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ub16c9@ub16c9-gpu:/ub16_prj/KeyPhrase-Extraction$ python main.py
len(train_data) 70484
len(valid_data) 7832
len(test_data) 33541
len(vocab) 240058
Train started!
2018-11-14 09:30:16.762768: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-11-14 09:30:16.917598: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-14 09:30:16.918011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:01:00.0
totalMemory: 10.92GiB freeMemory: 10.42GiB
2018-11-14 09:30:16.918040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1312] Adding visible gpu devices: 0
2018-11-14 09:30:17.675964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10081 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "main.py", line 186, in
main()
File "main.py", line 73, in main
model_cell='lstm'
File "/home/ub16c9/ub16_prj/KeyPhrase-Extraction/models/model.py", line 62, in init
self.init_state=single_cell1.zero_state(self.batch_size,dtype=tf.float32)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1004, in zero_state
return self._cell.zero_state(batch_size, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 252, in zero_state
output = _zero_state_tensors(state_size, batch_size, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 134, in _zero_state_tensors
return nest.map_structure(get_state_shape, state_size)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/nest.py", line 459, in map_structure
structure[0], [func(*x) for x in entries])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 128, in get_state_shape
c = _concat(batch_size, s)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 93, in _concat
"but saw tensor: %s" % p)
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder:0", dtype=int32)
ub16c9@ub16c9-gpu:/ub16_prj/KeyPhrase-Extraction$
The text was updated successfully, but these errors were encountered:
ub16c9@ub16c9-gpu:
/ub16_prj/KeyPhrase-Extraction$ python main.py/ub16_prj/KeyPhrase-Extraction$len(train_data) 70484
len(valid_data) 7832
len(test_data) 33541
len(vocab) 240058
Train started!
2018-11-14 09:30:16.762768: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-11-14 09:30:16.917598: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-14 09:30:16.918011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6575
pciBusID: 0000:01:00.0
totalMemory: 10.92GiB freeMemory: 10.42GiB
2018-11-14 09:30:16.918040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1312] Adding visible gpu devices: 0
2018-11-14 09:30:17.675964: I tensorflow/core/common_runtime/gpu/gpu_device.cc:993] Creating TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10081 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
File "main.py", line 186, in
main()
File "main.py", line 73, in main
model_cell='lstm'
File "/home/ub16c9/ub16_prj/KeyPhrase-Extraction/models/model.py", line 62, in init
self.init_state=single_cell1.zero_state(self.batch_size,dtype=tf.float32)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 1004, in zero_state
return self._cell.zero_state(batch_size, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 252, in zero_state
output = _zero_state_tensors(state_size, batch_size, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 134, in _zero_state_tensors
return nest.map_structure(get_state_shape, state_size)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/nest.py", line 459, in map_structure
structure[0], [func(*x) for x in entries])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 128, in get_state_shape
c = _concat(batch_size, s)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell_impl.py", line 93, in _concat
"but saw tensor: %s" % p)
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder:0", dtype=int32)
ub16c9@ub16c9-gpu:
The text was updated successfully, but these errors were encountered: