Concerns about Deterministic Bald (Softmax_Bald?)

[y0uCeF]

When reading the paper "Deep Bayesian Active Learning with Image Data", I was interested in the results of Figure 2. Specifically, I wanted to replicate the Bald VS Deterministic Bald part. I followed the file-naming logic which lead me to the code in Softmax_Bald_Q10_N1000.py. So my first question is whether this code is the one behind the results of Deterministic Bald, since it uses predict() instead of stochastic_predict()? Assuming I got it right, I wondered how the calculation of the average entropy have been made even though we only have one single instance of the predictions. When looking at the code, for softmax_iterations = 1, the values of G_X = Entropy_Average_Pi and F_X = Average_Entropy should be equal because there is no averaging operations involved. However, when I run the code, the values in U_X = G_X - F_X where, in fact, not zeroed-out which they should have been. Eventually, it turned out that the empty arrays created before the loop, namely score_All and All_Entropy_Softmax had the automatic dtype=np.float64 while the softmax_score resulting from model.predict() was of type np.float32. Hence, subtracting these arrays, or any subsequent results would produce a non-zero difference.

To verify this, it's just a matter of prefixing the dtype parameters as:

score_All = np.zeros(shape=(X_Pool_Dropout.shape[0], nb_classes), dtype=np.float32)
All_Entropy_Softmax = np.zeros(shape=X_Pool_Dropout.shape[0], dtype=np.float32)

Or removing the loop all together since it is only running for one iteration anyway.