tf.nn.max_pool
Performs the max pooling on the input.
tf.nn.max_pool(
input, ksize, strides, padding, data_format=None, name=None
)
Args |
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. |
Returns |
A Tensor of format specified by data_format . The max pooled output tensor. |