cost.py 文件源码

def normalized_mean_square_error(output, target):
    """Return the TensorFlow expression of normalized mean-square-error of two distributions.

    Parameters
    ----------
    output : 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, w, h] or [batch_size, w, h, c].
    target : 2D, 3D or 4D tensor.
    """
    with tf.name_scope("mean_squared_error_loss"):
        if output.get_shape().ndims == 2:   # [batch_size, n_feature]
            nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=1))
            nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=1))
        elif output.get_shape().ndims == 3:   # [batch_size, w, h]
            nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=[1,2]))
            nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=[1,2]))
        elif output.get_shape().ndims == 4: # [batch_size, w, h, c]
            nmse_a = tf.sqrt(tf.reduce_sum(tf.squared_difference(output, target), axis=[1,2,3]))
            nmse_b = tf.sqrt(tf.reduce_sum(tf.square(target), axis=[1,2,3]))
        nmse = tf.reduce_mean(nmse_a / nmse_b)
    return nmse