def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
return nllfun(w, train_images[idxs], train_labels[idxs])
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_images[idxs], train_labels[idxs]) * N_train
nlp = neg_log_prior(w)
return nll + nlp
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_images[idxs], train_labels[idxs]) * N_train
#nlp = neg_log_prior(w)
return nll # + nlp
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_inputs[idxs], train_targets[idxs]) * N_train
nlp = neg_log_prior(w)
return nll + nlp
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
nll = nllfun(w, train_inputs[idxs], train_targets[idxs]) * N_train
return nll
def indexed_loss_fun(w, i_iter):
rs = RandomState((seed, i, i_iter))
idxs = rs.randint(N_train, size=batch_size)
return nllfun(w, train_images[idxs], train_labels[idxs]) * N_train
