├── .gitignore ├── 01_intro.ipynb ├── 02_production.ipynb ├── 03_ethics.ipynb ├── 04_mnist_basics.ipynb ├── 05_pet_breeds.ipynb ├── 06_multicat.ipynb ├── 07_sizing_and_tta.ipynb ├── 08_collab.ipynb ├── 09_tabular.ipynb ├── 10_nlp.ipynb ├── 11_midlevel_data.ipynb ├── 12_nlp_dive.ipynb ├── 13_convolutions.ipynb ├── 14_resnet.ipynb ├── 15_arch_details.ipynb ├── 16_accel_sgd.ipynb ├── 17_foundations.ipynb ├── 18_CAM.ipynb ├── 19_learner.ipynb ├── 20_conclusion.ipynb ├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md ├── README_ar.md ├── README_bn.md ├── README_es.md ├── README_id.md ├── README_it.md ├── README_ja.md ├── README_ko.md ├── README_pt.md ├── README_tr.md ├── README_vn.md ├── README_zh.md ├── app_blog.ipynb ├── app_jupyter.ipynb ├── clean ├── 01_intro.ipynb ├── 02_production.ipynb ├── 03_ethics.ipynb ├── 04_mnist_basics.ipynb ├── 05_pet_breeds.ipynb ├── 06_multicat.ipynb ├── 07_sizing_and_tta.ipynb ├── 08_collab.ipynb ├── 09_tabular.ipynb ├── 10_nlp.ipynb ├── 11_midlevel_data.ipynb ├── 12_nlp_dive.ipynb ├── 13_convolutions.ipynb ├── 14_resnet.ipynb ├── 15_arch_details.ipynb ├── 16_accel_sgd.ipynb ├── 17_foundations.ipynb ├── 18_CAM.ipynb ├── 19_learner.ipynb ├── 20_conclusion.ipynb ├── app_blog.ipynb ├── app_jupyter.ipynb └── images ├── environment.yml ├── images ├── 0_jupyter.png ├── Dropout.png ├── Dropout1.png ├── LSTM.png ├── analytics_chain.gif ├── att_00000.png ├── att_00001.png ├── att_00002.png ├── att_00003.png ├── att_00004.png ├── att_00005.png ├── att_00006.png ├── att_00007.png ├── att_00008.png ├── att_00009.png ├── att_00010.png ├── att_00011.png ├── att_00012.png ├── att_00013.png ├── att_00014.png ├── att_00015.png ├── att_00016.png ├── att_00017.png ├── att_00018.png ├── att_00019.png ├── att_00020.png ├── att_00021.png ├── att_00022.png ├── att_00023.png ├── att_00024.png ├── att_00025.png ├── att_00026.png ├── att_00027.png ├── att_00028.png ├── att_00029.png ├── att_00030.png ├── att_00031.png ├── att_00032.png ├── att_00033.png ├── att_00034.png ├── att_00035.png ├── att_00036.png ├── att_00037.png ├── att_00038.png ├── att_00039.png ├── att_00040.png ├── att_00041.png ├── att_00042.png ├── att_00043.png ├── att_00044.png ├── att_00045.png ├── att_00046.png ├── att_00047.png ├── att_00048.png ├── att_00049.png ├── att_00050.png ├── att_00051.png ├── att_00052.png ├── att_00053.png ├── att_00054.png ├── att_00055.png ├── att_00056.png ├── att_00057.png ├── att_00058.png ├── att_00059.png ├── att_00060.png ├── att_00061.png ├── att_00062.png ├── att_00063.png ├── att_00064.png ├── att_00065.png ├── att_00066.png ├── att_00067.png ├── att_00068.png ├── att_00069.png ├── att_00070.png ├── att_00071.png ├── chapter1_add.png ├── chapter1_busy.png ├── chapter1_cat_example.jpg ├── chapter1_markdown.png ├── chapter1_new_notebook.png ├── chapter1_run.png ├── chapter1_save.png ├── chapter1_terminal.png ├── chapter2_bouncing.PNG ├── chapter2_bouncy.svg ├── chapter2_derivative.PNG ├── chapter2_div.svg ├── chapter2_layer3.PNG ├── chapter2_layer4and5.PNG ├── chapter2_perfect.svg ├── chapter2_sgd.PNG ├── chapter2_small.svg ├── chapter4_1cycle_schedule.png ├── chapter4_overfit.png ├── chapter7_neuron.png ├── chapter9_baseconv.svg ├── chapter9_bottleneck.png ├── chapter9_cat_conv.png ├── chapter9_conv_basic.png ├── chapter9_conv_pad.png ├── chapter9_conv_rgb.png ├── chapter9_conv_stride.png ├── chapter9_loss_landscape.png ├── chapter9_nopadconv.svg ├── chapter9_padconv.svg ├── chapter9_rgb_conv_stack.svg ├── chapter9_rgbconv.svg ├── chapter9_skip_connection.png ├── chapter9_strideconv.svg ├── colorful_dist.jpeg ├── colorful_summ.png ├── cover.png ├── decision_tree.PNG ├── doc_ex.png ├── driver.PNG ├── drivetrain-approach.png ├── ethics │ ├── image1.png │ ├── image10.png │ ├── image11.png │ ├── image12.png │ ├── image13.png │ ├── image14.png │ ├── image15.jpeg │ ├── image16.png │ ├── image17.jpeg │ ├── image17.png │ ├── image18.jpeg │ ├── image2.png │ ├── image3.jpeg │ ├── image4.png │ ├── image5.png │ ├── image6.png │ ├── image7.png │ ├── image8.png │ ├── image9.jpeg │ └── pipeline_diagram.svg ├── fast_template │ ├── image1.png │ ├── image10.png │ ├── image11.png │ ├── image12.png │ ├── image13.png │ ├── image14.png │ ├── image15.png │ ├── image16.png │ ├── image2.png │ ├── image3.png │ ├── image4.png │ ├── image5.png │ ├── image6.png │ ├── image7.png │ ├── image8.png │ └── image9.png ├── fltscale.svg ├── gitblog │ ├── commit.png │ ├── image1.png │ ├── image2.png │ ├── image3.png │ ├── image4.png │ └── image5.png ├── grad_illustration.svg ├── grizzly.jpg ├── healthy_skin.gif ├── layer1.png ├── layer2.png ├── matmul2.svg ├── mnist3_xl.gif ├── mnist3_xl_300.gif ├── mnist3_xl_custom.gif ├── pratchett.png ├── profit_drivers.gif ├── sklearn_features.png ├── tarsier.png ├── timeseries1.png ├── timeseries2.png └── timeseries3.png ├── requirements.txt ├── settings.ini ├── tools └── clean.py ├── translations └── cn │ ├── 01_intro.md │ ├── 02_production.md │ ├── 03_ethics.md │ ├── 04_mnist_basics.md │ ├── 05_pet_breeds.md │ ├── 06_multicat.md │ ├── 07_sizing_and_tta.md │ ├── 08_collab.md │ ├── 09_tabular.md │ ├── 10_nlp.md │ ├── 11_midlevel_data.md │ ├── 12_nlp_dive.md │ ├── 13_convolutions.md │ ├── 14_resnet.md │ ├── 15_arch_details.md │ ├── 16_accel_sgd.md │ ├── 17_foundations.md │ ├── 18_CAM.md │ ├── 19_learner.md │ ├── 20_conclusion.md │ └── img │ ├── dlcf_0101.png │ ├── dlcf_0102.png │ ├── dlcf_0103.png │ ├── dlcf_0104.png │ ├── dlcf_0105.png │ ├── dlcf_0106.png │ ├── dlcf_0107.png │ ├── dlcf_0108.png │ ├── dlcf_0109.png │ ├── dlcf_0110.png │ ├── dlcf_0111.png │ ├── dlcf_0112.png │ ├── dlcf_0113.png │ ├── dlcf_0114.png │ ├── dlcf_0115.png │ ├── dlcf_0116.png │ ├── dlcf_0117.png │ ├── dlcf_0118.png │ ├── dlcf_0119.png │ ├── dlcf_0120.png │ ├── dlcf_0121.png │ ├── dlcf_0122.png │ ├── dlcf_01in01.png │ ├── dlcf_01in02.png │ ├── dlcf_01in03.png │ ├── dlcf_01in04.png │ ├── dlcf_0201.png │ ├── dlcf_0202.png │ ├── dlcf_0203.png │ ├── dlcf_0204.png │ ├── dlcf_0205.png │ ├── dlcf_02in01.png │ ├── dlcf_02in02.png │ ├── dlcf_02in03.png │ ├── dlcf_02in04.png │ ├── dlcf_02in05.png │ ├── dlcf_02in06.png │ ├── dlcf_02in07.png │ ├── dlcf_02in08.png │ ├── dlcf_02in09.png │ ├── dlcf_02in11.png │ ├── dlcf_02in13.png │ ├── dlcf_0301.png │ ├── dlcf_0302.png │ ├── dlcf_0303.png │ ├── dlcf_0304.png │ ├── dlcf_0305.png │ ├── dlcf_0306.png │ ├── dlcf_0307.png │ ├── dlcf_0308.png │ ├── dlcf_0309.png │ ├── dlcf_0310.png │ ├── dlcf_0311.png │ ├── dlcf_0312.png │ ├── dlcf_0313.png │ ├── dlcf_0314.png │ ├── dlcf_0315.png │ ├── dlcf_0401.png │ ├── dlcf_0402.png │ ├── dlcf_0403.png │ ├── dlcf_0404.png │ ├── dlcf_0405.png │ ├── dlcf_0406.png │ ├── dlcf_04in01.png │ ├── dlcf_04in02.png │ ├── dlcf_04in03.png │ ├── dlcf_04in04.png │ ├── dlcf_04in05.png │ ├── dlcf_04in06.png │ ├── dlcf_04in07.png │ ├── dlcf_04in08.png │ ├── dlcf_04in09.png │ ├── dlcf_04in10.png │ ├── dlcf_04in11.png │ ├── dlcf_04in12.png │ ├── dlcf_04in13.png │ ├── dlcf_04in14.png │ ├── dlcf_04in15.png │ ├── dlcf_04in16.png │ ├── dlcf_04in17.png │ ├── dlcf_0501.png │ ├── dlcf_0502.png │ ├── dlcf_0503.png │ ├── dlcf_0504.png │ ├── dlcf_05in01.png │ ├── dlcf_05in02.png │ ├── dlcf_05in03.png │ ├── dlcf_05in04.png │ ├── dlcf_05in05.png │ ├── dlcf_05in06.png │ ├── dlcf_05in07.png │ ├── dlcf_06in01.png │ ├── dlcf_06in02.png │ ├── dlcf_06in03.png │ ├── dlcf_06in04.png │ ├── dlcf_06in05.png │ ├── dlcf_06in06.png │ ├── dlcf_06in07.png │ ├── dlcf_0701.png │ ├── dlcf_0702.png │ ├── dlcf_0801.png │ ├── dlcf_0802.png │ ├── dlcf_0803.png │ ├── dlcf_0804.png │ ├── dlcf_0901.png │ ├── dlcf_0902.png │ ├── dlcf_0903.png │ ├── dlcf_0904.png │ ├── dlcf_0905.png │ ├── dlcf_0906.png │ ├── dlcf_0907.png │ ├── dlcf_0908.png │ ├── dlcf_09in01.png │ ├── dlcf_09in02.png │ ├── dlcf_09in03.png │ ├── dlcf_09in04.png │ ├── dlcf_09in05.png │ ├── dlcf_09in06.png │ ├── dlcf_09in07.png │ ├── dlcf_09in08.png │ ├── dlcf_09in09.png │ ├── dlcf_09in10.png │ ├── dlcf_09in11.png │ ├── dlcf_09in12.png │ ├── dlcf_09in13.png │ ├── dlcf_09in14.png │ ├── dlcf_09in15.png │ ├── dlcf_1001.png │ ├── dlcf_1002.png │ ├── dlcf_1003.png │ ├── dlcf_1004.png │ ├── dlcf_11in01.png │ ├── dlcf_11in02.png │ ├── dlcf_11in03.png │ ├── dlcf_11in04.png │ ├── dlcf_11in05.png │ ├── dlcf_1201.png │ ├── dlcf_1202.png │ ├── dlcf_1203.png │ ├── dlcf_1204.png │ ├── dlcf_1205.png │ ├── dlcf_1206.png │ ├── dlcf_1207.png │ ├── dlcf_1208.png │ ├── dlcf_1209.png │ ├── dlcf_1210.png │ ├── dlcf_1211.png │ ├── dlcf_1301.png │ ├── dlcf_1302.png │ ├── dlcf_1303.png │ ├── dlcf_1304.png │ ├── dlcf_1305.png │ ├── dlcf_1306.png │ ├── dlcf_1307.png │ ├── dlcf_1308.png │ ├── dlcf_1309.png │ ├── dlcf_1310.png │ ├── dlcf_1311.png │ ├── dlcf_1312.png │ ├── dlcf_1313.png │ ├── dlcf_1314.png │ ├── dlcf_1315.png │ ├── dlcf_1316.png │ ├── dlcf_13in01.png │ ├── dlcf_13in02.png │ ├── dlcf_13in03.png │ ├── dlcf_13in04.png │ ├── dlcf_13in05.png │ ├── dlcf_13in06.png │ ├── dlcf_13in07.png │ ├── dlcf_13in08.png │ ├── dlcf_13in09.png │ ├── dlcf_13in10.png │ ├── dlcf_13in11.png │ ├── dlcf_13in12.png │ ├── dlcf_13in13.png │ ├── dlcf_13in14.png │ ├── dlcf_13in15.png │ ├── dlcf_13in16.png │ ├── dlcf_13in17.png │ ├── dlcf_13in18.png │ ├── dlcf_13in19.png │ ├── dlcf_13in20.png │ ├── dlcf_13in21.png │ ├── dlcf_13in22.png │ ├── dlcf_13in23.png │ ├── dlcf_13in24.png │ ├── dlcf_1401.png │ ├── dlcf_1402.png │ ├── dlcf_1403.png │ ├── dlcf_1404.png │ ├── dlcf_14in01.png │ ├── dlcf_14in02.png │ ├── dlcf_1501.png │ ├── dlcf_1502.png │ ├── dlcf_1503.png │ ├── dlcf_1601.png │ ├── dlcf_1602.png │ ├── dlcf_1603.png │ ├── dlcf_1604.png │ ├── dlcf_1605.png │ ├── dlcf_16in01.png │ ├── dlcf_16in02.png │ ├── dlcf_18in01.png │ ├── dlcf_18in02.png │ ├── dlcf_18in03.png │ ├── dlcf_19in01.png │ ├── dlcf_19in02.png │ ├── dlcf_19in03.png │ ├── dlcf_19in04.png │ ├── dlcf_2001.png │ ├── dlcf_aa01.png │ ├── dlcf_aa02.png │ ├── dlcf_aa03.png │ ├── dlcf_aa04.png │ ├── dlcf_aa05.png │ ├── dlcf_aa06.png │ ├── dlcf_aa07.png │ ├── dlcf_aa08.png │ ├── dlcf_aa09.png │ ├── dlcf_aa10.png │ ├── dlcf_aa11.png │ ├── dlcf_aa12.png │ ├── dlcf_aa13.png │ ├── dlcf_aa14.png │ ├── dlcf_aa15.png │ ├── dlcf_ab01.png │ └── dlcf_ab02.png └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | .gitattributes 2 | models/ 3 | tmp/ 4 | *.bak 5 | *.pkl 6 | bears/ 7 | __pycache__/ 8 | .last_checked 9 | .gitconfig 10 | .ipynb_checkpoints/ 11 | -------------------------------------------------------------------------------- /20_conclusion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#hide\n", 10 | "! [ -e /content ] && pip install -Uqq fastbook\n", 11 | "import fastbook\n", 12 | "fastbook.setup_book()" 13 | ] 14 | }, 15 | { 16 | "cell_type": "raw", 17 | "metadata": {}, 18 | "source": [ 19 | "[[chapter_conclusion]]" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "# Concluding Thoughts" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "Congratulations! You've made it! If you have worked through all of the notebooks to this point, then you have joined the small, but growing group of people that are able to harness the power of deep learning to solve real problems. You may not feel that way yet—in fact you probably don't. We have seen again and again that students that complete the fast.ai courses dramatically underestimate how effective they are as deep learning practitioners. We've also seen that these people are often underestimated by others with a classic academic background. So if you are to rise above your own expectations and the expectations of others, what you do next, after closing this book, is even more important than what you've done to get to this point.\n", 34 | "\n", 35 | "The most important thing is to keep the momentum going. In fact, as you know from your study of optimizers, momentum is something that can build upon itself! So think about what you can do now to maintain and accelerate your deep learning journey. <> can give you a few ideas." 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "\"What" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "We've talked a lot in this book about the value of writing, whether it be code or prose. But perhaps you haven't quite written as much as you had hoped so far. That's okay! Now is a great chance to turn that around. You have a lot to say, at this point. Perhaps you have tried some experiments on a dataset that other people don't seem to have looked at in quite the same way. Tell the world about it! Or perhaps thinking about trying out some ideas that occurred to you while you were reading—now is a great time to turn those ideas into code.\n", 50 | "\n", 51 | "If you'd like to share your ideas, one fairly low-key place to do so is the [fast.ai forums](https://forums.fast.ai/). You will find that the community there is very supportive and helpful, so please do drop by and let us know what you've been up to. Or see if you can answer a few questions for those folks who are earlier in their journey than you.\n", 52 | "\n", 53 | "And if you do have some successes, big or small, in your deep learning journey, be sure to let us know! It's especially helpful if you post about them on the forums, because learning about the successes of other students can be extremely motivating.\n", 54 | "\n", 55 | "Perhaps the most important approach for many people to stay connected with their learning journey is to build a community around it. For instance, you could try to set up a small deep learning meetup in your local neighborhood, or a study group, or even offer to do a talk at a local meetup about what you've learned so far or some particular aspect that interested you. It's okay that you are not the world's leading expert just yet—the important thing to remember is that you now know about plenty of stuff that other people don't, so they are very likely to appreciate your perspective.\n", 56 | "\n", 57 | "Another community event which many people find useful is a regular book club or paper reading club. You might find that there are some in your neighbourhood already, and if not you could try to get one started yourself. Even if there is just one other person doing it with you, it will help give you the support and encouragement to get going.\n", 58 | "\n", 59 | "If you are not in a geography where it's easy to get together with like-minded folks in person, drop by the forums, because there are always people starting up virtual study groups. These generally involve a bunch of folks getting together over video chat once a week or so to discuss some deep learning topic.\n", 60 | "\n", 61 | "Hopefully, by this point, you have a few little projects that you've put together and experiments that you've run. Our recommendation for the next step is to pick one of these and make it as good as you can. Really polish it up into the best piece of work that you can—something you are really proud of. This will force you to go much deeper into a topic, which will really test your understanding and give you the opportunity to see what you can do when you really put your mind to it.\n", 62 | "\n", 63 | "Also, you may want to take a look at the fast.ai free online course that covers the same material as this book. Sometimes, seeing the same material in two different ways can really help to crystallize the ideas. In fact, human learning researchers have found that one of the best ways to learn material is to see the same thing from different angles, described in different ways.\n", 64 | "\n", 65 | "Your final mission, should you choose to accept it, is to take this book and give it to somebody that you know—and get somebody else started on their own deep learning journey!" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": null, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [] 74 | } 75 | ], 76 | "metadata": { 77 | "jupytext": { 78 | "split_at_heading": true 79 | }, 80 | "kernelspec": { 81 | "display_name": "Python 3", 82 | "language": "python", 83 | "name": "python3" 84 | } 85 | }, 86 | "nbformat": 4, 87 | "nbformat_minor": 4 88 | } 89 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to make participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, gender identity and expression, level of experience, 9 | education, socio-economic status, nationality, personal appearance, race, 10 | religion, or sexual identity and orientation. 11 | 12 | Examples of unacceptable behavior by participants include: 13 | 14 | * The use of sexualized language or imagery and unwelcome sexual attention or 15 | advances 16 | * Trolling, insulting/derogatory comments, and personal or political attacks 17 | * Public or private harassment 18 | * Publishing others' private information, such as a physical or electronic 19 | address, without explicit permission 20 | 21 | These examples of unacceptable behaviour are requirements; we will not allow them 22 | in any fast.ai project, including this one. 23 | 24 | ## Our Standards 25 | 26 | Examples of behavior that contributes to creating a positive environment 27 | include: 28 | 29 | * Using welcoming and inclusive language 30 | * Being respectful of differing viewpoints and experiences 31 | * Gracefully accepting constructive criticism 32 | * Focusing on what is best for the community 33 | * Showing empathy towards other community members 34 | 35 | These examples are shown only to help you participate effectively -- they are not 36 | requirements, just requests and guidance. 37 | 38 | ## Our Responsibilities 39 | 40 | Project maintainers are responsible for clarifying the standards of acceptable 41 | behavior and are expected to take appropriate and fair corrective action in 42 | response to any instances of unacceptable behavior. 43 | 44 | Project maintainers have the right and responsibility to remove, edit, or 45 | reject comments, commits, code, wiki edits, issues, and other contributions 46 | that are not aligned to this Code of Conduct, or to ban temporarily or 47 | permanently any contributor for other behaviors that they deem inappropriate, 48 | threatening, offensive, or harmful. 49 | 50 | ## Scope 51 | 52 | This Code of Conduct applies both within project spaces and in public spaces 53 | when an individual is representing the project or its community. Examples of 54 | representing a project or community include using an official project e-mail 55 | address, posting via an official social media account or acting as an appointed 56 | representative at an online or offline event. Representation of a project may be 57 | further defined and clarified by project maintainers. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing or otherwise unacceptable behavior may be 62 | reported by contacting the project team at info@fast.ai. All 63 | complaints will be reviewed and investigated and will result in a response that 64 | is deemed necessary and appropriate to the circumstances. The project team is 65 | obligated to maintain confidentiality with regard to the reporter of an incident. 66 | Further details of specific enforcement policies may be posted separately. 67 | 68 | Project maintainers who do not follow or enforce the Code of Conduct in good 69 | faith may face temporary or permanent repercussions as determined by other 70 | members of the project's leadership. 71 | 72 | ## Attribution 73 | 74 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 75 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 76 | 77 | [homepage]: https://www.contributor-covenant.org 78 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 2 | 3 | # The fastai book 4 | 5 | These notebooks cover an introduction to deep learning, [fastai](https://docs.fast.ai/), and [PyTorch](https://pytorch.org/). fastai is a layered API for deep learning; for more information, see [the fastai paper](https://www.mdpi.com/2078-2489/11/2/108). Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards. A selection of chapters is available to [read online here](https://fastai.github.io/fastbook2e/). 6 | 7 | The notebooks in this repo are used for [a MOOC](https://course.fast.ai) and form the basis of [this book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. 8 | 9 | The code in the notebooks and python `.py` files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. We are making these materials freely available to help you learn deep learning, so please respect our copyright and these restrictions. 10 | 11 | If you see someone hosting a copy of these materials somewhere else, please let them know that their actions are not allowed and may lead to legal action. Moreover, they would be hurting the community because we're not likely to release additional materials in this way if people ignore our copyright. 12 | 13 | ## Colab 14 | 15 | Instead of cloning this repo and opening it on your machine, you can read and work with the notebooks using [Google Colab](https://research.google.com/colaboratory/). This is the recommended approach for folks who are just getting started -- there's no need to set up a Python development environment on your own machine, since you can just work directly in your web-browser. 16 | 17 | You can open any chapter of the book in Colab by clicking on one of these links: [Introduction to Jupyter](https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb) | [Chapter 1, Intro](https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb) | [Chapter 2, Production](https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb) | [Chapter 3, Ethics](https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb) | [Chapter 4, MNIST Basics](https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb) | [Chapter 5, Pet Breeds](https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb) | [Chapter 6, Multi-Category](https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb) | [Chapter 7, Sizing and TTA](https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb) | [Chapter 8, Collab](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) | [Chapter 9, Tabular](https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb) | [Chapter 10, NLP](https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb) | [Chapter 11, Mid-Level API](https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb) | [Chapter 12, NLP Deep-Dive](https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb) | [Chapter 13, Convolutions](https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb) | [Chapter 14, Resnet](https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb) | [Chapter 15, Arch Details](https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb) | [Chapter 16, Optimizers and Callbacks](https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb) | [Chapter 17, Foundations](https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb) | [Chapter 18, GradCAM](https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb) | [Chapter 19, Learner](https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb) | [Chapter 20, conclusion](https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb) 18 | 19 | 20 | ## Contributions 21 | 22 | If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's difficult for reviewers to know which corrections have already been made. Thank you.) 23 | 24 | ## Citations 25 | 26 | If you wish to cite the book, you may use the following: 27 | 28 | ``` 29 | @book{howard2020deep, 30 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 31 | author={Howard, J. and Gugger, S.}, 32 | isbn={9781492045526}, 33 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 34 | year={2020}, 35 | publisher={O'Reilly Media, Incorporated} 36 | } 37 | ``` 38 | 39 | -------------------------------------------------------------------------------- /README_ar.md: -------------------------------------------------------------------------------- 1 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Arabic](./README_ar.md) 2 | 3 | # Fastai كتاب 4 | 5 | 6 | [PyTorch](https://pytorch.org/) و [fastai](https://docs.fast.ai/) تغطي هذه الدفاتر مقدمة للتعلم العميق 7 | 8 | [The fastai paper](https://www.mdpi.com/2078-2489/11/2/108) عبارة عن واجهة برمجة متعددة الطبقات للتعلم العميق؛ لمزيد من المعلومات، راجع الورقة fastai 9 | 10 | بدءا من عام 2020 فصاعدا, حقوق الطبع والنشر لهذا المستودع ومحتوياته تعود إلى جيريمي هوارد وسيلفين غوججر. مجموعة مختارة من الفصول متاحة [اقرأ على الانترنت هنا](https://fastai.github.io/fastbook2e/) 11 | 12 | يتم استخدام دفاتر الملاحظات الموجودة في هذا المستودع في [دورة مفتوحة المصدر]( 13 | https://course.fast.ai) وتشكل أساس هذا [الكتاب](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) المتاح للشراء حاليًا 14 | و الذي بدوره لا يخضع لنفس قيود الملكية الفكرية المطبقة على هذا المستودع 15 | 16 | الكود الموجود في دفاتر الملاحظات وملفات البايثون يخضع لقوانين الرخصة العمومية الشاملة (الاصدار 3), راجع ملف الترخيص للحصول على تفاصيل اكثر. 17 | أما الباقي (بما في ذلك جميع خلايا النصوص في دفاتر الملاحظات وغيرها) فهي غير مرخصة لإعادة التوزيع أو تغيير في الشكل و النوع، بخلاف عمل نسخ من هذا المستودع لاستخدامك الشخصي.لايسمح أي استخدام تجاري لهذه المواد.هذا المستودع متاح مجانًا لمساعدتك على تعلم التعلم العميق، لذا يرجى احترام حقوق النشر الخاصة بنا 18 | 19 | إذا رأيت شخصًا يستضيف نسخة من هذه المواد في مكان آخر ، فيرجى إعلامه بأن أفعاله غير مسموح بها وقد تؤدي إلى الملاحقة القانونية. علاوة على ذلك ، سوف تضر افعاله المنفردة بالمجتمع ككل لأننا من غير المحتمل أن ننشر موادا إضافية بهذه الطريقة (مجانا) إذا تجاهل الناس حقوق الطبع والنشر الخاصة بنا 20 | 21 | ## Colab غوغل كولاب 22 | 23 | يمكنك القراءة والعمل مع دفاتر الملاحظات باستخدام [غوغل كولاب](https://research.google.com/colaboratory/) كبديل عوضا عن استنساخ هذا المستودع وفتحه على جهازك 24 | 25 | هذا هو الأسلوب الموصى به بالنسبة للأشخاص المبتدئين في هذا المجال حيث يمكنك العمل مباشرة في متصفح الويب الخاص بك و ليست هناك حاجة لإعداد بيئة تطوير بايثون على جهازك الخاص 26 | 27 | :يمكنك فتح أي فصل من الكتاب في منصة غوغل كولاب بالضغط على أحد هذه الروابط ادناه 28 | 29 | [مقدمة إلى جوبيتر](https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb)| [الفصل 1، المقدمة](https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb) | [الفصل 2، من النموذج إلى الإنتاج](https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb) |[الفصل 3، أخلاقيات معالحة البيانات](https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb) | 30 | [الفصل 4، تدريب مصنف أرقام](https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb)| [الفصل 5، تصنيف الصور](https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb) | [الفصل 6، تصنيف الصور（تابع）](https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb) | [الفصل 7، تدريب نموذج حديث](https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb) | [الفصل 8 ،نبذة مفصلة في التصفية التعاونية](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) | [الفصل 9، نبذة مفصلة في النمذجة الجدولية](https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb) |[الفصل 10، معالجة اللغات الطبيعية](https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb) | [الفصل 11، واجهة برمجة التطبيقات للمستوى المتوسط](https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb) | [الفصل 12، نبذة مفصلة في معالجة اللغات الطبيعية](https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb) | [الفصل 13، الشبكات العصبية التلافيفية ](https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb) | [الفصل 14، ريسنيت](https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb) | [الفصل 15، هندسة التطبيقات، نبذة مفصلة](https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb) | [الفصل 16، عملية التدريب](https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb) | [الفصل 17، الشبكة العصبية من الأسس](https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb) | [الفصل 18، تفسير الشبكات العصبية](https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb) | [fastai الفصل 19، متعلم سريع من الصفر](https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb) | 31 | 32 | [الفصل 20، الخاتمة](https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb) 33 | 34 | 35 | ## مساهمات 36 | 37 | إذا قمت بتقديم أي طلبات سحب لهذا المستودع، فإنك تقوم بتعيين حقوق الطبع والنشر لهذا العمل لجيريمي هوارد وسيلفان جوجر. (بالإضافة إلى ذلك، إذا كنت تقوم بإجراء تعديلات صغيرة على التهجئة أو النص، فيرجى تحديد اسم الملف وارفاق وصف موجز لما قمت بتعديله لتسهيل عمل المراجعين في معرفة التصحيحات التي تم إجراؤها بالفعل. شكرًا لك.) 38 | 39 | 40 | ## اقتباسات 41 | 42 | :إذا أردت الاستشهاد بالكتاب يمكنك استخدام الصيفة الموضحة في الاسفل 43 | 44 | ``` 45 | @book{howard2020deep, 46 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 47 | author={Howard, J. and Gugger, S.}, 48 | isbn={9781492045526}, 49 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 50 | year={2020}, 51 | publisher={O'Reilly Media, Incorporated} 52 | } 53 | ``` 54 | 55 | -------------------------------------------------------------------------------- /README_bn.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # fastai বই 5 | 6 | এই নোটবুকগুলি প্রারম্ভিক ডীপ লার্নিং, [fastai](https://docs.fast.ai/), এবং [PyTorch](https://pytorch.org/) কভার করে। fastai হল ডীপ লার্নিং -এর জন্য একটি স্তরবিশিষ্ট API; আরও জানার জন্য [fastai পেপারটি](https://www.mdpi.com/2078-2489/11/2/108) দেখুন। এই রেপোর অন্তর্ভুক্ত সমস্তকিছুর স্বত্ব ২০২০ সাল থেকে জেরেমি হাওয়ার্ড ও সিলভ্যাঁ গাগার দ্বারা রক্ষিত। 7 | 8 | এই নোটবুকগুলি [একটি MOOC -এ](https://course.fast.ai) ব্যবহৃত হয় এবং এগুলি [এই বইটার](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) ভিত্তি, যা এখন কিনতে পাওয়া যাচ্ছে। এই খসড়ার অন্তর্ভুক্ত বিষয়বস্তু GPL লাইসেন্সের আওতাধীন, কিন্তু বইটি GPL লাইসেন্সের বাঁধাধরার আওতাভুক্ত নয়। 9 | 10 | এই নোটবুকগুলিতে ব্যবহৃত কোড এবং পাইথন `.py` ফাইলগুলি GPL v3 লাইসেন্সের আওতাধীন। আরও জানার জন্য LICENSE ফাইলটি দেখুন। 11 | 12 | বাদবাকি সমস্তকিছুর (নোটবুকের সমস্ত মার্কডাউন সেল এবং অন্যান্য টেক্সটের) কোনরকম পুনর্বিতরণ বা তাদের আকার বা মাধ্যমের কোনরকম পরিবর্তন অনুমোদিত নয়। তবে এই নোটবুকগুলিকে ব্যক্তিগত ব্যবহারের প্রয়োজনে কপি করা যেতে পারে, বা রেপোটিকে ফর্ক করা যেতে পারে। এই নোটবুকগুলি ব্যবসায়িক উদ্দেশ্যে ব্যবহার করা বা কোনভাবে সম্প্রচার করা নিষিদ্ধ। আমরা আপনাদের ডীপ লার্নিং শেখায় সাহায্য করার জন্য এই মেটেরিয়ালগুলি বিনামূল্যে উপলব্ধ করছি। সুতরাং আমাদের স্বত্বাধিকারকে সম্মান করুন, এবং বিধিনিষেধগুলিকে মান্যতা দিন। 13 | 14 | যদি আপনি এই মেটেরিয়ালগুলি বা তাদের কোন কপিকে অন্য কোথাও কারও দ্বারা হোস্টেড হতে দেখেন, তাহলে তাঁদের জানান যে তাঁদের কার্যকলাপ অনুমোদিত নয়, এবং তাঁদের বিরুদ্ধে আইনী ব্যবস্থা নেওয়া হতে পারে। উপরন্তু, তাঁরা ব্যবহারকারী গোষ্ঠীর ক্ষতিসাধন করবেন, কারণ আমাদের স্বত্বাধিকার ক্ষতিগ্রস্ত হলে আমরা এইভাবে অতিরিক্ত মেটেরিয়াল প্রকাশ করার আগে অনেকবার ভাববো। 15 | 16 | এইটা একটা শুরুর দিকের খসড়া। যদি আপনার নোটবুকগুলি রান করতে কোনরকম সমস্যা হয়, তাহলে [fastai-dev ফোরামে](https://forums.fast.ai/c/fastai-users/fastai-dev/) উত্তরের খোঁজ করুন, অথবা সাহায্যের প্রয়োজন হলে সাহায্য চান। নোটবুকগুলি রান করতে কোনরকম সমস্যা হলে তা জানানোর জন্য অনুগ্রহ করে গিটহাব ইস্যুর ব্যবহার করবেন না। 17 | 18 | আপনি যদি এই রেপোতে কোন পুল রিকোয়েস্ট তৈরি করেন, তাহলে আপনি সেই কাজের স্বত্বাধিকার জেরেমি হাওয়ার্ড এবং সিলভ্যাঁ গাগারকে অর্পণ করছেন। (আরও জেনে রাখুন- আপনি যদি বানান বা লেখায় ছোট আকারের কোন ভুল সংশোধন করতে চান, তাহলে অনুগ্রহ করে ফাইলের নাম এবং আপনার করা পরিবর্তনের একটা অতিসংক্ষিপ্ত বিবরণ যোগ করুন। রিভিউয়ারদের পক্ষে বিভিন্ন ফাইল এবং সম্পর্কিত পরিবর্তনের হিসেব রাখা কঠিন হয়ে দাঁড়াচ্ছে। ধন্যবাদ।) 19 | -------------------------------------------------------------------------------- /README_es.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # The fastai book 5 | 6 | Una colección de Jupyter Notebooks que cubren una introducción al Deep Learning, [fastai](https://docs.fast.ai/) y [PyTorch](https://pytorch.org/). fastai es una API en capas para Deep Learning; para mayor información lea [la publicación](https://www.mdpi.com/2078-2489/11/2/108). Todos los documentos incluídos en este repositorio tienen derechos de autor para Jeremy Howard y Sylvain Gugger, desde 2020 en adelante. 7 | 8 | Estos Notebooks son usados para [el MOOC](https://course.fast.ai) (Massive Open Online Courses por sus siglas en ingles, cursos abiertos masivos en línea) y forman la base del [libro](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), el cual se encuentra actualmente disponible para su compra. El libro no tiene las mismas restricciones de GPL que se incluyen en este borrador. 9 | 10 | El código en los Notebooks y en los archivos de python `.py` está cubierto por la licencia GPL v3; consulte el archivo de LICENSE para obtener más detalles. 11 | 12 | El resto de textos (incluidas todas las celdas de Markdown en los Notebooks) no tienen licencia para su redistribución o cambio de formato o medio, más allá del uso de copias de los Notebooks o bifurcar el repositorio para uso privado. No se permite el uso comercial o de difusión. Ponemos estos materiales a su disposición de forma gratuita para ayudarle a aprender sobre Deep Learning, así que por favor respete las restricciones y nuestros derechos de autor. 13 | 14 | Si observa a alguien usando una copia de estos materiales incumpliendo la licencia, por favor infórmele que sus acciones no están permitidas y pueden dar lugar a acciones legales. Además, estarían perjudicando a la comunidad porque es probable que dejemos de publicar materiales adicionales si la gente ignora nuestros derechos de autor. 15 | 16 | Este es un borrador inicial. Si encuentra problemas ejecutando los Notebooks, podrá buscar ayuda y respuestas en el [foro fastai-dev](https://forums.fast.ai/c/fastai-users/fastai-dev/). Por favor, no utilice _GitHub issues_ para problemas relacionados con la ejecución de los Notebooks. 17 | 18 | Todo _pull request_ realizado a este repositorio queda asignado bajo derechos de autor a Jeremy Howard y Sylvain Gugger. (Además, si realiza pequeñas modificaciones en la ortografía o el texto, por favor indique el nombre del archivo y una descripción breve de lo que está corrigiendo. A los revisores les resulta cada vez más difícil saber qué correcciones ya se han realizado. Gracias). 19 | -------------------------------------------------------------------------------- /README_id.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # Buku fastai 5 | 6 | Notebook-notebook ini membahas pengantar deep learning, [fastai](https://docs.fast.ai/), dan [PyTorch](https://pytorch.org/). fastai adalah API berlapis untuk deep learning; untuk informasi lebih lanjut, lihat [the fastai paper](https://www.mdpi.com/2078-2489/11/2/108). Semua yang ada di repo ini adalah hak cipta Jeremy Howard dan Sylvain Gugger, 2020 dan seterusnya. 7 | 8 | Notebook ini digunakan untuk [MOOC](https://course.fast.ai) dan menjadi dasar untuk [buku ini](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), yang saat ini tersedia untuk dibeli. Buku tersebut tidak memiliki batasan GPL yang sama dengan yang ada di draf ini. 9 | 10 | Kode dalam notebook dan file python `.py` dilindungi oleh lisensi GPL v3; lihat file LICENSE untuk lebih detail. 11 | 12 | Lebih dari itu (termasuk semua sel-sel markdown di notebook-notebook dan prosa lainnya) tidak dilisensikan untuk pendistribusian ulang atau perubahan format atau media, selain untuk membuat salinan dari notebook-notebook atau me-fork repo ini untuk penggunaan pribadi anda sendiri. Tidak ada penggunaan komersial atau hak siaran diperbolehkan. Kami menyediakan materi ini secara gratis untuk membantu anda mempelajari deep learning, jadi mohon harap hormati hak cipta kami dan batasan-batasan ini. 13 | 14 | Jika anda melihat seseorang menghosting salinan materi ini di tempat lain, beri tahu mereka bahwa tindakan mereka tidak diizinkan dan dapat menyebabkan tindakan hukum. Selain itu, mereka akan merugikan komunitas karena kami tidak mungkin merilis materi tambahan dengan cara ini jika orang mengabaikan hak cipta kami. 15 | 16 | Ini adalah draf awal. Jika anda mengalami kebuntuan saat menjalankan notebook, silakan cari [forum fastai-dev](https://forums.fast.ai/c/fastai-users/fastai-dev/) untuk mendapatkan jawaban, dan mintalah bantuan di sana jika diperlukan. Harap jangan gunakan GitHub issues untuk masalah menjalankan notebook. 17 | 18 | Jika anda membuat pull request apa pun ke repo ini, anda memberikan hak cipta atas karya tersebut kepada Jeremy Howard dan Sylvain Gugger. (Selain itu, jika anda membuat pengeditan kecil pada ejaan atau teks, harap sebutkan nama file dan uraian singkat perihal apa saja yang anda perbaiki. Seiring berjalannya waktu, semakin sulit bagi peninjau untuk mengetahui koreksi mana yang telah dilakukan. Terima kasih.) 19 | 20 | ## Kutipan 21 | 22 | Jika Anda ingin mengutip buku tersebut, Anda dapat menggunakan: 23 | 24 | ``` 25 | @book{howard2020deep, 26 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 27 | author={Howard, J. and Gugger, S.}, 28 | isbn={9781492045526}, 29 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 30 | year={2020}, 31 | publisher={O'Reilly Media, Incorporated} 32 | } 33 | ``` -------------------------------------------------------------------------------- /README_it.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # Il libro di fastai 5 | 6 | Questi notebooks contengono un'introduzione al deep learning, [fastai](https://docs.fast.ai/), e [PyTorch](https://pytorch.org/). fastai è un API a più livelli per il deep learning; per ulteriori informazioni, si rimanda al [paper di fastai](https://www.mdpi.com/2078-2489/11/2/108). Tutto ciò che è contenuto in questa repo è copyright di Jeremy Howard e Sylvain Gugger, dal 2020 in poi. 7 | 8 | Questi notebooks sono utilizzati come [MOOC (Massive Open Online Courses)](https://course.fast.ai) e costituiscono la base di [questo libro](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), attualmente disponibile per l'acquisto. Il libro non ha le stesse restrizioni GPL presenti in questa bozza. 9 | 10 | Il codice presente nei notebook e i file python `.py` sono coperti dalla licena GPL v3; consulta il file LICENSE per maggiori dettagli. 11 | 12 | Il resto (incluse tutte le celle di markdown dei notebooks) non è concesso in licenza per alcuna ridistribuzione o cambio di formato o supporto, salvo la creazione di copie dei notebooks o fork della repo per uso privato. Non è consentito alcun uso commerciale o distributivo. Stiamo rendendo questi materiali disponibili gratuitamente per aiutarti ad apprendere il deep learning, quindi ti preghiamo di rispettare il nostro copyright e queste restrizioni. 13 | 14 | Qualora si notasse qualcuno intendo ad utilizzare una copia di questi materiali da qualche altra parte, si prega di informarlo che le sue azioni non sono consentite e potrebbero portare a un'azione legale. Inoltre, danneggerebbero la comunità perché è probabile che in questo modo non rilasceremo più materiale aggiuntivo se le persone ignorano il nostro copyright. 15 | 16 | Questa è una prima bozza. Se si incontrano difficoltà nell'eseguire i notebooks, si prega di cercare risposte nel [fastai-dev forum](https://forums.fast.ai/c/fastai-users/fastai-dev/), chiedendo aiuto lì se necessario. Si prega di non utilizzare GitHub issues per problemi relativi all'esecuzione dei notebooks. 17 | 18 | Qualsiasi pull request a questa repo assegna il copyright del lavoro a Jeremy Howard e Sylvain Gugger. (Inoltre, se si stanno apportando piccole modifiche all'ortografia o al testo, si prega di specificare il nome del file e una breve descrizione di ciò che si sta sistemando. È difficile per i revisori sapere quali correzioni sono già state apportate. Grazie.) 19 | 20 | ## Citazioni 21 | 22 | Qualora si desiderasse citare il libro, è possibile farlo nella seguente maniera: 23 | 24 | ``` 25 | @book{howard2020deep, 26 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 27 | author={Howard, J. and Gugger, S.}, 28 | isbn={9781492045526}, 29 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 30 | year={2020}, 31 | publisher={O'Reilly Media, Incorporated} 32 | } 33 | ``` 34 | -------------------------------------------------------------------------------- /README_ja.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # The fastai book 5 | 6 | このレポジトリにあるノートブックは、ディープラーニング、[fastai](https://docs.fast.ai/)、 [PyTorch](https://pytorch.org/)の入門を扱っています。fastaiはディープラーニングのための階層型のAPIを提供します。更に詳しく学びたい方は[the fastai paper](https://www.mdpi.com/2078-2489/11/2/108)をお読みください。このレポジトリの全てはJeremy HowardとSylvain Guggerが2020年以降の著作権を保持します。一部のセクションは[オンラインで読む](https://fastai.github.io/fastbook2e/)ことができます。 7 | 8 | このレポジトリにあるノートブックは[a MOOC](https://course.fast.ai)で使われており、また[この本](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527)のベースとなっています。このレポジトリにあるようなGPLの制限はありません。 9 | 10 | このレポジトリにあるノートブック内のコードやPythonのファイル`.py`はGPL v3ライセンスで公開されています。詳細は[LICENSE ファイル](./LICENSE)を参照してください。それ以外のもの（すべてのマークダウンセルやその他文章を含む）は私的利用の目的のみにおいて、再配布やフォーマット・媒体の変更、ノートブックのコピーを作成したり、レポジトリをフォークすることが可能です。営利目的、放映目的での利用は認められません。多くの方がディープラーニングを学ぶのを手助けするために自由に利用できるようにしていますので、著作権とこれらの制限を尊重してください。 11 | 12 | もし、これらの資料のコピーをどこかでホストしている人を見かけたら、その行為が許されないものであり、法的措置につながる可能性があることを教えてあげてください。また、この著作権を無視する人がいるようであれば、私たちは追加の資料を公開できなくなるため、コミュニティにも損害を与えることになります。 13 | 14 | ## Colab 15 | 16 | このレポジトリをクローンして自分のマシンで開く代わりに、[Google Colab](https://research.google.com/colaboratory/)を使ってノートブックを読んだり、作業することができます。この方法はPythonの開発環境をセットアップする必要がなく、ブラウザ上ですぐに作業を始められるので、勉強を始められたばかりの方には特におすすめです。 17 | 18 | この本の任意の章を次のリンクから開くことができます:: [Introduction to Jupyter](https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb) | [Chapter 1, Intro](https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb) | [Chapter 2, Production](https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb) | [Chapter 3, Ethics](https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb) | [Chapter 4, MNIST Basics](https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb) | [Chapter 5, Pet Breeds](https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb) | [Chapter 6, Multi-Category](https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb) | [Chapter 7, Sizing and TTA](https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb) | [Chapter 8, Collab](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) | [Chapter 9, Tabular](https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb) | [Chapter 10, NLP](https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb) | [Chapter 11, Mid-Level API](https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb) | [Chapter 12, NLP Deep-Dive](https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb) | [Chapter 13, Convolutions](https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb) | [Chapter 14, Resnet](https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb) | [Chapter 15, Arch Details](https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb) | [Chapter 16, Optimizers and Callbacks](https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb) | [Chapter 17, Foundations](https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb) | [Chapter 18, GradCAM](https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb) | [Chapter 19, Learner](https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb) | [Chapter 20, conclusion](https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb) 19 | 20 | 21 | ## コントリビューション 22 | 23 | このレポジトリに対してプルリクエストをした場合、その著作権はすべてJeremy HowardとSylvain Guggerに譲渡されます。（また、テキストやスペルの修正などの小さな変更を加える場合は、修正をするファイル名と修正事項に関する簡潔な説明文を含めてください。でないとレビュアーにはどこが修正されているか判断するのが困難です。よろしくおねがいします。） 24 | 25 | ## 引用 26 | 27 | この本を引用する場合は下記の内容をお使いください: 28 | 29 | ``` 30 | @book{howard2020deep, 31 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 32 | author={Howard, J. and Gugger, S.}, 33 | isbn={9781492045526}, 34 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 35 | year={2020}, 36 | publisher={O'Reilly Media, Incorporated} 37 | } 38 | ``` 39 | 40 | -------------------------------------------------------------------------------- /README_ko.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # The fastai book - draft 5 | 6 | 이 draft는 [fastai](https://docs.fast.ai/), [PyTorch](https://pytorch.org/) 에 대한 소개를 다루고 있습니다. fastai는 딥러닝을 위한 레이어드 API입니다. fastai에 대한 자세한 정보는 [the fastai paper](https://www.mdpi.com/2078-2489/11/2/108) 를 참고하실 수 있습니다. 이 저장소는 2020년부터 Jeremy Howard와 Sylvain Gugger가 저작권을 갖고 있습니다. 7 | 8 | 이 노트북들은 2020년 3월부터 샌프란시스코에서 [강의하는 과정](https://www.usfca.edu/data-institute/certificates/deep-learning-part-one) 에 사용되며, 2020년 7월경부터는 MOOC 강좌로 이용할 수 있을 것입니다. 우리의 계획은 이 노트북들이 [이 책](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) 의 기본이 되어 선주문할 수 있게 하는 것입니다. 그 책에는 이 초안에 있는 것과 같은 GPL 규제를 가지고 있지 않을 것입니다. 9 | 10 | 노트북에 포함된 코드와 python `.py` 파일의 코드는 GPL v3 라이센스로 처리됩니다. 자세한 내용은 License 파일을 참조 부탁드립니다. 11 | 12 | 나머지(노트북 및 기타 모든 마크다운 셀 포함)는 노트북의 복사본을 만들거나 개인적으로 이 리포(repo)를 요청하는 것 외에는, 형식이나 매체의 재배포 또는 변경에 대해 허가되지 않습니다. 또한, 상업적으로 사용하거나 방송용으로의 이용은 허용되지 않습니다. 딥러닝을 배우는 데 도움이 될 수 있도록 이러한 자료를 무료로 제공하고 있으니, 당사의 저작권 및 제한 조치를 따라 주세요. 13 | 14 | 다른 곳에서 이러한 자료의 사본을 호스팅하는 사람을 보게 되면, 불법 행위이며, 법적 조치로 이어질 수 있음을 알려주기 바랍니다. 또한 만약 사람들이 이 제한을 무시한다면, 더 이상 이런 식으로 추가 자료를 발표하지 않을 것이기 때문에 꼭 지켜주시길 바랍니다. 15 | 16 | 이것은 가장 초안입니다. 노트북을 실행할 때 오류가 발생한다면 [fastai-v2 포럼](https://forums.fast.ai/c/fastai-users/fastai-v2) 에서 답변을 검색하고, 도움을 요청하십시오. GitHub issue 에서는 이러한 문제를 다루지 않을 것이니 올려주시지 않기를 부탁드립니다. 17 | 18 | 만약 이 repo에 PR을 보낸다면, 해당 저작물의 저작권을 Jeremy Howard와 Sylvain Gugger에게 할당하는 것입니다. (추가적으로, 철자법이나 텍스트에 대해 편집을 한다면, 파일의 이름과 당신이 고치고 있는 것에 대한 간단한 설명을 함께 명시해주십시오. 검토자들이 어떤 수정이 이루어졌는지 확인하는 것이 어렵기 때문입니다. 감사합니다.) 19 | -------------------------------------------------------------------------------- /README_pt.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # O Livro fastai 5 | 6 | Estes cadernos Jupyter cobrem a introdução a _deep learning_, [fastai](https://docs.fast.ai/), e [PyTorch](https://pytorch.org/). A fastai é como uma interface por camadas para _deep learning_. Para mais informações, ler o [paper sobre a fastai](https://www.mdpi.com/2078-2489/11/2/108). Todos os direitos dos conteúdos deste repositório são reservados a Jeremy Howard e Sylvain Gugger, de 2020 ao presente. 7 | 8 | Os cadernos são usados para o [curso online](https://course.fast.ai) e são a base [deste livro](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), que se econtra atualmente para a venda. O livro não contém as mesmas restrições da licença GPL que constam neste rascunho. 9 | 10 | O código existente nos cadernos e ficheiros python `.py` estão abrangidos pela licença GPLv3. Ver o ficheiro LICENSE para mais detalhes. 11 | 12 | Os restantes conteúdos, incluindo todas as celas markdown nos cadernos e outros textos, não são licenciados para nenhuma redistribuição ou alteração de formato ou meio, além de cópias dos cadernos ou bifurcações do repositório para uso privado. Nenhum uso comercial ou de distribuição é permitido. Estes materiais são distribuídos gratuitamente para poder ajudar a aprender sobre _deep learning_ e, como tal, pede-se o respeito pelas licenças e estas restrições. 13 | 14 | Se souber de alguém a fornecer uma cópia destes materiais noutro sítio, por favor, indique-lhe que não é permitido e pode levar a ação judicial. Além do mais, estariam a prejudicar a comunidade pois deixaremos de publicar novos materiais se as licenças e restrições forem continuamente ignoradas. 15 | 16 | Este repositório é um esboço inicial. Se se deparar com algum problema ao correr os cadernos, por favor use o [fastai-dev forum](https://forums.fast.ai/c/fastai-users/fastai-dev/) para obter ajuda e crie novas perguntas se for necessário. Não utilize os _issues_ do GitHub para problemas relacionados com a execução dos cadernos. 17 | 18 | Ao fazer um pedido de junção de alterações (_pull request_) para este repositório está a transferir todos os direitos desse trabalho para o Jeremy Howard e Sylvain Gugger (Adicionalmente, se for uma alteração de texto mínima para corrigir ortografia ou melhorar gramática, por favor, indique o nome do ficheiro e uma breve descrição do que está a alterar. É difícil para quem mantém o repositório saber quais correções já foram feitas. Obrigado.) 19 | 20 | ## Citações 21 | 22 | Se deseja citar o livre, pode fazê-lo da seguinte forma: 23 | 24 | ``` 25 | @book{howard2020deep, 26 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 27 | author={Howard, J. and Gugger, S.}, 28 | isbn={9781492045526}, 29 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 30 | year={2020}, 31 | publisher={O'Reilly Media, Incorporated} 32 | } 33 | ``` 34 | -------------------------------------------------------------------------------- /README_tr.md: -------------------------------------------------------------------------------- 1 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) / [Turkish](./README_tr.md) 2 | # Fastai kitabı 3 | 4 | Bu Jupyter Notebook'ları, Derin Öğrenme, [fastai](https://docs.fast.ai/) ve [PyTorch](https://pytorch.org/) hakkında eğitim içermektedir. Fastai, Derin Öğrenme tekniklerine özel katmanlı API içermektedir; daha fazla bilgi için [bkz.](https://www.mdpi.com/2078-2489/11/2/108) Bu Repodaki her şeyin telif hakkı, 2020'den itiabaren, Jeremy Howard ve Sylvain Gugger'a aittir. Seçili okunabilen bölümlere [buradan ulaşabilirsiniz](https://fastai.github.io/fastbook2e/). 5 | 6 | Repoda bulunan Jupyter Notebook'ları, [bu çevrimiçi kurs](https://course.fast.ai) için hazırlanmıştır. Ayrıca, şuan satılık olan, [bu kitabın](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) temellerini oluşturmaktadır. 7 | Kitap, aynı GPL kısıtlamalarına tabii değildir. 8 | 9 | Jupyter Notebook'ları ve python `.py` dosyalarının tamamı GPL v3 ile lisanslanmıştı; daha fazla detay için LICENSE'a bakınız. Geri kalanı (notebooklarda bulunan markdown hücreleri dahil olmak üzere), herhangi bir yeniden dağıtım veya format değişmi için, Repo'yu çatallamak ve özel kullanımınız hariç, lisanslanmamıştır. Ticari veya yayın amaçlı kullanıma izin verilmez. Deri Öğrenme konularını öğrenebilmeniz amacıyla bu içeriği ücretsiz sunuyoruz. Dolayısıyla, lütfen bahsi geçen kısıtlamalara ve telif hakkına saygı gösteriniz. 10 | 11 | Bu içeriği başka yerlerde yayınlanan birisini görürseniz lütfen yaptıklarının izinli olmadığını ve onlar hakkında soruşturma başlatılabileceğini söyleyiniz. Dahası, topluluğa da zarar vermiş olacaklardır çünkü eğer insanlar telif hakkımızı göz ardı ederse bu şekilde içerik yayınlamamız pek olası değildir. 12 | 13 | ## Colab 14 | 15 | Repo'yu klonlayıp bilgisayarınızda çalıştırmak yerine, [Google Colab](https://research.google.com/colaboratory/) kullanarak Notebook'ları çalışabilirsiniz. Bu, yeni başlayanlar için önerilen yaklaşımdır; doğrudan web tarayıcınızda çalışabileceğiniz için kendi makinenizde bir Python geliştirme ortamı kurmanıza da gerek yoktur. 16 | 17 | Kitabın herhangi bir bölümüne Google Colab üzerine bu linklerden ulaşabilirsiniz: [Jupyter'e Giriş](https://colab.research.google.com/github/fastai/fastbook/blob/master/app_jupyter.ipynb) | [Bölüm 1, Giriş](https://colab.research.google.com/github/fastai/fastbook/blob/master/01_intro.ipynb) | [Bölüm 2, Production](https://colab.research.google.com/github/fastai/fastbook/blob/master/02_production.ipynb) | [Bölüm 3, Etikler](https://colab.research.google.com/github/fastai/fastbook/blob/master/03_ethics.ipynb) | [Bölüm 4, MNIST temelleri](https://colab.research.google.com/github/fastai/fastbook/blob/master/04_mnist_basics.ipynb) | [Bölüm 5, Pet Breeds](https://colab.research.google.com/github/fastai/fastbook/blob/master/05_pet_breeds.ipynb) | [Bölüm 6, Multi-Category](https://colab.research.google.com/github/fastai/fastbook/blob/master/06_multicat.ipynb) | [Bölüm 7, Sizing and TTA](https://colab.research.google.com/github/fastai/fastbook/blob/master/07_sizing_and_tta.ipynb) | [Bölüm 8, Collab](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) | [Bölüm 9, Tabular](https://colab.research.google.com/github/fastai/fastbook/blob/master/09_tabular.ipynb) | [Bölüm 10, NLP](https://colab.research.google.com/github/fastai/fastbook/blob/master/10_nlp.ipynb) | [Bölüm 11, Mid-Level API](https://colab.research.google.com/github/fastai/fastbook/blob/master/11_midlevel_data.ipynb) | [Bölüm 12, NLP Deep-Dive](https://colab.research.google.com/github/fastai/fastbook/blob/master/12_nlp_dive.ipynb) | [Bölüm 13, Convolutions](https://colab.research.google.com/github/fastai/fastbook/blob/master/13_convolutions.ipynb) | [Bölüm 14, Resnet](https://colab.research.google.com/github/fastai/fastbook/blob/master/14_resnet.ipynb) | [Bölüm 15, Arch Details](https://colab.research.google.com/github/fastai/fastbook/blob/master/15_arch_details.ipynb) | [Bölüm 16, Optimizers and Callbacks](https://colab.research.google.com/github/fastai/fastbook/blob/master/16_accel_sgd.ipynb) | [Bölüm 17, Foundations](https://colab.research.google.com/github/fastai/fastbook/blob/master/17_foundations.ipynb) | [Bölüm 18, GradCAM](https://colab.research.google.com/github/fastai/fastbook/blob/master/18_CAM.ipynb) | [Bölüm 19, Learner](https://colab.research.google.com/github/fastai/fastbook/blob/master/19_learner.ipynb) | [Bölüm 20, conclusion](https://colab.research.google.com/github/fastai/fastbook/blob/master/20_conclusion.ipynb) 18 | 19 | 20 | ## Contributions / Katkılar 21 | 22 | Bu Repo'ya herhangi bir Pull isteğinde bulunursanız bu çalışmanın telif hakkını Jeremy Howard ve Sylvain Gugger'a devretmiş olursunuz. (Ayrıca, yazım veya metin üzerinde küçük düzenlemeler yapıyorsanız, lütfen dosyanın adını ve neyi düzelttiğinizin çok kısa bir açıklamasını belirtin. İncelemeyi yapanların hangi düzeltmelerin daha önce yapıldığını bilmesi zordur. Teşekkür ederiz.) 23 | 24 | ## Atıf 25 | 26 | Kitaba atıfta bulunmak istiyorsanız, şunu kullanabilirsiniz: 27 | 28 | ``` 29 | @book{howard2020deep, 30 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 31 | author={Howard, J. and Gugger, S.}, 32 | isbn={9781492045526}, 33 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 34 | year={2020}, 35 | publisher={O'Reilly Media, Incorporated} 36 | } 37 | ``` 38 | 39 | -------------------------------------------------------------------------------- /README_vn.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # Cuốn sách fastai 5 | 6 | Những notebooks này giới thiệu về học sâu, sử dụng [fastai](https://docs.fast.ai/) và [PyTorch](https://pytorch.org/). Fastai là một thư viện API cho học sâu; để biết thêm thông tin, hãy xem [fastai paper](https://www.mdpi.com/2078-2489/11/2/108). Mọi tài liệu trong repo này thuộc bản quyền của Jeremy Howard và Sylvain Gugger, từ năm 2020 trở đi. 7 | 8 | Các notebooks được sử dụng cho khóa học online mở [a MOOC](https://course.fast.ai) và là nội dung cơ bản của cuốn [sách này](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), và hiện bạn có thể đặt mua. Cuốn sách này không có các hạn chế GPL giống như trong bản notebooks ở repo này. 9 | 10 | Mã nguồn trong các notebooks và file python `.py` được đăng ký giấy phép GPL v3; chi tiết xem tại file LICENSE. 11 | 12 | Những phần còn lại (bao gồm tất cả các markdown cells trong các notebooks cùng các nội dung khác) không được phép phân phối hoặc thay đổi định dạng hoặc phương tiện nào. Trừ trường hợp, bản sao của notebooks hoặc forking của notebooks này cho mục đích sử dụng cá nhân. Không được phép sử dụng notebooks cho mục đích thương mại hoặc quảng bá. Chúng tôi cung cấp miễn phí những tài liệu này để giúp bạn học về học sâu, vì vậy hãy tôn trọng bản quyền của chúng tôi và những hạn chế này. 13 | 14 | Nếu bạn thấy ai đó lưu trữ bản sao của những tài liệu này ở một nơi khác, vui lòng cho họ biết rằng hành động của họ là không được phép và có thể dẫn đến hành động pháp lý. Hơn nữa, chúng sẽ gây tổn hại cho cộng đồng vì chúng tôi không có khả năng sẽ không tiếp tục phát hành tài liệu bổ sung theo cách này nếu mọi người bỏ qua bản quyền của chúng tôi. 15 | 16 | Đây là một bản thảo draft. Nếu bạn gặp khó khăn khi chạy các notebooks, vui lòng tìm kiếm câu trả lời và yêu cầu trợ giúp nếu cần tại [diễn đàn fastai-dev](https://forums.fast.ai/c/fastai-users/fastai-dev/). Vui lòng không sử dụng GitHub cho các lỗi (issues) khi chạy notebooks. 17 | 18 | Nếu bạn thực hiện bất kỳ pull requests nào đối với repo này, thì bạn đang chuyển nhượng bản quyền của nội dung đó cho Jeremy Howard và Sylvain Gugger. (Ngoài ra, nếu bạn thực hiện các chỉnh sửa nhỏ như về lỗi chính tả, vui lòng nêu rõ tên của tệp file văn bản và mô tả rất ngắn gọn về những gì bạn đang sửa. Bởi vì rất khó để reviewers biết bạn đã sửa những nơi nào. Xin cảm ơn!) 19 | 20 | ## Trích dẫn 21 | 22 | Nếu bạn muốn trích dẫn cuốn sách, bạn có thể sử dụng như sau: 23 | 24 | ``` 25 | @book{howard2020deep, 26 | title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD}, 27 | author={Howard, J. and Gugger, S.}, 28 | isbn={9781492045526}, 29 | url={https://books.google.no/books?id=xd6LxgEACAAJ}, 30 | year={2020}, 31 | publisher={O'Reilly Media, Incorporated} 32 | } 33 | ``` 34 | -------------------------------------------------------------------------------- /README_zh.md: -------------------------------------------------------------------------------- 1 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastai/fastbook/master) 2 | [English](./README.md) / [Spanish](./README_es.md) / [Korean](./README_ko.md) / [Chinese](./README_zh.md) / [Bengali](./README_bn.md) / [Indonesian](./README_id.md) / [Italian](./README_it.md) / [Portuguese](./README_pt.md) / [Vietnamese](./README_vn.md) / [Japanese](./README_ja.md) 3 | 4 | # The fastai book 5 | 6 | 这些notebook包含了对深度学习，[fastai](https://docs.fast.ai/)，以及[PyTorch](https://pytorch.org/)的介绍。fastai是一个用于深度学习的分层API；要了解更多信息，请阅读[the fastai paper](https://www.mdpi.com/2078-2489/11/2/108)论文。本repo的所有内容的版权都属于Jeremy Howard和Sylvain Gugger，起自2020年。 7 | 8 | 这些notebook被用于[一个MOOC课程](https://course.fast.ai)，并且是[这本书](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527)（目前可供购买）的基础。书籍并没有本稿的GPL限制。 9 | 10 | notebook 里的代码以及 python 的 `.py` 文件受到 GPL v3 开源协议的保护；更多详情请查看 LICENSE 文件。 11 | 12 | 其余部分（包括 notebook 里的 markdown 单元以及其他文字内容）可以用于重新发布，调整格式或者载体。你可以复制这里的 notebook 或者 fork 本 repo 用于个人目的。但是任何商业传播行为都是不被允许的。我们制作这些资料是用来帮助你学习深度学习的，所以请遵守我们的版权以及上述限制. 13 | 14 | 如果你在别的地方发现任何人持有这些资料的副本，请告诉他们，他们的行为是不被允许的，并可能导致法律措施被采取。而且，他们可能会对社区造成伤害。这是因为如果人们忽视我们的版权，我们可能不会再像这样发布新的资料。 15 | 16 | 这些资料还属于初稿阶段。如果你在运行这些 notebook 时遇到困难，请尝试在[fastai-dev forum](https://forums.fast.ai/c/fastai-users/fastai-dev/)搜索或者寻求帮助。请不要因为运行 notebook 中遇到的问题而使用 GitHub issues。 17 | 18 | 若果你要为本 repo 创建 pull request，那么你的作品版权将归于 Jeremy Howard 和 Sylvain Gugger。（另外，如果你是对拼写和文本做一些小修改，请标注文件名并对你的修改提供一个简单的说明。因为对 reviewer 来说，越来越难发现哪些地方已经被改正了。非常感谢。） 19 | -------------------------------------------------------------------------------- /clean/07_sizing_and_tta.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#hide\n", 10 | "! [ -e /content ] && pip install -Uqq fastbook\n", 11 | "import fastbook\n", 12 | "fastbook.setup_book()" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "#hide\n", 22 | "from fastbook import *" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "# Training a State-of-the-Art Model" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## Imagenette" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "from fastai.vision.all import *\n", 46 | "path = untar_data(URLs.IMAGENETTE)" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "dblock = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n", 56 | " get_items=get_image_files,\n", 57 | " get_y=parent_label,\n", 58 | " item_tfms=Resize(460),\n", 59 | " batch_tfms=aug_transforms(size=224, min_scale=0.75))\n", 60 | "dls = dblock.dataloaders(path, bs=64)" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "model = xresnet50(n_out=dls.c)\n", 70 | "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", 71 | "learn.fit_one_cycle(5, 3e-3)" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "## Normalization" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": null, 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "x,y = dls.one_batch()\n", 88 | "x.mean(dim=[0,2,3]),x.std(dim=[0,2,3])" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": null, 94 | "metadata": {}, 95 | "outputs": [], 96 | "source": [ 97 | "def get_dls(bs, size):\n", 98 | " dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n", 99 | " get_items=get_image_files,\n", 100 | " get_y=parent_label,\n", 101 | " item_tfms=Resize(460),\n", 102 | " batch_tfms=[*aug_transforms(size=size, min_scale=0.75),\n", 103 | " Normalize.from_stats(*imagenet_stats)])\n", 104 | " return dblock.dataloaders(path, bs=bs)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [ 113 | "dls = get_dls(64, 224)" 114 | ] 115 | }, 116 | { 117 | "cell_type": "code", 118 | "execution_count": null, 119 | "metadata": {}, 120 | "outputs": [], 121 | "source": [ 122 | "x,y = dls.one_batch()\n", 123 | "x.mean(dim=[0,2,3]),x.std(dim=[0,2,3])" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": null, 129 | "metadata": {}, 130 | "outputs": [], 131 | "source": [ 132 | "model = xresnet50(n_out=dls.c)\n", 133 | "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", 134 | "learn.fit_one_cycle(5, 3e-3)" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": {}, 140 | "source": [ 141 | "## Progressive Resizing" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": null, 147 | "metadata": {}, 148 | "outputs": [], 149 | "source": [ 150 | "dls = get_dls(128, 128)\n", 151 | "learn = Learner(dls, xresnet50(n_out=dls.c), loss_func=CrossEntropyLossFlat(), \n", 152 | " metrics=accuracy)\n", 153 | "learn.fit_one_cycle(4, 3e-3)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "metadata": {}, 160 | "outputs": [], 161 | "source": [ 162 | "learn.dls = get_dls(64, 224)\n", 163 | "learn.fine_tune(5, 1e-3)" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "## Test Time Augmentation" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [ 179 | "preds,targs = learn.tta()\n", 180 | "accuracy(preds, targs).item()" 181 | ] 182 | }, 183 | { 184 | "cell_type": "markdown", 185 | "metadata": {}, 186 | "source": [ 187 | "## Mixup" 188 | ] 189 | }, 190 | { 191 | "cell_type": "markdown", 192 | "metadata": {}, 193 | "source": [ 194 | "### Sidebar: Papers and Math" 195 | ] 196 | }, 197 | { 198 | "cell_type": "markdown", 199 | "metadata": {}, 200 | "source": [ 201 | "### End sidebar" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "metadata": {}, 208 | "outputs": [], 209 | "source": [ 210 | "church = PILImage.create(get_image_files_sorted(path/'train'/'n03028079')[0])\n", 211 | "gas = PILImage.create(get_image_files_sorted(path/'train'/'n03425413')[0])\n", 212 | "church = church.resize((256,256))\n", 213 | "gas = gas.resize((256,256))\n", 214 | "tchurch = tensor(church).float() / 255.\n", 215 | "tgas = tensor(gas).float() / 255.\n", 216 | "\n", 217 | "_,axs = plt.subplots(1, 3, figsize=(12,4))\n", 218 | "show_image(tchurch, ax=axs[0]);\n", 219 | "show_image(tgas, ax=axs[1]);\n", 220 | "show_image((0.3*tchurch + 0.7*tgas), ax=axs[2]);" 221 | ] 222 | }, 223 | { 224 | "cell_type": "markdown", 225 | "metadata": {}, 226 | "source": [ 227 | "## Label Smoothing" 228 | ] 229 | }, 230 | { 231 | "cell_type": "markdown", 232 | "metadata": {}, 233 | "source": [ 234 | "### Sidebar: Label Smoothing, the Paper" 235 | ] 236 | }, 237 | { 238 | "cell_type": "markdown", 239 | "metadata": {}, 240 | "source": [ 241 | "### End sidebar" 242 | ] 243 | }, 244 | { 245 | "cell_type": "markdown", 246 | "metadata": {}, 247 | "source": [ 248 | "## Conclusion" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "## Questionnaire" 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "metadata": {}, 261 | "source": [ 262 | "1. What is the difference between ImageNet and Imagenette? When is it better to experiment on one versus the other?\n", 263 | "1. What is normalization?\n", 264 | "1. Why didn't we have to care about normalization when using a pretrained model?\n", 265 | "1. What is progressive resizing?\n", 266 | "1. Implement progressive resizing in your own project. Did it help?\n", 267 | "1. What is test time augmentation? How do you use it in fastai?\n", 268 | "1. Is using TTA at inference slower or faster than regular inference? Why?\n", 269 | "1. What is Mixup? How do you use it in fastai?\n", 270 | "1. Why does Mixup prevent the model from being too confident?\n", 271 | "1. Why does training with Mixup for five epochs end up worse than training without Mixup?\n", 272 | "1. What is the idea behind label smoothing?\n", 273 | "1. What problems in your data can label smoothing help with?\n", 274 | "1. When using label smoothing with five categories, what is the target associated with the index 1?\n", 275 | "1. What is the first step to take when you want to prototype quick experiments on a new dataset?" 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": {}, 281 | "source": [ 282 | "### Further Research\n", 283 | "\n", 284 | "1. Use the fastai documentation to build a function that crops an image to a square in each of the four corners, then implement a TTA method that averages the predictions on a center crop and those four crops. Did it help? Is it better than the TTA method of fastai?\n", 285 | "1. Find the Mixup paper on arXiv and read it. Pick one or two more recent articles introducing variants of Mixup and read them, then try to implement them on your problem.\n", 286 | "1. Find the script training Imagenette using Mixup and use it as an example to build a script for a long training on your own project. Execute it and see if it helps.\n", 287 | "1. Read the sidebar \"Label Smoothing, the Paper\", look at the relevant section of the original paper and see if you can follow it. Don't be afraid to ask for help!" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": null, 293 | "metadata": {}, 294 | "outputs": [], 295 | "source": [] 296 | } 297 | ], 298 | "metadata": { 299 | "jupytext": { 300 | "split_at_heading": true 301 | }, 302 | "kernelspec": { 303 | "display_name": "Python 3", 304 | "language": "python", 305 | "name": "python3" 306 | } 307 | }, 308 | "nbformat": 4, 309 | "nbformat_minor": 2 310 | } 311 | -------------------------------------------------------------------------------- /clean/18_CAM.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#hide\n", 10 | "! [ -e /content ] && pip install -Uqq fastbook\n", 11 | "import fastbook\n", 12 | "fastbook.setup_book()" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "#hide\n", 22 | "from fastbook import *" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "# CNN Interpretation with CAM" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## CAM and Hooks" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "path = untar_data(URLs.PETS)/'images'\n", 46 | "def is_cat(x): return x[0].isupper()\n", 47 | "dls = ImageDataLoaders.from_name_func(\n", 48 | " path, get_image_files(path), valid_pct=0.2, seed=21,\n", 49 | " label_func=is_cat, item_tfms=Resize(224))\n", 50 | "learn = vision_learner(dls, resnet34, metrics=error_rate)\n", 51 | "learn.fine_tune(1)" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "img = PILImage.create(image_cat())\n", 61 | "x, = first(dls.test_dl([img]))" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": null, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "class Hook():\n", 71 | " def hook_func(self, m, i, o): self.stored = o.detach().clone()" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": null, 77 | "metadata": {}, 78 | "outputs": [], 79 | "source": [ 80 | "hook_output = Hook()\n", 81 | "hook = learn.model[0].register_forward_hook(hook_output.hook_func)" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": null, 87 | "metadata": {}, 88 | "outputs": [], 89 | "source": [ 90 | "with torch.no_grad(): output = learn.model.eval()(x)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "act = hook_output.stored[0]" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": null, 105 | "metadata": {}, 106 | "outputs": [], 107 | "source": [ 108 | "F.softmax(output, dim=-1)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": null, 114 | "metadata": {}, 115 | "outputs": [], 116 | "source": [ 117 | "dls.vocab" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": null, 123 | "metadata": {}, 124 | "outputs": [], 125 | "source": [ 126 | "x.shape" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": {}, 133 | "outputs": [], 134 | "source": [ 135 | "cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, act)\n", 136 | "cam_map.shape" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": null, 142 | "metadata": {}, 143 | "outputs": [], 144 | "source": [ 145 | "x_dec = TensorImage(dls.train.decode((x,))[0][0])\n", 146 | "_,ax = plt.subplots()\n", 147 | "x_dec.show(ctx=ax)\n", 148 | "ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n", 149 | " interpolation='bilinear', cmap='magma');" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": null, 155 | "metadata": {}, 156 | "outputs": [], 157 | "source": [ 158 | "hook.remove()" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": null, 164 | "metadata": {}, 165 | "outputs": [], 166 | "source": [ 167 | "class Hook():\n", 168 | " def __init__(self, m):\n", 169 | " self.hook = m.register_forward_hook(self.hook_func) \n", 170 | " def hook_func(self, m, i, o): self.stored = o.detach().clone()\n", 171 | " def __enter__(self, *args): return self\n", 172 | " def __exit__(self, *args): self.hook.remove()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": null, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "with Hook(learn.model[0]) as hook:\n", 182 | " with torch.no_grad(): output = learn.model.eval()(x.cuda())\n", 183 | " act = hook.stored" 184 | ] 185 | }, 186 | { 187 | "cell_type": "markdown", 188 | "metadata": {}, 189 | "source": [ 190 | "## Gradient CAM" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": null, 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [ 199 | "class HookBwd():\n", 200 | " def __init__(self, m):\n", 201 | " self.hook = m.register_backward_hook(self.hook_func) \n", 202 | " def hook_func(self, m, gi, go): self.stored = go[0].detach().clone()\n", 203 | " def __enter__(self, *args): return self\n", 204 | " def __exit__(self, *args): self.hook.remove()" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": null, 210 | "metadata": {}, 211 | "outputs": [], 212 | "source": [ 213 | "cls = 1\n", 214 | "with HookBwd(learn.model[0]) as hookg:\n", 215 | " with Hook(learn.model[0]) as hook:\n", 216 | " output = learn.model.eval()(x.cuda())\n", 217 | " act = hook.stored\n", 218 | " output[0,cls].backward()\n", 219 | " grad = hookg.stored" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": null, 225 | "metadata": {}, 226 | "outputs": [], 227 | "source": [ 228 | "w = grad[0].mean(dim=[1,2], keepdim=True)\n", 229 | "cam_map = (w * act[0]).sum(0)" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": null, 235 | "metadata": {}, 236 | "outputs": [], 237 | "source": [ 238 | "_,ax = plt.subplots()\n", 239 | "x_dec.show(ctx=ax)\n", 240 | "ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n", 241 | " interpolation='bilinear', cmap='magma');" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "with HookBwd(learn.model[0][-2]) as hookg:\n", 251 | " with Hook(learn.model[0][-2]) as hook:\n", 252 | " output = learn.model.eval()(x.cuda())\n", 253 | " act = hook.stored\n", 254 | " output[0,cls].backward()\n", 255 | " grad = hookg.stored" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": null, 261 | "metadata": {}, 262 | "outputs": [], 263 | "source": [ 264 | "w = grad[0].mean(dim=[1,2], keepdim=True)\n", 265 | "cam_map = (w * act[0]).sum(0)" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": null, 271 | "metadata": {}, 272 | "outputs": [], 273 | "source": [ 274 | "_,ax = plt.subplots()\n", 275 | "x_dec.show(ctx=ax)\n", 276 | "ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0),\n", 277 | " interpolation='bilinear', cmap='magma');" 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": {}, 283 | "source": [ 284 | "## Conclusion" 285 | ] 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "metadata": {}, 290 | "source": [ 291 | "## Questionnaire" 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "1. What is a \"hook\" in PyTorch?\n", 299 | "1. Which layer does CAM use the outputs of?\n", 300 | "1. Why does CAM require a hook?\n", 301 | "1. Look at the source code of the `ActivationStats` class and see how it uses hooks.\n", 302 | "1. Write a hook that stores the activations of a given layer in a model (without peeking, if possible).\n", 303 | "1. Why do we call `eval` before getting the activations? Why do we use `no_grad`?\n", 304 | "1. Use `torch.einsum` to compute the \"dog\" or \"cat\" score of each of the locations in the last activation of the body of the model.\n", 305 | "1. How do you check which order the categories are in (i.e., the correspondence of index->category)?\n", 306 | "1. Why are we using `decode` when displaying the input image?\n", 307 | "1. What is a \"context manager\"? What special methods need to be defined to create one?\n", 308 | "1. Why can't we use plain CAM for the inner layers of a network?\n", 309 | "1. Why do we need to register a hook on the backward pass in order to do Grad-CAM?\n", 310 | "1. Why can't we call `output.backward()` when `output` is a rank-2 tensor of output activations per image per class?" 311 | ] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "metadata": {}, 316 | "source": [ 317 | "### Further Research" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": {}, 323 | "source": [ 324 | "1. Try removing `keepdim` and see what happens. Look up this parameter in the PyTorch docs. Why do we need it in this notebook?\n", 325 | "1. Create a notebook like this one, but for NLP, and use it to find which words in a movie review are most significant in assessing the sentiment of a particular movie review." 326 | ] 327 | }, 328 | { 329 | "cell_type": "code", 330 | "execution_count": null, 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [] 334 | } 335 | ], 336 | "metadata": { 337 | "jupytext": { 338 | "split_at_heading": true 339 | }, 340 | "kernelspec": { 341 | "display_name": "Python 3", 342 | "language": "python", 343 | "name": "python3" 344 | } 345 | }, 346 | "nbformat": 4, 347 | "nbformat_minor": 2 348 | } 349 | -------------------------------------------------------------------------------- /clean/20_conclusion.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#hide\n", 10 | "! [ -e /content ] && pip install -Uqq fastbook\n", 11 | "import fastbook\n", 12 | "fastbook.setup_book()" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# Concluding Thoughts" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": null, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [] 28 | } 29 | ], 30 | "metadata": { 31 | "jupytext": { 32 | "split_at_heading": true 33 | }, 34 | "kernelspec": { 35 | "display_name": "Python 3", 36 | "language": "python", 37 | "name": "python3" 38 | } 39 | }, 40 | "nbformat": 4, 41 | "nbformat_minor": 4 42 | } 43 | -------------------------------------------------------------------------------- /clean/app_blog.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Creating a Blog" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Blogging with GitHub Pages" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "### Creating the Repository" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "### Setting Up Your Home Page" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "### Creating Posts" 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "### Synchronizing GitHub and Your Computer" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "## Jupyter for Blogging" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": null, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [] 58 | } 59 | ], 60 | "metadata": { 61 | "jupytext": { 62 | "split_at_heading": true 63 | }, 64 | "kernelspec": { 65 | "display_name": "Python 3", 66 | "language": "python", 67 | "name": "python3" 68 | } 69 | }, 70 | "nbformat": 4, 71 | "nbformat_minor": 2 72 | } 73 | -------------------------------------------------------------------------------- /clean/app_jupyter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "#hide\n", 10 | "! [ -e /content ] && pip install -Uqq fastbook\n", 11 | "import fastbook\n", 12 | "fastbook.setup_book()" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "from fastbook import *" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "# Appendix: Jupyter Notebook 101" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## Introduction" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": null, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "1+1" 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "## Writing" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "3/2" 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": {}, 66 | "source": [ 67 | "## Modes" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "## Other Important Considerations" 75 | ] 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "## Markdown Formatting\n" 82 | ] 83 | }, 84 | { 85 | "cell_type": "markdown", 86 | "metadata": {}, 87 | "source": [ 88 | "### Italics, Bold, Strikethrough, Inline, Blockquotes and Links" 89 | ] 90 | }, 91 | { 92 | "cell_type": "markdown", 93 | "metadata": {}, 94 | "source": [ 95 | "### Headings" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "### Lists" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "## Code Capabilities" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": null, 115 | "metadata": {}, 116 | "outputs": [], 117 | "source": [ 118 | "# Import necessary libraries\n", 119 | "from fastai.vision.all import * \n", 120 | "import matplotlib.pyplot as plt" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": null, 126 | "metadata": {}, 127 | "outputs": [], 128 | "source": [ 129 | "from PIL import Image" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": null, 135 | "metadata": {}, 136 | "outputs": [], 137 | "source": [ 138 | "a = 1\n", 139 | "b = a + 1\n", 140 | "c = b + a + 1\n", 141 | "d = c + b + a + 1\n", 142 | "a, b, c ,d" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": null, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [ 151 | "plt.plot([a,b,c,d])\n", 152 | "plt.show()" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": null, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "Image.open(image_cat())" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "## Running the App Locally" 169 | ] 170 | }, 171 | { 172 | "cell_type": "markdown", 173 | "metadata": {}, 174 | "source": [ 175 | "## Creating a Notebook" 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "metadata": {}, 181 | "source": [ 182 | "## Shortcuts and Tricks" 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "### Command Mode Shortcuts" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "### Cell Tricks" 197 | ] 198 | }, 199 | { 200 | "cell_type": "markdown", 201 | "metadata": {}, 202 | "source": [ 203 | "### Line Magics" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": {}, 210 | "outputs": [], 211 | "source": [ 212 | "%timeit [i+1 for i in range(1000)]" 213 | ] 214 | } 215 | ], 216 | "metadata": { 217 | "kernelspec": { 218 | "display_name": "Python 3", 219 | "language": "python", 220 | "name": "python3" 221 | } 222 | }, 223 | "nbformat": 4, 224 | 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scikit_learn 8 | azure-cognitiveservices-search-imagesearch 9 | sentencepiece 10 | -------------------------------------------------------------------------------- /settings.ini: -------------------------------------------------------------------------------- 1 | [DEFAULT] 2 | lib_name = fastbook 3 | user = fastai 4 | description = fastbook 5 | keywords = jupyter notebook asciidoc 6 | author = Jeremy Howard and Sylvain Gugger 7 | author_email = info@fast.ai 8 | copyright = fast.ai 9 | branch = master 10 | version = 0.0.1 11 | min_python = 3.6 12 | audience = Developers 13 | language = English 14 | custom_sidebar = False 15 | license = custom 16 | status = 2 17 | nbs_path = . 18 | doc_path = docs 19 | git_url = https://github.com/fastai/fastbook/tree/master/ 20 | lib_path = fastbook 21 | title = fastbook 22 | doc_host = https://fastai.github.io 23 | doc_baseurl = /fastbook/ 24 | host = github 25 | 26 | -------------------------------------------------------------------------------- /tools/clean.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import nbformat 4 | from nbdev.export import * 5 | from nbdev.clean import * 6 | from fastcore.all import * 7 | 8 | _re_header = re.compile(r'^#+\s+\S+') 9 | _re_clean = re.compile(r'^\s*#\s*clean\s*') 10 | 11 | def is_header_cell(cell): return _re_header.search(cell['source']) is not None 12 | def is_clean_cell(cell): return _re_clean.search(cell['source']) is not None 13 | 14 | _re_questionnaire = re.compile(r'^#+\s+Questionnaire') 15 | 16 | def get_stop_idx(cells): 17 | i = 0 18 | while i < len(cells) and _re_questionnaire.search(cells[i]['source']) is None: i+=1 19 | return i 20 | 21 | def clean_tags(cell): 22 | if is_header_cell(cell): return cell 23 | for attr in ["id", "caption", "alt", "width", "hide_input", "hide_output", "clean"]: 24 | cell["source"] = re.sub(r'#\s*' + attr + r'.*?($|\n)', '', cell["source"]) 25 | return cell 26 | 27 | def proc_nb(fname, dest): 28 | nb = read_nb(fname) 29 | i = get_stop_idx(nb['cells']) 30 | nb['cells'] = [clean_tags(c) for j,c in enumerate(nb['cells']) if 31 | c['cell_type']=='code' or is_header_cell(c) or is_clean_cell(c) or j >= i] 32 | clean_nb(nb, clear_all=True) 33 | with open(dest/fname.name, 'w') as f: nbformat.write(nb, f, version=4) 34 | 35 | def proc_all(path='.', dest_path='clean'): 36 | path,dest_path = Path(path),Path(dest_path) 37 | fns = [f for f in path.iterdir() if f.suffix == '.ipynb' and not f.name.startswith('_')] 38 | for fn in fns: proc_nb(fn, dest=dest_path) 39 | 40 | if __name__=='__main__': proc_all() 41 | 42 | -------------------------------------------------------------------------------- /translations/cn/18_CAM.md: -------------------------------------------------------------------------------- 1 | # 第十八章：使用 CAM 解释 CNN 2 | 3 | 4 | 5 | 现在我们知道如何从头开始构建几乎任何东西，让我们利用这些知识来创建全新（并非常有用！）的功能：*类激活图*。它让我们对 CNN 为何做出预测有一些见解。 6 | 7 | 在这个过程中，我们将学习到 PyTorch 中一个我们之前没有见过的方便功能，*hook*，并且我们将应用本书中介绍的许多概念。如果你想真正测试你对本书材料的理解，完成本章后，尝试将其放在一边，从头开始重新创建这里的想法（不要偷看！）。 8 | 9 | # CAM 和 Hooks 10 | 11 | *类激活图*（CAM）是由周博磊等人在[“学习用于区分定位的深度特征”](https://oreil.ly/5hik3)中引入的。它使用最后一个卷积层的输出（就在平均池化层之前）以及预测结果，为我们提供一个热图可视化，解释模型为何做出决定。这是一个有用的解释工具。 12 | 13 | 更准确地说，在我们最终卷积层的每个位置，我们有与最后一个线性层中一样多的滤波器。因此，我们可以计算这些激活与最终权重的点积，以便为我们特征图上的每个位置得到用于做出决定的特征的分数。 14 | 15 | 在训练模型时，我们需要一种方法来访问模型内部的激活。在 PyTorch 中，可以通过 *hook* 来实现。Hook 是 PyTorch 的等价于 fastai 的回调。然而，与允许您像 fastai 的 `Learner` 回调一样将代码注入训练循环不同，hook 允许您将代码注入前向和反向计算本身。我们可以将 hook 附加到模型的任何层，并且在计算输出（前向 hook）或反向传播（后向 hook）时执行。前向 hook 是一个接受三个参数的函数——一个模块，它的输入和输出——它可以执行任何您想要的行为。（fastai 还提供了一个方便的 `HookCallback`，我们这里不涉及，但看看 fastai 文档；它使使用 hook 更容易一些。） 16 | 17 | 为了说明，我们将使用我们在第一章中训练的相同的猫狗模型： 18 | 19 | ```py 20 | path = untar_data(URLs.PETS)/'images' 21 | def is_cat(x): return x[0].isupper() 22 | dls = ImageDataLoaders.from_name_func( 23 | path, get_image_files(path), valid_pct=0.2, seed=21, 24 | label_func=is_cat, item_tfms=Resize(224)) 25 | learn = cnn_learner(dls, resnet34, metrics=error_rate) 26 | learn.fine_tune(1) 27 | ``` 28 | 29 | | 轮次 | 训练损失 | 验证损失 | 错误率 | 时间 | 30 | | --- | --- | --- | --- | --- | 31 | | 0 | 0.141987 | 0.018823 | 0.007442 | 00:16 | 32 | | 轮次 | 训练损失 | 验证损失 | 错误率 | 时间 | 33 | | --- | --- | --- | --- | --- | 34 | | 0 | 0.050934 | 0.015366 | 0.006766 | 00:21 | 35 | 36 | 首先，我们将获取一张猫的图片和一批数据： 37 | 38 | ```py 39 | img = PILImage.create('images/chapter1_cat_example.jpg') 40 | x, = first(dls.test_dl([img])) 41 | ``` 42 | 43 | 对于 CAM，我们想要存储最后一个卷积层的激活。我们将我们的 hook 函数放在一个类中，这样它就有一个我们稍后可以访问的状态，并且只存储输出的副本： 44 | 45 | ```py 46 | class Hook(): 47 | def hook_func(self, m, i, o): self.stored = o.detach().clone() 48 | ``` 49 | 50 | 然后我们可以实例化一个 `Hook` 并将其附加到我们想要的层，即 CNN 主体的最后一层： 51 | 52 | ```py 53 | hook_output = Hook() 54 | hook = learn.model[0].register_forward_hook(hook_output.hook_func) 55 | ``` 56 | 57 | 现在我们可以获取一个批次并将其通过我们的模型： 58 | 59 | ```py 60 | with torch.no_grad(): output = learn.model.eval()(x) 61 | ``` 62 | 63 | 我们可以访问我们存储的激活： 64 | 65 | ```py 66 | act = hook_output.stored[0] 67 | ``` 68 | 69 | 让我们再次双重检查我们的预测： 70 | 71 | ```py 72 | F.softmax(output, dim=-1) 73 | ``` 74 | 75 | ```py 76 | tensor([[7.3566e-07, 1.0000e+00]], device='cuda:0') 77 | ``` 78 | 79 | 我们知道 `0`（对于 `False`）是“狗”，因为在 fastai 中类别会自动排序，但我们仍然可以通过查看 `dls.vocab` 来进行双重检查： 80 | 81 | ```py 82 | dls.vocab 83 | ``` 84 | 85 | ```py 86 | (#2) [False,True] 87 | ``` 88 | 89 | 所以，我们的模型非常确信这是一张猫的图片。 90 | 91 | 为了对我们的权重矩阵（2 乘以激活数量）与激活（批次大小乘以激活乘以行乘以列）进行点积，我们使用自定义的 `einsum`： 92 | 93 | ```py 94 | x.shape 95 | ``` 96 | 97 | ```py 98 | torch.Size([1, 3, 224, 224]) 99 | ``` 100 | 101 | ```py 102 | cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, act) 103 | cam_map.shape 104 | ``` 105 | 106 | ```py 107 | torch.Size([2, 7, 7]) 108 | ``` 109 | 110 | 对于我们批次中的每个图像，对于每个类别，我们得到一个 7×7 的特征图，告诉我们激活较高和较低的位置。这将让我们看到哪些图片区域影响了模型的决策。 111 | 112 | 例如，我们可以找出哪些区域使模型决定这个动物是一只猫（请注意，由于`DataLoader`对输入`x`进行了归一化，我们需要`decode`，并且由于在撰写本书时，PyTorch 在索引时不保留类型，我们需要转换为`TensorImage`——这可能在您阅读本文时已经修复）： 113 | 114 | ```py 115 | x_dec = TensorImage(dls.train.decode((x,))[0][0]) 116 | _,ax = plt.subplots() 117 | x_dec.show(ctx=ax) 118 | ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,224,224,0), 119 | interpolation='bilinear', cmap='magma'); 120 | ``` 121 | 122 | ![](img/dlcf_18in01.png) 123 | 124 | 在这种情况下，明亮黄色的区域对应于高激活，紫色区域对应于低激活。在这种情况下，我们可以看到头部和前爪是使模型决定这是一张猫的图片的两个主要区域。 125 | 126 | 完成钩子后，应该将其删除，否则可能会泄漏一些内存： 127 | 128 | ```py 129 | hook.remove() 130 | ``` 131 | 132 | 这就是为什么将`Hook`类作为*上下文管理器*通常是一个好主意，当您进入时注册钩子，当您退出时删除它。上下文管理器是一个 Python 构造，在`with`子句中创建对象时调用`__enter__`，在`with`子句结束时调用`__exit__`。例如，这就是 Python 处理`with open(...) as f:`构造的方式，您经常会看到用于打开文件而不需要在最后显式调用`close(f)`。 133 | 134 | 如果我们将`Hook`定义如下 135 | 136 | ```py 137 | class Hook(): 138 | def __init__(self, m): 139 | self.hook = m.register_forward_hook(self.hook_func) 140 | def hook_func(self, m, i, o): self.stored = o.detach().clone() 141 | def __enter__(self, *args): return self 142 | def __exit__(self, *args): self.hook.remove() 143 | ``` 144 | 145 | 我们可以安全地这样使用它： 146 | 147 | ```py 148 | with Hook(learn.model[0]) as hook: 149 | with torch.no_grad(): output = learn.model.eval()(x.cuda()) 150 | act = hook.stored 151 | ``` 152 | 153 | fastai 为您提供了这个`Hook`类，以及一些其他方便的类，使使用钩子更容易。 154 | 155 | 这种方法很有用，但仅适用于最后一层。*梯度 CAM*是一个解决这个问题的变体。 156 | 157 | # 梯度 CAM 158 | 159 | 我们刚刚看到的方法让我们只能计算最后激活的热图，因为一旦我们有了我们的特征，我们必须将它们乘以最后的权重矩阵。这对网络中的内部层不起作用。2016 年的一篇论文[“Grad-CAM: Why Did You Say That?”](https://oreil.ly/4krXE)由 Ramprasaath R. Selvaraju 等人介绍了一种变体，使用所需类的最终激活的梯度。如果您还记得一点关于反向传播的知识，最后一层输出的梯度与该层输入的梯度相对应，因为它是一个线性层。 160 | 161 | 对于更深的层，我们仍然希望梯度，但它们不再等于权重。我们必须计算它们。PyTorch 在反向传播期间为我们计算每一层的梯度，但它们不会被存储（除了`requires_grad`为`True`的张量）。然而，我们可以在反向传播上注册一个钩子，PyTorch 将把梯度作为参数传递给它，因此我们可以在那里存储它们。为此，我们将使用一个`HookBwd`类，它的工作方式类似于`Hook`，但是拦截并存储梯度而不是激活： 162 | 163 | ```py 164 | class HookBwd(): 165 | def __init__(self, m): 166 | self.hook = m.register_backward_hook(self.hook_func) 167 | def hook_func(self, m, gi, go): self.stored = go[0].detach().clone() 168 | def __enter__(self, *args): return self 169 | def __exit__(self, *args): self.hook.remove() 170 | ``` 171 | 172 | 然后对于类索引`1`（对于`True`，即“猫”），我们拦截最后一个卷积层的特征，如前所述，计算我们类的输出激活的梯度。我们不能简单地调用`output.backward`，因为梯度只对标量有意义（通常是我们的损失），而`output`是一个秩为 2 的张量。但是，如果我们选择单个图像（我们将使用`0`）和单个类（我们将使用`1`），我们*可以*计算我们喜欢的任何权重或激活的梯度，与该单个值相关，使用`output[0,cls].backward`。我们的钩子拦截了我们将用作权重的梯度： 173 | 174 | ```py 175 | cls = 1 176 | with HookBwd(learn.model[0]) as hookg: 177 | with Hook(learn.model[0]) as hook: 178 | output = learn.model.eval()(x.cuda()) 179 | act = hook.stored 180 | output[0,cls].backward() 181 | grad = hookg.stored 182 | ``` 183 | 184 | Grad-CAM 的权重由特征图上的梯度平均值给出。然后就像以前一样： 185 | 186 | ```py 187 | w = grad[0].mean(dim=[1,2], keepdim=True) 188 | cam_map = (w * act[0]).sum(0) 189 | ``` 190 | 191 | ```py 192 | _,ax = plt.subplots() 193 | x_dec.show(ctx=ax) 194 | ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0), 195 | interpolation='bilinear', cmap='magma'); 196 | ``` 197 | 198 | ![](img/dlcf_18in02.png) 199 | 200 | Grad-CAM 的新颖之处在于我们可以在任何层上使用它。例如，在这里我们将其用于倒数第二个 ResNet 组的输出： 201 | 202 | ```py 203 | with HookBwd(learn.model[0][-2]) as hookg: 204 | with Hook(learn.model[0][-2]) as hook: 205 | output = learn.model.eval()(x.cuda()) 206 | act = hook.stored 207 | output[0,cls].backward() 208 | grad = hookg.stored 209 | ``` 210 | 211 | ```py 212 | w = grad[0].mean(dim=[1,2], keepdim=True) 213 | cam_map = (w * act[0]).sum(0) 214 | ``` 215 | 216 | 现在我们可以查看此层的激活图： 217 | 218 | ```py 219 | _,ax = plt.subplots() 220 | x_dec.show(ctx=ax) 221 | ax.imshow(cam_map.detach().cpu(), alpha=0.6, extent=(0,224,224,0), 222 | interpolation='bilinear', cmap='magma'); 223 | ``` 224 | 225 | ![](img/dlcf_18in03.png) 226 | 227 | # 结论 228 | 229 | 模型解释是一个活跃研究领域，我们只是在这一简短章节中探讨了可能性的一部分。类激活图让我们了解模型为什么预测了某个结果，它展示了图像中对于给定预测最负责的区域。这可以帮助我们分析假阳性，并找出在我们的训练中缺少了哪种数据以避免它们。 230 | 231 | # 问卷调查 232 | 233 | 1. PyTorch 中的 hook 是什么？ 234 | 235 | 1. CAM 使用哪个层的输出？ 236 | 237 | 1. 为什么 CAM 需要一个 hook？ 238 | 239 | 1. 查看`ActivationStats`类的源代码，看看它如何使用 hooks。 240 | 241 | 1. 编写一个 hook，用于存储模型中给定层的激活（如果可能的话，不要偷看）。 242 | 243 | 1. 为什么我们在获取激活之前要调用`eval`？为什么我们要使用`no_grad`？ 244 | 245 | 1. 使用`torch.einsum`来计算模型主体最后激活的每个位置的“狗”或“猫”得分。 246 | 247 | 1. 如何检查类别的顺序（即索引→类别的对应关系）？ 248 | 249 | 1. 为什么我们在显示输入图像时使用`decode`？ 250 | 251 | 1. 什么是上下文管理器？需要定义哪些特殊方法来创建一个？ 252 | 253 | 1. 为什么我们不能对网络的内部层使用普通的 CAM？ 254 | 255 | 1. 为了执行 Grad-CAM，为什么我们需要在反向传播中注册一个 hook？ 256 | 257 | 1. 当`output`是每个图像每个类别的输出激活的秩为 2 的张量时，为什么我们不能调用`output.backward`？ 258 | 259 | ## 进一步研究 260 | 261 | 1. 尝试移除`keepdim`，看看会发生什么。查阅 PyTorch 文档中的这个参数。为什么我们在这个笔记本中需要它？ 262 | 263 | 1. 创建一个类似这个的笔记本，但用于 NLP，并用它来找出电影评论中哪些词对于评估特定电影评论的情感最重要。 264 | -------------------------------------------------------------------------------- /translations/cn/20_conclusion.md: -------------------------------------------------------------------------------- 1 | # 第二十章：总结思考 2 | 3 | 4 | 5 | 恭喜！你成功了！如果你已经完成了到这一点的所有笔记本，你已经加入了一个小但不断增长的人群，他们能够利用深度学习的力量解决实际问题。你可能不会觉得这样—事实上，你可能不会。我们一再看到完成 fast.ai 课程的学生明显低估了自己作为深度学习从业者的效力。我们也看到这些人经常被具有传统学术背景的其他人低估。所以如果你要超越自己的期望和他人的期望，那么在关闭这本书后，你接下来要做的事情比你到目前为止所做的更重要。 6 | 7 | 最重要的是保持动力。事实上，从你对优化器的研究中知道，动量是可以自我增强的！所以想想你现在可以做些什么来维持和加速你的深度学习之旅。图 20-1 可以给你一些想法。 8 | 9 | ![接下来要做什么](img/dlcf_2001.png) 10 | 11 | ###### 图 20-1。接下来要做什么 12 | 13 | 在这本书中，我们谈了很多关于写作的价值，无论是代码还是散文。但也许你到目前为止还没有写得像你希望的那样多。没关系！现在是一个扭转局面的好机会。此时你有很多话要说。也许你已经在一个数据集上尝试了一些实验，其他人似乎没有以同样的方式看待。告诉世界！或者你可能正在考虑尝试一些在阅读时想到的想法——现在是把这些想法转化为代码的好时机。 14 | 15 | 如果你想分享你的想法，一个相对低调的地方是[fast.ai 论坛](https://forums.fast.ai)。你会发现那里的社区非常支持和乐于助人，所以请过来告诉我们你在做什么。或者看看你是否可以回答一些问题，帮助那些在学习早期的人。 16 | 17 | 如果你在深度学习之旅中取得了一些成功，无论大小，请务必告诉我们！在论坛上发布这些信息尤其有帮助，因为了解其他学生的成功可以极大地激励人们。 18 | 19 | 对许多人来说，保持与学习之旅的联系最重要的方法之一是围绕它建立一个社区。例如，你可以尝试在你当地社区设立一个小型深度学习聚会，或者一个学习小组，甚至可以在当地聚会上做一个关于你到目前为止学到的内容或者你感兴趣的某个特定方面的演讲。你现在还不是世界领先的专家也没关系——重要的是要记住你现在知道很多其他人不知道的东西，所以他们很可能会欣赏你的观点。 20 | 21 | 另一个许多人发现有用的社区活动是定期的读书俱乐部或论文阅读俱乐部。你可能已经在你的社区找到了一些，如果没有，你可以尝试开始一个。即使只有另一个人和你一起做，也会帮助你获得支持和鼓励，让你开始行动起来。 22 | 23 | 如果你不在一个容易与志同道合的人聚在一起的地方，可以去论坛，因为人们总是在组建虚拟学习小组。这些通常涉及一群人每周一次通过视频聊天讨论一个深度学习主题。 24 | 25 | 希望到这一点，你已经有了一些小项目和实验。我们建议你的下一步是选择其中一个，并尽可能把它做得更好。真正把它打磨成你能做到的最好作品——一件让你真正自豪的作品。这将迫使你更深入地了解一个主题，测试你的理解，并让你看到当你全力以赴时你能做到什么。 26 | 27 | 此外，您可能想看一下[fast.ai 免费在线课程](https://course.fast.ai)，它涵盖了与本书相同的内容。有时，以两种方式看同样的材料确实有助于澄清思路。事实上，人类学习研究人员发现，学习材料的最佳方式之一是从不同角度看同一件事，用不同的方式描述。 28 | 29 | 如果您选择接受最后的任务，那就是把这本书送给您认识的某人，并帮助另一个人开始他们自己的深度学习之旅！ 30 | -------------------------------------------------------------------------------- /translations/cn/img/dlcf_0101.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fastai/fastbook/e8baa81d89f0b7be816e35f1cc813ac02038db54/translations/cn/img/dlcf_0101.png -------------------------------------------------------------------------------- /translations/cn/img/dlcf_0102.png: 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mpl.rcParams['figure.dpi']= 200 11 | mpl.rcParams['savefig.dpi']= 200 12 | mpl.rcParams['font.size']=12 13 | 14 | set_seed(42) 15 | torch.backends.cudnn.deterministic = True 16 | torch.backends.cudnn.benchmark = False 17 | pd.set_option('display.max_columns',999) 18 | np.set_printoptions(linewidth=200) 19 | torch.set_printoptions(linewidth=200) 20 | 21 | import graphviz 22 | def gv(s): return graphviz.Source('digraph G{ rankdir="LR"' + s + '; }') 23 | 24 | def get_image_files_sorted(path, recurse=True, folders=None): return get_image_files(path, recurse, folders).sorted() 25 | 26 | 27 | # + 28 | # pip install azure-cognitiveservices-search-imagesearch 29 | 30 | from azure.cognitiveservices.search.imagesearch import ImageSearchClient as api 31 | from msrest.authentication import CognitiveServicesCredentials as auth 32 | 33 | def search_images_bing(key, term, min_sz=128, max_images=150): 34 | params = {'q':term, 'count':max_images, 'minHeight':min_sz, 'minWidth':min_sz, 'imageType':'photo'} 35 | headers = {"Ocp-Apim-Subscription-Key":key} 36 | search_url = "https://api.bing.microsoft.com/v7.0/images/search" 37 | response = requests.get(search_url, headers=headers, params=params) 38 | response.raise_for_status() 39 | search_results = response.json() 40 | return L(search_results['value']) 41 | 42 | 43 | # - 44 | 45 | def search_images_ddg(key,max_n=200): 46 | """Search for 'key' with DuckDuckGo and return a unique urls of 'max_n' images 47 | (Adopted from https://github.com/deepanprabhu/duckduckgo-images-api) 48 | """ 49 | url = 'https://duckduckgo.com/' 50 | params = {'q':key} 51 | res = requests.post(url,data=params) 52 | searchObj = re.search(r'vqd=([\d-]+)\&',res.text) 53 | if not searchObj: print('Token Parsing Failed !'); return 54 | requestUrl = url + 'i.js' 55 | headers = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:71.0) Gecko/20100101 Firefox/71.0'} 56 | params = (('l','us-en'),('o','json'),('q',key),('vqd',searchObj.group(1)),('f',',,,'),('p','1'),('v7exp','a')) 57 | urls = [] 58 | while True: 59 | try: 60 | res = requests.get(requestUrl,headers=headers,params=params) 61 | data = json.loads(res.text) 62 | for obj in data['results']: 63 | urls.append(obj['image']) 64 | max_n = max_n - 1 65 | if max_n < 1: return L(set(urls)) # dedupe 66 | if 'next' not in data: return L(set(urls)) 67 | requestUrl = url + data['next'] 68 | except: 69 | pass 70 | 71 | 72 | def plot_function(f, tx=None, ty=None, title=None, min=-2, max=2, figsize=(6,4)): 73 | x = torch.linspace(min,max) 74 | fig,ax = plt.subplots(figsize=figsize) 75 | ax.plot(x,f(x)) 76 | if tx is not None: ax.set_xlabel(tx) 77 | if ty is not None: ax.set_ylabel(ty) 78 | if title is not None: ax.set_title(title) 79 | 80 | # + 81 | from sklearn.tree import export_graphviz 82 | 83 | def draw_tree(t, df, size=10, ratio=0.6, precision=0, **kwargs): 84 | s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True, rounded=True, 85 | special_characters=True, rotate=False, precision=precision, **kwargs) 86 | return graphviz.Source(re.sub('Tree {', f'Tree {{ size={size}; ratio={ratio}', s)) 87 | 88 | 89 | # + 90 | from scipy.cluster import hierarchy as hc 91 | 92 | def cluster_columns(df, figsize=(10,6), font_size=12): 93 | corr = np.round(scipy.stats.spearmanr(df).correlation, 4) 94 | corr_condensed = hc.distance.squareform(1-corr) 95 | z = hc.linkage(corr_condensed, method='average') 96 | fig = plt.figure(figsize=figsize) 97 | hc.dendrogram(z, labels=df.columns, orientation='left', leaf_font_size=font_size) 98 | plt.show() 99 | --------------------------------------------------------------------------------