keras mnist

基于tensorflow2

最简单的keras demo，做一个备份，以后有需要直接复制

模型结构

 

代码 

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras import datasets
from tensorflow.python.keras.utils import np_utils


(x_train,y_train),(x_test,y_test)=datasets.mnist.load_data()
print(x_train.shape,x_test.shape)
print(y_train.shape,y_test.shape)


x_train=x_train.reshape(x_train.shape[0],x_train.shape[1],x_train.shape[2],1)
x_test=x_test.reshape(x_test.shape[0],x_test.shape[1],x_test.shape[2],1)
x_train=x_train/255
x_test=x_test/255
y_train=np_utils.to_categorical(y_train)
y_test=np_utils.to_categorical(y_test)
print(x_train.shape,x_test.shape)
print(y_train.shape,y_test.shape)


inp_img=layers.Input(shape=(28,28,1))
layer1_conv=layers.Conv2D(32,(3,3),padding='same',activation='relu')(inp_img)
layer1_pool=layers.AveragePooling2D((2,2))(layer1_conv) 
layer2_conv=layers.Conv2D(64,(3,3),padding='same',activation='relu')(layer1_pool)
layer2_pool=layers.AveragePooling2D((2,2))(layer2_conv)  
layer3_conv=layers.Conv2D(128,(3,3),padding='same',activation='relu')(layer2_pool)
layer3_pool=layers.AveragePooling2D((7,7))(layer3_conv)   
layer4_fc=layers.Flatten()(layer3_pool)
pred=layers.Dense(10,activation='softmax')(layer4_fc)
model=models.Model(inp_img,pred)
adam=optimizers.Adam(lr=0.01)
model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['acc'])
model.summary()


model.fit(x_train,y=y_train,epochs=10,batch_size=32)
model.save('mnist_cnn.h5')
loss,acc=model.evaluate(x_test,y_test)
print('\ntest loss: ',loss)
print('\ntest accuracy: ',acc)