diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py index d219613..0ec08c1 100644 --- a/deepface/DeepFace.py +++ b/deepface/DeepFace.py @@ -511,9 +511,7 @@ def analyze(img_path, actions = [], models = {}, enforce_detection = True, detec return resp_objects, orig_faces def detectFace(img_path, detector_backend='opencv'): - imgs = functions.preprocess_face(img=img_path, detector_backend=detector_backend)['processed'] #preprocess_face returns (1, 224, 224, 3) - for i in range(len(imgs)): - imgs[i] = imgs[i][0][:, :, ::-1] #bgr to rgb + imgs = functions.preprocess_face(img=img_path, detector_backend=detector_backend)['original'] #preprocess_face returns (1, 224, 224, 3) return imgs def find(img_path, db_path, model_name ='VGG-Face', distance_metric = 'cosine', model = None, enforce_detection = True, detector_backend = 'opencv'): diff --git a/deepface/commons/functions.py b/deepface/commons/functions.py index 1fb24d4..e8510d9 100644 --- a/deepface/commons/functions.py +++ b/deepface/commons/functions.py @@ -470,6 +470,7 @@ def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detect imgs = detect_face(img=img, detector_backend=detector_backend, grayscale=grayscale, enforce_detection=enforce_detection) + orig = imgs.copy() # -------------------------- @@ -505,7 +506,7 @@ def preprocess_face(img, target_size=(224, 224), grayscale=False, enforce_detect pixels.append(img_pixels) - return {'processed': pixels, 'original': imgs} + return {'processed': pixels, 'original': orig} def allocateMemory(): diff --git a/my_deepface.ipynb b/my_deepface.ipynb index 8c756b9..c8b9ce7 100644 --- a/my_deepface.ipynb +++ b/my_deepface.ipynb @@ -43,7 +43,7 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, "execution_count": 3, "metadata": {}, @@ -77,7 +77,7 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": "" }, "execution_count": 4, "metadata": {}, @@ -113,56 +113,147 @@ "name": "stderr", "output_type": "stream", "text": [ - "Action: emotion: 100%|██████████| 3/3 [00:01<00:00, 2.11it/s]\n" + "Action: emotion: 100%|██████████| 3/3 [00:14<00:00, 4.96s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:5 out of the last 17 calls to .predict_function at 0x7fe2ddd7caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:6 out of the last 19 calls to .predict_function at 0x7fe2dd798ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 20 calls to .predict_function at 0x7fe2dd9e09d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 21 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2de88c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dd9e0f70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2dd798160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ddc954c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd9e01f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 13 calls to .predict_function at 0x7fe2de88c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de3ec790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe31d495e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2dfd92430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dfcc93a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de5544c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2de554ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2147310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2147ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2df888940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de3ec160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de00a8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df888310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ddc95dc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 13 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e239fa60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de840b80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df2435e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2dd510700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de5540d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc9670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2de554c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e43f94c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e3d5c280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e4faad30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e444cc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d121f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfcc91f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dd5109d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df243820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e3d5c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de7e4670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ddc95040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de00a040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ddc95280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2de554af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2dfd92550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e239f820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e274c820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", "[262 48 88 88]\n", - "[57 68 88 88]\n", "[649 54 99 99]\n", + "[57 68 88 88]\n", "[448 57 92 92]\n", "[450 251 87 87]\n", "[831 250 96 96]\n", "[264 255 85 85]\n", "[653 272 84 84]\n", "[448 438 96 96]\n", - "[649 446 101 101]\n", "[256 456 93 93]\n", + "[649 446 101 101]\n", "[643 644 104 104]\n", "[ 65 639 79 79]\n", "[847 641 86 86]\n", "[248 655 114 114]\n", + "[835 819 92 92]\n", + "[450 830 93 93]\n", + "[256 832 88 88]\n", "[643 838 91 91]\n", - "[835 819 92 92]\n", - "[450 830 93 93]\n", - "[256 832 88 88]\n", - "[262 48 88 88]\n", - "[649 54 99 99]\n", - "[57 68 88 88]\n", - "[448 57 92 92]\n", - "[450 251 87 87]\n", - "[653 272 84 84]\n", - "[831 250 96 96]\n", - "[264 255 85 85]\n", - "[448 438 96 96]\n", - "[256 456 93 93]\n", - "[649 446 101 101]\n", - "[643 644 104 104]\n", - "[ 65 639 79 79]\n", - "[847 641 86 86]\n", - "[248 655 114 114]\n", - "[835 819 92 92]\n", - "[450 830 93 93]\n", - "[256 832 88 88]\n", - "[643 838 91 91]\n" + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e6572e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659be50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" ] } ], "source": [ - "demography, imgs = DeepFace.analyze(imgs[0], actions = ['age', 'gender', 'emotion'])\n", + "demography, imgs = DeepFace.analyze(imgs[0], actions=['age', 'gender', 'emotion'],\n", + " detector_backend='mtcnn')\n", "# print(\"Age: \", demography[\"age\"])\n", "# print(\"Gender: \", demography[\"gender\"])\n", "# print(\"Emotion: \", demography[\"dominant_emotion\"])" @@ -176,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "outputs": [ { "name": "stdout", @@ -189,16 +280,16 @@ }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 24, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -222,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "outputs": [ { "name": "stdout", @@ -235,9 +326,9 @@ }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 25, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -268,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "outputs": [ { "name": "stdout", @@ -281,16 +372,16 @@ }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 26, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -314,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "outputs": [ { "name": "stdout", @@ -322,14 +413,14 @@ "text": [ "Age: 35.126786088918394\n", "Gender: Man\n", - "Emotion: neutral\n" + "Emotion: happy\n" ] }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 27, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -360,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "outputs": [ { "name": "stdout", @@ -368,21 +459,21 @@ "text": [ "Age: 37.149563607061395\n", "Gender: Man\n", - "Emotion: fear\n" + "Emotion: happy\n" ] }, { "data": { - "text/plain": "" + "text/plain": "" }, - "execution_count": 28, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -406,7 +497,229 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "19" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(demography)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:5 out of the last 13 calls to .predict_function at 0x7fe2e444ce50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:6 out of the last 14 calls to .predict_function at 0x7fe2e274cd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 15 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 16 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 17 calls to .predict_function at 0x7fe2e43f9ca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e4a9d9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e65b71f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e2828670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e76cf8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e659b8b0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b160> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e87dcca0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e80c9d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e913f9d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e9125940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e896baf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e921eb80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea9b7550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea373ee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896bdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e9d5f280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e921ea60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea9b7c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e913f3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649daf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e76cf940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e896b700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e659b670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e4a9d5e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e649d310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e3d12940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e43f9550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e274cf70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 13 calls to .predict_function at 0x7fe2de554700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e9d5f940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ea373670> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e3d5caf0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ec5a40d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2ea373a60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ebd9fc10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2eac0c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ee2afe50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2e444c940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ee208d30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2eda6ac10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 11 calls to .predict_function at 0x7fe2ebd9f550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e3d5c4c0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2ec5a4430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2edd11430> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2ec5a4e50> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e6572af0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2df243c10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2df243550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e4a9d790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 13 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e444cee0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e2828310> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e649da60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e659b550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e896b040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 12 calls to .predict_function at 0x7fe2e91253a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 13 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:10 out of the last 12 calls to .predict_function at 0x7fe2e87dc3a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913fdc0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:8 out of the last 11 calls to .predict_function at 0x7fe2e921e280> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 12 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:9 out of the last 11 calls to .predict_function at 0x7fe2e913f040> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e9d5f1f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "WARNING:tensorflow:7 out of the last 11 calls to .predict_function at 0x7fe2e9d5fd30> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + } + ], + "source": [ + "faces = DeepFace.detectFace(img1, detector_backend='mtcnn')\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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