Description Usage Arguments Details References See Also
View source: R/layersconvolutional.R
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.
1 2 3 4 5 6 7 8 9  layer_conv_3d_transpose(object, filters, kernel_size, strides = c(1, 1,
1), padding = "valid", output_padding = NULL, data_format = NULL,
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 3 integers, specifying the depth, height, and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions. 
strides 
An integer or list of 3 integers, specifying the strides of
the convolution along the depth, height and width.. Can be a single integer
to specify the same value for all spatial dimensions. Specifying any stride
value != 1 is incompatible with specifying any 
padding 
one of 
output_padding 
An integer or list of 3 integers,
specifying the amount of padding along the depth, height, and width
of the output tensor. Can be a single integer to specify the same
value for all spatial dimensions. The amount of output padding along a
given dimension must be lower than the stride along that same dimension.
If set to 
data_format 
A string, one of 
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. 
When using this layer as the first layer in a model, provide the keyword argument
input_shape
(list of integers, does not include the sample axis), e.g.
input_shape = list(128, 128, 128, 3)
for a 128x128x128 volume with 3 channels if
data_format="channels_last"
.
Other convolutional layers: layer_conv_1d
,
layer_conv_2d_transpose
,
layer_conv_2d
, 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
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