神经网络入门代码(见系列博客)

from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD from matplotlib import pyplot as plt def read_data(num_classes): # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(60000, 784) x_test = x_test.reshape(10000, 784) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) return x_train, y_train, x_test, y_test def model(x_train, y_train, x_test, y_test, batch_size, epochs, num_classes): model = Sequential() model.add(Dense(15, activation='relu', input_shape=(784,))) model.add(Dense(num_classes, activation='softmax')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy']) history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) ### print the keys contained in the history object print(history.history.keys()) plot_training(history=history) model.save('model.json') score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) def plot_training(history): ### plot the training and validation loss for each epoch plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model mean squared error loss') plt.ylabel('mean squared error loss') plt.xlabel('epoch') plt.legend(['training set', 'validation set'], loc='upper right') plt.show() def show_samples(samples, labels): plt.figure(figsize=(12, 12)) for i in range(len(samples)): plt.subplot(4, 4, i+1) plt.imshow(samples[i], cmap='gray') plt.title(labels[i]) plt.show() if __name__ == '__main__': batch_size = 128 num_classes = 10 epochs = 20 x_train, y_train, x_test, y_test = read_data(num_classes) model(x_train, y_train, x_test, y_test, batch_size, epochs, num_classes)