7 Total params: 623290
8 ____________________________________________________________________________________________________
9 Train alt="物联网" width="550" height="138" />
代码：
1 #coding: utf-8
2 #Simple CNN
3 import numpy
4 from keras.datasets import mnist
5 from keras.models import Sequential
6 from keras.layers import Dense
7 from keras.layers import Dropout
8 from keras.layers import Flatten
9 from keras.layers.convolutional import Convolution2D
10 from keras.layers.convolutional import MaxPooling2D
11 from keras.utils import np_utils
12
13 seed = 7
14 numpy.random.seed(seed)
15
16 #加载数据
17 (X_train, y_train), (X_test, y_test) = mnist.load_data()
18 # reshape to be [samples][channels][width][height]
19 X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
20 X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')
21
22 # normalize inputs from 0-255 to 0-1
23 X_train = X_train / 255
24 X_test = X_test / 255
25
26 # one hot encode outputs
27 y_train = np_utils.to_categorical(y_train)
28 y_test = np_utils.to_categorical(y_test)
29 num_classes = y_test.shape[1]
30
31 # define a simple CNN model
32 def baseline_model():
33 # create model
34 model = Sequential()
35 model.add(Convolution2D(32, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu'))
36 model.add(MaxPooling2D(pool_size=(2, 2)))
37 model.add(Dropout(0.2))
38 model.add(Flatten())
39 model.add(Dense(128, activation='relu'))
40 model.add(Dense(num_classes, activation='softmax'))
41 # Compile model
42 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
43 return model
44
45 # build the model
46 model = baseline_model()
47
48 # Fit the model
49 model.fit(X_train, y_train, validation_data=http://www.netofthings.cn/JieJueFangAn/2016-07/(X_test, y_test), nb_epoch=10, batch_size=128, verbose=2)
50
51 # Final evaluation of the model
52 scores = model.evaluate(X_test, y_test, verbose=0)
53 print("CNN Error: %.2f%%" % (100-scores[1]*100))
结果：
1 ____________________________________________________________________________________________________
2 Layer (type) Output Shape Param # Connected to
3 ====================================================================================================
4 convolution2d_1 (Convolution2D) (None, 32, 24, 24) 832 convolution2d_input_1[0][0]
5 ____________________________________________________________________________________________________
6 maxpooling2d_1 (MaxPooling2D) (None, 32, 12, 12) 0 convolution2d_1[0][0]
7 ____________________________________________________________________________________________________
8 dropout_1 (Dropout) (None, 32, 12, 12) 0 maxpooling2d_1[0][0]
9 ____________________________________________________________________________________________________
10 flatten_1 (Flatten) (None, 4608) 0 dropout_1[0][0]
11 ____________________________________________________________________________________________________
12 dense_1 (Dense) (None, 128) 589952 flatten_1[0][0]
13 ____________________________________________________________________________________________________
14 dense_2 (Dense) (None, 10) 1290 dense_1[0][0]
15 ====================================================================================================
16 Total params: 592074
17 ____________________________________________________________________________________________________
18 Train on 60000 samples, validate on 10000 samples
19 Epoch 1/10
20 32s - loss: 0.2412 - acc: 0.9318 - val_loss: 0.0754 - val_acc: 0.9766
21 Epoch 2/10
22 32s - loss: 0.0726 - acc: 0.9781 - val_loss: 0.0534 - val_acc: 0.9829
23 Epoch 3/10
24 32s - loss: 0.0497 - acc: 0.9852 - val_loss: 0.0391 - val_acc: 0.9858
25 Epoch 4/10
26 32s - loss: 0.0413 - acc: 0.9870 - val_loss: 0.0432 - val_acc: 0.9854
27 Epoch 5/10
28 34s - loss: 0.0323 - acc: 0.9897 - val_loss: 0.0375 - val_acc: 0.9869
29 Epoch 6/10
30 36s - loss: 0.0281 - acc: 0.9909 - val_loss: 0.0424 - val_acc: 0.9864