Computes gradients of max pooling function.
tf.raw_ops.MaxPool3DGrad(
orig_input, orig_output, grad, ksize, strides, padding, data_format='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.
|
