tf.keras.layers.Cropping2D

Cropping layer for 2D input (e.g. picture).

Inherits From: Layer, Operation

tf.keras.layers.Cropping2D(
    cropping=((0, 0), (0, 0)), data_format=None, **kwargs
)

It crops along spatial dimensions, i.e. height and width.

Example:

input_shape = (2, 28, 28, 3)
x = np.arange(np.prod(input_shape)).reshape(input_shape)
y = keras.layers.Cropping2D(cropping=((2, 2), (4, 4)))(x)
y.shape
(2, 24, 20, 3)

Args
`cropping`	Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints. If int: the same symmetric cropping is applied to height and width. If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: `(symmetric_height_crop, symmetric_width_crop)`. If tuple of 2 tuples of 2 ints: interpreted as `((top_crop, bottom_crop), (left_crop, right_crop))`.
`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

cropping

Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.

If int: the same symmetric cropping is applied to height and width.
If tuple of 2 ints: interpreted as two different symmetric cropping values for height and width: (symmetric_height_crop, symmetric_width_crop).
If tuple of 2 tuples of 2 ints: interpreted as ((top_crop, bottom_crop), (left_crop, right_crop)).

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)`

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, cropped_height, cropped_width, channels)` If `data_format` is `"channels_first"`: `(batch_size, channels, cropped_height, cropped_width)`

Output shape

4D tensor with shape:

If data_format is "channels_last": (batch_size, cropped_height, cropped_width, channels)
If data_format is "channels_first": (batch_size, channels, cropped_height, cropped_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.

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
)