![]() ![]() ![]() The following is the 【loss curve】 from the 7th to the 12th. From the 6th to the 12th training, the weight B still did not change and remained at -0.5233551. But in the sixth training, the weight B did not change. Body mass index (BMI) details should also be. The temperature can be measured in Celsius and Fahrenheit. 12 Best Weight Loss Trackers (Free Templates) Weight tracking is an integral part of the whole process of weight loss. The temperature of the body is also measured and recorded in the tracker. The body measurements of different body parts such as chest, hip, legs, feet, shoulders are also recorded. It can be seen that in the first five training, the value of weight B has been changing. The weight can be measured in kilograms as well as in pounds. The model was trained 12 times (manual training), and the above 6 images were obtained. I only select a certain weight parameter(I call it weight B) in the model and observe the change of its value in the process of updating.Īfter the end of each time model training, I will draw the change of weight into a graph. Note: for each epoch, the parameter is updated 1180 times. I get the change of the weight parameter value in each epoch. I didn’t change the original code, I just ran it again. This time I set 【epoch = 1】, manual training. I don’t know how to use clone properly (this time I’m not going to delve into this one), so my choice is: clear all variables, restart the program, and restart training. However, the value of 【loss】 decreased normally.I don’t know why the value of the weight 【b】 is not updated. Print('Epoch: ', epoch, '| Step: ', i, '| loss: ',wc_loss)įor name, param in NET.named_parameters():ī.append(parm)īut the result of 【b】is always repeated,same result every updata,like this.( note:The first convolution layer is conv1d,Ĭonv1d(1,3,kernel=3)) ], Print('Epoch:', epoch + 1, 'Training.')įor i, (batch_x, batch_y) in enumerate(loader): I want to get the weight parameters of the first level convolution, for epoch in range(EPOCH): Hi,I have a question about this.Here is the code for my training phase. ![]()
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