QoDlTq8PA9MGRYBKJPlzAUNawPKg1n3w0RzTKTqSuKHjIclaDlbVjRHAXXs7ACXq0Tue48s/PEveVt2dpq5ovxnLqYjToXKy9EyJO19v8K69HRWTh1cSa4oJOBPqJVDKDUMioj5uLRKQfw6fKqxrgifODIFUQyGLtquaPzx3Tmw+CezYEMfUNUxNcH44M+Oc+hCLw0y3JB208Uo8KUFOPz5m6NzQngx+ztsuMUPNCxhOF4mZUzyA6kGyc0kIHjs1wOe+9VOklDzySh/AqjAC14wBgNoJlGrDAPU52lBJFe5z8+QsFQbomuC3P6haQH/cPcSrvZOz7u5XxkIuwGqCWxJw1Wp4ZgXb5ctPdAc8/qOdvaSKLqLosrkpyn3v3s2ejuYKafBaCcF6OHF2CCklEUPHclxOnB0KDcBSYC66ALPNBvQ9hripQ9ykYLs0xUzu3NvOxeEs/+WH50L5rzUKicrjCGD3xjhXxvKBQXj67Ahd/WnuPbQTgeTGzUlGMkXue/duDh/YOi3JPB/izl17O3jklT4sx0UIEYQbK401ZwBmKrlUW+S5agOWw/cYjncN8lr/JKd6J9mxIRHc4CdeH2zY4o/ogt0b4pwfyTXojCHmAj8hOJazg3FuoCYIDUzm+dGZq2SKLpbr0ZaIcPjA1pqLvVbeqV6c7/9/mANYBGbjSldP9XnkZO+CM7SPnuwnW7QZy9rkLZfmmIr/TvdUSjSbuqA5ZjKWtSp+P1s9Wi/NFAgX//LDT8VEdI109e8lnO5LoQlBc8zgDz8yNfuverFXh5cXh7Mzxvl337pt1Sx8H2tqLsBseunlU31SRZdXeyaRVWKf83mf1ngET0ocdyp798nbd9MSm7Kb/ujwamhCyUzFjNpfsStrUyhDLA80wTSj7UOi+AKelJw4OxQIfn7lvnfwuQ/tnTbZ595Du9i/rYUTZ4cUS1QTwWtXO9aUBzAbV9qf6vO1f76o4nck9x7aWVHqm8/7jGSKaEKwfUMikHY6uKM1mAQ80w7vSiiWWIb14JUZgTCnMD8stMTqzwDY3BRlIm9NI2v599X1JJmiw4/OXKXz0njQ7DVT++6e9ibFGSldmD+SfDVjTRmAuXCl93Q0k7dcckXVtruno3lBbtc9t21nJG3xg9cHuTqZR9c0miIGp/sm0TRRoSrjt5G2xAz6JgrBOYruzHU/iXqQIeQLzBcL/b6KJdq348kZpdkk6v5mii5gBXJx5aFnNcnslu3NtMR0NKGhCUlbMrKwi1xGrCkDALNzpTOWw6amCIau4bhe0MFXD7WqBp9+uJNc0cY09NI5XMDlD/+xi4/euo2JUr8A+LryEDd17n/P9fz5D7oDBtlcBJfDhb/8SEQNfua6Np47P1ox1akW0ctyPCxdEClpRWaLdpBL8kPFeMTgwkiWdMEmZ3lsTOoko5EVVfqZK9acAZgNB3e0kogY2I4763z2YLFbDomIwdfuP8TxrkGupgoIAW7BwSgJggIMp4v8zxcuI6USCjU10DSN7W1xCpYTjBJ/snuI/jJPIMTqga4pcY+tzYqkJXCDASERXcN2p1OKP7x/C/94aoDJnI0QgqYSoaw8VBRAR0uMdMHmfXvb+dQdN6wJSvA1ZwDm01L5jReuBCOgUwUnkHnyHwhRlaaTEFBIpZTETIOIoTOaKWLqGkdevELedrAdWTNHYAg1dSbEykCghnqkCw7ffbWPgu2ia6qz0zQ0TE3D9WRF3qYlbpC1HBIRHUMTOJ7k+TdHg3zAA4f38ejJXk72TFIobSSNWPzhePBFYC4tlWcGUvzjqT6VtJEyiMUPH9jK0c5ecpaDqWsUbBfLnQojNFS5yNQ1Pvv+m3n4+cvYjkvecshaM9OEw8W/spjq7qvc5R1P0t4SYWBCKUCbOoAgomvEDJ3OS+Oki04wF/IbP7nM5jPRYGagQGLqGr/SoElA4XjwOaLWiOa54nTfJBFDw9DVPt8cM4Kb97X7D/H7d+/nvnfvRtfUVJ9A/730esf1uDKWQ6CaixCh4u9axmi6iJRqVLjrKqUnXRPkLZfJvBP4gvGI6gA0dI1c0SZnOWxIRhFItrXFGy4NFo4Hr4P5Wslql+rgjlaSURMgGA1WXtv1E4JHO3tJlbr7DE25kBFDw3Y9ftozHvAM4qZBtji3RqFq+F5FiJWBBvzMdRvovDxRGv0FSIiZOnnbRaIUoDwpkVIihMBxPRKl58cvSzdFDI529izabV9OabA1awDmw8P2jYU/7NOfA1jdJFR983xv4BsvXFHhAkpU0nZVi+ilkSzJqJodf6J7eFrQrwklKjnb6PCmmEEiojOYKjbgmwkxGwQQNQSehKaoAUjODKaJR3QKtht0C45kirTFTZJRA6Ry83/m+g2887qNgUgoqGdxIqtKhUpNqnLW5HyxnNJga9YAzMdKnu6bJGc5jOdsXNfji4+9Fui579/WwrFTA0ELcCJiTGN6HdzVyonuq6pDcDxLzDQYTBWwXEkxa3F+OMNErjhtF2+Lmzie2jUQgkzBqdGGqpEuOHNSswmxeAhU/ubtu9u4MpZHoAg/juPR0RKjaLtT04GlxPYkiYhO0fYwdI1Xeyb46ZWJCo8R4Nf+5kXGslZgAMqfsYVguaTB1mwOoB41sxYO7milaLvYjocoxfN+XHVmIMUXH3uN8azFeM5mMm/z0LMXOTOQCnIMTREjMDatiSgffMsWhFBRoZRwuncymP3nw9BUcklJjAnet2cz8YhOtYBQoUyjPsTSoPwrV5Ucj65S3uj9+zrQhJrh8OZQRnkAgoDtCdDRHEMIyNsOE3mH8dJu7+eeTvdNBt4eqP+WP2OrGWvWA4D5WcmIoSOEg/Qkpl6pGKQJEJrAdjzGHYsfdw/x3PlRbNejYLtoQvDZ999U4fY92T1EznJwPEnWnxpbtpIdj2BXt1yHp7qHaIqaRHTFEivYXjgpuMEwfHqwAA1BzNR4/752nj0/Ss5S3X0+ckWXuKkGxU7mnYDr4fOAdE0Njo2bU2IyqbyN7SoZOF0TQdh5cEcrpq6jCaUCHTW1iiEiqxmL8gCEEL8ihOgSQnhCiFlnka8UTvdNEtEFN7U30RI3uffQzkrFIF1HllavEGrKa6pgMZwpkio4TORtvvLUmxVqMF+4ez8fumUL79uzeRpnQBfTr0ETGhFd8G/uuJ7733tDoE8YonHY2hqnOW6goSS9C47Lq70TaBpctylRsat7UuJ6ko7mGLUyNLbjYRp6qby3iy/cvR9TV1LwricxNK1igefLZMZyRZdPvHPXnDanxVSyGoHFegCvAb8M/L8NuJYlg58vyFtO0N/tw28g+vpzl2iOmfRN5BlKFRRPvOy5cKUMLL6fM1DuvHqkPCnRUAk9y/EwUTRSSam5RHpIVG/CibNDdYeLhlg4eifyFQQs14Pe8SlG5uamKJbj4qEW+C1bWxAImqLTFYVdqYRATV2wrS3Oc2+OMJG30ZQN4K597cECP903GZCHfANzbjhTcb5axJ7VMCZ8UQZASnkGCOLh1YpaWdXyG3L4wNZA2nlTMoLluJi6IG9Pcf1bSnoA5TkDXdeIGhqJiE5LPELesinYHkVHzZNrS5js29pM92AaCdiux//96GlylkMhZAUtCep9qze2N/HHH3srz5wd5ss/PEfB9nj6/AicHyFuaCQjOp4nKwaEOK4kW3SZyFo8fmqgov9jKF0I2oQP7milKWqQs9ygwatc8afeQl8NY8KXLQew0qPBqoUcq2+IbyAGJvIcefEyE3kHDTANwTt2b+TXbr8umAvnSaZqwaZOMmZiuy5521ODQVAPou1JmmMGErX79E3kp7HQyhHyAZYOmYLDkRcu892T/RRst8JQ5B2P5qhBU8LAzlhIKVVOQEBEh68+cwFNE0GzkBDwau8knz3ySrCYH/7Uu/jGC1cYTOf5+G07K0qA9Rb6ahgTPqsBEEL8EKildvgFKeV35/pGq2k0WK0b4os6nBlI8bfPXlIVA5TgxxuDKR483g3AqZ4JUnlbqcsCn33/TezckCiFBKLiwSraLv98boSi4zGZt4MscT2Ei39hiBkqC7NrY5xzQ9max7wxmOaNwXTNvwG0t0T52Nu2s6ejmXNDabr6JznZM0FLPBK0g29MRsgWHSKGTkdzrGIx79/Wwh9//K01z11voa/EKLBqzGoApJQfWo4LWU7MZnn9OE+i/tUcM8lZDl987DVF6pGSzU1RQJK3lcKrrqlY8eJwBl3TsD0Py5VYbindJ8AsNZPMdXxViLmh6Eiaovq05q16KK/Y+PdiImfx6Ml+Hji8j21tcfZ0NNPVn+bSSBYJbEoafPJdShn4wePd89q15zKSfqWwpsuAC8VMN+R032Rp9p8aD+YzwiK6RsQQbGiKkiqo8VCJqMmRF6+QLao+cNv12NQUZXNzhDevZij6nYOoB629JcpoxmJLa5SRtDUv8o9GmVEKUQGJ0oHwE2+zGVgp1Six37zrZh59tY/LIzk2JqOk8pXCH3fta+efTg+wuSlK3nLY1hbn7lu3cUN7ct679kov9HpYlAEQQnwc+G9AO/BPQoiTUsqfb8iVLTHq3ZCmiKFGf5f8cYHfRioBjbzlsCkZ4bZdrZi6xvdfGwxqx6m8TUvMZHCyECx+H3FTp3+igJQwMFGsWXqaCSJsNgIgbmgViTofeim8cqWc1QJIFEdjKFVgMudQcDwujWRpihpETS0IDdubo7QlIuQtZ5rrvhoX80Kw2CrAI8AjDbqWZUctNaAHj3cHkl9+/7flenie6g47fMs2TnQP83p/ismCU6Ho43gwkbdL4UElDu1u4+UrExRsb1oSqhwxQ6M1bnA1XSlYWUs5yNSUUVgPqkIdzVElziqYZgCmDLT6YTYlJt8+HOsaJF902NEWYzxns39bM1fG8hWTo3xJ8JWK0Zca6zIEgNqVgCmJJ53JHLQlIgxnijiuxNAFEUNQdD0EknjEYGCyWLHZGBpIKchbTsXvBaoikLdn7xYsOB7FdG212mqsJyLhSKZIU9Rg58Y42YJbobdY8ZXK2tWUrS1RxrMWRXfK98oUHFXmyysm54WRLIamiD/lff3X4sL3cU3RUebDqqrVc+0nBx3XQwhBwXEpjZLDcRWFeE97EwXHY7AkG7ZzY5xkRFFGNzVF2doa49N33shd+9qD95LAxmSEpqg+p8+xDjb0ecOTym2/MJTBnkVs1UM92DFDY2dbjI0JUzH/JDRFdaKGxvtuLvVmaFPnb4mZDe3rXwu4ZjyA+bKqalUCypODE1mLL//w7NQLBNy2q5VvvtSDrgl0IdiYjFC0lSjkZ+68kbZkhImsxUuXx3j+wmj5S9mYiKp+BKbc/6ihYZXc2XDRzw3VuZV6uKmjqcS5UN7age3NvNo7GZTvfub6Dbw+mMLzlHfneVK1/yYiAeGrluu/HFJdyyUHBteQAZiJVVVvPmCtSoCf4Dna2YOpa+TtKYqn7XrYjhs8RHft28I/nurHk5KHn7/M/e+5rkIV2IcExnJFbtnazNPnR4PwoFgVy+rAzFPuQ/jxfXXzVTX6JvI0RXVa4xFGMkU6mmMkItmK+H5PR3MFpXtrS5Rk1OCZs8N886WeaZtJrdFztWYFLAbLTQ++ZgxAvdr+TF/oTNncgztaaY6ZZIuqh78tbtLRHKOLdPAeEkm61AmYtVy++2qfUo2pcb6nz42QLaqyn88zqH6AEzGD9Dx1AZpjejD+ej1htlyKqWlYjuTCSBak5PFT/Xz2/TeTt6e+K7+kd7xrkL/554uc6lOh44sXx2hLmOxoS1RsJuWbzFC6UHNWACxuB19uevA1YwDq7ej1vtDZbpKvBnS8a5CRtMWT3UMcf30Qy/H4yK07+OTtuzneNRhQTwRw/aYkZwams82SEb00llqJTHhSactV04Lnu/gBXFcG1Yr1hNkmKmUth/fcuJGXLo1huzBZcPhvT56nJW7ieB5/98LlQLXneNdgxXcvpWrkqt5MyjcZ15NogprP1WJ28OWmB18zBgBq7+i1vtC53qTycOAHrw8wkXdwXY/vdQ3wydt3TykIF20SUZNfPLgdgCffGEYTkpzlkYjoSlIK5SVoQsX+v/3BPXz75Z661NW5wvE8NiajDKXXl5zYTOauJW5gCDXks+jKIGxIFx2KjodE4nkyUO2BSuqAEPC5D+wJ9B9qhY1NEaMmI3CxO/hy04OvKQNQC7W+0KOdPfO6SQd3tKp6u+uh61ogBnHvoV187f5DFQ+E7bhsTEaCpFNzzOTqZJ637mjlnddv4NnzI0RNnYefv8zbd7Xx5nB2Gi11PrBcgsW/0Hl584Wpg70Koo6ILlTJNu9UzFxI5R2ihkYyatASM5nM28FrfFEQU1fe2Jef6KajOcbm5ijZooMn4Xc+tIc797Zzum+Si8PZmovxhvZkzYXaiB18OYlG17wBgOlf6Hxv0v5tLfzRR99aoRtYzQqrNioHtrfS1Z/mwlAGDxVX+p5A1NDonyjw7JsjbG6KclN7ko3JCD1jOV4fSBM1NDwPVYacx+d05ezJsUZguRd/PcMokWQKjprTWDpO1xQhS0pJKm8TrZrO3BTVyRZdDCFI5R1+dGYIhGBTMsJn3ndjoBXhi8iOZiw2Jk2SUZMHDu8LjLzvOd57aFfF+Rezgy9n9t/HujAA1VjITZqNA15tVA4f2MrZq2n6JvKAWpT+w5opOohSS3Hedrn9xk3ETZ1jpwcV56CUG4ibU1WIuWIu8whXIxKmRsHxahov3+Oo/tPmZJSr6WJJAUj9zi+s+Gq/h67fyMmeSTJFm7zlkSuFYarVVwRkKtv1gvq/b8wNXcOTpYYuy+HE2aE5eY4L2cFXShxkXRoAWNhNmuk11UYF4NnzI9OO27Ehzmi6SN7xgvn0Dz93ibw9XRzUk+ohtebp1wsgZmo4nsdN7c0ztsGuBly3Mc779nTw3Vf7KhqkBIqc8+4bN9B5aZyxnF3xusF0EeFv/UBr3CBfasqazKs5fvfctpN7btvJFx97DYFLznbZuSFO0fEo2h5Wqcpj6hoDE3nODKQCYz6ZV0NgJ3I2hibY095E56XxJUnQrZQ4yDXFBFxp+CIPp/smOd41SNTU8T3QiC5ob44iPUkiahA1NDShxCcLjqIXV0NKb96L3+8YtFyJ58HgZJ6W+Oq281dTRR492YeUslI1WShBlt/58D5+8eB2YqaGUTpA11Tzb8TQMHUNXahBHi1xgw1Jk61tcTY1RchYDhlLlWqTUaUXmC6oGX5/8vGD/Oo7d/Gu6zcC8O3OHj575BUAvnLfOzh0XZsKK4SSfMvb7pyVqOeLlRIHWd1PxhpD9bx42/Eo9a/QFDX4w48cIGM5TGQt/vwH3crdlZKYoakyXpW7rwmNpohWMeJcF6UEFmDNICvmeRJdV3Huao8KCo5HwfGI6AR+voba/T9z540c7xrkB68Pqu+z9HfXU/McBSqxFzP1oF//S8fOkCs6wa4eN3XGsraazwD80sHtfPJ2pUr14PFuJnIW6YLD9ZuTFCwnSPAe2N7Kk28MqxxD6X0X6t7PFm6ulDjIujIAS51kqSaK5KypxTeZt3nuzRFu3dVG3nbZmDQBlQM4sL2Ffz4/Ou18H9zfwesDadwJSd5xg2RYU8zk3Tds5NjpqWnG5TA1gaFrSCkponbVRlUHFipmope8nZk9GkFLXOdnb26nLWEiEDz8/GVyRZtU0SVZEu9MRnQlytIc5cqYyrFkLZcfdw8BKp53XI90weEbP7mC60mipoZbEmfZ3BypiPU3lzQeRtMF2pLRYPetLvOWi8nOFfOJ7VeizXjdGIDlSLIc3NGKRNA7nlOTZLWp5eIBj58a4KnuISQCU9dLbr/OCxfHpi2q5qjOLx7czi8ehN/7zilwpjLdn7nzRvK2y/e7rk7p15WgCzXo1HYlxVIVoZGlwYWeypOQMHRMXZKto4to6ios2rOliaOdvYznLAqWS2vSxPNkQNYp2C5bW+OYemUE+0rPJOeHs+QsVYrNWWrgZ6boUCjzrh5+7hKHD2ytUIve2hLj3qrpvj4ZbDGbxmoQ/pwJ6yYHsJwTV5GSiKHRFDXRS1NmmqIGmpAqNHBd7j20k/fv61BjqapWaMzQSEQVr+DcUBpDFxiaOk9EV17D4QNb2doSI1JV5tI0wVjOJl10mEF/dEVgGgJTVyrKRimG18uC/qzlMpZzePnSOFdTBYq2kvCeyNpBbsPQBMmowb2HdnLX3vaK84tSHkBKSOVVgnU0Y1GsCq1SRYcvP6E0Hh84vI/33LyZL9y9n9/60N4KOu/Rzh6AQC9yIVgNwp8zYd14AIu9EXMJH073TQbjwsezRX6lrEYcN3X+/AfdatS0EMRNne93DU4TB4kbglhkSnQSIBExmMipRVBwJEdevMLhA1v52v2H+Ksnz/FPpVAAmGZMGoGmqE6muDhrYuqCvOWVSnqeEvAQlObuuRWhwen+STw5NTolUuqa1ISq70cMLdDmS5bq+nFTw3al0lwUlIZ6AEKSiBhcLRu86riSJ14fovPSOPGIgUDy3PlRzg2lK3gAjfAWV4Pw50xYNwZgsQSNuTwQtbgA/nFHO3vY1BRBSuXCdl4eQxNqzrxdGiDSEtNpjkUANXLaXwL3v+c6/uKJs8HwieF0kW+8cIU//vhbSUSNQLbMY2YXfaHxe36erkTEEGjA5mbVi//mcFa5/a5LGSkPDdi9MU664ASxPChpNU0IIoamZjq6XtA81Ro3+aOPvpWMpXQZt7XGuTqZ5z03beKe23Zy4uwQP3p9EEPXyRYdTE3D1AQdzVGaYwaXR7OKKASM52wcT7KlJcaFkSxff/Yij57s557btjfUbV/NEmKL1QR8EPgIYAFvAv9GSjnRgOtaEiz0Rsw1jpvJyDRFDIq2SkwJASd7JjF1naaIx4Tr0RI1MQ3BF+7eH3SoHe3s5dudPRQcr8JVlsB3Xunhk7fvpjVqzFksdKG+wXydCstRDUqf/xf7ee7NkaDLrhw+Y/HSSG5aI5OUKmTas7WJLc1Rnjs/QlPcJJO3+fU7rufuW7dx7NQAmaLLwGQBhKCrP83vfDgJdPDIK304nsoXbExMKfwAfOKrLzBRZoU0IRjJKGWnTc0xCqWKy2p22xuJxXoATwC/L6V0hBB/Bvw+8HuLv6zVhfmED7WMjK816EqJJyXNUYO87fLzt2yl4Lo8fXY4cPkzlhO0ngokG5JRrqbypYTiFHK2x+/971e5MpabkjFfpPcf0QVCCKKmRq7oUEN7c07QSmWzfzrdz5NvDNc8xtDUkI2C7U4zMBJIF22ujGYZmCiQiJpIT9KWjHL4wNbg+/Rbr3e2xSg6Hg89e5GdbXGihoZbYvxJWanw8ycfP8jnHzmNKyUtMZMv3L2fc0Npjnb2UiiJf64HLUAfixUFPV724wvAv1rc5axOzLSzzzU3YDsuGxIR0gUnYLv94PVB/vAjB+i8NM5QuoDrSZoi6paUG51k1OTwLZv45k+uVCyW0/0p1WasCdwa2/RsLbPV0AS8b+9mOppjPPZqfwUrzx+sORdvQC9dz/HXr2LXeEHC1PmZ69p49s3RmuczdEFzzGBLSzzIpWxri09r5trSEiM7kmUiZ5MLyoAiyKu4Uv1TbrBrU7q31Vzw1/LC99HIHMCngL+v98eVHg22WNTb2eeTG5jIWQgBolQitB2XjOXwiXfu4i+fPIcm4IuPvQaoB7WaWnz89asVbb++XmFcF7hCBDkCH/GIzlu2NPFq72RAZtnUFGEkbdWcQmQ5kld7J8kVR0mXJf2iuqA5bnLP27bzN89dmtHTiOgCTYBD/YSkoQvODKaJGVoFBdp3cg7fsoXXB6aEV/Z0NFeQoarLd7dsb+bVngm2tMTpHctiGhpuKfOfKTpcHM5W3Jda93I1x+lLiYaMBhNCfAF1z4/UO89qGg3WKMwlN+B7CA8c3se5oTRHXrzCaMbCkxLT0GmKGPz5998gZynFYA036FOvfij/8CMH+Ny3floRMwvgvTdvZktzjP/14pXKC5SSn/ZMBhOOdAHvvH4jP3pjqKIu7iNianQ0x+jOVsbsm5qj/N9338Ldt25jIFWoqDpUwyoJlNSbYyCAjpYYQ6k8uaprkBI2JEx+8wN7gu+3vM263MhWG8fPHnmF8WyRRNSk6FrBeyHhxNmhill95felKWI0XNZrLWHRo8GEEPcDvwR8UMq12ou2MMyWG6jlIezpaA5iUFPXODeURtdEoBYkNBHoDVwczvLoyV46mmPcd/t1ZCyHlrjBWHYqibUhYfLvPryPi8NZjrx4pcLdr15guiZ4+txIzcUPSgWnbyI3pbFfwmTO5kvHzgCwd0szP+ganDE/MJM6UVPMIJW3Aj1E3zPxxyb6L61us45FDEbTBY53DQZ/K1+wDxzexyMne9naHCdqCB567pKSCNdExaRemLov1e2+KzGee6Wx2CrAL6CSfj8npcw15pKWHo2iBM9WWqzlIYCqq/u/A1Xnt+OSiZxFa9wgETF4/vwIj57sDxb08dev8umfvWFaTP3Lb98BqF2uOW6QKShRC4Eamll0ZbCooqYekG+qF7mGGmd+Y3sTPWM5ooZO/0QO11Pipf2TeX7vO6dojRtEDT1oWZ4POpqjfPpnb+Crz1zA1HSKOFPhhFShgSdlsMhhil3pz+g7UvJy/Ky+v4t/6dgZrqYKSGBrS4z/+AtvofPyGB3NsUD1p/q++HRpU9cDclhoAOaHvwKiwBNCmfAXpJT/dtFXtYRoNCV4NmHRWh5CNVfAT0D57uhE1uI/f++Nit08lbf56jMXprHavvFSD9/ruorjeeSKaqhJtugihOot+tR7r+fKeI6O5hi7Nyb4ylNvUstRi0eUgu6n3nsDXzp2hnTBJh4xyBadYEfPFB22tMaIWg45a35lxZ1tMT5/9y1kLIeYoRE1dTJVA1RcT5KzXI529gYciv3bWrj30E6+/twlmmMG/RMFvv7cJY529gIgkBQcD8vxVH4FyFkOedvl3FCW1/tTPPvmaMV99u9LzlKkLNt1SUbNa7rcVw+LrQLc3KgLWS4sJze7nodQT47c90w6L49NY+3omiiNAatcdgLIFW02N8fIWy67NsYZmCwSj6gBJ81xkw9t2xrE0ppQr48agmKpm1AT8PG37+C+26+bOrGUxE0dr7Qo/VLjaLqArmtsSCrC0mTenuZN1CIcTeZtHjzezSfeuask/DHF9DN0QbS0G2/fkAg68vzvZCRtIaUM2JCbm6KMZIogFeuybyKH7SqdP4RSbAKC+3w1leehZy/yqTtuCIxKubZfmANYR1hubvZcM87lnonlyooVZGjwM7s38NLl8YrFFjOV7l2m4HB+KIMmYGCigCfBcT0MTeNoZ2+wS3qehyfVsosaOkXHKe2aSqvAj7nzlo0jIZ+30DQtqCdubopy37unRmTbjouhRRjNFINyXln/01R2H9jSGieVt/jqMxfQNYFjezTHDDJFB+lJkgkDU9eCWrwv3vrphzsZTBVAShIRnU1JNazTX+RD6QKpvENLTCdqGIFiM8CjJ/u5msozlrV5+uwwnZfGA09gvWb9q7HuDMBq5WaXeyZXU3kSJdlwiRpT/tyFUQy9JIZRSpztaIsznrXIl7r+fHpr1FBqQO/f28HJnnE2JKP0TeSYyKuWQo+poSQaKgE3nC5ytLOHswMpxnLlqjwuuzYmSBds7nv3bn7rQ3sBglr6wESeIy9eBkQwv8+TkoihFuvVVAFNCAqWyk3omqCjOUbvWBZdE9zU3sTVVIHbdrVyz207K3bjo5095EpiHgiBoQnue/fugBMA8NCzF/lx91DAGTi4q9LTeujZixVEq7l6fCuhz7cSWHcGAFZnzbe6lbgpZpCMGgxniiSjBrmiQ0ssgu1YmLqaAzCULpApumhClFiG6lwFx0Nz4Mdnh9A1DcvNowlBWyJCPKKTLtjYjkRDGY3WmMGJ7mGe6h5iIm9XuPASGM0UMXWN4XQxmLvoL46DO1p59GR/0Fd/Y3uSzckoP+2ZUKPUm6LBBJ1yKe1E1MR2Pa6mCuQsl5M9E5wbyk6L1RMRpQEgIOjJL793n7rjhroyXfu3tcz493pYKX2+lcC6NACrBeW7jA/Xk6Uefw0h1Nx7U1MCokLA1tYYuzbG6eqfpC0RIVvIV8TTflOQB2Qsl+aowHLgjps3cbJnEiElEV0jbgpaNsQZyRQ5dP0GXu9PsSEZJVWwp11n1nLRcPnWSz0cf/0qjqvENU1d8Gf/8m185b53cLxrkCMvXqHz0jgS2JSMcN+7d09bsL7n4GfuMwVbxe4IcpbD8a7Bip3XH84CsKejOaiklOdOZvLoFuLxrfYe/kYiNABLiJncyOpd5p7btuN4ShrLLWnXv+emTRy6biNtyUiQrPIXTs7yyBVVB108ogGKP+C4XqAkLKVqdpks2PzozFVa4pGKGN6Ppf2RZ1dTeXKWpxh6fojgE3pKFmYiZwWtuwUH/sO3T/Kdz97BtrY4tuMGmfhyld1aODeURiDZ1BylZyzPcKaILgRHXrxCRBcVO6+fDPz0w52BOs/X7j9UYQRmomfP1+Nb7T38jURoAJYIs7mR1bsMqN3fcZSIYNZyeenS+DS3+GhnDwLJjZuT9E3k0TXY0hLn0kiWREQPwgCjpI/vd74VHImdUe9TPhPvaGcvT5U49LftauPV3klaYiYXhrOoGToKnlTCmBF9elPS8a5BDh/YSiJqki6qfEQiYtAUMTja2VNhvPzkoSw1MWfzNppQCca85WC7HltaEtN23iMvXGZgUoUy6aJbwRWYz/c+F6zWPNFSIDQAS4TZ3Mha2gFxU+dPv/9GwPEXQrnF5a8t58FvSKhS3GhaEWC2tMQYzVrYrlWTr19eravuOPSHmZwbyiqOfWuMt+9q40dvDFEs6RFGTZ279rVz7LXB4Pyi7HzV7vqDx7sr2HaeVB5FeZMPEFQqzPiUFkL1gNfHTw0ERqiqMXJe3/tcsRrzREuB0AAsEWZzI2vx2c8NZ2hLREBKxnI24zkLXYigQ7De6/ydPG85tMZN/u37buTccIZc0eH7r10NuANNMaNC2LKWESrvijveNcjT54axPaHkzCM6v/mBPbxtZxtf/uE5hIANiUhwzvJFUz5cw2fbOZ5SAqpu8vnC3fuD7D8wbec93TcZ8Bd87OloXtD3HqISoQFYItRaqEc7e6aRf/z49rNHXmEyV2Qi7xA39cAtBlnRCee/DqYWym99aC+HD2wNdt8797bzmZ+7ia/++E1OdA8rCW1N8H/ddfO0rriZSElHO3uVko+UNMcM/uijbw2u2Z+dVy+/MTCRR6JyEuVsO78paiRt8aVjZ9TOX2NsezkO7mhF0zRK1UCaYsa072S2zxSiNkIDsISoXuD14lI1grwQtPN6Ui1+lQwzpu1itc4HBOW4R0/284l37uLPf9CtqgooubFvvtTDnXvbZ22NhSl9wxs3JxnJFPn1915f0VFX/rpjpwY4cXaIu/Z2cEN7Mrg2EIFWf/kOX67Fv70txmimyJef6OZ3Pryv5rXs39bCZ+68kT/9/hsA5IpuhVdU73sPMTtCA7AMmCku9XfajOUipRLPbIkbfPJduytEMGY7H1Dxu+NnlGS4oSvlHU1o82p4Kc81tJW5+dUZ9mOnBvjct36KlJJHXunjV9+5q+I6trXFKwxHhRZ/3qFnLI8EfnhmiK7+dEV2vxxtyQibmyKqccd163oAIeaH0AAsA2aKS/2ddteGOL3jaopNImJMq5/P5XwV8fz+LZy8MhFQhzUh67Ys13KXa7nStTyPE2eHSkq9OpbjMpQuzBiDlxuW5phBwXZKSr7TE57Vr0tGTWxnZRt3rjUdgdAALANmikv9BeEr3FYPp5jP+ap/t3NDghNnh9jT3kRbMlJTzqxWKFFdQ/dRy/O4a68S4Sw6qj340HUbA3Xeu/Z2zEjMKW/j9SQVY9fn8x0uF/zvK1u0GcvabGqKkIgYa5opGBqAFcZCH+y5NBndfeu2aUo45TjeNchEzgpq8Me7BoM8Qq1cRS3PY/+2FnrHc/zlk+eI6GqUF6g23c5L44GyUS1yDhDwEYAKw1fLMyl/3Upw9X0DaOo6UlolSfe1rSMQGoBlwGxJwJVIWvm5B1+kdGtLDGDGGno9Y9WWjNAWN9mQjNI7ngvadMvzE/U+f63PPtv3tVJcfd8AZotq9LjjejN6LWsBoQFoAGbbjVYjt9zPPVy/OclousC9h3ayp6OZguNxNZWvG2fXot36Jb/xbDFo0y33Eub7+Wc7fqW+z+rwJcwBhJjTbrQaySn+NRUsh7ZkNGDu+XJhDxzeV9N1L0f5ZwcRDOC4OJydlgOYz+ef7ftaye/zWisxhgZgkZjLbtSIBFajs8/V1+R/jo7mGEPpAifOqlHbtRR5a332q6k8vRN5Lg5ng9eU5wDm8/lnO341JASvFSxWFPSPgY+huk+HgF+XUvY34sLWCua6Gy1m51gqFdvqazINnaF0gdGMxY+7hzjxxlAFd79eP0O56k6918z38892/LW2E68UFjse/EEp5a1SytuAx4EvLv6S1hb83ehzH9q7ZMmomVRsGwX/c7xvbzsbkyZbWuKBDmGt5hx/dPZX7nsHP7evg01NETqaY3VfE2J1YrGioOUTJJIsfP7kmsZS70bLpWK7f1ulgk4iYgRqPjORgWZ7TYjVi0XnAIQQXwL+D2ASuGvRVxRiGhaTfZ5v7mC2+LpWzuPeQ7vCmHyNQsw2zGcuo8FKx/0+EJNS/kGd85TPBvyZy5cvL/iiQ8wN1bmDeESnJWbW5dvP55zrQS9vLUMI8bKU8tBsxy16NFgZvgH8E1DTAFyLswFXO/zdWko1ritXdMhZ9dV05oIwA39tYbFVgD1SynOlHz8KvLH4SwrRKPi5g1RBSYGJGZR05oMwA3/tYLE5gD8VQuxDlQEvA6t6LNh6g79b+4q9tuMG0tprAetFm38lsdgqwL9s1IWEWBr4u3W51NdaWExhrmF5EDIB1wnWmtu+GvsnrkUslggUIsSSYKF8f5+k5E8wCjEzQg8gRAWWM+6e6b0WUm0Iw4b5IzQAIQIs5wKay3vNN2wJw4b5IwwBQgQoX0Cz9Ros1tWez3vNFaux7Xq1I/QA1jnK3fC5LqBGeApLsVhDktL8ERqAdYxaC3kuC2g2mfO5LMClWqxrrdqx0ggNwDpGvcae2RZQvd17vp5BuFhXHqEBWMdYqBteb/cOk3BrD6EBWMdYjBtea/cOk3BrD6EBWOdopBseJuHWHkIDEKKhCOP6tYWQBxBiyRHSc1cvQg8gxJIipOeuboQeQIglxVIw/kI0DqEBCLGkCCsDqxthCBBiSRFWBlY3QgMQYskRVgZWL8IQIESIdYyGGAAhxH8QQkghxOZGnC9EiBDLg0UbACHELuDDwJXFX06IECGWE43wAL4M/C7rdC5giBBrGYsdDPJRoE9K+aqYZepE+WgwoCiEeG0x771KsRkYWemLWCJcq5/tWv1c++Zy0KJmAwKfBw5LKSeFEJeAQ1LKWb9MIUTnXOaWrTVcq58Lrt3Ptt4/14JnAwohDgI3AP7uvxN4RQjxLinl4DyvN0SIECuABYcAUsrTQIf/83w8gBAhQqwOrBQP4Ksr9L5LjWv1c8G1+9nW9eeaNQcQIkSIaxchEzBEiHWM0ACECLGOseIG4FqjEQshHhRCvCGEOCWEeEQI0bbS17QYCCF+QQjRLYQ4L4T4jyt9PY2CEGKXEOKEEOKMEKJLCPFbK31NjYQQQhdC/FQI8fhMx62oAbhGacRPAG+VUt4KnAV+f4WvZ8EQQujAXwP/ArgF+IQQ4paVvaqGwQH+vZRyP3A78H9eQ58N4LeAM7MdtNIewDVHI5ZSHpdSOqUfX0DxI9Yq3gWcl1JekFJawLeAj63wNTUEUsoBKeUrpf9PoxbLjpW9qsZACLET+EXga7Mdu2IGoJxGvFLXsAz4FPC9lb6IRWAH0FP2cy/XyCIphxDieuDtwIsrfCmNwn9BbazebAcuqSDIXGjES/n+S4WZPpeU8rulY76AcjOPLOe1NRi1GjyuGW8NQAjRBHwH+G0p5ZqXLRZC/BIwJKV8WQjx/tmOX1IDcK3SiOt9Lh9CiPuBXwI+KNc20aIX2FX2806gf4WupeEQQpioxX9ESvkPK309DcIdwEeFEHcDMaBFCPG/pJT/utbBq4IIdC3RiIUQvwD8BfBzUsrhlb6exUAIYaASmR8E+oCXgE9KKbtW9MIaAKF2noeBMSnlb6/w5SwJSh7Af5BS/lK9Y1Y6CXgt4q+AZuAJIcRJIcT/WOkLWihKyczfBH6ASpIdvRYWfwl3AL8GfKB0n06Wds11hVXhAYQIEWJlEHoAIUKsY4QGIESIdYzQAIQIsY4RGoAQIdYxQgMQIsQ6RmgAQoRYxwgNQIgQ6xj/P6LZH4SvO75VAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"zsamples = zdist.sample(moon_n)\n",
"plt.plot(zsamples[:, 0], zsamples[:, 1], \".\", alpha=0.8)\n",
@@ -226,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {
"vscode": {
"languageId": "python"
@@ -249,13 +303,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<tfp.distributions.TransformedDistribution 'chain_of_shift_of_scaleMultivariateNormalDiag' batch_shape=[] event_shape=[2] dtype=float32>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"td = tfd.TransformedDistribution(zdist, bijector=b)\n",
"td"
@@ -263,13 +328,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"zsamples = td.sample(moon_n)\n",
"plt.plot(zsamples[:, 0], zsamples[:, 1], \".\", alpha=0.8)\n",
@@ -287,13 +365,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# make points for grid\n",
"zpoints = np.linspace(-4, 4, 150)\n",
@@ -321,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {
"vscode": {
"languageId": "python"
@@ -359,13 +450,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"zpoints = np.linspace(-4, 4, 150)\n",
"(\n",
@@ -396,7 +500,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {
"vscode": {
"languageId": "python"
@@ -436,13 +540,68 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2023-03-24 12:43:25.921725: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x15126c005e60 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2023-03-24 12:43:25.921762: I tensorflow/compiler/xla/service/service.cc:181] StreamExecutor device (0): Tesla V100-PCIE-16GB, Compute Capability 7.0\n",
+ "2023-03-24 12:43:25.929117: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2023-03-24 12:43:26.211499: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "313/313 [==============================] - 5s 4ms/step - loss: 1.4593\n",
+ "Epoch 2/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.4612\n",
+ "Epoch 3/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.2859\n",
+ "Epoch 4/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.3626\n",
+ "Epoch 5/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.3028\n",
+ "Epoch 6/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.1770\n",
+ "Epoch 7/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.6166\n",
+ "Epoch 8/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.3097\n",
+ "Epoch 9/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.2793\n",
+ "Epoch 10/10\n",
+ "313/313 [==============================] - 1s 4ms/step - loss: 1.1443\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"result = model.fit(x=data, y=np.zeros(moon_n), epochs=10, verbose=1)\n",
"plt.plot(result.history[\"loss\"])\n",
@@ -458,13 +617,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"zpoints = np.linspace(-2.5, 2.5, 200)\n",
"(\n",
@@ -491,13 +663,26 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {
"vscode": {
"languageId": "python"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
"zsamples = td.sample(moon_n)\n",
"plt.plot(zsamples[:, 0], zsamples[:, 1], \".\", alpha=0.2, markeredgewidth=0.0)\n",
diff --git a/BLcourse4/vae_mod.ipynb b/BLcourse4/vae_mod.ipynb
index 909fc0c2c016bfa55dc92b9a9a229724d6950c68..8842d60d743262519dc6387727f3358ffe5ff60d 100644
--- a/BLcourse4/vae_mod.ipynb
+++ b/BLcourse4/vae_mod.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -29,17 +29,14 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "2023-03-19 17:40:56.021384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 38278 MB memory: -> device: 0, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:03:00.0, compute capability: 8.0\n",
- "2023-03-19 17:40:56.023169: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 38278 MB memory: -> device: 1, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:44:00.0, compute capability: 8.0\n",
- "2023-03-19 17:40:56.024878: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 38278 MB memory: -> device: 2, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:84:00.0, compute capability: 8.0\n",
- "2023-03-19 17:40:56.026497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 38278 MB memory: -> device: 3, name: NVIDIA A100-SXM4-40GB, pci bus id: 0000:c4:00.0, compute capability: 8.0\n"
+ "2023-03-24 11:44:49.157237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14650 MB memory: -> device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:01:00.0, compute capability: 7.0\n"
]
}
],
@@ -73,7 +70,7 @@
"source": [
"## Define the encoder and decoder networks with *tf.keras.Sequential*\n",
"\n",
- "In this VAE example, use two s ConvNets for the encoder and decoder networks. In the literature, these networks are also referred to as inference/recognition and generative models respectively. Use `tf.keras.Sequential` to simplify implementation. Let $x$ and $z$ denote the observation and latent variable respectively in the following descriptions.\n",
+ "In this VAE example, use two ConvNets for the encoder and decoder networks. In the literature, these networks are also referred to as inference/recognition and generative models respectively. Use `tf.keras.Sequential` to simplify implementation. Let $x$ and $z$ denote the observation and latent variable respectively in the following descriptions.\n",
"\n",
"### Encoder network\n",
"This defines the approximate posterior distribution $q(z|x)$, which takes as input an observation and outputs a set of parameters for specifying the conditional distribution of the latent representation $z$. \n",
@@ -226,7 +223,7 @@
"* During each iteration, pass the image to the encoder to obtain a set of mean and log-variance parameters of the approximate posterior $q(z|x)$\n",
"* then apply the *reparameterization trick* to sample from $q(z|x)$\n",
"* Finally, pass the reparameterized samples to the decoder to obtain the logits of the generative distribution $p(x|z)$\n",
- "* Note: Since you use the dataset loaded by keras with 60k datapoints in the training set and 10k datapoints in the test set, our resulting ELBO on the test set is slightly higher than reported results in the literature which uses dynamic binarization of Larochelle's MNIST.\n",
+ "\n",
"\n",
"### Generating images\n",
"\n",
@@ -998,7 +995,7 @@
" print(l,\" loss\")\n",
" print ('Time for epoch {} is {} sec'.format(epoch + 1, time.time()-start))\n",
" \n",
- " generate_and_save_images(model, 0, test_sample)\n",
+ " generate_and_save_images(model, epoch, test_sample)\n",
"\n",
"num_examples_to_generate = 16\n",
"\n",