Interface | Description |
---|---|
Storage.Matrix |
Abstract matrix interface
|
Storage.Tensor |
Abstract tensor interface
|
Storage.Vector |
Abstract vector interface
|
Class | Description |
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DeepLearning |
Deep Learning Neural Net implementation based on MRTask
|
DeepLearningModel |
The Deep Learning model
It contains a DeepLearningModelInfo with the most up-to-date model,
a scoring history, as well as some helpers to indicate the progress
|
DeepLearningModel.DeepLearningModelOutput |
The Deep Learning model output contains a few extra fields in addition to the metrics in Model.Output
1) Scoring history (raw data)
2) weights/biases (raw data)
3) variable importances (TwoDimTable)
|
DeepLearningModel.DeepLearningParameters |
Deep Learning Parameters
|
DeepLearningModelInfo |
This class contains the state of the Deep Learning model
This will be shared: one per node
|
DeepLearningModelInfo.GradientCheck | |
DeepLearningScoringInfo |
Lightweight DeepLearning scoring history.
|
DeepLearningTask | |
DeepLearningTask2 |
DRemoteTask-based Deep Learning.
|
Dropout |
Helper class for dropout training of Neural Nets
|
MurmurHash |
This is a very fast, non-cryptographic hash suitable for general hash-based
lookup.
|
Neurons |
This class implements the concept of a Neuron layer in a Neural Network
During training, every MRTask F/J thread is expected to create these neurons for every map call (Cheap to make).
|
Neurons.ExpRectifier | |
Neurons.ExpRectifierDropout |
Exponential Rectifier with dropout
|
Neurons.Input |
Input layer of the Neural Network
This layer is different from other layers as it has no incoming weights,
but instead gets its activation values from the training points.
|
Neurons.Linear |
Output neurons for regression - Linear units
|
Neurons.Maxout |
Maxout neurons (picks the max out of the k activation_j = sum(A_ij*x_i) + b_j)
Requires k times the model parameters (weights/biases) as a "normal" neuron
|
Neurons.MaxoutDropout |
Maxout neurons with dropout
|
Neurons.Output |
Abstract class for Output neurons
|
Neurons.Rectifier |
Rectifier linear unit (ReLU) neurons
|
Neurons.RectifierDropout |
Rectifier linear unit (ReLU) neurons with dropout
|
Neurons.Softmax |
Output neurons for classification - Softmax
|
Neurons.Tanh |
Tanh neurons - most common, most stable
|
Neurons.TanhDropout |
Tanh neurons with dropout
|
Storage | |
Storage.DenseColMatrix |
Dense column matrix implementation
|
Storage.DenseRowMatrix |
Dense row matrix implementation
|
Storage.DenseVector |
Dense vector implementation
|
Storage.SparseRowMatrix |
Sparse row matrix implementation
|
Enum | Description |
---|---|
DeepLearningModel.DeepLearningParameters.Activation |
Activation functions
|
DeepLearningModel.DeepLearningParameters.ClassSamplingMethod | |
DeepLearningModel.DeepLearningParameters.InitialWeightDistribution | |
DeepLearningModel.DeepLearningParameters.Loss |
Loss functions
Absolute, Quadratic, Huber, Quantile for regression
Quadratic, ModifiedHuber or CrossEntropy for classification
|
DeepLearningModel.DeepLearningParameters.MissingValuesHandling |