inst/examples_classification.R

In rz1988/deeplearning: An Implementation of Deep Neural Network for Regression and Classification

# Examples of classification

input <- matrix(runif(1000), 500, 2)
input_valid <- matrix(runif(100), 50, 2)
target <- (cos(rowSums(input + input^2)) > 0.5) * 1
target_valid <- (cos(rowSums(input_valid + input_valid^2)) > 0.5) * 1

# create a new deep neural network for classificaiton
dnn_classification <- new_dnn(
  c(2, 50, 50, 20, 1),  # The layer structure of the deep neural network.
                        # The first element is the number of input variables.
                        # The last element is the number of output variables.
  hidden_layer_default = rectified_linear_unit_function, # for hidden layers, use rectified_linear_unit_function
  output_layer_default = sigmoidUnitDerivative # for classification, use sigmoidUnitDerivative function
)

dnn_classification <- train_dnn(
  dnn_classification,

  # training data
  input, # input variable for training
  target, # target variable for training
  input_valid, # input variable for validation
  target_valid, # target variable for validation

  # training parameters
  learn_rate_weight = exp(-8) * 10, # learning rate for weights, higher if use dropout
  learn_rate_bias = exp(-8) * 10, # learning rate for biases, hihger if use dropout
  learn_rate_gamma = exp(-8) * 10, # learning rate for the gamma factor used
  batch_size = 10, # number of observations in a batch during training. Higher for faster training. Lower for faster convergence
  batch_normalization = T, # logical value, T to use batch normalization
  dropout_input = 0.2, # dropout ratio in input.
  dropout_hidden = 0.5, # dropout ratio in hidden layers
  momentum_initial = 0.6, # initial momentum in Stochastic Gradient Descent training
  momentum_final = 0.9, # final momentum in Stochastic Gradient Descent training
  momentum_switch = 100, # after which the momentum is switched from initial to final momentum
  num_epochs = 100, # number of iterations in training

  # Error function
  error_function = crossEntropyErr, # error function to minimize during training. For regression, use crossEntropyErr
  report_classification_error = T # whether to print classification error during training
)

# the prediciton by dnn_regression
pred <- predict(dnn_classification)

hist(pred)

# calculate the r-squared of the prediciton
AR(dnn_classification)

# calcualte the r-squared of the prediciton in validation
AR(dnn_classification, input = input_valid, target = target_valid)

# print the layer weights
# this function can print heatmap, histogram, or a surface
print_weight(dnn_regression, 1, type = "heatmap")

print_weight(dnn_regression, 2, type = "surface")

print_weight(dnn_regression, 3, type = "histogram")