#%%
x = load_image_data()
#%%
x = x.unbatch()
x = x.batch(batch_size=10)
shuffled_data = x.shuffle(buffer_size=SHUFFLE_BUFFER_SIZE)
test = shuffled_data.take(TEST_SIZE).repeat()
train = shuffled_data.skip(TEST_SIZE).repeat()
#%%
model = DnCNN(depth=17)
model.compile(optimizer=keras.optimizers.Adam(),
loss=dcnn_loss,
metrics=[psnr])
now = datetime.now()
tensorboard_callback = keras.callbacks.TensorBoard(
log_dir='logs\log_from_{}'.format(now.strftime("%Y-%m-%d_at_%H-%M-%S")),
histogram_freq=1)
model.fit(x=train,
steps_per_epoch=1000,
validation_data=test,
epochs=5,
validation_steps=50,
callbacks=[tensorboard_callback])
model.summary()
# nomal=True,
# fill_mode='constant')
# generator = train_datagen.flow_from_directory(file_path=train_file_path,
# data_dir=data_dir, data_suffix=data_suffix,
# label_dir=label_dir, label_suffix=label_suffix,
# target_size=target_shape, color_mode='grayscale',
# batch_size=batch_size, shuffle=True,
# loss_shape=None)
scheduler = LearningRateScheduler(lr_scheduler)
callbacks = [scheduler]
# ################### checkpoint saver#######################
checkpoint = ModelCheckpoint(filepath=os.path.join(save_path,
'checkpoint_weights.h5'),
save_weights_only=True) # .{epoch:d}
callbacks.append(checkpoint)
# model = srcnn(input_shape=input_shape, kernel_size=[3, 3])
model = DnCNN(input_shape=input_shape)
# model.load_weights('unet_optics_l2.h5')
model.compile(loss=mean_squared_error, optimizer='adadelta')
model.summary()
history = model.fit(input_data,
input_label,
batch_size=batch_size,
nb_epoch=epochs,
callbacks=callbacks,
verbose=1)
model.save_weights('DnCNN_l2_mnist_combinenoise200_noise.h5')