LilRhino (version 1.2.2)

Binary_Network: 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.

Value

Train

The training data in approximate probability space

Test

The testing data in 'double' approximate probability space

%% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ...

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.

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 {
# }
# 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)
  }
  
# }
# NOT RUN {
# }