训练过程

D:\ANACONDA\envs\pytorch\python.exe C:/Users/Administrator/Desktop/Code/MOOCCode/class1/p13_backpropagation.py
2024-01-03 15:03:58.722804: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2024-01-03 15:03:58.722804: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2024-01-03 15:04:00.472904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2024-01-03 15:04:00.490906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:01:00.0 name: GeForce GT 240 computeCapability: 1.2
coreClock: 1.34GHz coreCount: 6 deviceMemorySize: 256.00MiB deviceMemoryBandwidth: 19.88GiB/s
2024-01-03 15:04:00.491906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2024-01-03 15:04:00.491906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found
2024-01-03 15:04:00.492906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2024-01-03 15:04:00.492906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2024-01-03 15:04:00.493906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2024-01-03 15:04:00.493906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found
2024-01-03 15:04:00.494906: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudnn64_7.dll'; dlerror: cudnn64_7.dll not found
2024-01-03 15:04:00.494906: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1598] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2024-01-03 15:04:00.495906: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2024-01-03 15:04:00.503906: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1af3e2e0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2024-01-03 15:04:00.503906: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2024-01-03 15:04:00.503906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2024-01-03 15:04:00.504906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      
After 0 epoch,w is -6.988000,loss is 36.000000
After 1 epoch,w is 4.976024,loss is 35.856144
After 2 epoch,w is -6.964072,loss is 35.712860
After 3 epoch,w is 4.952145,loss is 35.570156
After 4 epoch,w is -6.940241,loss is 35.428024
After 5 epoch,w is 4.928361,loss is 35.286461
After 6 epoch,w is -6.916504,loss is 35.145462
After 7 epoch,w is 4.904671,loss is 35.005020
After 8 epoch,w is -6.892861,loss is 34.865135
After 9 epoch,w is 4.881076,loss is 34.725815
After 10 epoch,w is -6.869314,loss is 34.587051
After 11 epoch,w is 4.857575,loss is 34.448849
After 12 epoch,w is -6.845860,loss is 34.311192
After 13 epoch,w is 4.834168,loss is 34.174084
After 14 epoch,w is -6.822500,loss is 34.037521
After 15 epoch,w is 4.810855,loss is 33.901508
After 16 epoch,w is -6.799233,loss is 33.766033
After 17 epoch,w is 4.787635,loss is 33.631107
After 18 epoch,w is -6.776060,loss is 33.496716
After 19 epoch,w is 4.764508,loss is 33.362869
After 20 epoch,w is -6.752979,loss is 33.229557
After 21 epoch,w is 4.741473,loss is 33.096771
After 22 epoch,w is -6.729990,loss is 32.964516
After 23 epoch,w is 4.718530,loss is 32.832787
After 24 epoch,w is -6.707093,loss is 32.701580
After 25 epoch,w is 4.695680,loss is 32.570911
After 26 epoch,w is -6.684288,loss is 32.440765
After 27 epoch,w is 4.672919,loss is 32.311131
After 28 epoch,w is -6.661573,loss is 32.182014
After 29 epoch,w is 4.650250,loss is 32.053413
After 30 epoch,w is -6.638950,loss is 31.925329
After 31 epoch,w is 4.627672,loss is 31.797762
After 32 epoch,w is -6.616417,loss is 31.670694
After 33 epoch,w is 4.605185,loss is 31.544140
After 34 epoch,w is -6.593974,loss is 31.418095
After 35 epoch,w is 4.582787,loss is 31.292547
After 36 epoch,w is -6.571621,loss is 31.167505
After 37 epoch,w is 4.560478,loss is 31.042959
After 38 epoch,w is -6.549357,loss is 30.918919
After 39 epoch,w is 4.538259,loss is 30.795368

Process finished with exit code 0

对结果的内容分析

当学习率过大时，可能导致梯度下降算法无法收敛，甚至可能发生震荡或发散，导致损失函数值不断增加而无法找到最优解。在这个例子中，学习率的初始值为0.2。如果学习率设置得太大，每次更新参数的步长就会很大，可能会越过损失函数的最小值，导致算法无法收敛。

在输出中，你可以观察到 w 的值在每个 epoch 中发生剧烈的变化，而损失函数的值并没有明显地减小。这是学习率过大的典型表现，会导致算法不稳定，难以找到合适的参数值。

为了解决这个问题，可以尝试减小学习率，例如将其设置为 0.01 或更小的值，然后重新运行代码。适当选择学习率是优化算法中一个重要的调整参数，需要根据具体问题和数据来调整。