![]() This option defaults to 0.9.Įlastic_averaging_regularization: (Applicable only if elastic_averaging=True) Specify the elastic averaging regularization strength. This option defaults to False (disabled).Įlastic_averaging_moving_rate: (Applicable only if elastic_averaging=True) Specify the moving rate for elastic averaging. This option defaults to True (enabled).Įlastic_averaging: Specify whether to enable elastic averaging between computing nodes, which can improve distributed model convergence. For large networks, enabling this option can reduce speed. This option defaults to False (disabled).ĭiagnostics: Specify whether to compute the variable importances for input features (using the Gedeon method). This option can speed up forward propagation but may reduce the speed of back propagation. This option defaults to 0.Ĭol_major: Specify whether to use a column major weight matrix for the input layer. ![]() When the error is at or below this threshold, training stops. This option defaults to True (enabled).Ĭlassification_stop: This option specifies the stopping criteria in terms of classification error (1-accuracy) on the training data scoring dataset. Maxout (not supported when autoencoder is enabled)Īdaptive_rate: Specify whether to enable the adaptive learning rate (ADADELTA). text, audio, time-series), then RNNs are a good choice. MLPs work well on transactional (tabular) data however if you have image data, then CNNs are a great choice. Several other types of DNNs are popular as well, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Each compute node trains a copy of the global model parameters on its local data with multi-threading (asynchronously) and contributes periodically to the global model via model averaging across the network.Ī feedforward artificial neural network (ANN) model, also known as deep neural network (DNN) or multi-layer perceptron (MLP), is the most common type of Deep Neural Network and the only type that is supported natively in H2O-3. Advanced features such as adaptive learning rate, rate annealing, momentum training, dropout, L1 or L2 regularization, checkpointing, and grid search enable high predictive accuracy. The network can contain a large number of hidden layers consisting of neurons with tanh, rectifier, and maxout activation functions. ![]() H2O’s Deep Learning is based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using back-propagation.
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