Binary_Network {LilRhino}

Binary Decision Neural Network Wrapper

Description

Used as a function of Feed_Reduction, Binary_Networt uses a 3 layer neural network with an adam optimizer, leaky RELU for the first two activation functions, followed by a softmax on the last layer. The loss function is binary_crossentropy. This is a keras wrapper, and uses tensorflow in the backend.

Usage

Binary_Network(X, Y, X_test, val_split, nodes, epochs, batch_size, verbose = 0)

Arguments

X

Training data.

Y

Training Labels. These must be binary.

X_test

The test Data

val_split

The validation split for keras.

nodes

The number of nodes in the hidden layers.

epochs

The number of epochs for the network

batch_size

The batch size for the network

verbose

Weither or not you want details about the run as its happening. 0 = silent, 1 = progress bar, 2 = one line per epoch.

Details

This function is a subset for the larger function Feed_Network. The output is the list containing the training and testing data converted into an approximation of probability space for that binary decision.

Value

Train

The training data in approximate probability space

Test

The testing data in 'double' approximate probability space

Author(s)

Travis Barton

References

Check out http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf and Keras for details

See Also

Feed_Network

Examples

## Not run: 
if(8 * .Machine$sizeof.pointer == 64){
  #Feed Network Testing
  library(keras)
  library(dplyr)
    install_keras()
    dat <- keras::dataset_mnist()
    X_train = array_reshape(dat$train$x/255, c(nrow(dat$train$x/255), 784))
    y_train = to_categorical(dat$train$y, 10)
    X_test = array_reshape(dat$test$x/255, c(nrow(dat$test$x/255), 784))
    y_test =to_categorical(dat$test$y, 10)


    index_train = which(dat$train$y == 6 | dat$train$y == 5)
    index_train = sample(index_train, length(index_train))
    index_test = which(dat$test$y == 6 | dat$test$y == 5)
    index_test = sample(index_test, length(index_test))

    temp = Binary_Network(X_train[index_train,],
    y_train[index_train,c(7, 6)], X_test[index_test,], .3, 350, 30, 50)
  }
  
## End(Not run)