PredictDBN: Predict DBN
In TimoMatzen/RBM: Package for fitting RBM and DBN models in R.

Description Usage Arguments Value Examples

Function for predicting on a test set with a Deep Belief Network (Trained with DBN)

1	PredictDBN(test, labels, model, layers)

`test`	Is the test-data (matrix) on which the user wants to make predictions.
`labels`	Is a matrix with the corresponding labels for test-data.
`model`	Is the trained DBN model.
`layers`	Defines the number of layers.

A list with a confusion matrix and the accuracy of the predictions.

# Load the MNIST data
data(MNIST)

# Train the DBN model
modDBN <- DBN(MNIST$trainX, MNIST$trainY,n.iter = 500, nodes = c(500, 300, 150), learning.rate = 0.5,
size.minibatch = 10, n.iter.pre = 300, learning.rate.pre = 0.1, verbose = FALSE)

# Make predictions with PredictDBN
PredictDBN(MNIST$testX, MNIST$testY, model = modDBN, layers = 4)