self).__init__()self.linear=nn.Linear(1,1)defforward(self,x):returnself.linear(x)# 初始化模型、损失函数和优化器model=LinearRegressionModel()criterion=nn.MSELoss()optimizer=optim.SGD(model.parameters(),lr=0.01)# 生成一些
I have a question about ppo's policy_gradient_loss log. The following part https://github.com/DLR-RM/stable-baselines3/blob/master/stable_baselines3/ppo/ppo.py#L229-L231 Am I correct in understanding that policy_gradient_loss generally gets smaller as we learn? (It is a loss function ...
trainable=False)learning_rate=tf.train.exponential_decay(learning_rate=lr,# 初始化学习率global_step=global_step,decay_steps=1000,decay_rate=1.0,staircase=False)optimizer=tf.train.GradientDescentOptimizer(learning_rate)# train = optimizer.minimize(loss=loss, global_step=global_step)...
Alex graves的博士论文地址:https://www.cs.toronto.edu/~graves/preprint.pdf 1 Loss Function ctc loss定义为ground truth标签序列的概率的负对数。上式表示的样本集的loss,是对每个样本的loss求和得到。 因为这个loss函数是可导的,所以loss对网络权重的梯度是可以通过反向传播算法得到的。 样本集的loss对网络权重...
Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories. Extensive experiments on a long-tailed TCT WSI image dataset show that the mainstream detectors, e.g. RepPoints, FCOS, ATSS, YOLOF, etc. trained using our proposed Gradient-...
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss 改进点 修改后的Unet 1)就是移除了BN层,只用一层卷积(原始Unet用两层卷积)解释:这样做的原因是超分辨率重建是像素级的任务,因为插值问题的解决方案主要是考虑某个区域中的像素。直接放大的图像避免了由于冗余计算而导致的误差,...
New issue 使用解决了多卡gradient accumulation严重BUG的最新transformer库(以及对应的trl库),DPO训练的时候LOSS变为之前的好几倍 #5747 Closed 1 task done JianbangZ opened this issue Oct 18, 2024· 12 comments · Fixed by #5852 Comments
第一个输出就是loss, 第二个输出是gradient,这边不需要 最终的计算是由c++完成的 (在tensorflow里面,可以添加新的c++”操作”,然后写个python wrapper就可以用了https://www.tensorflow.org/guide/extend/op) 我们追踪到了SparseSoftmaxCrossEntropyWithLogits这个“操作”(英文版叫OPS)https://github.com/tensorflow...
We then develop scalable stochastic gradient descent solvers for non-decomposable loss functions. We show that for loss functions satisfying a certain uniform convergence property (that includes precision@k and partial AUC), our methods provably converge to the empirical ri...
由上面可得计算gradient的公式为: ∂L∂wi=2NXiT(XW+b−Y) ∂L∂b=2(XW+b−Y).mean() 其中: X是features,有n个samples,每个sample有m个features,形式如下: X=[x11x21x31...xm1x12x22x32...xm2...x1nx2nx3n...xmn] W是参数,是列向量,形式如下: W=[w1w2...wm] b是bias,...