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Isuru0x01

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tensorflow-issues

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/68478/checks?check_run_id=25304225149) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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[ "Hi @filip-halt ,\r\n\r\nCan you please provide the tflite model file so that i can replicate the issue?", "> Hi @filip-halt ,\r\n> \r\n> Can you please provide the tflite model file so that i can replicate the issue?\r\n\r\nYou can find a copy here: https://github.com/filip-halt/tflite_bug It was too large to upload directly into this chat.", "Turns out that this is most likely due to a conv2dtranspose layer in the model. I was under the impression that [conv2dtranpose](https://github.com/tensorflow/tensorflow/blob/43ce5b4267d89b9e7f96821495ce4f6bdc7aa1e6/tensorflow/lite/delegates/gpu/gl/kernels/transpose_conv.cc#L39) was supported but I could be wrong. \r\n\r\n\r\nAnother interesting thing that happens is that when you convert the model with:\r\n\r\n```python\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_types = [tf.float16]\r\n```\r\n\r\nthe resulting binary is 2x as large as float32 and 20% slower on mobile. When I inspected the graph with Netron, it looks like nothing was converted to float16, not even the conv2d's", "Hi @filip-halt ,\r\n\r\nI ran your model using GPU delegate on dimensity 9000 and it ran fine without giving any issues. Can you please try it out with a different device and let me know if it worked there. However the list of supported operators for tflite is [here](https://www.tensorflow.org/lite/performance/gpu) and TRANSPOSE_CONV is listed there.", "This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.", "I believe that this is a grouped TRANSPOSE_CONV conversion problem. Tensorflow seems to barely support this and is what is breaking when converting onnx to tf. The default conversion creates a large amount of layers that ultimately cause an OOM on the phone when loading. \r\n\r\n<img width=\"1403\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/81822489/5c93b64a-b188-4fa3-9ea8-8c01abdf073a\">\r\n" ]

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[ "Hi @gcelano,\r\n\r\nIn Keras3, In order to save weights using model.save_weights method you need to add `.weights.h5` extension to the path.\r\n\r\n`model.save_weights('dir/my_model.weights.h5')`\r\n\r\nPlease check the [doc](https://keras.io/api/models/model_saving_apis/weights_saving_and_loading/#load_weights-method) source for same.", "These issues might share common themes or underlying causes related to saving and loading model weights in TensorFlow. Exploring them further could provide insights into resolving the \"Model weights cannot be saved\" issue. #65279 , #62483 , #61859 , #61279 , #60064 , #59287", "@SuryanarayanaY , in my code at link above I use \".weights.h5\", and this has no impact on the problem. @testhowtest, is there a provisional workaround? Without saving, there is not much use in using TF " ]

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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/68447/checks?check_run_id=25294636701) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]

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