print('Validation steps per epoch {}'.format(validation_steps_per_epoch))
model_file = 'model_combined_last_0.2_drop_batch_new_augmentation.h5'
# Initializing a KerasMoel instance
k_model = KerasModel(1,
keras_model.NVIDIA_ARCHITECTURE,
dropout=0.2,
batch_norm=BATCH_NORM,
model_file=model_file,
multivariant=MULTIVARIANT,
gray=GRAY,
load=False)
# Training the KerasModel model and getting the metrics
model_history = k_model.train_model_with_generator(train_generator,
train_steps_per_epoch,
EPOCHS,
validation_generator,
validation_steps_per_epoch,
save_model_filepath=model_file)
# model_history = k_model.train_learned_model_with_generator(train_generator,
# train_steps_per_epoch,
# EPOCHS,
# validation_generator,
# validation_steps_per_epoch,
# save_model_filepath='model_transfer_Inceptionv3.h5')
# Plotting the model Loss
utils.plot_loss(model_history=model_history)
# Track 1 layers visualization
# k_model = KerasModel(load=True, model_file='./models/model_modular_nvidia.h5')
# test_image = np.asarray(Image.open(
# './assets/Layer_visualization/Track1/center_2018_05_07_18_39_19_350.jpg'))
# # Flattening the Images after the convolutional steps
# model.add(Flatten())
# # Fist dense layer
# model.add(Dense(120))
# # Second dense layer
# model.add(Dense(84))
# # Logits layer
# model.add(Dense(1))
# # Defining the loss function and optimizer
# model.compile(loss='mse', optimizer='adam')
training_lenght = math.ceil((len(train_samples)*3*2) / BATCHSIZE)
validation_length = math.ceil((len(validation_samples)*3*2) / BATCHSIZE)
# print(len(list(train_generator)))
k_model = KerasModel(1, keras_model.LENET_ARCHITECTURE)
model_history = k_model.train_model_with_generator(train_generator,
training_lenght,
EPOCHS,
validation_generator,
validation_length,
save_model_filepath='model_modular.h5')
# model_history = model.fit_generator(train_generator,
# steps_per_epoch=training_lenght,
# validation_data=validation_generator,
# validation_steps=validation_length,
# epochs=EPOCHS, verbose=1)
#
# model.save('model.h5')
# model.save('model_track2.h5')
plot_loss(model_history=model_history)
