class theanets.layers.base.Concatenate(size, inputs, name=None, activation='relu', **kwargs)

Concatenate multiple inputs along the last axis.


This layer concatenates multiple inputs along their last dimension; all inputs must have the same dimensionality and the same shape along all but the last dimension. The size of this layer must equal the sum of the sizes of the inputs.


  • out — inputs concatenated along last axis
__init__(size, inputs, name=None, activation='relu', **kwargs)


__init__(size, inputs[, name, activation])
add_bias(name, size[, mean, std]) Helper method to create a new bias vector.
add_weights(name, nin, nout[, mean, std, ...]) Helper method to create a new weight matrix.
connect(inputs) Create Theano variables representing the outputs of this layer.
find(key) Get a shared variable for a parameter by name.
log() Log some information about this layer.
output_name([name]) Return a fully-scoped name for the given layer output.
setup() Set up the parameters and initial values for this layer.
to_spec() Create a specification dictionary for this layer.
transform(inputs) Transform the inputs for this layer into an output for the layer.


input_size For networks with one input, get the input size.
num_params Total number of learnable parameters in this layer.
params A list of all parameters in this layer.

Transform the inputs for this layer into an output for the layer.


inputs : dict of Theano expressions

Symbolic inputs to this layer, given as a dictionary mapping string names to Theano expressions. See Layer.connect().


outputs : dict of Theano expressions

A map from string output names to Theano expressions for the outputs from this layer. This layer type generates a “pre” output that gives the unit activity before applying the layer’s activation function, and an “out” output that gives the post-activation output.

updates : list of update pairs

An empty sequence of updates.