applyDropoutMask | Applies the given dropout mask to the given data row-wise. |
AR | Calculates the Accuracy Ratio of a classifier |
AR.DArch | Calculates the Accruacy Ratio of a given set of probability |
AR.default | Calculates the Accruacy Ratio of a given set of probability |
AR.numeric | Calculates the Accruacy Ratio of a given set of probability |
backpropagate_delta_bn | Calculates the delta functions using backpropagation |
batch_normalization | Batch Normalization Function that normalizes the input before... |
batch_normalization_differential | Function that calcualtes the differentials in the batch... |
calcualte_population_mu_sigma | Calculates the mu and sigmas of a darch instance |
classification_error | Calculates the classification error |
convert_categorical | Data proprosess function that covnerts a categorical input to... |
crossEntropyErr | Calculates the cross entropy error |
finetune_SGD_bn | Updates a deep neural network's parameters using stochastic... |
generateDropoutMask | Generates the dropout mask for the deep neural network |
generateDropoutMasksForDarch | Generates dropout masks for dnn |
matMult | Calculates the outer product of two matricies |
meanSquareErr | Calculates the mean squared error |
new_dnn | Creats a new instance of darch class |
print_weight | Prints out the weight of a deep neural network |
rectified_linear_unit_function | Rectified Linear Unit Function |
reset_population_mu_sigma | Resets the mu and sigmas of a darch instance to 0 and 1 |
rsq | Calculate the RSQ of a regression model Utilitiy function... |
rsq.DArch | Utilitiy function that calcualtes RSQ of a DArch instance |
rsq.lm | Utilitiy function that calcualtes RSQ of a linear model |
run_dnn | Execution function that runs in the batch normalization mode |
train_dnn | Train a deep neural network |
verticalize | Creates a matrix by repeating a row vector N times |
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