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Sep 16th, 2019
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  1. from keras.datasets import mnist
  2. from keras.models import Sequential
  3. from keras.layers.core import Dense, Activation
  4. from keras.utils import np_utils
  5.  
  6. (X_train, Y_train), (X_test, Y_test), = mnist.load_data()
  7.  
  8. X_train = X_train.reshape(60000, 784)
  9. X_test = X_test.reshape(10000, 784)
  10.  
  11. classes = 10
  12. Y_train = np_utils.to_categorical(Y_train, classes)
  13. Y_test = np_utils.to_categorical(Y_test, classes)
  14. input_size = 784
  15. batch_size = 100
  16. hidden_neurons = 100
  17. epochs = 15
  18.  
  19.  
  20.  
  21. model = Sequential()
  22. model.add(Dense(hidden_neurons, input_dim=input_size))
  23. model.add(Activation('sigmoid'))
  24. model.add(Dense(classes, input_dim=hidden_neurons))
  25. model.add(Activation('softmax'))
  26. model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='sgd')
  27. model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=epochs, verbose=1)
  28. score = model.evaluate(X_test, Y_test, verbose=1)
  29. print('Dokladnosc testu:', score)
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