:class:`Dense` layer in Tensorflow uses a 2D kernel
of shape (n_input, n_output). This module adds new Dense layers with 2D, 3D and 4D kernel.
The names of the new Dense layers are defined as Dense<Input_dim>to<Output_dim> with the batch size dimension included.
For example, the layer :class:`Dense2Dto3D` takes as input a 2D tensor of shape (batch_size, n_input) and outputs a tensor of
shape (batch_size, units_dim1, units_dim2).
The new layers can be used as usual tensorflow layers. They are useful when the model outputs parameters of a
distribution. For instance, if the model predicts the mean and the variance of a gaussian distribution for 4 variables,
it is interesting to have an output shape equal to (batch_size, 4, 2). It is then possible using this piece
of code :
>>> inputs = Input(shape=(input_dim,))
>>> x = Dense(100, activation="relu")(inputs)
>>> outputs = Dense2Dto3D(4, 2, activation=MeanVarianceActivation)(x)
>>> model = Model(inputs=inputs, outputs=outputs)
>>> model.output_shape
(None, 4, 2)Here is the list of the new layers :
A detailed presentation of each layer is available below along with an image describing the operations performed by each layer.
.. autoclass:: purestochastic.model.layers.Dense2Dto3D
:no-undoc-members:
The following figure represents the linear operation performed by the layer :class:`Dense2Dto3D`.
If activation is specified, the activation function is applied to the output of the linear
operation described below.
.. autoclass:: purestochastic.model.layers.Dense3Dto3D
:no-undoc-members:
.. autoclass:: purestochastic.model.layers.Dense3Dto2D
:no-undoc-members:
.. autoclass:: purestochastic.model.layers.Dense3Dto4D
:no-undoc-members: