tf.raw_ops.MaxPool3DGrad

Computes gradients of 3D max pooling function.

Compat aliases for migration

tf.compat.v1.raw_ops.MaxPool3DGrad

tf.raw_ops.MaxPool3DGrad(
    orig_input, orig_output, grad, ksize, strides, padding,
    data_format=&#x27;NDHWC', name=None
)

Args
`orig_input`	A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. The original input tensor.
`orig_output`	A `Tensor`. Must have the same type as `orig_input`. The original output tensor.
`grad`	A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. Output backprop of shape `[batch, depth, rows, cols, channels]`.
`ksize`	A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`.
`strides`	A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`.
`padding`	A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use.
`data_format`	An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `grad`.

Args