More or less yes. But the other post was more about using scan in general, 
now its a special issue I have.

Am Montag, 17. April 2017 14:18:39 UTC+2 schrieb Triet Chau:
>
> Hi, does your code still serve the same purpose as that from your previous 
> post (https://groups.google.com/forum/#!topic/theano-users/gSZLwu8UrO0)?
>
> On Wednesday, 12 April 2017 13:04:30 UTC+2, [email protected] wrote:
>>
>> Hello,
>>
>> I try to loop with the scan function from theano.
>> I have a working example:
>>
>>
>>    1. import numpy as np
>>    2. import theano
>>    3. import theano.tensor as T 
>>    4.  
>>    5. N = 100
>>    6. M = 50
>>    7. D = 2
>>    8. EPhiTPhi = np.zeros((M,M))
>>    9. Z_n_W = T.dtensor3("Z_n_W")   
>>    10. MU_S_hat_minus = T.dtensor3("MU_S_hat_minus") 
>>    11.              
>>    12. def EPhiTPhi_loop(EPhiTPhi, Z_n_W, MU_S_hat_minus):
>>    13.    EPhiTPhi = EPhiTPhi + Z_n_W * (T.exp(-0.5 * (MU_S_hat_minus**2)
>>    .sum(2)));  
>>    14.    return EPhiTPhi
>>    15. 
>>    16. EPhiTPhi_out, _ = theano.scan(EPhiTPhi_loop,
>>    17.                        outputs_info = EPhiTPhi,
>>    18.                        sequences = [Z_n_W],
>>    19.                        non_sequences = [MU_S_hat_minus])
>>    20. 
>>    21. OUT = theano.function(inputs=[Z_n_W, MU_S_hat_minus], outputs = 
>>    EPhiTPhi_out)
>>    22. 
>>    23.  
>>    24. Z_n_W = np.ones((N,M,M))     
>>    25. MU_S_hat_minus = np.zeros((M,M,D))    
>>    26. #EPhiTPhi = EPhiTPhi.astype(np.float32)
>>    27. #Z_n_W = Z_n_W.astype(np.float32)
>>    28. #MU_S_hat_minus = MU_S_hat_minus.astype(np.float32)
>>    29. 
>>    30. LIST = {'Z_n_W': Z_n_W, 'MU_S_hat_minus': MU_S_hat_minus}
>>    31. 
>>    32. TEST = OUT(**LIST)
>>    
>>
>> When I try to extend it with one additional sequence variable of size 
>> (N,D), SIGMA_trf, I expect to hand over to EPhiTPhi_loop a vector with 
>> size(1,D).
>> When i want to make a calculation, where i have to blow up this vector to 
>> SIGMA_trf[None, None, :], I get an error.
>> Her first the extenden code.
>>
>>
>>    1. import numpy as np
>>    2. import theano
>>    3. import theano.tensor as T 
>>    4.  
>>    5. N = 100
>>    6. M = 50
>>    7. D = 2
>>    8. EPhiTPhi = np.zeros((M,M))
>>    9. SIGMA_trf = T.dmatrix("SIGMA_trf")  
>>    10. Z_n_W = T.dtensor3("Z_n_W")   
>>    11. MU_S_hat_minus = T.dtensor3("MU_S_hat_minus") 
>>    12. 
>>    13.              
>>    14. def EPhiTPhi_loop(EPhiTPhi, SIGMA_trf, Z_n_W, MU_S_hat_minus):
>>    15.    EPhiTPhi = EPhiTPhi + Z_n_W * (T.exp(-0.5 * (MU_S_hat_minus**2 
>>    * SIGMA_trf[None, None, :]).sum(2)));  
>>    16.    return EPhiTPhi
>>    17. 
>>    18. EPhiTPhi_out, _ = theano.scan(EPhiTPhi_loop,
>>    19.                        outputs_info = EPhiTPhi,
>>    20.                        sequences = [SIGMA_trf, Z_n_W],
>>    21.                        non_sequences = [MU_S_hat_minus])
>>    22. 
>>    23. OUT = theano.function(inputs=[SIGMA_trf, Z_n_W, MU_S_hat_minus], 
>>    outputs = EPhiTPhi_out)
>>    24. 
>>    25. SIGMA_trf = np.zeros((N,D))    
>>    26. Z_n_W = np.ones((N,M,M))     
>>    27. MU_S_hat_minus = np.zeros((M,M,D))    
>>    28. #EPhiTPhi = EPhiTPhi.astype(np.float32)
>>    29. #Z_n_W = Z_n_W.astype(np.float32)
>>    30. #MU_S_hat_minus = MU_S_hat_minus.astype(np.float32)
>>    31. 
>>    
>>    32.  
>>    33. LIST = {'SIGMA_trf': SIGMA_trf, 'Z_n_W': Z_n_W, 'MU_S_hat_minus': 
>>    MU_S_hat_minus}
>>    34. 
>>    35. TEST = OUT(**LIST)
>>    
>>
>> The error is:
>>
>> Traceback (most recent call last):
>>
>>   File "<ipython-input-1-dd8e5b5e726c>", line 22, in <module>
>>     non_sequences = [MU_S_hat_minus])
>>
>>   File 
>> "C:\ProgramData\Anaconda3\lib\site-packages\theano\scan_module\scan.py", 
>> line 773, in scan
>>     condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*
>> args))
>>
>>   File "<ipython-input-1-dd8e5b5e726c>", line 15, in EPhiTPhi_loop
>>     EPhiTPhi = EPhiTPhi + Z_n_W * (T.exp(-0.5 * (MU_S_hat_minus**2 * 
>> SIGMA_trf[None, None, :]).sum(2)));
>>
>>   File "C:\ProgramData\Anaconda3\lib\site-packages\theano\tensor\var.py", 
>> line 560, in __getitem__
>>     view = self.dimshuffle(pattern)
>>
>>   File "C:\ProgramData\Anaconda3\lib\site-packages\theano\tensor\var.py", 
>> line 355, in dimshuffle
>>     pattern)
>>
>>   File 
>> "C:\ProgramData\Anaconda3\lib\site-packages\theano\tensor\elemwise.py", 
>> line 177, in __init__
>>     (input_broadcastable, new_order))
>>
>> ValueError: ('You cannot drop a non-broadcastable dimension.', ((False, 
>> False), ('x', 'x', 0)))
>>
>>
>> Thanks for your help.
>>
>

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