Repeats the rows and columns of the data by size[[0]]
and size[[1]]
respectively.
layer_upsampling_2d(object, size = c(2L, 2L), data_format = NULL,
interpolation = "nearest", batch_size = NULL, name = NULL,
trainable = NULL, weights = NULL)
Model or layer object
int, or list of 2 integers. The upsampling factors for rows and columns.
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".
A string, one of nearest
or bilinear
.
Note that CNTK does not support yet the bilinear
upscaling
and that with Theano, only size=(2, 2)
is possible.
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
4D tensor with shape:
If data_format
is "channels_last"
: (batch, rows, cols, channels)
If data_format
is "channels_first"
: (batch, channels, rows, cols)
4D tensor with shape:
If data_format
is "channels_last"
: (batch, upsampled_rows, upsampled_cols, channels)
If data_format
is "channels_first"
: (batch, channels, upsampled_rows, upsampled_cols)
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_cropping_3d
,
layer_depthwise_conv_2d
,
layer_separable_conv_1d
,
layer_separable_conv_2d
,
layer_upsampling_1d
,
layer_upsampling_3d
,
layer_zero_padding_1d
,
layer_zero_padding_2d
,
layer_zero_padding_3d