tf.nn.max_pool

Performs the max pooling on the input.

tf.nn.max_pool(
    input, ksize, strides, padding, data_format=None, name=None
)

input Tensor of rank N+2, of shape [batch_size] + input_spatial_shape + [num_channels] if data_format does not start with "NC" (default), or [batch_size, num_channels] + input_spatial_shape if data_format starts with "NC". Pooling happens over the spatial dimensions only.
ksize An int or list of ints that has length 1, N or N+2. The size of the window for each dimension of the input tensor.
strides An int or list of ints that has length 1, N or N+2. The stride of the sliding window for each dimension of the input tensor.
padding Either the string"SAME"or"VALID"indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is"NHWC", this should be in the form[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]. When explicit padding used and data_format is"NCHW", this should be in the form[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]. When using explicit padding, the size of the paddings cannot be greater than the sliding window size. </td> </tr><tr> <td>data_format</td> <td> A string. Specifies the channel dimension. For N=1 it can be either "NWC" (default) or "NCW", for N=2 it can be either "NHWC" (default) or "NCHW" and for N=3 either "NDHWC" (default) or "NCDHW". </td> </tr><tr> <td>name` Optional name for the operation.

A Tensor of format specified by data_format. The max pooled output tensor.