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
)
What to compose the new Layer
instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()
).
The return value depends on object
. If object
is:
missing or NULL
, the Layer
instance is returned.
a Sequential
model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object)
is returned.
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.
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_1d()
,
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()