tf.compat.v1.layers.max_pooling2d
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Max pooling layer for 2D inputs (e.g. images).

tf.compat.v1.layers.max_pooling2d(
    inputs,
    pool_size,
    strides,
    padding='valid',
    data_format='channels_last',
    name=None
)

Migrate to TF2

This API is a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is tf.keras.layers.MaxPooling2D.

None of the supported arguments have changed name.

Before:

 y = tf.compat.v1.layers.max_pooling2d(x, pool_size=2, strides=2)

After:

To migrate code using TF1 functional layers use the Keras Functional API:

 x = tf.keras.Input((28, 28, 1))
 y = tf.keras.layers.MaxPooling2D(pool_size=2, strides=2)(x)
 model = tf.keras.Model(x, y)

Args
`inputs`	The tensor over which to pool. Must have rank 4.
`pool_size`	An integer or tuple/list of 2 integers: (pool_height, pool_width) specifying the size of the pooling window. Can be a single integer to specify the same value for all spatial dimensions.
`strides`	An integer or tuple/list of 2 integers, specifying the strides of the pooling operation. Can be a single integer to specify the same value for all spatial dimensions.
`padding`	A string. The padding method, either 'valid' or 'same'. Case-insensitive.
`data_format`	A string. The ordering of the dimensions in the inputs. `channels_last` (default) and `channels_first` are supported. `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`.
`name`	A string, the name of the layer.

Returns
Output tensor.

Raises
`ValueError`	if eager execution is enabled.