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