def train_model(self, x_train, y_train, x_val, y_val):
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model.summary()
# File used to save model checkpoints
model_filename = 'chargram-cnn.{0:03d}.hdf5'
if self.last_finished_epoch > 0:
self.model = load_model(
model_filename.format(self.last_finished_epoch - 1))
elif self.initial_weights_file is not None:
self.model = load_model(self.initial_weights_file)
print('Fitting model')
self.model.fit(x_train,
y_train,
batch_size=self.batch_size,
epochs=self.epochs,
validation_data=(x_val, y_val),
callbacks=[
keras_utils.ModelSaveCallback(model_filename),
keras_utils.TqdmProgressCallback()
],
verbose=1,
initial_epoch=self.last_finished_epoch or 0)
# from keras.models import load_model
# s = reset_tf_session()
# last_finished_epoch = 7
# model = load_model(model_filename.format(last_finished_epoch))
# fit model
model.fit(
x_train2,
y_train2, # prepared data
batch_size=BATCH_SIZE,
epochs=EPOCHS,
callbacks=[
keras.callbacks.LearningRateScheduler(lr_scheduler),
LrHistory(),
keras_utils.TqdmProgressCallback(),
keras_utils.ModelSaveCallback(model_filename)
],
validation_data=(x_test2, y_test2),
shuffle=True,
verbose=0,
initial_epoch=last_finished_epoch or 0)
# save weights to file
model.save_weights("weights.h5")
# load weights from file (can call without model.fit)
model.load_weights("weights.h5")
"""# Evaluate model"""
# make test predictions
y_pred_test = model.predict_proba(x_test2)