support Pooling ops with Sequence axis
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# CNTK Examples: Image/Regression ## Overview |Data: |The CIFAR-10 dataset (http://www.cs.toronto.edu/~kriz/cifar.html) of small images. |:---------|:--- |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. ## Running the example ### Getting the data 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](../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. ## Details ### RegrSimple_CIFAR10.cntk 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](../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](./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.
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