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.
layer_conv_3d_transpose( object, filters, kernel_size, strides = c(1, 1, 1), padding = "valid", output_padding = NULL, data_format = NULL, dilation_rate = c(1L, 1L, 1L), 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
The return value depends on
Layer instance is returned.
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 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.
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
dilation_rate value != 1.
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
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, depth, height, width, channels) while
channels_first corresponds to inputs with shape
(batch, channels, depth, height, width). It defaults to the
image_data_format value found in your Keras config file at
~/.keras/keras.json. If you never set it, then it will be
An integer or vector of 3 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions.
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
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.
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 (
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.
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
Other convolutional layers: