tf.raw_ops.MaxPool3DGradGrad

Computes second-order gradients of the maxpooling function.

tf.raw_ops.MaxPool3DGradGrad(
    orig_input,
    orig_output,
    grad,
    ksize,
    strides,
    padding,
    data_format='NDHWC',
    name=None
)

orig_input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, half, uint32, uint64. The original input tensor.
orig_output A Tensor. Must have the same type as orig_input. The original output tensor.
grad A Tensor. Must have the same type as orig_input. 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).

A Tensor. Has the same type as orig_input.