如何利用角蛋白提取CNN激活？
如何利用角蛋白提取CNN激活？
提问于 2020-04-07 19:49:18
我想提取CNN激活从第一个完全连接层使用角。Caffe中有这样一个函数，但我不能使用该框架，因为我面临安装问题。我正在读一篇的研究论文“”，它使用的是CNN的激活，但作者使用的是Caffe。
是否有一种方法可以提取这些CNN激活，以便我可以使用数据挖掘关联规则，apriori算法在中的项目。
当然，首先，我必须提取CNN激活的k最大震级。因此，每个映像都是一个事务，每个激活都是一个项。
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'])回答 1
Stack Overflow用户
回答已采纳
发布于 2020-06-02 13:03:35
提到下面使用Tensorflow Keras的解决方案。
为了能够访问Activations，我们首先应该传递一个或多个映像，然后激活对应于这些映像。
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(关于猫和狗数据集)访问任何层的Activations：
# 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的输出(关于猫和狗的数据)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：
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Deep Learning Using Python的章节5.4.1可视化中间激活的书籍，由Francois，Keras之父。
希望这能有所帮助。学习愉快！
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/61088262
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