TrainingOptionsSGDM

Training options for stochastic gradient descent with momentum

Description

Training options for stochastic gradient descent with momentum, including learning rate information, L2 regularization factor, and mini-batch size.

Creation

Create a TrainingOptionsSGDM object using trainingOptions and specifying 'sgdm' as the solverName input argument.

Properties

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Plots and Display

Plots to display during network training, specified as one of the following:

• 'none' — Do not display plots during training.

• 'training-progress'— Plot training progress. The plot shows mini-batch loss and accuracy, validation loss and accuracy, and additional information on the training progress. The plot has a stop button in the top-right corner. Click the button to stop training and return the current state of the network.

Indicator to display training progress information in the command window, specified as 1 (true) or 0 (false).

The displayed information includes the epoch number, iteration number, time elapsed, mini-batch loss, mini-batch accuracy, and base learning rate. When you train a regression network, root mean square error (RMSE) is shown instead of accuracy. If you validate the network during training, then the displayed information also includes the validation loss and validation accuracy (or RMSE).

Data Types: logical

Frequency of verbose printing, which is the number of iterations between printing to the command window, specified as a positive integer. This property only has an effect when the Verbose value equals true.

If you validate the network during training, then trainNetwork prints to the command window every time validation occurs.

Mini-Batch Options

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

An iteration is one step taken in the gradient descent algorithm towards minimizing the loss function using a mini-batch. An epoch is the full pass of the training algorithm over the entire training set.

Size of the mini-batch to use for each training iteration, specified as a positive integer. A mini-batch is a subset of the training set that is used to evaluate the gradient of the loss function and update the weights.

Option for data shuffling, specified as one of the following:

• 'once' — Shuffle the training and validation data once before training.

• 'never' — Do not shuffle the data.

• 'every-epoch' — Shuffle the training data before each training epoch, and shuffle the validation data before each network validation. If the mini-batch size does not evenly divide the number of training samples, then trainNetwork discards the training data that does not fit into the final complete mini-batch of each epoch. Set the Shuffle value to 'every-epoch' to avoid discarding the same data every epoch.

Validation

Data to use for validation during training, specified as a datastore, a table, or a cell array containing the validation predictors and responses.

You can specify validation predictors and responses using the same formats supported by the trainNetwork function. You can specify the validation data as a datastore, table, or the cell array {predictors,responses}, where predictors contains the validation predictors and responses contains the validation responses.

For more information, see the images, sequences, and features input arguments of the trainNetwork function.

During training, trainNetwork calculates the validation accuracy and validation loss on the validation data. To specify the validation frequency, use the 'ValidationFrequency' name-value pair argument. You can also use the validation data to stop training automatically when the validation loss stops decreasing. To turn on automatic validation stopping, use the 'ValidationPatience' name-value pair argument.

If your network has layers that behave differently during prediction than during training (for example, dropout layers), then the validation accuracy can be higher than the training (mini-batch) accuracy.

The validation data is shuffled according to the 'Shuffle' value. If the 'Shuffle' value equals 'every-epoch', then the validation data is shuffled before each network validation.

Frequency of network validation in number of iterations, specified as a positive integer.

The ValidationFrequency value is the number of iterations between evaluations of validation metrics.

Patience of validation stopping of network training, specified as a positive integer or Inf.

The 'ValidationPatience' value is the number of times that the loss on the validation set can be larger than or equal to the previously smallest loss before network training stops.

Solver Options

Initial learning rate used for training, specified as a positive scalar. If the learning rate is too low, then training takes a long time. If the learning rate is too high, then training can reach a suboptimal result.

Settings for the learning rate schedule, specified as a structure. LearnRateScheduleSettings has the field Method, which specifies the type of method for adjusting the learning rate. The possible methods are:

• 'none' — The learning rate is constant throughout training.

• 'piecewise' — The learning rate drops periodically during training.

If Method is 'piecewise', then LearnRateScheduleSettings contains two more fields:

• DropRateFactor — The multiplicative factor by which the learning rate drops during training

• DropPeriod — The number of epochs that passes between adjustments to the learning rate during training

Specify the settings for the learning schedule rate using trainingOptions.

Data Types: struct

Factor for L2 regularizer (weight decay), specified as a nonnegative scalar.

You can specify a multiplier for the L2 regularizer for network layers with learnable parameters.

Contribution of the gradient step from the previous iteration to the current iteration of the training, specified as a scalar value from 0 to 1. A value of 0 means no contribution from the previous step, whereas a value of 1 means maximal contribution from the previous step. For more information about the different solvers, see Stochastic Gradient Descent.

Mode to evaluate the statistics in batch normalization layers, specified as one of the following:

• 'population' – Use the population statistics. After training, the software finalizes the statistics by passing through the training data once more and uses the resulting mean and variance.

• 'moving' – Approximate the statistics during training using a running estimate given by update steps

$\begin{array}{l}{\mu }^{*}={\lambda }_{\mu }\stackrel{^}{\mu }+\left(1-{\lambda }_{\mu }\right)\mu \\ {\sigma }^{2}{}^{*}={\lambda }_{{\sigma }^{2}}\stackrel{^}{{\sigma }^{2}}\text{​}\text{+}\text{​}\text{(1-}{\lambda }_{{\sigma }^{2}}\right)\text{​}{\sigma }^{2}\end{array}$

where ${\mu }^{*}$ and ${\sigma }^{2}{}^{*}$ denote the updated mean and variance, respectively, ${\lambda }_{\mu }$ and ${\lambda }_{{\sigma }^{2}}$ denote the mean and variance decay values, respectively, $\stackrel{^}{\mu }$ and $\stackrel{^}{{\sigma }^{2}}$ denote the mean and variance of the layer input, respectively, and $\mu$ and ${\sigma }^{2}$ denote the latest values of the moving mean and variance values, respectively. After training, the software uses the most recent value of the moving mean and variance statistics. This option supports CPU and single GPU training only.

Gradient threshold method used to clip gradient values that exceed the gradient threshold, specified as one of the following:

• 'l2norm' — If the L2 norm of the gradient of a learnable parameter is larger than GradientThreshold, then scale the gradient so that the L2 norm equals GradientThreshold.

• 'global-l2norm' — If the global L2 norm, L, is larger than GradientThreshold, then scale all gradients by a factor of GradientThreshold/L. The global L2 norm considers all learnable parameters.

• 'absolute-value' — If the absolute value of an individual partial derivative in the gradient of a learnable parameter is larger than GradientThreshold, then scale the partial derivative to have magnitude equal to GradientThreshold and retain the sign of the partial derivative.

Option to reset input layer normalization, specified as one of the following:

• true – Reset the input layer normalization statistics and recalculate them at training time.

• false – Calculate normalization statistics at training time when they are empty.

Sequence Options

Option to pad, truncate, or split input sequences, specified as one of the following:

• 'longest' — Pad sequences in each mini-batch to have the same length as the longest sequence. This option does not discard any data, though padding can introduce noise to the network.

• 'shortest' — Truncate sequences in each mini-batch to have the same length as the shortest sequence. This option ensures that no padding is added, at the cost of discarding data.

• Positive integer — For each mini-batch, pad the sequences to the nearest multiple of the specified length that is greater than the longest sequence length in the mini-batch, and then split the sequences into smaller sequences of the specified length. If splitting occurs, then the software creates extra mini-batches. Use this option if the full sequences do not fit in memory. Alternatively, try reducing the number of sequences per mini-batch by setting the 'MiniBatchSize' option to a lower value.

Direction of padding or truncation, specified as one of the following:

• 'right' — Pad or truncate sequences on the right. The sequences start at the same time step and the software truncates or adds padding to the end of the sequences.

• 'left' — Pad or truncate sequences on the left. The software truncates or adds padding to the start of the sequences so that the sequences end at the same time step.

Because LSTM layers process sequence data one time step at a time, when the layer OutputMode property is 'last', any padding in the final time steps can negatively influence the layer output. To pad or truncate sequence data on the left, set the 'SequencePaddingDirection' option to 'left'.

For sequence-to-sequence networks (when the OutputMode property is 'sequence' for each LSTM layer), any padding in the first time steps can negatively influence the predictions for the earlier time steps. To pad or truncate sequence data on the right, set the 'SequencePaddingDirection' option to 'right'.

Value by which to pad input sequences, specified as a scalar. The option is valid only when SequenceLength is 'longest' or a positive integer. Do not pad sequences with NaN, because doing so can propagate errors throughout the network.

Hardware Options

Hardware resource for training network, specified as one of the following:

• 'auto' — Use a GPU if one is available. Otherwise, use the CPU.

• 'cpu' — Use the CPU.

• 'gpu' — Use the GPU.

• 'multi-gpu' — Use multiple GPUs on one machine, using a local parallel pool based on your default cluster profile. If there is no current parallel pool, the software starts a parallel pool with pool size equal to the number of available GPUs.

• 'parallel' — Use a local or remote parallel pool based on your default cluster profile. If there is no current parallel pool, the software starts one using the default cluster profile. If the pool has access to GPUs, then only workers with a unique GPU perform training computation. If the pool does not have GPUs, then training takes place on all available CPU workers instead.

For more information on when to use the different execution environments, see Scale Up Deep Learning in Parallel and in the Cloud.

'gpu', 'multi-gpu', and 'parallel' options require Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). If you choose one of these options and Parallel Computing Toolbox or a suitable GPU is not available, then the software returns an error.

To see an improvement in performance when training in parallel, try scaling up the MiniBatchSize and InitialLearnRate training options by the number of GPUs.

Training long short-term memory networks supports single CPU or single GPU training only.

Specify the execution environment using trainingOptions.

Data Types: char | string

Worker load division for GPUs or CPUs, specified as a scalar from 0 to 1, a positive integer, or a numeric vector. This property has an effect only when the ExecutionEnvironment value equals 'multi-gpu' or 'parallel'.

Checkpoints

Path where checkpoint networks are saved, specified as a character vector.

Data Types: char

Output functions to call during training, specified as a function handle or cell array of function handles. trainNetwork calls the specified functions once before the start of training, after each iteration, and once after training has finished. trainNetwork passes a structure containing information in the following fields:

FieldDescription
EpochCurrent epoch number
IterationCurrent iteration number
TimeSinceStartTime in seconds since the start of training
TrainingLossCurrent mini-batch loss
ValidationLossLoss on the validation data
BaseLearnRateCurrent base learning rate
TrainingAccuracy Accuracy on the current mini-batch (classification networks)
TrainingRMSERMSE on the current mini-batch (regression networks)
ValidationAccuracyAccuracy on the validation data (classification networks)
ValidationRMSERMSE on the validation data (regression networks)
StateCurrent training state, with a possible value of "start", "iteration", or "done".

If a field is not calculated or relevant for a certain call to the output functions, then that field contains an empty array.

You can use output functions to display or plot progress information, or to stop training. To stop training early, make your output function return true. If any output function returns true, then training finishes and trainNetwork returns the latest network. For an example showing how to use output functions, see Customize Output During Deep Learning Network Training .

Data Types: function_handle | cell

Examples

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Create a set of options for training a network using stochastic gradient descent with momentum. Reduce the learning rate by a factor of 0.2 every 5 epochs. Set the maximum number of epochs for training to 20, and use a mini-batch with 64 observations at each iteration. Turn on the training progress plot.

options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.2, ...
'LearnRateDropPeriod',5, ...
'MaxEpochs',20, ...
'MiniBatchSize',64, ...
'Plots','training-progress')
options =
TrainingOptionsSGDM with properties:

Momentum: 0.9000
InitialLearnRate: 0.0100
LearnRateSchedule: 'piecewise'
LearnRateDropFactor: 0.2000
LearnRateDropPeriod: 5
L2Regularization: 1.0000e-04
MaxEpochs: 20
MiniBatchSize: 64
Verbose: 1
VerboseFrequency: 50
ValidationData: []
ValidationFrequency: 50
ValidationPatience: Inf
Shuffle: 'once'
CheckpointPath: ''
ExecutionEnvironment: 'auto'
OutputFcn: []
Plots: 'training-progress'
SequenceLength: 'longest'