For the simple snippet
*def discrete_fourier_transform(signal_tensor):*
* """*
* :param signal_tensor: Tensor of form (number of signals, signals length)*
* :returns: Discrete fourier transform of each signal*
* Description:*
* """*
* x = T.dmatrix()*
* W_t = T.dmatrix()*
* f = T.dot(W_t, x)*
* # dft matrix*
* W = dft(signal_tensor.shape[1])*
* # bind fourier transform*
* disc_ft = theano.function([W_t, x], f)*
* # compute and return*
* return disc_ft(W, signal_tensor)*
TypeError: ('Bad input argument to theano function with name
"integral_transforms.py:41" at index 0(0-based)', 'TensorType(float64,
matrix) cannot store a value of dtype complex128 without risking loss of
precision. If you do not mind this loss, you can: 1) explicitly cast your
data to float64, or 2) set "allow_input_downcast=True" when calling
"function".', array([[ 1.00000000 +0.00000000e+00j, 1.00000000
+0.00000000e+00j,
I can handle it by seperating real and complex then adding them up at the
end however I would prefer not to do so
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