Use model_settings.py

train.py

@@ -5,6 +5,7 @@ from keras.models import Sequential
 from keras.layers import Embedding, Dense, Dropout, Flatten, PReLU
 from keras.preprocessing.text import Tokenizer
 from keras_self_attention import SeqSelfAttention, SeqWeightedAttention
+from model_settings import *
 
 
 with open("dataset.json", "r") as f: # TODO: move the outputs into a separate file, so it would be "key": 0, "key2": 1 etc
@@ -14,7 +15,6 @@ dset_size = len(dset)
 tokenizer = Tokenizer() # a tokenizer is a thing to split text into words, it might have some other stuff like making all the letters lowercase, etc.
 tokenizer.fit_on_texts(list(dset.keys()))
 
-emb_size = 128 # how big are the word vectors in the input (how much information can be fit into one word)
 vocab_size = len(tokenizer.get_vocabulary())
 inp_len = 10 # limit of the input length, after 10 words the