Closed twhughes closed 5 years ago
When I train simple linear models, the loss function oscillates wildly. For example, using inSituAdam:
inSituAdam
model_linear = neu.Sequential([ neu.ClementsLayer(N), neu.Activation(neu.Abs(N)), neu.DropMask(N, keep_ports=range(N_classes)) ]) losses = neu.InSituAdam(model_linear, neu.CategoricalCrossEntropy, step_size=step_size).fit(x_train_flattened, y_train_onehot, epochs=n_epochs, batch_size=batch_size)
This may be a sign that the gradients are incorrect. Should double check.
I think its fine. This is probably due to the way the batching works.
Just got this email notif, I don't think you would get this issue if you removed the abs nonlinearity and did a meansquareerror loss.
When I train simple linear models, the loss function oscillates wildly. For example, using
inSituAdam
:This may be a sign that the gradients are incorrect. Should double check.