theanets.layers.base.Reshape

class theanets.layers.base.Reshape(name=None, **kwargs)[source]

Reshape an input to have different numbers of dimensions.

Parameters:
shape : sequence of int

The desired shape of the output “vectors” for this layer. This should not include the leading axis of the actual shape of the data arrays processed by the graph! For example, to reshape input vectors of length a * b into 2D output “images” use (a, b) as the shape—not (batch-size, a, b).

Notes

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.

If you want to vectorize a data array, you could do that using (-1, ) as the shape for this layer. But it’s often easier to read if you use the Flatten layer type to reshape a layer’s output into a flat vector.

Outputs

  • out — reshaped inputs
Attributes:
shape : list of int

The desired shape of the output “vectors” for this layer.

__init__(name=None, **kwargs)

x.__init__(…) initializes x; see help(type(x)) for signature

Methods

__init__([name]) x.__init__(…) initializes x; see help(type(x)) for signature
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.
bind(graph[, reset, initialize]) Bind this layer into a computation graph.
connect(inputs) Create Theano variables representing the outputs of this layer.
find(key) Get a shared variable for a parameter by name.
full_name(name) Return a fully-scoped name for the given layer output.
log() Log some information about this layer.
log_params() Log information about this layer’s parameters.
resolve_inputs(layers) Resolve the names of inputs for this layer into shape tuples.
resolve_outputs() Resolve the names of outputs for this layer into shape tuples.
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.

Attributes

input_name Name of layer input (for layers with one input).
input_shape Shape of layer input (for layers with one input).
input_size Size of layer input (for layers with one input).
output_name Full name of the default output for this layer.
output_shape Shape of default output from this layer.
output_size Number of “neurons” in this layer’s default output.
params A list of all parameters in this layer.
resolve_outputs()[source]

Resolve the names of outputs for this layer into shape tuples.

transform(inputs)[source]

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

Parameters:
inputs : dict of Theano expressions

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

Returns:
output : Theano expression

The output for this layer is the same as the input.

updates : list

An empty updates list.