|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/layers/pooling/max_pooling2d.py#L5-L109) | Max pooling operation for 2D spatial data. Inherits From: [`Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/Layer), [`Operation`](https://www.tensorflow.org/api_docs/python/tf/keras/Operation) #### View aliases **Main aliases** [`tf.keras.layers.MaxPooling2D`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D)
tf.keras.layers.MaxPool2D( pool_size=(2, 2), strides=None, padding='valid', data_format=None, name=None, **kwargs ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Effective Tensorflow 2](https://www.tensorflow.org/guide/effective_tf2) - [Sparse weights using structural pruning](https://www.tensorflow.org/model_optimization/guide/pruning/pruning_with_sparsity_2_by_4) | - [Image classification](https://www.tensorflow.org/tutorials/images/classification) - [Data augmentation](https://www.tensorflow.org/tutorials/images/data_augmentation) - [Load and preprocess images](https://www.tensorflow.org/tutorials/load_data/images) - [Custom training with tf.distribute.Strategy](https://www.tensorflow.org/tutorials/distribute/custom_training) - [Convolutional Neural Network (CNN)](https://www.tensorflow.org/tutorials/images/cnn) | Downsamples the input along its spatial dimensions (height and width) by taking the maximum value over an input window (of size defined by `pool_size`) for each channel of the input. The window is shifted by `strides` along each dimension. The resulting output when using the `"valid"` padding option has a spatial shape (number of rows or columns) of: `output_shape = math.floor((input_shape - pool_size) / strides) + 1` (when `input_shape >= pool_size`) The resulting output shape when using the `"same"` padding option is: `output_shape = math.floor((input_shape - 1) / strides) + 1`


| ## Args || |---|---| | `pool_size` | int or tuple of 2 integers, factors by which to downscale (dim1, dim2). If only one integer is specified, the same window length will be used for all dimensions. | | `strides` | int or tuple of 2 integers, or None. Strides values. If None, it will default to `pool_size`. If only one int is specified, the same stride size will be used for all dimensions. | | `padding` | string, either `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"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. | | `data_format` | string, either `"channels_last"` or `"channels_first"`. The ordering of the dimensions in the inputs. `"channels_last"` corresponds to inputs with shape `(batch, height, width, channels)` while `"channels_first"` corresponds to inputs with shape `(batch, channels, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be `"channels_last"`. |
#### Input shape: - If `data_format="channels_last"`: 4D tensor with shape `(batch_size, height, width, channels)`. - If `data_format="channels_first"`: 4D tensor with shape `(batch_size, channels, height, width)`. #### Output shape: - If `data_format="channels_last"`: 4D tensor with shape `(batch_size, pooled_height, pooled_width, channels)`. - If `data_format="channels_first"`: 4D tensor with shape `(batch_size, channels, pooled_height, pooled_width)`. #### Examples: `strides=(1, 1)` and `padding="valid"`: x = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]) x = np.reshape(x, [1, 3, 3, 1]) max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="valid") max_pool_2d(x) `strides=(2, 2)` and `padding="valid"`: x = np.array([[1., 2., 3., 4.], [5., 6., 7., 8.], [9., 10., 11., 12.]]) x = np.reshape(x, [1, 3, 4, 1]) max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding="valid") max_pool_2d(x) `stride=(1, 1)` and `padding="same"`: x = np.array([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]) x = np.reshape(x, [1, 3, 3, 1]) max_pool_2d = keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding="same") max_pool_2d(x)


| ## Attributes || |---|---| | `input` | Retrieves the input tensor(s) of a symbolic operation.
Only returns the tensor(s) corresponding to the *first time* the operation was called. | | `output` | Retrieves the output tensor(s) of a layer.
Only returns the tensor(s) corresponding to the *first time* the operation was called. |
## Methods ### `from_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/ops/operation.py#L322-L362) @classmethod from_config( config ) Creates an operation from its config. This method is the reverse of `get_config`, capable of instantiating the same operation from the config dictionary. > [!NOTE] > **Note:** If you override this method, you might receive a serialized dtype config, which is a `dict`. You can deserialize it as follows: if "dtype" in config and isinstance(config["dtype"], dict): policy = dtype_policies.deserialize(config["dtype"])


| Args || |---|---| | `config` | A Python dictionary, typically the output of `get_config`. |



| Returns || |---|---| | An operation instance. ||