very good everyone, thank you for going into my problem, so I'm going to get to the point, it turns out that when I compile with pyinstaller everything works well, tensorflow is imported correctly, I don't see any problems when importing the h5 model either, the problem is when I want to do a prediction with .predict(), when you run the application with the predict line in the application, instead of opening the application an error window opens that says: the code is very simple, this is it import tensorflow as tf modelo = tf.keras.models.load_model("model.h5") numeros_variados = tf.random.uniform(shape=(1, 30), minval=2, maxval=11, dtype=tf.int32) y_pred= modelo.predict(numeros_variados) print(y_pred) Traceback (most recent call last): File "main.py", line 37, in <module> y_pred= modelo.predict(numeros_variados) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "keras\src\utils\traceback_utils.py", line 122, in error_handler File "keras\src\utils\traceback_utils.py", line 119, in error_handler File "keras\src\backend\tensorflow\trainer.py", line 506, in predict File "keras\src\callbacks\callback_list.py", line 126, in on_predict_batch_end File "keras\src\callbacks\progbar_logger.py", line 66, in on_predict_batch_end File "keras\src\callbacks\progbar_logger.py", line 95, in _update_progbar File "keras\src\utils\progbar.py", line 182, in update File "keras\src\utils\io_utils.py", line 98, in print_msg AttributeError: 'NoneType' object has no attribute 'write' I also attach a screenshot of the error, and thank you very much in advance I need to be able to make the .predict() predictions without the error I described above appearing I don't know if it helps but I get this message in the terminal every time I run the script 2024-05-02 13:48:11.074234: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2024-05-02 13:48:14.790349: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0. 2024-05-02 13:48:22.773016: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. model.compile_metrics will be empty until you train or evaluate the model. Continue reading...
