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I have installed all packages as per instruction given on main page. But When run this code I get following error
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape.
Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/keras/engine/training.py", line 429, in data_generator_task
generator_output = next(self._generator)
File "SSD_training.py", line 235, in generate
y = self.bbox_util.assign_boxes(y)
File "/home/aniket/PycharmProjects/myVirtual3/ssd_keras-master/ssd_utils.py", line 149, in assign_boxes
assignment[:, 5:-8][best_iou_mask] = boxes[best_iou_idx, 4:]
ValueError: shape mismatch: value array of shape (20,20) could not be broadcast to indexing result of shape (20,3)
Epoch 1/5
Traceback (most recent call last):
File "SSD_training.py", line 315, in
nb_worker=1)
File "/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/keras/engine/training.py", line 1532, in fit_generator
str(generator_output))
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
The text was updated successfully, but these errors were encountered:
AniketGurav
changed the title
I have installed all packages as per instruction given on main page.
I have installed all packages as per instruction given on main page. But When run this code I get following error
Jan 6, 2018
AniketGurav
changed the title
I have installed all packages as per instruction given on main page. But When run this code I get following error
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
Jan 6, 2018
I have installed all packages as per instruction given on main page. But When run this code I get following error
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:91: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape.
Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
self.run()
File "/usr/lib/python2.7/threading.py", line 754, in run
self.__target(*self.__args, **self.__kwargs)
File "/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/keras/engine/training.py", line 429, in data_generator_task
generator_output = next(self._generator)
File "SSD_training.py", line 235, in generate
y = self.bbox_util.assign_boxes(y)
File "/home/aniket/PycharmProjects/myVirtual3/ssd_keras-master/ssd_utils.py", line 149, in assign_boxes
assignment[:, 5:-8][best_iou_mask] = boxes[best_iou_idx, 4:]
ValueError: shape mismatch: value array of shape (20,20) could not be broadcast to indexing result of shape (20,3)
Epoch 1/5
Traceback (most recent call last):
File "SSD_training.py", line 315, in
nb_worker=1)
File "/home/aniket/PycharmProjects/myVirtual3/local/lib/python2.7/site-packages/keras/engine/training.py", line 1532, in fit_generator
str(generator_output))
ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
Please note this issue is not same as #61
The text was updated successfully, but these errors were encountered: