The need for transposed convolutions generally arise from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.
layer_conv_1d_transpose(
object,
filters,
kernel_size,
strides = 1L,
padding = "valid",
data_format = NULL,
dilation_rate = 1L,
activation = NULL,
use_bias = TRUE,
kernel_initializer = "glorot_uniform",
bias_initializer = "zeros",
kernel_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
bias_constraint = NULL,
...
)A 3D tensor representing
activation(conv1d_transpose(inputs, kernel) + bias).
Object to compose the layer with. A tensor, array, or sequential model.
int, the dimension of the output space (the number of filters in the transpose convolution).
int or list of 1 integer, specifying the size of the transposed convolution window.
int or list of 1 integer, specifying the stride length
of the transposed convolution. strides > 1 is incompatible with
dilation_rate > 1.
string, either "valid" or "same" (case-insensitive).
"valid" means no padding. "same" results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
string, either "channels_last" or "channels_first".
The ordering of the dimensions in the inputs. "channels_last"
corresponds to inputs with shape (batch, steps, features)
while "channels_first" corresponds to inputs with shape
(batch, features, steps). 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".
int or list of 1 integers, specifying the dilation rate to use for dilated transposed convolution.
Activation function. If NULL, no activation is applied.
bool, if TRUE, bias will be added to the output.
Initializer for the convolution kernel. If NULL,
the default initializer ("glorot_uniform") will be used.
Initializer for the bias vector. If NULL, the
default initializer ("zeros") will be used.
Optional regularizer for the convolution kernel.
Optional regularizer for the bias vector.
Optional regularizer function for the output.
Optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The
function must take as input the unprojected variable and must return
the projected variable (which must have the same shape). Constraints
are not safe to use when doing asynchronous distributed training.
Optional projection function to be applied to the
bias after being updated by an Optimizer.
For forward/backward compatability.
If data_format="channels_last":
A 3D tensor with shape: (batch_shape, steps, channels)
If data_format="channels_first":
A 3D tensor with shape: (batch_shape, channels, steps)
If data_format="channels_last":
A 3D tensor with shape: (batch_shape, new_steps, filters)
If data_format="channels_first":
A 3D tensor with shape: (batch_shape, filters, new_steps)
ValueError: when both strides > 1 and dilation_rate > 1.
x <- random_uniform(c(4, 10, 128))
y <- x |> layer_conv_1d_transpose(32, 3, 2, activation='relu')
shape(y)
## shape(4, 21, 32)Other convolutional layers:
layer_conv_1d()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_separable_conv_1d()
layer_separable_conv_2d()
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_auto_contrast()
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_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_equalization()
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_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_rotation()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
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_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
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_2d()
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()