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**KNIME Deeplearning4J Integration** version **4.3.1.v202101261633** by **KNIME AG, Zurich, Switzerland**

This node performs supervised training of a feedforward deep learning model for classification. Thereby, the learning procedure can be adjusted using several training methods and parameters, which can be customized in the node dialog. Additionally, the node supplies further methods for regularization, gradient normalization and learning refinements. The learner node automatically adds an output layer to the network configuration, which can be also configured in the node dialog. For classification, the output layer will always use 'softmax' as the activation function and the number of outputs will be automatically set to match the number of unique labels used for training. The output of the node is a trained deep learning model which can be used to predict the labels of unseen data instances.

- Number of Training Iterations
- The number of parameter updates that will be done on one batch of input data.
- Optimization Algorithm
- The type of optimization method to use. The following algorithms are available:
- Line Gradient Descent
- Conjugate Gradient Descent
- Hessian Free
- LBFGS
- Stochastic Gradient Descent

For Line Gradient Descent, Conjugate Gradient Descent, and LBFGS the maximum number of line search iterations can be specified. - Do Finetuning?
- Whether to to finetuning. If this option is chosen the learner will perform Stochastic Gradient Descent on the output layer of the network. This is usually done after the network was previously pretrained using an unsupervised learning algorithm. An example of a corresponding network architecture would be a Deep Belief Network consisting of RBMs.
- Updater
- The type of updater to use. These specify how the raw gradients will be modified. If this option is unchecked the node tries
to use an updater from a previously trained network if available. If not available the default will be used (NESTEROVS). Some
of the updater types may have additional coefficients which can be adjusted. The The following methods are available:
- SGD
- ADAM (ADAM Mean Decay, ADAM Var Decay)
- ADADELTA (RHO)
- NESTEROVS (Momentum, Schedule)

Nesterovs Schedule:

Schedule - Schedule for momentum value change during training. This is specified in the following format:

'iteration':'momentum rate','iteration':'momentum rate' ...

This creates a map, which maps the iteration to the momentum rate that should be used. E.g. '2:0.8' means that the rate '0.8' should be used in iteration '2'. Leave empty if you do not want to use a schedule. - ADAGRAD
- RMSPROP (RMS Decay)

An explanation of these methods and their coefficients can be found here. - Random Seed
- The seed value which should be used in order to compare training runs. Any Integer number may be used.
- Regularization
- The L1 and L2 regularization coefficients.
- Gradient Normalization
- Gradient normalization strategies. These are applied on raw gradients, before the gradients are passed to the updater.
An explanation can be found here.
- Renormalize L2 Per Layer
- Renormalize L2 Per Param Type
- ClipElement Wise Absolute Value
- Clip L2 Per Layer
- Clip L2 Per Param Type

For 'ClipElement Wise Absolute Value', 'Clip L2 Per Layer', and 'Clip L2 Per Param Type' you can additionally specify a threshold value.

- Global Learning Rate
- The learning rate for the whole network. If not used the learning rate specified in each layer will be used.
- Global Drop-Out Rate
- The drop-out rate for the whole network. If not used the drop-out rate specified in each layer will be used.
- Use Drop-Connect?
- Whether to use Drop Connect.
- Global Weight Initialization Strategy
- The weight initialization strategy to use for the whole network.
- Global Bias - Learning Rate
- The bias learning rate for the whole network if you want to use a different learning rate for the bias.
- Global Bias - Initialization
- The value to initialize all biases with.

- Batch Size
- The number of examples used for one minibatch.
- Epochs
- The number of epochs to train the network, hence the number of training runs on the whole data set.
- Size of Input Image
- If the input table contains images and a convolutional network is used, the dimensionality of the images needs to be specified. This value needs to be three numbers separated by a comma specifying the dimensionality of the used images (size x,size y,number of channels). E.g. 64,64,3

- Label Column
- The column of the input table containing labels for supervised learning.
- Feature Column Selection
- The columns of the input table containing the training data for the network.

- Learning Rate
- The learning rate that should be used for this layer.
- Weight Initialization Strategy
- The strategy which will be used to set the initial weights for this layer.
- Loss Function
- The type of loss function that should be used for this layer.

- Learning Status
- Shows information about the current learning run. Has an option for early stopping of training. If training is stopped before the last epoch the model will be saved in the current status.

- Dense Layer (29 %)
- Partitioning (10 %)
- Shuffle (7 %)
- DL4J Model Initializer (7 %)
- DL4J Feedforward Learner (Pretraining) (3 %)
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- DL4J Feedforward Learner (Classification) (2 %)
- DL4J Feedforward Learner (Pretraining) (2 %)
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To use this node in KNIME, install KNIME Deeplearning4J Integration (64bit only) from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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