Examples/Image/Regression/README.md
| Data: | The CIFAR-10 dataset (http://www.cs.toronto.edu/~kriz/cifar.html) of small images. |
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| Purpose | This folder contains a number of examples that demonstrate the usage of BrainScript to define deep learning networks for image regression tasks. |
| Network | Convolution neural networks. |
| Training | Stochastic gradient descent with momentum. |
| Comments | See below. |
we use the CIFAR-10 dataset to demonstrate how to perform regression on images. CIFAR-10 dataset is not included in the CNTK distribution but can be easily downloaded and converted by following the instructions in DataSets/CIFAR-10. We recommend you to keep the downloaded data in the respective folder while downloading, as the configuration files in this folder assumes that by default.
In this example, we set up a very simple task to have a neural network predict the average RGB values of images normalized to [0,1). To generate the ground truth labels for this regression task, the CIFAR-10 installation script in DataSets/CIFAR-10 will generate two additional files, train_regrLabels.txt and test_regrLabels.txt, for train and test respectively.
Run the example from the current folder using:
cntk configFile=RegrSimple_CIFAR10.cntk
The network produces root-mean-square error (rmse) of around 0.1257.
You may examine the cntk configuration file RegrSimple_CIFAR10.cntk for more details. Note the network is a linear one without nonlinearity. This is intended as we know that computing the average RGB values of images is a linear operation. The reader is a composite reader that uses the ImageReader to read images and the CNTKTextFormatReader to read the regression ground truth labels. The configuration file also demonstrates how to write the network prediction for the test data into an output file.