NeuralNet

add NeuralNet add(string name_, NeuralLayer layer, Optimizer opt): Name and add a layer to the net, without wiring it.
add NeuralNet add(NeuralLayer layer, Optimizer opt): Add a layer to the net, without wiring it.
add_root NeuralNet add_root(string name_, InputLayer layer): Name and add a root to the net.
add_root NeuralNet add_root(InputLayer root_): Add a root to the net.
backward_prop void backward_prop(V[] output_grad): Undocumented in source. Be warned that the author may not have intended to support it.
check_layer_here void check_layer_here(string name): Undocumented in source. Be warned that the author may not have intended to support it.
clear_opt void clear_opt(): Remove any optimizer defined on layers of the net.
dup NeuralNet dup(bool topology_only): Return a copy of the net.
initialize void initialize(double rand_scale): Initialize at random all the parameters of the net.
learn void learn(D data, S delegate(R net_out, ref T ex, ref V[] grad) grad_f, O opt, bool verbose, uint num_cores): Train neural network on some data, using specified gradient callback and optimizer.
learn void learn(D data, string loss, O opt, bool verbose, uint num_cores, bool monitor_loss): Train neural network on a dataset, using a predefined loss.
learn void learn(D data, float delegate(R net_out, ref T ex, ref V[] grad) grad_f, bool verbose, uint num_cores): Train neural network on some data, using a gradient callback.
learn void learn(D data, string loss, bool verbose, uint num_cores): Train neural network on some data, using a predefined loss.
predict float[] predict(T v): Compute the prediction of the net for $(PARAM v). Runs forward-propagation and outputs the predicted vector.
predict float[] predict(T args): Compute the prediction of the net when passing the arguments to the root(s) of the net.
reset void reset(): Reset any internal state variables of the net.
serialize void serialize(string path): Dump the neural net (topology and weight values) to the specified path.
share_params void share_params(NeuralNet net): Discard local weights and use those of the target net instead. However, the net keeps its own internal state. Useful for hogwild SGD implementation.
stack NeuralNet stack(NeuralLayer layer, Optimizer opt): Stack a layer on top of the former leaf of the net.
stack NeuralNet stack(string name_, NeuralLayer layer, Optimizer opt): Stack a layer on top of the former leaf of the net.
toString string toString(): Undocumented in source. Be warned that the author may not have intended to support it.
wire void wire(string parent, string child, bool with_alloc): Create a directed edge between parent and child nodes.
wire void wire(NeuralLayer parent, NeuralLayer child, bool with_alloc): Create a directed edge between parent and child nodes.

Mixins

__anonymous mixin opCallNew: Undocumented in source.

Properties

num_params ulong num_params [@property getter]: Return the total number of learnable parameters in the net.
out_layer NeuralLayer out_layer [@property getter]: reference to the leaf of the net
output float[] output [@property getter]: Return a reference to the dense output vector of the leaf of the net.

Static functions

deserialize NeuralNet deserialize(string path): Deserialize the neural net from the specified path.
is_upstream_stack bool is_upstream_stack(NeuralLayer layer): Undocumented in source. Be warned that the author may not have intended to support it.

Variables

edges string[][string] edges;: edges of the graph: src -> [dst1, ..., dstk]
layers NeuralLayer[] layers;: all nodes of the computational graph
layers_map NeuralLayer[string] layers_map;: map: name --> layer
leaves NeuralLayer[] leaves;: array of all the leaves
roots InputLayer[] roots;: array of all the roots

Mixed In Members

From `mixin opCallNew`

opCall auto opCall(T args): Undocumented in source. Be warned that the author may not have intended to support it.

Examples

auto nn = NeuralNet()
    .stack(DenseData(400))
    .stack(Linear(10));
// nn is a network working on 400-dimensions dense vectors and predicting
// a 10-dimensions vector

NeuralNet

Constructors

Members

Functions

Mixins

Properties

Static functions

Variables

Mixed In Members

From `mixin opCallNew`

Examples

Meta

Source

NeuralNet

Constructors

Members

Functions

Mixins

Properties

Static functions

Variables

Mixed In Members

From mixin opCallNew

Examples

Meta

Source

From `mixin opCallNew`