I am performing text classification as Good  or Bad . The texts are preprocessed and converted to Vectors using Google Word2Vec. Further CNN architecture is used for training. I have roughly 13000 texts as Bad and 5450 texts as Good for training (making it a roughly 70:30%) The issue starts when I realize I don't have enough compute power (2GB GPU). Hence I compromise and use 100 dimensions of word embeddings from Word2Vec (instead of the 300). After certain hyperparameter tuning in the CNN architecture, I am able to obtain a 30-35% precision, which I am happy with. After months, I have an 8GB GPU in the server and I implemented the 300-dimensional word embeddings in Word2Vec and kept for training. Ideally, I should have obtained better results; instead, the loss and accuracy don't change with time for every epoch. Thus it predicts all texts as Bad. Can you please help me identify the problem and If I am missing out anything here!
I would like to add some clarifications: In the Linux server with GPU 1070Ti 8GB, I tried with three experiments in this order: a) 300-dimensional word embeddings b) 100-dimensional word-embeddings and c) 105-dimensional word embeddings I have obtained no change in accuracy and loss for a) and c). However, for b), it is exactly the same results as I obtained with my local GPU(750Ti Nvidia 2GB). In short, its working fine for 100-dimensional in the server.
Now, since I can't assign 300d word vectors in my local GPU, I make up the same experiment as c) with 105d vectors in the local GPU to just check if there's any fault in code, and surprisingly it's giving around 30% precision much similar to earlier results.
I am having a hard time figuring out the issue in the server GPU as its working fine for 100d word vectors but fail to give proper predictions for other dimensional word-embeddings.
I am attaching some results which might make it a lot clear:
1.) Trained with 100d vectors in local(2GB) GPU precision recall f1-score support
0.0 0.70 0.83 0.76 1973 1.0 **0.31** 0.17 0.22 850
avg / total 0.58 0.63 0.60 2823
2.) Trained with 100d vectors in server (8GB) GPU precision recall f1-score support
0.0 0.70 0.77 0.73 1973 1.0 **0.30** 0.23 0.26 850
avg / total 0.58 0.61 0.59 2823
3.) This is the same result for both 105d and 300d vectors trained in server GPU precision recall f1-score support
0.0 0.70 1.00 0.82 1973 1.0 **0.00** 0.00 0.00 850
avg / total 0.49 0.70 0.58 2823
4.) Trained with 105d vectors in local(2GB) GPU precision recall f1-score support
0.0 0.70 0.83 0.76 1973 1.0 **0.30** 0.18 0.23 850
avg / total 0.57 0.64 0.61 2823