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  • Keras实现CIFAR-10分类

      仅仅为了学习Keras的使用,使用一个四层的全连接网络对MNIST数据集进行分类,网络模型各层结点数为:3072: : 1024 : 512:10;

      使用50000张图片进行训练,10000张测试:

                  precision    recall  f1-score   support
    
        airplane       0.61      0.69      0.65      1000
      automobile       0.69      0.67      0.68      1000
            bird       0.43      0.49      0.45      1000
             cat       0.40      0.32      0.36      1000
            dear       0.49      0.50      0.50      1000
             dog       0.45      0.48      0.47      1000
            frog       0.58      0.65      0.61      1000
           horse       0.63      0.60      0.62      1000
            ship       0.72      0.66      0.69      1000
           truck       0.63      0.58      0.60      1000
    
       micro avg       0.56      0.56      0.56     10000
       macro avg       0.56      0.56      0.56     10000
    weighted avg       0.56      0.56      0.56     10000
    
    

    训练过程中,损失和正确率曲线:

      可以看到,训练集的损失在一直降低,而测试集的损失出现大范围波动,并趋于上升,说明在一些epoch之后,出现过拟合;

      训练集的正确率也在一直上升,并接近100%;而测试集的正确率达到50%就趋于平稳了;

    代码:

    #!/usr/bin/env python
    # -*- coding: utf-8 -*-
    # @Time : 19-5-9
    
    """
    implement classification for CIFAR-10 with Keras
    """
    
    __author__ = 'Zhen Chen'
    
    # import the necessary packages
    from sklearn.preprocessing import LabelBinarizer
    from sklearn.metrics import classification_report
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.optimizers import SGD
    from keras.datasets import cifar10
    import matplotlib.pyplot as plt
    import numpy as np
    import argparse
    
    # construct the argument parse and parse the arguments
    parser = argparse.ArgumentParser()
    parser.add_argument("-o", "--output", default="./Training Loss and Accuracy_CIFAR10.png")
    args = parser.parse_args()
    
    # load the training and testing data, scale it into the range [0, 1],
    # then reshape the design matrix
    print("[INFO] loading CIFAR-10 data...")
    ((trainX, trainY), (testX, testY)) = cifar10.load_data()
    trainX = trainX.astype("float") / 255.0
    testX = testX.astype("float") / 255.0
    trainX = trainX.reshape((trainX.shape[0], 3072))
    testX = testX.reshape((testX.shape[0], 3072))
    
    # convert the labels from integers to vectors
    lb = LabelBinarizer()
    trainY = lb.fit_transform(trainY)
    testY = lb.fit_transform(testY)
    
    # initialize the label names for the CIFAR-10 dataset
    labelNames = ["airplane", "automobile", "bird", "cat", "dear", "dog", "frog", "horse", "ship", "truck"]
    
    # define the 2072-1024-512-10 architecture Keras
    model = Sequential()
    model.add(Dense(1024, input_shape=(3072,), activation="relu"))
    model.add(Dense(512, activation="relu"))
    model.add(Dense(10, activation="softmax"))
    
    # train the model using SGD
    print("[INFO] training network...")
    sgd = SGD(0.01)
    model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
    H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=100, batch_size=32)
    model.save_weights('SGD_100_32.h5')
    
    
    # evaluate the network
    print("[INFO] evaluating network...")
    predictions = model.predict(testX, batch_size=32)
    print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=labelNames))
    
    # plot the training losss and accuracy
    plt.style.use("ggplot")
    plt.figure()
    plt.plot(np.arange(0, 100), H.history["loss"], label="train_loss")
    plt.plot(np.arange(0, 100), H.history["val_loss"], label="val_loss")
    plt.plot(np.arange(0, 100), H.history["acc"], label="train_acc")
    plt.plot(np.arange(0, 100), H.history["val_acc"], label="val_acc")
    plt.title("Training Loss and Accuracy on CIRFAR-10")
    plt.xlabel("Epoch #")
    plt.ylabel("Loss/Accuracy")
    plt.legend()
    plt.savefig(args.output)
    
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  • 原文地址:https://www.cnblogs.com/chenzhen0530/p/10837622.html
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