def build_objective2(model, deterministic=False, bias = app.get_biases()[label_id], epsilon=1.e-7):
predictions = T.flatten(nn.layers.get_output(model.l_out, deterministic=deterministic))
targets = T.flatten(nn.layers.get_output(model.l_target))
preds = predictions*np.float32(0.5/bias)
preds = T.clip(preds, epsilon, 1.-epsilon)
#logs = [-T.log(preds), -T.log(1-preds)]
weighted_bce = - 5 * targets * T.log(preds) - (1-targets)*T.log(1-preds)
reg = nn.regularization.l2(predictions)
weight_decay=0.00004
return T.mean(weighted_bce) + weight_decay * reg
def build_test_objective(model,
deterministic=False,
bias=app.get_biases()[label_id],
epsilon=1.e-7):
predictions = T.flatten(
nn.layers.get_output(model.l_out, deterministic=deterministic))
targets = T.flatten(nn.layers.get_output(model.l_target))
preds = predictions * np.float32(0.5 / bias)
preds = T.clip(preds, epsilon, 1. - epsilon)
return T.mean(nn.objectives.binary_crossentropy(preds, targets))
def test_score(gts, preds, bias=app.get_biases()[label_id], epsilon=1.e-11):
predictions = np.array(preds) / 0.5 * bias
preds_cutoff = [1 if p > 0.5 else 0 for p in predictions]
return app.f2_score(gts, preds_cutoff, average=None)
def test_score(gts, preds, bias=app.get_biases()[label_id], epsilon=1.e-11):
predictions = expit(logit(preds) - logit(0.5) + logit(bias))
preds_cutoff = [1 if p > 0.5 else 0 for p in predictions]
return app.f2_score(gts, preds_cutoff, average=None)
