An example optical character recognition(OCR) Example test example CNn. m in http://www.mathworks.com/matlabcentral fileexchange /38310-deep-learning-toolbox Based on a data base(mnist uint8, from http://yann.lecun.com/exdb/mnist/) 60,000 training examples(e. g 28x28 pixels eac .0,000 testing samples a different dataset After training, given an unknown image, it willl tell whether it iso, or 1...9 etc https://towardsdatascience.com a-simple-2d-cnn-for-mnist-digit- recognition-a998dbcle79a ch9. CNN. V9b3
An example optical character recognition (OCR) • Example test_example_CNN.m in http://www.mathworks.com/matlabcentral /fileexchange/38310-deep-learning-toolbox • Based on a data base (mnist_uint8, from http://yann.lecun.com/exdb/mnist/) • 60,000 training examples (e.g. 28x28 pixels each) • 10,000 testing samples (a different dataset) – After training , given an unknown image, it will tell whether it is 0, or 1 ,..,9 etc. ch9. CNN. V9b3 6 https://towardsdatascience.com/ a-simple-2d-cnn-for-mnist-digit- recognition-a998dbc1e79a
The basic idea of convolution neural networks cnn Same idea as back-propagation- neural networks ( bpnn but different implementation After vectorized (vec), the 2d arranged inputs become 1D https://adeshpande3.github.io/adeshpande3.github.io/a-begInner%27s Guide- To-Understanding-Convolutional-Neural-Networks/ vectors then the network is just like a BPNN ch9. CNN. V9b3 (Back propagation neural networks
The basic idea of Convolution Neural Networks CNN Same idea as Back-propagation-neural networks (BPNN) but different implementation • ch9. CNN. V9b3 7 https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s- Guide-To-Understanding-Convolutional-Neural-Networks/ After vectorized (vec), the 2D arranged inputs become 1D vectors. Then the network is just like a BPNN (Back propagation neural networks )
Basic structure of cnn The convolution layer: see how to use convolution for feature identifier ch9. CNN. V9b3
Basic structure of CNN The convolution layer: see how to use convolution for feature identifier ch9. CNN. V9b3 8
The basic structure Input conv subs. conV subs fully fully output Alternating Convolution(conv) and subsampling layer(subs Subsampling allows the features to be flexibly positioned ch9. CNN. V9b3
The basic structure • ch9. CNN. V9b3 9 Input conv. subs. conv subs fully fully output • Alternating Convolution (conv) and subsampling layer (subs) • Subsampling allows the features to be flexibly positioned
Convolution(conv) layer Example: From the input layer to the first hidden layer · The first hidden layer represents the filter outputs of a certain input neurons first hidden layer feature ·So, what is a feature? · Answer is in the next slide isualization of 5 x 5 filter convolving around an input volume and producing an activation map ch9. CNN. V9b3
Convolution (conv) layer: Example: From the input layer to the first hidden layer • The first hidden layer represents the filter outputs of a certain feature • So, what is a feature? • Answer is in the next slide ch9. CNN. V9b3 10