-
-
Notifications
You must be signed in to change notification settings - Fork 0
/
slides_5_orig.html
568 lines (459 loc) · 21.7 KB
/
slides_5_orig.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<meta name="author" content="Emre Neftci">
<title>Neural Networks and Machine Learning</title>
<link rel="stylesheet" href="css/reset.css">
<link rel="stylesheet" href="css/reveal.css">
<link rel="stylesheet" href="css/theme/nmilab.css">
<link rel="stylesheet" type="text/css" href="https://cdn.rawgit.com/dreampulse/computer-modern-web-font/master/fonts.css">
<link rel="stylesheet" type="text/css" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
<link rel="stylesheet" type="text/css" href="lib/css/monokai.css">
<!-- Printing and PDF exports -->
<script>
var link = document.createElement( 'link' );
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = window.location.search.match( /print-pdf/gi ) ? 'css/print/pdf.css' : 'css/print/paper.css';
document.getElementsByTagName( 'head' )[0].appendChild( link );
</script>
<script async defer src="https://buttons.github.io/buttons.js"></script>
</head>
<body>
<div class="reveal">
<div class="slides">
<!--
<section data-markdown><textarea data-template>
##
</textarea></section>
[](https://colab.research.google.com/github/surrogate-gradient-learning/pytorch-lif-autograd/blob/master/tutorial01_dcll_localerrors.ipynb)
-->
<!-- .element: class="fragment" -->
<section data-markdown data-vertical-align-top data-background-color=#B2BA67><textarea data-template>
<h1> Neural Networks <br/> and <br/> Machine Learning </h1>
### Week 5: Modern ConvNets and Segmentation
### Instructor: Prof. Emre Neftci
<center>https://canvas.eee.uci.edu/courses/21750</center>
<center>http://tinyurl.com/nmi-lab-appointments</center>
[](?print-pdf)
</textarea>
</section>
<section data-markdown><textarea data-template>
<h1> Deep Learning Goody Bag II</h1>
<h2> Normalization, FashionMNIST, Resizing data </h2>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Normalization</h2>
<ul>
<li /> If preactivations drift during training, it is costly for subsequent layers to adapt to that drift.
<li /> Several normalization techniques exist, we will focus on the one use the most commonly (Batch Norm).
</ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Batch Normalization</h2>
<ul>
<li /> Batch Normalization (BN) is one solution to this problem. BN transforms the activations at a given layer x according to:
$$
\mathrm{BN}(\mathbf{x}) = \mathbf{\gamma} \odot \frac{\mathbf{x} - \hat{\mathbf{\mu}}}{\hat\sigma} + \mathbf{\beta}</div>
$$
where $\hat{\mathbf{\mu}}$ is the mean over the batch, $\hat\sigma$ is the standard deviation over the batch. $\gamma$ and $\beta$ are trainable scaling and offsets parameters.
<li /> Typically a noise term is added to the calculation of $\hat\sigma$. This noise term prevents a division by zero and also acts as a regularizer
<li /> BN speeds up the training in large networks
<li /> BN is applied right before the application of the activation function
</ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Goody II.1 Use BN in Feed Forward Neural Networks</h2>
<pre><code class="Python" data-trim data-noescape>
def __init__(self):
super(Net, self).__init__()
self.layer1 = torch.nn.Linear(128,64)
self.layer1_bn =nn.BatchNorm1d(64)
...
def forward(self, x):
y = self.layer1(x)
y = self.layer1_bn(y)
y = torch.relu(y)
...
</code></pre>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Goody II.1 Use BN in Convolutional Neural Networks</h2>
<pre><code class="Python" data-trim data-noescape>
def __init__(self):
super(Net, self).__init__()
self.layer1 = torch.nn.Conv2d(3,32,3,1)
self.layer1_bn =nn.BatchNorm2d(32)
...
def forward(self, x):
y = self.layer1(x)
y = self.layer1_bn(y)
y = torch.relu(y)
...
</code></pre>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Fashion MNIST </h2>
<ul>
<li/> MNIST is too easy: 99.70% classification means 30 misclassified samples
<li/> But the size is great for proof of concept. The Fashion MNIST dataset is the same size (28x28, 70k images, 10 classes) and is slightly harder
</ul>
<img src="images/fashion-mnist-sprite.png" class=stretch />
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Goody II.2 Use Fashion MNIST as drop-in replacement for MNIST </h2>
<ul>
<li /> Use fashion MNIST wherever you can use MNIST to test your model.
</ul>
<pre><code class="Python" data-trim data-noescape>
train_set = datasets.FashionMNIST('./data',
train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]),
target_transform = None,)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=100, shuffle=True)
</code></pre>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Goody II.3 Data pre-processing on-the-fly </h2>
<ul>
<li /> Often, computer vision models are optimized for a given size. For example in torchvision:
<blockquote>
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
</blockquote>
<li /> You can use the following transforms to make any color image compliant with this constraint:
<pre><code class="Python" data-trim data-noescape>
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
</code></pre>
</ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Goody II.3 Example CIFAR10 </h2>
<ul>
<li /> MNIST has only 1 channel, so you can't use it in pre-trained models (but you can modify the pre-defined models to accept a single channel)
<li /> CIFAR10 is another commonly used dataset. You can make CIFAR10 compliant with pre-trained models as follows
</ul>
<pre><code class="Python" data-trim data-noescape>
train_set = datasets.CIFAR10('./data',
train=True, download=True,
transform=preprocess,
target_transform = None,)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=100, shuffle=True)
</code></pre>
[](https://drive.google.com/open?id=1L0Q3f9m0xIyAx18GGhij-9rwZyQq-mYH)
</textarea></section>
<section data-markdown><textarea data-template>
<h1> Modern Convolutional Neural Networks </h1>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> The Rise of Deep Learning </h2>
<ul>
<li /> Until 2012, neural networks were often surpassed by <em>classical</em> machine learning methods, such as support vector machines.
<li /> Rather than training <em>end-to-end</em>, classical machine learning used hand-crafted features (SIFT, SURF, HOG).
<li /> In the 1990s, neural network accelerators were not sufficiently powerful. ConvNets were around, but couldn't scale to large problems.
<li /> GPU accelerators, large-scale labeled data (ImageNet) and the perserverence of neural network researchers changed this in late 2000'
</ ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> AlexNet </h2>
<div class=row>
<div class=column>
<ul>
<li /> In 2012, Krizhevsky, Sutskever, & Hinton implemented core operations (convolution operations) of CNNs on GPUs, which achieved excellent performance in the ImageNet challenge.
<li /> The capacity of the network was contolled using dropout and data augmentation.
<li /> Alexnet (Right) also had subtle differences with the LeNet (Left)
<li /> In 2012, training Alexnet tooks days! Today, AlexNets are superseded by more efficient networks.
</ul>
</div>
<div class=column>
<img src="images/alexnet.svg" class=large />
</div>
</div>
<pre><code class="Python" data-trim data-noescape>
torchvision.models.alexnet(pretrained=False)
</code></pre>
<p class=ref>Krizhevsky et al. 2012 "Imagenet classification with deep convolutional neural networks"</p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Modern ConvNets: Visual Geometry Groups (VGG) </h2>
<div class=row>
<div class=column>
<ul>
<li /> VGG networks introduced the use of <em> blocks </em> that consisted of multiple layers.
<li /> The original VGG block consists of multiple 3x3 convolution layers, a ReLU and a 2x2 max-pooling layer with stride of 2
<li /> The first block has 64 output channels and each subsequent block doubles the number of output channels, until that number reaches 512
<li /> The number of convolutional blocks used depended on the size of the input. The original VGG net used 5 blocks.
</ul>
</div>
<div class=column>
<img src="images/vgg.svg" class=large />
</div>
</div>
<pre><code class="Python" data-trim data-noescape>
torchvision.models.vgg16(pretrained=False)
</code></pre>
<p class=ref>Simonyan & Zisserman, 2014 </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Modern ConvNets: Network in Networks </h2>
<div class=row>
<div class=column>
<ul>
<li /> Fully Connected (FC) layers are important to mix the spatial features for a final classification.
<li /> However, if their size and depth is not chosen properly, they can remove spatial structure too early.
<li /> Networks in Networks perform mixing of channels (as in FC) while keeping the spatial structure. This is achieved using 1x1 convolutions.
<li /> A global mean pooling layer is used to produce the output
</ul>
[](https://drive.google.com/open?id=1ZUxCV0J-I13XV03WA-qkALeiIYoVjI-T)
</div>
<div class=column>
<img src="images/nin.svg" class=large />
<pre><code class="Python" data-trim data-noescape>
...
torch.nn.Conv2d(3, 64, 5, padding=2),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(64, 32, 1),
torch.nn.ReLU(inplace=True),
torch.nn.Conv2d(32, 32, 1),
torch.nn.ReLU(inplace=True),
torch.nn.MaxPool2d(3, stride=2),
torch.nn.Dropout()
...
nn.AvgPool2d(8, stride=1) #This after several nin blocks
</code></pre>
</div>
</div>
<p class=ref>Lin et al., 2013 </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Modern ConvNets: GoogLeNet </h2>
<div class=row>
<div class=column>
<ul>
<li /> Different convnets use different kernel sizes.
<li /> GoogLeNet combined multiple convolutions with different kernel sizes in a single block.
<li > The middle two paths perform a 1x1 convolution on the input to reduce the number of input channels, reducing the model's complexity
</ul>
<pre><code class="Python" data-trim data-noescape>
torchvision.models.googlenet(pretrained=False)
</code></pre>
</div>
<div class=column>
<img src="images/inception-full.svg" class=large />
<img src="images/inception-block.svg" class=large />
</div>
</div>
<p class=ref>Szegedy et al., 2015 </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Modern ConvNets: ResNet </h2>
<div class=row>
<div class=column>
<ul>
<li /> A residual block attempts to output $f(x) + x$, where $f$ is the function computed by the network in the dotted box.
<li /> In the residual block, $f(x)$ denotes the deviation from the original input
<li /> Standard Block (left), Residual Block (right)
<li /> Using resnets, much deeper layers could be constructed.
</ul>
</div>
<div class=column>
<img src="images/residual-block.svg" class=large />
</div>
</div>
<pre><code class="Python" data-trim data-noescape>
torchvision.models.resnet34(pretrained=False)
</code></pre>
<p class=ref>He et al., 2016 </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Training a pre-defined network on your own dataset </h2>
<ul>
<li /> Pre-defined networks are great for getting started on a project because the structure of the network has been optimized by the community
<li /> Note that torchvision networks are intended for ImageNet classification, *i.e.* 1000 classes. Remember to set num_classes accordingly.
</ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> In-class Assignment: Training your own model </h2>
Let's train our own resnet18/googlenet/vgg on CIFAR10.
:
<ul>
<li /> Use the num_classes=10 argument as follows:
<pre><code class="Python" data-trim data-noescape>
torchvision.models.resnet18(pretrained=False, num_classes=10)
</code></pre>
<li /> The Resnet class works for 3 inputs channels only. How would you modify the model class? (for resnets, it is defined here: <a href=https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py> https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py </a>)
</ul>
[](https://drive.google.com/open?id=1mdFea-ADcCsKTzZX7A2roqVgO5O9qsR7)
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Semantic Segmentation</h2>
<img src="https://miro.medium.com/max/1000/1*wbaUQkYzRhvmd7IjKJjjCg.gif" />
<ul>
<li /> Standard ConvNets produce one classification per image. Assuming the output of the network is an image, it should be possible to make a classification at every <em>pixel</em>.
<li /> Using a network composed only of convolutions, is it possible to obtain an image at the end of the neural network pipeline
<li /> Classification can then be done on every resulting pixel:
$$ Total Loss = \sum_{ij} Loss(Y_{ij},T_{ij}) $$
</ul>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> <s>Deconvolution</s> Transposed Convolution </h2>
<ul>
<li /> The output of a convolutional network has fewer pixels. Additional layers can be used to remap the classification to the original image.
<li /> Convolution operations can be reversed. In fact this is what backpropagation in convolutional layers do
<li /> Convolutional layers with stride 2 downsample by a factor 2 on each axis. Transposed convolutions do the opposite (a stride of 1/2 upsamples the image by 2).
<li /> Contrary to standard upsampling (e.g. bilinear), the backward convolution has learnable parameters.
</ul>
<img src=images/deconv.gif class=small />
<a href="https://github.com/vdumoulin/conv_arithmetic"> more animations here </a>
</textarea></section>
<section data-markdown><textarea data-template>
<h2> Fully Convolutional Networks for Semantic Segmentation </h2>
<div class=row>
<div class=column>
Without upsampling layer
<img src="images/fcn_conv.png" />
</div>
<div class=column>
With upsampling layer
<img src="images/fcn_semantic_segmentation.png" />
</div>
</div>
<p class=ref>Long et al. 2015</p>
[](https://drive.google.com/open?id=1EFTCtTz4swVicOf114o_PTe37lOm6eJa)
</textarea></section>
<section data-markdown><textarea data-template>
<h2> U-Nets </h2>
<div class=row>
<div class=column>
U-Net architecture
<img src="images/unet_arch.png" class=large/>
</div>
<div class=column>
With upsampling layer
<img src="images/unet_example.png" class=large/>
</div>
</div>
<p class=ref><a href="https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28">Ronneberger et al. 2015</a></p>
[](https://drive.google.com/open?id=1EFTCtTz4swVicOf114o_PTe37lOm6eJa)
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Analyzing Neural Networks</h2>
<ul>
<li/>Patterns that maximally activate the neuron roughly correspond to a local maximum of the tuning curve.
<li/>To find it, one can maximize activity by gradient descent.
$$ \hat{x} = \mathrm{argmax}_x y_i(x) $$
<li /> In practice, this doesn't produce nice images, but "optical illusions" of neural networks. The optimization must be regularized or a prior must be applied to obtain meaningful images.
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Analyzing Neural Networks: Which objective? </h2>
<img src="images/visualization_optimization_objective.png" />
<p class=ref> Olah et al. 2017, https://distill.pub/2017/feature-visualization/ </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Analyzing Neural Networks: What does a neural network respond to? </h2>
<img src="images/visualization_class.png" />
<p class=ref> Olah et al. 2017, https://distill.pub/2017/feature-visualization/ </p>
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Deep Dream</h2>
<iframe width="794" height="547" src="https://www.youtube.com/embed/SCE-QeDfXtA" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
<p class=ref> Google Blog, June 2015</p>
[](https://drive.google.com/open?id=1Uy0nWFaoNQ-QuLIH8-ANp2NMpo_WFNBU)
</textarea></section>
<section data-markdown><textarea data-template>
<h2>Machine Behavior - Cognitive Sciences of the Future?</h2>
<div class=row>
<div class=column>
<img src="images/machine_behavior_first.png" style="max-height:500px" />
</div>
<div class=column>
<img src="images/machine_behavior_fig1.png" style="max-height:500px" />
</div>
</div>
<a href=https://www.nature.com/articles/s41586-019-1138-y?error=cookies_not_supported&code=113da7cb-dcd8-44c3-b755-c33b6fec01fe>https://www.nature.com/articles/s41586-019-1138-y</a>
<p class=pl>Discusson on Week 6: Please read!</p>
</textarea></section>
</div>
</div>
<!-- End of slides -->
<script src="../reveal.js/lib/js/head.min.js"></script>
<script src="js/reveal.js"></script>
<script>
Reveal.configure({ pdfMaxPagesPerSlide: 1, hash: true, slideNumber: true})
Reveal.initialize({
mouseWheel: false,
width: 1280,
height: 720,
margin: 0.0,
navigationMode: 'grid',
transition: 'slide',
menu: { // Menu works best with font-awesome installed: sudo apt-get install fonts-font-awesome
themes: false,
transitions: false,
markers: true,
hideMissingTitles: true,
custom: [
{ title: 'Plugins', icon: '<i class="fa fa-external-link-alt"></i>', src: 'toc.html' },
{ title: 'About', icon: '<i class="fa fa-info"></i>', src: 'about.html' }
]
},
math: {
mathjax: 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js',
config: 'TeX-AMS_HTML-full', // See http://docs.mathjax.org/en/latest/config-files.html
// pass other options into `MathJax.Hub.Config()`
TeX: { Macros: { Dp: ["\\frac{\\partial #1}{\\partial #2}",2] }}
//TeX: { Macros: { Dp#1#2: },
},
chalkboard: {
src:'slides_2-chalkboard.json',
penWidth : 1.0,
chalkWidth : 1.5,
chalkEffect : .5,
readOnly: false,
toggleChalkboardButton: { left: "80px" },
toggleNotesButton: { left: "130px" },
transition: 100,
theme: "whiteboard",
},
menu : { titleSelector: 'h1', hideMissingTitles: true,},
keyboard: {
67: function() { RevealChalkboard.toggleNotesCanvas() }, // toggle notes canvas when 'c' is pressed
66: function() { RevealChalkboard.toggleChalkboard() }, // toggle chalkboard when 'b' is pressed
46: function() { RevealChalkboard.reset() }, // reset chalkboard data on current slide when 'BACKSPACE' is pressed
68: function() { RevealChalkboard.download() }, // downlad recorded chalkboard drawing when 'd' is pressed
88: function() { RevealChalkboard.colorNext() }, // cycle colors forward when 'x' is pressed
89: function() { RevealChalkboard.colorPrev() }, // cycle colors backward when 'y' is pressed
},
dependencies: [
{ src: '../reveal.js/lib/js/classList.js', condition: function() { return !document.body.classList; } },
{ src: 'plugin/markdown/marked.js' },
{ src: 'plugin/markdown/markdown.js' },
{ src: 'plugin/notes/notes.js', async: true },
{ src: 'plugin/highlight/highlight.js', async: true, languages: ["Python"] },
{ src: 'plugin/math/math.js', async: true },
{ src: 'external-plugins/chalkboard/chalkboard.js' },
//{ src: 'external-plugins/menu/menu.js'},
{ src: 'node_modules/reveal.js-menu/menu.js' }
]
});
</script>
<script type="text/bibliography">
@article{gregor2015draw,
title={DRAW: A recurrent neural network for image generation},
author={Gregor, Karol and Danihelka, Ivo and Graves, Alex and Rezende, Danilo Jimenez and Wierstra, Daan},
journal={arXivreprint arXiv:1502.04623},
year={2015},
url={https://arxiv.org/pdf/1502.04623.pdf}
}
</script>
</body>
</html>