kears sparse_categorical_crossentropy参数说明

kears sparse_categorical_crossentropy说明：
from_logits=False，output为经过softmax输出的概率值。
from_logits=True，output为经过网络直接输出的 logits张量。

def sparse_categorical_crossentropy(target, output, from_logits=False):
    """Categorical crossentropy with integer targets.

    # Arguments
        target: An integer tensor.
        output: A tensor resulting from a softmax
            (unless `from_logits` is True, in which
            case `output` is expected to be the logits).
        from_logits: Boolean, whether `output` is the
            result of a softmax, or is a tensor of logits.

    # Returns
        Output tensor.
    """
    # Note: tf.nn.sparse_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()
    targets = cast(flatten(target), 'int64')
    logits = tf.reshape(output, [-1, int(output_shape[-1])])
    res = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=targets,
        logits=logits)
    if len(output_shape) >= 3:
        # if our output includes timestep dimension
        # or spatial dimensions we need to reshape
        return tf.reshape(res, tf.shape(output)[:-1])
    else:
        return res