phone, batch_size, train_size, learning_rate, num_train_iters, \
w_content, w_color, w_texture, w_tv, \
dped_dir, vgg_dir, eval_step = utils.process_command_args(sys.argv)
Complex_args = utils.Complex_args()
np.random.seed(0)
# loading training and test data
print("Loading test data...")
test_data, test_answ = load_test_data(phone, dped_dir, PATCH_SIZE)
print("Test data was loaded\n")
print("Loading training data...")
train_data, train_answ = load_batch(phone, dped_dir, train_size, PATCH_SIZE)
print("Training data was loaded\n")
TEST_SIZE = test_data.shape[0]
num_test_batches = int(test_data.shape[0] / batch_size)
# defining system architecture
with tf.Graph().as_default(), tf.Session() as sess:
# placeholders for training data
K.set_session(sess)
phone_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
phone_image = tf.reshape(phone_, [-1, PATCH_HEIGHT, PATCH_WIDTH, 3])
dslr_ = tf.placeholder(tf.float32, [None, PATCH_SIZE])
# processing command arguments
phone, batch_size, train_size, starter_learning_rate, num_train_iters, \
w_content, w_color, w_gray, w_gradient, w_tv, w_laplacian, \
dped_dir, vgg_dir, eval_step, log_step, name = utils.process_command_args(sys.argv)
np.random.seed(0)
# loading training and test data
print("Loading test data...")
test_data, test_answ = load_test_data(phone, dped_dir, PATCH_SIZE)
print("Test data was loaded\n")
print("Loading training data...")
train_data, train_answ, num_of_image = load_batch(phone, dped_dir, train_size, PATCH_SIZE)
print("Training data was loaded\n")
print("Loading validation data...")
valid_data, valid_answ = load_valid_data(phone, dped_dir, PATCH_SIZE, num_of_image)
print("validation data was loaded\n")
TEST_SIZE = test_data.shape[0]
num_test_batches = int(test_data.shape[0]/batch_size)
VALID_SIZE = valid_data.shape[0]
num_valid_batches = int(valid_data.shape[0]/batch_size)
# defining system architecture
with tf.Graph().as_default(), tf.Session() as sess:
# placeholders for training data
