def add_model(self, model, x_train, y_train, x_test, y_test):
"""add one model while will be trained to history list
Returns:
History object.
"""
model.compile(loss=categorical_crossentropy,
optimizer=Adadelta(),
metrics=['accuracy'])
if self.verbose:
model.summary()
ModelTrainer(model, x_train, y_train, x_test, y_test,
self.verbose).train_model()
loss, accuracy = model.evaluate(x_test, y_test, verbose=self.verbose)
model.save(os.path.join(self.path, str(self.model_count) + '.h5'))
ret = {
'model_id': self.model_count,
'loss': loss,
'accuracy': accuracy
}
self.history.append(ret)
self.history_configs.append(extract_config(model))
self.model_count += 1
return ret
def _remove_duplicate(self, models):
"""Remove the duplicate in the history_models"""
ans = []
for model_a in models:
model_a_config = extract_config(model_a)
if model_a_config not in self.history_configs:
ans.append(model_a)
return ans
def add_model(self, model, x_train, y_train, x_test, y_test):
"""add one model while will be trained to history list
Returns:
History object.
"""
loss, accuracy = ModelTrainer(model, x_train, y_train, x_test, y_test,
False).train_model(**self.trainer_args)
accuracy += 0.005 * len(
Graph(model, False).extract_descriptor().skip_connections)
accuracy = min(accuracy, 1)
model.save(os.path.join(self.path, str(self.model_count) + '.h5'))
plot_model(model,
to_file=os.path.join(self.path,
str(self.model_count) + '.png'),
show_shapes=True)
model_id = self.model_count
ret = {'model_id': model_id, 'loss': loss, 'accuracy': accuracy}
self.history.append(ret)
self.history_configs.append(extract_config(model))
self.model_count += 1
self.descriptors[Graph(model, False).extract_descriptor()] = True
# Update best_model text file
if model_id == self.get_best_model_id():
file = open(os.path.join(self.path, 'best_model.txt'), 'w')
file.write('best model: ' + str(model_id))
file.close()
if self.verbose:
print('Model ID:', model_id)
print('Loss:', loss)
print('Accuracy', accuracy)
return ret