tf.reduce_sum()_tf.reduce_mean()_tf.reduce

本文详细解析了TensorFlow中的Reduce函数，包括tf.reduce_sum、tf.reduce_max和tf.reduce_mean等，介绍了它们如何用于压缩求和、求最大值和求平均值的操作，以及参数input_tensor、reduction_indices和keepdims的具体作用。

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tf.reduce_sum(input_tensor,axis=None,keepdims=None,name=None,reduction_indices=None,keep_dims=None）

Args:

input_tensor: The tensor to reduce. Should have numeric type. #输入
axis: The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range （rank(input_tensor), rank(input_tensor)).#取0第一维，取1第二维，取-1最后一维
keepdims: If true, retains reduced dimensions with length 1.#按照原来的维度
name: A name for the operation (optional).
reduction_indices: The old (deprecated) name for axis.#axis的原来的名字
keep_dims: Deprecated alias for keepdims.
reduce_sum应该理解为压缩求和用于降维

求最大值tf.reduce_max(input_tensor, reduction_indices=None, keep_dims=False, name=None)

求平均值tf.reduce_mean(input_tensor, reduction_indices=None, keep_dims=False, name=None)

参数1--input_tensor:待求值的tensor。

参数2--reduction_indices:在哪一维上求解。

参数（3）（4）可忽略