keras手写数字识别--入门

电玩女神 2022-05-25 05:10 259阅读 0赞

# 程序 #

由于mnist数据集直接使用

(x_train, y_train), (x_test, y_test) = mnist.load_data()

这种加载方式，有时候由于网络原因，很难加载成功。为此，可以直接通过地址其地址下载下来。然后使用numpy加载一下数据就行。

# -*- coding: utf-8 -*-
    import keras
    # from keras.datasets import mnist
    from keras.models import Sequential
    from keras.layers import Dense, Dropout
    from keras.optimizers import RMSprop
    import matplotlib.pyplot as plt
    import numpy as np
    batch_size = 128
    num_classes = 10
    epochs = 20
    
    #由于使用程序下载很困难，这里手动下载导入数据
    # the data, shuffled and split between train and test sets
    # (x_train, y_train), (x_test, y_test) = mnist.load_data()
    
    
    
    path='F:/program_work/python_work/KerasTest/data/mnist.npz'
    f = np.load(path)
    x_train, y_train = f['x_train'], f['y_train']
    x_test, y_test = f['x_test'], f['y_test']
    f.close()
    
    x_train = x_train.reshape(60000, 784).astype('float32')
    x_test = x_test.reshape(10000, 784).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
    # label为0~9共10个类别，keras要求格式为binary class matrices
    
    y_train = keras.utils.to_categorical(y_train, num_classes)
    y_test = keras.utils.to_categorical(y_test, num_classes)
    
    # 全连接模型
    model = Sequential()
    model.add(Dense(512, activation='relu', input_shape=(784,)))
    model.add(Dropout(0.2))
    model.add(Dense(512, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(num_classes, activation='softmax'))
    
    model.summary()
    
    #损失函数使用交叉熵
    model.compile(loss='categorical_crossentropy',
                  optimizer=RMSprop(),
                  metrics=['accuracy'])
    #模型估计
    model.fit(x_train, y_train,
                        batch_size=batch_size,
                        epochs=epochs,
                        verbose=1,
                        validation_data=(x_test, y_test))
    score = model.evaluate(x_test, y_test, verbose=0)
    print('Total loss on Test Set:', score[0])
    print('Accuracy of Testing Set:', score[1])
    
    #预测
    result = model.predict_classes(x_test)
    correct_indices = np.nonzero(result == y_test)[0]
    incorrect_indices = np.nonzero(result != y_test)[0]
    plt.figure()
    for i, correct in enumerate(correct_indices[:9]):
        plt.subplot(3,3,i+1)
        plt.imshow(x_test[correct].reshape(28,28), cmap='gray', interpolation='none')
        plt.title("Predicted {}, Class {}".format(result[correct], y_test[correct]))
    
    plt.figure()
    for i, incorrect in enumerate(incorrect_indices[:9]):
        plt.subplot(3,3,i+1)
        plt.imshow(x_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')
        plt.title("Predicted {}, Class {}".format(result[incorrect], y_test[incorrect]))
        plt.show()

上面程序中，我们可以查看一些训练集的例子。如下图所示：

![这里写图片描述][Image 1]

训练结果为：

![这里写图片描述][Image 1]

关于全连接的理解，可以参考李宏毅的ppt。

![这里写图片描述][Image 1]

损失函数通常使用的有以下两种。

![这里写图片描述][Image 1]  
  
对应的程序为：

model.compile(loss='mse',
                  optimizer=RMSprop(),
                  metrics=['accuracy'])

model.compile(loss='categorical_crossentropy',
                  optimizer=RMSprop(),
                  metrics=['accuracy'])

同时，模型的激活函数也有其他的，如ReLU,sigmoid等。对应的程序调整为：

model.add(Dense(num_classes, activation='relu'))