Hi Arjun, thanks a lot for the quick response. 

But even then, I read somewhere that the probability for no label should be 
completely 1, because it will always predict every label with some non-zero 
probability - is that incorrect?

Yes, I just tried it with the training data, it's predicting 0 (with 1 
probability) for all the training examples.

But I think I found the problem. The training data just contains one sample 
with label 1 (like you said, imbalanced class distribution). I'll try 
training the model on more data, and get back. Thanks! 

 

On Monday, September 26, 2016 at 10:13:11 PM UTC+5:30, Arjun Jain wrote:
>
> Hi Mallika,
>
> It is indeed a non-theano specific question buy I will try to answer it 
> nevertheless - there can be a variety of reasons you are always getting the 
> first class e.g. if the first class is the dominant class (more number of 
> training examples and you do not use a weight in the cost function), a good 
> local minima for the network to learn is to output the first class all the 
> time as it would be a good guess. 
>
> Have you tested your trained model on the training data?
>
> Best,
> Arjun
>
> On Mon, Sep 26, 2016 at 8:11 PM, Mallika Agarwal <[email protected] 
> <javascript:>> wrote:
>
>> I suppose this isn't theano specific, let me know if I should shift the 
>> question somewhere else. 
>>
>> I am trying to perform verification for face recognition. (Binary 
>> classification)
>>
>> I trained a CNN using ~1000 examples, and stored the model to file. 
>>
>> I am now testing this on  ~5000 examples. I seem to be getting something 
>> very incorrect. When I output the probabilities from logistic regression 
>> (for each test example I get [1.0, 0.0] this prob distribution). 
>>
>> Is there a way I could check where the problem lies - in my trained 
>> parameters or in the test method? 
>>
>> Any help would be appreciated, thanks. :S
>>
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