Keras handwritten digit recognition notes

Keras handwritten digit recognition

Handwritten digit recognition can be said to be the "hello world" in the field of machine learning.
For starters, this may be a good case. The program is run on jupyter, so there are many intermediate results. The reference book is "Python Deep Learning".

import keras
from keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np

Using TensorFlow backend.

#获取mnist数据集（如果本地没有，会连网下载）
#训练集image格式（60000,28,28）
#训练集label格式（60000，）
#测试集image格式（10000,28,28）
#测试集label格式（10000，）
(train_img,train_label),(test_img,test_label)=mnist.load_data()
#拷贝一份测试数据以备后续预测
test_predict=test_img.copy()
#分离训练集和验证集
val_img=train_img[:30000]
train_img=train_img[30000:]
val_label=train_label[:30000]
train_label=train_label[30000:]
#将图片展平+归一化（0~1）
train_img=train_img.reshape((-1,28*28)).astype('float64')
train_img=train_img/255
#验证集
val_img=val_img.reshape((-1,28*28)).astype('float64')
val_img=val_img/255

test_img=test_img.reshape((10000,28*28)).astype('float64')
test_img=test_img/255
#标签one-hot表示
train_label=keras.utils.to_categorical(train_label)
val_label=keras.utils.to_categorical(val_label) #验证集的标签
test_label=keras.utils.to_categorical(test_label)

#显示一张手写数字
plt.imshow(test_predict[0])

#搭建网络
#全连接（relu）+全连接（softmax）
net=keras.models.Sequential()
net.add(keras.layers.Dense(512,activation='relu',input_shape=(28*28,)))
net.add(keras.layers.Dense(10,activation='softmax'))

#定义优化器和损失函数
net.compile(optimizer='rmsprop',
            loss='categorical_crossentropy',
            metrics=['accuracy'])

#fit训练+验证，最终结果保存到his
his=net.fit(train_img,
            train_label,
            batch_size=128,
            epochs=10,
            validation_data=(val_img,val_label))

Train on 30000 samples, validate on 30000 samples
Epoch 1/10
30000/30000 [] - 5s 157us/step - loss: 0.3495 - acc: 0.8992 - val_loss: 0.2031 - val_acc: 0.9410
Epoch 2/10
30000/30000 [] - 4s 131us/step - loss: 0.1550 - acc: 0.9543 - val_loss: 0.1517 - val_acc: 0.9552
Epoch 3/10
30000/30000 [] - 3s 114us/step - loss: 0.1035 - acc: 0.9694 - val_loss: 0.1174 - val_acc: 0.9647
Epoch 4/10
30000/30000 [] - 3s 113us/step - loss: 0.0744 - acc: 0.9774 - val_loss: 0.1060 - val_acc: 0.9682
Epoch 5/10
30000/30000 [] - 3s 109us/step - loss: 0.0532 - acc: 0.9841 - val_loss: 0.0975 - val_acc: 0.9715
Epoch 6/10
30000/30000 [] - 3s 103us/step - loss: 0.0404 - acc: 0.9878 - val_loss: 0.1001 - val_acc: 0.9709
Epoch 7/10
30000/30000 [] - 4s 122us/step - loss: 0.0301 - acc: 0.9915 - val_loss: 0.0979 - val_acc: 0.9724
Epoch 8/10
30000/30000 [] - 4s 130us/step - loss: 0.0223 - acc: 0.9939 - val_loss: 0.1012 - val_acc: 0.9725
Epoch 9/10
30000/30000 [] - 3s 109us/step - loss: 0.0164 - acc: 0.9957 - val_loss: 0.0963 - val_acc: 0.9744
Epoch 10/10
30000/30000 [] - 4s 118us/step - loss: 0.0125 - acc: 0.9971 - val_loss: 0.0986 - val_acc: 0.9737

#提取his中的信息
his_dict=his.history
loss=his_dict['loss']
val_loss=his_dict['val_loss']
acc=his_dict['acc']
val_acc=his_dict['val_acc']
epoch=range(1,len(loss)+1)

#画图
plt.plot(epoch,loss,'k',label='train_loss')
plt.plot(epoch,val_loss,'b',label='validation_loss')
plt.plot(epoch,acc,'r',label='accuracy')
plt.plot(epoch,val_acc,'g',label='accuracy')
plt.title('train ans valid')
plt.xlabel('epoch')
plt.ylabel('loss/acc')
plt.legend()

plt.show()

#进行测试
test_loss,test_acu=net.evaluate(test_img,test_label)

10000/10000 [==============================] - 1s 56us/step

print('test_loss=',test_loss,'\ntest_accuracy=',test_acu)

test_loss= 0.0941627221799965
test_accuracy= 0.9742

#图片的单独预测
prediction=net.predict(test_img)

plt.imshow(test_predict[11])
plt.show()
print('result:',np.argmax(prediction[11]))

result: 6

The final result is correct and the prediction is complete.