但是BN层的mean和variance都会改变| | self._mean.stop_gradient=True |从源码里看到 BN 的 mean 与...
但是BN层的mean和variance都会改变| | self._mean.stop_gradient=True |从源码里看到 BN 的 mean 与...
q,k,v的3个输入只支持全部stop_gradient=True和stop_gradient=False的反向。 不使用memory_efficent和flash attn的话,反向正常。 例如, 当qkv的stop gradient分别为 T,T,T 的时候反向不报错 T,F,F 的时候反向报错 F,T,T 的时候反向报错 importpaddle,paddle.nnasnnfromfunctoolsimportpartialfrompaddle.incubate...
If not, please set stop_gradient to True for its input and output variables using var.stop_gradient=True. [Hint: grad_op_maker_ should not be null.] at (/paddle/paddle/fluid/framework/op_info.h:77) 0 收藏 回复 全部评论(1) 时间顺序 thinc #2 回复于2020-11 is_test=False 0...
ValueError: Target(Tensor(shape=[1], dtype=int64, place=CUDAPlace(0), stop_gradient=True, [102])) is out of class_dimension's upper bound(12)
# b_IJ += tf.reduce_sum(u_produce_v, axis=0, keep_dims=True) b_IJ += u_produce_v 在这个循环里,循环中间的u_hat实际上在是在一个batch里面的,不应该反向传播。但是,tensorflow对这个for循环的执行,实际上是展成一个长链(用tf.variable_scope('iter_' + str(r_iter))充当namespace,区分不同...
例子: v1 = [1,2] v2 = [0,1] a = Variable('a') b = Variable('b') b_stop_grad =stop_gradient(3* b) loss = MakeLoss(b_stop_grad + a) executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2)) executor.forward(is_train=True, a=v1, b=v2) executor.outputs [1.5...
v1 = [1, 2] v2 = [0, 1] a = Variable('a') b = Variable('b') b_stop_grad = stop_gradient(3 * b) loss = MakeLoss(b_stop_grad + a) executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2)) executor.forward(is_train=True, a=v1, b=v2) executor.outputs [ 1...
Hello! Pytorch has a facility todetacha tensor so that it will never require a gradient, i.e. (fromhere): In order to enable automatic differentiation, PyTorch keeps track of all operations involving tensors for which the gradient may need to be computed (i.e...
Window{ visible:true width:400 height:400 Rectangle{ width:300 height:300 anchors.centerIn:parent gradient:LinearGradient{ startX:0// 渐变开始点 X 坐标 startY:0// 渐变开始点 Y 坐标 endX:width// 渐变结束点 X 坐标 endY:height// 渐变结束点 Y 坐标 ...