From ee86578a9bdc1fd32bc0e882d53b6f11b8cb4d0f Mon Sep 17 00:00:00 2001
From: Ama <85511496+amaboh@users.noreply.github.com>
Date: Fri, 19 May 2023 07:27:15 +0100
Subject: [PATCH] Typo error: np.clip() method for numpy refered to Pytorch
 instead of torch.clib()

---
 .devcontainer/Dockerfile                      |  12 +
 .devcontainer/devcontainer.json               |  21 +
 .devcontainer/requirements.txt                |  17 +
 .devcontainer/welcome-message.txt             |   7 +
 01-jupyter-notebook-101.ipynb                 |   2 +-
 04-how-does-a-neural-net-really-work.ipynb    | 863 +++++++++++++++++-
 ...ar-model-and-neural-net-from-scratch.ipynb |   2 +-
 README.md                                     |   1 +
 getting-started-with-codespaces.md            |  30 +
 9 files changed, 952 insertions(+), 3 deletions(-)
 create mode 100644 .devcontainer/Dockerfile
 create mode 100644 .devcontainer/devcontainer.json
 create mode 100644 .devcontainer/requirements.txt
 create mode 100644 .devcontainer/welcome-message.txt
 create mode 100644 getting-started-with-codespaces.md

diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
new file mode 100644
index 0000000000..1174208462
--- /dev/null
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,12 @@
+FROM mcr.microsoft.com/devcontainers/python:0-3.10
+
+COPY requirements.txt /tmp/
+
+RUN echo "(*) Installing tools..." \
+    && su - vscode -c "pip install -r /tmp/requirements.txt" \
+    && apt-get update \
+    && apt-get -y install --no-install-recommends graphviz
+
+ENV PATH="/home/vscode/.local/bin:${PATH}"
+
+COPY welcome-message.txt /usr/local/etc/vscode-dev-containers/first-run-notice.txt
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000000..7b7928701a
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,21 @@
+// For more details, see https://aka.ms/devcontainer.json.
+{
+    "build": {
+        "context": ".",
+        "dockerfile": "Dockerfile"
+    },
+
+    // Uncomment to install NVIDIA CUDA - required for a GPU-powered codespace. For more details, see https://github.com/fastai/course22/blob/master/getting-started-with-codespaces.md#gpu-powered-codespaces
+    // "features": {
+    //     "ghcr.io/devcontainers/features/nvidia-cuda:1": {
+    //         "installCudnn": true
+    //     }
+    // },
+
+    "hostRequirements": {
+        "storage": "64gb"
+    },
+    "runArgs": [
+        "--shm-size=16g"
+    ]
+}
diff --git a/.devcontainer/requirements.txt b/.devcontainer/requirements.txt
new file mode 100644
index 0000000000..5d09ceca78
--- /dev/null
+++ b/.devcontainer/requirements.txt
@@ -0,0 +1,17 @@
+duckduckgo_search
+fastai
+fastkaggle
+gradio
+graphviz
+ipywidgets
+jupyterlab
+kaggle
+nbdev
+plotly
+seaborn
+statsmodels
+sympy
+timm
+torch
+torchvision
+transformers
\ No newline at end of file
diff --git a/.devcontainer/welcome-message.txt b/.devcontainer/welcome-message.txt
new file mode 100644
index 0000000000..c23139dbad
--- /dev/null
+++ b/.devcontainer/welcome-message.txt
@@ -0,0 +1,7 @@
+👋 Welcome to "Practical Deep Learning for Coders" in Codespaces!
+
+🛠️  Your environment is fully setup with all the required software and machine learning libraries.
+
+🚀 To get started, either open the notebook file in the VS Code editor,
+   or open this Codespace with "Open in Jupyterlab" at https://github.com/codespaces
+
diff --git a/01-jupyter-notebook-101.ipynb b/01-jupyter-notebook-101.ipynb
index 4df3292802..cabb464f71 100644
--- a/01-jupyter-notebook-101.ipynb
+++ b/01-jupyter-notebook-101.ipynb
@@ -1 +1 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Introduction","metadata":{}},{"cell_type":"markdown","source":"Let's build up from the basics: what is a Jupyter Notebook? A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this:","metadata":{}},{"cell_type":"code","source":"1+1","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:00:06.294808Z","iopub.execute_input":"2022-04-24T20:00:06.295342Z","iopub.status.idle":"2022-04-24T20:00:06.32586Z","shell.execute_reply.started":"2022-04-24T20:00:06.295213Z","shell.execute_reply":"2022-04-24T20:00:06.324876Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. \n\nTry it yourself now. Click \"Copy & Edit\" in the top right to get your own editable version of this notebook, then click the cell above and hit `Shift`-`Enter`.\n\nOther renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists [use Jupyter Notebooks](https://paulromer.net/jupyter-mathematica-and-the-future-of-the-research-paper/)  for their experiments and some suggest that Jupyter Notebooks will be the [new format for research papers](https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/).\n","metadata":{}},{"cell_type":"markdown","source":"## Writing","metadata":{}},{"cell_type":"markdown","source":"A type of cell in which you can write text is called a _Markdown cell_. [_Markdown_](https://en.wikipedia.org/wiki/Markdown) is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown.\n\nClick the '+ Markdown' button below. Now you can type your first Markdown cell. Write 'My first markdown cell' and press run.\n\nYou should see something like this:","metadata":{}},{"cell_type":"markdown","source":"My first markdown cell","metadata":{}},{"cell_type":"markdown","source":"Now try making your first _Code_ cell: follow the same steps as before but click \"+ Code\". Type something like 3/2. You should see '1.5' as output.","metadata":{}},{"cell_type":"code","source":"3/2","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:01:13.917336Z","iopub.execute_input":"2022-04-24T20:01:13.917666Z","iopub.status.idle":"2022-04-24T20:01:13.92343Z","shell.execute_reply.started":"2022-04-24T20:01:13.917621Z","shell.execute_reply":"2022-04-24T20:01:13.922665Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Modes","metadata":{}},{"cell_type":"markdown","source":"If you made a mistake in your *Markdown* cell and you have already run it, you will notice that you cannot edit it just by clicking on it. This is because you are in **Command Mode**. Jupyter Notebooks have two distinct modes:\n\n- Edit Mode:: Allows you to edit a cell's content.\n\n- Command Mode:: Allows you to edit the notebook as a whole and use keyboard shortcuts but not edit a cell's content. \n\nYou can toggle between these two by either pressing <kbd>ESC</kbd> and <kbd>Enter</kbd> or clicking outside a cell or inside it (you need to double click if it's a Markdown cell). You can always tell which mode you're on: the current cell will have a green border in **Edit Mode** and a blue border in **Command Mode**. Try it!\n","metadata":{}},{"cell_type":"markdown","source":"## Other Important Considerations","metadata":{}},{"cell_type":"markdown","source":"Your notebook is autosaved every 120 seconds. If you want to manually save it you can just press the \"save version\" button on the upper right corner.\n\nTo know if your *kernel* (the Python engine executing your instructions behind the scenes) is computing or not, you can check the icon to the left of your cell. If the dot spinning, it means that the kernel is working. If not, it is idle.\n\nThere are a couple of shortcuts you must know about which we use **all** the time (always in **Command Mode**). These are:\n\n- <kbd>Shift</kbd>+<kbd>Enter</kbd>: Run the code or markdown on a cell\n- <kbd>Up Arrow</kbd> / <kbd>Down Arrow</kbd>: Toggle across cells\n- <kbd>b</kbd>: Create new cell underneath this one\n- <kbd>0</kbd>+<kbd>0</kbd>: Reset Kernel\n\nYou can find more shortcuts by typing <kbd>h</kbd> (for help).\n\nYou may need to use shell commands, like `ls` or `cat` in a Jupyter Notebook environment. That is very easy to do: just type `!` before you shell command, like so:","metadata":{}},{"cell_type":"code","source":"!pwd","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:05:01.750722Z","iopub.execute_input":"2022-04-24T20:05:01.751028Z","iopub.status.idle":"2022-04-24T20:05:02.49447Z","shell.execute_reply.started":"2022-04-24T20:05:01.750992Z","shell.execute_reply":"2022-04-24T20:05:02.493731Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That's it. This is all you need to know to use Jupyter Notebooks. That said, we have more tips and tricks below... There's a lot you can do with notebooks -- in fact, we published [a whole book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) written entirely as notebooks!","metadata":{}},{"cell_type":"markdown","source":"## Markdown Formatting\n","metadata":{}},{"cell_type":"markdown","source":"### Images","metadata":{}},{"cell_type":"markdown","source":"Did you know that the Jupyter Notebook team won the highest honor for a software system, the ACM Software System Award?","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:e8fe737f-c301-41ec-a462-cf16cee3cbb7.png)","metadata":{},"attachments":{"e8fe737f-c301-41ec-a462-cf16cee3cbb7.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"And did you know that you can add images to your notebook, like I did in the cell above? To do so, just copy your image on your computer, and paste it into a markdown cell. You'll see something like this:\n\n    ![image.png](attachment:e8fe737f-c301-41ec-a462-cf16cee3cbb7.png)\n\nWhen you execute the cell, you'll see the image appear.","metadata":{}},{"cell_type":"markdown","source":"### Italics, Bold, Strikethrough, Inline, Blockquotes and Links","metadata":{}},{"cell_type":"markdown","source":"The five most important concepts to format your code appropriately when using Markdown are:\n    \n- *Italics*:: Surround your text with \\_ or \\*.\n\n- **Bold**:: Surround your text with \\__ or \\**.\n\n- `inline`:: Surround your text with \\`.\n\n- blockquote:: Place \\> before your text.\n\n- [Links](http://course-v3.fast.ai/):: Surround the text you want to link with \\[\\] and place the link adjacent to the text, surrounded with ().\n\n","metadata":{}},{"cell_type":"markdown","source":"### Headings","metadata":{}},{"cell_type":"markdown","source":"Notice that including a hashtag before the text in a markdown cell makes the text a heading. The number of hashtags you include will determine the priority of the header (# is level one, ## is level two, ### is level three and #### is level four). We will add three new cells with the + button on the left to see how every level of heading looks.\n\nIn the notebook, double click on some headings and find out what level they are!\n","metadata":{}},{"cell_type":"markdown","source":"### Lists","metadata":{}},{"cell_type":"markdown","source":"There are three types of lists in markdown.\n\nOrdered list:\n\n1. Step 1\n    2. Step 1B\n3. Step 3\n\nUnordered list\n\n* learning rate\n* cycle length\n* weight decay\n\nTask list\n\n- [x] Learn Jupyter Notebooks\n    - [x] Writing\n    - [x] Modes\n    - [x] Other Considerations\n- [ ] Change the world\n\nIn the notebook, double click on them to see how they are built! \n","metadata":{}},{"cell_type":"markdown","source":"## Code Capabilities","metadata":{}},{"cell_type":"markdown","source":"**Code** cells are different than **Markdown** cells in that they have an output cell. This means that we can _keep_ the results of our code within the notebook and share them. Let's say we want to show a graph that explains the result of an experiment. We can just run the necessary cells and save the notebook. The output will be there when we open it again! Try it out by running the next two cells.","metadata":{}},{"cell_type":"code","source":"a = 1\nb = a + 1\nc = b + a + 1\nd = c + b + a + 1\na, b, c ,d","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:17:26.204129Z","iopub.execute_input":"2022-04-24T20:17:26.204587Z","iopub.status.idle":"2022-04-24T20:17:26.210903Z","shell.execute_reply.started":"2022-04-24T20:17:26.204552Z","shell.execute_reply":"2022-04-24T20:17:26.210032Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot([a,b,c,d])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:17:27.213474Z","iopub.execute_input":"2022-04-24T20:17:27.213767Z","iopub.status.idle":"2022-04-24T20:17:27.428066Z","shell.execute_reply.started":"2022-04-24T20:17:27.213738Z","shell.execute_reply":"2022-04-24T20:17:27.426435Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Running Jupyter Locally","metadata":{}},{"cell_type":"markdown","source":"This notebook is running inside Kaggle. You can also run notebooks inside other cloud environments such as [Colab](https://colab.research.google.com), [Sagemaker Studio Lab](https://studiolab.sagemaker.aws/), and [Paperspace Gradient](https://gradient.run/notebooks). Or you can run a Jupyter Notebook server from your local computer. What's more, if you have installed Anaconda you don't even need to install Jupyter (if not, just `pip install jupyter`).\n\nYou just need to run `jupyter notebook` in your terminal. Remember to run it from a folder that contains all the folders/files you will want to access. You will be able to open, view, and edit files located within the directory in which you run this command but not files in parent directories.\n\nIf a browser tab does not open automatically once you run the command, you should CTRL+CLICK the link starting with 'http://localhost:' and this will open a new tab in your default browser.\n","metadata":{}},{"cell_type":"markdown","source":"## Shortcuts and Tricks","metadata":{}},{"cell_type":"markdown","source":"Here is a list of useful tricks when in a Jupyter Notebook. Make sure you learn them early and use them as often as you can!\n","metadata":{}},{"cell_type":"markdown","source":"### Command Mode Shortcuts","metadata":{}},{"cell_type":"markdown","source":"There are a couple of useful keyboard shortcuts in `Command Mode` that you can leverage to make Jupyter Notebook faster to use. Remember that you can switch back and forth between `Command Mode` and `Edit Mode` with <kbd>Esc</kbd> and <kbd>Enter</kbd>.\n\n- m:: Convert cell to Markdown\n- y:: Convert cell to Code\n- d+d:: Delete cell\n- o:: Toggle between hide or show output\n- Shift+Arrow up/Arrow down:: Select multiple cells. Once you have selected them you can operate on them like a batch (run, copy, paste etc).\n- Shift+M:: Merge selected cells\n","metadata":{}},{"cell_type":"markdown","source":"### Cell Tricks","metadata":{}},{"cell_type":"markdown","source":"There are also some tricks that you can code into a cell:\n\n- `?function-name`:: Shows the definition and docstring for that function\n- `??function-name`:: Shows the source code for that function\n- `doc(function-name)`:: Shows the definition, docstring **and links to the documentation** of the function\n(only works with fastai library imported)\n- Shift+Tab (press once):: See which parameters to pass to a function \n- Shift+Tab (press three times):: Get additional information on the method\n\nHere's an example of using `?` to learn about Python's `print()` function:","metadata":{}},{"cell_type":"code","source":"?print","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:19:08.469088Z","iopub.execute_input":"2022-04-24T20:19:08.469889Z","iopub.status.idle":"2022-04-24T20:19:08.474222Z","shell.execute_reply.started":"2022-04-24T20:19:08.469852Z","shell.execute_reply":"2022-04-24T20:19:08.473469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Line Magics","metadata":{}},{"cell_type":"markdown","source":"Line magics are functions that you can run on cells. They should be at the beginning of a line and take as an argument the rest of the line from where they are called. You call them by placing a '%' sign before the command. The most useful ones are:\n\n- `%matplotlib inline`:: Ensures that all matplotlib plots will be plotted in the output cell within the notebook and will be kept in the notebook when saved.\n\nThis command is always called together at the beginning of every notebook of the fast.ai course.\n\n``` python\n%matplotlib inline\n```\n\n- `%timeit`:: Runs a line ten thousand times and displays the average time it took to run.","metadata":{}},{"cell_type":"code","source":"%timeit [i+1 for i in range(1000)]","metadata":{"execution":{"iopub.status.busy":"2022-04-24T20:10:13.839549Z","iopub.execute_input":"2022-04-24T20:10:13.839892Z","iopub.status.idle":"2022-04-24T20:10:18.121665Z","shell.execute_reply.started":"2022-04-24T20:10:13.839856Z","shell.execute_reply":"2022-04-24T20:10:18.120991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"`%debug`: Inspects a function which is showing an error using the [Python debugger](https://docs.python.org/3/library/pdb.html). If you type this in a cell just after an error, you will be directed to a console where you can inspect the values of all the variables.\n","metadata":{}},{"cell_type":"markdown","source":"## Thanks for reading!","metadata":{}},{"cell_type":"markdown","source":"If you found this notebook useful, I'd greatly appreciate an upvote (on my original notebook [here](https://www.kaggle.com/code/jhoward/jupyter-notebook-101), not on the copy you made of it!) Don't hesitate to add a comment if you have any questions or thoughts to add, or have your own favorite Jupyter tips.","metadata":{}}]}
\ No newline at end of file
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Introduction"]},{"cell_type":"markdown","metadata":{},"source":["Let's build up from the basics: what is a Jupyter Notebook? A notebook is a document made of cells. You can write in some of them (markdown cells) or you can perform calculations in Python (code cells) and run them like this:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:00:06.295342Z","iopub.status.busy":"2022-04-24T20:00:06.294808Z","iopub.status.idle":"2022-04-24T20:00:06.32586Z","shell.execute_reply":"2022-04-24T20:00:06.324876Z","shell.execute_reply.started":"2022-04-24T20:00:06.295213Z"},"trusted":true},"outputs":[],"source":["1+1"]},{"cell_type":"markdown","metadata":{},"source":["Cool, huh? This combination of prose and code makes Jupyter Notebook ideal for experimentation: we can see the rationale for each experiment, the code, and the results in one comprehensive document. \n","\n","Try it yourself now. Click \"Copy & Edit\" in the top right to get your own editable version of this notebook, then click the cell above and hit `Shift`-`Enter`.\n","\n","Other renowned institutions in academia and industry use Jupyter Notebook, including Google, Microsoft, IBM, Bloomberg, Berkeley and NASA among others. Even Nobel-winning economists [use Jupyter Notebooks](https://paulromer.net/jupyter-mathematica-and-the-future-of-the-research-paper/)  for their experiments and some suggest that Jupyter Notebooks will be the [new format for research papers](https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/).\n"]},{"cell_type":"markdown","metadata":{},"source":["## Writing"]},{"cell_type":"markdown","metadata":{},"source":["A type of cell in which you can write text is called a _Markdown cell_. [_Markdown_](https://en.wikipedia.org/wiki/Markdown) is a very popular markup language. To specify that a cell is Markdown you need to click in the drop-down menu in the toolbar and select Markdown.\n","\n","Click the '+ Markdown' button below. Now you can type your first Markdown cell. Write 'My first markdown cell' and press run.\n","\n","You should see something like this:"]},{"cell_type":"markdown","metadata":{},"source":["My first markdown cell"]},{"cell_type":"markdown","metadata":{},"source":["Now try making your first _Code_ cell: follow the same steps as before but click \"+ Code\". Type something like 3/2. You should see '1.5' as output."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:01:13.917666Z","iopub.status.busy":"2022-04-24T20:01:13.917336Z","iopub.status.idle":"2022-04-24T20:01:13.92343Z","shell.execute_reply":"2022-04-24T20:01:13.922665Z","shell.execute_reply.started":"2022-04-24T20:01:13.917621Z"},"trusted":true},"outputs":[],"source":["3/2"]},{"cell_type":"markdown","metadata":{},"source":["## Modes"]},{"cell_type":"markdown","metadata":{},"source":["If you made a mistake in your *Markdown* cell and you have already run it, you will notice that you cannot edit it just by clicking on it. This is because you are in **Command Mode**. Jupyter Notebooks have two distinct modes:\n","\n","- Edit Mode:: Allows you to edit a cell's content.\n","\n","- Command Mode:: Allows you to edit the notebook as a whole and use keyboard shortcuts but not edit a cell's content. \n","\n","You can toggle between these two by either pressing <kbd>ESC</kbd> and <kbd>Enter</kbd> or clicking outside a cell or inside it (you need to double click if it's a Markdown cell). You can always tell which mode you're on: the current cell will have a green border in **Edit Mode** and a blue border in **Command Mode**. Try it!\n"]},{"cell_type":"markdown","metadata":{},"source":["## Other Important Considerations"]},{"cell_type":"markdown","metadata":{},"source":["Your notebook is autosaved every 120 seconds. If you want to manually save it you can just press the \"save version\" button on the upper right corner.\n","\n","To know if your *kernel* (the Python engine executing your instructions behind the scenes) is computing or not, you can check the icon to the left of your cell. If the dot spinning, it means that the kernel is working. If not, it is idle.\n","\n","There are a couple of shortcuts you must know about which we use **all** the time (always in **Command Mode**). These are:\n","\n","- <kbd>Shift</kbd>+<kbd>Enter</kbd>: Run the code or markdown on a cell\n","- <kbd>Up Arrow</kbd> / <kbd>Down Arrow</kbd>: Toggle across cells\n","- <kbd>b</kbd>: Create new cell underneath this one\n","- <kbd>0</kbd>+<kbd>0</kbd>: Reset Kernel\n","\n","You can find more shortcuts by typing <kbd>h</kbd> (for help).\n","\n","You may need to use shell commands, like `ls` or `cat` in a Jupyter Notebook environment. That is very easy to do: just type `!` before you shell command, like so:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:05:01.751028Z","iopub.status.busy":"2022-04-24T20:05:01.750722Z","iopub.status.idle":"2022-04-24T20:05:02.49447Z","shell.execute_reply":"2022-04-24T20:05:02.493731Z","shell.execute_reply.started":"2022-04-24T20:05:01.750992Z"},"trusted":true},"outputs":[],"source":["!pwd"]},{"cell_type":"markdown","metadata":{},"source":["That's it. This is all you need to know to use Jupyter Notebooks. That said, we have more tips and tricks below... There's a lot you can do with notebooks -- in fact, we published [a whole book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527) written entirely as notebooks!"]},{"cell_type":"markdown","metadata":{},"source":["## Markdown Formatting\n"]},{"cell_type":"markdown","metadata":{},"source":["### Images"]},{"cell_type":"markdown","metadata":{},"source":["Did you know that the Jupyter Notebook team won the highest honor for a software system, the ACM Software System Award?"]},{"attachments":{"e8fe737f-c301-41ec-a462-cf16cee3cbb7.png":{"image/png":"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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:e8fe737f-c301-41ec-a462-cf16cee3cbb7.png)"]},{"cell_type":"markdown","metadata":{},"source":["And did you know that you can add images to your notebook, like I did in the cell above? To do so, just copy your image on your computer, and paste it into a markdown cell. You'll see something like this:\n","\n","    ![image.png](attachment:e8fe737f-c301-41ec-a462-cf16cee3cbb7.png)\n","\n","When you execute the cell, you'll see the image appear."]},{"cell_type":"markdown","metadata":{},"source":["### Italics, Bold, Strikethrough, Inline, Blockquotes and Links"]},{"cell_type":"markdown","metadata":{},"source":["The five most important concepts to format your code appropriately when using Markdown are:\n","    \n","- *Italics*:: Surround your text with \\_ or \\*.\n","\n","- **Bold**:: Surround your text with \\__ or \\**.\n","\n","- `inline`:: Surround your text with \\`.\n","\n","- blockquote:: Place \\> before your text.\n","\n","- [Links](http://course-v3.fast.ai/):: Surround the text you want to link with \\[\\] and place the link adjacent to the text, surrounded with ().\n","\n"]},{"cell_type":"markdown","metadata":{},"source":["### Headings"]},{"cell_type":"markdown","metadata":{},"source":["Notice that including a hashtag before the text in a markdown cell makes the text a heading. The number of hashtags you include will determine the priority of the header (# is level one, ## is level two, ### is level three and #### is level four). We will add three new cells with the + button on the left to see how every level of heading looks.\n","\n","In the notebook, double click on some headings and find out what level they are!\n"]},{"cell_type":"markdown","metadata":{},"source":["### Lists"]},{"cell_type":"markdown","metadata":{},"source":["There are three types of lists in markdown.\n","\n","Ordered list:\n","\n","1. Step 1\n","    2. Step 1B\n","3. Step 3\n","\n","Unordered list\n","\n","* learning rate\n","* cycle length\n","* weight decay\n","\n","Task list\n","\n","- [x] Learn Jupyter Notebooks\n","    - [x] Writing\n","    - [x] Modes\n","    - [x] Other Considerations\n","- [ ] Change the world\n","\n","In the notebook, double click on them to see how they are built! \n"]},{"cell_type":"markdown","metadata":{},"source":["## Code Capabilities"]},{"cell_type":"markdown","metadata":{},"source":["**Code** cells are different than **Markdown** cells in that they have an output cell. This means that we can _keep_ the results of our code within the notebook and share them. Let's say we want to show a graph that explains the result of an experiment. We can just run the necessary cells and save the notebook. The output will be there when we open it again! Try it out by running the next two cells."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:17:26.204587Z","iopub.status.busy":"2022-04-24T20:17:26.204129Z","iopub.status.idle":"2022-04-24T20:17:26.210903Z","shell.execute_reply":"2022-04-24T20:17:26.210032Z","shell.execute_reply.started":"2022-04-24T20:17:26.204552Z"},"trusted":true},"outputs":[],"source":["a = 1\n","b = a + 1\n","c = b + a + 1\n","d = c + b + a + 1\n","a, b, c ,d"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:17:27.213767Z","iopub.status.busy":"2022-04-24T20:17:27.213474Z","iopub.status.idle":"2022-04-24T20:17:27.428066Z","shell.execute_reply":"2022-04-24T20:17:27.426435Z","shell.execute_reply.started":"2022-04-24T20:17:27.213738Z"},"trusted":true},"outputs":[],"source":["import matplotlib.pyplot as plt\n","\n","plt.plot([a,b,c,d])\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["## Running Jupyter Locally"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["This notebook is running inside Kaggle or a [GitHub Codespace](https://github.com/features/codespaces). You can also run notebooks inside other cloud environments such as [Colab](https://colab.research.google.com), [Sagemaker Studio Lab](https://studiolab.sagemaker.aws/), and [Paperspace Gradient](https://gradient.run/notebooks). Or you can run a Jupyter Notebook server from your local computer. What's more, if you have installed Anaconda you don't even need to install Jupyter (if not, just `pip install jupyter`).\n","\n","You just need to run `jupyter notebook` in your terminal. Remember to run it from a folder that contains all the folders/files you will want to access. You will be able to open, view, and edit files located within the directory in which you run this command but not files in parent directories.\n","\n","If a browser tab does not open automatically once you run the command, you should CTRL+CLICK the link starting with 'http://localhost:' and this will open a new tab in your default browser.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Shortcuts and Tricks"]},{"cell_type":"markdown","metadata":{},"source":["Here is a list of useful tricks when in a Jupyter Notebook. Make sure you learn them early and use them as often as you can!\n"]},{"cell_type":"markdown","metadata":{},"source":["### Command Mode Shortcuts"]},{"cell_type":"markdown","metadata":{},"source":["There are a couple of useful keyboard shortcuts in `Command Mode` that you can leverage to make Jupyter Notebook faster to use. Remember that you can switch back and forth between `Command Mode` and `Edit Mode` with <kbd>Esc</kbd> and <kbd>Enter</kbd>.\n","\n","- m:: Convert cell to Markdown\n","- y:: Convert cell to Code\n","- d+d:: Delete cell\n","- o:: Toggle between hide or show output\n","- Shift+Arrow up/Arrow down:: Select multiple cells. Once you have selected them you can operate on them like a batch (run, copy, paste etc).\n","- Shift+M:: Merge selected cells\n"]},{"cell_type":"markdown","metadata":{},"source":["### Cell Tricks"]},{"cell_type":"markdown","metadata":{},"source":["There are also some tricks that you can code into a cell:\n","\n","- `?function-name`:: Shows the definition and docstring for that function\n","- `??function-name`:: Shows the source code for that function\n","- `doc(function-name)`:: Shows the definition, docstring **and links to the documentation** of the function\n","(only works with fastai library imported)\n","- Shift+Tab (press once):: See which parameters to pass to a function \n","- Shift+Tab (press three times):: Get additional information on the method\n","\n","Here's an example of using `?` to learn about Python's `print()` function:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:19:08.469889Z","iopub.status.busy":"2022-04-24T20:19:08.469088Z","iopub.status.idle":"2022-04-24T20:19:08.474222Z","shell.execute_reply":"2022-04-24T20:19:08.473469Z","shell.execute_reply.started":"2022-04-24T20:19:08.469852Z"},"trusted":true},"outputs":[],"source":["?print"]},{"cell_type":"markdown","metadata":{},"source":["### Line Magics"]},{"cell_type":"markdown","metadata":{},"source":["Line magics are functions that you can run on cells. They should be at the beginning of a line and take as an argument the rest of the line from where they are called. You call them by placing a '%' sign before the command. The most useful ones are:\n","\n","- `%matplotlib inline`:: Ensures that all matplotlib plots will be plotted in the output cell within the notebook and will be kept in the notebook when saved.\n","\n","This command is always called together at the beginning of every notebook of the fast.ai course.\n","\n","``` python\n","%matplotlib inline\n","```\n","\n","- `%timeit`:: Runs a line ten thousand times and displays the average time it took to run."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-04-24T20:10:13.839892Z","iopub.status.busy":"2022-04-24T20:10:13.839549Z","iopub.status.idle":"2022-04-24T20:10:18.121665Z","shell.execute_reply":"2022-04-24T20:10:18.120991Z","shell.execute_reply.started":"2022-04-24T20:10:13.839856Z"},"trusted":true},"outputs":[],"source":["%timeit [i+1 for i in range(1000)]"]},{"cell_type":"markdown","metadata":{},"source":["`%debug`: Inspects a function which is showing an error using the [Python debugger](https://docs.python.org/3/library/pdb.html). If you type this in a cell just after an error, you will be directed to a console where you can inspect the values of all the variables.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Thanks for reading!"]},{"cell_type":"markdown","metadata":{},"source":["If you found this notebook useful, I'd greatly appreciate an upvote (on my original notebook [here](https://www.kaggle.com/code/jhoward/jupyter-notebook-101), not on the copy you made of it!) Don't hesitate to add a comment if you have any questions or thoughts to add, or have your own favorite Jupyter tips."]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.4"}},"nbformat":4,"nbformat_minor":4}
diff --git a/04-how-does-a-neural-net-really-work.ipynb b/04-how-does-a-neural-net-really-work.ipynb
index 41e13c6499..4a72c2d123 100644
--- a/04-how-does-a-neural-net-really-work.ipynb
+++ b/04-how-does-a-neural-net-really-work.ipynb
@@ -1 +1,862 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**Important**: The interactive features of this notebook don't work in Kaggle's *Reader* mode. They only work in *Edit* mode. Therefore, before starting reading this, please click \"**Copy & Edit**\" in the top right of this window, then in the menu click *Run* and then *Run all*. Then you'll be able to use all the interactive sliders in this notebook.","metadata":{}},{"cell_type":"markdown","source":"## Fitting a function with *gradient descent*","metadata":{}},{"cell_type":"markdown","source":"A neural network is just a mathematical function. In the most standard kind of neural network, the function:\n\n1. Multiplies each input by a number of values. These values are known as *parameters*\n1. Adds them up for each group of values\n1. Replaces the negative numbers with zeros\n\nThis represents one \"layer\". Then these three steps are repeated, using the outputs of the previous layer as the inputs to the next layer. Initially, the parameters in this function are selected randomly. Therefore a newly created neural network doesn't do anything useful at all -- it's just random!\n\nTo get the function to \"learn\" to do something useful, we have to change the parameters to make them \"better\" in some way. We do this using *gradient descent*. Let's see how this works...","metadata":{}},{"cell_type":"code","source":"from ipywidgets import interact\nfrom fastai.basics import *\n\nplt.rc('figure', dpi=90)\n\ndef plot_function(f, title=None, min=-2.1, max=2.1, color='r', ylim=None):\n    x = torch.linspace(min,max, 100)[:,None]\n    if ylim: plt.ylim(ylim)\n    plt.plot(x, f(x), color)\n    if title is not None: plt.title(title)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-04-23T08:54:34.585263Z","iopub.execute_input":"2022-04-23T08:54:34.587766Z","iopub.status.idle":"2022-04-23T08:54:36.961606Z","shell.execute_reply.started":"2022-04-23T08:54:34.587646Z","shell.execute_reply":"2022-04-23T08:54:36.960846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To learn how gradient descent works, we're going to start by fitting a quadratic, since that's a function most of us are probably more familiar with than a neural network. Here's the quadratic we're going to try to fit:","metadata":{}},{"cell_type":"code","source":"def f(x): return 3*x**2 + 2*x + 1\n\nplot_function(f, \"$3x^2 + 2x + 1$\")","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:36.963135Z","iopub.execute_input":"2022-04-23T08:54:36.96361Z","iopub.status.idle":"2022-04-23T08:54:37.603953Z","shell.execute_reply.started":"2022-04-23T08:54:36.963574Z","shell.execute_reply":"2022-04-23T08:54:37.60309Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This quadratic is of the form $ax^2+bx+c$, with parameters $a=3$, $b=2$, $c=1$. To make it easier to try out different quadratics for fitting a model to the data we'll create, let's create a function that calculates the value of a point on any quadratic:","metadata":{}},{"cell_type":"code","source":"def quad(a, b, c, x): return a*x**2 + b*x + c","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.605073Z","iopub.execute_input":"2022-04-23T08:54:37.605295Z","iopub.status.idle":"2022-04-23T08:54:37.610024Z","shell.execute_reply.started":"2022-04-23T08:54:37.605268Z","shell.execute_reply":"2022-04-23T08:54:37.609103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"If we fix some particular values of a, b, and c, then we'll have made a quadratic. To fix values passed to a function in python, we use the `partial` function, like so:","metadata":{}},{"cell_type":"code","source":"def mk_quad(a,b,c): return partial(quad, a,b,c)","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.611932Z","iopub.execute_input":"2022-04-23T08:54:37.612415Z","iopub.status.idle":"2022-04-23T08:54:37.622926Z","shell.execute_reply.started":"2022-04-23T08:54:37.612377Z","shell.execute_reply":"2022-04-23T08:54:37.622077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"So for instance, we can recreate our previous quadratic:","metadata":{}},{"cell_type":"code","source":"f2 = mk_quad(3,2,1)\nplot_function(f2)","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.624649Z","iopub.execute_input":"2022-04-23T08:54:37.625357Z","iopub.status.idle":"2022-04-23T08:54:37.829696Z","shell.execute_reply.started":"2022-04-23T08:54:37.62531Z","shell.execute_reply":"2022-04-23T08:54:37.828997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now let's simulate making some noisy measurements of our quadratic `f`. We'll then use gradient descent to see if we can recreate the original function from the data.\n\nHere's a couple of functions to add some random noise to data:","metadata":{}},{"cell_type":"code","source":"def noise(x, scale): return np.random.normal(scale=scale, size=x.shape)\ndef add_noise(x, mult, add): return x * (1+noise(x,mult)) + noise(x,add)","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.831069Z","iopub.execute_input":"2022-04-23T08:54:37.831627Z","iopub.status.idle":"2022-04-23T08:54:37.838391Z","shell.execute_reply.started":"2022-04-23T08:54:37.831581Z","shell.execute_reply":"2022-04-23T08:54:37.837454Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's use the now to create our noisy measurements based on the quadratic above:","metadata":{}},{"cell_type":"code","source":"np.random.seed(42)\n\nx = torch.linspace(-2, 2, steps=20)[:,None]\ny = add_noise(f(x), 0.15, 1.5)","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.839727Z","iopub.execute_input":"2022-04-23T08:54:37.83997Z","iopub.status.idle":"2022-04-23T08:54:37.86166Z","shell.execute_reply.started":"2022-04-23T08:54:37.839939Z","shell.execute_reply":"2022-04-23T08:54:37.861067Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's the first few values of each of `x` and `y`:","metadata":{}},{"cell_type":"code","source":"x[:5],y[:5]","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.862576Z","iopub.execute_input":"2022-04-23T08:54:37.863311Z","iopub.status.idle":"2022-04-23T08:54:37.901746Z","shell.execute_reply.started":"2022-04-23T08:54:37.863275Z","shell.execute_reply":"2022-04-23T08:54:37.9009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As you can see, they're *tensors*. A tensor is just like an `array` in numpy (if you're not familiar with numpy, I strongly recommend reading [this great book](https://wesmckinney.com/book/), because it's a critical foundation for nearly all numeric programming in Python. Furthermore, PyTorch, which most researchers use for deep learning, is modeled closely on numpy.) A tensor can be a single number (a *scalar* or *rank-0 tensor*), a list of numbers (a *vector* or *rank-1 tensor*), a table of numbers (a *matrix* or *rank-0 tensor*), a table of tables of numbers (a *rank-3 tensor*), and so forth.\n\nWe're not going to learn much about our data by just looking at the raw numbers, so let's draw a picture:","metadata":{}},{"cell_type":"code","source":"plt.scatter(x,y);","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:37.903248Z","iopub.execute_input":"2022-04-23T08:54:37.903736Z","iopub.status.idle":"2022-04-23T08:54:38.141765Z","shell.execute_reply.started":"2022-04-23T08:54:37.903689Z","shell.execute_reply":"2022-04-23T08:54:38.140818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"How do we find values of a, b, and c which fit this data? One approach is to try a few values and see what fits. Here's a function which overlays a quadratic on top of our data, along with some sliders to change a, b, and c, and see how it looks:","metadata":{}},{"cell_type":"code","source":"@interact(a=1.1, b=1.1, c=1.1)\ndef plot_quad(a, b, c):\n    plt.scatter(x,y)\n    plot_function(mk_quad(a,b,c), ylim=(-3,13))","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:54:38.143544Z","iopub.execute_input":"2022-04-23T08:54:38.143753Z","iopub.status.idle":"2022-04-23T08:54:38.372553Z","shell.execute_reply.started":"2022-04-23T08:54:38.143725Z","shell.execute_reply":"2022-04-23T08:54:38.37172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Reminder**: If the sliders above aren't working for you, that's because the interactive features of this notebook don't work in Kaggle's *Reader* mode. They only work in *Edit* mode. Please click \"**Copy & Edit**\" in the top right of this window, then in the menu click *Run* and then *Run all*. Then you'll be able to use all the interactive sliders in this notebook.","metadata":{}},{"cell_type":"markdown","source":"Try moving slider `a` a bit to the left. Does that look better or worse? How about if you move it a bit to the right? Find out which direction seems to improve the fit of the quadratic to the data, and move the slider a bit in that direction. Next, do the same for slider `b`: first figure out which direction improves the fit, then move it a bit in that direction. Then do the same for `c`.\n\nOK, now go back to slider `a` and repeat the process. Do it again for `b` and `c` as well.\n\nDid you notice that by going back and doing the sliders a second time that you were able to improve things a bit further? That's an important insight -- it's only after changing `b` and `c`, for instance, that you realise that `a` actually needs some adjustment based on those new values.\n\nOne thing that's making this tricky is that we don't really have a great sense of whether our fit is really better or worse. It would be easier if we had a numeric measure of that. On easy metric we could use is *mean absolute error* -- which is the distance from each data point to the curve:","metadata":{}},{"cell_type":"code","source":"def mae(preds, acts): return (torch.abs(preds-acts)).mean()","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:55:06.428918Z","iopub.execute_input":"2022-04-23T08:55:06.429237Z","iopub.status.idle":"2022-04-23T08:55:06.433738Z","shell.execute_reply.started":"2022-04-23T08:55:06.429205Z","shell.execute_reply":"2022-04-23T08:55:06.432713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll update our interactive function to print this at the top for us.\n\nUse this to repeat the approach we took before to try to find the best fit, but this time just use the value of the metric to decide which direction to move each slider, and how far to move it.\n\nThis time around, try doing it in the opposite order: `c`, then `b`, then `a`.\n\nYou'll probably find that you have to go through the set of sliders a couple of times to get the best fit.","metadata":{}},{"cell_type":"code","source":"@interact(a=1.1, b=1.1, c=1.1)\ndef plot_quad(a, b, c):\n    f = mk_quad(a,b,c)\n    plt.scatter(x,y)\n    loss = mae(f(x), y)\n    plot_function(f, ylim=(-3,12), title=f\"MAE: {loss:.2f}\")","metadata":{"execution":{"iopub.status.busy":"2022-04-23T08:55:07.451295Z","iopub.execute_input":"2022-04-23T08:55:07.452152Z","iopub.status.idle":"2022-04-23T08:55:07.701428Z","shell.execute_reply.started":"2022-04-23T08:55:07.452102Z","shell.execute_reply":"2022-04-23T08:55:07.700643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In a modern neural network we'll often have tens of millions of parameters to fit, or more, and thousands or millions of data points to fit them to. We're not going to be able to do that by moving sliders around! We'll need to automate this process.\n\nThankfully, that turns out to be pretty straightforward. We can use calculus to figure out, for each parameter, whether we should increase or decrease it.\n\nUh oh, calculus! If you haven't touched calculus since school, you might be getting ready to run away at this point. But don't worry, we don't actually need much calculus at all. Just derivatives, which measure the rate of change of a function. We don't even need to calculate them ourselves, because the computer will do it for us! If you've forgotten what a derivitive is, then watch the first three of these fantastic [videos by Professor Dave](https://www.youtube.com/playlist?list=PLybg94GvOJ9ELZEe9s2NXTKr41Yedbw7M). It's only 15 minutes in total, so give it a go! Then come back here and we'll continue on our journey...","metadata":{}},{"cell_type":"markdown","source":"## Automating gradient descent","metadata":{}},{"cell_type":"markdown","source":"The basic idea is this: if we know the *gradient* of our `mae()` function *with respect to* our parameters, `a`, `b`, and `c`, then that means we know how adjusting (for instance) `a` will change the value of `mae()`. If, say, `a` has a *negative* gradient, then we know that increasing `a` will decrease `mae()`. Then we know that's what we need to do, since we trying to make `mae()` as low as possible.\n\nSo, we find the gradient of `mae()` for each of our parameters, and then adjust our parameters a bit in the *opposite* direction to the sign of the gradient.\n\nTo do this, first we need a function that takes all the parameters `a`, `b`, and `c` as a single vector input, and returns the value `mae()` based on those parameters:","metadata":{}},{"cell_type":"code","source":"def quad_mae(params):\n    f = mk_quad(*params)\n    return mae(f(x), y)","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.831976Z","iopub.execute_input":"2022-04-22T22:28:41.832211Z","iopub.status.idle":"2022-04-22T22:28:41.837761Z","shell.execute_reply.started":"2022-04-22T22:28:41.832181Z","shell.execute_reply":"2022-04-22T22:28:41.836892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's try it:","metadata":{}},{"cell_type":"code","source":"quad_mae([1.1, 1.1, 1.1])","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.839427Z","iopub.execute_input":"2022-04-22T22:28:41.839663Z","iopub.status.idle":"2022-04-22T22:28:41.853343Z","shell.execute_reply.started":"2022-04-22T22:28:41.839635Z","shell.execute_reply":"2022-04-22T22:28:41.852644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Yup, that's the same as the starting `mae()` we had in our plot before.\n\nWe're first going to do exactly the same thing as we did manually -- pick some arbritrary starting point for our parameters. We'll put them all into a single tensor:","metadata":{}},{"cell_type":"code","source":"abc = torch.tensor([1.1,1.1,1.1])","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.854708Z","iopub.execute_input":"2022-04-22T22:28:41.855001Z","iopub.status.idle":"2022-04-22T22:28:41.863677Z","shell.execute_reply.started":"2022-04-22T22:28:41.854965Z","shell.execute_reply":"2022-04-22T22:28:41.862424Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To tell PyTorch that we want it to calculate gradients for these parameters, we need to call `requires_grad_()`:","metadata":{}},{"cell_type":"code","source":"abc.requires_grad_()","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.865105Z","iopub.execute_input":"2022-04-22T22:28:41.86557Z","iopub.status.idle":"2022-04-22T22:28:41.881016Z","shell.execute_reply.started":"2022-04-22T22:28:41.865497Z","shell.execute_reply":"2022-04-22T22:28:41.879681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can now calculate `mae()`. Generally, when doing gradient descent, the thing we're trying to minimise is called the *loss*:","metadata":{}},{"cell_type":"code","source":"loss = quad_mae(abc)\nloss","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.882498Z","iopub.execute_input":"2022-04-22T22:28:41.882908Z","iopub.status.idle":"2022-04-22T22:28:41.894811Z","shell.execute_reply.started":"2022-04-22T22:28:41.882865Z","shell.execute_reply":"2022-04-22T22:28:41.893828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To get PyTorch to now calculate the gradients, we need to call `backward()`","metadata":{}},{"cell_type":"code","source":"loss.backward()","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.896144Z","iopub.execute_input":"2022-04-22T22:28:41.896493Z","iopub.status.idle":"2022-04-22T22:28:41.914719Z","shell.execute_reply.started":"2022-04-22T22:28:41.896462Z","shell.execute_reply":"2022-04-22T22:28:41.913717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The gradients will be stored for us in an attribute called `grad`:","metadata":{}},{"cell_type":"code","source":"abc.grad","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.916372Z","iopub.execute_input":"2022-04-22T22:28:41.91704Z","iopub.status.idle":"2022-04-22T22:28:41.925299Z","shell.execute_reply.started":"2022-04-22T22:28:41.916988Z","shell.execute_reply":"2022-04-22T22:28:41.924407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"According to these gradients, all our parameters are a little low. So let's increase them a bit. If we subtract the gradient, multiplied by a small number, that should improve them a bit:","metadata":{}},{"cell_type":"code","source":"with torch.no_grad():\n    abc -= abc.grad*0.01\n    loss = quad_mae(abc)\n    \nprint(f'loss={loss:.2f}')","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.927221Z","iopub.execute_input":"2022-04-22T22:28:41.927714Z","iopub.status.idle":"2022-04-22T22:28:41.941471Z","shell.execute_reply.started":"2022-04-22T22:28:41.927665Z","shell.execute_reply":"2022-04-22T22:28:41.940784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Yes, our loss has gone down!\n\nThe \"small number\" we multiply is called the *learning rate*, and is the most important *hyper-parameter* to set when training a neural network.\n\nBTW, you'll see we had to wrap our calculation of the new parameters in `with torch.no_grad()`. That disables the calculation of gradients for any operations inside that context manager. We have to do that, because `abc -= abc.grad*0.01` isn't actually part of our quadratic model, so we don't want derivitives to include that calculation.\n\nWe can use a loop to do a few more iterations of this:","metadata":{}},{"cell_type":"code","source":"for i in range(10):\n    loss = quad_mae(abc)\n    loss.backward()\n    with torch.no_grad(): abc -= abc.grad*0.01\n    print(f'step={i}; loss={loss:.2f}')","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.942822Z","iopub.execute_input":"2022-04-22T22:28:41.943242Z","iopub.status.idle":"2022-04-22T22:28:41.968186Z","shell.execute_reply.started":"2022-04-22T22:28:41.943202Z","shell.execute_reply":"2022-04-22T22:28:41.967081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As you can see, our loss keeps going down!\n\nIf you keep running this loop for long enough however, you'll see that the loss eventually starts increasing for a while. That's because once the parameters get close to the correct answer, our parameter updates will jump right over the correct answer! To avoid this, we need to decrease our learning rate as we train. This is done using a *learning rate schedule*, and can be automated in most deep learning frameworks, such as fastai and PyTorch.","metadata":{}},{"cell_type":"markdown","source":"## How a neural network approximates any given function","metadata":{}},{"cell_type":"markdown","source":"But neural nets are much more convenient and powerful than this example showed, because we can learn much more than just a quadratic with them. How does *that* work?\n\nThe trick is that a neural network is a very expressive function. In fact -- it's [infinitely expressive](https://en.wikipedia.org/wiki/Universal_approximation_theorem). A neural network can approximate any computable function, given enough parameters. A \"computable function\" can cover just about anything you can imagine: understand and translate human speech; paint a picture; diagnose a disease from medical imaging; write an essay; etc...\n\nThe way a neural network approximates a function actually turns out to be very simple. The key trick is to combine two extremely basic steps:\n\n1. Matrix multiplication, which is just multiplying things together and then adding them up\n1. The function $max(x,0)$, which simply replaces all negative numbers with zero.\n\nIn PyTorch, the function $max(x,0)$ is written as `np.clip(x,0)`. The combination of a linear function and this *max()* is called a *rectified linear function*, and it can be implemented like this:","metadata":{}},{"cell_type":"code","source":"def rectified_linear(m,b,x):\n    y = m*x+b\n    return torch.clip(y, 0.)","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.970051Z","iopub.execute_input":"2022-04-22T22:28:41.970814Z","iopub.status.idle":"2022-04-22T22:28:41.976113Z","shell.execute_reply.started":"2022-04-22T22:28:41.970763Z","shell.execute_reply":"2022-04-22T22:28:41.975338Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's what it looks like:","metadata":{}},{"cell_type":"code","source":"plot_function(partial(rectified_linear, 1,1))","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:41.977336Z","iopub.execute_input":"2022-04-22T22:28:41.978052Z","iopub.status.idle":"2022-04-22T22:28:42.197682Z","shell.execute_reply.started":"2022-04-22T22:28:41.978012Z","shell.execute_reply":"2022-04-22T22:28:42.196709Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"BTW, instead of `torch.clip(y, 0.)`, we can instead use `F.relu(x)`, which does exactly the same thing. In PyTorch, `F` refers to the `torch.nn.functional` module.","metadata":{}},{"cell_type":"code","source":"import torch.nn.functional as F\ndef rectified_linear2(m,b,x): return F.relu(m*x+b)\nplot_function(partial(rectified_linear2, 1,1))","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:42.199561Z","iopub.execute_input":"2022-04-22T22:28:42.200133Z","iopub.status.idle":"2022-04-22T22:28:42.40433Z","shell.execute_reply.started":"2022-04-22T22:28:42.200083Z","shell.execute_reply":"2022-04-22T22:28:42.403323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To understand how this function works, try using this interactive version to play around with the parameters `m` and `b`:","metadata":{}},{"cell_type":"code","source":"@interact(m=1.5, b=1.5)\ndef plot_relu(m, b):\n    plot_function(partial(rectified_linear, m,b), ylim=(-1,4))","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:42.405808Z","iopub.execute_input":"2022-04-22T22:28:42.406108Z","iopub.status.idle":"2022-04-22T22:28:42.61219Z","shell.execute_reply.started":"2022-04-22T22:28:42.406071Z","shell.execute_reply":"2022-04-22T22:28:42.61144Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As you see, `m` changes the slope, and `b` changes where the \"hook\" appears. This function doesn't do much on its own, but look what happens when we add two of them together:","metadata":{}},{"cell_type":"code","source":"def double_relu(m1,b1,m2,b2,x):\n    return rectified_linear(m1,b1,x) + rectified_linear(m2,b2,x)\n\n@interact(m1=-1.5, b1=-1.5, m2=1.5, b2=1.5)\ndef plot_double_relu(m1, b1, m2, b2):\n    plot_function(partial(double_relu, m1,b1,m2,b2), ylim=(-1,6))","metadata":{"execution":{"iopub.status.busy":"2022-04-22T22:28:42.613745Z","iopub.execute_input":"2022-04-22T22:28:42.614042Z","iopub.status.idle":"2022-04-22T22:28:42.851224Z","shell.execute_reply.started":"2022-04-22T22:28:42.614007Z","shell.execute_reply":"2022-04-22T22:28:42.85035Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"If you play around with that for a while, you notice something quite profound: with enough of these rectified linear functions added together, you could approximate any function with a single input, to whatever accuracy you like! Any time the function doesn't quite match, you can just add a few more additions to the mix to make it a bit closer. As an experiment, perhaps you'd like to try creating your own `plot_triple_relu` interactive function, and maybe even include the scatter plot of our data from before, to see how close you can get?\n\nThis exact same approach can be expanded to functions of 2, 3, or more parameters.","metadata":{}},{"cell_type":"markdown","source":"## How to recognise an owl","metadata":{}},{"cell_type":"markdown","source":"OK great, we've created a nifty little example showing that we can drawing squiggly lines that go through some points. So what?\n\nWell... the truth is that actually drawing squiggly lines (or planes, or high-dimensional hyperplanes...) through some points is literally *all that deep learning does*! If your data points are, say, the RGB values of pixels in photos of owls, then you can create an owl-recogniser model by following the exact steps above.\n\nThis may, at first, sound about as useful as the classic \"how to draw an owl\" guide:","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:c66592d3-c997-4c72-aed4-2dea579b96e1.png)","metadata":{},"attachments":{"c66592d3-c997-4c72-aed4-2dea579b96e1.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Students often ask me at this point \"OK Jeremy, but how do neural nets *actually work*\". But at a foundational level, there is no \"step 2\". We're done -- the above steps will, given enough time and enough data, create (for example) an owl recogniser, if you feed in enough owls (and non-owls).\n\nThe devil, I guess, is in the \"given enough time and enough data\" part of the above sentence. There's a *lot* of tweaks we can make to reduce both of these things. For instance, instead of running our calculations on a normal CPU, as we've done above, we could do thousands of them simultaneously by taking advantage of a GPU. We could greatly reduce the amount of computation and data needed by using a convolution instead of a matrix multiplication, which basically means skipping over a bunch of the multiplications and additions for bits that you'd guess won't be important. We could make things much faster if, instead of starting with random parameters, we start with parameters of someone else's model that does something similar to what we want (this is called *transfer learning*).\n\nAnd, of course, there's lots of helpful software out there to do this stuff for you without too much fuss. Like, say, [fastai](https://docs.fast.ai).\n\nLearning these things is what we teach in our [course](https://course.fast.ai), which, like everything we make, is totally free. So if you're interested in learning more, do check it out!","metadata":{}},{"cell_type":"markdown","source":"As always, if you enjoyed this notebook, please upvote it to help others find it, and to encourage me to write more. If you upvote it, be careful you don't accidentally upvote your copy that's created when you click \"Copy & Edit\" -- you can find my original at [this link](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work).","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: The interactive features of this notebook don't work in Kaggle's *Reader* mode. They only work in *Edit* mode. Therefore, before starting reading this, please click \"**Copy & Edit**\" in the top right of this window, then in the menu click *Run* and then *Run all*. Then you'll be able to use all the interactive sliders in this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fitting a function with *gradient descent*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A neural network is just a mathematical function. In the most standard kind of neural network, the function:\n",
+    "\n",
+    "1. Multiplies each input by a number of values. These values are known as *parameters*\n",
+    "1. Adds them up for each group of values\n",
+    "1. Replaces the negative numbers with zeros\n",
+    "\n",
+    "This represents one \"layer\". Then these three steps are repeated, using the outputs of the previous layer as the inputs to the next layer. Initially, the parameters in this function are selected randomly. Therefore a newly created neural network doesn't do anything useful at all -- it's just random!\n",
+    "\n",
+    "To get the function to \"learn\" to do something useful, we have to change the parameters to make them \"better\" in some way. We do this using *gradient descent*. Let's see how this works..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "_kg_hide-input": true,
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:34.587766Z",
+     "iopub.status.busy": "2022-04-23T08:54:34.585263Z",
+     "iopub.status.idle": "2022-04-23T08:54:36.961606Z",
+     "shell.execute_reply": "2022-04-23T08:54:36.960846Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:34.587646Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact\n",
+    "from fastai.basics import *\n",
+    "\n",
+    "plt.rc('figure', dpi=90)\n",
+    "\n",
+    "def plot_function(f, title=None, min=-2.1, max=2.1, color='r', ylim=None):\n",
+    "    x = torch.linspace(min,max, 100)[:,None]\n",
+    "    if ylim: plt.ylim(ylim)\n",
+    "    plt.plot(x, f(x), color)\n",
+    "    if title is not None: plt.title(title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To learn how gradient descent works, we're going to start by fitting a quadratic, since that's a function most of us are probably more familiar with than a neural network. Here's the quadratic we're going to try to fit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:36.96361Z",
+     "iopub.status.busy": "2022-04-23T08:54:36.963135Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.603953Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.60309Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:36.963574Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def f(x): return 3*x**2 + 2*x + 1\n",
+    "\n",
+    "plot_function(f, \"$3x^2 + 2x + 1$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This quadratic is of the form $ax^2+bx+c$, with parameters $a=3$, $b=2$, $c=1$. To make it easier to try out different quadratics for fitting a model to the data we'll create, let's create a function that calculates the value of a point on any quadratic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.605295Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.605073Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.610024Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.609103Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.605268Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def quad(a, b, c, x): return a*x**2 + b*x + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we fix some particular values of a, b, and c, then we'll have made a quadratic. To fix values passed to a function in python, we use the `partial` function, like so:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.612415Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.611932Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.622926Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.622077Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.612377Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def mk_quad(a,b,c): return partial(quad, a,b,c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So for instance, we can recreate our previous quadratic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.625357Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.624649Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.829696Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.828997Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.62531Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "f2 = mk_quad(3,2,1)\n",
+    "plot_function(f2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's simulate making some noisy measurements of our quadratic `f`. We'll then use gradient descent to see if we can recreate the original function from the data.\n",
+    "\n",
+    "Here's a couple of functions to add some random noise to data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.831627Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.831069Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.838391Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.837454Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.831581Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def noise(x, scale): return np.random.normal(scale=scale, size=x.shape)\n",
+    "def add_noise(x, mult, add): return x * (1+noise(x,mult)) + noise(x,add)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's use the now to create our noisy measurements based on the quadratic above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.83997Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.839727Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.86166Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.861067Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.839939Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)\n",
+    "\n",
+    "x = torch.linspace(-2, 2, steps=20)[:,None]\n",
+    "y = add_noise(f(x), 0.15, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the first few values of each of `x` and `y`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.863311Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.862576Z",
+     "iopub.status.idle": "2022-04-23T08:54:37.901746Z",
+     "shell.execute_reply": "2022-04-23T08:54:37.9009Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.863275Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x[:5],y[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, they're *tensors*. A tensor is just like an `array` in numpy (if you're not familiar with numpy, I strongly recommend reading [this great book](https://wesmckinney.com/book/), because it's a critical foundation for nearly all numeric programming in Python. Furthermore, PyTorch, which most researchers use for deep learning, is modeled closely on numpy.) A tensor can be a single number (a *scalar* or *rank-0 tensor*), a list of numbers (a *vector* or *rank-1 tensor*), a table of numbers (a *matrix* or *rank-0 tensor*), a table of tables of numbers (a *rank-3 tensor*), and so forth.\n",
+    "\n",
+    "We're not going to learn much about our data by just looking at the raw numbers, so let's draw a picture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:37.903736Z",
+     "iopub.status.busy": "2022-04-23T08:54:37.903248Z",
+     "iopub.status.idle": "2022-04-23T08:54:38.141765Z",
+     "shell.execute_reply": "2022-04-23T08:54:38.140818Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:37.903689Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(x,y);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do we find values of a, b, and c which fit this data? One approach is to try a few values and see what fits. Here's a function which overlays a quadratic on top of our data, along with some sliders to change a, b, and c, and see how it looks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:54:38.143753Z",
+     "iopub.status.busy": "2022-04-23T08:54:38.143544Z",
+     "iopub.status.idle": "2022-04-23T08:54:38.372553Z",
+     "shell.execute_reply": "2022-04-23T08:54:38.37172Z",
+     "shell.execute_reply.started": "2022-04-23T08:54:38.143725Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@interact(a=1.1, b=1.1, c=1.1)\n",
+    "def plot_quad(a, b, c):\n",
+    "    plt.scatter(x,y)\n",
+    "    plot_function(mk_quad(a,b,c), ylim=(-3,13))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Reminder**: If the sliders above aren't working for you, that's because the interactive features of this notebook don't work in Kaggle's *Reader* mode. They only work in *Edit* mode. Please click \"**Copy & Edit**\" in the top right of this window, then in the menu click *Run* and then *Run all*. Then you'll be able to use all the interactive sliders in this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try moving slider `a` a bit to the left. Does that look better or worse? How about if you move it a bit to the right? Find out which direction seems to improve the fit of the quadratic to the data, and move the slider a bit in that direction. Next, do the same for slider `b`: first figure out which direction improves the fit, then move it a bit in that direction. Then do the same for `c`.\n",
+    "\n",
+    "OK, now go back to slider `a` and repeat the process. Do it again for `b` and `c` as well.\n",
+    "\n",
+    "Did you notice that by going back and doing the sliders a second time that you were able to improve things a bit further? That's an important insight -- it's only after changing `b` and `c`, for instance, that you realise that `a` actually needs some adjustment based on those new values.\n",
+    "\n",
+    "One thing that's making this tricky is that we don't really have a great sense of whether our fit is really better or worse. It would be easier if we had a numeric measure of that. On easy metric we could use is *mean absolute error* -- which is the distance from each data point to the curve:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:55:06.429237Z",
+     "iopub.status.busy": "2022-04-23T08:55:06.428918Z",
+     "iopub.status.idle": "2022-04-23T08:55:06.433738Z",
+     "shell.execute_reply": "2022-04-23T08:55:06.432713Z",
+     "shell.execute_reply.started": "2022-04-23T08:55:06.429205Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def mae(preds, acts): return (torch.abs(preds-acts)).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll update our interactive function to print this at the top for us.\n",
+    "\n",
+    "Use this to repeat the approach we took before to try to find the best fit, but this time just use the value of the metric to decide which direction to move each slider, and how far to move it.\n",
+    "\n",
+    "This time around, try doing it in the opposite order: `c`, then `b`, then `a`.\n",
+    "\n",
+    "You'll probably find that you have to go through the set of sliders a couple of times to get the best fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-23T08:55:07.452152Z",
+     "iopub.status.busy": "2022-04-23T08:55:07.451295Z",
+     "iopub.status.idle": "2022-04-23T08:55:07.701428Z",
+     "shell.execute_reply": "2022-04-23T08:55:07.700643Z",
+     "shell.execute_reply.started": "2022-04-23T08:55:07.452102Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@interact(a=1.1, b=1.1, c=1.1)\n",
+    "def plot_quad(a, b, c):\n",
+    "    f = mk_quad(a,b,c)\n",
+    "    plt.scatter(x,y)\n",
+    "    loss = mae(f(x), y)\n",
+    "    plot_function(f, ylim=(-3,12), title=f\"MAE: {loss:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a modern neural network we'll often have tens of millions of parameters to fit, or more, and thousands or millions of data points to fit them to. We're not going to be able to do that by moving sliders around! We'll need to automate this process.\n",
+    "\n",
+    "Thankfully, that turns out to be pretty straightforward. We can use calculus to figure out, for each parameter, whether we should increase or decrease it.\n",
+    "\n",
+    "Uh oh, calculus! If you haven't touched calculus since school, you might be getting ready to run away at this point. But don't worry, we don't actually need much calculus at all. Just derivatives, which measure the rate of change of a function. We don't even need to calculate them ourselves, because the computer will do it for us! If you've forgotten what a derivitive is, then watch the first three of these fantastic [videos by Professor Dave](https://www.youtube.com/playlist?list=PLybg94GvOJ9ELZEe9s2NXTKr41Yedbw7M). It's only 15 minutes in total, so give it a go! Then come back here and we'll continue on our journey..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automating gradient descent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic idea is this: if we know the *gradient* of our `mae()` function *with respect to* our parameters, `a`, `b`, and `c`, then that means we know how adjusting (for instance) `a` will change the value of `mae()`. If, say, `a` has a *negative* gradient, then we know that increasing `a` will decrease `mae()`. Then we know that's what we need to do, since we trying to make `mae()` as low as possible.\n",
+    "\n",
+    "So, we find the gradient of `mae()` for each of our parameters, and then adjust our parameters a bit in the *opposite* direction to the sign of the gradient.\n",
+    "\n",
+    "To do this, first we need a function that takes all the parameters `a`, `b`, and `c` as a single vector input, and returns the value `mae()` based on those parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.832211Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.831976Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.837761Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.836892Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.832181Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def quad_mae(params):\n",
+    "    f = mk_quad(*params)\n",
+    "    return mae(f(x), y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.839663Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.839427Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.853343Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.852644Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.839635Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "quad_mae([1.1, 1.1, 1.1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yup, that's the same as the starting `mae()` we had in our plot before.\n",
+    "\n",
+    "We're first going to do exactly the same thing as we did manually -- pick some arbritrary starting point for our parameters. We'll put them all into a single tensor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.855001Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.854708Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.863677Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.862424Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.854965Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "abc = torch.tensor([1.1,1.1,1.1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To tell PyTorch that we want it to calculate gradients for these parameters, we need to call `requires_grad_()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.86557Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.865105Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.881016Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.879681Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.865497Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "abc.requires_grad_()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now calculate `mae()`. Generally, when doing gradient descent, the thing we're trying to minimise is called the *loss*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.882908Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.882498Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.894811Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.893828Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.882865Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loss = quad_mae(abc)\n",
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get PyTorch to now calculate the gradients, we need to call `backward()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.896493Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.896144Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.914719Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.913717Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.896462Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "loss.backward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The gradients will be stored for us in an attribute called `grad`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.91704Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.916372Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.925299Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.924407Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.916988Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "abc.grad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "According to these gradients, all our parameters are a little low. So let's increase them a bit. If we subtract the gradient, multiplied by a small number, that should improve them a bit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.927714Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.927221Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.941471Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.940784Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.927665Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    abc -= abc.grad*0.01\n",
+    "    loss = quad_mae(abc)\n",
+    "    \n",
+    "print(f'loss={loss:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, our loss has gone down!\n",
+    "\n",
+    "The \"small number\" we multiply is called the *learning rate*, and is the most important *hyper-parameter* to set when training a neural network.\n",
+    "\n",
+    "BTW, you'll see we had to wrap our calculation of the new parameters in `with torch.no_grad()`. That disables the calculation of gradients for any operations inside that context manager. We have to do that, because `abc -= abc.grad*0.01` isn't actually part of our quadratic model, so we don't want derivitives to include that calculation.\n",
+    "\n",
+    "We can use a loop to do a few more iterations of this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.943242Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.942822Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.968186Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.967081Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.943202Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(10):\n",
+    "    loss = quad_mae(abc)\n",
+    "    loss.backward()\n",
+    "    with torch.no_grad(): abc -= abc.grad*0.01\n",
+    "    print(f'step={i}; loss={loss:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, our loss keeps going down!\n",
+    "\n",
+    "If you keep running this loop for long enough however, you'll see that the loss eventually starts increasing for a while. That's because once the parameters get close to the correct answer, our parameter updates will jump right over the correct answer! To avoid this, we need to decrease our learning rate as we train. This is done using a *learning rate schedule*, and can be automated in most deep learning frameworks, such as fastai and PyTorch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How a neural network approximates any given function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But neural nets are much more convenient and powerful than this example showed, because we can learn much more than just a quadratic with them. How does *that* work?\n",
+    "\n",
+    "The trick is that a neural network is a very expressive function. In fact -- it's [infinitely expressive](https://en.wikipedia.org/wiki/Universal_approximation_theorem). A neural network can approximate any computable function, given enough parameters. A \"computable function\" can cover just about anything you can imagine: understand and translate human speech; paint a picture; diagnose a disease from medical imaging; write an essay; etc...\n",
+    "\n",
+    "The way a neural network approximates a function actually turns out to be very simple. The key trick is to combine two extremely basic steps:\n",
+    "\n",
+    "1. Matrix multiplication, which is just multiplying things together and then adding them up\n",
+    "1. The function $max(x,0)$, which simply replaces all negative numbers with zero.\n",
+    "\n",
+    "In PyTorch, the function $max(x,0)$ is written as `torch.clip(x,0)` which is an alias for `torch.clamp()`. The combination of a linear function and this *max()* is called a *rectified linear function*, and it can be implemented like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.970814Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.970051Z",
+     "iopub.status.idle": "2022-04-22T22:28:41.976113Z",
+     "shell.execute_reply": "2022-04-22T22:28:41.975338Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.970763Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def rectified_linear(m,b,x):\n",
+    "    y = m*x+b\n",
+    "    return torch.clip(y, 0.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:41.978052Z",
+     "iopub.status.busy": "2022-04-22T22:28:41.977336Z",
+     "iopub.status.idle": "2022-04-22T22:28:42.197682Z",
+     "shell.execute_reply": "2022-04-22T22:28:42.196709Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:41.978012Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_function(partial(rectified_linear, 1,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "BTW, instead of `torch.clip(y, 0.)`, we can instead use `F.relu(x)`, which does exactly the same thing. In PyTorch, `F` refers to the `torch.nn.functional` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:42.200133Z",
+     "iopub.status.busy": "2022-04-22T22:28:42.199561Z",
+     "iopub.status.idle": "2022-04-22T22:28:42.40433Z",
+     "shell.execute_reply": "2022-04-22T22:28:42.403323Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:42.200083Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "def rectified_linear2(m,b,x): return F.relu(m*x+b)\n",
+    "plot_function(partial(rectified_linear2, 1,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand how this function works, try using this interactive version to play around with the parameters `m` and `b`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:42.406108Z",
+     "iopub.status.busy": "2022-04-22T22:28:42.405808Z",
+     "iopub.status.idle": "2022-04-22T22:28:42.61219Z",
+     "shell.execute_reply": "2022-04-22T22:28:42.61144Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:42.406071Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "@interact(m=1.5, b=1.5)\n",
+    "def plot_relu(m, b):\n",
+    "    plot_function(partial(rectified_linear, m,b), ylim=(-1,4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you see, `m` changes the slope, and `b` changes where the \"hook\" appears. This function doesn't do much on its own, but look what happens when we add two of them together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2022-04-22T22:28:42.614042Z",
+     "iopub.status.busy": "2022-04-22T22:28:42.613745Z",
+     "iopub.status.idle": "2022-04-22T22:28:42.851224Z",
+     "shell.execute_reply": "2022-04-22T22:28:42.85035Z",
+     "shell.execute_reply.started": "2022-04-22T22:28:42.614007Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def double_relu(m1,b1,m2,b2,x):\n",
+    "    return rectified_linear(m1,b1,x) + rectified_linear(m2,b2,x)\n",
+    "\n",
+    "@interact(m1=-1.5, b1=-1.5, m2=1.5, b2=1.5)\n",
+    "def plot_double_relu(m1, b1, m2, b2):\n",
+    "    plot_function(partial(double_relu, m1,b1,m2,b2), ylim=(-1,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you play around with that for a while, you notice something quite profound: with enough of these rectified linear functions added together, you could approximate any function with a single input, to whatever accuracy you like! Any time the function doesn't quite match, you can just add a few more additions to the mix to make it a bit closer. As an experiment, perhaps you'd like to try creating your own `plot_triple_relu` interactive function, and maybe even include the scatter plot of our data from before, to see how close you can get?\n",
+    "\n",
+    "This exact same approach can be expanded to functions of 2, 3, or more parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to recognise an owl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "OK great, we've created a nifty little example showing that we can drawing squiggly lines that go through some points. So what?\n",
+    "\n",
+    "Well... the truth is that actually drawing squiggly lines (or planes, or high-dimensional hyperplanes...) through some points is literally *all that deep learning does*! If your data points are, say, the RGB values of pixels in photos of owls, then you can create an owl-recogniser model by following the exact steps above.\n",
+    "\n",
+    "This may, at first, sound about as useful as the classic \"how to draw an owl\" guide:"
+   ]
+  },
+  {
+   "attachments": {
+    "c66592d3-c997-4c72-aed4-2dea579b96e1.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:c66592d3-c997-4c72-aed4-2dea579b96e1.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Students often ask me at this point \"OK Jeremy, but how do neural nets *actually work*\". But at a foundational level, there is no \"step 2\". We're done -- the above steps will, given enough time and enough data, create (for example) an owl recogniser, if you feed in enough owls (and non-owls).\n",
+    "\n",
+    "The devil, I guess, is in the \"given enough time and enough data\" part of the above sentence. There's a *lot* of tweaks we can make to reduce both of these things. For instance, instead of running our calculations on a normal CPU, as we've done above, we could do thousands of them simultaneously by taking advantage of a GPU. We could greatly reduce the amount of computation and data needed by using a convolution instead of a matrix multiplication, which basically means skipping over a bunch of the multiplications and additions for bits that you'd guess won't be important. We could make things much faster if, instead of starting with random parameters, we start with parameters of someone else's model that does something similar to what we want (this is called *transfer learning*).\n",
+    "\n",
+    "And, of course, there's lots of helpful software out there to do this stuff for you without too much fuss. Like, say, [fastai](https://docs.fast.ai).\n",
+    "\n",
+    "Learning these things is what we teach in our [course](https://course.fast.ai), which, like everything we make, is totally free. So if you're interested in learning more, do check it out!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, if you enjoyed this notebook, please upvote it to help others find it, and to encourage me to write more. If you upvote it, be careful you don't accidentally upvote your copy that's created when you click \"Copy & Edit\" -- you can find my original at [this link](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05-linear-model-and-neural-net-from-scratch.ipynb b/05-linear-model-and-neural-net-from-scratch.ipynb
index 4bcd006535..2378a0df27 100644
--- a/05-linear-model-and-neural-net-from-scratch.ipynb
+++ b/05-linear-model-and-neural-net-from-scratch.ipynb
@@ -1 +1 @@
-{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Introduction","metadata":{}},{"cell_type":"markdown","source":"In this notebook we're going to build and train a deep learning model \"from scratch\" -- by which I mean that we're not going to use any pre-built architecture, or optimizers, or data loading frameworks, etc.\n\nWe'll be assuming you already know the basics of how a neural network works. If you don't, read this notebook first: [How does a neural net really work?\n](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work). We'll be using Kaggle's [Titanic](https://www.kaggle.com/competitions/titanic/) competition in this notebook, because it's very small and simple, but also has displays many of the tricky real-life issues that we need to handle in most practical projects. (Note, however, that this competition is a small \"learner\" competition on Kaggle, so don't expect to actually see much benefits from using a neural net just yet; that will come once we try our some real competitions!)\n\nIt's great to be able to run the same notebook on your own machine or Colab, as well as Kaggle. To allow for this, we use this code to download the data as needed when not on Kaggle (see [this notebook](https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners/) for details about this technique):","metadata":{}},{"cell_type":"code","source":"import os\nfrom pathlib import Path\n\niskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '')\nif iskaggle: path = Path('../input/titanic')\nelse:\n    path = Path('titanic')\n    if not path.exists():\n        import zipfile,kaggle\n        kaggle.api.competition_download_cli(str(path))\n        zipfile.ZipFile(f'{path}.zip').extractall(path)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:17.763494Z","iopub.execute_input":"2022-05-30T22:34:17.763822Z","iopub.status.idle":"2022-05-30T22:34:17.771348Z","shell.execute_reply.started":"2022-05-30T22:34:17.763787Z","shell.execute_reply":"2022-05-30T22:34:17.770444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that the data for Kaggle comps always lives in the `../input` folder. The easiest way to get the path is to click the \"K\" button in the top-right of the Kaggle notebook, click on the folder shown there, and click the copy button.\n\nWe'll be using *numpy* and *pytorch* for array calculations in this notebook, and *pandas* for working with tabular data, so we'll import them and set them to display using a bit more space than they default to.","metadata":{"hidden":true}},{"cell_type":"code","source":"import torch, numpy as np, pandas as pd\nnp.set_printoptions(linewidth=140)\ntorch.set_printoptions(linewidth=140, sci_mode=False, edgeitems=7)\npd.set_option('display.width', 140)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:17.810967Z","iopub.execute_input":"2022-05-30T22:34:17.811857Z","iopub.status.idle":"2022-05-30T22:34:17.817725Z","shell.execute_reply.started":"2022-05-30T22:34:17.811797Z","shell.execute_reply":"2022-05-30T22:34:17.816849Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Cleaning the data","metadata":{"heading_collapsed":true}},{"cell_type":"markdown","source":"This is a *tabular data* competition -- the data is in the form of a table. It's provided as a Comma Separated Values (CSV) file. We can open it using the *pandas* library, which will create a `DataFrame`.","metadata":{"hidden":true}},{"cell_type":"code","source":"df = pd.read_csv(path/'train.csv')\ndf","metadata":{"hidden":true,"scrolled":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:17.898249Z","iopub.execute_input":"2022-05-30T22:34:17.899238Z","iopub.status.idle":"2022-05-30T22:34:17.932714Z","shell.execute_reply.started":"2022-05-30T22:34:17.899131Z","shell.execute_reply":"2022-05-30T22:34:17.931738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we learned in the *How does a neural net really work* notebook, we going to want to multiply each column by some coefficients. But we can see in the `Cabin` column that there are `NaN` values, which is how Pandas refers to missing values. We can't multiply something by a missing value!\n\nLet's check which columns contain `NaN` values. Pandas' `isna()` function returns `True` (which is treated as `1` when used as a number) for `NaN` values, so we can just add them up for each column:","metadata":{"hidden":true}},{"cell_type":"code","source":"df.isna().sum()","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:17.95524Z","iopub.execute_input":"2022-05-30T22:34:17.955557Z","iopub.status.idle":"2022-05-30T22:34:17.966199Z","shell.execute_reply.started":"2022-05-30T22:34:17.955525Z","shell.execute_reply":"2022-05-30T22:34:17.96534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Notice that by default Pandas sums over columns.\n\nWe'll need to replace the missing values with something. It doesn't generally matter too much what we choose. We'll use the most common value (the \"*mode*\"). We can use the `mode` function for that. One wrinkle is that it returns more than one row in the case of ties, so we just grab the first row with `iloc[0]`:","metadata":{"hidden":true}},{"cell_type":"code","source":"modes = df.mode().iloc[0]\nmodes","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.011876Z","iopub.execute_input":"2022-05-30T22:34:18.012387Z","iopub.status.idle":"2022-05-30T22:34:18.030165Z","shell.execute_reply.started":"2022-05-30T22:34:18.012338Z","shell.execute_reply":"2022-05-30T22:34:18.029316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"BTW, it's never a good idea to use functions without understanding them. So be sure to google for anything you're not familiar with. E.g if you want to learn about `iloc` (which is a very important function indeed!) then Google will give you a link to a [great tutorial](https://www.shanelynn.ie/pandas-iloc-loc-select-rows-and-columns-dataframe/).\n\nNow that we've got the mode of each column, we can use `fillna` to replace the missing values with the mode of each column. We'll do it \"in place\" -- meaning that we'll change the dataframe itself, rather than returning a new one.","metadata":{"hidden":true}},{"cell_type":"code","source":"df.fillna(modes, inplace=True)","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.069123Z","iopub.execute_input":"2022-05-30T22:34:18.069624Z","iopub.status.idle":"2022-05-30T22:34:18.078805Z","shell.execute_reply.started":"2022-05-30T22:34:18.069573Z","shell.execute_reply":"2022-05-30T22:34:18.077978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can now check there's no missing values left:","metadata":{"hidden":true}},{"cell_type":"code","source":"df.isna().sum()","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.13936Z","iopub.execute_input":"2022-05-30T22:34:18.139844Z","iopub.status.idle":"2022-05-30T22:34:18.149896Z","shell.execute_reply.started":"2022-05-30T22:34:18.139793Z","shell.execute_reply":"2022-05-30T22:34:18.148884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's how we get a quick summary of all the numeric columns in the dataset:","metadata":{"hidden":true}},{"cell_type":"code","source":"import numpy as np\n\ndf.describe(include=(np.number))","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.205483Z","iopub.execute_input":"2022-05-30T22:34:18.205953Z","iopub.status.idle":"2022-05-30T22:34:18.240196Z","shell.execute_reply.started":"2022-05-30T22:34:18.205897Z","shell.execute_reply":"2022-05-30T22:34:18.239106Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see that `Fare` contains mainly values of around `0` to `30`, but there's a few really big ones. This is very common with fields contain monetary values, and it can cause problems for our model, because once that column is multiplied by a coefficient later, the few rows with really big values will dominate the result.\n\nYou can see the issue most clearly visually by looking at a histogram, which shows a long tail to the right (and don't forget: if you're not entirely sure what a histogram is, Google \"[histogram tutorial](https://www.google.com/search?q=histogram+tutorial&oq=histogram+tutorial)\" and do a bit of reading before continuing on):","metadata":{"execution":{"iopub.execute_input":"2022-05-13T11:02:34.328433Z","iopub.status.busy":"2022-05-13T11:02:34.327999Z","iopub.status.idle":"2022-05-13T11:02:34.336993Z","shell.execute_reply":"2022-05-13T11:02:34.335466Z","shell.execute_reply.started":"2022-05-13T11:02:34.32838Z"},"hidden":true}},{"cell_type":"code","source":"df['Fare'].hist();","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.241874Z","iopub.execute_input":"2022-05-30T22:34:18.242123Z","iopub.status.idle":"2022-05-30T22:34:18.471439Z","shell.execute_reply.started":"2022-05-30T22:34:18.242094Z","shell.execute_reply":"2022-05-30T22:34:18.470441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To fix this, the most common approach is to take the logarithm, which squishes the big numbers and makes the distribution more reasonable. Note, however, that there are zeros in the `Fare` column, and `log(0)` is infinite -- to fix this, we'll simply add `1` to all values first:","metadata":{"execution":{"iopub.execute_input":"2022-05-13T11:02:34.328433Z","iopub.status.busy":"2022-05-13T11:02:34.327999Z","iopub.status.idle":"2022-05-13T11:02:34.336993Z","shell.execute_reply":"2022-05-13T11:02:34.335466Z","shell.execute_reply.started":"2022-05-13T11:02:34.32838Z"},"hidden":true}},{"cell_type":"code","source":"df['LogFare'] = np.log(df['Fare']+1)","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.473109Z","iopub.execute_input":"2022-05-30T22:34:18.47382Z","iopub.status.idle":"2022-05-30T22:34:18.478776Z","shell.execute_reply.started":"2022-05-30T22:34:18.473778Z","shell.execute_reply":"2022-05-30T22:34:18.478149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The histogram now shows a more even distribution of values without the long tail:","metadata":{"hidden":true}},{"cell_type":"code","source":"df['LogFare'].hist();","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.480088Z","iopub.execute_input":"2022-05-30T22:34:18.480926Z","iopub.status.idle":"2022-05-30T22:34:18.855605Z","shell.execute_reply.started":"2022-05-30T22:34:18.480885Z","shell.execute_reply":"2022-05-30T22:34:18.854915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It looks from the `describe()` output like `Pclass` contains just 3 values, which we can confirm by looking at the [Data Dictionary](https://www.kaggle.com/competitions/titanic/data) (which you should always study carefully for any project!) -- ","metadata":{"hidden":true}},{"cell_type":"code","source":"pclasses = sorted(df.Pclass.unique())\npclasses","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.857312Z","iopub.execute_input":"2022-05-30T22:34:18.857676Z","iopub.status.idle":"2022-05-30T22:34:18.863672Z","shell.execute_reply.started":"2022-05-30T22:34:18.857643Z","shell.execute_reply":"2022-05-30T22:34:18.862791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's how we get a quick summary of all the non-numeric columns in the dataset:","metadata":{"hidden":true}},{"cell_type":"code","source":"df.describe(include=[object])","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.864893Z","iopub.execute_input":"2022-05-30T22:34:18.865154Z","iopub.status.idle":"2022-05-30T22:34:18.89758Z","shell.execute_reply.started":"2022-05-30T22:34:18.865115Z","shell.execute_reply":"2022-05-30T22:34:18.896706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Clearly we can't multiply strings like `male` or `S` by coefficients, so we need to replace those with numbers.\n\nWe do that by creating new columns containing *dummy variables*. A dummy variable is a column that contains a `1` where a particular column contains a particular value, or a `0` otherwise. For instance, we could create a dummy variable for `Sex='male'`, which would be a new column containing `1` for rows where `Sex` is `'male'`, and 0 for rows where it isn't.\n\nPandas can create these automatically using `get_dummies`, which also remove the original columns. We'll create dummy variables for `Pclass`, even although it's numeric, since the numbers `1`, `2`, and `3` correspond to first, second, and third class cabins - not to counts or measures that make sense to multiply by. We'll also create dummies for `Sex` and `Embarked` since we'll want to use those as predictors in our model. On the other hand, `Cabin`, `Name`, and `Ticket` have too many unique values for it to make sense creating dummy variables for them.","metadata":{"hidden":true}},{"cell_type":"code","source":"df = pd.get_dummies(df, columns=[\"Sex\",\"Pclass\",\"Embarked\"])\ndf.columns","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.899127Z","iopub.execute_input":"2022-05-30T22:34:18.899369Z","iopub.status.idle":"2022-05-30T22:34:18.914993Z","shell.execute_reply.started":"2022-05-30T22:34:18.899338Z","shell.execute_reply":"2022-05-30T22:34:18.914167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see that 5 columns have been added to the end -- one for each of the possible values of each of the three columns we requested, and that those three requested columns have been removed.\n\nHere's what the first few rows of those newly added columns look like:","metadata":{"hidden":true}},{"cell_type":"code","source":"added_cols = ['Sex_male', 'Sex_female', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Embarked_C', 'Embarked_Q', 'Embarked_S']\ndf[added_cols].head()","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.916186Z","iopub.execute_input":"2022-05-30T22:34:18.916456Z","iopub.status.idle":"2022-05-30T22:34:18.933746Z","shell.execute_reply.started":"2022-05-30T22:34:18.916426Z","shell.execute_reply":"2022-05-30T22:34:18.933135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can create our independent (predictors) and dependent (target) variables. They both need to be PyTorch tensors. Our dependent variable is `Survived`:","metadata":{"hidden":true}},{"cell_type":"code","source":"from torch import tensor\n\nt_dep = tensor(df.Survived)","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.934648Z","iopub.execute_input":"2022-05-30T22:34:18.935422Z","iopub.status.idle":"2022-05-30T22:34:18.944596Z","shell.execute_reply.started":"2022-05-30T22:34:18.935384Z","shell.execute_reply":"2022-05-30T22:34:18.943444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Our independent variables are all the continuous variables of interest plus all the dummy variables we just created:","metadata":{"hidden":true}},{"cell_type":"code","source":"indep_cols = ['Age', 'SibSp', 'Parch', 'LogFare'] + added_cols\n\nt_indep = tensor(df[indep_cols].values, dtype=torch.float)\nt_indep","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.94652Z","iopub.execute_input":"2022-05-30T22:34:18.946772Z","iopub.status.idle":"2022-05-30T22:34:18.96487Z","shell.execute_reply.started":"2022-05-30T22:34:18.946741Z","shell.execute_reply":"2022-05-30T22:34:18.963667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here's the number of rows and columns we have for our independent variables:","metadata":{"hidden":true}},{"cell_type":"code","source":"t_indep.shape","metadata":{"hidden":true,"execution":{"iopub.status.busy":"2022-05-30T22:34:18.968005Z","iopub.execute_input":"2022-05-30T22:34:18.969136Z","iopub.status.idle":"2022-05-30T22:34:18.98114Z","shell.execute_reply.started":"2022-05-30T22:34:18.969092Z","shell.execute_reply":"2022-05-30T22:34:18.980184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Setting up a linear model","metadata":{}},{"cell_type":"markdown","source":"Now that we've got a matrix of independent variables and a dependent variable vector, we can work on calculating our predictions and our loss. In this section, we're going to manually do a single step of calculating predictions and loss for every row of our data.\n\nOur first model will be a simple linear model. We'll need a coefficient for each column in `t_indep`. We'll pick random numbers in the range `(-0.5,0.5)`, and set our manual seed so that my explanations in the prose in this notebook will be consistent with what you see when you run it.","metadata":{}},{"cell_type":"code","source":"torch.manual_seed(442)\n\nn_coeff = t_indep.shape[1]\ncoeffs = torch.rand(n_coeff)-0.5\ncoeffs","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:18.982492Z","iopub.execute_input":"2022-05-30T22:34:18.983237Z","iopub.status.idle":"2022-05-30T22:34:18.995038Z","shell.execute_reply.started":"2022-05-30T22:34:18.983187Z","shell.execute_reply":"2022-05-30T22:34:18.994437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Our predictions will be calculated by multiplying each row by the coefficients, and adding them up. One interesting point here is that we don't need a separate constant term (also known as a \"bias\" or \"intercept\" term), or a column of all `1`s to give the same effect has having a constant term. That's because our dummy variables already cover the entire dataset -- e.g. there's a column for \"male\" and a column for \"female\", and everyone in the dataset is in exactly one of these; therefore, we don't need a separate intercept term to cover rows that aren't otherwise part of a column.\n\nHere's what the multiplication looks like:","metadata":{}},{"cell_type":"code","source":"t_indep*coeffs","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:18.999409Z","iopub.execute_input":"2022-05-30T22:34:19.000637Z","iopub.status.idle":"2022-05-30T22:34:19.009362Z","shell.execute_reply.started":"2022-05-30T22:34:19.000588Z","shell.execute_reply":"2022-05-30T22:34:19.00847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see we've got a problem here. The sums of each row will be dominated by the first column, which is `Age`, since that's bigger on average than all the others.\n\nLet's make all the columns contain numbers from `0` to `1`, by dividing each column by its `max()`:","metadata":{}},{"cell_type":"code","source":"vals,indices = t_indep.max(dim=0)\nt_indep = t_indep / vals","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.010954Z","iopub.execute_input":"2022-05-30T22:34:19.011202Z","iopub.status.idle":"2022-05-30T22:34:19.02202Z","shell.execute_reply.started":"2022-05-30T22:34:19.011171Z","shell.execute_reply":"2022-05-30T22:34:19.02133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we see, that removes the problem of one column dominating all the others:","metadata":{}},{"cell_type":"code","source":"t_indep*coeffs","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.042223Z","iopub.execute_input":"2022-05-30T22:34:19.04269Z","iopub.status.idle":"2022-05-30T22:34:19.050475Z","shell.execute_reply.started":"2022-05-30T22:34:19.042652Z","shell.execute_reply":"2022-05-30T22:34:19.049515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"One thing you hopefully noticed is how amazingly cool this line of code is:\n\n    t_indep = t_indep / vals\n\nThat is dividing a matrix by a vector -- what on earth does that mean?!? The trick here is that we're taking advantage of a technique in numpy and PyTorch (and many other languages, going all the way back to APL) called [broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html). In short, this acts as if there's a separate copy of the vector for every row of the matrix, so it divides each row of the matrix by the vector. In practice, it doesn't actually make any copies, and does the whole thing in a highly optimized way, taking full advantage of modern CPUs (or, indeed, GPUs, if we're using them). Broadcasting is one of the most important techniques for making your code concise, maintainable, and fast, so it's well worth studying and practicing.\n\nWe can now create predictions from our linear model, by adding up the rows of the product:","metadata":{}},{"cell_type":"code","source":"preds = (t_indep*coeffs).sum(axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.096685Z","iopub.execute_input":"2022-05-30T22:34:19.097604Z","iopub.status.idle":"2022-05-30T22:34:19.1028Z","shell.execute_reply.started":"2022-05-30T22:34:19.097545Z","shell.execute_reply":"2022-05-30T22:34:19.10218Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's take a look at the first few:","metadata":{}},{"cell_type":"code","source":"preds[:10]","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.160318Z","iopub.execute_input":"2022-05-30T22:34:19.160794Z","iopub.status.idle":"2022-05-30T22:34:19.168117Z","shell.execute_reply.started":"2022-05-30T22:34:19.160761Z","shell.execute_reply":"2022-05-30T22:34:19.167355Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Of course, these predictions aren't going to be any use, since our coefficients are random -- they're just a starting point for our gradient descent process.\n\nTo do gradient descent, we need a loss function. Taking the average error of the rows (i.e. the absolute value of the difference between the prediction and the dependent) is generally a reasonable approach:","metadata":{}},{"cell_type":"code","source":"loss = torch.abs(preds-t_dep).mean()\nloss","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.235318Z","iopub.execute_input":"2022-05-30T22:34:19.235623Z","iopub.status.idle":"2022-05-30T22:34:19.24312Z","shell.execute_reply.started":"2022-05-30T22:34:19.235589Z","shell.execute_reply":"2022-05-30T22:34:19.242356Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now that we've tested out a way of calculating predictions, and loss, let's pop them into functions to make life easier:","metadata":{}},{"cell_type":"code","source":"def calc_preds(coeffs, indeps): return (indeps*coeffs).sum(axis=1)\ndef calc_loss(coeffs, indeps, deps): return torch.abs(calc_preds(coeffs, indeps)-deps).mean()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.295577Z","iopub.execute_input":"2022-05-30T22:34:19.296558Z","iopub.status.idle":"2022-05-30T22:34:19.301478Z","shell.execute_reply.started":"2022-05-30T22:34:19.296517Z","shell.execute_reply":"2022-05-30T22:34:19.300683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Doing a gradient descent step","metadata":{}},{"cell_type":"markdown","source":"In this section, we're going to do a single \"epoch\" of gradient descent manually. The only thing we're going to automate is calculating gradients, because let's face it that's pretty tedious and entirely pointless to do by hand! To get PyTorch to calculate gradients, we'll need to call `requires_grad_()` on our `coeffs` (if you're not sure why, review the previous notebook, [How does a neural net really work?](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work), before continuing):","metadata":{}},{"cell_type":"code","source":"coeffs.requires_grad_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.375387Z","iopub.execute_input":"2022-05-30T22:34:19.376212Z","iopub.status.idle":"2022-05-30T22:34:19.382205Z","shell.execute_reply.started":"2022-05-30T22:34:19.376163Z","shell.execute_reply":"2022-05-30T22:34:19.381536Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now when we calculate our loss, PyTorch will keep track of all the steps, so we'll be able to get the gradients afterwards:","metadata":{}},{"cell_type":"code","source":"loss = calc_loss(coeffs, t_indep, t_dep)\nloss","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.437208Z","iopub.execute_input":"2022-05-30T22:34:19.438026Z","iopub.status.idle":"2022-05-30T22:34:19.444641Z","shell.execute_reply.started":"2022-05-30T22:34:19.437985Z","shell.execute_reply":"2022-05-30T22:34:19.443791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Use `backward()` to ask PyTorch to calculate gradients now:","metadata":{}},{"cell_type":"code","source":"loss.backward()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.491601Z","iopub.execute_input":"2022-05-30T22:34:19.49239Z","iopub.status.idle":"2022-05-30T22:34:19.496556Z","shell.execute_reply.started":"2022-05-30T22:34:19.492353Z","shell.execute_reply":"2022-05-30T22:34:19.495831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's see what they look like:","metadata":{}},{"cell_type":"code","source":"coeffs.grad","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.549651Z","iopub.execute_input":"2022-05-30T22:34:19.550501Z","iopub.status.idle":"2022-05-30T22:34:19.555905Z","shell.execute_reply.started":"2022-05-30T22:34:19.550456Z","shell.execute_reply":"2022-05-30T22:34:19.555257Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that each time we call `backward`, the gradients are actually *added* to whatever is in the `.grad` attribute. Let's try running the above steps again:","metadata":{}},{"cell_type":"code","source":"loss = calc_loss(coeffs, t_indep, t_dep)\nloss.backward()\ncoeffs.grad","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.60283Z","iopub.execute_input":"2022-05-30T22:34:19.603236Z","iopub.status.idle":"2022-05-30T22:34:19.610594Z","shell.execute_reply.started":"2022-05-30T22:34:19.603206Z","shell.execute_reply":"2022-05-30T22:34:19.609647Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As you see, our `.grad` values are have doubled. That's because it added the gradients a second time. For this reason, after we use the gradients to do a gradient descent step, we need to set them back to zero.\n\nWe can now do one gradient descent step, and check that our loss decreases:","metadata":{}},{"cell_type":"code","source":"loss = calc_loss(coeffs, t_indep, t_dep)\nloss.backward()\nwith torch.no_grad():\n    coeffs.sub_(coeffs.grad * 0.1)\n    coeffs.grad.zero_()\n    print(calc_loss(coeffs, t_indep, t_dep))","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:34:19.665238Z","iopub.execute_input":"2022-05-30T22:34:19.665739Z","iopub.status.idle":"2022-05-30T22:34:19.674839Z","shell.execute_reply.started":"2022-05-30T22:34:19.665705Z","shell.execute_reply":"2022-05-30T22:34:19.673727Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that `a.sub_(b)` subtracts `b` from `a` in-place. In PyTorch, any method that ends in `_` changes its object in-place. Similarly, `a.zero_()` sets all elements of a tensor to zero.","metadata":{}},{"cell_type":"markdown","source":"## Training the linear model","metadata":{}},{"cell_type":"markdown","source":"Before we begin training our model, we'll need to ensure that we hold out a validation set for calculating our metrics (for details on this, see \"[Getting started with NLP for absolute beginners](https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners#Test-and-validation-sets)\".\n\nThere's lots of different ways we can do this. In the next notebook we'll be comparing our approach here to what the fastai library does, so we'll want to ensure we split the data in the same way. So let's use `RandomSplitter` to get indices that will split our data into training and validation sets:","metadata":{}},{"cell_type":"code","source":"from fastai.data.transforms import RandomSplitter\ntrn_split,val_split=RandomSplitter(seed=42)(df)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:26.57605Z","iopub.execute_input":"2022-05-30T22:35:26.57635Z","iopub.status.idle":"2022-05-30T22:35:27.821258Z","shell.execute_reply.started":"2022-05-30T22:35:26.57632Z","shell.execute_reply":"2022-05-30T22:35:27.820329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can apply those indicies to our independent and dependent variables:","metadata":{}},{"cell_type":"code","source":"trn_indep,val_indep = t_indep[trn_split],t_indep[val_split]\ntrn_dep,val_dep = t_dep[trn_split],t_dep[val_split]\nlen(trn_indep),len(val_indep)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:27.822671Z","iopub.execute_input":"2022-05-30T22:35:27.822887Z","iopub.status.idle":"2022-05-30T22:35:27.836937Z","shell.execute_reply.started":"2022-05-30T22:35:27.822859Z","shell.execute_reply":"2022-05-30T22:35:27.836081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll create functions for the three things we did manually above: updating `coeffs`, doing one full gradient descent step, and initilising `coeffs` to random numbers:","metadata":{}},{"cell_type":"code","source":"def update_coeffs(coeffs, lr):\n    coeffs.sub_(coeffs.grad * lr)\n    coeffs.grad.zero_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:35.028649Z","iopub.execute_input":"2022-05-30T22:35:35.029177Z","iopub.status.idle":"2022-05-30T22:35:35.034346Z","shell.execute_reply.started":"2022-05-30T22:35:35.029143Z","shell.execute_reply":"2022-05-30T22:35:35.033261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def one_epoch(coeffs, lr):\n    loss = calc_loss(coeffs, trn_indep, trn_dep)\n    loss.backward()\n    with torch.no_grad(): update_coeffs(coeffs, lr)\n    print(f\"{loss:.3f}\", end=\"; \")","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:35.633703Z","iopub.execute_input":"2022-05-30T22:35:35.633995Z","iopub.status.idle":"2022-05-30T22:35:35.639103Z","shell.execute_reply.started":"2022-05-30T22:35:35.633964Z","shell.execute_reply":"2022-05-30T22:35:35.63814Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def init_coeffs(): return (torch.rand(n_coeff)-0.5).requires_grad_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:36.293565Z","iopub.execute_input":"2022-05-30T22:35:36.293837Z","iopub.status.idle":"2022-05-30T22:35:36.297457Z","shell.execute_reply.started":"2022-05-30T22:35:36.293808Z","shell.execute_reply":"2022-05-30T22:35:36.296816Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can now use these functions to train our model:","metadata":{}},{"cell_type":"code","source":"def train_model(epochs=30, lr=0.01):\n    torch.manual_seed(442)\n    coeffs = init_coeffs()\n    for i in range(epochs): one_epoch(coeffs, lr=lr)\n    return coeffs","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:35:38.771827Z","iopub.execute_input":"2022-05-30T22:35:38.772314Z","iopub.status.idle":"2022-05-30T22:35:38.777313Z","shell.execute_reply.started":"2022-05-30T22:35:38.772247Z","shell.execute_reply":"2022-05-30T22:35:38.776439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's try it. Our loss will print at the end of every step, so we hope we'll see it going down:","metadata":{}},{"cell_type":"code","source":"coeffs = train_model(18, lr=0.2)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:14.16706Z","iopub.execute_input":"2022-05-30T22:36:14.16788Z","iopub.status.idle":"2022-05-30T22:36:14.181652Z","shell.execute_reply.started":"2022-05-30T22:36:14.167812Z","shell.execute_reply":"2022-05-30T22:36:14.180496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It does!\n\nLet's take a look at the coefficients for each column:","metadata":{}},{"cell_type":"code","source":"def show_coeffs(): return dict(zip(indep_cols, coeffs.requires_grad_(False)))\nshow_coeffs()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:21.97957Z","iopub.execute_input":"2022-05-30T22:36:21.980389Z","iopub.status.idle":"2022-05-30T22:36:21.990088Z","shell.execute_reply.started":"2022-05-30T22:36:21.98035Z","shell.execute_reply":"2022-05-30T22:36:21.989021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Measuring accuracy","metadata":{}},{"cell_type":"markdown","source":"The Kaggle competition is not, however, scored by absolute error (which is our loss function). It's scored by *accuracy* -- the proportion of rows where we correctly predict survival. Let's see how accurate we were on the validation set. First, calculate the predictions:","metadata":{}},{"cell_type":"code","source":"preds = calc_preds(coeffs, val_indep)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:30.825856Z","iopub.execute_input":"2022-05-30T22:36:30.826372Z","iopub.status.idle":"2022-05-30T22:36:30.831613Z","shell.execute_reply.started":"2022-05-30T22:36:30.826322Z","shell.execute_reply":"2022-05-30T22:36:30.830756Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll assume that any passenger with a score of over `0.5` is predicted to survive. So that means we're correct for each row where `preds>0.5` is the same as the dependent variable:","metadata":{}},{"cell_type":"code","source":"results = val_dep.bool()==(preds>0.5)\nresults[:16]","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:33.618455Z","iopub.execute_input":"2022-05-30T22:36:33.618899Z","iopub.status.idle":"2022-05-30T22:36:33.62703Z","shell.execute_reply.started":"2022-05-30T22:36:33.618867Z","shell.execute_reply":"2022-05-30T22:36:33.625949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's see what our average accuracy is:","metadata":{}},{"cell_type":"code","source":"results.float().mean()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:35.725112Z","iopub.execute_input":"2022-05-30T22:36:35.725637Z","iopub.status.idle":"2022-05-30T22:36:35.732969Z","shell.execute_reply.started":"2022-05-30T22:36:35.725599Z","shell.execute_reply":"2022-05-30T22:36:35.732241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That's not a bad start at all! We'll create a function so we can calcuate the accuracy easy for other models we train:","metadata":{}},{"cell_type":"code","source":"def acc(coeffs): return (val_dep.bool()==(calc_preds(coeffs, val_indep)>0.5)).float().mean()\nacc(coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:40.356043Z","iopub.execute_input":"2022-05-30T22:36:40.356505Z","iopub.status.idle":"2022-05-30T22:36:40.365187Z","shell.execute_reply.started":"2022-05-30T22:36:40.356471Z","shell.execute_reply":"2022-05-30T22:36:40.364153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Using sigmoid","metadata":{}},{"cell_type":"markdown","source":"Looking at our predictions, there's one obvious problem -- some of our predictions of the probability of survival are `>1`, and some are `<0`:","metadata":{}},{"cell_type":"code","source":"preds[:28]","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:42.344533Z","iopub.execute_input":"2022-05-30T22:36:42.344823Z","iopub.status.idle":"2022-05-30T22:36:42.352948Z","shell.execute_reply.started":"2022-05-30T22:36:42.344794Z","shell.execute_reply":"2022-05-30T22:36:42.351968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To fix this, we should pass every prediction through the *sigmoid function*, which has a minimum at zero and maximum at one, and is defined as follows:","metadata":{}},{"cell_type":"code","source":"import sympy\nsympy.plot(\"1/(1+exp(-x))\", xlim=(-5,5));","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:44.913101Z","iopub.execute_input":"2022-05-30T22:36:44.914015Z","iopub.status.idle":"2022-05-30T22:36:46.311818Z","shell.execute_reply.started":"2022-05-30T22:36:44.913968Z","shell.execute_reply":"2022-05-30T22:36:46.311008Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"PyTorch already defines that function for us, so we can modify `calc_preds` to use it:","metadata":{}},{"cell_type":"code","source":"def calc_preds(coeffs, indeps): return torch.sigmoid((indeps*coeffs).sum(axis=1))","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:36:46.313435Z","iopub.execute_input":"2022-05-30T22:36:46.313644Z","iopub.status.idle":"2022-05-30T22:36:46.317749Z","shell.execute_reply.started":"2022-05-30T22:36:46.313618Z","shell.execute_reply":"2022-05-30T22:36:46.3169Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's train a new model now, using this updated function to calculate predictions:","metadata":{}},{"cell_type":"code","source":"coeffs = train_model(lr=100)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:23.225051Z","iopub.execute_input":"2022-05-30T22:38:23.22576Z","iopub.status.idle":"2022-05-30T22:38:23.250206Z","shell.execute_reply.started":"2022-05-30T22:38:23.225722Z","shell.execute_reply":"2022-05-30T22:38:23.249321Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The loss has improved by a lot. Let's check the accuracy:","metadata":{}},{"cell_type":"code","source":"acc(coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:28.518642Z","iopub.execute_input":"2022-05-30T22:38:28.519132Z","iopub.status.idle":"2022-05-30T22:38:28.527145Z","shell.execute_reply.started":"2022-05-30T22:38:28.519078Z","shell.execute_reply":"2022-05-30T22:38:28.526248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That's improved too! Here's the coefficients of our trained model:","metadata":{}},{"cell_type":"code","source":"show_coeffs()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:32.015953Z","iopub.execute_input":"2022-05-30T22:38:32.01697Z","iopub.status.idle":"2022-05-30T22:38:32.02724Z","shell.execute_reply.started":"2022-05-30T22:38:32.016924Z","shell.execute_reply":"2022-05-30T22:38:32.026125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"These coefficients seem reasonable -- in general, older people and males were less likely to survive, and first class passengers were more likely to survive.","metadata":{}},{"cell_type":"markdown","source":"## Submitting to Kaggle","metadata":{}},{"cell_type":"markdown","source":"Now that we've got a trained model, we can prepare a submission to Kaggle. To do that, first we need to read the test set:","metadata":{}},{"cell_type":"code","source":"tst_df = pd.read_csv(path/'test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:35.172343Z","iopub.execute_input":"2022-05-30T22:38:35.172909Z","iopub.status.idle":"2022-05-30T22:38:35.188597Z","shell.execute_reply.started":"2022-05-30T22:38:35.172873Z","shell.execute_reply":"2022-05-30T22:38:35.187826Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this case, it turns out that the test set is missing `Fare` for one passenger. We'll just fill it with `0` to avoid problems:","metadata":{}},{"cell_type":"code","source":"tst_df['Fare'] = tst_df.Fare.fillna(0)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:36.352687Z","iopub.execute_input":"2022-05-30T22:38:36.353392Z","iopub.status.idle":"2022-05-30T22:38:36.358237Z","shell.execute_reply.started":"2022-05-30T22:38:36.353355Z","shell.execute_reply":"2022-05-30T22:38:36.35739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can just copy the same steps we did to our training set and do the same exact things on our test set to preprocess the data:","metadata":{}},{"cell_type":"code","source":"tst_df.fillna(modes, inplace=True)\ntst_df['LogFare'] = np.log(tst_df['Fare']+1)\ntst_df = pd.get_dummies(tst_df, columns=[\"Sex\",\"Pclass\",\"Embarked\"])\n\ntst_indep = tensor(tst_df[indep_cols].values, dtype=torch.float)\ntst_indep = tst_indep / vals","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:38.198257Z","iopub.execute_input":"2022-05-30T22:38:38.198549Z","iopub.status.idle":"2022-05-30T22:38:38.220592Z","shell.execute_reply.started":"2022-05-30T22:38:38.198519Z","shell.execute_reply":"2022-05-30T22:38:38.219629Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's calculate our predictions of which passengers survived in the test set:","metadata":{}},{"cell_type":"code","source":"tst_df['Survived'] = (calc_preds(tst_indep, coeffs)>0.5).int()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:39.206216Z","iopub.execute_input":"2022-05-30T22:38:39.206547Z","iopub.status.idle":"2022-05-30T22:38:39.212386Z","shell.execute_reply.started":"2022-05-30T22:38:39.206512Z","shell.execute_reply":"2022-05-30T22:38:39.211631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The sample submission on the Kaggle competition site shows that we're expected to upload a CSV with just `PassengerId` and `Survived`, so let's create that and save it:","metadata":{}},{"cell_type":"code","source":"sub_df = tst_df[['PassengerId','Survived']]\nsub_df.to_csv('sub.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:40.161034Z","iopub.execute_input":"2022-05-30T22:38:40.161382Z","iopub.status.idle":"2022-05-30T22:38:40.173242Z","shell.execute_reply.started":"2022-05-30T22:38:40.161336Z","shell.execute_reply":"2022-05-30T22:38:40.17258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can check the first few rows of the file to make sure it looks reasonable:","metadata":{}},{"cell_type":"code","source":"!head sub.csv","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:42.86855Z","iopub.execute_input":"2022-05-30T22:38:42.869402Z","iopub.status.idle":"2022-05-30T22:38:43.638832Z","shell.execute_reply.started":"2022-05-30T22:38:42.869362Z","shell.execute_reply":"2022-05-30T22:38:43.637559Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"When you click \"save version\" in Kaggle, and wait for the notebook to run, you'll see that `sub.csv` appears in the \"Data\" tab. Clicking on that file will show a *Submit* button, which allows you to submit to the competition.","metadata":{}},{"cell_type":"markdown","source":"## Using matrix product","metadata":{}},{"cell_type":"markdown","source":"We can make things quite a bit neater...\n\nTake a look at the inner-most calculation we're doing to get the predictions:","metadata":{}},{"cell_type":"code","source":"(val_indep*coeffs).sum(axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:48.247768Z","iopub.execute_input":"2022-05-30T22:38:48.248084Z","iopub.status.idle":"2022-05-30T22:38:48.258935Z","shell.execute_reply.started":"2022-05-30T22:38:48.248052Z","shell.execute_reply":"2022-05-30T22:38:48.258184Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Multiplying elements together and then adding across rows is identical to doing a matrix-vector product! Python uses the `@` operator to indicate matrix products, and is supported by PyTorch tensors. Therefore, we can replicate the above calculate more simply like so:","metadata":{}},{"cell_type":"code","source":"val_indep@coeffs","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:51.959362Z","iopub.execute_input":"2022-05-30T22:38:51.959798Z","iopub.status.idle":"2022-05-30T22:38:51.97614Z","shell.execute_reply.started":"2022-05-30T22:38:51.959765Z","shell.execute_reply":"2022-05-30T22:38:51.975461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It also turns out that this is much faster, because matrix products in PyTorch are very highly optimised.\n\nLet's use this to replace how `calc_preds` works:","metadata":{}},{"cell_type":"code","source":"def calc_preds(coeffs, indeps): return torch.sigmoid(indeps@coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:56.321807Z","iopub.execute_input":"2022-05-30T22:38:56.322255Z","iopub.status.idle":"2022-05-30T22:38:56.326812Z","shell.execute_reply.started":"2022-05-30T22:38:56.322213Z","shell.execute_reply":"2022-05-30T22:38:56.32606Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In order to do matrix-matrix products (which we'll need in the next section), we need to turn `coeffs` into a column vector (i.e. a matrix with a single column), which we can do by passing a second argument `1` to `torch.rand()`, indicating that we want our coefficients to have one column:","metadata":{}},{"cell_type":"code","source":"def init_coeffs(): return (torch.rand(n_coeff, 1)*0.1).requires_grad_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:58.895467Z","iopub.execute_input":"2022-05-30T22:38:58.895779Z","iopub.status.idle":"2022-05-30T22:38:58.900851Z","shell.execute_reply.started":"2022-05-30T22:38:58.895744Z","shell.execute_reply":"2022-05-30T22:38:58.899931Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll also need to turn our dependent variable into a column vector, which we can do by indexing the column dimension with the special value `None`, which tells PyTorch to add a new dimension in this position:","metadata":{}},{"cell_type":"code","source":"trn_dep = trn_dep[:,None]\nval_dep = val_dep[:,None]","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:38:59.788799Z","iopub.execute_input":"2022-05-30T22:38:59.789678Z","iopub.status.idle":"2022-05-30T22:38:59.794227Z","shell.execute_reply.started":"2022-05-30T22:38:59.789625Z","shell.execute_reply":"2022-05-30T22:38:59.793326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can now train our model as before and confirm we get identical outputs...:","metadata":{}},{"cell_type":"code","source":"coeffs = train_model(lr=100)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:38.070545Z","iopub.execute_input":"2022-05-30T22:39:38.071003Z","iopub.status.idle":"2022-05-30T22:39:38.094666Z","shell.execute_reply.started":"2022-05-30T22:39:38.070972Z","shell.execute_reply":"2022-05-30T22:39:38.093641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"...and identical accuracy:","metadata":{}},{"cell_type":"code","source":"acc(coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:40.463301Z","iopub.execute_input":"2022-05-30T22:39:40.463735Z","iopub.status.idle":"2022-05-30T22:39:40.469684Z","shell.execute_reply.started":"2022-05-30T22:39:40.463702Z","shell.execute_reply":"2022-05-30T22:39:40.468652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## A neural network","metadata":{}},{"cell_type":"markdown","source":"We've now got what we need to implement our neural network.\n\nFirst, we'll need to create coefficients for each of our layers. Our first set of coefficients will take our `n_coeff` inputs, and create `n_hidden` outputs. We can choose whatever `n_hidden` we like -- a higher number gives our network more flexibility, but makes it slower and harder to train. So we need a matrix of size `n_coeff` by `n_hidden`. We'll divide these coefficients by `n_hidden` so that when we sum them up in the next layer we'll end up with similar magnitude numbers to what we started with.\n\nThen our second layer will need to take the `n_hidden` inputs and create a single output, so that means we need a `n_hidden` by `1` matrix there. The second layer will also need a constant term added.","metadata":{}},{"cell_type":"code","source":"def init_coeffs(n_hidden=20):\n    layer1 = (torch.rand(n_coeff, n_hidden)-0.5)/n_hidden\n    layer2 = torch.rand(n_hidden, 1)-0.3\n    const = torch.rand(1)[0]\n    return layer1.requires_grad_(),layer2.requires_grad_(),const.requires_grad_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:44.428254Z","iopub.execute_input":"2022-05-30T22:39:44.428599Z","iopub.status.idle":"2022-05-30T22:39:44.434009Z","shell.execute_reply.started":"2022-05-30T22:39:44.428563Z","shell.execute_reply":"2022-05-30T22:39:44.433164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we have our coefficients, we can create our neural net. The key steps are the two matrix products, `indeps@l1` and `res@l2` (where `res` is the output of the first layer). The first layer output is passed to `F.relu` (that's our non-linearity), and the second is passed to `torch.sigmoid` as before.","metadata":{}},{"cell_type":"code","source":"import torch.nn.functional as F\n\ndef calc_preds(coeffs, indeps):\n    l1,l2,const = coeffs\n    res = F.relu(indeps@l1)\n    res = res@l2 + const\n    return torch.sigmoid(res)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:45.302573Z","iopub.execute_input":"2022-05-30T22:39:45.302903Z","iopub.status.idle":"2022-05-30T22:39:45.309472Z","shell.execute_reply.started":"2022-05-30T22:39:45.302864Z","shell.execute_reply":"2022-05-30T22:39:45.308498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Finally, now that we have more than one set of coefficients, we need to add a loop to update each one:","metadata":{}},{"cell_type":"code","source":"def update_coeffs(coeffs, lr):\n    for layer in coeffs:\n        layer.sub_(layer.grad * lr)\n        layer.grad.zero_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:55.3665Z","iopub.execute_input":"2022-05-30T22:39:55.366945Z","iopub.status.idle":"2022-05-30T22:39:55.371578Z","shell.execute_reply.started":"2022-05-30T22:39:55.366914Z","shell.execute_reply":"2022-05-30T22:39:55.370699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That's it -- we're now ready to train our model!","metadata":{}},{"cell_type":"code","source":"coeffs = train_model(lr=1.4)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:39:58.189651Z","iopub.execute_input":"2022-05-30T22:39:58.189982Z","iopub.status.idle":"2022-05-30T22:39:58.227202Z","shell.execute_reply.started":"2022-05-30T22:39:58.189951Z","shell.execute_reply":"2022-05-30T22:39:58.226226Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"coeffs = train_model(lr=20)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:40:16.337512Z","iopub.execute_input":"2022-05-30T22:40:16.338016Z","iopub.status.idle":"2022-05-30T22:40:16.368327Z","shell.execute_reply.started":"2022-05-30T22:40:16.337959Z","shell.execute_reply":"2022-05-30T22:40:16.367439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It's looking good -- our loss is lower than before. Let's see if that translates to a better result on the validation set:","metadata":{}},{"cell_type":"code","source":"acc(coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:40:18.644153Z","iopub.execute_input":"2022-05-30T22:40:18.644458Z","iopub.status.idle":"2022-05-30T22:40:18.651372Z","shell.execute_reply.started":"2022-05-30T22:40:18.644427Z","shell.execute_reply":"2022-05-30T22:40:18.650102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this case our neural net isn't showing better results than the linear model. That's not surprising; this dataset is very small and very simple, and isn't the kind of thing we'd expect to see neural networks excel at. Furthermore, our validation set is too small to reliably see much accuracy difference. But the key thing is that we now know exactly what a real neural net looks like!","metadata":{}},{"cell_type":"markdown","source":"## Deep learning","metadata":{}},{"cell_type":"markdown","source":"The neural net in the previous section only uses one hidden layer, so it doesn't count as \"deep\" learning. But we can use the exact same technique to make our neural net deep, by adding more matrix multiplications.\n\nFirst, we'll need to create additional coefficients for each layer:","metadata":{}},{"cell_type":"code","source":"def init_coeffs():\n    hiddens = [10, 10]  # <-- set this to the size of each hidden layer you want\n    sizes = [n_coeff] + hiddens + [1]\n    n = len(sizes)\n    layers = [(torch.rand(sizes[i], sizes[i+1])-0.3)/sizes[i+1]*4 for i in range(n-1)]\n    consts = [(torch.rand(1)[0]-0.5)*0.1 for i in range(n-1)]\n    for l in layers+consts: l.requires_grad_()\n    return layers,consts","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:40:55.25457Z","iopub.execute_input":"2022-05-30T22:40:55.255291Z","iopub.status.idle":"2022-05-30T22:40:55.261806Z","shell.execute_reply.started":"2022-05-30T22:40:55.255242Z","shell.execute_reply":"2022-05-30T22:40:55.261271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You'll notice here that there's a lot of messy constants to get the random numbers in just the right ranges. When you train the model in a moment, you'll see that the tiniest changes to these initialisations can cause our model to fail to train at all! This is a key reason that deep learning failed to make much progress in the early days -- it's very finicky to get a good starting point for our coefficients. Nowadays, we have ways to deal with that, which we'll learn about in other notebooks.\n\nOur deep learning `calc_preds` looks much the same as before, but now we loop through each layer, instead of listing them separately:","metadata":{}},{"cell_type":"code","source":"import torch.nn.functional as F\n\ndef calc_preds(coeffs, indeps):\n    layers,consts = coeffs\n    n = len(layers)\n    res = indeps\n    for i,l in enumerate(layers):\n        res = res@l + consts[i]\n        if i!=n-1: res = F.relu(res)\n    return torch.sigmoid(res)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:40:57.610142Z","iopub.execute_input":"2022-05-30T22:40:57.610974Z","iopub.status.idle":"2022-05-30T22:40:57.618154Z","shell.execute_reply.started":"2022-05-30T22:40:57.610916Z","shell.execute_reply":"2022-05-30T22:40:57.617329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We also need a minor update to `update_coeffs` since we've got `layers` and `consts` separated now:","metadata":{}},{"cell_type":"code","source":"def update_coeffs(coeffs, lr):\n    layers,consts = coeffs\n    for layer in layers+consts:\n        layer.sub_(layer.grad * lr)\n        layer.grad.zero_()","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:41:08.512494Z","iopub.execute_input":"2022-05-30T22:41:08.513049Z","iopub.status.idle":"2022-05-30T22:41:08.519219Z","shell.execute_reply.started":"2022-05-30T22:41:08.512999Z","shell.execute_reply":"2022-05-30T22:41:08.518093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's train our model...","metadata":{}},{"cell_type":"code","source":"coeffs = train_model(lr=4)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:41:23.632516Z","iopub.execute_input":"2022-05-30T22:41:23.633004Z","iopub.status.idle":"2022-05-30T22:41:23.666981Z","shell.execute_reply.started":"2022-05-30T22:41:23.632953Z","shell.execute_reply":"2022-05-30T22:41:23.666048Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"...and check its accuracy:","metadata":{}},{"cell_type":"code","source":"acc(coeffs)","metadata":{"execution":{"iopub.status.busy":"2022-05-30T22:41:25.490656Z","iopub.execute_input":"2022-05-30T22:41:25.491182Z","iopub.status.idle":"2022-05-30T22:41:25.497888Z","shell.execute_reply.started":"2022-05-30T22:41:25.491146Z","shell.execute_reply":"2022-05-30T22:41:25.49695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Final thoughts","metadata":{}},{"cell_type":"markdown","source":"It's actually pretty cool that we've managed to create a real deep learning model from scratch and trained it to get over 80% accuracy on this task, all in the course of a single notebook!\n\nThe \"real\" deep learning models that are used in research and industry look very similar to this, and in fact if you look inside the source code of any deep learning model you'll recognise the basic steps are the same.\n\nThe biggest differences in practical models to what we have above are:\n\n- How initialisation and normalisation is done to ensure the model trains correctly every time\n- Regularization (to avoid over-fitting)\n- Modifying the neural net itself to take advantage of knowledge of the problem domain\n- Doing gradient descent steps on smaller batches, rather than the whole dataset.\n\nI'll be adding notebooks about all these later, and will add links here once they're ready.\n\nIf you found this notebook useful, please remember to click the little up-arrow at the top to upvote it, since I like to know when people have found my work useful, and it helps others find it too. (BTW, be sure you're looking at my [original notebook here](https://www.kaggle.com/code/jhoward/linear-model-and-neural-net-from-scratch) when you do that, and are not on your own copy of it, otherwise your upvote won't get counted!) And if you have any questions or comments, please pop them below -- I read every comment I receive!","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Introduction"]},{"cell_type":"markdown","metadata":{},"source":["In this notebook we're going to build and train a deep learning model \"from scratch\" -- by which I mean that we're not going to use any pre-built architecture, or optimizers, or data loading frameworks, etc.\n","\n","We'll be assuming you already know the basics of how a neural network works. If you don't, read this notebook first: [How does a neural net really work?\n","](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work). We'll be using Kaggle's [Titanic](https://www.kaggle.com/competitions/titanic/) competition in this notebook, because it's very small and simple, but also has displays many of the tricky real-life issues that we need to handle in most practical projects. (Note, however, that this competition is a small \"learner\" competition on Kaggle, so don't expect to actually see much benefits from using a neural net just yet; that will come once we try our some real competitions!)\n","\n","It's great to be able to run the same notebook on your own machine or Colab, as well as Kaggle. To allow for this, we use this code to download the data as needed when not on Kaggle (see [this notebook](https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners/) for details about this technique):"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:17.763822Z","iopub.status.busy":"2022-05-30T22:34:17.763494Z","iopub.status.idle":"2022-05-30T22:34:17.771348Z","shell.execute_reply":"2022-05-30T22:34:17.770444Z","shell.execute_reply.started":"2022-05-30T22:34:17.763787Z"},"trusted":true},"outputs":[],"source":["import os\n","from pathlib import Path\n","\n","iskaggle = os.environ.get('KAGGLE_KERNEL_RUN_TYPE', '')\n","if iskaggle: path = Path('../input/titanic')\n","else:\n","    path = Path('titanic')\n","    if not path.exists():\n","        import zipfile,kaggle\n","        kaggle.api.competition_download_cli(str(path))\n","        zipfile.ZipFile(f'{path}.zip').extractall(path)"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Note that the data for Kaggle comps always lives in the `../input` folder. The easiest way to get the path is to click the \"K\" button in the top-right of the Kaggle notebook, click on the folder shown there, and click the copy button.\n","\n","We'll be using *numpy* and *pytorch* for array calculations in this notebook, and *pandas* for working with tabular data, so we'll import them and set them to display using a bit more space than they default to."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:17.811857Z","iopub.status.busy":"2022-05-30T22:34:17.810967Z","iopub.status.idle":"2022-05-30T22:34:17.817725Z","shell.execute_reply":"2022-05-30T22:34:17.816849Z","shell.execute_reply.started":"2022-05-30T22:34:17.811797Z"},"trusted":true},"outputs":[],"source":["import torch, numpy as np, pandas as pd\n","np.set_printoptions(linewidth=140)\n","torch.set_printoptions(linewidth=140, sci_mode=False, edgeitems=7)\n","pd.set_option('display.width', 140)"]},{"cell_type":"markdown","metadata":{"heading_collapsed":true},"source":["## Cleaning the data"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["This is a *tabular data* competition -- the data is in the form of a table. It's provided as a Comma Separated Values (CSV) file. We can open it using the *pandas* library, which will create a `DataFrame`."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:17.899238Z","iopub.status.busy":"2022-05-30T22:34:17.898249Z","iopub.status.idle":"2022-05-30T22:34:17.932714Z","shell.execute_reply":"2022-05-30T22:34:17.931738Z","shell.execute_reply.started":"2022-05-30T22:34:17.899131Z"},"hidden":true,"scrolled":true,"trusted":true},"outputs":[],"source":["df = pd.read_csv(path/'train.csv')\n","df"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["As we learned in the *How does a neural net really work* notebook, we going to want to multiply each column by some coefficients. But we can see in the `Cabin` column that there are `NaN` values, which is how Pandas refers to missing values. We can't multiply something by a missing value!\n","\n","Let's check which columns contain `NaN` values. Pandas' `isna()` function returns `True` (which is treated as `1` when used as a number) for `NaN` values, so we can just add them up for each column:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:17.955557Z","iopub.status.busy":"2022-05-30T22:34:17.95524Z","iopub.status.idle":"2022-05-30T22:34:17.966199Z","shell.execute_reply":"2022-05-30T22:34:17.96534Z","shell.execute_reply.started":"2022-05-30T22:34:17.955525Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df.isna().sum()"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Notice that by default Pandas sums over columns.\n","\n","We'll need to replace the missing values with something. It doesn't generally matter too much what we choose. We'll use the most common value (the \"*mode*\"). We can use the `mode` function for that. One wrinkle is that it returns more than one row in the case of ties, so we just grab the first row with `iloc[0]`:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.012387Z","iopub.status.busy":"2022-05-30T22:34:18.011876Z","iopub.status.idle":"2022-05-30T22:34:18.030165Z","shell.execute_reply":"2022-05-30T22:34:18.029316Z","shell.execute_reply.started":"2022-05-30T22:34:18.012338Z"},"hidden":true,"trusted":true},"outputs":[],"source":["modes = df.mode().iloc[0]\n","modes"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["BTW, it's never a good idea to use functions without understanding them. So be sure to google for anything you're not familiar with. E.g if you want to learn about `iloc` (which is a very important function indeed!) then Google will give you a link to a [great tutorial](https://www.shanelynn.ie/pandas-iloc-loc-select-rows-and-columns-dataframe/).\n","\n","Now that we've got the mode of each column, we can use `fillna` to replace the missing values with the mode of each column. We'll do it \"in place\" -- meaning that we'll change the dataframe itself, rather than returning a new one."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.069624Z","iopub.status.busy":"2022-05-30T22:34:18.069123Z","iopub.status.idle":"2022-05-30T22:34:18.078805Z","shell.execute_reply":"2022-05-30T22:34:18.077978Z","shell.execute_reply.started":"2022-05-30T22:34:18.069573Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df.fillna(modes, inplace=True)"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["We can now check there's no missing values left:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.139844Z","iopub.status.busy":"2022-05-30T22:34:18.13936Z","iopub.status.idle":"2022-05-30T22:34:18.149896Z","shell.execute_reply":"2022-05-30T22:34:18.148884Z","shell.execute_reply.started":"2022-05-30T22:34:18.139793Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df.isna().sum()"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Here's how we get a quick summary of all the numeric columns in the dataset:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.205953Z","iopub.status.busy":"2022-05-30T22:34:18.205483Z","iopub.status.idle":"2022-05-30T22:34:18.240196Z","shell.execute_reply":"2022-05-30T22:34:18.239106Z","shell.execute_reply.started":"2022-05-30T22:34:18.205897Z"},"hidden":true,"trusted":true},"outputs":[],"source":["import numpy as np\n","\n","df.describe(include=(np.number))"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2022-05-13T11:02:34.328433Z","iopub.status.busy":"2022-05-13T11:02:34.327999Z","iopub.status.idle":"2022-05-13T11:02:34.336993Z","shell.execute_reply":"2022-05-13T11:02:34.335466Z","shell.execute_reply.started":"2022-05-13T11:02:34.32838Z"},"hidden":true},"source":["We can see that `Fare` contains mainly values of around `0` to `30`, but there's a few really big ones. This is very common with fields contain monetary values, and it can cause problems for our model, because once that column is multiplied by a coefficient later, the few rows with really big values will dominate the result.\n","\n","You can see the issue most clearly visually by looking at a histogram, which shows a long tail to the right (and don't forget: if you're not entirely sure what a histogram is, Google \"[histogram tutorial](https://www.google.com/search?q=histogram+tutorial&oq=histogram+tutorial)\" and do a bit of reading before continuing on):"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.242123Z","iopub.status.busy":"2022-05-30T22:34:18.241874Z","iopub.status.idle":"2022-05-30T22:34:18.471439Z","shell.execute_reply":"2022-05-30T22:34:18.470441Z","shell.execute_reply.started":"2022-05-30T22:34:18.242094Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df['Fare'].hist();"]},{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2022-05-13T11:02:34.328433Z","iopub.status.busy":"2022-05-13T11:02:34.327999Z","iopub.status.idle":"2022-05-13T11:02:34.336993Z","shell.execute_reply":"2022-05-13T11:02:34.335466Z","shell.execute_reply.started":"2022-05-13T11:02:34.32838Z"},"hidden":true},"source":["To fix this, the most common approach is to take the logarithm, which squishes the big numbers and makes the distribution more reasonable. Note, however, that there are zeros in the `Fare` column, and `log(0)` is infinite -- to fix this, we'll simply add `1` to all values first:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.47382Z","iopub.status.busy":"2022-05-30T22:34:18.473109Z","iopub.status.idle":"2022-05-30T22:34:18.478776Z","shell.execute_reply":"2022-05-30T22:34:18.478149Z","shell.execute_reply.started":"2022-05-30T22:34:18.473778Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df['LogFare'] = np.log(df['Fare']+1)"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["The histogram now shows a more even distribution of values without the long tail:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.480926Z","iopub.status.busy":"2022-05-30T22:34:18.480088Z","iopub.status.idle":"2022-05-30T22:34:18.855605Z","shell.execute_reply":"2022-05-30T22:34:18.854915Z","shell.execute_reply.started":"2022-05-30T22:34:18.480885Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df['LogFare'].hist();"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["It looks from the `describe()` output like `Pclass` contains just 3 values, which we can confirm by looking at the [Data Dictionary](https://www.kaggle.com/competitions/titanic/data) (which you should always study carefully for any project!) -- "]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.857676Z","iopub.status.busy":"2022-05-30T22:34:18.857312Z","iopub.status.idle":"2022-05-30T22:34:18.863672Z","shell.execute_reply":"2022-05-30T22:34:18.862791Z","shell.execute_reply.started":"2022-05-30T22:34:18.857643Z"},"hidden":true,"trusted":true},"outputs":[],"source":["pclasses = sorted(df.Pclass.unique())\n","pclasses"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Here's how we get a quick summary of all the non-numeric columns in the dataset:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.865154Z","iopub.status.busy":"2022-05-30T22:34:18.864893Z","iopub.status.idle":"2022-05-30T22:34:18.89758Z","shell.execute_reply":"2022-05-30T22:34:18.896706Z","shell.execute_reply.started":"2022-05-30T22:34:18.865115Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df.describe(include=[object])"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Clearly we can't multiply strings like `male` or `S` by coefficients, so we need to replace those with numbers.\n","\n","We do that by creating new columns containing *dummy variables*. A dummy variable is a column that contains a `1` where a particular column contains a particular value, or a `0` otherwise. For instance, we could create a dummy variable for `Sex='male'`, which would be a new column containing `1` for rows where `Sex` is `'male'`, and 0 for rows where it isn't.\n","\n","Pandas can create these automatically using `get_dummies`, which also remove the original columns. We'll create dummy variables for `Pclass`, even although it's numeric, since the numbers `1`, `2`, and `3` correspond to first, second, and third class cabins - not to counts or measures that make sense to multiply by. We'll also create dummies for `Sex` and `Embarked` since we'll want to use those as predictors in our model. On the other hand, `Cabin`, `Name`, and `Ticket` have too many unique values for it to make sense creating dummy variables for them."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.899369Z","iopub.status.busy":"2022-05-30T22:34:18.899127Z","iopub.status.idle":"2022-05-30T22:34:18.914993Z","shell.execute_reply":"2022-05-30T22:34:18.914167Z","shell.execute_reply.started":"2022-05-30T22:34:18.899338Z"},"hidden":true,"trusted":true},"outputs":[],"source":["df = pd.get_dummies(df, columns=[\"Sex\",\"Pclass\",\"Embarked\"])\n","df.columns"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["We can see that 5 columns have been added to the end -- one for each of the possible values of each of the three columns we requested, and that those three requested columns have been removed.\n","\n","Here's what the first few rows of those newly added columns look like:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.916456Z","iopub.status.busy":"2022-05-30T22:34:18.916186Z","iopub.status.idle":"2022-05-30T22:34:18.933746Z","shell.execute_reply":"2022-05-30T22:34:18.933135Z","shell.execute_reply.started":"2022-05-30T22:34:18.916426Z"},"hidden":true,"trusted":true},"outputs":[],"source":["added_cols = ['Sex_male', 'Sex_female', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Embarked_C', 'Embarked_Q', 'Embarked_S']\n","df[added_cols].head()"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Now we can create our independent (predictors) and dependent (target) variables. They both need to be PyTorch tensors. Our dependent variable is `Survived`:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.935422Z","iopub.status.busy":"2022-05-30T22:34:18.934648Z","iopub.status.idle":"2022-05-30T22:34:18.944596Z","shell.execute_reply":"2022-05-30T22:34:18.943444Z","shell.execute_reply.started":"2022-05-30T22:34:18.935384Z"},"hidden":true,"trusted":true},"outputs":[],"source":["from torch import tensor\n","\n","t_dep = tensor(df.Survived)"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Our independent variables are all the continuous variables of interest plus all the dummy variables we just created:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.946772Z","iopub.status.busy":"2022-05-30T22:34:18.94652Z","iopub.status.idle":"2022-05-30T22:34:18.96487Z","shell.execute_reply":"2022-05-30T22:34:18.963667Z","shell.execute_reply.started":"2022-05-30T22:34:18.946741Z"},"hidden":true,"trusted":true},"outputs":[],"source":["indep_cols = ['Age', 'SibSp', 'Parch', 'LogFare'] + added_cols\n","\n","t_indep = tensor(df[indep_cols].values, dtype=torch.float)\n","t_indep"]},{"cell_type":"markdown","metadata":{"hidden":true},"source":["Here's the number of rows and columns we have for our independent variables:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.969136Z","iopub.status.busy":"2022-05-30T22:34:18.968005Z","iopub.status.idle":"2022-05-30T22:34:18.98114Z","shell.execute_reply":"2022-05-30T22:34:18.980184Z","shell.execute_reply.started":"2022-05-30T22:34:18.969092Z"},"hidden":true,"trusted":true},"outputs":[],"source":["t_indep.shape"]},{"cell_type":"markdown","metadata":{},"source":["## Setting up a linear model"]},{"cell_type":"markdown","metadata":{},"source":["Now that we've got a matrix of independent variables and a dependent variable vector, we can work on calculating our predictions and our loss. In this section, we're going to manually do a single step of calculating predictions and loss for every row of our data.\n","\n","Our first model will be a simple linear model. We'll need a coefficient for each column in `t_indep`. We'll pick random numbers in the range `(-0.5,0.5)`, and set our manual seed so that my explanations in the prose in this notebook will be consistent with what you see when you run it."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:18.983237Z","iopub.status.busy":"2022-05-30T22:34:18.982492Z","iopub.status.idle":"2022-05-30T22:34:18.995038Z","shell.execute_reply":"2022-05-30T22:34:18.994437Z","shell.execute_reply.started":"2022-05-30T22:34:18.983187Z"},"trusted":true},"outputs":[],"source":["torch.manual_seed(442)\n","\n","n_coeff = t_indep.shape[1]\n","coeffs = torch.rand(n_coeff)-0.5\n","coeffs"]},{"cell_type":"markdown","metadata":{},"source":["Our predictions will be calculated by multiplying each row by the coefficients, and adding them up. One interesting point here is that we don't need a separate constant term (also known as a \"bias\" or \"intercept\" term), or a column of all `1`s to give the same effect has having a constant term. That's because our dummy variables already cover the entire dataset -- e.g. there's a column for \"male\" and a column for \"female\", and everyone in the dataset is in exactly one of these; therefore, we don't need a separate intercept term to cover rows that aren't otherwise part of a column.\n","\n","Here's what the multiplication looks like:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.000637Z","iopub.status.busy":"2022-05-30T22:34:18.999409Z","iopub.status.idle":"2022-05-30T22:34:19.009362Z","shell.execute_reply":"2022-05-30T22:34:19.00847Z","shell.execute_reply.started":"2022-05-30T22:34:19.000588Z"},"trusted":true},"outputs":[],"source":["t_indep*coeffs"]},{"cell_type":"markdown","metadata":{},"source":["We can see we've got a problem here. The sums of each row will be dominated by the first column, which is `Age`, since that's bigger on average than all the others.\n","\n","Let's make all the columns contain numbers from `0` to `1`, by dividing each column by its `max()`:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.011202Z","iopub.status.busy":"2022-05-30T22:34:19.010954Z","iopub.status.idle":"2022-05-30T22:34:19.02202Z","shell.execute_reply":"2022-05-30T22:34:19.02133Z","shell.execute_reply.started":"2022-05-30T22:34:19.011171Z"},"trusted":true},"outputs":[],"source":["vals,indices = t_indep.max(dim=0)\n","t_indep = t_indep / vals"]},{"cell_type":"markdown","metadata":{},"source":["As we see, that removes the problem of one column dominating all the others:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.04269Z","iopub.status.busy":"2022-05-30T22:34:19.042223Z","iopub.status.idle":"2022-05-30T22:34:19.050475Z","shell.execute_reply":"2022-05-30T22:34:19.049515Z","shell.execute_reply.started":"2022-05-30T22:34:19.042652Z"},"trusted":true},"outputs":[],"source":["t_indep*coeffs"]},{"cell_type":"markdown","metadata":{},"source":["One thing you hopefully noticed is how amazingly cool this line of code is:\n","\n","    t_indep = t_indep / vals\n","\n","That is dividing a matrix by a vector -- what on earth does that mean?!? The trick here is that we're taking advantage of a technique in numpy and PyTorch (and many other languages, going all the way back to APL) called [broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html). In short, this acts as if there's a separate copy of the vector for every row of the matrix, so it divides each row of the matrix by the vector. In practice, it doesn't actually make any copies, and does the whole thing in a highly optimized way, taking full advantage of modern CPUs (or, indeed, GPUs, if we're using them). Broadcasting is one of the most important techniques for making your code concise, maintainable, and fast, so it's well worth studying and practicing.\n","\n","We can now create predictions from our linear model, by adding up the rows of the product:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.097604Z","iopub.status.busy":"2022-05-30T22:34:19.096685Z","iopub.status.idle":"2022-05-30T22:34:19.1028Z","shell.execute_reply":"2022-05-30T22:34:19.10218Z","shell.execute_reply.started":"2022-05-30T22:34:19.097545Z"},"trusted":true},"outputs":[],"source":["preds = (t_indep*coeffs).sum(axis=1)"]},{"cell_type":"markdown","metadata":{},"source":["Let's take a look at the first few:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.160794Z","iopub.status.busy":"2022-05-30T22:34:19.160318Z","iopub.status.idle":"2022-05-30T22:34:19.168117Z","shell.execute_reply":"2022-05-30T22:34:19.167355Z","shell.execute_reply.started":"2022-05-30T22:34:19.160761Z"},"trusted":true},"outputs":[],"source":["preds[:10]"]},{"cell_type":"markdown","metadata":{},"source":["Of course, these predictions aren't going to be any use, since our coefficients are random -- they're just a starting point for our gradient descent process.\n","\n","To do gradient descent, we need a loss function. Taking the average error of the rows (i.e. the absolute value of the difference between the prediction and the dependent) is generally a reasonable approach:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.235623Z","iopub.status.busy":"2022-05-30T22:34:19.235318Z","iopub.status.idle":"2022-05-30T22:34:19.24312Z","shell.execute_reply":"2022-05-30T22:34:19.242356Z","shell.execute_reply.started":"2022-05-30T22:34:19.235589Z"},"trusted":true},"outputs":[],"source":["loss = torch.abs(preds-t_dep).mean()\n","loss"]},{"cell_type":"markdown","metadata":{},"source":["Now that we've tested out a way of calculating predictions, and loss, let's pop them into functions to make life easier:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.296558Z","iopub.status.busy":"2022-05-30T22:34:19.295577Z","iopub.status.idle":"2022-05-30T22:34:19.301478Z","shell.execute_reply":"2022-05-30T22:34:19.300683Z","shell.execute_reply.started":"2022-05-30T22:34:19.296517Z"},"trusted":true},"outputs":[],"source":["def calc_preds(coeffs, indeps): return (indeps*coeffs).sum(axis=1)\n","def calc_loss(coeffs, indeps, deps): return torch.abs(calc_preds(coeffs, indeps)-deps).mean()"]},{"cell_type":"markdown","metadata":{},"source":["## Doing a gradient descent step"]},{"cell_type":"markdown","metadata":{},"source":["In this section, we're going to do a single \"epoch\" of gradient descent manually. The only thing we're going to automate is calculating gradients, because let's face it that's pretty tedious and entirely pointless to do by hand! To get PyTorch to calculate gradients, we'll need to call `requires_grad_()` on our `coeffs` (if you're not sure why, review the previous notebook, [How does a neural net really work?](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work), before continuing):"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.376212Z","iopub.status.busy":"2022-05-30T22:34:19.375387Z","iopub.status.idle":"2022-05-30T22:34:19.382205Z","shell.execute_reply":"2022-05-30T22:34:19.381536Z","shell.execute_reply.started":"2022-05-30T22:34:19.376163Z"},"trusted":true},"outputs":[],"source":["coeffs.requires_grad_()"]},{"cell_type":"markdown","metadata":{},"source":["Now when we calculate our loss, PyTorch will keep track of all the steps, so we'll be able to get the gradients afterwards:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.438026Z","iopub.status.busy":"2022-05-30T22:34:19.437208Z","iopub.status.idle":"2022-05-30T22:34:19.444641Z","shell.execute_reply":"2022-05-30T22:34:19.443791Z","shell.execute_reply.started":"2022-05-30T22:34:19.437985Z"},"trusted":true},"outputs":[],"source":["loss = calc_loss(coeffs, t_indep, t_dep)\n","loss"]},{"cell_type":"markdown","metadata":{},"source":["Use `backward()` to ask PyTorch to calculate gradients now:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.49239Z","iopub.status.busy":"2022-05-30T22:34:19.491601Z","iopub.status.idle":"2022-05-30T22:34:19.496556Z","shell.execute_reply":"2022-05-30T22:34:19.495831Z","shell.execute_reply.started":"2022-05-30T22:34:19.492353Z"},"trusted":true},"outputs":[],"source":["loss.backward()"]},{"cell_type":"markdown","metadata":{},"source":["Let's see what they look like:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.550501Z","iopub.status.busy":"2022-05-30T22:34:19.549651Z","iopub.status.idle":"2022-05-30T22:34:19.555905Z","shell.execute_reply":"2022-05-30T22:34:19.555257Z","shell.execute_reply.started":"2022-05-30T22:34:19.550456Z"},"trusted":true},"outputs":[],"source":["coeffs.grad"]},{"cell_type":"markdown","metadata":{},"source":["Note that each time we call `backward`, the gradients are actually *added* to whatever is in the `.grad` attribute. Let's try running the above steps again:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.603236Z","iopub.status.busy":"2022-05-30T22:34:19.60283Z","iopub.status.idle":"2022-05-30T22:34:19.610594Z","shell.execute_reply":"2022-05-30T22:34:19.609647Z","shell.execute_reply.started":"2022-05-30T22:34:19.603206Z"},"trusted":true},"outputs":[],"source":["loss = calc_loss(coeffs, t_indep, t_dep)\n","loss.backward()\n","coeffs.grad"]},{"cell_type":"markdown","metadata":{},"source":["As you see, our `.grad` values are have doubled. That's because it added the gradients a second time. For this reason, after we use the gradients to do a gradient descent step, we need to set them back to zero.\n","\n","We can now do one gradient descent step, and check that our loss decreases:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:34:19.665739Z","iopub.status.busy":"2022-05-30T22:34:19.665238Z","iopub.status.idle":"2022-05-30T22:34:19.674839Z","shell.execute_reply":"2022-05-30T22:34:19.673727Z","shell.execute_reply.started":"2022-05-30T22:34:19.665705Z"},"trusted":true},"outputs":[],"source":["loss = calc_loss(coeffs, t_indep, t_dep)\n","loss.backward()\n","with torch.no_grad():\n","    coeffs.sub_(coeffs.grad * 0.1)\n","    coeffs.grad.zero_()\n","    print(calc_loss(coeffs, t_indep, t_dep))"]},{"cell_type":"markdown","metadata":{},"source":["Note that `a.sub_(b)` subtracts `b` from `a` in-place. In PyTorch, any method that ends in `_` changes its object in-place. Similarly, `a.zero_()` sets all elements of a tensor to zero."]},{"cell_type":"markdown","metadata":{},"source":["## Training the linear model"]},{"cell_type":"markdown","metadata":{},"source":["Before we begin training our model, we'll need to ensure that we hold out a validation set for calculating our metrics (for details on this, see \"[Getting started with NLP for absolute beginners](https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners#Test-and-validation-sets)\".\n","\n","There's lots of different ways we can do this. In the next notebook we'll be comparing our approach here to what the fastai library does, so we'll want to ensure we split the data in the same way. So let's use `RandomSplitter` to get indices that will split our data into training and validation sets:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:26.57635Z","iopub.status.busy":"2022-05-30T22:35:26.57605Z","iopub.status.idle":"2022-05-30T22:35:27.821258Z","shell.execute_reply":"2022-05-30T22:35:27.820329Z","shell.execute_reply.started":"2022-05-30T22:35:26.57632Z"},"trusted":true},"outputs":[],"source":["from fastai.data.transforms import RandomSplitter\n","trn_split,val_split=RandomSplitter(seed=42)(df)"]},{"cell_type":"markdown","metadata":{},"source":["Now we can apply those indicies to our independent and dependent variables:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:27.822887Z","iopub.status.busy":"2022-05-30T22:35:27.822671Z","iopub.status.idle":"2022-05-30T22:35:27.836937Z","shell.execute_reply":"2022-05-30T22:35:27.836081Z","shell.execute_reply.started":"2022-05-30T22:35:27.822859Z"},"trusted":true},"outputs":[],"source":["trn_indep,val_indep = t_indep[trn_split],t_indep[val_split]\n","trn_dep,val_dep = t_dep[trn_split],t_dep[val_split]\n","len(trn_indep),len(val_indep)"]},{"cell_type":"markdown","metadata":{},"source":["We'll create functions for the three things we did manually above: updating `coeffs`, doing one full gradient descent step, and initilising `coeffs` to random numbers:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:35.029177Z","iopub.status.busy":"2022-05-30T22:35:35.028649Z","iopub.status.idle":"2022-05-30T22:35:35.034346Z","shell.execute_reply":"2022-05-30T22:35:35.033261Z","shell.execute_reply.started":"2022-05-30T22:35:35.029143Z"},"trusted":true},"outputs":[],"source":["def update_coeffs(coeffs, lr):\n","    coeffs.sub_(coeffs.grad * lr)\n","    coeffs.grad.zero_()"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:35.633995Z","iopub.status.busy":"2022-05-30T22:35:35.633703Z","iopub.status.idle":"2022-05-30T22:35:35.639103Z","shell.execute_reply":"2022-05-30T22:35:35.63814Z","shell.execute_reply.started":"2022-05-30T22:35:35.633964Z"},"trusted":true},"outputs":[],"source":["def one_epoch(coeffs, lr):\n","    loss = calc_loss(coeffs, trn_indep, trn_dep)\n","    loss.backward()\n","    with torch.no_grad(): update_coeffs(coeffs, lr)\n","    print(f\"{loss:.3f}\", end=\"; \")"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:36.293837Z","iopub.status.busy":"2022-05-30T22:35:36.293565Z","iopub.status.idle":"2022-05-30T22:35:36.297457Z","shell.execute_reply":"2022-05-30T22:35:36.296816Z","shell.execute_reply.started":"2022-05-30T22:35:36.293808Z"},"trusted":true},"outputs":[],"source":["def init_coeffs(): return (torch.rand(n_coeff)-0.5).requires_grad_()"]},{"cell_type":"markdown","metadata":{},"source":["We can now use these functions to train our model:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:35:38.772314Z","iopub.status.busy":"2022-05-30T22:35:38.771827Z","iopub.status.idle":"2022-05-30T22:35:38.777313Z","shell.execute_reply":"2022-05-30T22:35:38.776439Z","shell.execute_reply.started":"2022-05-30T22:35:38.772247Z"},"trusted":true},"outputs":[],"source":["def train_model(epochs=30, lr=0.01):\n","    torch.manual_seed(442)\n","    coeffs = init_coeffs()\n","    for i in range(epochs): one_epoch(coeffs, lr=lr)\n","    return coeffs"]},{"cell_type":"markdown","metadata":{},"source":["Let's try it. Our loss will print at the end of every step, so we hope we'll see it going down:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:14.16788Z","iopub.status.busy":"2022-05-30T22:36:14.16706Z","iopub.status.idle":"2022-05-30T22:36:14.181652Z","shell.execute_reply":"2022-05-30T22:36:14.180496Z","shell.execute_reply.started":"2022-05-30T22:36:14.167812Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(18, lr=0.2)"]},{"cell_type":"markdown","metadata":{},"source":["It does!\n","\n","Let's take a look at the coefficients for each column:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:21.980389Z","iopub.status.busy":"2022-05-30T22:36:21.97957Z","iopub.status.idle":"2022-05-30T22:36:21.990088Z","shell.execute_reply":"2022-05-30T22:36:21.989021Z","shell.execute_reply.started":"2022-05-30T22:36:21.98035Z"},"trusted":true},"outputs":[],"source":["def show_coeffs(): return dict(zip(indep_cols, coeffs.requires_grad_(False)))\n","show_coeffs()"]},{"cell_type":"markdown","metadata":{},"source":["## Measuring accuracy"]},{"cell_type":"markdown","metadata":{},"source":["The Kaggle competition is not, however, scored by absolute error (which is our loss function). It's scored by *accuracy* -- the proportion of rows where we correctly predict survival. Let's see how accurate we were on the validation set. First, calculate the predictions:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:30.826372Z","iopub.status.busy":"2022-05-30T22:36:30.825856Z","iopub.status.idle":"2022-05-30T22:36:30.831613Z","shell.execute_reply":"2022-05-30T22:36:30.830756Z","shell.execute_reply.started":"2022-05-30T22:36:30.826322Z"},"trusted":true},"outputs":[],"source":["preds = calc_preds(coeffs, val_indep)"]},{"cell_type":"markdown","metadata":{},"source":["We'll assume that any passenger with a score of over `0.5` is predicted to survive. So that means we're correct for each row where `preds>0.5` is the same as the dependent variable:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:33.618899Z","iopub.status.busy":"2022-05-30T22:36:33.618455Z","iopub.status.idle":"2022-05-30T22:36:33.62703Z","shell.execute_reply":"2022-05-30T22:36:33.625949Z","shell.execute_reply.started":"2022-05-30T22:36:33.618867Z"},"trusted":true},"outputs":[],"source":["results = val_dep.bool()==(preds>0.5)\n","results[:16]"]},{"cell_type":"markdown","metadata":{},"source":["Let's see what our average accuracy is:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:35.725637Z","iopub.status.busy":"2022-05-30T22:36:35.725112Z","iopub.status.idle":"2022-05-30T22:36:35.732969Z","shell.execute_reply":"2022-05-30T22:36:35.732241Z","shell.execute_reply.started":"2022-05-30T22:36:35.725599Z"},"trusted":true},"outputs":[],"source":["results.float().mean()"]},{"cell_type":"markdown","metadata":{},"source":["That's not a bad start at all! We'll create a function so we can calcuate the accuracy easy for other models we train:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:40.356505Z","iopub.status.busy":"2022-05-30T22:36:40.356043Z","iopub.status.idle":"2022-05-30T22:36:40.365187Z","shell.execute_reply":"2022-05-30T22:36:40.364153Z","shell.execute_reply.started":"2022-05-30T22:36:40.356471Z"},"trusted":true},"outputs":[],"source":["def acc(coeffs): return (val_dep.bool()==(calc_preds(coeffs, val_indep)>0.5)).float().mean()\n","acc(coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["## Using sigmoid"]},{"cell_type":"markdown","metadata":{},"source":["Looking at our predictions, there's one obvious problem -- some of our predictions of the probability of survival are `>1`, and some are `<0`:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:42.344823Z","iopub.status.busy":"2022-05-30T22:36:42.344533Z","iopub.status.idle":"2022-05-30T22:36:42.352948Z","shell.execute_reply":"2022-05-30T22:36:42.351968Z","shell.execute_reply.started":"2022-05-30T22:36:42.344794Z"},"trusted":true},"outputs":[],"source":["preds[:28]"]},{"cell_type":"markdown","metadata":{},"source":["To fix this, we should pass every prediction through the *sigmoid function*, which has a minimum at zero and maximum at one, and is defined as follows:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:44.914015Z","iopub.status.busy":"2022-05-30T22:36:44.913101Z","iopub.status.idle":"2022-05-30T22:36:46.311818Z","shell.execute_reply":"2022-05-30T22:36:46.311008Z","shell.execute_reply.started":"2022-05-30T22:36:44.913968Z"},"trusted":true},"outputs":[],"source":["import sympy\n","sympy.plot(\"1/(1+exp(-x))\", xlim=(-5,5));"]},{"cell_type":"markdown","metadata":{},"source":["PyTorch already defines that function for us, so we can modify `calc_preds` to use it:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:36:46.313644Z","iopub.status.busy":"2022-05-30T22:36:46.313435Z","iopub.status.idle":"2022-05-30T22:36:46.317749Z","shell.execute_reply":"2022-05-30T22:36:46.3169Z","shell.execute_reply.started":"2022-05-30T22:36:46.313618Z"},"trusted":true},"outputs":[],"source":["def calc_preds(coeffs, indeps): return torch.sigmoid((indeps*coeffs).sum(axis=1))"]},{"cell_type":"markdown","metadata":{},"source":["Let's train a new model now, using this updated function to calculate predictions:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:23.22576Z","iopub.status.busy":"2022-05-30T22:38:23.225051Z","iopub.status.idle":"2022-05-30T22:38:23.250206Z","shell.execute_reply":"2022-05-30T22:38:23.249321Z","shell.execute_reply.started":"2022-05-30T22:38:23.225722Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(lr=100)"]},{"cell_type":"markdown","metadata":{},"source":["The loss has improved by a lot. Let's check the accuracy:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:28.519132Z","iopub.status.busy":"2022-05-30T22:38:28.518642Z","iopub.status.idle":"2022-05-30T22:38:28.527145Z","shell.execute_reply":"2022-05-30T22:38:28.526248Z","shell.execute_reply.started":"2022-05-30T22:38:28.519078Z"},"trusted":true},"outputs":[],"source":["acc(coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["That's improved too! Here's the coefficients of our trained model:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:32.01697Z","iopub.status.busy":"2022-05-30T22:38:32.015953Z","iopub.status.idle":"2022-05-30T22:38:32.02724Z","shell.execute_reply":"2022-05-30T22:38:32.026125Z","shell.execute_reply.started":"2022-05-30T22:38:32.016924Z"},"trusted":true},"outputs":[],"source":["show_coeffs()"]},{"cell_type":"markdown","metadata":{},"source":["These coefficients seem reasonable -- in general, older people and males were less likely to survive, and first class passengers were more likely to survive."]},{"cell_type":"markdown","metadata":{},"source":["## Submitting to Kaggle"]},{"cell_type":"markdown","metadata":{},"source":["Now that we've got a trained model, we can prepare a submission to Kaggle. To do that, first we need to read the test set:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:35.172909Z","iopub.status.busy":"2022-05-30T22:38:35.172343Z","iopub.status.idle":"2022-05-30T22:38:35.188597Z","shell.execute_reply":"2022-05-30T22:38:35.187826Z","shell.execute_reply.started":"2022-05-30T22:38:35.172873Z"},"trusted":true},"outputs":[],"source":["tst_df = pd.read_csv(path/'test.csv')"]},{"cell_type":"markdown","metadata":{},"source":["In this case, it turns out that the test set is missing `Fare` for one passenger. We'll just fill it with `0` to avoid problems:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:36.353392Z","iopub.status.busy":"2022-05-30T22:38:36.352687Z","iopub.status.idle":"2022-05-30T22:38:36.358237Z","shell.execute_reply":"2022-05-30T22:38:36.35739Z","shell.execute_reply.started":"2022-05-30T22:38:36.353355Z"},"trusted":true},"outputs":[],"source":["tst_df['Fare'] = tst_df.Fare.fillna(0)"]},{"cell_type":"markdown","metadata":{},"source":["Now we can just copy the same steps we did to our training set and do the same exact things on our test set to preprocess the data:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:38.198549Z","iopub.status.busy":"2022-05-30T22:38:38.198257Z","iopub.status.idle":"2022-05-30T22:38:38.220592Z","shell.execute_reply":"2022-05-30T22:38:38.219629Z","shell.execute_reply.started":"2022-05-30T22:38:38.198519Z"},"trusted":true},"outputs":[],"source":["tst_df.fillna(modes, inplace=True)\n","tst_df['LogFare'] = np.log(tst_df['Fare']+1)\n","tst_df = pd.get_dummies(tst_df, columns=[\"Sex\",\"Pclass\",\"Embarked\"])\n","\n","tst_indep = tensor(tst_df[indep_cols].values, dtype=torch.float)\n","tst_indep = tst_indep / vals"]},{"cell_type":"markdown","metadata":{},"source":["Let's calculate our predictions of which passengers survived in the test set:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:39.206547Z","iopub.status.busy":"2022-05-30T22:38:39.206216Z","iopub.status.idle":"2022-05-30T22:38:39.212386Z","shell.execute_reply":"2022-05-30T22:38:39.211631Z","shell.execute_reply.started":"2022-05-30T22:38:39.206512Z"},"trusted":true},"outputs":[],"source":["tst_df['Survived'] = (calc_preds(tst_indep, coeffs)>0.5).int()"]},{"cell_type":"markdown","metadata":{},"source":["The sample submission on the Kaggle competition site shows that we're expected to upload a CSV with just `PassengerId` and `Survived`, so let's create that and save it:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:40.161382Z","iopub.status.busy":"2022-05-30T22:38:40.161034Z","iopub.status.idle":"2022-05-30T22:38:40.173242Z","shell.execute_reply":"2022-05-30T22:38:40.17258Z","shell.execute_reply.started":"2022-05-30T22:38:40.161336Z"},"trusted":true},"outputs":[],"source":["sub_df = tst_df[['PassengerId','Survived']]\n","sub_df.to_csv('sub.csv', index=False)"]},{"cell_type":"markdown","metadata":{},"source":["We can check the first few rows of the file to make sure it looks reasonable:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:42.869402Z","iopub.status.busy":"2022-05-30T22:38:42.86855Z","iopub.status.idle":"2022-05-30T22:38:43.638832Z","shell.execute_reply":"2022-05-30T22:38:43.637559Z","shell.execute_reply.started":"2022-05-30T22:38:42.869362Z"},"trusted":true},"outputs":[],"source":["!head sub.csv"]},{"cell_type":"markdown","metadata":{},"source":["When you click \"save version\" in Kaggle, and wait for the notebook to run, you'll see that `sub.csv` appears in the \"Data\" tab. Clicking on that file will show a *Submit* button, which allows you to submit to the competition."]},{"cell_type":"markdown","metadata":{},"source":["## Using matrix product"]},{"cell_type":"markdown","metadata":{},"source":["We can make things quite a bit neater...\n","\n","Take a look at the inner-most calculation we're doing to get the predictions:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:48.248084Z","iopub.status.busy":"2022-05-30T22:38:48.247768Z","iopub.status.idle":"2022-05-30T22:38:48.258935Z","shell.execute_reply":"2022-05-30T22:38:48.258184Z","shell.execute_reply.started":"2022-05-30T22:38:48.248052Z"},"trusted":true},"outputs":[],"source":["(val_indep*coeffs).sum(axis=1)"]},{"cell_type":"markdown","metadata":{},"source":["Multiplying elements together and then adding across rows is identical to doing a matrix-vector product! Python uses the `@` operator to indicate matrix products, and is supported by PyTorch tensors. Therefore, we can replicate the above calculate more simply like so:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:51.959798Z","iopub.status.busy":"2022-05-30T22:38:51.959362Z","iopub.status.idle":"2022-05-30T22:38:51.97614Z","shell.execute_reply":"2022-05-30T22:38:51.975461Z","shell.execute_reply.started":"2022-05-30T22:38:51.959765Z"},"trusted":true},"outputs":[],"source":["val_indep@coeffs"]},{"cell_type":"markdown","metadata":{},"source":["It also turns out that this is much faster, because matrix products in PyTorch are very highly optimised.\n","\n","Let's use this to replace how `calc_preds` works:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:56.322255Z","iopub.status.busy":"2022-05-30T22:38:56.321807Z","iopub.status.idle":"2022-05-30T22:38:56.326812Z","shell.execute_reply":"2022-05-30T22:38:56.32606Z","shell.execute_reply.started":"2022-05-30T22:38:56.322213Z"},"trusted":true},"outputs":[],"source":["def calc_preds(coeffs, indeps): return torch.sigmoid(indeps@coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["In order to do matrix-matrix products (which we'll need in the next section), we need to turn `coeffs` into a column vector (i.e. a matrix with a single column), which we can do by passing a second argument `1` to `torch.rand()`, indicating that we want our coefficients to have one column:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:58.895779Z","iopub.status.busy":"2022-05-30T22:38:58.895467Z","iopub.status.idle":"2022-05-30T22:38:58.900851Z","shell.execute_reply":"2022-05-30T22:38:58.899931Z","shell.execute_reply.started":"2022-05-30T22:38:58.895744Z"},"trusted":true},"outputs":[],"source":["def init_coeffs(): return (torch.rand(n_coeff, 1)*0.1).requires_grad_()"]},{"cell_type":"markdown","metadata":{},"source":["We'll also need to turn our dependent variable into a column vector, which we can do by indexing the column dimension with the special value `None`, which tells PyTorch to add a new dimension in this position:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:38:59.789678Z","iopub.status.busy":"2022-05-30T22:38:59.788799Z","iopub.status.idle":"2022-05-30T22:38:59.794227Z","shell.execute_reply":"2022-05-30T22:38:59.793326Z","shell.execute_reply.started":"2022-05-30T22:38:59.789625Z"},"trusted":true},"outputs":[],"source":["trn_dep = trn_dep[:,None]\n","val_dep = val_dep[:,None]"]},{"cell_type":"markdown","metadata":{},"source":["We can now train our model as before and confirm we get identical outputs...:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:38.071003Z","iopub.status.busy":"2022-05-30T22:39:38.070545Z","iopub.status.idle":"2022-05-30T22:39:38.094666Z","shell.execute_reply":"2022-05-30T22:39:38.093641Z","shell.execute_reply.started":"2022-05-30T22:39:38.070972Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(lr=100)"]},{"cell_type":"markdown","metadata":{},"source":["...and identical accuracy:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:40.463735Z","iopub.status.busy":"2022-05-30T22:39:40.463301Z","iopub.status.idle":"2022-05-30T22:39:40.469684Z","shell.execute_reply":"2022-05-30T22:39:40.468652Z","shell.execute_reply.started":"2022-05-30T22:39:40.463702Z"},"trusted":true},"outputs":[],"source":["acc(coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["## A neural network"]},{"cell_type":"markdown","metadata":{},"source":["We've now got what we need to implement our neural network.\n","\n","First, we'll need to create coefficients for each of our layers. Our first set of coefficients will take our `n_coeff` inputs, and create `n_hidden` outputs. We can choose whatever `n_hidden` we like -- a higher number gives our network more flexibility, but makes it slower and harder to train. So we need a matrix of size `n_coeff` by `n_hidden`. We'll divide these coefficients by `n_hidden` so that when we sum them up in the next layer we'll end up with similar magnitude numbers to what we started with.\n","\n","Then our second layer will need to take the `n_hidden` inputs and create a single output, so that means we need a `n_hidden` by `1` matrix there. The second layer will also need a constant term added."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:44.428599Z","iopub.status.busy":"2022-05-30T22:39:44.428254Z","iopub.status.idle":"2022-05-30T22:39:44.434009Z","shell.execute_reply":"2022-05-30T22:39:44.433164Z","shell.execute_reply.started":"2022-05-30T22:39:44.428563Z"},"trusted":true},"outputs":[],"source":["def init_coeffs(n_hidden=20):\n","    layer1 = (torch.rand(n_coeff, n_hidden)-0.5)/n_hidden\n","    layer2 = torch.rand(n_hidden, 1)-0.3\n","    const = torch.rand(1)[0]\n","    return layer1.requires_grad_(),layer2.requires_grad_(),const.requires_grad_()"]},{"cell_type":"markdown","metadata":{},"source":["Now we have our coefficients, we can create our neural net. The key steps are the two matrix products, `indeps@l1` and `res@l2` (where `res` is the output of the first layer). The first layer output is passed to `F.relu` (that's our non-linearity), and the second is passed to `torch.sigmoid` as before."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:45.302903Z","iopub.status.busy":"2022-05-30T22:39:45.302573Z","iopub.status.idle":"2022-05-30T22:39:45.309472Z","shell.execute_reply":"2022-05-30T22:39:45.308498Z","shell.execute_reply.started":"2022-05-30T22:39:45.302864Z"},"trusted":true},"outputs":[],"source":["import torch.nn.functional as F\n","\n","def calc_preds(coeffs, indeps):\n","    l1,l2,const = coeffs\n","    res = F.relu(indeps@l1)\n","    res = res@l2 + const\n","    return torch.sigmoid(res)"]},{"cell_type":"markdown","metadata":{},"source":["Finally, now that we have more than one set of coefficients, we need to add a loop to update each one:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:55.366945Z","iopub.status.busy":"2022-05-30T22:39:55.3665Z","iopub.status.idle":"2022-05-30T22:39:55.371578Z","shell.execute_reply":"2022-05-30T22:39:55.370699Z","shell.execute_reply.started":"2022-05-30T22:39:55.366914Z"},"trusted":true},"outputs":[],"source":["def update_coeffs(coeffs, lr):\n","    for layer in coeffs:\n","        layer.sub_(layer.grad * lr)\n","        layer.grad.zero_()"]},{"cell_type":"markdown","metadata":{},"source":["That's it -- we're now ready to train our model!"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:39:58.189982Z","iopub.status.busy":"2022-05-30T22:39:58.189651Z","iopub.status.idle":"2022-05-30T22:39:58.227202Z","shell.execute_reply":"2022-05-30T22:39:58.226226Z","shell.execute_reply.started":"2022-05-30T22:39:58.189951Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(lr=1.4)"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:40:16.338016Z","iopub.status.busy":"2022-05-30T22:40:16.337512Z","iopub.status.idle":"2022-05-30T22:40:16.368327Z","shell.execute_reply":"2022-05-30T22:40:16.367439Z","shell.execute_reply.started":"2022-05-30T22:40:16.337959Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(lr=20)"]},{"cell_type":"markdown","metadata":{},"source":["It's looking good -- our loss is lower than before. Let's see if that translates to a better result on the validation set:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:40:18.644458Z","iopub.status.busy":"2022-05-30T22:40:18.644153Z","iopub.status.idle":"2022-05-30T22:40:18.651372Z","shell.execute_reply":"2022-05-30T22:40:18.650102Z","shell.execute_reply.started":"2022-05-30T22:40:18.644427Z"},"trusted":true},"outputs":[],"source":["acc(coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["In this case our neural net isn't showing better results than the linear model. That's not surprising; this dataset is very small and very simple, and isn't the kind of thing we'd expect to see neural networks excel at. Furthermore, our validation set is too small to reliably see much accuracy difference. But the key thing is that we now know exactly what a real neural net looks like!"]},{"cell_type":"markdown","metadata":{},"source":["## Deep learning"]},{"cell_type":"markdown","metadata":{},"source":["The neural net in the previous section only uses one hidden layer, so it doesn't count as \"deep\" learning. But we can use the exact same technique to make our neural net deep, by adding more matrix multiplications.\n","\n","First, we'll need to create additional coefficients for each layer:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:40:55.255291Z","iopub.status.busy":"2022-05-30T22:40:55.25457Z","iopub.status.idle":"2022-05-30T22:40:55.261806Z","shell.execute_reply":"2022-05-30T22:40:55.261271Z","shell.execute_reply.started":"2022-05-30T22:40:55.255242Z"},"trusted":true},"outputs":[],"source":["def init_coeffs():\n","    hiddens = [10, 10]  # <-- set this to the size of each hidden layer you want\n","    sizes = [n_coeff] + hiddens + [1]\n","    n = len(sizes)\n","    layers = [(torch.rand(sizes[i], sizes[i+1])-0.3)/sizes[i+1]*4 for i in range(n-1)]\n","    consts = [(torch.rand(1)[0]-0.5)*0.1 for i in range(n-1)]\n","    for l in layers+consts: l.requires_grad_()\n","    return layers,consts"]},{"cell_type":"markdown","metadata":{},"source":["You'll notice here that there's a lot of messy constants to get the random numbers in just the right ranges. When you train the model in a moment, you'll see that the tiniest changes to these initialisations can cause our model to fail to train at all! This is a key reason that deep learning failed to make much progress in the early days -- it's very finicky to get a good starting point for our coefficients. Nowadays, we have ways to deal with that, which we'll learn about in other notebooks.\n","\n","Our deep learning `calc_preds` looks much the same as before, but now we loop through each layer, instead of listing them separately:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:40:57.610974Z","iopub.status.busy":"2022-05-30T22:40:57.610142Z","iopub.status.idle":"2022-05-30T22:40:57.618154Z","shell.execute_reply":"2022-05-30T22:40:57.617329Z","shell.execute_reply.started":"2022-05-30T22:40:57.610916Z"},"trusted":true},"outputs":[],"source":["import torch.nn.functional as F\n","\n","def calc_preds(coeffs, indeps):\n","    layers,consts = coeffs\n","    n = len(layers)\n","    res = indeps\n","    for i,l in enumerate(layers):\n","        res = res@l + consts[i]\n","        if i!=n-1: res = F.relu(res)\n","    return torch.sigmoid(res)"]},{"cell_type":"markdown","metadata":{},"source":["We also need a minor update to `update_coeffs` since we've got `layers` and `consts` separated now:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:41:08.513049Z","iopub.status.busy":"2022-05-30T22:41:08.512494Z","iopub.status.idle":"2022-05-30T22:41:08.519219Z","shell.execute_reply":"2022-05-30T22:41:08.518093Z","shell.execute_reply.started":"2022-05-30T22:41:08.512999Z"},"trusted":true},"outputs":[],"source":["def update_coeffs(coeffs, lr):\n","    layers,consts = coeffs\n","    for layer in layers+consts:\n","        layer.sub_(layer.grad * lr)\n","        layer.grad.zero_()"]},{"cell_type":"markdown","metadata":{},"source":["Let's train our model..."]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:41:23.633004Z","iopub.status.busy":"2022-05-30T22:41:23.632516Z","iopub.status.idle":"2022-05-30T22:41:23.666981Z","shell.execute_reply":"2022-05-30T22:41:23.666048Z","shell.execute_reply.started":"2022-05-30T22:41:23.632953Z"},"trusted":true},"outputs":[],"source":["coeffs = train_model(lr=4)"]},{"cell_type":"markdown","metadata":{},"source":["...and check its accuracy:"]},{"cell_type":"code","execution_count":null,"metadata":{"execution":{"iopub.execute_input":"2022-05-30T22:41:25.491182Z","iopub.status.busy":"2022-05-30T22:41:25.490656Z","iopub.status.idle":"2022-05-30T22:41:25.497888Z","shell.execute_reply":"2022-05-30T22:41:25.49695Z","shell.execute_reply.started":"2022-05-30T22:41:25.491146Z"},"trusted":true},"outputs":[],"source":["acc(coeffs)"]},{"cell_type":"markdown","metadata":{},"source":["## Final thoughts"]},{"cell_type":"markdown","metadata":{},"source":["It's actually pretty cool that we've managed to create a real deep learning model from scratch and trained it to get over 80% accuracy on this task, all in the course of a single notebook!\n","\n","The \"real\" deep learning models that are used in research and industry look very similar to this, and in fact if you look inside the source code of any deep learning model you'll recognise the basic steps are the same.\n","\n","The biggest differences in practical models to what we have above are:\n","\n","- How initialisation and normalisation is done to ensure the model trains correctly every time\n","- Regularization (to avoid over-fitting)\n","- Modifying the neural net itself to take advantage of knowledge of the problem domain\n","- Doing gradient descent steps on smaller batches, rather than the whole dataset.\n","\n","I'll be adding notebooks about all these later, and will add links here once they're ready.\n","\n","If you found this notebook useful, please remember to click the little up-arrow at the top to upvote it, since I like to know when people have found my work useful, and it helps others find it too. (BTW, be sure you're looking at my [original notebook here](https://www.kaggle.com/code/jhoward/linear-model-and-neural-net-from-scratch) when you do that, and are not on your own copy of it, otherwise your upvote won't get counted!) And if you have any questions or comments, please pop them below -- I read every comment I receive!"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.4"}},"nbformat":4,"nbformat_minor":4}
diff --git a/README.md b/README.md
index 3999515c0f..1159deff3f 100644
--- a/README.md
+++ b/README.md
@@ -9,3 +9,4 @@ This is where you'll find the notebooks, slides, and spreadsheets for the 2022 e
 - `xl`: Excel spreadsheets
 - `slides`: Jeremy's slide decks
 - `tools`: Ignore (tools for creating this repo)
+- `getting-started-with-codespaces`: A document to help run the notebooks in a GitHub Codespace
diff --git a/getting-started-with-codespaces.md b/getting-started-with-codespaces.md
new file mode 100644
index 0000000000..8a9b1b15b8
--- /dev/null
+++ b/getting-started-with-codespaces.md
@@ -0,0 +1,30 @@
+# Using Codespaces to work with the "Practical Deep Learning for Coders" course
+
+
+To get started, create a codespace for this repository by clicking this 👇 
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=master&repo=485606685)
+
+A codespace will open in a web-based version of Visual Studio Code.
+
+**Note**: Dev containers is an open spec which is supported by [GitHub Codespaces](https://github.com/codespaces) and [other supporting tools](https://containers.dev/supporting).
+
+## Opening a notebook
+
+The [dev container](.devcontainer/devcontainer.json) is fully configured with software and [machine learning libraries](.devcontainer/requirements.txt) needed for this course.
+
+In the VS Code editor, open any notebook file and start executing the notebook's cells.
+
+## Opening your codespace in JupyterLab
+
+You can open your codespace in JupyterLab from the "Your codespaces" page at [github.com/codespaces](https://github.com/codespaces), or by using [GitHub CLI](https://docs.github.com/en/codespaces/developing-in-codespaces/opening-an-existing-codespace?tool=cli#opening-an-existing-codespace) with `gh codespace jupyter`. For more information, see "[Opening an existing codespace](https://docs.github.com/en/codespaces/developing-in-codespaces/opening-an-existing-codespace)".
+
+## GPU-powered Codespaces
+
+GPU-powered Codespaces are now available in limited beta. Having access to a GPU from within a codespace allows developers to run complex Machine Learning models much more quickly. 
+
+To request access to the GPU machine types, or any additional machine type, [please complete the sign up form](https://github.surveymonkey.com/r/Y75GX9T).
+
+Once, GPU is enabled and configured for your codespace, uncomment [this section](.devcontainer/devcontainer.json#L9-L13) which installs NVIDIA CUDA.
+
+**Note**: Notebooks [09-small-models-road-to-the-top-part-2](09-small-models-road-to-the-top-part-2.ipynb) and [10-scaling-up-road-to-the-top-part-3](10-scaling-up-road-to-the-top-part-3.ipynb) requires a powerful machine to ensure that the kernel does not crash. Hence, some notebook cells for these two notebooks might not execute without a GPU-powered codespace.