Image Recognition Using Deep Learning

‹›Image and Signal ProcessingImage Recognition Using Deep Learning Deep learning can be applied to many image processing and computer vision problems with great success. Using NetChain and NetTrain, you can define and train a neural network that categorizes a handwritten digit given an image. Obtain training and validation data from the MNIST database of handwritten digits. In[1]:= resource = ResourceObject[“MNIST”]; trainingData = ResourceData[resource, “TrainingData”]; testData = ResourceData[resource, “TestData”]; In[2]:= RandomSample[trainingData, 5] Out[2]= Design a convolutional neural network architected for recognizing 28×28 grayscale images. In[3]:= lenet = NetChain[ {ConvolutionLayer[20, 5], Ramp, PoolingLayer[2, 2], ConvolutionLayer[50, 5], Ramp, PoolingLayer[2, 2], FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, “Output” -> NetDecoder[{“Class”, Range[0, 9]}], “Input” -> NetEncoder[{“Image”, {28, 28}, “Grayscale”}] ] Out[3]= Train the network for three training rounds. In[4]:= lenet = NetTrain[lenet, trainingData, ValidationSet -> testData,…


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