Created by: liuxianyi

在命令行模式下，以下代码会报报错，导致后向传播出现问题？求解答

错误日志：

代码

import numpy as np
import paddle,itertools
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable
import paddle.fluid.dygraph as nn
class adaILN(nn.Layer):
    def __init__(self, num_features, eps=1e-5):
        super(adaILN, self).__init__()
        self.num_features = num_features
        self.eps = eps
        self.rho = fluid.layers.create_parameter(shape=[1,num_features,1,1], dtype='float32',is_bias=True, 
        default_initializer=fluid.initializer.Constant(0.9))
    def forward(self, input):
        ln_mean= fluid.layers.reduce_mean(input, dim=[1, 2, 3], keep_dim=True)
      
        out_ln = input - ln_mean
        
        out1 = (1-fluid.layers.expand(x=self.rho, expand_times=[input.shape[0], 1, 1, 1])) * out_ln
        # 经过初步调试，确定发生错误位置在此处，将此处注释不会报错

        return out1
#高层API的组网方式需要继承Model，Model类实现了模型执行所需的逻辑
class SimpleNet(paddle.fluid.dygraph.Layer):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.conv = paddle.fluid.dygraph.Conv2D(1,1,filter_size=3,stride=1,padding=1)
        self.iln = adaILN(1)
    def forward(self, x):
        x = self.conv(x)
        x = self.iln(x)
        return x

with paddle.fluid.dygraph.guard(paddle.fluid.CUDAPlace(0)):
    
    model1 = SimpleNet()
    Loss = nn.L1Loss()
    optim = fluid.optimizer.Adam(learning_rate=0.001,
        beta1=0.5,
        beta2=0.999,
        parameter_list=model1.parameters()
            )
    model1.train()

    for i in range(20):
        x1 = np.random.random((2, 1, 256, 256)).astype('float32')
        x1 = to_variable(x1)
        e1= model1(x1)
        #print(e1)
        # e2 = model1(e1)
        loss2 = Loss(e1, x1)
        #print('loss:',loss.numpy())
        # loss = loss1+loss2
        print(loss2.numpy())
        loss2.backward()
        optim.minimize(loss2)
        optim.clear_gradients()

Originally posted by @liuxianyi in https://github.com/PaddlePaddle/Paddle/issues/26192#issuecomment-673821904