Commit cee8e8b

committed

finish para initialize

1 parent 56c7064 commit cee8e8bCopy full SHA for cee8e8b

File tree

+473

-4

lines changed

+473

-4

lines changed

Lines changed: 3 additions & 3 deletions

Original file line number	Diff line number	Diff line change
`@@ -27,11 +27,11 @@ Learn Deep Learning with PyTorch`
`27`	`27`	`- [线性模型与梯度下降](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/linear-regression-gradient-descend.ipynb)`
`28`	`28`	`- [Logistic 回归与优化器](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/logistic-regression/logistic-regression.ipynb)`
`29`	`29`	`- [多层神经网络,Sequential 和 Module](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/nn-sequential-module.ipynb)`
`30`		`- - 参数初始化方法`
	`30`	`+ - [参数初始化方法](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/param_initialize.ipynb)`
`31`	`31`	`- 优化算法`
`32`	`32`
`33`	`33`	`- Chapter 4: 卷积神经网络`
`34`		`- - 卷积网络介绍以及 PyTorch 中的卷积模块`
	`34`	`+ - PyTorch 中的卷积模块`
`35`	`35`	`- 使用重复元素的深度网络,VGG`
`36`	`36`	`- 更加丰富化结构的网络,GoogLeNet`
`37`	`37`	`- 深度残差网络,ResNet`
`@@ -41,7 +41,7 @@ Learn Deep Learning with PyTorch`
`41`	`41`	`- Fine-tuning: 通过微调进行迁移学习`
`42`	`42`
`43`	`43`	`- Chapter 5: 循环神经网络`
`44`		`- - LSTM和GRU`
	`44`	`+ - LSTM 和 GRU`
`45`	`45`	`- 使用RNN进行时间序列分析`
`46`	`46`	`- 使用RNN进行图像分类`
`47`	`47`	`- Word Embedding和N-Gram模型`

Lines changed: 1 addition & 1 deletion

Original file line number	Diff line number	Diff line change
`@@ -78,7 +78,7 @@`
`78`	`78`	`img = Variable(img, volatile=True).cuda()`
`79`	`79`	`label = Variable(label, volatile=True).cuda()`
`80`	`80`	`else:`
`81`		`- img = Variabel(img, volatile=True)`
	`81`	`+ img = Variable(img, volatile=True)`
`82`	`82`	`label = Variable(label, volatile=True)`
`83`	`83`	`out = model(img)`
`84`	`84`	`loss = criterion(out, label)`

Comments

(0)