tf.keras.ops.max_pool

Max pooling operation.

Main aliases

tf.keras.ops.nn.max_pool

tf.keras.ops.max_pool(
    inputs,
    pool_size,
    strides=None,
    padding=&#x27;valid',
    data_format=None
)

Args
`inputs`	Tensor of rank N+2. `inputs` has shape `(batch_size,) + inputs_spatial_shape + (num_channels,)` if `data_format="channels_last"`, or `(batch_size, num_channels) + inputs_spatial_shape` if `data_format="channels_first"`. Pooling happens over the spatial dimensions only.
`pool_size`	int or tuple/list of integers of size `len(inputs_spatial_shape)`, specifying the size of the pooling window for each spatial dimension of the input tensor. If `pool_size` is int, then every spatial dimension shares the same `pool_size`.
`strides`	int or tuple/list of integers of size `len(inputs_spatial_shape)`. The stride of the sliding window for each spatial dimension of the input tensor. If `strides` is int, then every spatial dimension shares the same `strides`.
`padding`	string, either `"valid"` or `"same"`. `"valid"` means no padding is applied, and `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input when `strides=1`.
`data_format`	A string, either `"channels_last"` or `"channels_first"`. `data_format` determines the ordering of the dimensions in the inputs. If `data_format="channels_last"`, `inputs` is of shape `(batch_size, ..., channels)` while if `data_format="channels_first"`, `inputs` is of shape `(batch_size, channels, ...)`.

Returns
A tensor of rank N+2, the result of the max pooling operation.

Args