def softmax(logits, axis=None, name=None, dim=None):
This function performs the equivalent of
softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)
logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension.
name: A name for the operation (optional).
dim: Deprecated alias for `axis`.
Returns: A `Tensor`. Has the same type and shape as `logits`.
通过Softmax回归，将logistic的预测二分类的概率的问题推广到n分类的概率的问题
