layer_cropping_3d {keras} | R Documentation

## 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

`object` | Model or layer object
`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.

### 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`, `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_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`

[Package keras version 2.2.4 Index]