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)
