fauxgrad_walkthrough.ipynb

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        "<a href=\"https://colab.research.google.com/github/ksanjeevan/fauxgrad/blob/master/fauxgrad_walkthrough.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "<p style=\"text-align:left;\">\n",
        "<font size=6>\n",
        "    <b>fauxgrad: simple autodiff in python</b>\n",
        "</font>\n",
        "    <span style=\"float:right;\">\n",
        "        <a href=\"https://github.com/ksanjeevan/fauxgrad\"><b>See on GitHub!</b></a>\n",
        "    </span>\n",
        "</p>\n",
        "\n",
        "----"
      ]
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        "Install `fauxgrad` and its dependencies by running the cell below:"
      ]
    },
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        "try:\n",
        "  from fauxgrad import Value\n",
        "except:\n",
        "  !pip install fauxgrad"
      ],
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      "outputs": []
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        "## Table of Contents\n",
        "* 0\\. [Introduction](#intro)\n",
        "* 1\\. [Implementation: the `Value` class](#val)\n",
        "\n",
        "    * 1.1\\. [Addition](#ad)\n",
        "       \n",
        "    * 1.2\\. [Multiplication](#mul)\n",
        "    \n",
        "    * 1.3\\. [Backward](#back)\n",
        "    \n",
        "    * 1.4\\. [Other functions](#func)\n",
        "    \n",
        "    \n",
        "* 2\\. [Verification: Training a Neural Net from scratch](#demo)"
      ]
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        "<a name=\"intro\"/></a>\n",
        "# 0. Introduction\n",
        "\n",
        "There's plenty of excellent ([tinygrad](https://github.com/geohot/tinygrad)) and minimalist ([micrograd](https://github.com/karpathy/micrograd)) built-from-scratch, deep learning frameworks out there, so the goal of `fauxgrad` is to sacrifice some of the full functionality, and focus on the general idea and building blocks for writing your own library."
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        "import inspect\n",
        "from IPython import display\n",
        "#helper display function \n",
        "def show_code(method, cls=Value):\n",
        "    return display.Code(inspect.getsource(getattr(cls, method)), language='python')"
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        "<a name=\"val\"/></a>\n",
        "# 1. Implementing the `Value` class\n",
        "\n",
        "Automatic differentiation is the technique which allows us to train deep neural networks. The idea is that whenever a function is applied to a value (*forward* pass), we should also keep track of its derivative (*backward* pass).\n",
        "\n",
        "So if we take $a = f(x)$, then we also want to compute $grad = \\partial f(x)/\\partial x$. If we have a set of operations that define a [computational](https://colah.github.io/posts/2015-08-Backprop/) [graph](https://www.geeksforgeeks.org/computational-graphs-in-deep-learning/), we'll have to keep track of these derivative values at each node. This will allow use of the chain rule to cheaply evaluate the derivative of any variable with respect to another. An example:\n",
        "\n",
        "```\n",
        "a = 5\n",
        "b = -3\n",
        "c = a * b = 5 * -3\n",
        "d = a + c = 5 + (-15)\n",
        "e = d * 2 = -10 * 2 = -20\n",
        "```\n",
        "\n",
        "Doing these operations defines the computational graph, which can be visualized below:\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img src=\"https://user-images.githubusercontent.com/12011058/133184213-ddb42e07-0333-4ad5-b7e6-f57ad15a7616.png\" width=\"1050px\"/>\n",
        "</p>\n",
        "\n",
        "\n",
        "\n",
        "We might want to know $\\partial e/ \\partial a$. Well, we can use the chain rule to figure that out by reversing the forward pass of the graph:\n",
        "\n",
        "$$\\partial e/ \\partial a = \\partial e/ \\partial d * \\partial d/ \\partial a = \\partial e/ \\partial d * (\\partial a/ \\partial a + \\partial c/ \\partial a) = 2 * (1 + (-3)) = -4$$\n",
        "\n",
        "We can automate these calculations by: \n",
        "\n",
        "* A) **Wrapping our variables** with a class that handles/implements the derivatives of the functions we wish to use. See the `__init__` of our `Value` class below where we are using:\n",
        "    \n",
        "    1. `self.parents` to keep track of the `Value`s that resulted in this one being created (if we're talking about `d` from the example above, its parents would be `[a, c]`, while `a` has parents `[]`)\n",
        "\n",
        "    2. `self.grad` to keep track of the cumulated derivative on this node\n",
        "\n",
        "    3. `self.diff` to store the partial derivatives of the function that end up acting on this `Value` (more explanation of this later)\n",
        "    \n",
        "    The code used is:"
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"fm\">__init__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[]):</span>\n",
              "    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span> <span class=\"o\">=</span> <span class=\"nb\">float</span><span class=\"p\">(</span><span class=\"n\">val</span><span class=\"p\">)</span>\n",
              "    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">parents</span> <span class=\"o\">=</span> <span class=\"n\">parents</span>\n",
              "    \n",
              "    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">grad</span> <span class=\"o\">=</span> <span class=\"mi\">0</span>\n",
              "    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[]</span>\n",
              "</pre></div>\n"
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              "  def __init__(self, val, parents=[]):\n",
              "    self.val = float(val)\n",
              "    self.parents = parents\n",
              "    \n",
              "    self.grad = 0\n",
              "    self.diff = lambda grad: []"
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        "\n",
        "* B) **Implementing each function** we want to use and its derivative (i.e. the forward and backward pass). To keep the amount of code small, let's focus on the minimum number of operations that we'll need:\n",
        "\n",
        "    - `__add__(x, y)`: adding two elements\n",
        "    - `__mul__(x, y)`: multiplying two elements\n",
        "    - `backward()`: calculating the gradient from this point backward through the computational graph\n",
        "    - `activation & loss functions`: any activation functions that we might use, or that we might use in the loss function"
      ]
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        "<a name=\"ad\"/></a>\n",
        "## 1.1. `__add__`"
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        "Our `Value` class stores the numeric data in the attribute `a.val`. So the forward pass when doing `c = a + b` will simply be `a.val + b.val` and storing it in a new `Value` (and making sure to set `c`'s parents to be `a` and `b`).\n",
        "\n",
        "Let's see the code for implementing the addition function:"
      ]
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"fm\">__add__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">other</span><span class=\"p\">):</span>\n",
              "    <span class=\"n\">other</span> <span class=\"o\">=</span> <span class=\"n\">other</span> <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">other</span><span class=\"p\">,</span> <span class=\"n\">Value</span><span class=\"p\">)</span> <span class=\"k\">else</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"n\">other</span><span class=\"p\">)</span>\n",
              "    <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span> <span class=\"o\">+</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">other</span><span class=\"p\">])</span>\n",
              "    <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">grad</span><span class=\"p\">,</span> <span class=\"n\">grad</span><span class=\"p\">]</span>\n",
              "    <span class=\"k\">return</span> <span class=\"n\">ret</span>\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def __add__(self, other):\n",
              "    other = other if isinstance(other, Value) else Value(other)\n",
              "    ret = Value(self.val + other.val, parents=[self, other])\n",
              "    ret.diff = lambda grad: [grad, grad]\n",
              "    return ret"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gmZ77X3xte9"
      },
      "source": [
        "<a name=\"mul\"/></a>\n",
        "## 1.2. `__mul__`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsGWn3nrxte9"
      },
      "source": [
        "The forward pass for the multiplication function follows the same approach as in `__add__`, but let's look at the backward pass which we ignored before.\n",
        "\n",
        "We want to take in the gradient that was propagated up to this node (formally: $\\partial J/ \\partial c$) and multiply it by our derivative value. Since we have two variables that formed `c`, our `diff` will return two values as well, corresponding to [$\\partial c/ \\partial a$ , $\\partial c/ \\partial b$] times $\\partial J/ \\partial c$. \n",
        "\n",
        "That's why in the case of `__add__` above, both derivatives are `1` and we just propagate the value of the gradient, and in `__mul__` the derivatve $\\partial c/ \\partial a = b$ and $\\partial c/ \\partial b = a$. Let's look at the `__mul__` code:\n"
      ]
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      "source": [
        "show_code('__mul__')"
      ],
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"fm\">__mul__</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">other</span><span class=\"p\">):</span>\n",
              "    <span class=\"n\">other</span> <span class=\"o\">=</span> <span class=\"n\">other</span> <span class=\"k\">if</span> <span class=\"nb\">isinstance</span><span class=\"p\">(</span><span class=\"n\">other</span><span class=\"p\">,</span> <span class=\"n\">Value</span><span class=\"p\">)</span> <span class=\"k\">else</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"n\">other</span><span class=\"p\">)</span>\n",
              "    <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span> <span class=\"o\">*</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"p\">,</span> <span class=\"n\">other</span><span class=\"p\">])</span>\n",
              "    <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">grad</span> <span class=\"o\">*</span> <span class=\"n\">other</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">,</span> <span class=\"n\">grad</span> <span class=\"o\">*</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">]</span>\n",
              "    <span class=\"k\">return</span> <span class=\"n\">ret</span>\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def __mul__(self, other):\n",
              "    other = other if isinstance(other, Value) else Value(other)\n",
              "    ret = Value(self.val * other.val, parents=[self, other])\n",
              "    ret.diff = lambda grad: [grad * other.val, grad * self.val]\n",
              "    return ret"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ig2PvmqNxte-"
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      "source": [
        "In order for this to work fully, we'd also implement `__sub__`, `__pow__`, etc. but they all follow the same idea, and are fully done in [micrograd](https://github.com/karpathy/micrograd). Here we only implement the essential operations to later train a neural net (even though it will make some later code slightly ugly... it's a bit of a tradeoff).\n",
        "\n",
        "This does mean that this happens:\n",
        "\n",
        "```python\n",
        "from fauxgrad import Value\n",
        "Value(3) + (-2)\n",
        ">>> Value(1.00; grad=0.00)\n",
        "Value(3) - 2\n",
        ">>> Traceback (most recent call last):\n",
        ">>>   File \"<stdin>\", line 1, in <module>\n",
        ">>> TypeError: unsupported operand type(s) for -: 'Value' and 'int'\n",
        "```\n",
        "\n",
        "Which is a bit annoying but again we're not going for completeness."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDA8Mjuhxte-"
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      "source": [
        "<a name=\"back\"/></a>\n",
        "## 1.3. `backward`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3AoNZLyxte-"
      },
      "source": [
        "This function should now backtrack from the current node back through all the parent variables, calculating their respective partial derivatives along the way. We can split this up into:\n",
        "\n",
        "* A) **Traversing the graph**. If `c` is the result of some function of `a`, we won't be able to get the gradient of `a` until we know the gradient of `c` (chain rule). So what we need is a *reverse* [topologcal ordering](https://www.geeksforgeeks.org/topological-sorting/) of the nodes in the computational graph (e.g. in the example from above we would want the ordering `[e, d, c, b, a]`).\n",
        "\n",
        "    This can be seen in the helper function:"
      ]
    },
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      "source": [
        "show_code('_rev_topo_sort')"
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"nf\">_rev_topo_sort</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
              "    <span class=\"k\">def</span> <span class=\"nf\">traverse</span><span class=\"p\">(</span><span class=\"n\">node</span><span class=\"p\">):</span>\n",
              "      <span class=\"k\">if</span> <span class=\"n\">node</span> <span class=\"ow\">in</span> <span class=\"n\">visit</span><span class=\"p\">:</span> <span class=\"k\">return</span>\n",
              "      <span class=\"n\">visit</span><span class=\"o\">.</span><span class=\"n\">add</span><span class=\"p\">(</span><span class=\"n\">node</span><span class=\"p\">)</span>\n",
              "      <span class=\"k\">for</span> <span class=\"n\">p</span> <span class=\"ow\">in</span> <span class=\"n\">node</span><span class=\"o\">.</span><span class=\"n\">parents</span><span class=\"p\">:</span> <span class=\"n\">traverse</span><span class=\"p\">(</span><span class=\"n\">p</span><span class=\"p\">)</span>\n",
              "      <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">node</span><span class=\"p\">)</span>\n",
              "\n",
              "    <span class=\"n\">visit</span> <span class=\"o\">=</span> <span class=\"nb\">set</span><span class=\"p\">();</span> <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"p\">[]</span>\n",
              "    <span class=\"n\">traverse</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">)</span>\n",
              "    <span class=\"k\">return</span> <span class=\"nb\">reversed</span><span class=\"p\">(</span><span class=\"n\">ret</span><span class=\"p\">)</span>\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def _rev_topo_sort(self):\n",
              "    def traverse(node):\n",
              "      if node in visit: return\n",
              "      visit.add(node)\n",
              "      for p in node.parents: traverse(p)\n",
              "      ret.append(node)\n",
              "\n",
              "    visit = set(); ret = []\n",
              "    traverse(self)\n",
              "    return reversed(ret)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lF4uqIWPxte-"
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      "source": [
        "* B) **Applying the gradients**. As we now can take each node, we use its `diff` function to propagate the gradient to their parents. This can be seen in the `backward` function: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "e263e16b-3f8b-451a-9841-29753cb52142"
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      "source": [
        "show_code('backward')"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
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            "text/latex": "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n  \\PY{k}{def} \\PY{n+nf}{backward}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n    \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{grad} \\PY{o}{=} \\PY{l+m+mf}{1.0} \\PY{c+c1}{\\PYZsh{} implicit gradient creation}\n    \\PY{k}{for} \\PY{n}{node} \\PY{o+ow}{in} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{\\PYZus{}rev\\PYZus{}topo\\PYZus{}sort}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n      \\PY{n}{gradients} \\PY{o}{=} \\PY{n}{node}\\PY{o}{.}\\PY{n}{diff}\\PY{p}{(}\\PY{n}{node}\\PY{o}{.}\\PY{n}{grad}\\PY{p}{)}\n      \\PY{k}{for} \\PY{n}{p}\\PY{p}{,} \\PY{n}{g} \\PY{o+ow}{in} \\PY{n+nb}{zip}\\PY{p}{(}\\PY{n}{node}\\PY{o}{.}\\PY{n}{parents}\\PY{p}{,} \\PY{n}{gradients}\\PY{p}{)}\\PY{p}{:} \\PY{n}{p}\\PY{o}{.}\\PY{n}{grad} \\PY{o}{+}\\PY{o}{=} \\PY{n}{g}    \n\\end{Verbatim}\n",
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"nf\">backward</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
              "    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">grad</span> <span class=\"o\">=</span> <span class=\"mf\">1.0</span> <span class=\"c1\"># implicit gradient creation</span>\n",
              "    <span class=\"k\">for</span> <span class=\"n\">node</span> <span class=\"ow\">in</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">_rev_topo_sort</span><span class=\"p\">():</span>\n",
              "      <span class=\"n\">gradients</span> <span class=\"o\">=</span> <span class=\"n\">node</span><span class=\"o\">.</span><span class=\"n\">diff</span><span class=\"p\">(</span><span class=\"n\">node</span><span class=\"o\">.</span><span class=\"n\">grad</span><span class=\"p\">)</span>\n",
              "      <span class=\"k\">for</span> <span class=\"n\">p</span><span class=\"p\">,</span> <span class=\"n\">g</span> <span class=\"ow\">in</span> <span class=\"nb\">zip</span><span class=\"p\">(</span><span class=\"n\">node</span><span class=\"o\">.</span><span class=\"n\">parents</span><span class=\"p\">,</span> <span class=\"n\">gradients</span><span class=\"p\">):</span> <span class=\"n\">p</span><span class=\"o\">.</span><span class=\"n\">grad</span> <span class=\"o\">+=</span> <span class=\"n\">g</span>    \n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def backward(self):\n",
              "    self.grad = 1.0 # implicit gradient creation\n",
              "    for node in self._rev_topo_sort():\n",
              "      gradients = node.diff(node.grad)\n",
              "      for p, g in zip(node.parents, gradients): p.grad += g    "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCycP6BZxte_"
      },
      "source": [
        "We start with a `gradient=1` (since we've done the ordering correctly in `_rev_topo_sort`, `self == nodes[0]`), and for each node we update their parents' cumulated gradient. \n",
        "\n",
        "The mathematical pseudocode of what is being computed at each iteration in the outter loop is:\n",
        "\n",
        "```\n",
        "for ...:\n",
        "\n",
        "    dc/da, dc/db = c.diff(dJ/dc)\n",
        "\n",
        "    a.grad += dc/da\n",
        "    b.grad += dc/db\n",
        "```\n",
        "\n",
        "where of course a node can have two parents, one parent or no parents.\n",
        "\n",
        "Let's run the example from above again using `fauxgrad` and see the results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3H_AIlMQvvNK",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "source": [
        "a = Value(5)\n",
        "b = Value(-3)\n",
        "c = a * b\n",
        "d = a + c\n",
        "e = d * 2\n",
        "e.backward()\n",
        "\n",
        "print(f'The derivative that we computed before, de/da = {a.grad}')\n",
        "print(f'and de/db = {b.grad}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The derivative that we computed before, de/da = -4.0\n",
            "and de/db = 10.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89y_6DtpN7Rj"
      },
      "source": [
        "and we can see the backward flow of the computaional graph:\n",
        "\n",
        "<p align=\"center\">\n",
        "  <img src=\"https://user-images.githubusercontent.com/12011058/133184219-2ef548ca-346f-4c39-b3f4-dad47b11952e.png\" width=\"1050px\"/>\n",
        "\n",
        "</p>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4R4BvJTyxte_"
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      "source": [
        "<a name=\"func\"/></a>\n",
        "## 1.4. Other Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Op25AZMCxte_"
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      "source": [
        "We implement three more functions for our `Value` class in order to be able to run the demo in section [2.](#demo). First we can see the `relu`:"
      ]
    },
    {
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      "source": [
        "show_code('relu')"
      ],
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        {
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          "data": {
            "text/latex": "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n  \\PY{k}{def} \\PY{n+nf}{relu}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n    \\PY{n}{ret} \\PY{o}{=} \\PY{n}{Value}\\PY{p}{(}\\PY{n+nb}{max}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{val}\\PY{p}{)}\\PY{p}{,} \\PY{n}{parents}\\PY{o}{=}\\PY{p}{[}\\PY{n+nb+bp}{self}\\PY{p}{]}\\PY{p}{)}\n    \\PY{n}{ret}\\PY{o}{.}\\PY{n}{diff} \\PY{o}{=} \\PY{k}{lambda} \\PY{n}{grad}\\PY{p}{:} \\PY{p}{[}\\PY{n}{grad} \\PY{o}{*} \\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{val} \\PY{o}{\\PYZgt{}} \\PY{l+m+mi}{0}\\PY{p}{)}\\PY{p}{]}\n    \\PY{k}{return} \\PY{n}{ret}\n\\end{Verbatim}\n",
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"nf\">relu</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
              "    <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"nb\">max</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">),</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"p\">])</span>\n",
              "    <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">grad</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span> <span class=\"o\">&gt;</span> <span class=\"mi\">0</span><span class=\"p\">)]</span>\n",
              "    <span class=\"k\">return</span> <span class=\"n\">ret</span>\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def relu(self):\n",
              "    ret = Value(max(0, self.val), parents=[self])\n",
              "    ret.diff = lambda grad: [grad * (self.val > 0)]\n",
              "    return ret"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
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        "Unlike `__add__` and `__mul__`, `relu` only has one parent (the `Value` the `relu` was applied to). As we're going to solve a binary classification problem, we'll also use a `sigmoid` activation for the final layer:"
      ]
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        "show_code('sigmoid')"
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"nf\">sigmoid</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
              "    <span class=\"n\">_sigmoid</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"mi\">1</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"mi\">1</span> <span class=\"o\">+</span> <span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">exp</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"n\">x</span><span class=\"p\">))</span>\n",
              "    <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"n\">_sigmoid</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">),</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"p\">])</span>\n",
              "    <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">grad</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"o\">-</span><span class=\"n\">_sigmoid</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">_sigmoid</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">)]</span>\n",
              "    <span class=\"k\">return</span> <span class=\"n\">ret</span>\n",
              "</pre></div>\n"
            ],
            "text/plain": [
              "  def sigmoid(self):\n",
              "    _sigmoid = lambda x: 1/(1 + math.exp(-x))\n",
              "    ret = Value(_sigmoid(self.val), parents=[self])\n",
              "    ret.diff = lambda grad: [grad * (1-_sigmoid(self.val))*_sigmoid(self.val)]\n",
              "    return ret"
            ]
          },
          "metadata": {},
          "execution_count": 29
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      "source": [
        "Here making use of $\\sigma(x)'=\\sigma(x) * (1 - \\sigma(x))$. Finally, for the implementation of our [cross entropy](https://en.wikipedia.org/wiki/Cross_entropy) loss function, we'll also need the `log`:"
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        "show_code('log')"
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        {
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            "text/latex": "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n  \\PY{k}{def} \\PY{n+nf}{log}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n    \\PY{n}{ret} \\PY{o}{=} \\PY{n}{Value}\\PY{p}{(}\\PY{n}{math}\\PY{o}{.}\\PY{n}{log}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{val}\\PY{p}{)}\\PY{p}{,} \\PY{n}{parents}\\PY{o}{=}\\PY{p}{[}\\PY{n+nb+bp}{self}\\PY{p}{]}\\PY{p}{)}\n    \\PY{n}{ret}\\PY{o}{.}\\PY{n}{diff} \\PY{o}{=} \\PY{k}{lambda} \\PY{n}{grad}\\PY{p}{:} \\PY{p}{[}\\PY{n}{grad} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1}\\PY{o}{/}\\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{val}\\PY{p}{)}\\PY{p}{]}\n    \\PY{k}{return} \\PY{n}{ret}\n\\end{Verbatim}\n",
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              ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span>  <span class=\"k\">def</span> <span class=\"nf\">log</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
              "    <span class=\"n\">ret</span> <span class=\"o\">=</span> <span class=\"n\">Value</span><span class=\"p\">(</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">log</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">),</span> <span class=\"n\">parents</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"bp\">self</span><span class=\"p\">])</span>\n",
              "    <span class=\"n\">ret</span><span class=\"o\">.</span><span class=\"n\">diff</span> <span class=\"o\">=</span> <span class=\"k\">lambda</span> <span class=\"n\">grad</span><span class=\"p\">:</span> <span class=\"p\">[</span><span class=\"n\">grad</span> <span class=\"o\">*</span> <span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"o\">/</span><span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">val</span><span class=\"p\">)]</span>\n",
              "    <span class=\"k\">return</span> <span class=\"n\">ret</span>\n",
              "</pre></div>\n"
            ],
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              "  def log(self):\n",
              "    ret = Value(math.log(self.val), parents=[self])\n",
              "    ret.diff = lambda grad: [grad * (1/self.val)]\n",
              "    return ret"
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        "Implementing all these function follows the same procedure, and extending this to `__pow__`, `__sub__` would follow the same. \n",
        "\n",
        "With a bit of helper code, we now can move on to using this minimal engine to train a neural network! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxJZ92TyMAS4"
      },
      "source": [
        "<a name=\"demo\"/></a>\n",
        "# Using `fauxgrad` to train a Neural Net for binary classification "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uv36xWWbMc3f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz5ZNXoKMjJ5",
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          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "37fb3610-ce26-41fe-cd99-5a3c2f818ad4"
      },
      "source": [
        "# collect 2d toy data of a donut in a donut\n",
        "from fauxgrad.utils import generate_circles, visualize_plot\n",
        "X, Y = generate_circles(num_samples=1600)\n",
        "\n",
        "# Let's display the data\n",
        "plt.figure(figsize=(10,7))\n",
        "plt.scatter(X[:,0], X[:,1], c=np.squeeze(Y), cmap='plasma', s=100, alpha=0.7)\n",
        "ax = plt.gca()\n",
        "ax.set_facecolor((0.95, 0.95, 0.95)); plt.axis('off'); plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKHXf1pLMkoH"
      },
      "source": [
        "# Linear simply implements `List[Value]` based matrix multiplication with a choice of activation\n",
        "# the gradients will be computed automatically!\n",
        "from fauxgrad import Linear, cross_entropy\n",
        "\n",
        "class MyNet:\n",
        "    \n",
        "    def __init__(self, hidden_size=12):\n",
        "        self.l1 = Linear(2, hidden_size, 'relu')\n",
        "        self.l2 = Linear(hidden_size, 1, 'sigmoid')\n",
        "        \n",
        "    def __call__(self, x):\n",
        "        x = self.l1(x)\n",
        "        x = self.l2(x)\n",
        "        return x\n",
        "\n",
        "    def parameters(self):\n",
        "        return self.l1.parameters() + self.l2.parameters()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DbV4J3GMm08"
      },
      "source": [
        "# SGD will just loop over the flattened parameters that were passed to it,\n",
        "# and for each one compute the update `p.val -= lr * p.grad`\n",
        "from fauxgrad.optim import SGD\n",
        "\n",
        "m = MyNet()\n",
        "\n",
        "data = X.reshape(100, 16, 2) \n",
        "labels = Y.reshape(100, 16)\n",
        "\n",
        "epochs = 14\n",
        "lr = 0.001\n",
        "opt = SGD(m.parameters(), lr=lr)\n",
        "\n",
        "for epoch in range(1, epochs+1):\n",
        "    losses = []; accs = []\n",
        "    \n",
        "    for i in range(data.shape[0]):\n",
        "        x = [[Value(d[0]), Value(d[1])] for d in data[i]]\n",
        "\n",
        "        opt.zero_grad()\n",
        "        yhat = [m(datum)[0] for datum in x]\n",
        "\n",
        "        loss = cross_entropy(yhat, labels[i])\n",
        "        loss.backward()\n",
        "        \n",
        "        losses.append(loss.val)\n",
        "        accs.extend([int(y.val>0.5)==l for y, l in zip(yhat, labels[i])])\n",
        "\n",
        "        opt.step()\n",
        "    title = 'Epoch %d, Loss %.3f; Acc %.1f%%'%(epoch,\n",
        "                                               sum(losses)/len(losses), \n",
        "                                               100*sum(accs)/len(accs))\n",
        "    visualize_plot(X, Y, m, title=title)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}