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
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 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. |
Section
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
4D tensor with shape:
If
data_format
is"channels_last"
:(batch, cropped_rows, cropped_cols, channels)
If
data_format
is"channels_first"
:(batch, channels, cropped_rows, cropped_cols)
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_3d()
, 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()