R/layers-convolutional.R

layer_cropping_3d

Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Description

Cropping layer for 3D data (e.g. spatial or spatio-temporal).

Usage

 
layer_cropping_3d( 
  object, 
  cropping = list(c(1L, 1L), c(1L, 1L), c(1L, 1L)), 
  data_format = NULL, 
  batch_size = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 
)

Arguments

Arguments Description
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 3 ints, or list of 3 lists of 2 ints.
- If int: the same symmetric cropping is applied to depth, height, and width.
- If list of 3 ints: interpreted as two different symmetric cropping values for depth, height, and width: (symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop).
- If list of 3 list of 2 ints: interpreted as ((left_dim1_crop, right_dim1_crop), (left_dim2_crop, right_dim2_crop), (left_dim3_crop, right_dim3_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, spatial_dim1, spatial_dim2, spatial_dim3, channels) while channels_first corresponds to inputs with shape (batch, channels, spatial_dim1, spatial_dim2, spatial_dim3). 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.

Section

Input shape

5D tensor with shape:

  • If data_format is "channels_last": (batch, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop, depth)

  • If data_format is "channels_first": (batch, depth, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)

Output shape

5D tensor with shape:

  • If data_format is "channels_last": (batch, first_cropped_axis, second_cropped_axis, third_cropped_axis, depth)

  • If data_format is "channels_first": (batch, depth, first_cropped_axis, second_cropped_axis, third_cropped_axis)

See Also

Other convolutional layers: layer_conv_1d_transpose(), layer_conv_1d(), layer_conv_2d_transpose(), layer_conv_2d(), layer_conv_3d_transpose(), layer_conv_3d(), layer_conv_lstm_2d(), layer_cropping_1d(), layer_cropping_2d(), layer_depthwise_conv_1d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()