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
(tuple of integers, does not include the sample axis),
input_shape=(128, 3) for data with 128 time steps and 3 channels.
layer_conv_1d_transpose( object, filters, kernel_size, strides = 1, padding = "valid", output_padding = NULL, 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 )
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
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
A string, one of
an integer or list of a single integer, specifying the
dilation rate to use for dilated convolution. Currently, specifying any
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation:
Boolean, whether the layer uses a bias vector.
Initializer for the
Initializer for the bias vector.
Regularizer function applied to the
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,
Fixed batch size for layer
The data type expected by the input, as a string (
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.
3D tensor with shape:
(batch, steps, channels)
3D tensor with shape:
(batch, new_steps, filters)
output_padding is specified:
new_timesteps = ((timesteps - 1) * strides + kernel_size - 2 * padding + output_padding)
Other convolutional layers: