tf.keras.ops.adaptive_max_pool

Adaptive max pooling operation.

tf.keras.ops.adaptive_max_pool(
    inputs, output_size, data_format=None
)

Applies an adaptive max pooling operation that automatically computes the kernel size and stride to pool the input to the specified output_size. This operation is useful when you want a fixed output size regardless of input size, commonly used in models like ResNet for global feature extraction. Args: inputs: Tensor of rank 4. Input tensor of shape:

    - If `data_format="channels_last"`:
        `(batch_size, height, width, channels)`.
    - If `data_format="channels_first"`:
        `(batch_size, channels, height, width)`.
output_size: Integer or tuple/list of 2 integers, specifying the target
    output spatial dimensions `(output_height, output_width)`. If a
    single
    integer is provided, the same value is used for both dimensions.
data_format: string, either `"channels_last"` or `"channels_first"`.
    Defaults to the value found in your Keras config file at
    `~/.keras/keras.json`. If never set, defaults to `"channels_last"`.

A tensor of rank 4 representing the adaptive max pooled result.

Example:

x = np.random.rand(2, 64, 64, 3)
y = keras.ops.adaptive_max_pool(x, output_size=(32, 32))
y.shape
(2, 32, 32, 3)
 
# Works with any input size
x = np.random.rand(2, 100, 80, 3)
y = keras.ops.adaptive_max_pool(x, output_size=7)
y.shape
(2, 7, 7, 3)