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cs501r_f2018:lab3 [2018/09/14 10:49]
wingated
cs501r_f2018:lab3 [2018/09/20 08:50] (current)
sadler [Detailed specs:]
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      ​https://​pytorch.org/​docs/​stable/​torch.html#​torch.log      ​https://​pytorch.org/​docs/​stable/​torch.html#​torch.log
            
-**Part 2:** Implement a few initialization strategies ​including ​Xe initialization (sometimes called Xavier), Orthogonal initailization,​ and uniform random. You can specify which strategy you want to use with a parameter. Helpful links include:+**Part 2:** Implement a few initialization strategies ​which can include ​Xe initialization (sometimes called Xavier), Orthogonal initailization,​ and uniform random. You can specify which strategy you want to use with a parameter. Helpful links include:
     https://​hjweide.github.io/​orthogonal-initialization-in-convolutional-layers (or the orignal paper: http://​arxiv.org/​abs/​1312.6120)     https://​hjweide.github.io/​orthogonal-initialization-in-convolutional-layers (or the orignal paper: http://​arxiv.org/​abs/​1312.6120)
     http://​andyljones.tumblr.com/​post/​110998971763/​an-explanation-of-xavier-initialization     http://​andyljones.tumblr.com/​post/​110998971763/​an-explanation-of-xavier-initialization
  
-**Part 3:** Print the number of parameters in your network and plot accuracy of your training and validation set over time. You should experiment with some deep networks and see if you can'​t ​get a network with close to 1,000,000 parameters.+**Part 3:** Print the number of parameters in your network and plot accuracy of your training and validation set over time. You should experiment with some deep networks and see if you can get a network with close to 1,000,000 parameters.
  
 **Part 4:** Learn about how convolution layers affect the shape of outputs, and answer the following quiz questions. Include these in a new markdown cell in your jupyter notebook. ​ **Part 4:** Learn about how convolution layers affect the shape of outputs, and answer the following quiz questions. Include these in a new markdown cell in your jupyter notebook. ​
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 (c=3, h=10, w=10) => (c=22, ​ h=10, w=10) :  (c=3, h=10, w=10) => (c=22, ​ h=10, w=10) : 
  
-(c=3, h=10, w=10) => (c=65, ​ h=11, w=11) : +(c=3, h=10, w=10) => (c=65, ​ h=12, w=12) : 
  
-(c=3, h=10, w=10) => (c=7,   h=15, w=15) : +(c=3, h=10, w=10) => (c=7,   h=20, w=20) : 
   ​   ​
 Using a Kernel size of 5x5: Using a Kernel size of 5x5:
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 Using Kernel size of 5x3: Using Kernel size of 5x3:
  
-(c=3, h=10, w=10) => (c=10, ​ h=8,  w=8)  : (out_channels=10, ​ kernel_size=(5,​ 5), padding=(1, 0))+(c=3, h=10, w=10) => (c=10, ​ h=8,  w=8)  : 
  
 (c=3, h=10, w=10) => (c=100, h=10, w=10) :  (c=3, h=10, w=10) => (c=100, h=10, w=10) : 
cs501r_f2018/lab3.1536947395.txt.gz ยท Last modified: 2018/09/14 10:49 by wingated