testing_correct +=torch.sum(pred == y_test.data) print("Loss is:{:.4f}, Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}".format(torch.true_divide(running_loss, len(data_train)), torch.true_divide(100*running_
transpose_(self, dim0, dim1) triangular_solve(self, A, upper=True, transpose=False, unitriangular=False) tril(self, k=0) tril_(self, k=0) triu(self, k=0) triu_(self, k=0) true_divide(self, value) true_divide_(self, value) trunc(self) trunc_(self) type(self, dtype=None, n...
true_divide:返回除法的浮点数结果而不作整数处理。 import torch d = [[1,2,3], [4,5,6], [7,8,9]] t = torch.tensor(d) td = t.true_divide(2)#每个元素÷2 print(td) 结果: floor_divide(地板除):先进行除运算,再对结果(浮点数)进行向下取整并返回整数。 import torch d = [[1,2,...
testing_correct += torch.sum(pred == y_test.data) print("Loss is:{:.4f}, Train Accuracy is:{:.4f}%, Test Accuracy is:{:.4f}".format(torch.true_divide(running_loss, len(data_train)), torch.true_divide(100*running_correct, len(data_train)), torch.true_divide(100*testing_correct...
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * torch.true_divide(correct, total))) # Save the Model for i in range(1,4): plt.imshow(train_dataset.train_data[i].numpy(), cmap='gray') plt.title('%i' % train_dataset.train_labels[i]) ...
torch.true_divide(100*running_correct, len(data_train)), torch.true_divide(100*testing_correct, len(data_test))) torch.save(model.state_dict(), "model_parameter.pkl") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14
true_divide(correct, total))) # Save the Model for i in range(1,4): plt.imshow(train_dataset.train_data[i].numpy(), cmap='gray') plt.title('%i' % train_dataset.train_labels[i]) plt.show() torch.save(net.state_dict(), 'model.pkl') #net.state_dict(),模型文件 test_output ...
true_divide(l, m)) 输出:Tensor 除法: tensor([2., 3.]) 3.4 幂运算 q = torch.tensor([2, 3]) ## 方法1 print("Tensor 幂运算:", torch.pow(q, 2)) ## 方法2 print("Tensor 幂运算:", q.pow(2)) ## 方法3 print("Tensor 幂运算:", q**2) 输出:Tensor 幂运算: tensor([4, ...
下图形象的展示了Softmax,Exponent这里指指数,和上面我们说的一样,先求指数,这样有了分子,再将所有指数求和,最后一一divide,得到了每一个概率。 接下来我们一起来看看损失函数 如果使用numpy进行实现,根据刘二大人的代码,可以进行如下的实现 代码语言:javascript ...
D = np.true_divide(B, A) # 第二种方法实现元素除法(点除) F = np.divide(B, A) # 第三种方法实现元素除法(点除) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 运行结果如下: 10-矩阵的元素除法(点除)取余 有三种方法实现元素除法(点除)取余,示例代码如下 ...