Buddle_Main {Buddle} | R Documentation |
Building a multi-layer feed-forward neural network model for statistical classification
Buddle_Main(Data, Label, Train_Size, Batch_Size, Optimization = "SGD", Learning_Rate = 0.05, Iteration = 100, Layer = 3, Neuron = 20, Activation = "Sigmoid")
Data |
- Input matrix. |
Label |
- Vector of training labels. |
Train_Size |
- Size of data which is used for training . |
Batch_Size |
- Batch size. |
Optimization |
- Method used to minimize loss fucntion. It can take one of "SGD", "Moment", or "AdaGrad." |
Learning_Rate |
- Default is 0.05. |
Iteration |
- Number of iterations. Default is 100. |
Layer |
- Number of layers. Default is 3. |
Neuron |
- Number of neurons. Default is 20. |
Activation |
- The name of activation function. It takes either "Relu" or "Sigmoid." |
Loss - Vector of values of the loss function.
W - Matrix of weights in the first layer
b - Vector of weights in the first layer
ZList - List of matrices of weights in the middle layers
cList - List of vectors of weights in the middle layers
ZFinal - Matrix of weights in the final layer
cFinal - Vector of weights in the final layer
Train_acc - Accuracy of the classifier when applied to the train data
Test_acc - Accuracy of the classifier when applied to the test data
Epoch - Number of epoch.
[1] Geron, A. Hand-On Machine Learning with Scikit-Learn and TensorFlow. Sebastopol: O'Reilly, 2017. Print.
[2] Han, J., Pei, J, Kamber, M. Data Mining: Concepts and Techniques. New York: Elsevier, 2011. Print.
Buddle_Predict
#################### n <- 50 p <- 3 Data <- matrix(runif(n*p, 0,50), nrow=n, ncol=p) #### Generate n-by-p input matrix for data Label = sample.int(n, n, replace=TRUE) #### Generate n-by-1 vector for the label Layer = 6 #### Number of layers Neuron = 20 #### Number of neurons lr = 0.005 #### Learning rate Iter = 100 #### Iteration Opt = "SGD" #### Method to optimize the loss function Act = "Sigmoid" ##### Activation function TrSize = 20 ##### Train_Size BatSize = 5 ##### Batch_Size DLResult = Buddle_Main(Data, Label, TrSize, BatSize, Opt, lr, Iter, Layer, Neuron, Act) Loss_Vector = DLResult$Loss Train_Accuracy = DLResult$Train_acc Test_Accuracy = DLResult$Test_acc