Fresh clone, data/bitcoin.csv unzipped, Keras(2.0.6) TensorFlow(1.2.1) Python(3.6.2) (full pip freeze).

[Edit] Also tried on Python 2.7, same error. (full pip freeze)

Full error:

(btc3) lefnire@lefnire-ubuntu:~/Sites/btc/github/Multidimensional-LSTM-BitCoin-Time-Series$ python run.py 
Using TensorFlow backend.
> Creating x & y data files...
> Clean datasets created in file `data/clean_data.h5.h5`
> 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 :  0.010142087936401367
> Testing model on 36122 data rows with 361 steps
2017-08-09 17:15:15.447882: 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.
2017-08-09 17:15:15.447905: 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.
2017-08-09 17:15:15.447909: 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.
2017-08-09 17:15:15.447913: 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.
2017-08-09 17:15:15.447917: 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.
2017-08-09 17:15:15.563391: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-08-09 17:15:15.563705: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] 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: 10.02GiB
2017-08-09 17:15:15.563716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 
2017-08-09 17:15:15.563719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   Y 
2017-08-09 17:15:15.563724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
> Compilation Time :  0.009964227676391602
Epoch 1/2
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 942, in _run
    allow_operation=False)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2663, 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/btc3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
    self.run()
  File "/home/lefnire/anaconda3/envs/btc3/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/btc3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/keras/models.py", line 1117, in fit_generator
    initial_epoch=initial_epoch)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 87, in wrapper
    return func(*args, **kwargs)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/keras/engine/training.py", line 1840, in fit_generator
    class_weight=class_weight)
  File "/home/lefnire/anaconda3/envs/btc3/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/btc3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 2268, in __call__
    **self.session_kwargs)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)
  File "/home/lefnire/anaconda3/envs/btc3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 945, 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.

I realize you're likely not keen on supporting a blog-post's code-demo, but just in case someone has top-of-the-dome.

As you can see I scaled back everything to the original. You can also notice the SP500 trend is usually upward rather the scaled graph predicted many down trends.

Hi,

When I try to train the LSTM I get the following error:

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.

Any suggestions? Thanks!

Dears,

I am confused about the standardise method in the code -

    def zero_base_standardise(self, data, abs_base=pd.DataFrame()):
        """Standardise dataframe to be zero based percentage returns from i=0"""
        if(abs_base.empty): abs_base = data.iloc[0]
        data_standardised = (data/abs_base)-1
        return (abs_base, data_standardised)

My understanding about the standarise is -

(x - mean) / standard_deviation
mean = sum(x) / count(x)
standard_deviation = sqrt( sum( (x - mean)^2 ) / count(x))

I think (data/abs_base)-1 should be far from standardised values, could you please kindly let me know if I misunderstood anything? Thank you very much!

I coppied and pasted code from multidementional forcasting into a spreadsheet for the bitcoin multi dimentional forcasting software to run on 300 lines.

I am not shure why the data line plotted only and the predicted line was behind it or invisible or not plotted.

I pasted it for all the columns the 2 columns of data.

here is where I got my data set from https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc451.htm