The need for transposed convolutions generally arises 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. When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.

layer_conv_1d_transpose(
object,
filters,
kernel_size,
strides = 1,
data_format = NULL,
dilation_rate = 1,
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,
input_shape = NULL,
batch_input_shape = NULL,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)

## Arguments

object Model or layer object Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). An integer or list of a single integer, specifying the length of the 1D convolution window. An integer or list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1. one of "valid" or "same" (case-insensitive). An integer specifying the amount of padding along the time dimension of the output tensor. The amount of output padding must be lower than the stride. If set to NULL (default), the output shape is inferred. 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, length, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, length). an integer or list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1. Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x). Boolean, whether the layer uses a bias vector. Initializer for the kernel weights matrix. Initializer for the bias vector. Regularizer function applied to the kernel weights matrix. Regularizer function applied to the bias vector. Regularizer function applied to the output of the layer (its "activation").. Constraint function applied to the kernel matrix. Constraint function applied to the bias vector. Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors. Fixed batch size for layer The data type expected by the input, as a string (float32, float64, int32...) 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.

## Input shape

3D tensor with shape: (batch, steps, channels)

## Output shape

3D tensor with shape: (batch, new_steps, filters) If output_padding is specified:

new_timesteps = ((timesteps - 1) * strides + kernel_size - 2 * padding + output_padding)

## References

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_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()