LeNet-5模型的keras实现

 import keras
 from keras.models import Sequential
 from keras.layers import Input,Dense,Activation,Conv2D,MaxPooling2D,Flatten
 from keras.datasets import mnist

 (x_train,y_train),(x_test,y_test) = mnist.load_data()
 x_train = x_train.reshape(-1, 28, 28, 1)    #######
 x_train = x_train.astype("float32")
 print(x_train.shape)
 y_train = y_train.astype("float32")
 x_test = x_test.reshape(-1,28,28,1)
 x_test = x_test.astype("float32")
 y_test = y_test.astype("float32")

 print(y_train)
 x_train /= 255
 x_test /= 255

 from keras.utils import np_utils
 y_train_new = np_utils.to_categorical(num_classes=10,y=y_train)
 print(y_train_new)
 y_test_new = np_utils.to_categorical(num_classes=10,y=y_test)

 def LeNet_5():
     model = Sequential()
     model.add(Conv2D(filters=6,kernel_size=(5,5),padding="valid",activation="tanh",input_shape=[28, 28, 1]))
     model.add(MaxPooling2D(pool_size=(2,2)))
     model.add(Conv2D(filters=16,kernel_size=(5,5),padding="valid",activation="tanh"))
     model.add(MaxPooling2D(pool_size=(2,2)))
     model.add(Flatten())
     model.add(Dense(120,activation="tanh"))
     model.add(Dense(84,activation="tanh"))
     model.add(Dense(10,activation="softmax"))
     return model

 def train_model():
     model = LeNet_5()
     model.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy"])
     model.fit(x_train,y_train_new,batch_size=64,epochs=1,verbose=1,validation_split=0.2,shuffle=True)
     return model

 model = train_model()

 loss,accuracy = model.evaluate(x_test,y_test_new)
 print(loss,accuracy)