Layer base classes

class npdl.layers.Layer

The Layer class represents a single layer of a neural network. It should be subclassed when implementing new types of layers.

Because each layer can keep track of the layer(s) feeding into it, a network’s output Layer instance can double as a handle to the full network.

Parameters:

incoming : a Layer instance or a tuple

The layer feeding into this layer, or the expected input shape.

name : a string or None

An optional name to attach to this layer.

backward(pre_grad, *args, **kwargs)

calculate the input gradient

connect_to(prev_layer)

Propagates the given input through this layer (and only this layer).

Parameters:

input : Theano expression

The expression to propagate through this layer.

Returns:

output : Theano expression

The output of this layer given the input to this layer.

Notes

This is called by the base lasagne.layers.get_output() to propagate data through a network.

This method should be overridden when implementing a new Layer class. By default it raises NotImplementedError.

forward(input, *args, **kwargs)

calculate layer output for given input (forward propagation).

grads

Get layer parameter gradients as calculated from backward().

param_grads

layer parameters and corresponding gradients.

params

layer parameters.

Returns a list of Theano shared variables or expressions that parameterize the layer.

By default, all shared variables that participate in the forward pass will be returned (in the order they were registered in the Layer’s constructor via add_param()). The list can optionally be filtered by specifying tags as keyword arguments. For example, trainable=True will only return trainable parameters, and regularizable=True will only return parameters that can be regularized (e.g., by L2 decay).

If any of the layer’s parameters was set to a Theano expression instead of a shared variable, unwrap_shared controls whether to return the shared variables involved in that expression (unwrap_shared=True, the default), or the expression itself (unwrap_shared=False). In either case, tag filtering applies to the expressions, considering all variables within an expression to be tagged the same.

Parameters:

unwrap_shared : bool (default: True)

Affects only parameters that were set to a Theano expression. If True the function returns the shared variables contained in the expression, otherwise the Theano expression itself.

**tags (optional)

tags can be specified to filter the list. Specifying tag1=True will limit the list to parameters that are tagged with tag1. Specifying tag1=False will limit the list to parameters that are not tagged with tag1. Commonly used tags are regularizable and trainable.

Returns:

list of Theano shared variables or expressions

A list of variables that parameterize the layer

Notes

For layers without any parameters, this will return an empty list.

to_json()

To configuration