What is the usage of SECOND_ORDER_GRAD_ITER=0 and self.total_loss1? about mzsr HOT 3 OPEN

MAML requires 2nd-order gradients, which requires large computation.
For the fast training, we use 1st-order approximation of the gradients at the beginning of the training, and then after SECOND_ORDER_GRAD_ITER, we use the full gradients.

Therefore, SECOND_ORDER_GRAD_ITER is to decide how many steps to approximate the gradients within 1st-order.

For the self.total_loss1, you are right. it is not used for the training. You may ignore that loss and corresponding optimizer.

from mzsr.

Dear Sir,
Amazing work ! Congratulation!!
please , I have a question.can you kindly provide me with the full path I should insert of checkpoint the trained large scale training model to be able to use it as a pre-trained to meta transfer training? as it says that there is no check point file
I'm waiting for your reply.
Thanks in advance

from mzsr.

Please can you kindly explain me how to calculate this weight loss ?

def get_loss_weights(self):
loss_weights = tf.ones(shape=[self.TASK_ITER]) * (1.0/self.TASK_ITER)
decay_rate = 1.0 / self.TASK_ITER / (10000 / 3)
min_value= 0.03 / self.TASK_ITER

    loss_weights_pre = tf.maximum(loss_weights[:-1] - (tf.multiply(tf.to_float(self.global_step), decay_rate)), min_value)

    loss_weight_cur= tf.minimum(loss_weights[-1] + (tf.multiply(tf.to_float(self.global_step),(self.TASK_ITER- 1) * decay_rate)), 1.0 - ((self.TASK_ITER - 1) * min_value))
    loss_weights = tf.concat([[loss_weights_pre], [[loss_weight_cur]]], axis=1)
    return loss_weights

from mzsr.