This example shows how to train a deep learning network for classification by using Experiment Manager. In this example, you train two networks to classify images of MathWorks merchandise into five classes. Each network is trained using three algorithms. In each case, a confusion matrix compares the true classes for a set of validation images with the classes predicted by the trained network. For more information on training a network for image classification, see Train Deep Learning Network to Classify New Images.
First, open the example. Experiment Manager loads a project with a preconfigured experiment that you can inspect and run. To open the experiment, in the Experiment Browser, double-click the name of the experiment (
An experiment definition consists of a description, a hyperparameter table, a setup function, and (optionally) a collection of metric functions to evaluate the results of the experiment. For more information, see Configure Deep Learning Experiment.
The Description box contains a textual description of the experiment. For this example, the description is:
Merchandise image classification using: - an untrained network (default) or a pretrained network (googlenet) - various solvers for training networks (sgdm, rmsprop, or adam)
The Hyperparameter Table contains the names and values of the hyperparameters used in the experiment. When you run the experiment, Experiment Manager sweeps through the hyperparameter values and trains the network multiple times. Each trial uses a different combination of the hyperparameter values specified in the table. This example uses two hyperparameters:
Network specifies the network to train. The options include
"default" (the default classification network provided by the setup function template) and
"googlenet" (a pretrained GoogLeNet network with modified layers for transfer learning).
Solver indicates the algorithm used to train the network. The options include
"sgdm" (stochastic gradient descent with momentum),
"rmsprop" (root mean square propagation), and
"adam" (adaptive moment estimation). For more information about these algorithms, see Stochastic Gradient Descent.
The Setup Function configures the training data, network architecture, and training options for the experiment. To inspect the setup function, under Setup Function, click Edit. The setup function opens in MATLAB Editor.
In this example, the input to the setup function is a
struct with fields from the hyperparameter table. The setup function returns three outputs that you use to train a network for image classification problems. The setup function has three sections.
Load Image Data defines image datastores containing the training and validation data. This example loads images from the file
MerchData.zip. This small data set contains 75 images of MathWorks merchandise, belonging to five different classes. The images are of size 227-by-227-by-3. For more information on this data set, see Image Data Sets.
Define Network Architecture defines the architecture for a convolutional neural network for deep learning classification. In this example, the choice of network to train depends on the value of the hyperparameter
Specify Training Options defines a
object for the experiment. The example trains the network for 10 epochs using the algorithm specified by the
Solver entry in the hyperparameter table.
The Metrics section specifies optional functions that evaluate the results of the experiment. This example does not include any metric functions.
On the Experiment Manager tab, click Run. Experiment Manager trains the network defined by the setup function six times. Each trial uses a different combination of hyperparameters. A table of results displays the accuracy and loss for each trial.
While the experiment is running, click Training Plot to display the training plot and track the progress of each trial.
To find the best result for your experiment, sort the table of results by validation accuracy.
Point to the Validation Accuracy column.
Click the triangle icon.
Select Sort in Descending Order.
The trial with the highest validation accuracy appears at the top of the results table.
To display the confusion matrix for this trial, select the top row in the results table and click Confusion Matrix.
In the Experiment Browser, right-click the name of the project and select Close Project. Experiment Manager saves your results and closes all of the experiments contained in the project.