NeuralNetwork (Deep Netts Core Pro 3.0.0 API)

java.lang.Object
- deepnetts.net.NeuralNetwork<T>

All Implemented Interfaces:

TrainerProvider<T>, Serializable

Direct Known Subclasses:

ConvolutionalNetwork, FeedForwardNetwork
```
public class NeuralNetwork<T extends Trainer>
extends Object
implements TrainerProvider<T>, Serializable
```
Base class for all neural networks in Deep Netts. Holds a list of abstract layers and loss function. Provides methods for forward and backward calculation, and to access input and output layers. Also provides network and output labels.

See Also:

AbstractLayer, LossFunction, Serialized Form

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`applyWeightChanges()` Applies calculated weight changes to all layers.
`void`	`backward()` Performs a backward bass across all layers in neural network, which is the calculation of corrections for the network internal parameters (weights).
`InputLayer`	`getInputLayer()` Returns the input layer of this neural network.
`float`	`getL1RegSum()` Calculates and returns L1 regularization sum of the entire network (all layers included).
`float`	`getL2RegSum()` Calculates and returns L2 regularization sum of the entire network (all layers included).
`String`	`getLabel()` Returns the label(name) of this neural network
`List<AbstractLayer>`	`getLayers()` Gets layers of this neural network.
`LossFunction`	`getLossFunction()` Returns a loss function of this network, which is used to calculate total network error during the training.
`AbstractScaler`	`getNormalizer()` Returns data normalization method that is applied to network's inputs.
`float[]`	`getOutput()` Returns network's output.
`String`	`getOutputLabel(int i)` Gets a label of the i-th output this network.
`String[]`	`getOutputLabels()` Returns all labels for outputs of this network.
`OutputLayer`	`getOutputLayer()` Returns the output layer of this network.
`Preprocessing<Tensor>`	`getPreprocessing()` Gets preprocessing that needs to be performed before input is fed to this network.
`T`	`getTrainer()` Returns a training algorithm of this neural network.
`static <T> T`	`load(String fileName, Class<T> clazz)` Loads and returns neural network previously saved to a file.
`Tensor`	`predict(Tensor input)` Returns the prediction of this neural network for the given input.
`void`	`save(String fileName)` Saves this network using serialization to file with specified fileName.
`void`	`setInput(Tensor inputs)` Sets network input and calculates entire network (triggers forward pass).
`void`	`setLabel(String label)` Sets label(name) for this neural network.
`void`	`setLossFunction(LossFunction lossFunction)` Sets a loss function of this network, which is used to calculate total network error during the training.
`void`	`setNormalizer(AbstractScaler normalizer)` Sets normalization data normalization method that is applied to network's inputs.
`void`	`setOutputError(float[] outputErrors)` Sets the network's output errors, which are a difference between actual(predicted) and target output.
`void`	`setOutputLabels(String... outputLabels)` Sets output labels of this network.
`void`	`setPreprocessing(Preprocessing<Tensor> preprocessing)` Sets preprocessing that needs to be performed before input is fed to this network.
`void`	`setTrainer(T trainer)` Sets the training algorithm of this neural network.
`javax.visrec.ml.eval.EvaluationMetrics`	`test(javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)` Tests how good are predictions of this network using specified test set.
`String`	`toString()` Returns string representation of this network including all layers and settings.
`void`	`train(javax.visrec.ml.data.DataSet<? extends MLDataItem> trainingSet)` Trains the neural network using specified training set.

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Method Detail
  - setInput
```
public void setInput(Tensor inputs)
```
    Sets network input and calculates entire network (triggers forward pass).
    
    Parameters:
    
    inputs - input tensor
  - getOutput
```
public float[] getOutput()
```
    Returns network's output.
    
    Returns:
    
    network's output
  - setOutputError
```
public void setOutputError(float[] outputErrors)
```
    Sets the network's output errors, which are a difference between actual(predicted) and target output.
    
    Parameters:
    
    outputErrors - array of errors for each output, a difference between actual(predicted) and target output
  - train
```
public void train(javax.visrec.ml.data.DataSet<? extends MLDataItem> trainingSet)
```
    Trains the neural network using specified training set.
    
    Parameters:
    
    trainingSet - example data given as (input, output) pairs to train the network
  - predict
```
public Tensor predict(Tensor input)
```
    Returns the prediction of this neural network for the given input. This is the main method to use a trained neural network for inference/prediction. A well trained neural network should provide predictions with low error. Both input and returned prediction are tensors, which are essentially multidimensional arrays.
    
    Parameters:
    
    input - input for the neural network
    
    Returns:
    
    network's prediction.
    
    See Also:
    
    Tensor
  - test
```
public javax.visrec.ml.eval.EvaluationMetrics test(javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)
```
    Tests how good are predictions of this network using specified test set. Automatically detects which type of task is network configured to perform and applies appropriate evaluation/test procedure using corresponding Evaluator.
    
    Parameters:
    
    testSet - data set to test/evaluate predictions
    
    Returns:
    
    evaluation metrics that show how good this network is at predicting unseen data
    
    See Also:
    
    EvaluationMetrics, ClassificationMetrics, RegressionMetrics, Evaluators
  - applyWeightChanges
```
public void applyWeightChanges()
```
    Applies calculated weight changes to all layers.
  - backward
```
public void backward()
```
    Performs a backward bass across all layers in neural network, which is the calculation of corrections for the network internal parameters (weights). This method invokes the training steps for all the layers starting from the last/output layer and going backwards to first/input layer.
  - getLayers
```
public List<AbstractLayer> getLayers()
```
    Gets layers of this neural network.
    
    Returns:
    
    layers of this neural network
  - getInputLayer
```
public InputLayer getInputLayer()
```
    Returns the input layer of this neural network. Input layer is the first layer in network which accepts the external input for the network, and forwards it to the next layer in the network.
    
    Returns:
    
    input layer of this neural network
    
    See Also:
    
    InputLayer
  - getOutputLayer
```
public OutputLayer getOutputLayer()
```
    Returns the output layer of this network. Output layer is the last layer of the network which provides final result of the network - predictions.
    
    Returns:
    
    output layer of this network.
    
    See Also:
    
    OutputLayer
  - setOutputLabels
```
public void setOutputLabels(String... outputLabels)
```
    Sets output labels of this network.
    
    Parameters:
    
    outputLabels - labels which correspond to outputs of the network.
  - getOutputLabels
```
public String[] getOutputLabels()
```
    Returns all labels for outputs of this network. Each output of the network should have a label which describes what that output represents.
    
    Returns:
    
    labels for outputs of this network.
  - getOutputLabel
```
public String getOutputLabel(int i)
```
    Gets a label of the i-th output this network. Each output of the network should have a label which describes what that output represents.
    
    Parameters:
    
    i - idx position of the output
    
    Returns:
    
    label for the i-th output
  - getLossFunction
```
public LossFunction getLossFunction()
```
    Returns a loss function of this network, which is used to calculate total network error during the training.
    
    Returns:
    
    loss function of this network
    
    See Also:
    
    LossFunction, LossType
  - setLossFunction
```
public void setLossFunction(LossFunction lossFunction)
```
    Sets a loss function of this network, which is used to calculate total network error during the training.
    
    Parameters:
    
    lossFunction - loss function to use during the training
  - getLabel
```
public String getLabel()
```
    Returns the label(name) of this neural network
    
    Returns:
    
    label of this network
  - setLabel
```
public void setLabel(String label)
```
    Sets label(name) for this neural network.
    
    Parameters:
    
    label - label for this network
  - getL2RegSum
```
public float getL2RegSum()
```
    Calculates and returns L2 regularization sum of the entire network (all layers included). This value is used during the training to prevent over-fitting.
    
    Returns:
    
    L2 regularization sum
  - getL1RegSum
```
public float getL1RegSum()
```
    Calculates and returns L1 regularization sum of the entire network (all layers included). This value is used during the training to prevent over-fitting.
    
    Returns:
    
    L2 regularization sum
  - getTrainer
```
public T getTrainer()
```
    Returns a training algorithm of this neural network. Training algorithm performs tuning of the network's internal parameter(weights) in order to minimize an error.
    
    Specified by:
    
    getTrainer in interface TrainerProvider<T extends Trainer>
    
    Returns:
    
    training algorithm of this network
    
    See Also:
    
    BackpropagationTrainer
  - setTrainer
```
public void setTrainer(T trainer)
```
    Sets the training algorithm of this neural network.
    
    Specified by:
    
    setTrainer in interface TrainerProvider<T extends Trainer>
    
    Parameters:
    
    trainer - training algorithm to use for this network
    
    See Also:
    
    BackpropagationTrainer
  - getNormalizer
```
public AbstractScaler getNormalizer()
```
    Returns data normalization method that is applied to network's inputs.
    
    Returns:
  - setNormalizer
```
public void setNormalizer(AbstractScaler normalizer)
```
    Sets normalization data normalization method that is applied to network's inputs.
    
    Parameters:
    
    normalizer -
  - toString
```
public String toString()
```
    Returns string representation of this network including all layers and settings.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    string representation of this network
  - getPreprocessing
```
public Preprocessing<Tensor> getPreprocessing()
```
    Gets preprocessing that needs to be performed before input is fed to this network.
    
    Returns:
  - setPreprocessing
```
public void setPreprocessing(Preprocessing<Tensor> preprocessing)
```
    Sets preprocessing that needs to be performed before input is fed to this network.
    
    Parameters:
    
    preprocessing -
  - save
```
public void save(String fileName)
          throws IOException
```
    Saves this network using serialization to file with specified fileName.
    
    Parameters:
    
    fileName - name of the file to save network
    
    Throws:
    
    IOException
  - load
```
public static <T> T load(String fileName,
                         Class<T> clazz)
                  throws IOException,
                         ClassNotFoundException
```
    Loads and returns neural network previously saved to a file.
    
    Type Parameters:
    
    T - type(class) of the network to lead and return.
    
    Parameters:
    
    fileName - name of the file to load network from
    
    clazz - class of the neural network to load
    
    Returns:
    
    loaded neural network
    
    Throws:
    
    IOException
    
    ClassNotFoundException

Class NeuralNetwork<T extends Trainer>

Method Summary

Methods inherited from class java.lang.Object

Method Detail

setInput

getOutput

setOutputError

train

predict

test

applyWeightChanges

backward

getLayers

getInputLayer

getOutputLayer

setOutputLabels

getOutputLabels

getOutputLabel

getLossFunction

setLossFunction

getLabel

setLabel

getL2RegSum

getL1RegSum

getTrainer

setTrainer

getNormalizer

setNormalizer

toString

getPreprocessing

setPreprocessing

save

load