Update main_dl.py

main_dl.py

@@ -225,8 +225,8 @@ def load_all_data():
    
     merge = Merge( dataPath+"/rnn_en-fr_split",  dataPath, "seq2seq_rnn-model-en-fr.h5").merge(cleanup=False)
     merge = Merge( dataPath+"/rnn_fr-en_split",  dataPath, "seq2seq_rnn-model-fr-en.h5").merge(cleanup=False)
-    rnn_en_fr = keras.models.load_model(dataPath+"/seq2seq_rnn-model-en-fr.h5", compile=False)
-    rnn_fr_en = keras.models.load_model(dataPath+"/seq2seq_rnn-model-fr-en.h5", compile=False)
+    rnn_en_fr = keras.models.load_model(dataPath+"/seq2seq_rnn-model-en-fr.h5") # , compile=False)
+    rnn_fr_en = keras.models.load_model(dataPath+"/seq2seq_rnn-model-fr-en.h5") # , compile=False)
     rnn_en_fr.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
     rnn_fr_en.compile(optimizer="rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
     
@@ -271,7 +271,7 @@ def init_dl_identifier():
     lan_identified = [lan_to_language[l] for l in list_lan]
     label_encoder.fit(list_lan)
     merge = Merge(dataPath+"/dl_id_lang_split",  dataPath, "dl_tiktoken_id_language_model.h5").merge(cleanup=False)
-    dl_model = keras.models.load_model(dataPath+"/dl_tiktoken_id_language_model.h5", compile=False)
+    dl_model = keras.models.load_model(dataPath+"/dl_tiktoken_id_language_model.h5") #, compile=False)
     
     if (dl_model is not None): print("dl_model OK")
     else: print("dl_model vide")