PROSAGA码农传奇-pytorch-如何将跟随张量流转换为pytorch

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  <P>
    代码的张量流部分的pytorch等价物，用代码本身的注释解释，你必须从scipy导入truncnorm。
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   <pre>
    <code>
      from scipy.stats import truncnorm #extra import equivalent to tf.trunc initialise

pooled_outputs_u = []

for i, filter_size in enumerate(filter_sizes):

filter_shape = [filter_size, embedding_size, 1, num_filters]

#W is just a tensor now that will act as weight to the conv layer
    W=torch.tensor(truncnorm.rvs(-1,1,size=[10,10]))
    #bias initialized with 0.1 initial values 
    b=torch.zeros([num_filters])+0.1
    #conv layer with the same parameters as the tensorflow layer more on this in the link
    conv=torch.nn.functional.conv2d(self.embedded_users,W,bias=b,strides=[1,1,1,1],padding=0)
    #can use torch sequential to include it all in a single line but did it like this for better understanding.
    h=torch.relu(conv)
    #look at link2 for what a max pool layer does, basically it is a kind of feature extraction
    pooled=torch.nn.functional.max_pool(h,kernal_size=[1,user_length-filter_size+1,1,1],strides=[1,1,1,1],padding=0)
    pooled_outputs_u.append(pooled)
num_filters_total = num_filters * len(filter_sizes)
self.h_pool_u=torch.concat(3,pooled_outputs_u)
self.h_pool_flat_u=torch.reshape(self.h_pool_u,[-1,num_filters_total])

</code>
  </pre>
  <P>
    参考：
  </p>
  <P>
    <a href="https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/" rel="nofollow noreferrer">
      链接1
    </A>
  </p>
  <P>
    <a href="https://computersciencewiki.org/index.php/Max-pooling_/_Pooling" rel="nofollow noreferrer">
      链接2
    </A>
  </p>
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