如何使用keras提取CNN激活？

Question

我想使用keras从第一个完全连接的层提取CNN激活。在Caffe中有这样一个函数，但我不能使用这个框架，因为我面临安装问题。我正在读一篇使用CNN激活的研究论文，但作者使用的是Caffe

有没有办法提取这些CNN激活，这样我就可以使用数据挖掘关联规则，apriori算法将它们作为事务中的项目使用

当然，首先我必须提取CNN激活的k最大量级。因此，每个图像都是一个事务，每个激活都是一个项目

到目前为止，我有以下代码：

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.layers import Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
import matplotlib.pylab as plt

model = Sequential()
model.add(Conv2D(32, kernel_size=(5, 5), strides=(1, 1),
                 activation='relu',
                 input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])

Answer 1

使用Tensorflow Keras提到下面的解决方案

为了能够访问Activations，首先我们应该传递一个或多个图像，然后激活对应于这些图像

传递Input Image及其preprocessing的代码如下所示：

from tensorflow.keras.preprocessing import image

Test_Dir = '/Deep_Learning_With_Python_Book/Dogs_Vs_Cats_Small/test/cats'
Image_File = os.path.join(Test_Dir, 'cat.1545.jpg')

Image = image.load_img(Image_File, target_size = (150,150))

Image_Tensor = image.img_to_array(Image)

print(Image_Tensor.shape)

Image_Tensor = tf.expand_dims(Image_Tensor, axis = 0)

Image_Tensor = Image_Tensor/255.0

一旦定义了模型，我们就可以使用下面所示的代码访问任意层的Activations（关于猫和狗数据集）：

# Extract the Model Outputs for all the Layers
Model_Outputs = [layer.output for layer in model.layers]
# Create a Model with Model Input as Input and the Model Outputs as Output
Activation_Model = Model(model.input, Model_Outputs)
Activations = Activation_Model.predict(Image_Tensor)

First Fully Connected Layer（关于猫和狗的数据）的输出是：

print('Shape of Activation of First Fully Connected Layer is', Activations[-2].shape)
print('                                             ')
print('Activation of First Fully Connected Layer is', Activations[-2])

其输出如下所示：

Shape of Activation of First Fully Connected Layer is (1, 512)
                                             
Activation of First Fully Connected Layer is [[0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.02759874 0.         0.         0.         0.
  0.         0.         0.00079661 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.04887392 0.         0.
  0.04422646 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.01124999
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.00286965 0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.00027195 0.
  0.         0.02132209 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.00511147 0.         0.         0.02347952 0.
  0.         0.         0.         0.         0.         0.
  0.02570331 0.         0.         0.         0.         0.03443285
  0.         0.         0.         0.         0.         0.
  0.         0.0068848  0.         0.         0.         0.
  0.         0.         0.         0.         0.00936454 0.
  0.00389365 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.00152553 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.09215052 0.         0.         0.0284613  0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.00198757 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.02395868 0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.01150922 0.0119792
  0.         0.         0.         0.         0.         0.
  0.00775307 0.         0.         0.         0.         0.
  0.         0.         0.         0.01026413 0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.01522083 0.         0.00377031 0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.02235368 0.         0.         0.         0.
  0.         0.         0.         0.         0.00317057 0.
  0.         0.         0.         0.         0.         0.
  0.03029975 0.         0.         0.         0.         0.
  0.         0.         0.03843511 0.         0.         0.
  0.         0.         0.         0.         0.         0.02327696
  0.00557329 0.         0.02251234 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.01655817 0.         0.
  0.         0.         0.         0.         0.00221658 0.
  0.         0.         0.         0.02087847 0.         0.
  0.         0.         0.02594821 0.         0.         0.
  0.         0.         0.01515464 0.         0.         0.
  0.         0.         0.         0.         0.00019883 0.
  0.         0.         0.         0.         0.         0.00213376
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.00237587
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.02521542 0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.00490679 0.         0.04504126 0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.        ]]

欲了解更多信息，请参阅Keras之父Francois Chollet所著《中间激活可视化》一书的第5.4.1节

希望这有帮助。学习愉快