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bitcoinforecast's Issues

Error while running the program

File "network.py", line 117, in
net.fit(data[:el],labels[:el],epochs=epochs,batch_size=300)
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 965, in fit
validation_steps=validation_steps)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1593, in fit
batch_size=batch_size)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1426, in _standardize_user_data
exception_prefix='input')
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 70, in _standardize_input_data
data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 70, in
data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data]
AttributeError: 'list' object has no attribute 'ndim'

grabber option to grab old data

Hello,

I found your project very interesting and was wondering if you could add a script to grab data from the past to train the model with.

Thanks

Data Source

Data source dataset.csv Where to get updates

help me run this

how do i run this, i have no clue, i tried running the commands with python3 butit just returns errors, i have installed all libraries but cant, please help

Mistake util.py

I think, you have mistake in util.py.
line 15: shoud be "return data[:-2],label[2:]"

Question?

What's the purpose of running grabber 2/3 days to get the new data when I can just fetch it all in a second?

Could you explain your point of view please?

thanks mate

Predicted Price

I seem to get the same predicted price every-time whether I retrain the model or not.

AttributeError: 'list' object has no attribute 'ndim'

Hi,

I'm total AI newbie trying to run your piece of code, but i'm getting error. Any hint about that ?

python3 network.py -train dataset.csv -iterations 100 15:10:20 Using TensorFlow backend. /home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6 return f(*args, **kwds) Namespace(finetune=None, iterations=100, model=None, run=None, train='dataset.csv') Building net..done! Loading data...1022 chunk loaded! training... Traceback (most recent call last): File "network.py", line 117, in <module> net.fit(data[:el],labels[:el],epochs=epochs,batch_size=300) File "/home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/site-packages/keras/models.py", line 965, in fit validation_steps=validation_steps) File "/home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/site-packages/keras/engine/training.py", line 1593, in fit batch_size=batch_size) File "/home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/site-packages/keras/engine/training.py", line 1426, in _standardize_user_data exception_prefix='input') File "/home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/site-packages/keras/engine/training.py", line 70, in _standardize_input_data data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data] File "/home/jfburdet/tmp/BitcoinForecast/venv3/lib/python3.6/site-packages/keras/engine/training.py", line 70, in <listcomp> data = [np.expand_dims(x, 1) if x is not None and x.ndim == 1 else x for x in data] AttributeError: 'list' object has no attribute 'ndim'

Why is the price deviation calculated so much?

Starting main loop...
[23:08:22] Actual:4644.28$ Last Prediction:0$ Next 9m:1087.96$
[23:08:27] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:31] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:36] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:40] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:45] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:49] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:55] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$
[23:08:59] Actual:4644.28$ Last Prediction:1087.96$ Next 9m:1087.96$

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