The *training *components provide different methods to train the data with:

Adds the ability to perform classification.

**Parameters**:

**Epochs**: sets the number of epochs to perform. One epoch corresponds to the number of iterations it takes to go through the entire dataset one time.**Batch Size**: the number of samples that the algorithm should train on at a time, before updating the weights in the model.**Loss function**: specifies which loss function to apply.**Optimizer**: specifies which optimizer algorithm to use**Beta 1**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Beta 2**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Learning rate**: sets the learning rate for the algorithm. The value must be between 0 and 1.**Additional Stop Condition**: allows you to specify an additional condition for when to stop training. Selecting**Target Accuracy**displays an edit field where you can specify a training accuracy percentage threshold, after which training will stop.

Attempts to find a line of "best fit" on a group of points in a space.

**Parameters**:

**Epochs**: sets the number of epochs to perform. One epoch corresponds to the number of iterations it takes to go through the entire dataset one time.**Batch Size**: the number of samples that the algorithm should train on at a time, before updating the weights in the model.**Loss Function**: specifies which loss function to apply.**Optimizer**: specifies which optimizer algorithm to use.**Beta 1**: optimizer-specific parameter. See the TensorFlow Optimizers pagefor optimizer-specific definitions.**Beta 2**: optimizer-specific parameter. See the TensorFlow Optimizers pagefor optimizer-specific definitions.**Learning Rate**: sets the learning rate for the algorithm. The value must be between 0 and 1.

Adds the ability to train a reinforcement learning algorithm.

**Parameters**:

**Method**: specifies which reinforcement learning method to use.**Optimizer**: specifies which optimizer algorithm to use.**History length**: specifies how many frames there should be inside each sample.**Batch size**: the number of samples that the algorithm should train on at a time, before updating the weights in the model.**Learning rate**: sets the learning rate for the optimizer algorithm.**Max steps**: specifies how many steps the agent is allowed to take before a*Done*state is enforced.**Episodes**: specifies how many*episodes*the algorithm will run before the training ends. An episode transitions into a new one whenever a*Done*state is received and is a way of defining how long the training will run. For example, if one training session consists of 15 episodes where reinforcement learning is training on a game, then the agent will have won, lost or reached max steps 15 times before the training ends.

Adds the ability to train a Generative Adversarial Network (GAN).

**Parameters**:

**Switch**: specifies which Switch component to use. This dropdown will only be populated if one or more Switch components exist in the model workspace.**Real Data**: specifies the Data component that contains the "real" data to imitate. This dropdown will only be populated if one or more Data components exist in the model workspace.**Epochs**: sets the number of iterations to perform.**Optimizer**: specifies which optimizer algorithm to use.**Beta 1**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Beta 2**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Learning Rate**: sets the learning rate for the algorithm. The value must be between 0 and 1.**Batch Size**: the number of samples that the algorithm should train on at a time, before updating the weights in the model.**Additional Stop Condition**: allows you to specify an additional condition for when to stop training. Selecting**Target Accuracy**displays an edit field where you can specify a training accuracy percentage threshold, after which training will stop.

Adds a Detector (e.g., for object detection).

**Parameters**:

**Epochs**: sets the number of epochs to perform. One epoch corresponds to the number of iterations it takes to go through the entire dataset one time.**Grid Size**: the size that the grid image is divided into for the Yolo V1 model.**Batch Size**: the number of samples that the algorithm should train on at a time, before updating the weights in the model.**Number of Boxes**: the number of predicted bounding boxes per object by the Yolo V1 model.**Threshold**: the lower threshold on the probability for boundary boxes.**λclassification**: the coefficient of loss corresponding to the object detection loss in the Yolo V1 model.**λnon object**: the coefficient of loss corresponding to the non-object detection loss in the Yolo V1 model.**Optimizer**: specifies which optimizer algorithm to use.**Beta 1**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Beta 2**: optimizer-specific parameter. See the TensorFlow Optimizers page for optimizer-specific definitions.**Learning Rate**: sets the learning rate for the algorithm. The value must be between 0 and 1.**Additional Stop Condition**: allows you to specify an additional condition for when to stop training. Selecting**Target Accuracy**displays an edit field where you can specify a training accuracy percentage threshold, after which training will stop.