My bechamel takes over an hour to thicken, what am I doing wrong. IMAGES_PER_GPU 1 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy Checking the docs for the difference between model.save_weights and model.save, we are pointed to keras' serialization and saving guide. Not the answer you're looking for? Does save_best_only in Keras prevents overfitting? y.append(deepcopy(a, memo)) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy Have a question about this project? IMAGE_MIN_DIM 800 Does model.save_weights include optimizer state? name: GeForce GTX 1080 y.append(deepcopy(a, memo)) y = copier(x, memo) y = _reconstruct(x, rv, 1, memo) y.append(deepcopy(a, memo)) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict state = deepcopy(state, memo) y.append(deepcopy(a, memo)) state = deepcopy(state, memo) 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 155, in deepcopy state = deepcopy(state, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) To learn more, see our tips on writing great answers. The only option now is to save the weights only and save the model architecture separately and combine them together during testing. The ModelCheckpoint callback in particular gets called after every epoch (if you keep the default period=1) and saves your model to disk in the filename you specify to the filepath argument. y.append(deepcopy(a, memo)) Just to add what ModelCheckPoint's output is, if it's relevant for anyone else: used as a callback during model training, it can either save the whole model or just the weights depending on what state the save_weights_only argument is set to. y = _reconstruct(x, rv, 1, memo) ModelCheckpoint [source] ModelCheckpoint class tf.keras.callbacks.ModelCheckpoint( filepath, monitor: str = "val_loss", verbose: int = 0, save_best_only: bool = False, save_weights_only: bool = False, mode: str = "auto", save_freq="epoch", options=None, initial_value_threshold=None, **kwargs ) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy You can try to do that. How would a city look like that adapted to sporadic tsunami like flash floods? y[deepcopy(key, memo)] = deepcopy(value, memo) Saving model weights in Keras: what is model weights? y = copier(x, memo) You should keep it there rather than deleting it. 58/100 [================>.] - ETA: 24s - loss: 2.3975 - rpn_class_loss: 0.0255 - rpn_bbox_loss: 0.8030 - mrcnn_class_loss: 0.1923 - mrcnn_bbox_loss: 0.7560 - mrcnn_mask_loss: 0.6208 RPN_NMS_THRESHOLD 0.7 y = copier(x, memo) Tensorflow / Keras - Using both ModelCheckpoint: save_best_only and EarlyStopping: restore_best_weights. If you tried to use model.save it will return the same error again from deepcopy. The only solution right now is to set save_weights_only=True in the ModelCheckPoint callback. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy Full code: This is probably due to `ModelCheckpoint.save_weights_only` being set to `True` Imanuel_Roz (Imanuel Rozenberg) November 15, 2021, 11:35am 1. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. ModelCheckpoint_.modelcheckpoint_szZack-CSDN y = copier(x, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) 75/100 [=====================>..] - ETA: 14s - loss: 2.1696 - rpn_class_loss: 0.0245 - rpn_bbox_loss: 0.6787 - mrcnn_class_loss: 0.1646 - mrcnn_bbox_loss: 0.7162 - mrcnn_mask_loss: 0.5855 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list Making statements based on opinion; back them up with references or personal experience. If a crystal has alternating layers of different atoms, will it display different properties depending on which layer is exposed? 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 300, in _reconstruct you can try: state = deepcopy(state, memo) mrcnn_mask_conv1 (TimeDistributed) y[deepcopy(key, memo)] = deepcopy(value, memo) If no epoch improves on baseline, training will run for patience epochs and restore weights from the best epoch in that set. 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 219, in _deepcopy_list Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy My ckpt . y = copier(x, memo) y = copier(x, memo) - ETA: 1s - loss: 2.0079 - rpn_class_loss: 0.0219 - rpn_bbox_loss: 0.5857 - mrcnn_class_loss: 0.1496 - mrcnn_bbox_loss: 0.6871 - mrcnn_mask_loss: 0.5636 Difference between Keras model.save() and model.save_weights()? @xiaolisarah 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 155, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct y = copier(x, memo) 592), How the Python team is adapting the language for an AI future (Ep. 62/100 [=================>] - ETA: 21s - loss: 2.3536 - rpn_class_loss: 0.0260 - rpn_bbox_loss: 0.7846 - mrcnn_class_loss: 0.1848 - mrcnn_bbox_loss: 0.7423 - mrcnn_mask_loss: 0.6159 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 300, in _reconstruct mrcnn_class_bn2 (TimeDistributed) The text was updated successfully, but these errors were encountered: save_weight_only is too large to save,i just want save_best_only,here are my code What would naval warfare look like if Dreadnaughts never came to be? y.append(deepcopy(a, memo)) y.append(deepcopy(a, memo)) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy Do the subject and object have to agree in number? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy Making statements based on opinion; back them up with references or personal experience. 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 155, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct ModelCheckpoint ( filepath=checkpoint_filepath , save_weights_only=False , monitor='loss' , mode='max' , save_best_only=True ) train_mode. y.append(deepcopy(a, memo)) 27/100 [=======>.] - ETA: 47s - loss: 2.9387 - rpn_class_loss: 0.0311 - rpn_bbox_loss: 1.3674 - mrcnn_class_loss: 0.2707 - mrcnn_bbox_loss: 0.7049 - mrcnn_mask_loss: 0.5647 If False, you could load the model with having to redefine it. y = _reconstruct(x, rv, 1, memo) ModelCheckpoint - save_best_only=True - Stack Overflow [ 16 16]] 34/100 [=========>..] - ETA: 41s - loss: 2.7392 - rpn_class_loss: 0.0298 - rpn_bbox_loss: 1.1679 - mrcnn_class_loss: 0.2477 - mrcnn_bbox_loss: 0.7080 - mrcnn_mask_loss: 0.5859 tf.keras how to save ModelCheckPoint object - Stack Overflow File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy 593), Stack Overflow at WeAreDevelopers World Congress in Berlin, Temporary policy: Generative AI (e.g., ChatGPT) is banned. state = deepcopy(state, memo) It doesn't affect the return history of fit () method. y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/keras/engine/training.py", line 2082, in fit_generator 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 300, in _reconstruct 18/100 [====>.] - ETA: 59s - loss: 3.0089 - rpn_class_loss: 0.0356 - rpn_bbox_loss: 1.4572 - mrcnn_class_loss: 0.3307 - mrcnn_bbox_loss: 0.6677 - mrcnn_mask_loss: 0.5178 tensorflow - Does Keras ModelCheckpoint save the best model across If you have limited memory, you may only keep the best model's weights in memory, and use the ModelCheckpoint to periodically save the best weights to disk. y = copier(x, memo) USE_MINI_MASK True KeyError: 'Trying to restore training state but checkpoint - GitHub y = copier(x, memo) and then I want to see the accuracy of that best model: I got the array ie. 592), How the Python team is adapting the language for an AI future (Ep. 82/100 [=======================>] - ETA: 10s - loss: 2.1098 - rpn_class_loss: 0.0233 - rpn_bbox_loss: 0.6370 - mrcnn_class_loss: 0.1572 - mrcnn_bbox_loss: 0.7177 - mrcnn_mask_loss: 0.5746 restore_best_weights: Whether to restore model weights from the epoch with the best value of the monitored quantity. 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 300, in _reconstruct 21/100 [=====>] - ETA: 54s - loss: 3.0412 - rpn_class_loss: 0.0366 - rpn_bbox_loss: 1.5670 - mrcnn_class_loss: 0.2925 - mrcnn_bbox_loss: 0.6199 - mrcnn_mask_loss: 0.5251 y[deepcopy(key, memo)] = deepcopy(value, memo) y.append(deepcopy(a, memo)) Airline refuses to issue proper receipt. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy 89/100 [=========================>.] - ETA: 6s - loss: 2.0713 - rpn_class_loss: 0.0224 - rpn_bbox_loss: 0.6134 - mrcnn_class_loss: 0.1550 - mrcnn_bbox_loss: 0.7070 - mrcnn_mask_loss: 0.5735 Once the training is complete, you can restore the weights of the best performing model by loading them back into the model architecture, and then use the model for predictions. 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 155, in deepcopy How can I modify ModelCheckPoint in keras to monitor both val_acc and val_loss and save accordingly the best model? model.save('my_model.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. Total memory: 7.92GiB 23/100 [=====>] - ETA: 52s - loss: 2.9014 - rpn_class_loss: 0.0344 - rpn_bbox_loss: 1.4518 - mrcnn_class_loss: 0.2767 - mrcnn_bbox_loss: 0.6211 - mrcnn_mask_loss: 0.5174 The only solution right now is to set save_weights_only=True in the ModelCheckPoint callback. Where By default, the ModelCheckpoint will save files into the Trainer.log_dir. The purpose of saving the weights is to be able to restore them later for predictions, in case you need to stop the training for some reason, or if you want to use the weights for inference on a different dataset. Reason not to use aluminium wires, other than higher resitance. Setting 'save_weights_only' to False in the Keras callback 'ModelCheckpoint' will save the full model; this example taken from the link above will save a full model every epoch, regardless of performance: keras.callbacks.ModelCheckpoint (filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto . Logs: /media/jgq/GXL/project/2018/DDIM-OD/logs, Configurations: Why can't sunlight reach the very deep parts of an ocean? rev2023.7.25.43544. is that make sense ? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The future of collective knowledge sharing. Why do capacitors have less energy density than batteries? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct You could just manually add in the N i.e., 1,2,3,N for each fit(). y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list Conclusions from title-drafting and question-content assistance experiments How to use ModelCheckpoint with custom metrics in Keras? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct 42/100 [===========>] - ETA: 35s - loss: 2.5803 - rpn_class_loss: 0.0277 - rpn_bbox_loss: 1.0036 - mrcnn_class_loss: 0.2217 - mrcnn_bbox_loss: 0.7391 - mrcnn_mask_loss: 0.5882 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy Who counts as pupils or as a student in Germany? Saving model weights in Keras: what is model weights? y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict According to Tensorflow, both save weights and save best are both set to False by default. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy 31/100 [========>] - ETA: 44s - loss: 2.8123 - rpn_class_loss: 0.0322 - rpn_bbox_loss: 1.2309 - mrcnn_class_loss: 0.2539 - mrcnn_bbox_loss: 0.7100 - mrcnn_mask_loss: 0.5854 Roboflow has free tools for each stage of the computer vision pipeline that will streamline your workflows and supercharge your productivity. TRAIN_ROIS_PER_IMAGE 200 rev2023.7.25.43544. 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 182, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy y.append(deepcopy(a, memo)) Just use np.max to get the best acc from acc history will do your job. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy 592), How the Python team is adapting the language for an AI future (Ep. This way, you will save the weights and then when testing you have to build the model and load the . 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 fpn_c5p5 (Conv2D) 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 First, let's import it and create a ModelCheckpoint object: from tensorflow.keras.callbacks import ModelCheckpoint checkpoint_path = 'model_checkpoints/' checkpoint = ModelCheckpoint(filepath=checkpoint_path, save_freq='epoch', save_weights_only=True, verbose=1) Next, let's pass the checkpoint object to model.fit() method for training. y = _reconstruct(x, rv, 1, memo) y.append(deepcopy(a, memo)) state = deepcopy(state, memo) When you save the weights of a model using the ModelCheckpoint callback during training, the weights are saved to disk (e.g., to a .h5 file) at specified . 16/100 [===>..] - ETA: 62s - loss: 3.1388 - rpn_class_loss: 0.0368 - rpn_bbox_loss: 1.6199 - mrcnn_class_loss: 0.3278 - mrcnn_bbox_loss: 0.6515 - mrcnn_mask_loss: 0.5028 Should I trigger a chargeback? How do you analyse the rank of a matrix depending on a parameter. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy Then you can use that HDF5 file with load() to reconstruct the whole model, including weights. 85/100 [========================>..] - ETA: 8s - loss: 2.0850 - rpn_class_loss: 0.0229 - rpn_bbox_loss: 0.6213 - mrcnn_class_loss: 0.1548 - mrcnn_bbox_loss: 0.7068 - mrcnn_mask_loss: 0.5791 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 Reason not to use aluminium wires, other than higher resitance, How to use wc command with find and exec commands. Making statements based on opinion; back them up with references or personal experience. Anthology TV series, episodes include people forced to dance, waking up from a virtual reality and an acidic rain, Physical interpretation of the inner product between two quantum states. 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 219, in _deepcopy_list File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list Keras: How to restore initial weights when using EarlyStopping, Is this mold/mildew? It seems that the lambda layer is causing the problem but couldn't find a solution yet. 47/100 [=============>.] - ETA: 31s - loss: 2.5437 - rpn_class_loss: 0.0270 - rpn_bbox_loss: 0.9406 - mrcnn_class_loss: 0.2123 - mrcnn_bbox_loss: 0.7358 - mrcnn_mask_loss: 0.6279 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy What's the DC of a Devourer's "trap essence" attack? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict state = deepcopy(state, memo) Training network heads, Checkpoint Path: /media/jgq/GXL/project/2018/DDIM-OD/logs/bioisland20180508T0934/mask_rcnn_bioisland_{epoch:04d}.h5 91/100 [==========================>] - ETA: 5s - loss: 2.0626 - rpn_class_loss: 0.0224 - rpn_bbox_loss: 0.6145 - mrcnn_class_loss: 0.1550 - mrcnn_bbox_loss: 0.7043 - mrcnn_mask_loss: 0.5664 y.append(deepcopy(a, memo)) change save_weight_only to save_best_only caused problem #530 - GitHub 55/100 [===============>..] - ETA: 26s - loss: 2.4351 - rpn_class_loss: 0.0262 - rpn_bbox_loss: 0.8310 - mrcnn_class_loss: 0.1980 - mrcnn_bbox_loss: 0.7547 - mrcnn_mask_loss: 0.6253 By clicking Sign up for GitHub, you agree to our terms of service and File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy File "/home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/keras/engine/topology.py", line 2553, in save y.append(deepcopy(a, memo)) To see all available qualifiers, see our documentation. This way it will save the best model for a particular fit() and you can easily compare them later. Find centralized, trusted content and collaborate around the technologies you use most. state = deepcopy(state, memo) y[deepcopy(key, memo)] = deepcopy(value, memo) callbacks = [ Learn more about Stack Overflow the company, and our products. state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list MEAN_PIXEL [123.7 116.8 103.9] y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy 90/100 [==========================>] - ETA: 5s - loss: 2.0624 - rpn_class_loss: 0.0225 - rpn_bbox_loss: 0.6204 - mrcnn_class_loss: 0.1532 - mrcnn_bbox_loss: 0.6992 - mrcnn_mask_loss: 0.5672 Dataset: data_process y = _reconstruct(x, rv, 1, memo) 592), How the Python team is adapting the language for an AI future (Ep. RPN_ANCHOR_SCALES (32, 64, 128, 256, 512) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 182, in deepcopy state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy 592), How the Python team is adapting the language for an AI future (Ep. y = copier(x, memo) 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. y = _reconstruct(x, rv, 1, memo) I am trying to understand what happens here when I use the Keras ModelCheckpoint callback without setting either save_best_only & save_weights_only to True. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy state = deepcopy(state, memo) 11/100 [==>] - ETA: 75s - loss: 3.7439 - rpn_class_loss: 0.0435 - rpn_bbox_loss: 2.2545 - mrcnn_class_loss: 0.4008 - mrcnn_bbox_loss: 0.5820 - mrcnn_mask_loss: 0.4631 y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/keras/models.py", line 107, in save_model File "/media/jgq/GXL/project/2018/DDIM-OD/train_ddim.py", line 329, in /home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) state = deepcopy(state, memo) y = copier(x, memo) 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 219, in _deepcopy_list What are the pitfalls of indirect implicit casting? y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict Then you can use that HDF5 file with load() to reconstruct the whole model, including weights.. save_weights() only saves the weights to HDF5 and nothing else. Empirically, what are the implementation-complexity and performance implications of "unboxed" primitives? y.append(deepcopy(a, memo)) state = deepcopy(state, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict What its like to be on the Python Steering Council (Ep. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] 39/100 [==========>.] - ETA: 37s - loss: 2.6040 - rpn_class_loss: 0.0281 - rpn_bbox_loss: 1.0419 - mrcnn_class_loss: 0.2337 - mrcnn_bbox_loss: 0.7211 - mrcnn_mask_loss: 0.5791 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy What should I do? y = copier(x, memo) y = copier(x, memo) To learn more, see our tips on writing great answers. y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/site-packages/keras/legacy/interfaces.py", line 87, in wrapper So both 'save_best_only and save_weights_only' have default value as False and will save all weights and full model if not True. 44/100 [============>..] - ETA: 33s - loss: 2.5287 - rpn_class_loss: 0.0284 - rpn_bbox_loss: 0.9633 - mrcnn_class_loss: 0.2175 - mrcnn_bbox_loss: 0.7276 - mrcnn_mask_loss: 0.5919 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 246, in _deepcopy_dict y.append(deepcopy(a, memo)) 2) mode.save_weights ---> this will save only the weights. To learn more, see our tips on writing great answers. It is not difficult to do. May I reveal my identity as an author during peer review? The model is saved in the same way described here. 70/100 [====================>] - ETA: 17s - loss: 2.2425 - rpn_class_loss: 0.0250 - rpn_bbox_loss: 0.7183 - mrcnn_class_loss: 0.1723 - mrcnn_bbox_loss: 0.7306 - mrcnn_mask_loss: 0.5963 Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, The future of collective knowledge sharing. File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = copier(x, memo) 1 Answer Sorted by: 1 ModelCheckpoint is a callback function used to save model file (h5) after epochs. y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y[deepcopy(key, memo)] = deepcopy(value, 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 219, in _deepcopy_list @MatiasValdenegro Would you care to explain why one would like to save the state of the optimizer? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y[deepcopy(key, memo)] = deepcopy(value, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy y = _reconstruct(x, rv, 1, memo) HansBambel commented Jun 9, 2020. y = copier(x, memo) In tensorflow's documentation, the description of save_weights_only parameter of ModelCheckpoint is as follows: if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)). y = _reconstruct(x, rv, 1, 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 300, in _reconstruct 3/100 [] - ETA: 167s - loss: 3.8355 - rpn_class_loss: 0.0296 - rpn_bbox_loss: 1.4527 - mrcnn_class_loss: 1.1266 - mrcnn_bbox_loss: 0.7389 - mrcnn_mask_loss: 0.4877 What could go wrong from just loading the weights when trying to keep training the same model, but in a different session (closing python and continuing training some other day, for example). fpn_c2p2 (Conv2D) . NUM_CLASSES 5 mAP scores on tensorboard (Tensorflow Object Detection API) are all 0 even though the loss value is low, Using class weights in Keras with multiple binary outputs which are not simply one-hot-encoded, How gradients are flown back to Network in siamese architecture? 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 182, in deepcopy y = copier(x, memo) File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 246, in _deepcopy_dict y = copier(x, memo) If filepath doesn't contain formatting options like {epoch} then filepath will be overwritten by each new better model. Are there any practical use cases for subtyping primitive types? File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 219, in _deepcopy_list 26/100 [======>..] - ETA: 48s - loss: 2.9103 - rpn_class_loss: 0.0318 - rpn_bbox_loss: 1.3994 - mrcnn_class_loss: 0.2798 - mrcnn_bbox_loss: 0.6401 - mrcnn_mask_loss: 0.5592 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 300, in _reconstruct 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 File "/home/jgq/anaconda3/envs/python34/lib/python3.4/copy.py", line 155, in deepcopy
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