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']
