tf.keras.layers.ZeroPadding1D

Zero-padding layer for 1D input (e.g. temporal sequence).

Inherits From: Layer, Operation

tf.keras.layers.ZeroPadding1D(
    padding=1, data_format=None, **kwargs
)

Example:

input_shape = (2, 2, 3)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
x
[[[ 0  1  2]
  [ 3  4  5]]
 [[ 6  7  8]
  [ 9 10 11]]]
y = keras.layers.ZeroPadding1D(padding=2)(x)
y
[[[ 0  0  0]
  [ 0  0  0]
  [ 0  1  2]
  [ 3  4  5]
  [ 0  0  0]
  [ 0  0  0]]
 [[ 0  0  0]
  [ 0  0  0]
  [ 6  7  8]
  [ 9 10 11]
  [ 0  0  0]
  [ 0  0  0]]]

Args
padding	Int, or tuple of int (length 2), or dictionary. If int: how many zeros to add at the beginning and end of the padding dimension (axis 1). If tuple of 2 ints: how many zeros to add at the beginning and the end of the padding dimension ((left_pad, right_pad)).
data_format	A string, one of "channels_last" (default) or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch_size, axis_to_pad, channels) while "channels_first" corresponds to inputs with shape (batch_size, channels, axis_to_pad). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists). Defaults to "channels_last".

Input shape
3D tensor with shape: If data_format is "channels_last": (batch_size, axis_to_pad, features) If data_format is "channels_first": (batch_size, features, axis_to_pad)

Output shape
3D tensor with shape: If data_format is "channels_last": (batch_size, padded_axis, features) If data_format is "channels_first": (batch_size, features, padded_axis)

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 an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

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.