tf.keras.layers.MaxPool2D | TensorFlow v2.16.1 (original) (raw)

tf.keras.layers.MaxPool2D

Stay organized with collections Save and categorize content based on your preferences.

Max pooling operation for 2D spatial data.

View aliases

Main aliases

tf.keras.layers.MaxPooling2D

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 Sparse weights using structural pruning	Image classification Data augmentation Load and preprocess images Custom training with tf.distribute.Strategy Convolutional Neural Network (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 theimage_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":

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

@classmethod from_config( config )

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

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

Returns
A layer instance.

`symbolic_call`

View source

symbolic_call(
    *args, **kwargs
)