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)
