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August 10, 2017 01:07
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| (tensorflow1) lefnire@lefnire-ubuntu:~/Sites/btc/github/Multidimensional-LSTM-BitCoin-Time-Series$ python run.py | |
| Using TensorFlow backend. | |
| I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally | |
| I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally | |
| I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally | |
| I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally | |
| I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally | |
| > Generating clean data from: data/clean_data.h5 with batch_size: 100 | |
| > Clean data has 180610 data rows. Training on 144488 rows with 722 steps-per-epoch | |
| > Compilation Time : 0.029917478561401367 | |
| > Testing model on 36122 data rows with 361 steps | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. | |
| W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. | |
| I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | |
| I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: | |
| name: GeForce GTX 1080 Ti | |
| major: 6 minor: 1 memoryClockRate (GHz) 1.582 | |
| pciBusID 0000:01:00.0 | |
| Total memory: 10.90GiB | |
| Free memory: 10.15GiB | |
| I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 | |
| I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y | |
| 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: 0000:01:00.0) | |
| > Compilation Time : 0.00948023796081543 | |
| Epoch 1/2 | |
| Exception in thread Thread-1: | |
| Traceback (most recent call last): | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 919, in _run | |
| allow_operation=False) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2473, in as_graph_element | |
| return self._as_graph_element_locked(obj, allow_tensor, allow_operation) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2552, in _as_graph_element_locked | |
| raise ValueError("Tensor %s is not an element of this graph." % obj) | |
| ValueError: Tensor Tensor("lstm_1_input:0", shape=(?, ?, 4), dtype=float32) is not an element of this graph. | |
| During handling of the above exception, another exception occurred: | |
| Traceback (most recent call last): | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/threading.py", line 916, in _bootstrap_inner | |
| self.run() | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/threading.py", line 864, in run | |
| self._target(*self._args, **self._kwargs) | |
| File "run.py", line 64, in fit_model_threaded | |
| epochs=configs['model']['epochs'] | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper | |
| return func(*args, **kwargs) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/models.py", line 1117, in fit_generator | |
| initial_epoch=initial_epoch) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper | |
| return func(*args, **kwargs) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/engine/training.py", line 1840, in fit_generator | |
| class_weight=class_weight) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/engine/training.py", line 1565, in train_on_batch | |
| outputs = self.train_function(ins) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2268, in __call__ | |
| **self.session_kwargs) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 767, in run | |
| run_metadata_ptr) | |
| File "/home/lefnire/anaconda3/envs/tensorflow1/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 922, in _run | |
| + e.args[0]) | |
| TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("lstm_1_input:0", shape=(?, ?, 4), dtype=float32) is not an element of this graph. |
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