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Training feedforward neural network for OCR [closed]

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问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Currently I'm learning about neural networks and I'm trying to create an application that can be trained to recognize handwritten characters. For this problem I use a feed-forward neural network and it seems to work when I train it to recognize 1, 2 or 3 different characters. But

Neural network gives different results for each execution

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问题 This is the exact code I'm running with Keras and TensorFlow as a back end. For each run with the same program, the training results are different. Some times it gets 100% accuracy in 400th iteration and some times in the 200th. training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32") target_data = np.array([[0],[1],[1],[0]], "float32") model = Sequential() model.add(Dense(4, input_dim=2, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='mean_squared

Training feedforward neural network for OCR [closed]

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Currently I'm learning about neural networks and I'm trying to create an application that can be trained to recognize handwritten characters. For this problem I use a feed-forward neural network and it seems to work when I train it to recognize 1, 2 or 3 different characters. But when I try to make the network learn more than 3 characters it will stagnate at a error percentage around the 40 - 60%. I tried with multiple layers and less/more neurons but I can't seem to get it right, now I'm wondering if a feedforward neural network is capable of recognizing that much information. Some statistics