tf.compat.v1.nn.max_pool_with

Calculate the mean and variance of x.

tf.compat.v1.nn.moments(
    x, axes, shift=None, name=None, keep_dims=None, keepdims=None
)

The mean and variance are calculated by aggregating the contents of x across axes. If x is 1-D and axes = [0] this is just the mean and variance of a vector.

When using these moments for batch normalization (see tf.nn.batch_normalization):

for so-called "global normalization", used with convolutional filters with shape [batch, height, width, depth], pass axes=[0, 1, 2].
for simple batch normalization pass axes=[0] (batch only).

Args
`x`	A `Tensor`.
`axes`	Array of ints. Axes along which to compute mean and variance.
`shift`	Not used in the current implementation
`name`	Name used to scope the operations that compute the moments.
`keep_dims`	produce moments with the same dimensionality as the input.
`keepdims`	Alias to keep_dims.

Returns
Two `Tensor` objects: `mean` and `variance`.

TensorFlow

tf.compat / v1 / v1.nn.max_pool_with_argmax

Args

Returns

TensorFlow

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tf.compat / v1 / v1.nn.max_pool_with_argmax

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