keras-入门-03-全连接网络

淩亂°似流年 2023-02-23 08:24 18阅读 0赞

#%%导入数据
    from keras.datasets import mnist
    import matplotlib.pyplot as plt
    import numpy as np
    
    (train_data,train_labels),(test_data,test_labels) = mnist.load_data()
    
    #%%数据预处理
    train_data = train_data.reshape([len(train_data),28*28])
    test_data = test_data.reshape([len(test_data),28*28])
    ax = plt.figure().add_subplot()
    ax.imshow(train_data[0].reshape([28,28]),cmap = 'bone')
    def label_to_one_hot(labels):
        l = np.zeros([len(labels),10])
        for i in range(len(labels)):
            l[i,labels[i]]=1
        return l
    train_labels = label_to_one_hot(train_labels)
    test_labels = label_to_one_hot(test_labels)
    #%%定义模型
    from keras import models,layers,losses,optimizers,metrics
    def build_model():
        model = models.Sequential()
        model.add(layers.Dense(28*28,activation = 'relu',input_shape = (len(train_data[0]),)))
        #model.add(layers.Dense(100,activation='relu'))
        model.add(layers.Dense(10,activation='softmax'))
        model.compile(optimizer=optimizers.RMSprop(lr=0.001),
            loss = losses.categorical_crossentropy,
            metrics=[metrics.accuracy])
        return model
    model = build_model()
    #%%测试验证参数和最优迭代次数
    x_train = train_data[10000:]
    y_train = train_labels[10000:]
    x_val = test_data[:10000]
    y_val = test_labels[:10000]
    history = model.fit(x_train,y_train,512,80,validation_data=(x_val,y_val))
    #%%绘制准确率图
    print(history.history.keys())
    acc = history.history['accuracy']
    acc_val = history.history['val_accuracy']
    ax = plt.figure().add_subplot()
    ax.plot(range(len(acc)),acc)
    ax.plot(range(len(acc)),acc_val)
    #%%在整个训练集上重新训练
    model_train = build_model()
    model_train.fit(train_data,train_labels,512,50)
    #%%预测测试集
    predict_labels = model_train.predict(test_data)
    print("预测标签：")
    print(predict_labels)
    evaluate = model_train.evaluate(test_data,test_labels)
    print("损失值与准确率：")
    print(evaluate)