tf.keras.layers.MaxPool1D

Max pooling operation for 1D temporal data.

Inherits From: Layer, Operation

tf.keras.layers.MaxPool1D(
    pool_size=2,
    strides=None,
    padding='valid',
    data_format=None,
    name=None,
    **kwargs
)

Used in the notebooks

Used in the guide Used in the tutorials

Downsamples the input representation by taking the maximum value over a spatial window of size pool_size. The window is shifted by strides.

The resulting output when using the "valid" padding option has a shape of: output_shape = (input_shape - pool_size + 1) / strides).

The resulting output shape when using the "same" padding option is: output_shape = input_shape / strides

pool_size int, size of the max pooling window.
strides int or None. Specifies how much the pooling window moves for each pooling step. If None, it will default to pool_size.
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, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). 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": 3D tensor with shape (batch_size, steps, features).
  • If data_format="channels_first": 3D tensor with shape (batch_size, features, steps).

Output shape:

  • If data_format="channels_last": 3D tensor with shape (batch_size, downsampled_steps, features).
  • If data_format="channels_first": 3D tensor with shape (batch_size, features, downsampled_steps).

Examples:

strides=1 and padding="valid":

x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
   strides=1, padding="valid")
max_pool_1d(x)

strides=2 and padding="valid":

x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
   strides=2, padding="valid")
max_pool_1d(x)

strides=1 and padding="same":

x = np.array([1., 2., 3., 4., 5.])
x = np.reshape(x, [1, 5, 1])
max_pool_1d = keras.layers.MaxPooling1D(pool_size=2,
   strides=1, padding="same")
max_pool_1d(x)

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
)