Also, will this issue be addressed by the HF team? 77 def functions_to_serialize(self, serialization_cache): The text was updated successfully, but these errors were encountered: On which version are you running? Train model Save the model. Thanks for the reply and pointing out that static XlaDeviceOpRegistrations* registrations = looks fine. In the toy example being used it worked correctly, although this may not be the case. Have a question about this project? You signed in with another tab or window. I am having the same problem. It is possible to reproduce with the MNIST/ fashion MNIST or really any other dataset, simply change the 10 from the line I've managed to find a workaround for this issue which satisfied me. 109 training, 114 saved_model_save.save(model, filepath, overwrite, include_optimizer, 392 I believe this should be documented then in the warning, as the warning seems to suggest that there is not a clear method for saving these models outside of saving the weights independently from the model, which is obviously more complicated and prone to error. @gthb What do you think about adding something like from_pretrained to MainLayer, and pulling out the logic from TFPreTrainedModel.from_pretrained to support both? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. privacy statement. I can't save a custom model even though I successfully call model.predict() before the save. Thanks! I'd like to export the model to TensorFlow's SavedModel format. Yes Sign in Earlier, it used to leak ~100MB per 100 iterations. kernel_size=(self.hparams.conv_kernel_size[i], self.hparams.conv_kernel_size[i]), Another important thing as mentioned by @k-w-w in another post. Have a question about this project? Currently, "importKerasNetwork"supports TensorFlow-Keras versions as follows: Keras model throws an error when I'm trying to save it in .tf format GitHub to your account, Please make sure that this is a bug. --> 110 lambda: replace_training_and_call(True), The H5 format does not support saving Subclassed models, so the initial error stating that only Sequential and Functional models may be saved is intended. 513 layer, inputs=inputs, build_graph=False, training=training, Current Warning message is as follows to your account. for i in range(len(self.hparams.conv_filters)): inner = tf.nn.relu6(inner). I have not had to deploy a subclasses model in TF JS so I could not say if this is usable or not. --> 103 functions = save_impl.wrap_layer_functions(self.obj, serialization_cache) We read every piece of feedback, and take your input very seriously. The issue is that there is no error or warning if this method is used to save the model, even though it explicitly states that you should not be able to save a subclassed models, yet it clearly allows it if you change the saved model extension to be ".h5py". @zakizhou The error ValueError: Model <__main__.Model object at 0x7f7a6bad1e48> cannot be saved because the input shapes have not been set. Consider saving to the Tensorflow SavedModel format (by setting save_format="tf") or using save_weights. We give Please let me know if I am missing anything. You switched accounts on another tab or window. Already on GitHub? ---> 54 return true_fn() The SavedModel format is able to handle these models, so you should not get an error. 104 def replace_training_and_call(training): I am closing this issue as it was resolved. Usually, input shapes are automatically determined from calling .fit() or .predict(). True that. 412 Describe the expected behavior ---> 57 outputs, losses = fn(inputs, *args, **kwargs) However, I think it should not be necessary if the model is called and fed an input. Yes I don't think that is problematic to configure. A model is, abstractly: A function that computes something on tensors (a forward pass) Some. After training tf.keras model with TF Hub model as keray layer, it must be saved with TF Format. You signed in with another tab or window. Fork 88.7k. Args: model: Keras model instance to be saved. While using the subclassing API for a subclassed layer and model, I was unable to use the model.save_model() function for h5 or hdf5 file types, but I could successfully save and load the model if it was saved as h5py file type. If you specify the file format, e.g. Currently, tf.keras.callbacks.ModelCheckpoint does not accept a save_model parameter, while tf.Keras.Model.save does accept it to specify whether to use Tensorflow SavedModel or HDF5 file. 393 def _merge_function(self, inputs): ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. We read every piece of feedback, and take your input very seriously. Provide a reproducible test case that is the bare minimum necessary to generate the problem. Cannot save Keras model to pb using model.save() #35365 - GitHub (The documentation describes the argument on this page: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model#save). --> 538 return super(LayerCall, self).get_concrete_function(*args, **kwargs) In this tutorial, you will: Train a tf.keras model for MNIST from scratch. The only code not provided was the actual data for the train and test data objects. HDF5 is the default in versions 1.x, So your model saved will be the TensorFlow Protobuf file in version 2.9.x in the ModelCheckpoint callback. Star 176k. To see all available qualifiers, see our documentation. model.save("NameOfYourModel", save_format='tf') 849 except errors.OperatorNotAllowedInGraphError as e: ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in return_outputs_and_add_losses(*args, **kwargs) Hence, only HDF5 models will be stored by the callback. I am running 2.0 and will try tf-nightly-gpu. Sign in All rights reserved.Licensed under the Creative Commons Attribution License 3.0.Code samples licensed under the Apache 2.0 License. filters=self.hparams.conv_filters[i], By clicking Sign up for GitHub, you agree to our terms of service and How do I solve this ?? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Isn't your Conv1D layer intended to convolve over the token sequence? I've been running OP's snippet and the bug is clearly reproducible. @gowthamkpr ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py in _maybe_build(self, inputs) No, Hi @Ryandry1st, may I have help on this similar issue please, using tf==2.2.0 Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X.` script ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in wrapper(instance, input_shape) Well occasionally send you account related emails. Loading the saved models from tf.keras in different versions Thanks! ---> 53 serialization_cache)) This guide uses tf.keras a high-level API to build and train models in TensorFlow. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: TensorFlow installed from (source or binary): TensorFlow version (use command below): tensorflow-gpu==2.0.0. I've installed Keras-Core with pip install keras-core and created a simple Sequential model using the TensorFlow backend, and I'm trying to export it to TensorFlow's SavedModel format using tf.saved_model.save(), but this failed with the following error: Here is the code to reproduce the error (gist): I've tried with both TensorFlow 2.12 and 2.13, and got the same error. ---> 59 pred, true_fn=true_fn, false_fn=false_fn, name=name) The pooled output produces a single vector representing the whole sequence, not separate vectors for each token of the sequence. subclassed model needs to be called on some data in order to create its weights. Error Saving tf.keras model with save_mode='tf'. diagnose the problem. You signed in with another tab or window. use_bias=False, I am running my processes inside dockers and after a while dockers shut down my processes because they acquire memory and never release it. 56 return false_fn(), ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in () Or there is something I could not have taken into account? Pull requests. The text was updated successfully, but these errors were encountered: I think you're supposed to use the new keras.saving namespace: Hi @bermeitinger-b , I am closing this issue as it was resolved in the tf-nightly. The text was updated successfully, but these errors were encountered: I am able to replicate this issue, please find the gist here Thanks! 62 # If the user did not specify signatures, check the root object for a function As you said, I just save the weights and then instantiate a new model and load the weights. like this: I'm getting this error, using transformers 2.11.0 version : @PoriNiki yeah, from a quick git log -S pretrained_model_archive_map that attribute went away in #4636 Kill model archive maps merged to master in d4c2cb4 and first released in v2.11.0. TF-2.0: Keras model save load memory leakage, https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/compiler/jit/xla_cpu_device.cc#L67, https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/compiler/jit/xla_device.cc#L525, Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes, OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04.3 LTS. Well occasionally send you account related emails. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. Save, serialize, and export models - Keras 105 # function dict, even if the value is None. script @Ryandry1st Similar issue, I'm trying to save a subclass model and convert it to TensorFlowJS layers model format. Does anyone solve it? Note that the model weights may have different scoped names after being loaded. https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/models/save_model, https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/models/save_model. I get all kinds of error. 415 else: ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in trace_with_training(value, fn) But I think it is still a huge problem because leak increases with complicating the model. By clicking Sign up for GitHub, you agree to our terms of service and The problem I have now is that the custom signatures that I've to your account. ---> 91 fns = self.functions_to_serialize(serialization_cache) @gadagashwini @jvishnuvardhan I think this issue should be reopend, as the stack overflow post you linked only worksaround the fact that I can't call the function from_pretrained. 360, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrapper(*args, **kwargs) 707 inputs, training=training, mask=mask, ValueError: Exception encountered when calling layer "dcn" (type DCN). keras - tensorflow 2.1 loading model when saved with `tf` format does 308 if output_shape is not None: ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/merge.py in build(self, input_shape) Perhaps docstring for the save_format is unclear? Can you please check with latest tf-2.0--nightly version? Let me add to this thread what I saw w.r.t. 516 _restore_layer_losses(original_losses) Python version: 3.7. Thanks for your response. I compared results from my earlier run (posted above) with the current run with tf-nightly-gpu. Error Saving tf.keras model with save_mode='tf' #2721 - GitHub 539 It is saved there with GPU versions 1.15 and 2.2.4 for both respectively. tf.saved_model.save() raises ValueError when saving a model - GitHub --> 166 '{}_layer_call_and_return_conditional_losses'.format(layer.name)) Mobile device (e.g. 112, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in replace_training_and_call(training) View aliases Compat aliases for migration See Migration guide for more details. Sign in 113 # Create arg spec for decorated function. Also if you need immediate memory deallocation, you can use tf.keras.backend.clear_session() and gc.collect(), they work fine now. You signed in with another tab or window. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Sign in You switched accounts on another tab or window. 414 trace_with_training(False) 102 objects = save_impl.wrap_layer_objects(self.obj, serialization_cache) Save and load Keras models - Google Colab Eventually it crashes with OSError: [Errno 12] Cannot allocate memory. actually it is a known issue you can check it here. Can anyone provide another feedback on that? 105 set_training_arg(training, training_arg_index, args, kwargs) I have created an image classification model using TensorFlow and Keras in google colab. 109 training, 406 self._concrete_stateful_fn = ( Thank you! We're looking into this issue. Setup import numpy as np import tensorflow as tf import keras Saving This section is about saving an entire model to a single file.
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