layer_cropping_2d: Cropping layer for 2D input (e.g. picture).

Description

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

Usage

layer_cropping_2d(
  object,
  cropping = list(c(0L, 0L), c(0L, 0L)),
  data_format = NULL,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.

cropping

int, or list of 2 ints, or list of 2 lists of 2 ints.

If int: the same symmetric cropping is applied to width and height.
If list of 2 ints: interpreted as two different symmetric cropping values for height and width: (symmetric_height_crop, symmetric_width_crop).
If list of 2 lists 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, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). 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".

batch_size

Fixed batch size for layer

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

weights

Initial weights for layer.

Input shape

4D tensor with shape:

If data_format is "channels_last": (batch, rows, cols, channels)
If data_format is "channels_first": (batch, channels, rows, cols)

Output shape