tf.keras.layers.ZeroPadding2D | TensorFlow v2.16.1 (original) (raw)

Zero-padding layer for 2D input (e.g. picture).

tf.keras.layers.ZeroPadding2D(
    padding=(1, 1), data_format=None, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Pruning for on-device inference w/ XNNPACK	pix2pix: Image-to-image translation with a conditional GAN

This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.

Example:

input_shape = (1, 1, 2, 2) x = np.arange(np.prod(input_shape)).reshape(input_shape) x [[[[0 1] [2 3]]]] y = keras.layers.ZeroPadding2D(padding=1)(x) y [[[[0 0] [0 0] [0 0] [0 0]] [[0 0] [0 1] [2 3] [0 0]] [[0 0] [0 0] [0 0] [0 0]]]]

Args
padding	Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. If int: the same symmetric padding is applied to height and width. If tuple of 2 ints: interpreted as two different symmetric padding values for height and width:(symmetric_height_pad, symmetric_width_pad). If tuple of 2 tuples of 2 ints: interpreted as((top_pad, bottom_pad), (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, height, width, channels) while "channels_first"corresponds to inputs with shape(batch_size, channels, height, width). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists). Defaults to"channels_last".

Args

padding

Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. If int: the same symmetric padding is applied to height and width. If tuple of 2 ints: interpreted as two different symmetric padding values for height and width:(symmetric_height_pad, symmetric_width_pad). If tuple of 2 tuples of 2 ints: interpreted as((top_pad, bottom_pad), (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, height, width, channels) while "channels_first"corresponds to inputs with shape(batch_size, channels, height, width). 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
4D tensor with shape: If data_format is "channels_last":(batch_size, height, width, channels) If data_format is "channels_first":(batch_size, channels, height, width)

Output shape
4D tensor with shape: If data_format is "channels_last":(batch_size, padded_height, padded_width, channels) If data_format is "channels_first":(batch_size, channels, padded_height, padded_width)

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`

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@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`

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symbolic_call(
    *args, **kwargs
)