R/layersconvolutional.R
layer_conv_1d_transpose.Rd
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, 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 )
object  Model or layer object 

filters  Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). 
kernel_size  An integer or list of a single integer, specifying the length of the 1D convolution window. 
strides  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 
padding  one of 
output_padding  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 
data_format  A string, one of 
dilation_rate  an integer or list of a single integer, specifying the
dilation rate to use for dilated convolution. Currently, specifying any

activation  Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: 
use_bias  Boolean, whether the layer uses a bias vector. 
kernel_initializer  Initializer for the 
bias_initializer  Initializer for the bias vector. 
kernel_regularizer  Regularizer function applied to the 
bias_regularizer  Regularizer function applied to the bias vector. 
activity_regularizer  Regularizer function applied to the output of the layer (its "activation").. 
kernel_constraint  Constraint function applied to the kernel matrix. 
bias_constraint  Constraint function applied to the bias vector. 
input_shape  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. 
batch_input_shape  Shapes, including the batch size. For instance,

batch_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
name  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. 
trainable  Whether the layer weights will be updated during training. 
weights  Initial weights for layer. 
3D tensor with shape: (batch, steps, channels)
3D tensor with shape: (batch, new_steps, filters)
If output_padding
is specified:
new_timesteps = ((timesteps  1) * strides + kernel_size  2 * padding + output_padding)
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()