# Old Neural Net API

Warning

This API is deprecated since v1.4.2, users are advised to use the new neural stack API.

## Feed-forward Network¶

To create a feedforward network we need three entities.

• The training data (type parameter D)
• A data pipe which transforms the original data into a data structure that understood by the FeedForwardNetwork
• The network architecture (i.e. the network as a graph object)

### Network graph¶

A standard feedforward network can be created by first initializing the network architecture/graph.

val gr = FFNeuralGraph(num_inputs = 3, num_outputs = 1,
hidden_layers = 1, List("logsig", "linear"), List(5))


This creates a neural network graph with one hidden layer, 3 input nodes, 1 output node and assigns sigmoid activation in the hidden layer. It also creates 5 neurons in the hidden layer.

Next we create a data transform pipe which converts instances of the data input-output patterns to (DenseVector[Double], DenseVector[Double]), this is required in many data processing applications where the data structure storing the training data is not a breeze vector.

Lets say we have data in the form trainingdata: Stream[(DenseVector[Double], Double)], i.e. we have input features as breeze vectors and scalar output values which help the network learn an unknown function. We can write the transform as.

val transform = DataPipe(
(d: Stream[(DenseVector[Double], Double)]) =>
d.map(el => (el._1, DenseVector(el._2)))
)


### Model Building¶

We are now in a position to initialize a feed forward neural network model.

val model = new FeedForwardNetwork[
Stream[(DenseVector[Double], Double)]
](trainingdata, gr, transform)


Here the variable trainingdata represents the training input output pairs, which must conform to the type argument given in square brackets (i.e. Stream[(DenseVector[Double], Double)]).

Training the model using back propagation can be done as follows, you can set custom values for the backpropagation parameters like the learning rate, momentum factor, mini batch fraction, regularization and number of learning iterations.

model.setLearningRate(0.09)
.setMaxIterations(100)
.setBatchFraction(0.85)
.setMomentum(0.45)
.setRegParam(0.0001)
.learn()


The trained model can now be used for prediction, by using either the predict() method or the feedForward() value member both of which are members of FeedForwardNetwork (refer to the api docs for more details).

val pattern = DenseVector(2.0, 3.5, 2.5)
val prediction = model.predict(pattern)


## Sparse Autoencoder¶

Sparse autoencoders are a feedforward architecture that are useful for unsupervised feature learning. They learn a compressed (or expanded) vector representation of the original data features. This process is known by various terms like feature learning, feature engineering, representation learning etc. Autoencoders are amongst several models used for feature learning. Other notable examples include convolutional neural networks (CNN), principal component analysis (PCA), Singular Value Decomposition (PCA) (a variant of PCA), Discrete Wavelet Transform (DWT), etc.

### Creation¶

Autoencoders can be created using the AutoEncoder class. Its constructor has the following arguments.

import io.github.mandar2812.dynaml.models.neuralnets._
import io.github.mandar2812.dynaml.models.neuralnets.TransferFunctions._
import io.github.mandar2812.dynaml.optimization.BackPropagation

//Cast the training data as a stream of (x,x),
//where x are the DenseVector of features
val trainingData: Stream[(DenseVector[Double], DenseVector[Double])] = ...

val testData = ...

val enc = new AutoEncoder(
outDim = 4, acts = List(SIGMOID, LIN))


### Training¶

The training algorithm used is a modified version of standard back-propagation. The objective function can be seen as an addition of three terms.

\begin{align} \mathcal{J}(\mathbf{W}, \mathbf{X}; \lambda, \rho) &= \mathcal{L}(\mathbf{W}, \mathbf{X}) + \lambda \mathcal{R}(\mathbf{W}) + KL(\hat{\rho}\ ||\ \rho) \\ KL(\hat{\rho}\ ||\ \rho) &= \sum_{i = 1}^{n_h} \rho log(\frac{\rho}{\hat{\rho}_i}) + (1 - \rho) log(\frac{1-\rho}{1-\hat{\rho}_i}) \\ \hat{\rho}_i &= \frac{1}{m} \sum_{j = 1}^{N} a_{i}(x_j) \end{align}
• $\mathcal{L}(\mathbf{W}, \mathbf{X})$ is the least squares loss.

• $\mathcal{R}(\mathbf{W})$ is the regularization penalty, with parameter $\lambda$.

• $KL(\hat{\rho} \| \rho)$ is the Kullback Leibler divergence, between the average activation (over all data instances $x \in \mathbf{X}$) of each hidden node and a specified value $\rho \in [0,1]$ which is also known as the sparsity weight.

//Set sparsity parameter for back propagation
BackPropagation.rho = 0.5

enc.optimizer
.setRegParam(0.0)
.setStepSize(1.5)
.setNumIterations(200)
.setMomentum(0.4)
.setSparsityWeight(0.9)

enc.learn(trainingData.toStream)

val metrics = new MultiRegressionMetrics(
testData.map(c => (enc.i(enc(c._1)), c._2)).toList,
testData.length)