filepath = "model/resnet_{epoch:03d}-{val_loss:.4f}.h5"
checkpoint = ModelCheckpoint(filepath,
save_best_only=False,
verbose=1,
mode='auto',
save_weights_only=True)
reducelr = ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=2,
verbose=1,
mode='min',
epsilon=0.0001,
cooldown=0,
min_lr=0.00000001)
es = EarlyStopping(monitor='val_loss', patience=3, verbose=1, mode='auto')
callbacks_list = [checkpoint, reducelr, es]
return model, callbacks_list
if __name__ == "__main__":
x_train, y_train, x_val, y_val, x_test = get_data(max_num_word=7400,
max_len_word=1200,
level='char')
model, callbacks_list = build_resnet(max_num_word=7400, max_len_word=1200)
model.fit(x_train,
y_train,
epochs=15,
batch_size=32,
validation_data=(x_val, y_val),
callbacks=callbacks_list)
def main():
name = sys.argv[1]
height = sys.argv[2]
width = sys.argv[3]
mode = sys.argv[4]
content_path = tf.keras.utils.get_file(
'ken.jpg',
'https://scontent.fzty2-1.fna.fbcdn.net/v/t1.0-9/69497958_2478425912438909_3450352180421197824_n.jpg?_nc_cat=101&_nc_ohc=V4z59bUbz1oAQlwF5rMLHtqypJHMG_t17mpiHmFlL-AKOhXrEHJnVc9qw&_nc_ht=scontent.fzty2-1.fna&oh=29ebb194584872745009fbe4e856a904&oe=5E834777'
)
style_path = tf.keras.utils.get_file(
's.jpg',
'https://iheartintelligence.com/wp-content/uploads/2015/09/THE-STARRY-NIGHT.jpg'
)
content_path_1 = tf.keras.utils.get_file(
's.jpg',
'https://iheartintelligence.com/wp-content/uploads/2015/09/THE-STARRY-NIGHT.jpg'
)
content_image, style_image, content_image_1 = get_images(
content_path, style_path, content_path_1, height, width)
dataset = get_data('./data_1', 4)
content_layers = ['block2_conv2']
style_layers = [
'block1_conv1', 'block2_conv2', 'block3_conv3', 'block4_conv3'
]
content_layers_1 = ['block5_conv2']
num_content_layers = len(content_layers)
num_style_layers = len(style_layers)
extractor = MergeModel(style_layers, content_layers, content_layers_1,
name)
style_targets = extractor.style_content(style_image)[1]
checkpoint_dir = './checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(extractor=extractor)
manager = tf.train.CheckpointManager(checkpoint,
checkpoint_dir,
max_to_keep=3)
if not os.path.exists('./out_dir'):
os.makedirs('./out_dir')
image = tf.constant(content_image)
if (mode == "test"):
checkpoint.restore(manager.latest_checkpoint)
im = ((extractor.get_image(image).numpy()) * 255)[0]
tensor = np.array(im, dtype=np.uint8)
s = './out_dir' + '/' + 'test' + '.png'
imwrite(s, im)
return
if (mode == "train"):
epochs = 3
steps_per_epoch = 8000
for i in range(epochs):
for j in range(steps_per_epoch):
print(i, "and", j)
content_targets_1 = extractor.style_content(
tf.constant(dataset[j][0], dtype='float32'))[0]
content_targets = extractor.style_content(
tf.constant(dataset[j][0], dtype='float32'))[0]
train(image, extractor, style_targets, content_targets,
num_style_layers, num_content_layers, content_targets_1)
if (j % 400 == 0):
im = ((extractor.get_image(image).numpy()) * 255)[0]
tensor = np.array(im, dtype=np.uint8)
s = './out_dir' + '/' + str(j) + '.png'
imwrite(s, im)
manager.save()
print("==saving progress===")
im = ((extractor.get_image(image).numpy()) * 255)[0]
tensor = np.array(im, dtype=np.uint8)
s = './out_dir' + '/' + str(i) + '.png'
imwrite(s, im)
manager.save()
print("==saving progress===")