andabi
loss graph
have you set 100k epochs for the train execution?
our loss graph using mir-1k dataset on 20k epochs doesn't show the correct behavior, expected to be a decreasing curve.
can you post your loss
graph result?
@vrosato The loss graph I've got was a lot like that of yours. When you set the smooth rate to 0.99, you probably see a decreasing loss graph. Actually I have no good explanation about why the graph is so oscillating. BTW If you have any chance, I recommend you to try L-BFGS optimization method. And then please let me know what the result become ;)
thx for the answer. we set the smooth rate to 0.99 and this is the result. have you used ikala or mir-1k dataset in the training process with 100k epochs?
@vrosato I used only ikala (was better than mixing two) and I remember that over 20k steps training was enough to get a generalized model. I hope you get a right result.
we are training the network with mir-1k dataset. after that we try with ikala dataset and the we compare the results.
for the optimizer L-BFGS we found this https://github.com/midori1/pylbfgs . is it the correct implementation?
thx
why GNSDR, GSIR, GSAR assume the same values? the paper expected results are different
did you separe the dataset for train and eval? using 175 clip for training and 825 for eval like the paper [3]
@balenko1992 yes i did, but the amount of each is not the same like that in the paper.
Dear andabi,
I am trying to reproduce the work you have done. I read your code and I am writing on my own using the same neural network architecture. Although the code of mine looks quite different to yours, but I believe the core training part are the almost the same. I was using y_tilde_src1 = y_hat_src1 / (y_hat_src1 + y_hat_src2 + np.finfo(float).eps) * self.x_mixed y_tilde_src2 = y_hat_src2 / (y_hat_src1 + y_hat_src2 + np.finfo(float).eps) * self.x_mixed as the output of neural network and apply them to the MSE loss function: loss = tf.reduce_mean(tf.square(self.y_src1 - self.y_pred_src1) + tf.square(self.y_src2 - self.y_pred_src2), name = 'MSE_loss') During training, I could see training loss drop from initial 8-9 to 4 in 500 time steps (i.e. 500 mini-batches), however, after another 20K time steps, the training loss is still around 3-4. So I came here to read your post.
I wonder what is the smooth rate you guys talked above? What is the definition of epoch here (Is it number of mini-batches)? Thank you.
why GNSDR, GSIR, GSAR assume the same values? the paper expected results are different
How do you see the GNSDR,GSIR,GSAR, I run eval.py,but I only get separation result without GNSDR,GSIR,GSAR ,How can I get it.
