Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. For example, the overview page of quantization-aware training is here. An issue for anything not supported should be a feature request.
Describe the bug
A clear and concise description of what the bug is.
System information
TensorFlow version (installed from source or binary): 2.7.0
TensorFlow Model Optimization version (installed from source or binary): 0.7.1
Python version: 3.8
Describe the expected behavior
Describe the current behavior
Hi, i can use the prune_low_magnitude for Dense and Conv2D. And When use the prune_low_magnitude, it failed when wrapped with GRU.
Code to reproduce the issue
Provide a reproducible code that is the bare minimum necessary to generate the
problem.
Screenshots
If applicable, add screenshots to help explain your problem.
2022-03-15 15:41:00.249444: I tensorflow/stream_executor/cuda/cuda_dnn.cc:366] Loaded cuDNN version 8101
2022-03-15 15:41:00.626781: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
10/10 [==============================] - ETA: 0s - loss: 867.6863 - ola_layer_loss: 6.6291 - mask_layer_loss: 16.1240 - vad_output_loss: 1.0971/home/test/dev/anaconda3/envs/tf2.7_py3.8/lib/python3.8/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.
layer_config = serialize_layer_fn(layer)
Traceback (most recent call last):
File "dpcrn/bin/train.py", line 97, in <module>
main()
File "dpcrn/bin/train.py", line 87, in main
excuctor.train(loss_wrapper=loss_wrapper,
File "/home/test/code/speech_enhance/DPCRN_DNS3/dpcrn/utils/executor.py", line 136, in train
self.model.fit_generator(data_generator.generator(batch_size=batch_size, validation=False),
File "/home/test/dev/anaconda3/envs/tf2.7_py3.8/lib/python3.8/site-packages/keras/engine/training.py", line 2016, in fit_generator
return self.fit(
File "/home/test/dev/anaconda3/envs/tf2.7_py3.8/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/test/dev/anaconda3/envs/tf2.7_py3.8/lib/python3.8/site-packages/h5py/_hl/group.py", line 149, in create_dataset
dsid = dataset.make_new_dset(group, shape, dtype, data, name, **kwds)
File "/home/test/dev/anaconda3/envs/tf2.7_py3.8/lib/python3.8/site-packages/h5py/_hl/dataset.py", line 142, in make_new_dset
dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5d.pyx", line 87, in h5py.h5d.create
ValueError: Unable to create dataset (name already exists)
2022-03-15 15:41:04.771988: W tensorflow/core/kernels/data/generator_dataset_op.cc:107] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.
[[{{node PyFunc}}]]
Additional context
Add any other context about the problem here.
Prior to filing: check that this should be a bug instead of a feature request. Everything supported, including the compatible versions of TensorFlow, is listed in the overview page of each technique. For example, the overview page of quantization-aware training is here. An issue for anything not supported should be a feature request.
Describe the bug A clear and concise description of what the bug is.
System information
TensorFlow version (installed from source or binary): 2.7.0
TensorFlow Model Optimization version (installed from source or binary): 0.7.1
Python version: 3.8
Describe the expected behavior
Describe the current behavior Hi, i can use the prune_low_magnitude for Dense and Conv2D. And When use the prune_low_magnitude, it failed when wrapped with GRU.
Also, when i remove the ModelCheckpoint callback, it can run with GRU, but i cannot save the model.
Code to reproduce the issue Provide a reproducible code that is the bare minimum necessary to generate the problem.
Screenshots If applicable, add screenshots to help explain your problem.
Additional context Add any other context about the problem here.