def multi_task_loss(y, t):
cross_entropy = categorical_crossentropy(y[:, :num_class], t)
regress_predictions = discrete_predict(y[:, -1])
mse = squared_loss(regress_predictions, t)
log_loss = cross_entropy.mean()
reg_loss = mse.mean()
return log_loss, reg_loss, log_loss + 3 * reg_loss
def multi_task_loss(y, t):
cross_entropy = categorical_crossentropy(y[:, :num_class], t)
regress_predictions = discrete_predict(y[:, -1])
mse = squared_loss(regress_predictions, t)
log_loss = cross_entropy.mean()
reg_loss = mse.mean()
return log_loss, reg_loss, log_loss + 3 * reg_loss
def multi_task_loss(y, t):
softmax_predictions = categorical_crossentropy(y[:, :num_class], t)
regress_predictions = squared_loss(y[:, -1], t)
log_loss = softmax_predictions.mean()
reg_loss = regress_predictions.mean()
return log_loss, reg_loss, log_loss + 0.1 * reg_loss
def multi_task_loss(y, t):
softmax_predictions = categorical_crossentropy(y[:, :num_class], t)
regress_predictions = squared_loss(y[:, -1], t)
log_loss = softmax_predictions.mean()
reg_loss = regress_predictions.mean()
return log_loss, reg_loss, 0.75*log_loss + 0.25*reg_loss
