diff --git a/src/modules/model_setup.py b/src/modules/model_setup.py
index 6cd0d6879333fee775a48b1a70a09c91796dbb24..925d83d733b8cb5715b515856e09e773737778a9 100644
--- a/src/modules/model_setup.py
+++ b/src/modules/model_setup.py
@@ -9,6 +9,7 @@ from keras.regularizers import l2
from keras.optimizers import Adam, SGD
import tensorflow as tf
import logging
+import os
from src.modules.run_environment import RunEnvironment
from src.helpers import l_p_loss, LearningRateDecay
@@ -25,6 +26,7 @@ class ModelSetup(RunEnvironment):
self.model = None
self.model_name = self.data_store.get("experiment_name", "general") + "model-best.h5"
self.scope = "general.model"
+ self._run()
def _run(self):
@@ -51,61 +53,63 @@ class ModelSetup(RunEnvironment):
optimizer = self.data_store.get("optimizer", self.scope)
loss = self.data_store.get("loss", self.scope)
self.model.compile(optimizer=optimizer, loss=loss, metrics=["mse", "mae"])
+ self.data_store.put("model", self.model, self.scope)
def _set_checkpoint(self):
- ModelCheckpoint(self.model_name, verbose=1, monitor='val_loss', save_best_only=True, mode='auto')
+ checkpoint = ModelCheckpoint(self.model_name, verbose=1, monitor='val_loss', save_best_only=True, mode='auto')
+ self.data_store.put("checkpoint", checkpoint, self.scope)
def load_weights(self):
- #try:
- logging.debug('reload weights...')
- self.model.load_weights(self.model_name)
- #except:
- # print('no weights to reload...')
+ try:
+ logging.debug('reload weights...')
+ self.model.load_weights(self.model_name)
+ except OSError:
+ logging.debug('no weights to reload...')
def build_model(self):
- args_list = ["activation", "window_size", "channels", "regularizer", "dropout_rate", "window_lead_time"]
+ args_list = ["activation", "window_history_size", "channels", "regularizer", "dropout_rate", "window_lead_time"]
args = self.data_store.create_args_dict(args_list, self.scope)
self.model = my_model(**args)
- def plot_model(self):
+ def plot_model(self): # pragma: no cover
with tf.device("/cpu:0"):
- file_name = self.data_store.get("experiment_name", "general") + "model.pdf"
+ path = self.data_store.get("experiment_path", "general")
+ name = self.data_store.get("experiment_name", "general") + "model.pdf"
+ file_name = os.path.join(path, name)
keras.utils.plot_model(self.model, to_file=file_name, show_shapes=True, show_layer_names=True)
def my_model_settings(self):
- scope = "general.model"
-
# channels
- X, y = self.data_store.get("generator", "general.train")[0]
+ X, _ = self.data_store.get("generator", "general.train")[0]
channels = X.shape[-1] # input variables
- self.data_store.put("channels", channels, scope)
+ self.data_store.put("channels", channels, self.scope)
# dropout
- self.data_store.put("dropout_rate", 0.1, scope)
+ self.data_store.put("dropout_rate", 0.1, self.scope)
# regularizer
- self.data_store.put("regularizer", l2(0.1), scope)
+ self.data_store.put("regularizer", l2(0.1), self.scope)
# learning rate
initial_lr = 1e-2
- self.data_store.put("initial_lr", initial_lr, scope)
+ self.data_store.put("initial_lr", initial_lr, self.scope)
optimizer = SGD(lr=initial_lr, momentum=0.9)
# optimizer=Adam(lr=initial_lr, amsgrad=True)
- self.data_store.put("optimizer", optimizer, scope)
- self.data_store.put("lr_decay", LearningRateDecay(base_lr=initial_lr, drop=.94, epochs_drop=10), scope)
+ self.data_store.put("optimizer", optimizer, self.scope)
+ self.data_store.put("lr_decay", LearningRateDecay(base_lr=initial_lr, drop=.94, epochs_drop=10), self.scope)
# learning settings
- self.data_store.put("epochs", 2, scope)
- self.data_store.put("batch_size", int(256), scope)
+ self.data_store.put("epochs", 2, self.scope)
+ self.data_store.put("batch_size", int(256), self.scope)
# activation
activation = layers.PReLU # ELU #LeakyReLU keras.activations.tanh #
- self.data_store.put("activation", activation, scope)
+ self.data_store.put("activation", activation, self.scope)
# set los
loss_all = my_loss()
- self.data_store.put("loss", loss_all, scope)
+ self.data_store.put("loss", loss_all, self.scope)
def my_loss():
@@ -115,7 +119,7 @@ def my_loss():
return loss_all
-def my_model(activation, window_size, channels, regularizer, dropout_rate, window_lead_time):
+def my_model(activation, window_history_size, channels, regularizer, dropout_rate, window_lead_time):
conv_settings_dict1 = {
'tower_1': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (3, 1), 'activation': activation},
@@ -147,7 +151,7 @@ def my_model(activation, window_size, channels, regularizer, dropout_rate, windo
##########################################
inception_model = InceptionModelBase()
- X_input = layers.Input(shape=(window_size + 1, 1, channels)) # add 1 to window_size to include current time step t0
+ X_input = layers.Input(shape=(window_history_size + 1, 1, channels)) # add 1 to window_size to include current time step t0
X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1, regularizer=regularizer,
batch_normalisation=True)
diff --git a/test/test_modules/test_model_setup.py b/test/test_modules/test_model_setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..d595463ff0f42818d634018fa8ddb03e5b54a2db
--- /dev/null
+++ b/test/test_modules/test_model_setup.py
@@ -0,0 +1,56 @@
+import logging
+import pytest
+import os
+import keras
+
+from src.modules.model_setup import ModelSetup
+from src.modules.run_environment import RunEnvironment
+from src.data_generator import DataGenerator
+
+
+class TestModelSetup:
+
+ @pytest.fixture
+ def setup(self):
+ obj = object.__new__(ModelSetup)
+ super(ModelSetup, obj).__init__()
+ obj.scope = "general.modeltest"
+ obj.model = None
+ yield obj
+ RunEnvironment().__del__()
+
+ @pytest.fixture
+ def gen(self):
+ return DataGenerator(os.path.join(os.path.dirname(__file__), 'data'), 'AIRBASE', 'DEBW107', ['o3', 'temp'],
+ 'datetime', 'variables', 'o3', statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'})
+
+ @pytest.fixture
+ def setup_with_gen(self, setup, gen):
+ setup.data_store.put("generator", gen, "general.train")
+ return setup
+
+ def test_set_checkpoint(self, setup):
+ assert "general.modeltest" not in setup.data_store.search_name("checkpoint")
+ setup.model_name = "TestName"
+ setup._set_checkpoint()
+ assert "general.modeltest" in setup.data_store.search_name("checkpoint")
+
+ def test_my_model_settings(self, setup_with_gen):
+ setup_with_gen.my_model_settings()
+ expected = {"channels", "dropout_rate", "regularizer", "initial_lr", "optimizer", "lr_decay", "epochs",
+ "batch_size", "activation", "loss"}
+ assert expected <= set(setup_with_gen.data_store.search_scope(setup_with_gen.scope, current_scope_only=True))
+
+ def test_build_model(self, setup_with_gen):
+ setup_with_gen.my_model_settings()
+ setup_with_gen.data_store.put("window_lead_time", 2, "general")
+ assert setup_with_gen.model is None
+ setup_with_gen.build_model()
+ assert isinstance(setup_with_gen.model, keras.Model)
+
+ def test_load_weights(self):
+ pass
+
+ def test_compile_model(self):
+ pass
+