def train():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train_t1, imgs_train_t2, imgs_gtruth_train = load_train_data()
print('-'*30)
print('Loading and preprocessing validation data...')
print('-'*30)
imgs_val_t1, imgs_val_t2, imgs_gtruth_val = load_validatation_data()
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
if unet_model_type == 'default':
model = get_unet_default()
elif unet_model_type == 'reduced':
model = get_unet_reduced()
elif unet_model_type == 'extended':
model = get_unet_extended()
model.summary()
print('-'*30)
print('Fitting model...')
print('-'*30)
#============================================================================
print('training starting..')
#2d_whole_image_model_train.csv
log_filename = 'outputs_wnet_depthwise/' + image_type +'_model_train.csv'
#Callback that streams epoch results to a csv file.
csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
early_stopping = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=PATIENCE, verbose=0, mode='min')
#checkpoint_filepath = 'outputs/' + image_type +"_best_weight_model_{epoch:03d}_{val_loss:.4f}.hdf5"
checkpoint_filepath = 'outputs_wnet_depthwise/' + 'weights.h5'
checkpoint = callbacks.ModelCheckpoint(checkpoint_filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
#callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log, early_stopping, checkpoint]
#============================================================================
hist = model.fit([ imgs_train_t1, imgs_train_t2], imgs_gtruth_train, batch_size=batch_size, nb_epoch=nb_epochs, verbose=1, validation_data=([imgs_val_t1, imgs_val_t2],imgs_gtruth_val), shuffle=True, callbacks=callbacks_list) # validation_split=0.2,
model_name = 'outputs_wnet_depthwise/' + image_type + '_model_last'
model.save(model_name) # creates a HDF5 file 'my_model.h5'
def train():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
print('-'*30)
print('Loading and preprocessing validation data...')
print('-'*30)
imgs_train, imgs_gtruth_train ,imgs_val, imgs_gtruth_val= load_train_data()
print('train shape:',imgs_train.shape)
print('val shape:',imgs_val.shape)
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
if unet_model_type == 'default':
model = get_unet_default()
elif unet_model_type == 'reduced':
model = get_unet_reduced()
elif unet_model_type == 'extended':
model = get_unet_extended()
model.summary()
print('-'*30)
print('Fitting model...')
print('-'*30)
#============================================================================
print('training starting..')
log_filename = 'outputs/' + image_type +'_model_train.csv'
#Callback that streams epoch results to a csv file.
csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
early_stopping = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=PATIENCE, verbose=0, mode='min')
#checkpoint_filepath = 'outputs/' + image_type +"_best_weight_model_{epoch:03d}_{val_loss:.4f}.hdf5"
checkpoint_filepath = 'outputs/' + 'weights.h5'
checkpoint = callbacks.ModelCheckpoint(checkpoint_filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
#callbacks_list = [csv_log, checkpoint]
callbacks_list = [csv_log, early_stopping, checkpoint]
#============================================================================
history = model.fit(imgs_train, imgs_gtruth_train, batch_size=batch_size, nb_epoch=nb_epochs, verbose=1, validation_data=(imgs_val,imgs_gtruth_val), shuffle=True, callbacks=callbacks_list) # validation_split=0.2,
print('Saving model')
model_name = 'outputs/' + image_type + '_model_last'
model.save(model_name) # creates a HDF5 file 'my_model.h5'
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()