cnn_lstm_memory_normalization_model.py 文件源码

def layer_normalize(self, input_raw, epsilon=1e-8):
    feature_dim = len(input_raw.get_shape()) - 1
    mean_input = tf.reduce_mean(input_raw, axis=feature_dim, keep_dims=True)
    std_input = tf.sqrt(tf.reduce_mean(tf.square(input_raw-mean_input), axis=feature_dim, keep_dims=True))
    std_input = tf.maximum(std_input, epsilon)
    output = (input_raw - mean_input) / std_input
    return output