CS231n 2022 作业代码实现
以及带笔记的课件
感谢B站 心里冷风 同学对knn答案的指正,欢迎各位同学交流作业和深度学习相关的问题!
有需要计算机课程辅导的同学可以通过github issue的方式私信我
CS231n作业代码实现
我在跑Transformer_Captioning.ipynb的第一个计算,也就是测试MultiHeadAttention的相对错误,我甚至复制了学长的代码,可是运行结果仍然是:
self_attn_output error: 0.449382070034207
masked_self_attn_output error: 1.0
attn_output error: 1.0
并未得到和学长一样的结果,也就是作业要求的: The relative error should be less than e-3.
请问学长,问题出在哪里呢~
所有作业都是按照作者的代码来理解的,写的清晰明了,对我帮助非常大,十分佩服,感谢~作者未来可期啊
Hello!
你好,我想问问你可以分享一下cs231n 2022的视频嘛?2017的视频感觉和作业在Lecture 11后有明显脱节了(捂脸)
hello,I think this place in knn part :
for i in range(N):
X_train_folds.append(X_train[i*length: (i+1)length])
y_train_folds.append(y_train[ilength: (i+1)length])
may should be:
for i in range(num_folds):
X_train_folds.append(X_train[ilength: (i+1)length])
y_train_folds.append(y_train[ilength: (i+1)*length])
我觉得softmax.ipynb的Inline question 2 答案不对
False
设原来有n个数据点,新增的 datapoint 会新增 2*n 个 max(0,-) 的和,而新增的和是大于0的
For SVM it's impossible. A new datapoint would increase the sum of the per-datapoint loss due to the fixed margin
which means that
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