ELEC 576: Training Convnets Lecture 5. Ankit B. Patel Baylor College of Medicine (Neuroscience Dept.) Rice University (ECE Dept.
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1 ELEC 576: Training Convnets Lecture 5 Ankit B. Patel Baylor College of Medicine (Neuroscience Dept.) Rice University (ECE Dept.)
2 Administrivia RCSG will be giving us a 30 minute tutorial today on how to use their commodity computing services. Please start Assignment #1 ASAP!!!
3 Latest News
4 Better Generative Models for Images of Products
5 Google Brain Residency Program
6 Training Convnets: Problems and Solutions
7 Training on CIFAR10 demo/cifar10.html
8 Data Preprocessing
9 Zero-Center & Normalize Data
10 PCA & Whitening
11 In Practice, for Images: Center Only
12 Data Augmentation During training: Random crops on the original image Horizontal reflections During testing: Average prediction of image augmented by the four corner patches and the center patch + flipped image (10 augmentations of the image Data augmentation reduces overfitting
13 Weight Initialization
14 Interesting Question: What happens when the weights are initialized to 0? (2 min)
15 Answer
16 Random Initialization W = 0.01 * np.random.randn(d, H) Works fine for small networks, but can lead to non-homogeneous distributions of activations across the layers of a network.
17 Look at Some Activation Statistics Setup: 10-layer net with 500 neurons on each layer, using tanh nonlinearities, and initializing as described in last slide.
18 Random Initialization
19 Random Initialization
20 Random Initialization Interesting Question: What will the gradients look like in the backward pass when all activations become zero?
21 Answer: The gradients in the backward pass will become zero!
22 Xavier Initialization W = np.random.randn(fan_in, fan_out) / np.sqrt(fan_in) Reasonable initialization (Mathematical derivation assumes linear activations)
23 Xavier Initialization W = np.random.randn(fan_in, fan_out) / np.sqrt(fan_in) but it breaks when using ReLU non-linearity
24 More Initialization Techniques Understanding the difficulty of training deep feedforward neural networks by Glorot and Bengio, 2010 Exact solutions to the nonlinear dynamics of learning in deep linear neural networks by Saxe et al, 2013 Random walk initialization for training very deep feedforward networks by Sussillo and Abbott, 2014 Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification by He et al., 2015 Data-dependent Initializations of Convolutional Neural Networks by Krähenbühl et al., 2015 All you need is a good init by Mishkin and Matas, 2015
25 Choosing an Activation Function that Helps the Training
26 Sigmoid
27 Tanh
28 ReLU dead in -region
29 Leaky ReLU
30 Exponential Linear Unit
31 Maxout
32 In Practice
33 Training Algorithms
34 Stochastic Gradient Descent
35 Stochastic Gradient Descent
36 Stochastic Gradient Descent for Neural Networks
37 Batch GD vs Stochastic GD
38 Mini-batch SGD
39 Momentum Update
40 Nesterov Momentum Update
41 Nesterov Momentum Update express the update in term of x_ahead, instead of x
42 Adagrad Per-parameter adaptive learning rate methods RMSprop Adam
43 Annealing the Learning Rates
44 Compare Learning Methods
45 In Practice Adam is the default choice in most cases Instead, SGD variants based on (Nesterov s) momentum are more standard than second-order methods because they are simpler and scale more easily. If you can afford to do full batch updates then try out L-BFGS (Limited-memory version of Broyden Fletcher Goldfarb Shanno (BFGS) algorithm). Don t forget to disable all sources of noise.
46 Regularization
47 DropOut
48 DropOut
49 DropOut
50 DropOut
51 DropOut
52 Normalization
53 Batch Normalization
54 Batch Normalization
55 Batch Normalization
56 Batch Normalization
57 Ensembles
58 Model Ensembles
59 Model Ensembles
60 Hyperparameter Optimization
61 Hyperparameter Optimization
62 Hyperparameter Optimization
63 Hyperparameter Optimization
64 Hyperparameter Optimization
65 Hyperparameter Optimization
66 Synaptic Pruning
67 Monitoring the Learning Process
68 Double-check that the Loss is Reasonable
69 Double-check that the Loss is Reasonable
70 Overfit Very Small Portion of the Training Data
71
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76
77
78
79 Transfer Learning
80 Transfer Learning
81 Transfer Learning
82 Species of Convnets
83 Alex Net
84 VGG Net
85 GoogLenet
86 ResNet
87 MDNet: Convnet for Object Tracking
88 Convnet for Brain Tumor Segmentation (Top 4 in BRATS 2015)
89 U-Net: Convnet for Segmentation of Neuronal Structures in Electron Microscopic Stacks (Won the ISBI Cell Tracking Challenge 2015)
90 DeepBind: Convnet for Predicting the Sequence Specificities of DNA- and RNA- Binding Proteins
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