Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? One can load back the model similarly like -. Sign in https://www.tensorflow.org/guide/saved_model#exporting_custom_models, UnboundLocalError: local variable 'concrete_func' referenced before assignment, Unable to load custom model when using tf.keras.callbacks.ModelCheckpoint, Differences between tf.saved_model.save and model.save, Have I written custom code (as opposed to using a stock example script provided in TensorFlow): NA, OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Mac, Mobile device (e.g. or, are there any alternatives in tensorflow? 58 return smart_module.smart_cond( @lgeiger Sure. model1.compile(optimizer=tf.keras.optimizers.Adam(learning_rate)) 2039 arg_names=arg_names, View aliases Compat aliases for migration See Migration guide for more details. Oops, yes, there's that little thing! created are not saved. Reopening this issue considering the comments above. 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) 974 saving.save_model(self, filepath, overwrite, include_optimizer, save_format, --> 817 self._maybe_build(inputs) 531 if not self.call_collection.tracing: we have to initiate a new instance for the subclass Model then compile and train_on_batch with one record and load_weights of built model. The text was updated successfully, but these errors were encountered: The default file format is the Keras Model.Save API is the Tensorflow format in versions >2.x. --> 106 return wrapped_call(*args, **kwargs) I agree with the save format, my question comes down to the use of h5py, which seems to allow the model to be saved, and it is able to load the model correctly then too. The point of this issue is that if it cannot be safely serialized then saving as an h5py either is handling it improperly, and should raise an error similar to the above, OR it works as it should with h5py (based on only this one test case and limited testing besides checking weights and accuracy) and the documentation should include this information, and probably adopt the method/recommend it. If possible, please share a link to Colab/Jupyter/any notebook. 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) 62 # If the user did not specify signatures, check the root object for a function but it doesn't fit with CNN: Also, will this issue be addressed by the HF team? tf.saved_model.save. My suspicion is based on the fact that the provided work around works and also based on the error message here: tensorflow/tensorflow/python/keras/saving/save.py. Can you please check with latest tf-2.0--nightly version? like this: Oops, yes, there's that little thing! Consider saving to the Tensorflow SavedModel format (by setting save_format="tf") or using save_weights. Sign in Thanks! Adding to @hanzigs , When I train the model and the save the weights using, model1 = DCN( ) I have an internal change that should fix the ValueError (Python inputs incompatible with input_signature), but I can't be sure because the examples above do not raise the error. Have a question about this project? We have fixed several memory leaking issues recently, and this problem may be potentially fixed already. If save format is hdf5, and h5py is not available. Please feel free to reopen if the issue persists again. Looks like KerasVariable needs to inherit from tensorflow.python.trackable.base.Trackable to work with tf.saved_model.save? Saves a model as a TensorFlow SavedModel or HDF5 file. privacy statement. If you have installed latest tf-nightly as in my google colab, you should be noticing similar performance. You switched accounts on another tab or window. Thanks! ---> 12 decoder.save("decoder.tf", save_format="tf"). --> 494 self.add_trace(*self._input_signature) Additionally, TF does not provide any documentation regarding saving a model with custom training (without using .compile() and .fit()). Thanks! 517 return ret, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in wrap_with_training_arg(*args, **kwargs) Already on GitHub? Please feel free to reopen if the issue persists again. privacy statement. 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. I am closing this issue as it was resolved. Thanks! Well occasionally send you account related emails. 59 return outputs, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in call(self, *args, **kwargs) I don't think this is the root cause. ---> 64 functions = saveable_view.list_functions(saveable_view.root) Note that when you want to store your model's layers in a list attribute you do want it to be wrapped through this . Have a question about this project? If including tracebacks, please include the full --> 306 output_shape = fn(instance, input_shape) @tf.function-decorated methods are also saved. #41543, and I tried with tf=2.0.0 version, getting same error, I need of help please The prediction got in the first session from model and the prediction got from the second session model1 is completely different. embedding = model([word_inputs, mask_inputs, seg_inputs])[0] You switched accounts on another tab or window. !!! For other approaches see the TensorFlow Save and Restore guide or Saving in eager. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"index","path":"index","contentType":"directory"},{"name":".gitignore","path":".gitignore . Yes the model. You can load the weights e.g. @taki0112 As mentioned in my former reply, if you assign a list as attribute of a tf.keras.layers.Layer-inheriting instance, it will automatically be wrapped through a ListWrapper object, which as you notices causes some issues when said list does not comprise keras components. Connect and share knowledge within a single location that is structured and easy to search. 389 'inputs with matching shapes ' Currently, it says: The NotImplementedError error from an earlier post above states that HDF5 cannot save out subclassed models. --> 515 ret = method(*args, **kwargs) For other approaches, refer to the Using the SavedModel format guide and the Save and load Keras models guide. NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. Would love to jump in. I have been able to serialize SavedModels to GCS Bucket folders directly from AI Platform Notebooks in the following manner: where export_module_dir is either a GCS Bucket or a SavedModel name inside a GCs Bucket. The text was updated successfully, but these errors were encountered: I am able to replicate this issue, please find the gist here Thanks! This feature as been enabled in regular keras. Have I written custom code (as opposed to using a stock example script provided in TensorFlow): OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Mobile device (e.g. As per our 10 Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Specifying that the SavedModel format is able to handle custom models sounds reasonable and would remove the ambiguity. 556 def call_and_return_conditional_losses(inputs, *args, **kwargs): ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/utils/tf_utils.py in wrapper(instance, input_shape) Any luck on this issue? Save and load Keras models - Google Colab Thanks! Star 176k. TensorFlow for R - Save and load - RStudio .h5, .keras, then it will not work for a subclassed model. tf.keras.callbacks.ModelCheckpoint accepts hdf5 file format. Keras - databricks.my.site.com Well occasionally send you account related emails. 90 """ Thanks! 109 training, 55 return objects, functions. my environment : docker image - tensorflow/tensorflow-latest-py3 (tensorflow 2.1.0, python 3.6.9). Thank you! I'm doing something similar but it didnt work for me! We read every piece of feedback, and take your input very seriously. kernel_size=(self.hparams.conv_kernel_size[i], self.hparams.conv_kernel_size[i]), I trained a tensorflow model in python and then loaded this model in C++ to do inference, then I detected memory leak using -fsanitize leak. @k-w-w This issue has been tagged as 2.1, has there been any progress on it so far? ***> wrote: You signed in with another tab or window. 409 utils.set_training_arg(value, self._training_arg_index, args, kwargs) Already on GitHub? To see all available qualifiers, see our documentation. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow. ValueError: Input 0 of layer input is incompatible with the layer: expected ndim=3, found ndim=2. Desktop (please complete the following information): The text was updated successfully, but these errors were encountered: @noumanriazkhan would you mind to attach / upload to colab your minimum reproducible example? 2152 return graph_function, args, kwargs, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) It required lots of saving and loading. If you have installed libtensorflow and configed using pkg_config, you may alternative use: The above compiled executable run to complete successfully, however, memory leak detected. 1846 if self.input_signature: To prevent further memory leakage, creating a nightly build with ASAN, USAN, LeakSanitizer and cover the core APIs may be a good idea to detect them early. 56 return false_fn(), ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/utils.py in () You signed in with another tab or window. tag:bug_template. Describe the current behavior During real training it happens quite fast. I want to save / load my keras model to / from Google storage bucket. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Or there is something I could not have taken into account? Here is the gist for your reference. 116 But are you sure you need to be using TFBertModel and not TFBertMainLayer, for your hidden layer? To see all available qualifiers, see our documentation. # Rank of 0 is the master node so we want that one to save the model; if hvd. Using: <. Provide a reproducible test case that is the bare minimum necessary to generate inner = tf.nn.relu6(inner). That resource has an example which is very useful. The Keras-Core announcement does say that Keras-Core models can be exported to the SavedModel format: @ageron aah sorry my bad. ---> 57 outputs, losses = fn(inputs, *args, **kwargs) I expect that memory should be cleaned. Mobile device (e.g. ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in wrap_layer_functions(layer, serialization_cache) 53 if pred_value: 861 `tf.keras.Model.save` does not support subclassed model when - GitHub https://colab.research.google.com/drive/18HYwffkXCylPqeA-8raL82vfwOjb-aLP, https://stackoverflow.com/questions/62482511/tfbertmainlayer-gets-less-accuracy-compared-to-tfbertmodel/64000378#64000378, Tensorflow NotImplementedError error with Camembert. 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. Code to reproduce the issue. @julyrashchenko I just ran your code and I cannot reproduce the issue. 495 return fn 54 functions['_default_save_signature'] = default_signature --> 847 outputs = call_fn(cast_inputs, *args, **kwargs) 2139 # operations. I ran your code in nightly and do not face the value error as faced in 2.0. Saving a Keras model: model = . Error message is very misleading and needs to be changed. This seems to be because the KerasVariable within the Dense layer isn't a TF trackable obj like tf.Variable etc. Full shape received: [None, 768], Do you have an idea how I should reshape it to fit with a conv1D layer ? How to save and load a model If you only have 10 seconds to read this guide, here's what you need to know. 537 self.call_collection.add_trace(*args, **kwargs) I compared results from my earlier run (posted above) with the current run with tf-nightly-gpu. The SavedModel format is able to handle these models, so you should not get an error. Thanks! Are you satisfied with the resolution of your issue? Subclassing tf.keras.models.Model save_model saves h5py but - GitHub By clicking Sign up for GitHub, you agree to our terms of service and This guide uses Keras, a high-level API to build and train models in TensorFlow. A use-case where this is a problem is when you want to use ModelCheckpoint with a model that contains weights that are not instances of tf.Variable, such as tf.keras.layers.TextVectorization. 534, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in add_trace(self, *args, **kwargs) 540, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in get_concrete_function(self, *args, **kwargs) 977 def save_weights(self, filepath, overwrite=True, save_format=None): ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options) Describe the expected behavior Describe the current behavior 412 845 outputs = base_layer_utils.mark_as_return(outputs, acd) I was waiting till the end, but memory was never freed, it just decreased to zero, and then the program stopped. I've updated my bug report to make it clear that I'm trying to export to the SavedModel format. Well occasionally send you account related emails. Step2: main.cpp in C++ Below is the C++ main.cpp which simply loads saved model from a directory, check status and then exit. 410 def invalid_creator_scope(*unused_args, **unused_kwds): ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) This guide uses tf.keras a high-level API to build and train models in TensorFlow. Create 3x smaller TF and TFLite models. Hi @jordisoler, As per the description mentioned here, dtype=tf.float32, Besides I tested it on one other machine and got the same results. 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. These functions will not be directly callable after loading. Keras documentation: Efficient Object Detection with YOLOV8 and KerasCV 5 x, emb = inputs I am having the same problem. 1850, ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs) 104 def replace_training_and_call(training): Yes 165 _wrap_call_and_conditional_losses(layer), \n; filepath (required): the path where we wish to write our model to. I compared results from my earlier run (posted above) with the current run with tf-nightly-gpu. 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) @asingh9530 , thanks for your message. The file will include: The model's architecture/config The model's weight values (which were learned during training) The model's compilation information (if compile () was called) Saving a Keras model: model = . Defaults to 'tf' in TF 2.X, and 'h5' in TF 1.X.` You switched accounts on another tab or window. 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. Can you try tf-nightly for your real training job? Can't save my model, only saving weight. To see all available qualifiers, see our documentation. -> 2150 graph_function = self._create_graph_function(args, kwargs) I also tried using the preview with the more complicated code and found that it throws a new error, which appears to be due to improperly recalling custom objects. model.save is not correct approach. You switched accounts on another tab or window. privacy statement. To reproduce this memory leak, any arbitrary simple TF model can be used. WARNING:absl:Found untraced functions such as ranking_layer_call_fn, ranking_layer_call_and_return_conditional_losses, dense_layer_call_fn, dense_layer_call_and_return_conditional_losses, ranking_layer_call_fn while saving (showing 5 of 10). Notifications. ValueError: Exception encountered when calling layer "dcn" (type DCN). The text was updated successfully, but these errors were encountered: I am able to reproduce the issue with TF version 2.0.0-dev20190724 on Colab. Instead, you need to save the weights in h5 format then make a new model the same way, train_on_batch as shown above, and then load the weights. 113 else: 413 trace_with_training(True), ~/.pyenv/versions/3.7.6/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/saved_model/save_impl.py in get_concrete_function(self, *args, **kwargs) Bash cmd to compile main.cpp with ASAN, USAN, and leak detection. BTW, It won't be easy because MoodelCheckpoint is using not only save(), but also load_weights, save_weights. It seems like a regression especially given that saver.restore(sess, gs://path_to_checkpoint) worked in the past, and using model.load_weights() works in regular keras, but this behavior is not maintained for tf.keras. Sign in You signed in with another tab or window. It works fine, but it saves the model using the Keras format. NOTE: If I save the model using, Also I attached output of my code running. @aginpatrick You can store Keras model checkpoints to google storage by creating a custom GCS callback as shown here, It seems it's now possible to save model to Google Storage without the GCS Setup import numpy as np import tensorflow as tf import keras Saving This section is about saving an entire model to a single file. # pass This behaviour is expected based on the API docs: There is no real new code or any ideas and it was no stretch to reach the warning using the previous code, and using random data as was suggested a month ago. With tf-nightly my output looks little different. Well occasionally send you account related emails. tf.compat.v1.keras.models.save_model tf.keras.models.save_model ( model, filepath, overwrite=True, include_optimizer=True, save_format=None, signatures=None, options=None ) Usage: I still have this issue. Well occasionally send you account related emails. Description: Train custom YOLOV8 object detection model with KerasCV. 356 # wrapped allows AutoGraph to swap in a converted function. 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) To see all available qualifiers, see our documentation. My model as follow: ##python 2020 The TensorFlow Authors. Having a one liner to load from GCS buckets would be highly desirable. Sign in -> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs) To avoid running into memory leakage caused by XLA support, we recompiled tensorflow library without XLA support e.g., build:xla --define with_xla_support=false, and confirmed that Leak sanitizer check runs OK. # Get model (Sequential, Functional Model, or Model subclass) model.save('path/to/location.keras') # The file needs to end with the .keras extension Loading the model back: 859 # Compute outputs. If you specify the file format, e.g. The text was updated successfully, but these errors were encountered: @julyrashchenko Can you please provide the github gist of the issue as I am unable to reproduce it on my side. ValueError Traceback (most recent call last) By clicking Sign up for GitHub, you agree to our terms of service and We read every piece of feedback, and take your input very seriously. It seems like it would be useful to smooth out this workflow, as many people using keras will run into this issue when they try to save their model. Simply train tf.keras. Is it okay? @julyrashchenko Can you try !pip install -q tf-nightly-gpu-2.0-preview and let us know. How to load a SavedModel downloaded from TFHub as a Keras model? Graph networks are used in the Keras Functional and Sequential APIs. Yes ---> 91 fns = self.functions_to_serialize(serialization_cache) Loading saved model from keras importer. What is the status of this one so far? How to save and load a model If you only have 10 seconds to read this guide, here's what you need to know. And another question is how should I call this model for prediction ? TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. To see all available qualifiers, see our documentation. rank == 0: signature = get_signature_mlflow (args, landmarks . After training tf.keras model with TF Hub model as keray layer, it must be saved with TF Format. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Since we have static XlaDeviceOpRegistrations* registrations = the allocation should happen only once and not cause a gradual memory leak.