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A Comprehension based Question Answering System
There is a bit of redundant code in the functions written in path_utils
. We can refactor it to reuse as much code as possible
calculate and add unknown words' vectors instead of taking random values.
Group the whole model in name_scope to use in tensorboard.
Train and test with the exponential decay function of tensorflow for learning rate in abcnn
Calculating gradient of the inner scan
loop in DT_RNN
gives an error, which I can't seem to fix currently. I'll keep trying to fix it. In the meantime, I guess it'll be better if we start coding the DT-RNN
in TensorFlow
.
I have written the DT-RNN
in numpy
, which can be found here
You can refer to that when making the function in TensorFlow
@mitrjain96 Please change the predictx
function in all the SentEmbd
models to take as input the actual sentence and not the sequence of word vectors as is being done currently.
Line 206 in 4acd8e0
This line uses the python string function split()
for tokenization. Using the nltk
tokenizer would prove better for this purpose.
@mitrjain96 please correct the cosine_similarity
function in Helpers/nn_utils.py
to return the following:
T.dot(A,T.transpose(B))/T.sqrt(T.dot(A,T.transpose(A))*T.dot(B,T.transpose(B)))
Change inputs
to a dict
and pass inputs as inputs1['word_vectors']
instead of inputs1[0]
here
Pass word vectors created in IR centroid to subsequent modules to eliminate redundant vectorization.
@virajshastri97 I have made some updates to the file architecture, so please check if our bAbI model and training and testing codes work properly. Also, check that the new state being pickled is pickled under the path states/dmn_basic/file_name
. Let me know if you get any errors
Add feature to shuffle the input data after each epoch in dtrnnTrain.py
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