import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flatten from keras.optimizers import Adam
# 载入数据 (x_train,y_train),(x_test,y_test) = mnist.load_data() # (60000,28,28)->(60000,28,28,1) x_train = x_train.reshape(-1,28,28,1)/255.0 x_test = x_test.reshape(-1,28,28,1)/255.0 # 换one hot格式 y_train = np_utils.to_categorical(y_train,num_classes=10) y_test = np_utils.to_categorical(y_test,num_classes=10) # 定义顺序模型 model = Sequential() # 第一个卷积层 # input_shape 输入平面 # filters 卷积核/滤波器个数 # kernel_size 卷积窗口大小 # strides 步长 # padding padding方式 same/valid # activation 激活函数 model.add(Convolution2D( input_shape = (28,28,1), filters = 32, kernel_size = 5, strides = 1, padding = \'same\', activation = \'relu\' )) # 第一个池化层 model.add(MaxPooling2D( pool_size = 2, strides = 2, padding = \'same\', )) # 第二个卷积层 model.add(Convolution2D(64,5,strides=1,padding=\'same\',activation = \'relu\')) # 第二个池化层 model.add(MaxPooling2D(2,2,\'same\')) # 把第二个池化层的输出扁平化为1维 model.add(Flatten()) # 第一个全连接层 model.add(Dense(1024,activation = \'relu\')) # Dropout model.add(Dropout(0.5)) # 第二个全连接层 model.add(Dense(10,activation=\'softmax\')) # 定义优化器 adam = Adam(lr=1e-4) # 定义优化器，loss function，训练过程中计算准确率 model.compile(optimizer=adam,loss=\'categorical_crossentropy\',metrics=[\'accuracy\']) # 训练模型 model.fit(x_train,y_train,batch_size=64,epochs=10) # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print(\'test loss\',loss) print(\'test accuracy\',accuracy)