tensorflow_backend.py 文件源码

def sparse_categorical_crossentropy(output, target, from_logits=False):
    '''Categorical crossentropy between an output tensor
    and a target tensor, where the target is an integer tensor.
    '''
    # Note: tf.nn.softmax_cross_entropy_with_logits
    # expects logits, Keras expects probabilities.
    if not from_logits:
        epsilon = _to_tensor(_EPSILON, output.dtype.base_dtype)
        output = tf.clip_by_value(output, epsilon, 1 - epsilon)
        output = tf.log(output)

    output_shape = output.get_shape()
    res = tf.nn.sparse_softmax_cross_entropy_with_logits(
        tf.reshape(output, [-1, int(output_shape[-1])]),
        cast(flatten(target), 'int64'))
    if len(output_shape) == 3:
        # if our output includes timesteps we need to reshape
        return tf.reshape(res, [-1, int(output_shape[-2])])
    else:
        return res