cnn-text-classification-tf | Convolutional Neural Network for Text Classification | Machine Learning library

i came across Denny Britz implementation in tensorflow link of yoon kim's work (Convolutional Neural Networks for Sentence Classification), i would like to perform SMOTE (oversampling) on the embedding vectors of each sentence, am not sure on how to get thies embeddings, or if i can do oversampling before the embeddings,here is Denny's embedding part :

I am referring https://github.com/dennybritz/cnn-text-classification-tf as a reference. My goal is to build frozen graph from model files. I want to know input and output node in the signature to effectively build the frozen graph. I am printing the proto file from the graph definition using the following code.

I am referring (here) to freeze models into .pb file. My model is CNN for text classification I am using (Github) link to train CNN for text classification and exporting in form of models. I have trained models to 4 epoch and My checkpoints folders look as follows:

I want to freeze this model into (.pb file). For that I am using following script:

I have trained a model using the Wild ML implementation of a CNN which can be found here, and deployed it to Google Cloud Platform. I am now trying to send a JSON prediction request to the model, but I am getting the following error:

I use python and tensorflow CNN for text classification. lrp_toolbox is a explain model of the neural network, it will provide the reason of classification of neural network model like "CNN", "RNN". The input of lrp_toolbox is trained "CNN" model with the following format.

I don't know how to make this format from the sess or graph of tensorflow CNN. I try to pickle the sess use code:

filename = 'trainedCNN_model_%s.pickle' % str(current_step) pickle.dump(sess, open(filename, 'wb')),

but it fails and shows TypeError: can't pickle module objects.

And actually, I think sess can't meet the input format of lrp_toolbox. The example input of lrp_toolbox is text format like

Linear 2 3 -2.01595799878 -2.05379403106 0.688953420218 1.20338836267 -1.7518249173 -1.90515935663 -0.519917325831 0.400368842573 0.0699950389094 Tanh Linear 3 3 -1.18542075899 -1.62921811225 0.134698917906 0.111469267787 1.85227669488 -0.350827317469 0.102194311719 -1.67678396935 0.256312518679 0.116095097279 -0.0138454065897 0.0469443958438 Tanh Linear 3 2 1.10940574175 0.26799513777 2.51842248017 -1.5497671807 -0.606042655911 0.197763706892 -0.115832556216 0.115832556216 SoftMax Here is the manual of lrp_toolbox.

I am new to tensorflow and neural network. I started a project which is about detecting errors in persian texts. I used the code in this address and developed the code in here. please check the code because I can not put all the code here.

What I want to do is to give several persian sentences to the model for training and then see if model can detect wrong sentences. The model works fine with english data but when I use it for persian data I encounter this issue.

The code is too long to be written here so I try to point to the part I think might be causing the issue. I used these lines in train.py which works fine and stores vocabularies:

I am following this tutorial in order to understand CNNs in NLP. There are a few things which I don't understand despite having the code in front of me. I hope somebody can clear a few things up here.

The first rather minor thing is the sequence_lengthparameter of the TextCNN object. In the example on github this is just 56 which I think is the max-length of all sentences in the training data. This means that self.input_x is a 56-dimensional vector which will contain just the indices from the dictionary of a sentence for each word.

This list goes into tf.nn.embedding_lookup(W, self.intput_x) which will return a matrix consisting of the word embeddings of those words given by self.input_x. According to this answer this operation is similar to using indexing with numpy:

I've tried to compare the tutorial code for text classification from tflearn : https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py

And the one from dennybritz : https://github.com/dennybritz/cnn-text-classification-tf

These 2 codes shows different result, i understand that it can be because the tflearn tutorial use 1d convolution, but there is one line of code that i don't understand:

network = global_max_pool(network)

What is the difference between global_max_pool and max_pool_2d?