def simple_cnn():
 input_data = Input(shape=(28, 28, 1))
 x = Conv2D(64, kernel_size=3, padding='same', activation='relu', name='conv1')(input_data)
 x = MaxPooling2D(pool_size=2, strides=2, name='maxpool1')(x)
 x = Conv2D(32, kernel_size=3, padding='same', activation='relu', name='conv2')(x)
 x = MaxPooling2D(pool_size=2, strides=2, name='maxpool2')(x)
 x = Dropout(0.25)(x)
 # 获得最后一层卷积层的输出
 # 添加自己的全连接
 x = Flatten(name='flatten')(x)
 x = Dense(128, activation='relu', name='fc1')(x)
 x = Dropout(0.25)(x)
 x = Dense(10, activation='softmax', name='fc2')(x)
 model = Model(inputs=input_data, outputs=x)

input_data = Input(shape=(28, 28, 1))
 x = Conv2D(64, kernel_size=3, padding='same', activation='relu', name='conv1')(input_data)
 x = MaxPooling2D(pool_size=2, strides=2, name='maxpool1')(x)
 x = Conv2D(32, kernel_size=3, padding='same', activation='relu', name='conv2')(x)
 x = MaxPooling2D(pool_size=2, strides=2, name='maxpool2')(x)
 x = Dropout(0.25)(x)
 x = Flatten(name='flatten')(x)
 x = Dense(128, activation='relu', name='fc1')(x)
 x = Dropout(0.25)(x)
 x = Dense(10, activation='softmax', name='fc2')(x)
 model = Model(inputs=input_data, outputs=x)
 
 model.load_weights('final_model_mnist_2019_1_28.h5')
 
 raw_img = cv2.imread('test.png')
 test_img = load_img('test.png', color_mode='grayscale', target_size=(28, 28))
 test_img = np.array(test_img)
 test_img = np.expand_dims(test_img, axis=0)
 test_img = np.expand_dims(test_img, axis=3)
 
 conv1_layer = Model(inputs=input_data, outputs=model.get_layer(index=1).output)
 
 conv1_output = conv1_layer.predict(test_img)
 
 for i in range(64):
  show_img = conv1_output[:, :, :, i]
  print(show_img.shape)
  show_img.shape = [28,28]
  cv2.imshow('img', show_img)
  cv2.waitKey(0)