• 【Keras案例学习】 多层感知机做手写字符分类(mnist_mlp )


    from __future__ import print_function
    # 导入numpy库, numpy是一个常用的科学计算库,优化矩阵的运算
    import numpy as np
    np.random.seed(1337)
    
    # 导入mnist数据库, mnist是常用的手写数字库
    from keras.datasets import mnist
    # 导入顺序模型
    from keras.models import Sequential
    # 导入全连接层Dense, 激活层Activation 以及 Dropout层
    from keras.layers.core import Dense, Dropout, Activation
    # 导入优化器RMSProp
    from keras.optimizers import RMSprop
    # 导入numpy工具,主要是用to_categorical来转换类别向量
    from keras.utils import np_utils
    
    # 设置batch的大小
    batch_size = 128
    # 设置类别的个数
    nb_classes = 10
    # 设置迭代的次数
    nb_epoch = 20
    
    # keras中的mnist数据集已经被划分成了60,000个训练集,10,000个测试集的形式,按以下格式调用即可
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    
    # X_train原本是一个60000*28*28的三维向量,将其转换为60000*784的二维向量
    X_train = X_train.reshape(60000, 784)
    # X_test原本是一个10000*28*28的三维向量,将其转换为10000*784的二维向量
    X_test = X_test.reshape(10000, 784)
    # 将X_train, X_test的数据格式转为float32存储
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    # 归一化
    X_train /= 255
    X_test /= 255
    # 打印出训练集和测试集的信息
    print(X_train.shape[0], 'train samples')
    print(X_test.shape[0], 'test samples')
    
    60000 train samples
    10000 test samples
    
    '''
    将类别向量(从0到nb_classes的整数向量)映射为二值类别矩阵,
    相当于将向量用one-hot重新编码'''
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)
    
    # 建立顺序型模型
    model = Sequential()
    '''
    模型需要知道输入数据的shape,
    因此,Sequential的第一层需要接受一个关于输入数据shape的参数,
    后面的各个层则可以自动推导出中间数据的shape,
    因此不需要为每个层都指定这个参数
    ''' 
    
    # 输入层有784个神经元
    # 第一个隐层有512个神经元,激活函数为ReLu,Dropout比例为0.2
    model.add(Dense(512, input_shape=(784,)))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))
    
    # 第二个隐层有512个神经元,激活函数为ReLu,Dropout比例为0.2
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.2))
    
    # 输出层有10个神经元,激活函数为SoftMax,得到分类结果
    model.add(Dense(10))
    model.add(Activation('softmax'))
    
    # 输出模型的整体信息
    # 总共参数数量为784*512+512 + 512*512+512 + 512*10+10 = 669706
    model.summary()
    
    ____________________________________________________________________________________________________
    Layer (type)                     Output Shape          Param #     Connected to                     
    ====================================================================================================
    dense_4 (Dense)                  (None, 512)           401920      dense_input_2[0][0]              
    ____________________________________________________________________________________________________
    activation_4 (Activation)        (None, 512)           0           dense_4[0][0]                    
    ____________________________________________________________________________________________________
    dropout_3 (Dropout)              (None, 512)           0           activation_4[0][0]               
    ____________________________________________________________________________________________________
    dense_5 (Dense)                  (None, 512)           262656      dropout_3[0][0]                  
    ____________________________________________________________________________________________________
    activation_5 (Activation)        (None, 512)           0           dense_5[0][0]                    
    ____________________________________________________________________________________________________
    dropout_4 (Dropout)              (None, 512)           0           activation_5[0][0]               
    ____________________________________________________________________________________________________
    dense_6 (Dense)                  (None, 10)            5130        dropout_4[0][0]                  
    ____________________________________________________________________________________________________
    activation_6 (Activation)        (None, 10)            0           dense_6[0][0]                    
    ====================================================================================================
    Total params: 669,706
    Trainable params: 669,706
    Non-trainable params: 0
    ____________________________________________________________________________________________________
    
    '''
    配置模型的学习过程
    compile接收三个参数:
    1.优化器optimizer:参数可指定为已预定义的优化器名,如rmsprop、adagrad,
    或一个Optimizer类对象,如此处的RMSprop()
    2.损失函数loss:参数为模型试图最小化的目标函数,可为预定义的损失函数,
    如categorical_crossentropy、mse,也可以为一个损失函数
    3.指标列表:对于分类问题,一般将该列表设置为metrics=['accuracy']
    '''
    model.compile(loss='categorical_crossentropy',
                  optimizer=RMSprop(),
                  metrics=['accuracy'])
    
    '''
    训练模型
    batch_size:指定梯度下降时每个batch包含的样本数
    nb_epoch:训练的轮数,nb指number of
    verbose:日志显示,0为不在标准输出流输出日志信息,1为输出进度条记录,2为epoch输出一行记录
    validation_data:指定验证集
    fit函数返回一个History的对象,其History.history属性记录了损失函数和其他指标的数值随epoch变化的情况,
    如果有验证集的话,也包含了验证集的这些指标变化情况
    '''
    history = model.fit(X_train, Y_train,
                        batch_size = batch_size,
                        nb_epoch = nb_epoch,
                        verbose = 1,
                        validation_data = (X_test, Y_test))
    
    # 按batch计算在某些输入数据上模型的误差
    score = model.evaluate(X_test, Y_test, verbose=0)
    
    Train on 60000 samples, validate on 10000 samples
    Epoch 1/20
    60000/60000 [==============================] - 3s - loss: 0.2468 - acc: 0.9245 - val_loss: 0.1062 - val_acc: 0.9662
    Epoch 2/20
    60000/60000 [==============================] - 3s - loss: 0.1027 - acc: 0.9687 - val_loss: 0.0885 - val_acc: 0.9744
    Epoch 3/20
    60000/60000 [==============================] - 3s - loss: 0.0755 - acc: 0.9772 - val_loss: 0.0798 - val_acc: 0.9763
    Epoch 4/20
    60000/60000 [==============================] - 3s - loss: 0.0617 - acc: 0.9810 - val_loss: 0.1023 - val_acc: 0.9692
    Epoch 5/20
    60000/60000 [==============================] - 3s - loss: 0.0512 - acc: 0.9847 - val_loss: 0.0832 - val_acc: 0.9791
    Epoch 6/20
    60000/60000 [==============================] - 3s - loss: 0.0447 - acc: 0.9866 - val_loss: 0.0778 - val_acc: 0.9796
    Epoch 7/20
    60000/60000 [==============================] - 3s - loss: 0.0392 - acc: 0.9883 - val_loss: 0.0822 - val_acc: 0.9798
    Epoch 8/20
    60000/60000 [==============================] - 3s - loss: 0.0336 - acc: 0.9899 - val_loss: 0.0784 - val_acc: 0.9820
    Epoch 9/20
    60000/60000 [==============================] - 3s - loss: 0.0336 - acc: 0.9904 - val_loss: 0.0937 - val_acc: 0.9809
    Epoch 10/20
    60000/60000 [==============================] - 3s - loss: 0.0293 - acc: 0.9917 - val_loss: 0.0802 - val_acc: 0.9829
    Epoch 11/20
    60000/60000 [==============================] - 3s - loss: 0.0260 - acc: 0.9924 - val_loss: 0.0966 - val_acc: 0.9821
    Epoch 12/20
    60000/60000 [==============================] - 3s - loss: 0.0240 - acc: 0.9932 - val_loss: 0.0984 - val_acc: 0.9836
    Epoch 13/20
    60000/60000 [==============================] - 3s - loss: 0.0230 - acc: 0.9939 - val_loss: 0.1032 - val_acc: 0.9822
    Epoch 14/20
    60000/60000 [==============================] - 3s - loss: 0.0236 - acc: 0.9933 - val_loss: 0.1002 - val_acc: 0.9843
    Epoch 15/20
    60000/60000 [==============================] - 3s - loss: 0.0184 - acc: 0.9945 - val_loss: 0.1111 - val_acc: 0.9811
    Epoch 16/20
    60000/60000 [==============================] - 3s - loss: 0.0201 - acc: 0.9944 - val_loss: 0.0982 - val_acc: 0.9837
    Epoch 17/20
    60000/60000 [==============================] - 3s - loss: 0.0186 - acc: 0.9949 - val_loss: 0.1012 - val_acc: 0.9841
    Epoch 18/20
    60000/60000 [==============================] - 3s - loss: 0.0179 - acc: 0.9951 - val_loss: 0.1132 - val_acc: 0.9824
    Epoch 19/20
    60000/60000 [==============================] - 3s - loss: 0.0189 - acc: 0.9950 - val_loss: 0.1081 - val_acc: 0.9842
    Epoch 20/20
    60000/60000 [==============================] - 3s - loss: 0.0168 - acc: 0.9956 - val_loss: 0.1109 - val_acc: 0.9837
    
    # 输出训练好的模型在测试集上的表现
    print('Test score:', score[0])
    print('Test accuracy:', score[1])
    
    Test score: 0.110892460335
    Test accuracy: 0.9837
    
    
    
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  • 原文地址:https://www.cnblogs.com/surfzjy/p/6419201.html
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