tf.raw_ops.MaxPoolGradGrad

Computes second-order gradients of the maxpooling function.

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

Args:

  • 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. 4-D. Gradients of gradients w.r.t. the input of max_pool.
  • ksize: A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.
  • strides: A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.
  • padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
  • data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
  • name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as orig_input.