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girl2010 04 oct 03 bu.zip cdcdex 2012 1001+ tim.zip.rar 2013,09092013-09-09 03:39:57269.75 Lg Bu Gc - November 2014 you paid for an album, but you receive only one song. It is like buying a CD and receiving just one song. The real problem is that you have already paid for the album and don’t want to pay for the other songs. Or you receive the album, but all the songs are in another format than you expect. And for that you lose your original data, or something. Data recovery is the solution. You choose what data recovery tool you want. Search in the internet and you can find good data recovery tools for Free Download!Q: Tensorflow - replace a specific feature in a tensor I'm working on a binary classification classification task, which uses BOW features and a embedding layer. It is trained on a csv file, which is loaded into a variable called X_train. I would like to alter the weight of some specific elements in the embedding layer. Since I know where the weight needs to be altered, I want to use Tensorflow's built in functions to update the weights directly. The question is, what tensor does Tensorflow have to return to replace a specific feature in a tensor? For example, if I would like to modify the weight of 'item_name_1' in the embedding layer, I want to use the built in functions tf.nn.dropout, where I provide the first item name and the corresponding value as arguments. If it's possible at all, I would also like to apply a sigmoid to this value, which turns it into a probability. The input for tf.nn.dropout is a tensor of size [batch_size, embedding_size]. If I would like to change the weight of a specific feature and applying a sigmoid afterwards, I would have to produce a second tensor of size [batch_size, embedding_size] and concatenate it to the original tensor. The code would look like this, and the comment marked as the problem is the part where I don't know what should I provide as an argument to tf.nn.dropout: # sigmoid(x) = (1+x)/(1+e^x)

 

 

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