tf.raw_ops.MaxPoolGrad

Computes gradients of the maxpooling function.

tf.raw_ops.MaxPoolGrad(
    orig_input,
    orig_output,
    grad,
    ksize,
    strides,
    padding,
    explicit_paddings=[],
    data_format='NHWC',
    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. 4-D. Gradients w.r.t. the output 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", "EXPLICIT". The type of padding algorithm to use.
explicit_paddings An optional list of ints. Defaults to [].
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).

A Tensor. Has the same type as orig_input.