Tensor x's grad is: Tensor(shape=[3], dtype=float32, place=CPUPlace, stop_gradient=False, [1., 1., 1.]) 3、因为backward()会累积梯度,所以飞桨还提供了clear_grad()函数来清除当前Tensor的梯度。 In [15] import paddle import numpy as np x = np.ones([2, 2], np.float32) inputs2...
# 清除梯度 opt.clear_grad() #保存模型参数 paddle.save(model.state_dict(), 'model/mnist_cnn_3.pdparams') model = MNIST() train(model) 这里跟前期代码重点关注的是如下代码,loss我们是使用交叉熵损失函数 #计算损失,使用交叉熵损失函数,取一个批次样本损失的平均值 loss = F.cross_entropy(predicts, ...
optim.step() # 更新参数 optim.clear_grad() # 清除梯度 model = Mnist() train(model) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 2.4、高层API进阶用法 2.4.1 自定义Loss 有时我们会遇到特定任务的Loss计算方式在框架既有的Loss...
获取预测评分predictions=model(sparse_ratings)# 计算损失loss=criterion(predictions,sparse_ratings.values())# 反向传播loss.backward()# 更新模型参数optimizer.step()# 清空梯度optimizer.clear_grad()#
optimizer.clear_grad() print("Pass:%d, Cost:%0.5f" % (pass_id, loss)) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. Pass:0, Cost:0.02406 Pass:1, Cost:0.02354 Pass:2, Cost:0.02302 Pass:3, Cost:0.02252 Pass:4, Cost:0.02202 ...
我们还需要在调用backward()函数之前调用optim.clear_grad(),原因是Paddle是默认梯度累加而不是梯度更新 In [18] # create a simple model model = paddle.nn.Linear(1,1) # create a simple dataset X_simple = paddle.to_tensor([[1.]]) y_simple = paddle.to_tensor([[2.]]) # create our ...
opt.step()#反馈器,修改回调函数opt.clear_grad() model.eval()#评价模式accuracies = []#初始化两个列表losses =[]forbatch_id, datainenumerate(valid_loader()): img= data[0]#把图片放到评价集中label = data[1]#计算模型输出logits =model(img)#计算损失函数loss_func = paddle.nn.CrossEntropyLoss...
opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( e, i, np.mean(loss.numpy())) # 开始评估 net.eval() plt.scatter(x, y, color='blue', label="act") x = sorted(x) z = np.array([net(paddle.to_tensor(i)).numpy()[0] for i in x]) plt...
新增前向和反向高阶自动微分API,paddle.incubate.autograd.forward_grad,paddle.incubate.autograd.grad。#43354 新增18个高阶自动微分算子sin,cos,exp,erf,abs,log,cast,where,equal,not_equal,greater_than,greater_equal,elementwise_powsquare,elementwise_max,gelu,reduce_mean,size。#46184,#46024,#45888,#...
jit.to_staticdefforward(self, x):y = self._linear(x)returny# create networklayer = LinearNet()adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters())forbatch_id, xinenumerate(data_loader()):out = layer(image)loss = paddle.mean(out)loss.backward()opt.step()opt.clear_grad...