本文共 1664 字，大约阅读时间需要 5 分钟。

1.前言

自己定义的CNN结构，基于Keras实现，处理MNIST数据集。

2.Python代码

加载相应库：

import numpy as np np.random.seed(1337)from keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flattenfrom keras.optimizers import Adam

定义训练、测试集：

(X_train,y_train),(X_test,y_test)=mnist.load_data()X_train = X_train.reshape(-1,1,28,28)X_test = X_test.reshape(-1,1,28,28)y_train = np_utils.to_categorical(y_train,num_classes=10)y_test = np_utils.to_categorical(y_test,num_classes=10)

定义网络结构：

model=Sequential()# Conv layer 1 output shape (32,28,28)model.add(Convolution2D(nb_filter=32,nb_row=5,nb_col=5,border_mode='same',    # padding methodinput_shape=(1,28,28),))model.add(Activation('relu'))# Pooling layer 1 (max pooling) output shape (32,14,14)model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode='same',))# Conv layer 2 output shape (64,14,14)model.add(Convolution2D(64,5,5,border_mode='same'))model.add(Activation('relu'))# Pooling layer 2 (max pooling) output shape (64,7,7)model.add(MaxPooling2D(pool_size=(2,2),border_mode='same'))# Fully connected layer 1 input shape (64*7*7)=(3136)model.add(Flatten())model.add(Dense(1024))model.add(Activation('relu'))# Fully connected layer 2 to shape (10) for 10 classesmodel.add(Dense(10))model.add(Activation('softmax'))adam = Adam(lr=1e-4)model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])

print('Training----------')model.fit(X_train,y_train,nb_epoch=1,batch_size=32,)print('Testing----------')loss,accuracy = model.evaluate(X_test,y_test)print('\ntest loss:',loss)print('\naccyracy:',accuracy)

转载地址：http://gihnwy.baihongyu.com/