nssTrainingLBFGS

Solver used to update network parameters, returned as a string. This property is read-only.

Use nssTrainingOptions("adam"), nssTrainingOptions("sgdm"), or nssTrainingOptions("rmsprop") to return an options set object for the Adam, SGDM, or RMSProp solvers respectively. For more information on these algorithms, see the Algorithms section of trainingOptions (Deep Learning Toolbox).

Maximum number of iterations to use for training, specified as a positive integer.

The L-BFGS solver is a full-batch solver, which means that it processes the entire training set in a single iteration.

Method to find suitable learning rate, specified as one of these values:

Number of state updates to store, specified as a positive integer. Values between 3 and 20 suit most tasks.

The L-BFGS algorithm uses a history of gradient calculations to approximate the Hessian matrix recursively. For more information, see Limited-Memory BFGS (Deep Learning Toolbox).

Initial value that characterizes the approximate inverse Hessian matrix, specified as a positive scalar.

To save memory, the L-BFGS algorithm does not store and invert the dense Hessian matrix B. Instead, the algorithm uses the approximation $$B_{k-m}^{-1}\approx {\lambda }_{k}I$$, where m is the history size, the inverse Hessian factor $${\lambda }_k$$ is a scalar, and I is the identity matrix. The algorithm then stores the scalar inverse Hessian factor only. The algorithm updates the inverse Hessian factor at each step.

The initial inverse hessian factor is the value of $${\lambda }_0$$.

For more information, see Limited-Memory BFGS (Deep Learning Toolbox).

Maximum number of line search iterations to determine the learning rate, specified as a positive integer.

Relative gradient tolerance, specified as a positive scalar.

The software stops training when the relative gradient is less than or equal to GradientTolerance.

Step size tolerance, specified as a positive scalar.

The software stops training when the step that the algorithm takes is less than or equal to StepTolerance.

Type of function used to calculate loss, specified as one of the following:

Option to plot the value of the loss function during training, specified as one of the following:

Constant coefficient applied to the regularization term added to the loss function, specified as a positive scalar.

The loss function with the regularization term is given by:

where t is the time variable, N is the size of the batch, ε is the sum of the reconstruction loss and autoencoder loss, θ is a concatenated vector of weights and biases of the neural network, and λ is the regularization constant that you can tune.

For more information, see Regularized Estimates of Model Parameters.

Coefficient applied to tune the reconstruction loss of an autoencoder, specified as a nonnegative scalar.

Reconstruction loss measures the difference between the original input (x) and its reconstruction (xr) after encoding and decoding. You calculate this loss as the L2 norm of (x - xr) divided by the batch size (N).

Number of samples in each frame or batch when segmenting data for model training, specified as a positive integer.

Number of samples in the overlap between successive frames when segmenting data for model training, specified as an integer. A negative integer indicates that certain data samples are skipped when creating the data frames.

ODE solver options to integrate continuous-time neural state-space systems, specified as an nssDLODE45 object.

Use dot notation to access properties such as the following:

For more information, see odeset.

Input interpolation method, specified as one of the following:

This is the interpolation method used to interpolate the input when integrating continuous-time neural state-space systems. For more information, see interpolation methods in interp1.