logging.basicConfig(level=logging.INFO) # Load up the training data _LOGGER.info('Loading training data') input_file = 'data_prepared/bach_goldberg_aria_10' # X_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims) X_train_freq = np.load(input_file + '_x.npy') # y_train is a tensor of size (num_train_examples, num_timesteps, num_frequency_dims) y_train_freq = np.load(input_file + '_y.npy') # X_mean is a matrix of size (num_frequency_dims,) containing the mean for each frequency dimension X_mean_freq = np.load(input_file + '_mean.npy') # X_var is a matrix of size (num_frequency_dims,) containing the variance for each frequency dimension X_var_freq = np.load(input_file + '_var.npy') _LOGGER.info('Finished loading training data') config = base_config.get_config() network = SRNetwork(config['network']) input_tensor = X_train_freq[0:2] max_value = np.max(input_tensor) input_tensor /= max_value input_tensor = (input_tensor + 1.0) / 2.0 epochs = config['global']['epochs'] output_ = [] output = mp.Queue() args = [input_tensor[i][15:16] for i in range(input_tensor.shape[0])]
def test_structure(self): conf = base_config.get_config() print conf['network']