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

Flatten all but the batch index of the input.


In theanets, the leading axis of a data array always runs over the examples in a mini-batch. Since the number of examples in a mini-batch is constant throughout a network graph, this layer always preserves the shape of the leading axis of its inputs.

This layer type flattens all of the non-leading dimensions of its inputs into one dimension. If you’d like to perform an arbitrary reshape of the input data, use a Reshape layer.


  • out — flattened inputs
__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.