def train():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_gtruth_train = load_train_data()
print('-'*30)
print('Loading and preprocessing validation data...')
print('-'*30)
imgs_val, imgs_gtruth_val = load_validatation_data()
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
# create a model
model = unet_model_3d(input_shape=config["input_shape"],
depth=config["depth"],
pool_size=config["pool_size"],
n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
deconvolution=config["deconvolution"])
model.summary()
print('-'*30)
print('Fitting model...')
print('-'*30)
#============================================================================
print('training starting..')
log_filename = 'outputs/' + image_type +'_model_train.csv'
csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
# early_stopping = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, 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.append(ReduceLROnPlateau(factor=config["learning_rate_drop"], patience=config["patience"],
verbose=True))
callbacks_list.append(EarlyStopping(verbose=True, patience=config["early_stop"]))
#============================================================================
hist = model.fit(imgs_train, imgs_gtruth_train, batch_size=config["batch_size"], nb_epoch=config["n_epochs"], verbose=1, validation_data=(imgs_val,imgs_gtruth_val), shuffle=True, callbacks=callbacks_list) # validation_split=0.2,
model_name = 'outputs/' + image_type + '_model_last'
model.save(model_name) # creates a HDF5 file 'my_model.h5'
def train_and_predict():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_gtruth_train = load_train_data()
imgs_train = np.transpose(imgs_train, (0, 4, 1, 2, 3))
imgs_gtruth_train = np.transpose(imgs_gtruth_train, (0, 4, 1, 2, 3))
print('-'*30)
print('Loading and preprocessing validation data...')
print('-'*30)
imgs_val, imgs_gtruth_val = load_validatation_data()
imgs_val = np.transpose(imgs_val, (0, 4, 1, 2, 3))
imgs_gtruth_val = np.transpose(imgs_gtruth_val, (0, 4, 1, 2, 3))
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
# create a model
model = isensee2017_model(input_shape=config["input_shape"], n_labels=config["n_labels"],
initial_learning_rate=config["initial_learning_rate"],
n_base_filters=config["n_base_filters"],loss_function=dice_coef_loss)
model.summary()
#summarize layers
#print(model.summary())
# plot graph
#plot_model(model, to_file='3d_unet.png')
print('-'*30)
print('Fitting model...')
print('-'*30)
#============================================================================
print('training starting..')
log_filename = 'outputs/' + image_type +'_model_train.csv'
csv_log = callbacks.CSVLogger(log_filename, separator=',', append=True)
# early_stopping = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, 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.append(ReduceLROnPlateau(factor=config["learning_rate_drop"], patience=config["patience"],
verbose=True))
callbacks_list.append(EarlyStopping(verbose=True, patience=config["early_stop"]))
#============================================================================
hist = model.fit(imgs_train, imgs_gtruth_train, batch_size=config["batch_size"], nb_epoch=config["n_epochs"], verbose=1, validation_data=(imgs_val,imgs_gtruth_val), shuffle=True, callbacks=callbacks_list) # validation_split=0.2,
model_name = 'outputs/' + image_type + '_model_last'
model.save(model_name) # creates a HDF5 file 'my_model.h5'