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:book: Some language modeling tools for Keras

Results 29 keras-language-modeling issues
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Hi, I try changing the attention model to cnn without success. I get complaints about shape of input layers. Can you give me some ideas what to fix in order...

I checked out code from master branch and printed sims shape. in insurance_qa_eval.py, after line 179 (sims = self.model.predict([question, answers]) ), I printed sims shape "print(sims.shape)" nb_epoch is set to...

In the blog post and in the related literature about attention LSTM, attention is incorporated like `attention_state = tanh(dot(attention_vec, W_attn) + dot(new_hidden_state, U_attn))` However, in attention_lstm.py it is incorporated like:...

Hi, I am trying your work on different data sets, such as the WikiQA. So I think I need to retrain word2vec weights and change the line: 'initial_embed_weights': np.load('word2vec_100_dim.embeddings'). But...

Hi, I mean without doing something not in the paper [dos santos 2016](http://arxiv.org/abs/1602.03609) I am mentioning 70% coz it is what the author of this paper reported on using the...

Thank you for your great works and support. Continue to do testing on "insurance_qa_eval.py", encountered error below: ###### Using Theano backend. WARNING (theano.configdefaults): g++ not detected ! Theano will be...

Hi, I am wondering why you calculate the top 1 precision by check whether the answer assigned the maximum score by the model is the good answer assigned the maximum...

I use the latest version and check the similarity part of the model, i.e., qa_model = merge([question_output, answer_output], mode=similarity, output_shape=lambda x: x[:-1],name='similarity') When I invoke qa_model.summary(), here is the screen...

There are four possibilities validation V1 validation V2 test V1 test V2 Or you split any of them? Many thanks.

Hi, I can't find the implementation of this model that was added recently to the [results](https://github.com/codekansas/keras-language-modeling/blob/master/results.notes), and that appears to perform much better than the other ones.