1.代码实战
#!/usr/bin/env python #! _*_ coding:UTF-8 _*_ # 导入numpy import numpy as np np.random.seed(1337) # 导入验证码图片数据集 from keras.datasets import mnist from keras.utils import np_utils # 导入kearas的模型 from keras.models import Sequential # 导入keras的层和激励函数 from keras.layers import Dense, Activation # 导入keras的优化器 from keras.optimizers import RMSprop (X_train, y_train), (X_test, y_test) = mnist.load_data() # 生成训练数据和测试数据 X_train = X_train.reshape(X_train.shape[0], -1) / 255. X_test = X_test.reshape(X_test.shape[0], -1) / 255. y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) # 生成训练模型，传入每个层及激励函数构造训练模型 model = Sequential([ Dense(32, input_dim=784), Activation('relu'), Dense(10), Activation('softmax'), ]) # 自定义优化器 rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0) # 使用优化器，和误差函数等编译训练模型 model.compile(optimizer=rmsprop, loss='categorical_crossentropy', metrics=['accuracy']) # 开始训练神经网络 model.fit(X_train, y_train, epochs=2, batch_size=32) # 开始测试神经网络 loss, accuracy = model.evaluate(X_test, y_test) print('test loss: ', loss) print('test accuracy: ', accuracy)
结果：
/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/keras_day03/classifier.py Using Theano backend. 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ETA: 0s - loss: 0.1955 - acc: 0.9438 55072/60000 [==========================>...] - ETA: 0s - loss: 0.1954 - acc: 0.9438 56864/60000 [===========================>..] - ETA: 0s - loss: 0.1954 - acc: 0.9439 58688/60000 [============================>.] - ETA: 0s - loss: 0.1942 - acc: 0.9440 60000/60000 [==============================] - 2s 30us/step - loss: 0.1937 - acc: 0.9440 32/10000 [..............................] - ETA: 0s 6784/10000 [===================>..........] - ETA: 0s 10000/10000 [==============================] - 0s 7us/step ('test loss: ', 0.18316557179167867) ('test accuracy: ', 0.9466) Process finished with exit code 0