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theptrk/the-spelled-out-intro-to-neural-networks-part-1.ipynb

Created October 20, 2022 15:05
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The spelled-out intro to neural networks - Part 1.ipynb
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the-spelled-out-intro-to-neural-networks-part-1.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNzWmQ/q9rzDagkkRShbcGC",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/theptrk/bdeb2ad8afb311c638e4c3e1359dad14/the-spelled-out-intro-to-neural-networks-part-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# The spelled-out intro to neural networks and backpropagation: building micrograd\n",
"\n",
"https://www.youtube.com/watch?v=VMj-3S1tku0"
],
"metadata": {
"id": "psZrGr42YKER"
}
},
{
"cell_type": "code",
"source": [
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"metadata": {
"id": "4nQNt1OZYPci"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def f(x):\n",
" return 3*x**2 - 4*x + 5\n",
" # 6x - 4"
],
"metadata": {
"id": "Y6Lz_qVQaAYB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"f(3.0)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DIJgNdZxaESQ",
"outputId": "da60ef68-a95e-4663-e968-4d84c0075467"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"20.0"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"h = 0.0001\n",
"(f(3.0 + h) - f(3.0)) / h"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XrqEqypjndiT",
"outputId": "35f11a79-7b50-40f9-bab3-0e8d5b0c6b67"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.0002999999981767587"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"xs = np.arange(-5, 5, 0.25)\n",
"xs"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dUEBVQIPaNNL",
"outputId": "432838b1-fe0d-466a-b4fe-88bc87a96e90"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-5. , -4.75, -4.5 , -4.25, -4. , -3.75, -3.5 , -3.25, -3. ,\n",
" -2.75, -2.5 , -2.25, -2. , -1.75, -1.5 , -1.25, -1. , -0.75,\n",
" -0.5 , -0.25, 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 ,\n",
" 1.75, 2. , 2.25, 2.5 , 2.75, 3. , 3.25, 3.5 , 3.75,\n",
" 4. , 4.25, 4.5 , 4.75])"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"ys = f(xs)\n",
"ys"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-rOJ8YeoaUcU",
"outputId": "0f73fdf4-9f2d-4fdd-ad44-69d66e1669b4"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([100. , 91.6875, 83.75 , 76.1875, 69. , 62.1875,\n",
" 55.75 , 49.6875, 44. , 38.6875, 33.75 , 29.1875,\n",
" 25. , 21.1875, 17.75 , 14.6875, 12. , 9.6875,\n",
" 7.75 , 6.1875, 5. , 4.1875, 3.75 , 3.6875,\n",
" 4. , 4.6875, 5.75 , 7.1875, 9. , 11.1875,\n",
" 13.75 , 16.6875, 20. , 23.6875, 27.75 , 32.1875,\n",
" 37. , 42.1875, 47.75 , 53.6875])"
]
},
"metadata": {},
"execution_count": 46
}
]
},
{
"cell_type": "code",
"source": [
"f(-5)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qIpgNpjYbAq4",
"outputId": "9a5f9799-9f56-4cce-e95e-29bb5a86868e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"100"
]
},
"metadata": {},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"source": [
"f(4.75)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iiBYoFirbLYC",
"outputId": "cded2f8e-62a3-43ad-d0e9-2f865ad06f3d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"53.6875"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"plt.plot(xs,ys)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "6TQHixq7bN4X",
"outputId": "a55293de-6fed-4b15-e668-6bcd6dc42132"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7efdaf901a50>]"
]
},
"metadata": {},
"execution_count": 49
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"class Value: \n",
"\n",
" def __init__(self, data, _children=(), _op='', label=''):\n",
" self.data = data\n",
" self.grad = 0.0\n",
" self._backward = lambda: None\n",
" self._prev = set(_children)\n",
" self._op = _op\n",
" self.label = label\n",
"\n",
" def __repr__(self):\n",
" return f\"Value(data={self.data})\"\n",
"\n",
" def __add__(self, other):\n",
" out = Value(self.data + other.data, (self, other), '+')\n",
"\n",
" def _backward():\n",
" self.grad += 1 * out.grad \n",
" other.grad += 1 * out.grad \n",
" out._backward = _backward\n",
"\n",
" return out\n",
"\n",
" def __mul__(self, other):\n",
" out = Value(self.data * other.data, (self, other), '*')\n",
" \n",
" def _backward():\n",
" self.grad += other.data * out.grad\n",
" other.grad += self.data * out.grad\n",
" out._backward = _backward\n",
"\n",
" return out\n",
"\n",
" def tanh(self):\n",
" x = self.data\n",
" t = (math.exp(2*x) - 1) / (math.exp(2*x) + 1)\n",
" out = Value(t, (self, ), 'tanh')\n",
"\n",
" def _backward():\n",
" self.grad += (1 - t**2) * out.grad \n",
" out._backward = _backward\n",
"\n",
" return out\n",
"\n",
" def backward(self):\n",
" topo = []\n",
" visited = set()\n",
" def build_topo(v):\n",
" if v not in visited:\n",
" visited.add(v)\n",
" for child in v._prev:\n",
" build_topo(child)\n",
" topo.append(v)\n",
" build_topo(self)\n",
" \n",
" self.grad = 1.0\n",
" for node in reversed(topo):\n",
" node._backward()\n",
" \n",
"\n",
"a = Value(2.0, label='a')\n",
"b = Value(-3.0, label='b')\n",
"c = Value(10.0, label='c')\n",
"e = a*b; e.label = 'e'\n",
"d = e+c; d.label = 'd'\n",
"f = Value(-2.0, label='f')\n",
"L = d*f; L.label = 'L'\n",
"L\n",
"L.backward()"
],
"metadata": {
"id": "VA3ykAhqbSR8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from graphviz import Digraph\n",
"\n",
"def trace(root):\n",
" # builds a set of all nodes and edges in a graph\n",
" nodes, edges = set(), set()\n",
" def build(v):\n",
" if v not in nodes:\n",
" nodes.add(v)\n",
" for child in v._prev:\n",
" edges.add((child, v))\n",
" build(child)\n",
" build(root)\n",
" return nodes, edges\n",
"\n",
"def draw_dot(root):\n",
" dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n",
" \n",
" nodes, edges = trace(root)\n",
" for n in nodes:\n",
" uid = str(id(n))\n",
" # for any value in the graph, create a rectangular ('record') node for it\n",
" dot.node(name = uid, label = \"{%s | data %.4f | grad %.4f }\" % (n.label, n.data, n.grad ), shape='record')\n",
" if n._op:\n",
" # if this value is a result of some operation, create an op node for it\n",
" dot.node(name = uid + n._op, label = n._op)\n",
" # and connect this node to it\n",
" dot.edge(uid + n._op, uid)\n",
"\n",
" for n1, n2 in edges:\n",
" # connect n1 to the op node of n2\n",
" dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n",
"\n",
" return dot"
],
"metadata": {
"id": "7vyET18YiKPU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def lol():\n",
"\n",
" h = 0.0001\n",
"\n",
" a = Value(2.0, label='a')\n",
" b = Value(-3.0, label='b')\n",
" c = Value(10.0, label='c')\n",
" e = a*b; e.label = 'e'\n",
" d = e+c; d.label = 'd'\n",
" f = Value(-2.0, label='f')\n",
" L = d*f; L.label = 'L'\n",
" L1 = L.data \n",
"\n",
" a = Value(2.0, label='a')\n",
" b = Value(-3.0+h, label='b')\n",
" c = Value(10.0, label='c')\n",
" e = a*b; e.label = 'e'\n",
" d = e+c; d.label = 'd'\n",
" f = Value(-2.0, label='f')\n",
" L = d*f; L.label = 'L'\n",
" L2 = L.data\n",
"\n",
" print((L2 - L1)/h)\n",
"\n",
"lol()"
],
"metadata": {
"id": "t8BU0WdjkuQ9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "be1b867c-89a7-4437-bf64-c8b7b73d1e5d"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-4.000000000008441\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"L.grad = 1.0\n",
"f.grad = 4.0\n",
"d.grad = -2.0\n",
"\n",
"# c,e is solved by the chain rule!\n",
"\n",
"# dL/dc = dL/dd * dd/dc\n",
"# where dd/dc is summation so its = 1.0\n",
"# c = d.grad * 1.0\n",
"# c = -2.0 * 1.0\n",
"c.grad = -2.0\n",
"e.grad = -2.0\n",
"\n",
"# dL/de = -2.0\n",
"# want: dL/da\n",
"# dL/da = dL/de * de/da = -2.0 * de/da\n",
"\n",
"# e = a * b\n",
"# de/da = b = -3.0\n",
"a.grad = -2.0 * b.data\n",
"b.grad = -2.0 * a.data"
],
"metadata": {
"id": "l0cRGvgamrbM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"draw_dot(L)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 247
},
"id": "D4qvSQOSkrkD",
"outputId": "8fb7e79a-a4dc-4801-c4d9-f42e4b99b47f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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},
"metadata": {},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"source": [
"a.data += 0.01 * a.grad\n",
"b.data += 0.01 * b.grad\n",
"c.data += 0.01 * c.grad\n",
"f.data += 0.01 * f.grad\n",
"e = a*b\n",
"d = e+c\n",
"L = d*f\n",
"print(L.data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yWWDXJiuxFue",
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},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-7.286496\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"plt.plot(np.arange(-5,5,0.2), np.tanh(np.arange(-5,5,0.2))); \n",
"plt.grid();"
],
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"colab": {
"base_uri": "https://localhost:8080/",
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},
"id": "vvAxevYTyIaY",
"outputId": "201bc9c5-0908-414a-8af0-9cc2767ea8fc"
},
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"outputs": [
{
"output_type": "display_data",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"L.tanh()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Zs8t7jPLkOcI",
"outputId": "ad70fbf0-c4d6-4784-d5f1-1a5336b95cb7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Value(data=-0.9999997749296758)"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"a = Value(3.0, label='a')\n",
"b = a+a; b.label = 'b'\n",
"b.backward()\n",
"draw_dot(b)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
},
"id": "lpyFiNvBlhbo",
"outputId": "487f06b2-aca1-4316-d162-1c8af39025d7"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<graphviz.dot.Digraph at 0x7f9c4bcf20d0>"
],
"image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"525pt\" height=\"45pt\"\n viewBox=\"0.00 0.00 525.00 45.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 41)\">\n<title>%3</title>\n<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-41 521,-41 521,4 -4,4\"/>\n<!-- 140309263491216 -->\n<g id=\"node1\" class=\"node\">\n<title>140309263491216</title>\n<polygon fill=\"none\" stroke=\"#000000\" points=\"321,-.5 321,-36.5 517,-36.5 517,-.5 321,-.5\"/>\n<text text-anchor=\"middle\" x=\"333\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">b</text>\n<polyline fill=\"none\" stroke=\"#000000\" points=\"345,-.5 345,-36.5 \"/>\n<text text-anchor=\"middle\" x=\"387.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">data 6.0000</text>\n<polyline fill=\"none\" stroke=\"#000000\" points=\"430,-.5 430,-36.5 \"/>\n<text text-anchor=\"middle\" x=\"473.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">grad 1.0000</text>\n</g>\n<!-- 140309263491216+ -->\n<g id=\"node2\" class=\"node\">\n<title>140309263491216+</title>\n<ellipse fill=\"none\" stroke=\"#000000\" cx=\"258\" cy=\"-18.5\" rx=\"27\" ry=\"18\"/>\n<text text-anchor=\"middle\" x=\"258\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">+</text>\n</g>\n<!-- 140309263491216+&#45;&gt;140309263491216 -->\n<g id=\"edge1\" class=\"edge\">\n<title>140309263491216+&#45;&gt;140309263491216</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M285.3115,-18.5C292.8223,-18.5 301.4512,-18.5 310.6318,-18.5\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"310.7835,-22.0001 320.7835,-18.5 310.7835,-15.0001 310.7835,-22.0001\"/>\n</g>\n<!-- 140309263491664 -->\n<g id=\"node3\" class=\"node\">\n<title>140309263491664</title>\n<polygon fill=\"none\" stroke=\"#000000\" points=\"0,-.5 0,-36.5 195,-36.5 195,-.5 0,-.5\"/>\n<text text-anchor=\"middle\" x=\"11.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">a</text>\n<polyline fill=\"none\" stroke=\"#000000\" points=\"23,-.5 23,-36.5 \"/>\n<text text-anchor=\"middle\" x=\"65.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">data 3.0000</text>\n<polyline fill=\"none\" stroke=\"#000000\" points=\"108,-.5 108,-36.5 \"/>\n<text text-anchor=\"middle\" x=\"151.5\" y=\"-14.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">grad 2.0000</text>\n</g>\n<!-- 140309263491664&#45;&gt;140309263491216+ -->\n<g id=\"edge2\" class=\"edge\">\n<title>140309263491664&#45;&gt;140309263491216+</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M195.0268,-18.5C204.1551,-18.5 212.9704,-18.5 220.9692,-18.5\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"220.9719,-22.0001 230.9718,-18.5 220.9718,-15.0001 220.9719,-22.0001\"/>\n</g>\n</g>\n</svg>\n"
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"a = Value(-2.0, label='a')\n",
"b = Value(3.0, label='b')\n",
"d = a*b; d.label = 'd'\n",
"e = a+b; d.label = 'e'\n",
"f = d*e; d.label = 'f'\n",
"\n",
"f.backward()\n",
"\n",
"draw_dot(f)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 175
},
"id": "zKvQupSnyVtA",
"outputId": "fc975fb0-d7b4-4c5c-d833-b0f92cab56d2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<graphviz.dot.Digraph at 0x7f9c4bcb7210>"
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},
"metadata": {},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"source": [
"class Neuron:\n",
" \n",
" def __init__(self, nin):\n",
" self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n",
" self.b = Value(random.uniform(-1,1))\n",
" \n",
" def __call__(self, x):\n",
" # w * x + b\n",
" act = sum((wi*xi for wi, xi in zip(self.w, x)), self.b)\n",
" out = act.tanh()\n",
" return out\n",
" \n",
" def parameters(self):\n",
" return self.w + [self.b]"
],
"metadata": {
"id": "X9W1tzjKyrnD"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"x = [2.0, 3.0]\n",
"n = Neuron(2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
},
"id": "wvWq8RJOxige",
"outputId": "9994e426-e467-4f83-ead2-2ee2ea9df1e5"
},
"execution_count": null,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-dba6f26906f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNeuron\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-3-1c32feb1c296>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, nin)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-3-1c32feb1c296>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Value' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "n_5ZMyvMxlbt"
},
"execution_count": null,
"outputs": []
}
]
}
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