如何使用 keras 提取 CNN 激活？

Question

我想使用 keras 从第一个全连接层中提取 CNN 激活。 Caffe 中有这样的功能，但我无法使用该框架，因为我遇到了安装问题。我正在阅读使用这些 CNN 激活的研究论文，但作者使用的是 Caffe。

有没有办法提取那些 CNN 激活，所以我可以通过使用数据挖掘关联规则、先验算法将它们用作事务中的项目。

当然，首先我必须提取 CNN 激活的 k 个最大幅度。所以每张图片都是一笔交易，每一次激活都是一件物品。

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 提及下面的解决方案。

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 和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

定义模型后，我们可以使用下面显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])

它的输出如下所示：

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.        ]]

有关更多信息，请参阅本书的Section 5.4.1 Visualizing intermediate activations，Deep Learning Using Python，作者是 Keras 之父 Francois Chollet。

希望这会有所帮助。快乐学习！