update the hparameters for weatherbench model

video_prediction_tools/hparams/era5/weatherBench/model_hparams_template.json

 
 {
     "batch_size": 4,
-    "lr": 0.001,
+    "lr": 0.0001,
     "max_epochs":20,
     "context_frames":12,
     "loss_fun":"mse",
     "opt_var": "0",
-    "shuffle_on_val":true
+    "shuffle_on_val":true,
+    "filters": [64, 64, 64, 64, 2],
+    "kernels": [5, 5, 5, 5, 5]
 }
 
 

video_prediction_tools/model_modules/video_prediction/models/weatherBench3DCNN.py

@@ -24,14 +24,11 @@ class WeatherBenchModel(object):
         self.hparams = self.parse_hparams()
         self.learning_rate = self.hparams.lr
         self.filters = self.hparams.filters
-        self.kernels = self.hparams.kernes
+        self.kernels = self.hparams.kernels
         self.context_frames = self.hparams.context_frames
-        self.sequence_length = self.hparams.sequence_length
-        self.predict_frames = self.sequence_length- self.context_frames
         self.max_epochs = self.hparams.max_epochs
         self.loss_fun = self.hparams.loss_fun
         self.batch_size = self.hparams.batch_size
-        self.recon_weight = self.hparams.recon_weight
         self.outputs = {}
         self.total_loss = None
 
@@ -60,15 +57,13 @@ class WeatherBenchModel(object):
             """
         hparams = dict(
             context_frames =12,
-            sequence_length =24,
             max_epochs = 20,
             batch_size = 40,
             lr = 0.001,
             loss_fun = "mse",
             shuffle_on_val= True,
-            filter = 4,
-            kernels = 4,
-    
+            filter = [64, 64, 64, 64, 2],
+            kernels = [5, 5, 5, 5, 5]
         )
         return hparams
 
@@ -85,8 +80,10 @@ class WeatherBenchModel(object):
         x_hat = self.build_model(x, self.filters, self.kernels, dr=0)
         # Loss
         self.total_loss = l1_loss(x[...,0], x_hat[...,0])
+
         # Optimizer
-        self.train_op = tf.train.AdamOptimizer(learning_rate = self.learning_rate).minimize(self.total_loss, var_list=self.gen_vars)
+        self.train_op = tf.train.AdamOptimizer(
+            learning_rate = self.learning_rate).minimize(self.total_loss, global_step = self.global_step)
 
         # outputs
         self.outputs["total_loss"] = self.total_loss
@@ -111,7 +108,7 @@ class WeatherBenchModel(object):
 
 
 class PeriodicPadding2D(object):
-    def __init__(self, x, pad_width):
+    def __init__(self, pad_width):
 
         self.pad_width = pad_width
 
@@ -127,7 +124,6 @@ class PeriodicPadding2D(object):
         return inputs_padded
 
 
-
 class PeriodicConv2D(object):
 
     def __init__(self, filters, kernel_size, conv_kwargs={}):