Tensorflow Argmax："axis" 和 "dimension" 参数有什么区别？

Question

当前版本的 Tensorflow Argmax 未指定 "axis" 和 "dimension" 参数之间的区别。这是official manual中给出的唯一信息：

tf.argmax(input, axis=None, name=None, dimension=None) {#argmax}

Returns the index with the largest value across axes of a tensor.

Args:

input: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.

axis: A Tensor. Must be one of the following types: int32, int64. int32, 0 <= axis < rank(input). Describes which axis of the input Tensor to reduce across. For vectors, use axis = 0.

name: A name for the operation (optional).

Returns:

A Tensor of type int64.

有人可以澄清一下吗？哪一个才是实际降维的？

Answer 1

TensorFlow 正在过渡到使用 axis 而不是即将弃用的 dimension：https://www.tensorflow.org/install/migration

Tensorflow Argmax："axis" 和 "dimension" 参数有什么区别？

Tensorflow Argmax: What's the difference between "axis" and "dimension" parameter?

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