I try to use validation_data
method, but have a problem
model.fit([X['macd_train'], X['rsi_train'],X['ema_train']],
Y['train'],
sample_weight=sample_weight,
validation_data=([X['macd_valid'],
X['rsi_valid'],
X['ema_valid']],
Y['valid']),
epochs=nb_epochs,
batch_size=512,
verbose=True,
callbacks=callbacks)
I get an error :
ValueError: The model expects 3 arrays, but only received one array. Found: array with shape (127, 100, 8)
My code can run properly if I use validation_data=None
Here is my variables information
X['macd_train'].shape, X['macd_valid'].shape
(507, 100, 2), (127, 100, 2)
X['rsi_train'].shape, X['rsi_valid'].shape
(507, 100, 1), (127, 100, 1)
X['ema_train'].shape, X['ema_valid'].shape
(507, 100, 6), (127, 100, 6)
Y['train'].shape, Y['valid'].shape
(507, 1), (127, 1)
model.fit()
takes as first argument the data input and as the second one the data output. You attempt to do that by using [X['macd_train'], X['rsi_train'], X['ema_train']]
However, you are not concatenating your data but only increasing the dimension of your array. You should use the numpy.concatenate()
to have control over your concatenation over the proper axis.
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