Keras学习（二）-mnist手写数据处理（卷积神经网络形式）

1.导入运行库

from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
np.random.seed(10)

2.进行数据预处理

(x_Train, y_Train), (x_Test, y_Test) = mnist.load_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')

x_Train4D_normalize = x_Train4D / 255
x_Test4D_normalize = x_Test4D / 255

y_TrainOneHot = np_utils.to_categorical(y_Train)
y_TestOneHot = np_utils.to_categorical(y_Test)

3.建立模型

from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
model = Sequential()#建立基本堆叠模型

#卷积层1
model.add(Conv2D(filters=16,    #会产生16个特征图
                 kernel_size=(5,5), #过滤窗大小
                 padding='same',  #过边框
                 input_shape=(28,28,1), #输入数据形状
                 activation='relu')) #采用的激活函数
#池化层1
model.add(MaxPooling2D(pool_size=(2, 2))) #池化层-图像缩减采样

#卷积层2
model.add(Conv2D(filters=36,
                 kernel_size=(5,5),
                 padding='same', #产生的卷积图像大小不变
                 activation='relu'))
#池化层2
model.add(MaxPooling2D(pool_size=(2, 2)))

#dropout层1
model.add(Dropout(0.25))#dropout层

#平坦层
model.add(Flatten())#平坦层，下一步将进行全连接层

#全连接隐层1
model.add(Dense(128, activation='relu'))

#dropout层2
model.add(Dropout(0.5))

#全连接输出层
model.add(Dense(10,activation='softmax')) #全连接输出分类

#显示模型概要
print(model.summary())

4.训练模型

#定义训练模式
model.compile(loss='categorical_crossentropy',
              optimizer='adam',metrics=['accuracy']) #定义训练模式

#传入训练数据，进行划分
train_history=model.fit(x=x_Train4D_normalize, 
                        y=y_TrainOneHot,validation_split=0.2, 
                        epochs=20, batch_size=300,verbose=2)
#显示训练准确度和损失值
import matplotlib.pyplot as plt
def show_train_history(train_acc,test_acc):
    plt.plot(train_history.history[train_acc])
    plt.plot(train_history.history[test_acc])
    plt.title('Train History')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.show()
show_train_history('acc','val_acc')
show_train_history('loss','val_loss')

5.进行模型准确率评估

scores = model.evaluate(x_Test4D_normalize , y_TestOneHot)
scores[1]

6.预测结果

• 查看预测结果

import matplotlib.pyplot as plt
def plot_images_labels_prediction(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')

        ax.set_title("label=" +str(labels[idx])+
                     ",predict="+str(prediction[idx])
                     ,fontsize=10) 
        
        ax.set_xticks([]);ax.set_yticks([])        
        idx+=1 
    plt.show()
plot_images_labels_prediction(x_Test,y_Test,prediction,idx=0)

• 建立混淆矩阵

import pandas as pd
pd.crosstab(y_Test,prediction,
            rownames=['label'],colnames=['predict'])

• 建立竖直查看

df = pd.DataFrame({'label':y_Test, 'predict':prediction})