Faster training

app.py

@@ -59,16 +59,14 @@ def train(data: str, message: str):
             for p in range(maxshift):
                 tokens = tokenizer.texts_to_sequences([key,])[0]
                 X.append(np.array(([0,]*p+list(tokens)+[0,]*inp_len)[:inp_len]))
-                output_array = np.zeros(resps_len)
-                output_array[dset[key]] = 1
-                y.append(output_array)
+                y.append(dset[key])
         
         X = np.array(X)
         y = np.array(y)
         
-        model.compile(loss="categorical_crossentropy", metrics=["accuracy",])
+        model.compile(loss="sparse_categorical_crossentropy", metrics=["accuracy",])
         
-        model.fit(X, y, epochs=64, batch_size=8, workers=4, use_multiprocessing=True)
+        model.fit(X, y, epochs=32, batch_size=8, workers=4, use_multiprocessing=True)
         model.save("cache/{data_hash}.keras")
     tokens = tokenizer.texts_to_sequences([message,])[0]
     prediction = model.predict(np.array([(list(tokens)+[0,]*inp_len)[:inp_len],]))[0]