{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceType":"modelInstanceVersion","sourceId":827684,"databundleVersionId":16609959,"modelInstanceId":629366,"modelId":641290}],"dockerImageVersionId":31328,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Load Model","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2026-04-11T20:17:21.615247Z","iopub.execute_input":"2026-04-11T20:17:21.615619Z","iopub.status.idle":"2026-04-11T20:17:58.316214Z","shell.execute_reply.started":"2026-04-11T20:17:21.615585Z","shell.execute_reply":"2026-04-11T20:17:58.315187Z"}}},{"cell_type":"code","source":"import tensorflow as tf\nimport numpy as np\n\n# ==========================================\n# 1. HIDE THE GPU (CRITICAL FIX FOR CUDNN)\n# ==========================================\ntf.config.set_visible_devices([], 'GPU')\n\nprint(\"Loading model...\")\nmodel = tf.keras.models.load_model('/kaggle/input/models/basanttyasser/violencemodel/keras/default/1/modelv2.keras')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-12T11:29:48.116003Z","iopub.execute_input":"2026-04-12T11:29:48.116813Z","iopub.status.idle":"2026-04-12T11:30:16.389896Z","shell.execute_reply.started":"2026-04-12T11:29:48.116778Z","shell.execute_reply":"2026-04-12T11:30:16.389137Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stderr","text":"2026-04-12 11:29:49.690710: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1775993389.872067      55 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1775993389.926223      55 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\nW0000 00:00:1775993390.365196      55 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1775993390.365241      55 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1775993390.365244      55 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\nW0000 00:00:1775993390.365246      55 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n","output_type":"stream"},{"name":"stdout","text":"Loading model...\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.12/dist-packages/keras/src/saving/saving_lib.py:802: UserWarning: Skipping variable loading for optimizer 'adam', because it has 104 variables whereas the saved optimizer has 108 variables. \n  saveable.load_own_variables(weights_store.get(inner_path))\n","output_type":"stream"}],"execution_count":1},{"cell_type":"markdown","source":"## A: Dynamic-Range-Quantization","metadata":{}},{"cell_type":"code","source":"# 2. Initialize converter\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\n\n# 3. Add the flags to prevent the converter from hanging / failing on the LSTM\nconverter.target_spec.supported_ops = [\n    tf.lite.OpsSet.TFLITE_BUILTINS, # Standard TFLite ops\n    tf.lite.OpsSet.SELECT_TF_OPS    # Let it use TF ops for the complex LSTM parts\n]\nconverter._experimental_lower_tensor_list_ops = False\n\n# 4. Convert (This should be much faster!)\nprint(\"Starting conversion... this may still take 2-5 minutes, please wait.\")\ntflite_quant_model = converter.convert()\n\n# 5. Save the file\nwith open('model_dynamic_quant.tflite', 'wb') as f:\n    f.write(tflite_quant_model)\n    \nprint(\"Quantized model saved successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-12T10:52:55.277403Z","iopub.execute_input":"2026-04-12T10:52:55.277778Z","iopub.status.idle":"2026-04-12T10:52:59.089497Z","shell.execute_reply.started":"2026-04-12T10:52:55.277702Z","shell.execute_reply":"2026-04-12T10:52:59.087660Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"Starting conversion... this may still take 2-5 minutes, please wait.\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_55/2824784063.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# 4. Convert (This should be much faster!)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Starting conversion... this may still take 2-5 minutes, please wait.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtflite_quant_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# 5. Save the file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/tensorflow/lite/python/lite.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1248\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1249\u001b[0m     \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1250\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_and_export_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconvert_func\u001b[0m\u001b[0;34m,\u001b[0m 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Call add_endpoint().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m             )\n\u001b[0;32m--> 510\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_and_track_resources\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m         \u001b[0msignatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/export/saved_model.py\u001b[0m in \u001b[0;36m_filter_and_track_resources\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;31m# Start by extracting variables from endpoints.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m         \u001b[0mfns\u001b[0m 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definition of the function for these arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m     self._concrete_variable_creation_fn = tracing_compilation.trace_function(\n\u001b[0m\u001b[1;32m    697\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m     )\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py\u001b[0m in \u001b[0;36mtrace_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m    176\u001b[0m       \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m 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self._run_through_graph(\n\u001b[0m\u001b[1;32m    184\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m             operation_fn=lambda op: operation_fn(\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/ops/function.py\u001b[0m in \u001b[0;36m_run_through_graph\u001b[0;34m(self, inputs, operation_fn, call_fn)\u001b[0m\n\u001b[1;32m    175\u001b[0m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;31m# Update tensor_dict.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    646\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         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\u001b[0;31m# Implementation #1: is the time axis is static, use a Python for loop.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/models/functional.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask, **kwargs)\u001b[0m\n\u001b[1;32m    181\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_keras_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m         outputs = self._run_through_graph(\n\u001b[0m\u001b[1;32m    184\u001b[0m             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layout for the layer output if needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    938\u001b[0m                 \u001b[0;31m# This is useful for relayout intermediate tensor in the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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)\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/keras/src/backend/tensorflow/nn.py\u001b[0m in \u001b[0;36mbatch_normalization\u001b[0;34m(x, mean, variance, axis, offset, scale, epsilon)\u001b[0m\n\u001b[1;32m    877\u001b[0m             \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 879\u001b[0;31m     return tf.nn.batch_normalization(\n\u001b[0m\u001b[1;32m    880\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m         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below.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m 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behavior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   5621\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5622\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name_scope\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5623\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name_scope\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5624\u001b[0m     \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5625\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_g_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/lib/python3.12/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    138\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mname_scope\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   3387\u001b[0m         \u001b[0;31m# that are illegal as the initial character of an op name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3388\u001b[0m         \u001b[0;31m# (viz. '-', '\\', '/', and '_').\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3389\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_VALID_SCOPE_NAME_REGEX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3390\u001b[0m           raise ValueError(\n\u001b[1;32m   3391\u001b[0m               \u001b[0;34mf\"'{name}' is not a valid scope name. A scope name has to match \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":3},{"cell_type":"markdown","source":"## B: Float16-Quantization","metadata":{}},{"cell_type":"code","source":"# 3. Initialize converter\nconverter = tf.lite.TFLiteConverter.from_keras_model(model)\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\n\n# 4. Set to Float16 (Method B)\nconverter.target_spec.supported_types = [tf.float16]\n\n# 5. Fix for dynamic LSTM loops\nconverter.target_spec.supported_ops = [\n    tf.lite.OpsSet.TFLITE_BUILTINS, \n    tf.lite.OpsSet.SELECT_TF_OPS    \n]\nconverter._experimental_lower_tensor_list_ops = False\n\nprint(\"Starting conversion... please wait. This may take a few minutes.\")\n\n# 6. Convert the model\ntflite_fp16_model = converter.convert()\n\n# 7. Save the file\nwith open('model_fp16_quant.tflite', 'wb') as f:\n    f.write(tflite_fp16_model)\n    \nprint(\"Float16 Quantized model saved successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-12T11:20:56.261584Z","iopub.execute_input":"2026-04-12T11:20:56.262606Z","iopub.status.idle":"2026-04-12T11:21:39.513520Z","shell.execute_reply.started":"2026-04-12T11:20:56.262570Z","shell.execute_reply":"2026-04-12T11:21:39.512638Z"},"collapsed":true,"jupyter":{"outputs_hidden":true}},"outputs":[{"name":"stdout","text":"Starting conversion... please wait. This may take a few minutes.\nINFO:tensorflow:Assets written to: /tmp/tmp6cqcvgmr/assets\n","output_type":"stream"},{"name":"stderr","text":"INFO:tensorflow:Assets written to: /tmp/tmp6cqcvgmr/assets\n","output_type":"stream"},{"name":"stdout","text":"Saved artifact at '/tmp/tmp6cqcvgmr'. The following endpoints are available:\n\n* Endpoint 'serve'\n  args_0 (POSITIONAL_ONLY): TensorSpec(shape=(None, 16, 224, 224, 3), dtype=tf.float32, name='input_layer_1')\nOutput Type:\n  TensorSpec(shape=(None, 2), dtype=tf.float32, name=None)\nCaptures:\n  134557616995216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612229648: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612229840: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557616996176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612229072: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612230416: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612231376: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612230800: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612230992: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612231952: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612230032: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612232720: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612233680: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612233296: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612232528: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612230608: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612234448: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612234256: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612233104: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612235024: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612231568: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612236176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612236368: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612235600: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612233872: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612235216: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612234832: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612234640: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612235792: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612237328: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612236752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612236944: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612237136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612235984: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612238288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612237712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612237904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612238096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612234064: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612239248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612239632: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612239824: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612238864: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612239440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240400: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240784: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240976: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240016: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240592: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612241552: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612239056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612241168: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612241360: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612238480: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612242512: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612241936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612242128: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612242320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612238672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612243472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612242896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612243088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612243280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612240208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612244432: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612243856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612241744: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612244624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612244048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612244816: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612244240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608444752: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608444560: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612242704: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557612243664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608443984: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608445136: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608444944: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608444176: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608446288: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608445712: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608445904: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608446096: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608445328: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608447248: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608446672: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608446864: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608447056: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608444368: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448208: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608447632: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608447824: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448016: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608445520: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608449168: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448592: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448784: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448976: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608446480: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608450128: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608449552: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608449744: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608449936: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608447440: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608451088: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608450512: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608450704: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608450896: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608448400: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608452048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608451472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608451664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608451856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608449360: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453008: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608452432: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608452624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608452816: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608450320: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453968: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453392: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453584: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608451280: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608454928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608454352: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608454544: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608454736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608452240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608455888: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608455312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608455504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608455696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608453200: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608456848: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608456272: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608456464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608456656: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608454160: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608457808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608457232: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608457424: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608457616: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608455120: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458768: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458192: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458384: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458576: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608456080: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608459728: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608459152: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608457040: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608459920: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608459344: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608460112: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608459536: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501244624: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501244816: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458000: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557608458960: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501243472: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501244240: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501244432: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501244048: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501245776: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501245200: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501245392: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501245584: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501243664: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501246736: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501246160: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501246352: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501246544: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501243856: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501247696: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501247120: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501247312: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501247504: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557501245008: 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TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502166544: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502166928: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502168656: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502169808: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502168080: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502168464: TensorSpec(shape=(), dtype=tf.resource, name=None)\n  134557502170384: TensorSpec(shape=(), dtype=tf.resource, name=None)\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nW0000 00:00:1775992887.465960      55 tf_tfl_flatbuffer_helpers.cc:365] Ignored output_format.\nW0000 00:00:1775992887.466041      55 tf_tfl_flatbuffer_helpers.cc:368] Ignored drop_control_dependency.\nI0000 00:00:1775992888.143179      55 mlir_graph_optimization_pass.cc:425] MLIR V1 optimization pass is not enabled\n","output_type":"stream"},{"name":"stdout","text":"Float16 Quantized model saved successfully!\n","output_type":"stream"}],"execution_count":2},{"cell_type":"markdown","source":"## C: Full-Intger-Quantization","metadata":{}},{"cell_type":"code","source":"# ==========================================\n# 2. CREATE A REPRESENTATIVE DATASET\n# ==========================================\n# TFLite needs to push data through the model to calibrate INT8 ranges.\n# IMPORTANT: Replace `dummy_data` below with a real slice of your training video data!\ndef representative_data_gen():\n    # We run 100 samples through the model for calibration\n    for _ in range(100):\n        # Based on your logs, your model expects: (Batch=1, Frames=16, H=224, W=224, Channels=3)\n        # Ideally, load 1 real video array here instead of random numbers.\n        dummy_data = np.random.rand(1, 16, 224, 224, 3).astype(np.float32)\n        yield [dummy_data]\n\n# ==========================================\n# 3. FORCE A STATIC SHAPE (Crucial for INT8 LSTMs)\n# ==========================================\n# We replace the dynamic \"None\" batch size with a strict \"1\"\nrun_model = tf.function(lambda x: model(x))\nconcrete_func = run_model.get_concrete_function(\n    tf.TensorSpec(shape=(1, 16, 224, 224, 3), dtype=tf.float32)\n)\n\n# Initialize the converter using the locked-shape function\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\n\n# ==========================================\n# 4. INT8 & LSTM FLAGS\n# ==========================================\n# Provide the dataset generator\nconverter.representative_dataset = representative_data_gen\n\n# Set supported ops to allow INT8, standard ops, AND fallback TF ops for the LSTM loops\nconverter.target_spec.supported_ops = [\n    tf.lite.OpsSet.TFLITE_BUILTINS_INT8, # Force INT8 where possible (CNN parts)\n    tf.lite.OpsSet.TFLITE_BUILTINS,      # Fallback to Float32 standard ops if needed\n    tf.lite.OpsSet.SELECT_TF_OPS         # Fallback to TF ops for LSTM loops\n]\nconverter._experimental_lower_tensor_list_ops = False\n\n# Force the Input and Output arrays to be purely INT8\nconverter.inference_input_type = tf.int8\nconverter.inference_output_type = tf.int8\n\n# ==========================================\n# 5. CONVERT AND SAVE\n# ==========================================\nprint(\"Starting Full INT8 Quantization... (This will take a few minutes)\")\ntflite_int8_model = converter.convert()\n\nwith open('model_full_int8.tflite', 'wb') as f:\n    f.write(tflite_int8_model)\n    \nprint(\"Full INT8 Quantized model saved successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-12T11:30:23.279895Z","iopub.execute_input":"2026-04-12T11:30:23.281064Z","iopub.status.idle":"2026-04-12T11:39:27.485795Z","shell.execute_reply.started":"2026-04-12T11:30:23.281027Z","shell.execute_reply":"2026-04-12T11:39:27.484477Z"}},"outputs":[{"name":"stderr","text":"WARNING:absl:Please consider providing the trackable_obj argument in the from_concrete_functions. Providing without the trackable_obj argument is deprecated and it will use the deprecated conversion path.\nI0000 00:00:1775993433.934218      55 devices.cc:67] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n","output_type":"stream"},{"name":"stdout","text":"Starting Full INT8 Quantization... (This will take a few minutes)\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1775993433.934488      55 single_machine.cc:374] Starting new session\nI0000 00:00:1775993434.038307      55 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15511 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n/usr/local/lib/python3.12/dist-packages/tensorflow/lite/python/convert.py:854: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n  warnings.warn(\nW0000 00:00:1775993448.507319      55 tf_tfl_flatbuffer_helpers.cc:365] Ignored output_format.\nW0000 00:00:1775993448.507369      55 tf_tfl_flatbuffer_helpers.cc:368] Ignored drop_control_dependency.\nINFO: Created TensorFlow Lite delegate for select TF ops.\nI0000 00:00:1775993451.281622      55 gpu_device.cc:2019] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15511 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\nINFO: TfLiteFlexDelegate delegate: 1661 nodes delegated out of 5075 nodes with 57 partitions.\n\nfully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\nerror: scale out of expressed type range [5.960464e-08, 6.550400e+04]\n","output_type":"stream"},{"name":"stdout","text":"Full INT8 Quantized model saved successfully!\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}