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    1. LeNet-5模型的keras实现

       1 import keras
       2 from keras.models import Sequential
       3 from keras.layers import Input,Dense,Activation,Conv2D,MaxPooling2D,Flatten
       4 from keras.datasets import mnist
       5 
       6 
       7 (x_train,y_train),(x_test,y_test) = mnist.load_data()
       8 x_train = x_train.reshape(-1, 28, 28, 1)    #######
       9 x_train = x_train.astype("float32")
      10 print(x_train.shape)
      11 y_train = y_train.astype("float32")
      12 x_test = x_test.reshape(-1,28,28,1)
      13 x_test = x_test.astype("float32")
      14 y_test = y_test.astype("float32")
      15 
      16 print(y_train)
      17 x_train /= 255
      18 x_test /= 255
      19 
      20 from keras.utils import np_utils
      21 y_train_new = np_utils.to_categorical(num_classes=10,y=y_train)
      22 print(y_train_new)
      23 y_test_new = np_utils.to_categorical(num_classes=10,y=y_test)
      24 
      25 def LeNet_5():
      26     model = Sequential()
      27     model.add(Conv2D(filters=6,kernel_size=(5,5),padding="valid",activation="tanh",input_shape=[28, 28, 1]))
      28     model.add(MaxPooling2D(pool_size=(2,2)))
      29     model.add(Conv2D(filters=16,kernel_size=(5,5),padding="valid",activation="tanh"))
      30     model.add(MaxPooling2D(pool_size=(2,2)))
      31     model.add(Flatten())
      32     model.add(Dense(120,activation="tanh"))
      33     model.add(Dense(84,activation="tanh"))
      34     model.add(Dense(10,activation="softmax"))
      35     return model
      36 
      37 def train_model():
      38     model = LeNet_5()
      39     model.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy"])
      40     model.fit(x_train,y_train_new,batch_size=64,epochs=1,verbose=1,validation_split=0.2,shuffle=True)
      41     return model
      42 
      43 model = train_model()
      44 
      45 loss,accuracy = model.evaluate(x_test,y_test_new)
      46 print(loss,accuracy)
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