Update train.py

move len(dset) into dset_size

train.py

@@ -9,6 +9,7 @@ from keras_self_attention import SeqSelfAttention, SeqWeightedAttention
 with open("dataset.json", "r") as f:
     dset = json.load(f)
 
+dset_size = len(dset)
 tokenizer = Tokenizer()
 tokenizer.fit_on_texts(list(dset.keys()))
 
@@ -22,6 +23,6 @@ model.add(SeqSelfAttention()) # an ATTENTION LAYER makes the model LEARN the MAI
 model.add(Flatten()) # SelfAttention and the embedding layer outputs a 2D array, it's a list of words with a list of numbers for each word
 model.add(Dense(1024, activation="relu"))
 model.add(Dropout(0.5)) # dropout makes ___ task harder __ removing ____ information, 0.5 means delete 50% (it resets neurons to 0 so the model will truly focus on what's important, and not learn on some data that's there by accident)
-model.add(Dense(len(dset), activation="linear")) # TBH it doesn't matter that much what activation function to use, just linear does nothing at all to the output, that might be something like softmax but i'll test that later
+model.add(Dense(dset_size, activation="linear")) # TBH it doesn't matter that much what activation function to use, just linear does nothing at all to the output, that might be something like softmax but i'll test that later
 
 model.save("chatbot.keras") # It's obvious what it does, saves the model to a file