layer_upsampling_2d {keras3}

Upsampling layer for 2D inputs.

Description

The implementation uses interpolative resizing, given the resize method (specified by the interpolation argument). Use interpolation=nearest to repeat the rows and columns of the data.

Usage

layer_upsampling_2d(
  object,
  size = list(2L, 2L),
  data_format = NULL,
  interpolation = "nearest",
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

size

Int, or list of 2 integers. The upsampling factors for rows and columns.

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_size, height, width, channels)⁠ while "channels_first" corresponds to inputs with shape ⁠(batch_size, channels, height, width)⁠. When unspecified, uses image_data_format value found in your Keras config file at ⁠~/.keras/keras.json⁠ (if exists) else "channels_last". Defaults to "channels_last".

interpolation

A string, one of "bicubic", "bilinear", "lanczos3", "lanczos5", "nearest".

...

For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

• a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.

• a keras_input(), then the output tensor from calling layer(input) is returned.

• NULL or missing, then a Layer instance is returned.

Example

input_shape <- c(2, 2, 1, 3)
x <- op_reshape(seq_len(prod(input_shape)), input_shape)
print(x)

## tf.Tensor(
## [[[[ 1  2  3]]
##
##   [[ 4  5  6]]]
##
##
##  [[[ 7  8  9]]
##
##   [[10 11 12]]]], shape=(2, 2, 1, 3), dtype=int32)

y <- layer_upsampling_2d(x, size = c(1, 2))
print(y)

## tf.Tensor(
## [[[[ 1  2  3]
##    [ 1  2  3]]
##
##   [[ 4  5  6]
##    [ 4  5  6]]]
##
##
##  [[[ 7  8  9]
##    [ 7  8  9]]
##
##   [[10 11 12]
##    [10 11 12]]]], shape=(2, 2, 2, 3), dtype=int32)

Input Shape

4D tensor with shape:

• If data_format is "channels_last": ⁠(batch_size, rows, cols, channels)⁠

• If data_format is "channels_first": ⁠(batch_size, channels, rows, cols)⁠

Output Shape

4D tensor with shape:

• If data_format is "channels_last": ⁠(batch_size, upsampled_rows, upsampled_cols, channels)⁠

• If data_format is "channels_first": ⁠(batch_size, channels, upsampled_rows, upsampled_cols)⁠

See Also

• https://keras.io/api/layers/reshaping_layers/up_sampling2d#upsampling2d-class

Other reshaping layers:
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_flatten()
layer_permute()
layer_repeat_vector()
layer_reshape()
layer_upsampling_1d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()