Convolutional Neural Networks (CNNs) are responsible for the major breakthroughs in image recognition made in the past few years. In this chapter we will cover:
- Implementing a Simpler CNN
- Implementing an Advanced CNN
- Retraining Existing CNN models
- Applying Stylenet/Neural-Style
- Implementing DeepDream
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million (八千万) tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.
Click here: https://www.cs.toronto.edu/~kriz/cifar.html
Visual dictionary
Click on top of the map to visualize the images in that region of the visual dictionary.
We present a visualization of all the nouns in the English language arranged by semantic meaning. 根据语义可视化名词
Each of the tiles in the mosaic is an arithmetic average of images relating to one of 53,464 nouns. 与五万多个名词之一相关的一个由一大堆图像算数平均后的像素点
The images for each word were obtained using Google's Image Search and other engines. A total of 7,527,697 images were used, each tile being the average of 140 images.
大概就是如此，如下：
The CIFAR-10 dataset
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class.
There are 50000 training images and 10000 test images.
Here are the classes in the dataset, as well as 10 random images from each:
| airplane | ||||||||||
| automobile | ||||||||||
| bird | ||||||||||
| cat | ||||||||||
| deer | ||||||||||
| dog | ||||||||||
| frog | ||||||||||
| horse | ||||||||||
| ship | ||||||||||
| truck |
The classes are completely mutually exclusive. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.
This recipe is an adapted version of the official TensorFlow CIFAR-10 tutorial, which is
available under the See also section at the end of this chapter. We have condensed the
tutorial into one script and will go through it line-by-line and explain all the code that is
necessary. We also revert some constants and parameters to the original cited paper values,
which we will point out in the following appropriated steps.
前言：使用我的四核i7小笔记本训练了一次数据，CPU满负荷，生怕机器就此挂了。
Starting Training Generation 50: Loss = 1.99133 Generation 100: Loss = 1.82077 Generation 150: Loss = 2.08245 Generation 200: Loss = 1.60232 Generation 250: Loss = 1.73661 Generation 300: Loss = 1.71986 Generation 350: Loss = 1.45252 Generation 400: Loss = 1.51505 Generation 450: Loss = 1.50190 Generation 500: Loss = 1.43989 --- Test Accuracy = 49.22%. Generation 550: Loss = 1.34899 Generation 600: Loss = 1.28325 Generation 650: Loss = 1.45376 Generation 700: Loss = 1.22179 Generation 750: Loss = 1.40790 Generation 800: Loss = 1.23635 Generation 850: Loss = 1.36577 Generation 900: Loss = 1.29193 Generation 950: Loss = 1.16195 Generation 1000: Loss = 1.30807 --- Test Accuracy = 53.91%. Generation 1050: Loss = 1.53120 Generation 1100: Loss = 1.19605 Generation 1150: Loss = 1.07220 Generation 1200: Loss = 1.03782 Generation 1250: Loss = 1.22976 Generation 1300: Loss = 0.96371 Generation 1350: Loss = 1.06199 Generation 1400: Loss = 1.04158 Generation 1450: Loss = 1.09863 Generation 1500: Loss = 1.00462 --- Test Accuracy = 61.72%. Generation 1550: Loss = 0.93589 Generation 1600: Loss = 0.94716 Generation 1650: Loss = 0.97767 Generation 1700: Loss = 0.89214 Generation 1750: Loss = 0.93194 Generation 1800: Loss = 0.78864 Generation 1850: Loss = 0.79083 Generation 1900: Loss = 1.16496 Generation 1950: Loss = 0.95690 Generation 2000: Loss = 0.71276 --- Test Accuracy = 64.84%. Generation 2050: Loss = 0.90579 Generation 2100: Loss = 0.82735 Generation 2150: Loss = 0.89798 Generation 2200: Loss = 0.90343 Generation 2250: Loss = 0.83713 Generation 2300: Loss = 0.85635 Generation 2350: Loss = 0.83437 Generation 2400: Loss = 0.83430 Generation 2450: Loss = 0.87104 Generation 2500: Loss = 0.84299 --- Test Accuracy = 74.22%. Generation 2550: Loss = 0.79991 Generation 2600: Loss = 0.80746 Generation 2650: Loss = 0.82027 Generation 2700: Loss = 0.84372 Generation 2750: Loss = 0.81977 Generation 2800: Loss = 0.77057 Generation 2850: Loss = 0.75629 Generation 2900: Loss = 0.82681 Generation 2950: Loss = 0.88289 Generation 3000: Loss = 0.94536 --- Test Accuracy = 71.09%. Generation 3050: Loss = 0.76870 Generation 3100: Loss = 0.80715 Generation 3150: Loss = 0.80056 Generation 3200: Loss = 0.78387 Generation 3250: Loss = 0.59328 Generation 3300: Loss = 0.84897 Generation 3350: Loss = 0.67461 Generation 3400: Loss = 0.64628 Generation 3450: Loss = 0.64160 Generation 3500: Loss = 0.63691 --- Test Accuracy = 70.31%. Generation 3550: Loss = 0.63177 Generation 3600: Loss = 0.74349 Generation 3650: Loss = 0.64307 Generation 3700: Loss = 0.61021 Generation 3750: Loss = 0.64688 Generation 3800: Loss = 0.63159 Generation 3850: Loss = 0.78472 Generation 3900: Loss = 0.75076 Generation 3950: Loss = 0.53717 Generation 4000: Loss = 0.46514 --- Test Accuracy = 65.62%. Generation 4050: Loss = 0.68460 Generation 4100: Loss = 0.58425 Generation 4150: Loss = 0.47215 Generation 4200: Loss = 0.58976 Generation 4250: Loss = 0.64681 Generation 4300: Loss = 0.77239 Generation 4350: Loss = 0.58956 Generation 4400: Loss = 0.70569 Generation 4450: Loss = 0.66185 Generation 4500: Loss = 0.46662 --- Test Accuracy = 76.56%. Generation 4550: Loss = 0.49475 Generation 4600: Loss = 0.54739 Generation 4650: Loss = 0.52838 Generation 4700: Loss = 0.81228 Generation 4750: Loss = 0.49100 Generation 4800: Loss = 0.51341 Generation 4850: Loss = 0.47875 Generation 4900: Loss = 0.37848 Generation 4950: Loss = 0.52750 Generation 5000: Loss = 0.53570 --- Test Accuracy = 63.28%. Generation 5050: Loss = 0.63138 Generation 5100: Loss = 0.49153 Generation 5150: Loss = 0.54037 Generation 5200: Loss = 0.72630 Generation 5250: Loss = 0.44166 Generation 5300: Loss = 0.51812 Generation 5350: Loss = 0.51912 Generation 5400: Loss = 0.54622 Generation 5450: Loss = 0.41648 Generation 5500: Loss = 0.57976 --- Test Accuracy = 71.88%. Generation 5550: Loss = 0.55666 Generation 5600: Loss = 0.44564 Generation 5650: Loss = 0.46812 ... 未完，但担心机器挂了