机器学习代码学习(五)->>手写辨识mnist

灰太狼 2022-04-22 12:46 119阅读 0赞

下面代码为: .ipynb文件, 可以在Jupyter上运行  
求: 预测手写辨识的数字

from keras.datasets import mnist  
    from keras.utils import np_utils  
    import numpy as np  
    np.random.seed(10)  
    (X_Train, y_Train), (X_Test, y_Test) = mnist.load_data()  
      
    # Translation of data  
    X_Train4D = X_Train.reshape(X_Train.shape[0], 28, 28, 1).astype('float32')  
    X_Test4D = X_Test.reshape(X_Test.shape[0], 28, 28, 1).astype('float32')  
    
    # Standardize feature data  
    X_Train4D_norm = X_Train4D / 255  
    X_Test4D_norm = X_Test4D /255  
      
    # Label Onehot-encoding  
    y_TrainOneHot = np_utils.to_categorical(y_Train)  
    y_TestOneHot = np_utils.to_categorical(y_Test) 
    
    from keras.models import Sequential  
    from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D  
      
    model = Sequential()  
    # Create CN layer 1  
    model.add(Conv2D(filters=16,kernel_size=(5,5),padding='same',input_shape=(28,28,1),activation='relu'))  
    # Create Max-Pool 1  
    model.add(MaxPooling2D(pool_size=(2,2)))  
      
    # Create CN layer 2  
    model.add(Conv2D(filters=36,kernel_size=(5,5),padding='same',activation='relu'))  
      
    # Create Max-Pool 2  
    model.add(MaxPooling2D(pool_size=(2,2)))  
      
    # Add Dropout layer  
    model.add(Dropout(0.25))  
    model.add(Flatten())  
    model.add(Dense(128, activation='relu'))  
    model.add(Dropout(0.5)) 
    model.add(Dense(10, activation='softmax'))  
    model.summary()  
    print("")  
    from keras import optimizers
    
    # 定義訓練方式  
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])  
      
    # 開始訓練  
    train_history = model.fit(x=X_Train4D_norm,y=y_TrainOneHot, validation_split=0.2,epochs=10, batch_size=300, verbose=1) 
    
    import matplotlib.pyplot as plt  
    def show_train_history(train_history, train, validation):  
        plt.plot(train_history.history[train])  
        plt.plot(train_history.history[validation])  
        plt.title('Train History')  
        plt.ylabel(train)  
        plt.xlabel('Epoch')  
        plt.legend(['train', 'validation'], loc='upper left')  
        plt.show() 
        
    def plot_images_labels_predict(images, labels, prediction, idx, num=10):  
        fig = plt.gcf()  
        fig.set_size_inches(12, 14)  
        if num > 25: num = 25  
        for i in range(0, num):  
            ax=plt.subplot(5,5, 1+i)  
            ax.imshow(images[idx], cmap='binary')  
            title = "l=" + str(labels[idx])  
            if len(prediction) > 0:  
                title = "l={},p={}".format(str(labels[idx]), str(prediction[idx]))  
            else:  
                title = "l={}".format(str(labels[idx]))  
            ax.set_title(title, fontsize=10)  
            ax.set_xticks([]); ax.set_yticks([])  
            idx+=1  
    plt.show()  
    
    show_train_history(train_history, 'acc', 'val_acc')  
    show_train_history(train_history, 'loss', 'val_loss') 
    
    scores = model.evaluate(X_Test4D_norm, y_TestOneHot)  
    print()  
    print("\t[Info] Accuracy of testing data = {:2.1f}%".format(scores[1]*100.0))   
    
    print("\t[Info] Making prediction of X_Test4D_norm")  
    prediction = model.predict_classes(X_Test4D_norm)  # Making prediction and save result to prediction  
    print()  
    print("\t[Info] Show 10 prediction result (From 240):")  
    print("%s\n" % (prediction[240:250]))
    plot_images_labels_predict(X_Test, y_Test, prediction, idx=240)  
    
    import pandas as pd  
    print("\t[Info] Display Confusion Matrix:")  
    print("%s\n" % pd.crosstab(y_Test, prediction, rownames=['label'], colnames=['predict'])