layer_zero_padding_2d
Zero-padding layer for 2D input (e.g. picture).
Description
This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.
Usage
layer_zero_padding_2d(
object,
padding = 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. |
| padding | int, or list of 2 ints, or list of 2 lists of 2 ints. - If int: the same symmetric padding is applied to width and height. - If list of 2 ints: interpreted as two different symmetric padding values for height and width: (symmetric_height_pad, symmetric_width_pad). - If list of 2 lists of 2 ints: interpreted as ((top_pad, bottom_pad), (left_pad, right_pad)) |
| 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_formatis"channels_last":(batch, rows, cols, channels)If
data_formatis"channels_first":(batch, channels, rows, cols)
Output shape
4D tensor with shape:
If
data_formatis"channels_last":(batch, padded_rows, padded_cols, channels)If
data_formatis"channels_first":(batch, channels, padded_rows, padded_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_2d(), 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_3d()