y[deepcopy(key, memo)] = deepcopy(value, memo) state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy MEAN_PIXEL [123.7 116.8 103.9] 60/100 [=================>] - ETA: 23s - loss: 2.3913 - rpn_class_loss: 0.0266 - rpn_bbox_loss: 0.8070 - mrcnn_class_loss: 0.1888 - mrcnn_bbox_loss: 0.7484 - mrcnn_mask_loss: 0.6204 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict 69/100 [===================>.] - ETA: 17s - loss: 2.2470 - rpn_class_loss: 0.0252 - rpn_bbox_loss: 0.7173 - mrcnn_class_loss: 0.1743 - mrcnn_bbox_loss: 0.7320 - mrcnn_mask_loss: 0.5982 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy y = copier(x, memo) You need extra code to reconstruct the model from a JSON file. y = _reconstruct(x, rv, 1, memo) Was the release of "Barbie" intentionally coordinated to be on the same day as "Oppenheimer"? state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy state = deepcopy(state, memo) y = copier(x, memo) y = copier(x, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) mrcnn_mask_conv2 (TimeDistributed) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict Save_weights_only means it only saves the weights and not the full model. The model is saved in the same way described here. 98/100 [============================>.] y = copier(x, memo) 54/100 [===============>..] - ETA: 27s - loss: 2.4641 - rpn_class_loss: 0.0266 - rpn_bbox_loss: 0.8463 - mrcnn_class_loss: 0.2004 - mrcnn_bbox_loss: 0.7622 - mrcnn_mask_loss: 0.6286 y = copier(x, memo) I am currently working on a Flask app and facing an issue with loading an HDF5 model that contains only weights. This can also serve as a back up in case training got interrupted. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) Keras EarlyStopping callback: Why would I ever set restore_best_weights=False? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. 2018-05-08 09:34:54.940052: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero y = copier(x, memo) 36/100 [=========>..] - ETA: 39s - loss: 2.6515 - rpn_class_loss: 0.0285 - rpn_bbox_loss: 1.1112 - mrcnn_class_loss: 0.2396 - mrcnn_bbox_loss: 0.6964 - mrcnn_mask_loss: 0.5758 If a crystal has alternating layers of different atoms, will it display different properties depending on which layer is exposed? y = _reconstruct(x, rv, 1, memo) MASK_POOL_SIZE 14 592), How the Python team is adapting the language for an AI future (Ep. Could ChatGPT etcetera undermine community by making statements less significant for us? fpn_p2 (Conv2D) y.append(deepcopy(a, memo)) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy mrcnn_mask_bn1 (TimeDistributed) fit ( { "inputs": X, "targets": Y }, epochs=5000, verbose=1, callbacks= [ model_checkpoint_callback ])` The Error/Console Output y = copier(x, memo) I tried to use model.save and then get the error: TypeError: can't pickle _thread.RLock objects, @angelbaowei File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict y = copier(x, memo) rev2023.7.25.43544. y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy 22/100 [=====>] - ETA: 53s - loss: 2.9590 - rpn_class_loss: 0.0355 - rpn_bbox_loss: 1.5090 - mrcnn_class_loss: 0.2831 - mrcnn_bbox_loss: 0.6127 - mrcnn_mask_loss: 0.5186 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy KerasModelCheckpoint_save_best_only_-CSDN XXX.h5) And from the above training log, I think your model never compiles with monitored metric; it seems like, only the AUC metric was used but the ModelCheckpoint is used to monitor the Accuracy metric. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/keras/callbacks.py", line 77, in on_epoch_end How weights of all CNN models are same even when using different models, Keras 1D CNN always predicts the same result even if accuracy is high on training set. y = copier(x, memo) 65/100 [==================>..] - ETA: 20s - loss: 2.3006 - rpn_class_loss: 0.0256 - rpn_bbox_loss: 0.7541 - mrcnn_class_loss: 0.1805 - mrcnn_bbox_loss: 0.7343 - mrcnn_mask_loss: 0.6061 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct y[deepcopy(key, memo)] = deepcopy(value, memo) y = copier(x, memo) rev2023.7.25.43544. json_model = model.to_json() state = deepcopy(state, memo) y = copier(x, memo) This way, you will save the weights and then when testing you have to build the model and load the weights separately. Then if this is correct which would be the preferred method to use? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) state = deepcopy(state, memo) "the returned array has changed. Full code: use_multiprocessing=True, Using TensorFlow backend. save() saves the weights and the model structure to a single HDF5 file. Asking for help, clarification, or responding to other answers. (As I understand, models are sometimes held in memory EarlyStopping for but not sure about model_checkpoint ModelCheckpoint). Conclusions from title-drafting and question-content assistance experiments NotImplementedError: Layers with arguments in `__init__` must override `get_config`, i can load my deep model in colab bu when i want load that model in pc i can't, Difference between model_weights and optimizer_weights in keras. Say period = 5, so that weights are saved every 5 epochs. Connect and share knowledge within a single location that is structured and easy to search. 17/100 [====>.] - ETA: 60s - loss: 3.0607 - rpn_class_loss: 0.0352 - rpn_bbox_loss: 1.5324 - mrcnn_class_loss: 0.3310 - mrcnn_bbox_loss: 0.6547 - mrcnn_mask_loss: 0.5075 @Ash1995 In keras there are two functions: 1) model.save ---> this will save the model and weights together which won't work with Mask RCNN. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy How to use wc command with find and exec commands, Anthology TV series, episodes include people forced to dance, waking up from a virtual reality and an acidic rain. Different balances between fullnode and bitcoin explorer. Is this possible? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict 50/100 [==============>] - ETA: 29s - loss: 2.5201 - rpn_class_loss: 0.0265 - rpn_bbox_loss: 0.8928 - mrcnn_class_loss: 0.2037 - mrcnn_bbox_loss: 0.7615 - mrcnn_mask_loss: 0.6357 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy state = deepcopy(state, memo) y.append(deepcopy(a, memo)) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1) Let's discuss in detail each of its arguments: filepath: This is the path to save your model. 86/100 [========================>..] - ETA: 7s - loss: 2.0820 - rpn_class_loss: 0.0229 - rpn_bbox_loss: 0.6153 - mrcnn_class_loss: 0.1557 - mrcnn_bbox_loss: 0.7089 - mrcnn_mask_loss: 0.5792 y[deepcopy(key, memo)] = deepcopy(value, memo) I believe it also includes things like the optimizer state. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) 593), Stack Overflow at WeAreDevelopers World Congress in Berlin, Temporary policy: Generative AI (e.g., ChatGPT) is banned. These weights can be used to make predictions as is or as the basis for ongoing training. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy On the other hand, with ModelCheckpoint, you have more control over when to save the weights, but you have to manually stop the training when the performance is no longer improving. y[deepcopy(key, memo)] = deepcopy(value, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) y = copier(x, memo) Sign in 37/100 [==========>.] - ETA: 38s - loss: 2.6511 - rpn_class_loss: 0.0279 - rpn_bbox_loss: 1.0910 - mrcnn_class_loss: 0.2358 - mrcnn_bbox_loss: 0.7180 - mrcnn_mask_loss: 0.5784 y = copier(x, memo) Total memory: 7.92GiB In summary, saving the weights during training allows you to persist the state of the model, so that you can continue training or use the model for predictions later. In model: rpn_model Share Improve this answer Follow answered Aug 27, 2019 at 5:44 meowongac 702 3 12 Thank you on your explanation. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict 41/100 [===========>] - ETA: 35s - loss: 2.5873 - rpn_class_loss: 0.0282 - rpn_bbox_loss: 1.0149 - mrcnn_class_loss: 0.2246 - mrcnn_bbox_loss: 0.7337 - mrcnn_mask_loss: 0.5858 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 309, in _reconstruct y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = _reconstruct(x, rv, 1, memo) Who counts as pupils or as a student in Germany? For a limited time, you can join WeightWatchers for just $10 a month for 10 months. y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) is that make sense ? Airline refuses to issue proper receipt. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict "Converting sparse IndexedSlices to a dense Tensor of unknown shape. " File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list y[deepcopy(key, memo)] = deepcopy(value, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list state = deepcopy(state, memo) save_weights_only: if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)). fpn_c4p4 (Conv2D) 71/100 [====================>] - ETA: 16s - loss: 2.2329 - rpn_class_loss: 0.0252 - rpn_bbox_loss: 0.7120 - mrcnn_class_loss: 0.1704 - mrcnn_bbox_loss: 0.7306 - mrcnn_mask_loss: 0.5948 keras.callbacks.ModelCheckpoint(self.checkpoint_path,monitor='val_loss', Is it a concern? Is it appropriate to try to contact the referee of a paper after it has been accepted and published? y = copier(x, memo) Airline refuses to issue proper receipt. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct y = copier(x, memo) Q1: musdb18pl.Trainer (resume_from_checkpoint='path/to/checkpoint.pth') /opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py save_weights_only: bool = Falsecheckpointkeystepmodelmodelkey model.stft.conv_real.weight model.stft.conv_imag.weight 592), How the Python team is adapting the language for an AI future (Ep. y = _reconstruct(x, rv, 1, memo) Keras Callbacks - ModelCheckpoint | TheAILearner y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct every_n_train_steps (Optional [int]) - Number of training steps between checkpoints. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict state = deepcopy(state, memo) If you tried to use model.save it will return the same error again from deepcopy. Checking the docs for the difference between model.save_weights and model.save, we are pointed to keras' serialization and saving guide. - ETA: 0s - loss: 2.0148 - rpn_class_loss: 0.0216 - rpn_bbox_loss: 0.5795 - mrcnn_class_loss: 0.1480 - mrcnn_bbox_loss: 0.6850 - mrcnn_mask_loss: 0.5807Epoch 00000: val_loss improved from inf to 1.17010, saving model to /media/jgq/GXL/project/2018/DDIM-OD/logs/bioisland20180508T0934/mask_rcnn_bioisland_0000.h5 Making statements based on opinion; back them up with references or personal experience. Why evaluation of saved model by using ModelCheckpoint is different from results in training history? y = copier(x, memo) return func(*args, **kwargs)