Also, the return values of this loss function are small compared to cross-entropy, some sample values after random initialization were around +/- 0.01. There is a LSTM layer and the input sequences are thousands of elements long, so I suspected vanishing gradients. However, I'm printing out the min, max, and mean of the gradients w.r.t each parameter, and they are all exactly equal to 0, which seems to indicate a different problem.
On Sunday, March 5, 2017 at 3:59:42 PM UTC-6, [email protected] wrote: > > I have defined a custom loss function, and despite the loss function > returning correct values given the inputs, the gradients are all always 0 > w.r.t each of my parameters. I am not suppressing any theano errors > including the disconnected input error, so I can't explain what is causing > this. I have copied the loss function below; in words, I first convert a 3 > class softmax output into a one hot representation, then I compare a subset > of it to the response and compute a quantity of interest. More generally, I > was under the impression that if one could express a function using theano > ops, it could be used as a loss function. Is this not the case? > > def calc_one_hot_loss(pred, y, mask): > mask_flat = T.flatten(mask) > length = T.sum(mask_flat, dtype='int32') > pred_unmasked = pred[mask_flat.nonzero()] > max_indices = T.argmax(pred_unmasked, axis=1) > pred_zero = T.set_subtensor(pred_unmasked[:], 0) > pred_one_hot = T.set_subtensor(pred_zero[T.arange(length), max_indices], > 1) > y_unmasked = y[mask_flat.nonzero()] > unchanged_col = pred_one_hot[:, preprocess.unchanged_index] > pred_up = T.flatten(pred_one_hot[T.eq(unchanged_col, 0).nonzero(), > preprocess.up_index]) > pred_down = T.flatten(pred_one_hot[T.eq(unchanged_col, 0).nonzero(), > preprocess.down_index]) > y_up = T.flatten(y_unmasked[T.eq(unchanged_col, 0).nonzero(), > preprocess.up_index]) > y_down = T.flatten(y_unmasked[T.eq(unchanged_col, 0).nonzero(), > preprocess.down_index]) > diff_up = T.abs_(pred_up - y_up) > diff_down = T.abs_(pred_down - y_down) > diff_sum = diff_up + diff_down > num_win = T.sum(T.eq(diff_sum, 0)) > num_lose = T.sum(T.eq(diff_sum, 2)) > loss = -1 * (num_win - num_lose) / length > return loss > > > > > > > > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
