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InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1' with dtype float [[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"](
#1
Closed
johndpope opened this issue
Oct 30, 2017
· 2 comments
I have successfully ran the trainer. / when I go to backtest - it fails.
Actually on closer inspection - I can see a bug. saving doesn't recognize tensor shape.
Tensor("output:0", shape=(?, 1), dtype=float32)
which version of tensorflow are you using?
python driver.py -t feedforward
2017-10-30 15:01:45.434854: 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. 2017-10-30 15:01:45.435181: 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. 2017-10-30 15:01:45.435203: 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. 2017-10-30 15:01:45.435214: 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. Lag 0 epoch 0 loss: 59128972527.1 Lag 0 epoch 1 loss: 46587011848.4 Lag 0 epoch 2 loss: 26121063660.8 Lag 0 epoch 3 loss: 29749124009.9 Lag 0 epoch 4 loss: 14901238999.3 The best lag is: 0 Epoch 0 completed out of 5 loss: 418341134612.0 Epoch 1 completed out of 5 loss: 49366656580.6 Epoch 2 completed out of 5 loss: 25395777597.4 Epoch 3 completed out of 5 loss: 17968284636.1 Epoch 4 completed out of 5 loss: 24333348789.0 Accuracy: 0.009250693802035153 Model saved in file: data/model/feedforward.ckpt Tensor("output:0", **shape=(?,** 1), dtype=float32)
python driver.py -b feedforward
Value before transactions: 100000
Loading pre-trained model...
2017-10-30 15:15:29.298250: 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.
2017-10-30 15:15:29.298280: 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.
2017-10-30 15:15:29.298295: 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.
2017-10-30 15:15:29.298299: 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.
Model loaded...
Traceback (most recent call last):
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
status, run_metadata)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input_1' with dtype float
[[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "driver.py", line 8, in <module>
main()
File "driver.py", line 5, in main
inputHandler(inputs)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/misc/arg_handler.py", line 31, in __init__
self.run(FeedforwardStrategy)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/misc/arg_handler.py", line 35, in run
backtest_obj.run(plot=False)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/pipeline/backtest.py", line 110, in run
self.cerebro.run()
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/cerebro.py", line 1127, in run
runstrat = self.runstrategies(iterstrat)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/cerebro.py", line 1214, in runstrategies
strat = stratcls(*sargs, **skwargs)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/metabase.py", line 88, in __call__
_obj, args, kwargs = cls.doinit(_obj, *args, **kwargs)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/metabase.py", line 78, in doinit
_obj.__init__(*args, **kwargs)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/pipeline/strategies/ff_strat.py", line 30, in __init__
print(self.sess.run(prediction, feed_dict={x: [[10.0]]}))
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
options, run_metadata)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'input_1' with dtype float
[[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'input_1', defined at:
File "driver.py", line 8, in <module>
main()
File "driver.py", line 5, in main
inputHandler(inputs)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/misc/arg_handler.py", line 31, in __init__
self.run(FeedforwardStrategy)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/misc/arg_handler.py", line 35, in run
backtest_obj.run(plot=False)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/pipeline/backtest.py", line 110, in run
self.cerebro.run()
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/cerebro.py", line 1127, in run
runstrat = self.runstrategies(iterstrat)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/cerebro.py", line 1214, in runstrategies
strat = stratcls(*sargs, **skwargs)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/metabase.py", line 88, in __call__
_obj, args, kwargs = cls.doinit(_obj, *args, **kwargs)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/backtrader/metabase.py", line 78, in doinit
_obj.__init__(*args, **kwargs)
File "/Users/jpope/Documents/cryptoWorkspace/Quant_stock/pipeline/strategies/ff_strat.py", line 23, in __init__
self.saver = tf.train.import_meta_graph("data/model/feedforward.ckpt.meta")
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1698, in import_meta_graph
**kwargs)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/meta_graph.py", line 656, in import_scoped_meta_graph
producer_op_list=producer_op_list)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def
op_def=op_def)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/jpope/miniconda2/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1' with dtype float
[[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"](
The text was updated successfully, but these errors were encountered:
hey @johndpope sorry for a late reply. There were some small issue with the earlier version of the software, but I've just pushed a big update, so it should work correctly now.
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I have successfully ran the trainer. / when I go to backtest - it fails.
Actually on closer inspection - I can see a bug. saving doesn't recognize tensor shape.
Tensor("output:0", shape=(?, 1), dtype=float32)
which version of tensorflow are you using?
python driver.py -t feedforward
2017-10-30 15:01:45.434854: 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. 2017-10-30 15:01:45.435181: 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. 2017-10-30 15:01:45.435203: 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. 2017-10-30 15:01:45.435214: 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. Lag 0 epoch 0 loss: 59128972527.1 Lag 0 epoch 1 loss: 46587011848.4 Lag 0 epoch 2 loss: 26121063660.8 Lag 0 epoch 3 loss: 29749124009.9 Lag 0 epoch 4 loss: 14901238999.3 The best lag is: 0 Epoch 0 completed out of 5 loss: 418341134612.0 Epoch 1 completed out of 5 loss: 49366656580.6 Epoch 2 completed out of 5 loss: 25395777597.4 Epoch 3 completed out of 5 loss: 17968284636.1 Epoch 4 completed out of 5 loss: 24333348789.0 Accuracy: 0.009250693802035153 Model saved in file: data/model/feedforward.ckpt Tensor("output:0", **shape=(?,** 1), dtype=float32)
The text was updated successfully, but these errors were encountered: