batch_norm_make_more_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMknz+8cSUkegPi4JQRUvg2",
      "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/github/01PrathamS/make_more/blob/main/batch_norm_make_more_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "EPMt9o8lbS7i"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words = open('make_more_names.txt', 'r').read().splitlines()\n",
        "words[:8]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9kDYOkUbqfE",
        "outputId": "bbb4691f-6659-4c82-e0e5-7acebb58a6fe"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['emma', 'olivia', 'ava', 'isabella', 'sophia', 'charlotte', 'mia', 'amelia']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chars = sorted(list(set(\"\".join(words))))\n",
        "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
        "stoi['.'] = 0\n",
        "itos = {i:s for s, i in stoi.items()}\n",
        "vocab_size = len(itos)\n",
        "print(itos)\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6AMqlc1bvRh",
        "outputId": "ff7eb31f-329b-4548-9472-fb52012b2562"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e', 6: 'f', 7: 'g', 8: 'h', 9: 'i', 10: 'j', 11: 'k', 12: 'l', 13: 'm', 14: 'n', 15: 'o', 16: 'p', 17: 'q', 18: 'r', 19: 's', 20: 't', 21: 'u', 22: 'v', 23: 'w', 24: 'x', 25: 'y', 26: 'z', 0: '.'}\n",
            "27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# build the dataset\n",
        "block_size = 3 # context length: how many characters do we take to predict the next one?\n",
        "\n",
        "def build_dataset(words):\n",
        "  X, Y = [], []\n",
        "  for w in words:\n",
        "\n",
        "    #print(w)\n",
        "    context = [0] * block_size\n",
        "    for ch in w + '.':\n",
        "      ix = stoi[ch]\n",
        "      X.append(context)\n",
        "      Y.append(ix)\n",
        "      #print(''.join(itos[i] for i in context), '--->', itos[ix])\n",
        "      context = context[1:] + [ix] # crop and append\n",
        "\n",
        "  X = torch.tensor(X)\n",
        "  Y = torch.tensor(Y)\n",
        "  print(X.shape, Y.shape)\n",
        "  return X, Y\n",
        "\n",
        "import random\n",
        "random.seed(42)\n",
        "random.shuffle(words)\n",
        "n1 = int(0.8*len(words))\n",
        "n2 = int(0.9*len(words))\n",
        "\n",
        "Xtr, Ytr = build_dataset(words[:n1])\n",
        "Xdev, Ydev = build_dataset(words[n1:n2])\n",
        "Xte, Yte = build_dataset(words[n2:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjFpHtG4b-a7",
        "outputId": "0f105a8f-8b3c-4b4e-eb0d-2ed1a99edadb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([182625, 3]) torch.Size([182625])\n",
            "torch.Size([22655, 3]) torch.Size([22655])\n",
            "torch.Size([22866, 3]) torch.Size([22866])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_embd = 10 ## the dimensionality of the character embedding vectors\n",
        "n_hidden = 200 ## the number of neurons in the hidden layer of the MLP\n",
        "\n",
        "g = torch.Generator().manual_seed(2147483647) # for reproducibility\n",
        "C = torch.randn((27, 10), generator=g)\n",
        "W1 = torch.randn((30, 200), generator=g) * (5/3)/((n_embd * block_size)**0.5)\n",
        "b1 = torch.randn(200, generator=g) * 0.01\n",
        "W2 = torch.randn((200, 27), generator=g) * 0.01\n",
        "b2 = torch.randn(27, generator=g)  * 0\n",
        "\n",
        "bngain = torch.ones((1, n_hidden))\n",
        "bnbias = torch.zeros((1, n_hidden))\n",
        "\n",
        "bnmean_running = torch.zeros((1, n_hidden))\n",
        "bnstd_running = torch.ones((1, n_hidden))\n",
        "\n",
        "parameters = [C, W1, b1, W2, b2, bngain, bnbias]\n",
        "\n",
        "print(sum(p.nelement() for p in parameters))\n",
        "for p in parameters:\n",
        "  p.requires_grad = True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzyJWqlIjj3D",
        "outputId": "cca63545-61ca-4939-8d63-1dc311a2c27a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12297\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.randn(1000, 10)\n",
        "w = torch.randn(10, 200)\n",
        "y = x @ w\n",
        "print(x.mean(), x.std())\n",
        "print(y.mean(), y.std())\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(121)\n",
        "plt.hist(y.view(-1).tolist(), 50, density=True)\n",
        "plt.subplot(122)\n",
        "plt.hist(x.view(-1).tolist(), 50, density=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "e2vVaLom07b6",
        "outputId": "9a130f65-f31c-4e2d-e67d-d417a2902a71"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(-0.0104) tensor(1.0051)\n",
            "tensor(0.0025) tensor(3.1347)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.00060279, 0.00060279, 0.00060279, 0.00060279, 0.00060279,\n",
              "        0.        , 0.00482231, 0.00180837, 0.00361673, 0.01567251,\n",
              "        0.01808367, 0.02893387, 0.03194781, 0.05726495, 0.06389563,\n",
              "        0.08981555, 0.119955  , 0.16094464, 0.18324784, 0.20253708,\n",
              "        0.2887359 , 0.31224467, 0.35142595, 0.36408451, 0.39723791,\n",
              "        0.3809626 , 0.41712994, 0.36348173, 0.3755375 , 0.31706698,\n",
              "        0.29657215, 0.26402155, 0.21700402, 0.16275301, 0.13803867,\n",
              "        0.11392711, 0.08921276, 0.0705263 , 0.03857849, 0.02953666,\n",
              "        0.01205578, 0.01868646, 0.00783626, 0.00723347, 0.00120558,\n",
              "        0.00361673, 0.00241116, 0.00060279, 0.        , 0.00060279]),\n",
              " array([-4.29091692, -4.12502137, -3.95912582, -3.79323028, -3.62733473,\n",
              "        -3.46143918, -3.29554363, -3.12964808, -2.96375254, -2.79785699,\n",
              "        -2.63196144, -2.46606589, -2.30017035, -2.1342748 , -1.96837925,\n",
              "        -1.8024837 , -1.63658815, -1.47069261, -1.30479706, -1.13890151,\n",
              "        -0.97300596, -0.80711041, -0.64121487, -0.47531932, -0.30942377,\n",
              "        -0.14352822,  0.02236732,  0.18826287,  0.35415842,  0.52005397,\n",
              "         0.68594952,  0.85184506,  1.01774061,  1.18363616,  1.34953171,\n",
              "         1.51542726,  1.6813228 ,  1.84721835,  2.0131139 ,  2.17900945,\n",
              "         2.34490499,  2.51080054,  2.67669609,  2.84259164,  3.00848719,\n",
              "         3.17438273,  3.34027828,  3.50617383,  3.67206938,  3.83796493,\n",
              "         4.00386047]),\n",
              " <BarContainer object of 50 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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0ZNmyZdm4cWOmTZuWpqambNu2rd/+Dz30UC644IJceOGF+elPf5p58+Zl3rx5efTRR/fo+53vfCcPP/xwxo0bV/qRAAAAAAAAlGhEoVAolLJDQ0NDZs6cmZtvvjlJ0tvbmwkTJuSTn/xkFi9evEf/+fPnZ+fOnbn33nuLbW9729tSX1+f1atXF9uee+65NDQ05Hvf+17mzp2byy67LJdddlm/NfT09KSnp6f4ubu7OxMmTEhXV1dGjx5dyuEA7KFu8X1DXQLsYfPyuUNdAnCI6e7uTnV1tXtg9ptrBsqD5xk49Hieg6FRyv1vSTNKdu3alQ0bNqSxsfH3A1RUpLGxMe3t7f3u097e3qd/kjQ1NfXp39vbm4985CO5/PLL88d//MevWkdra2uqq6uL24QJE0o5DAAAAAAAgCQlBiU7duzI7t27U1NT06e9pqYmHR0d/e7T0dHxqv2/8IUv5IgjjsinPvWp/apjyZIl6erqKm7PPvtsKYcBAAAAAACQJDliqAvYsGFDvvzlL2fjxo0ZMWLEfu1TWVmZysrKQa4MAAAAYOh4jRYAHBwlzSgZM2ZMRo4cmc7Ozj7tnZ2dqa2t7Xef2traffb/f//v/2Xbtm1505velCOOOCJHHHFEfvnLX+bTn/506urqSikPAAAAAACgJCUFJaNGjcr06dPT1tZWbOvt7U1bW1tmz57d7z6zZ8/u0z9JHnjggWL/j3zkI/nZz36WTZs2Fbdx48bl8ssvz/e+971SjwcAAAAAAGC/lfzqrZaWlixcuDAzZszIrFmzsnLlyuzcuTPNzc1JkgULFmT8+PFpbW1Nklx66aU5++yz88UvfjFz587NXXfdlZ/85Cf52te+liQ54YQTcsIJJ/T5Ga973etSW1ubU0899bUeHwAAAAAAwF6VHJTMnz8/27dvz9KlS9PR0ZH6+vqsXbu2uGD7li1bUlHx+4kqZ555Zu68885cffXVueqqqzJ58uTcc889Of300wfuKAAAAAAAAA7AiEKhUBjqIl6r7u7uVFdXp6urK6NHjx7qcoDDnAUTORRtXj53qEsADjHugSmVawYOP55NoHx5BoTXrpT735LWKAEAAAAAABhOBCUAAAAAAEDZEpQAAAAAAABlS1ACAAAAAACULUEJAAAAAABQtgQlAAAAAABA2RKUAAAAAAAAZUtQAgAAAAAAlC1BCQAAwABbtWpV6urqUlVVlYaGhqxfv36vfR977LGcd955qaury4gRI7Jy5crXPCYAALD/BCUAAAADaM2aNWlpacmyZcuycePGTJs2LU1NTdm2bVu//V966aVMmjQpy5cvT21t7YCMCQAA7D9BCQAAwAC68cYbc9FFF6W5uTmnnXZaVq9enaOOOiq33XZbv/1nzpyZG264Ieeff34qKysHZMwk6enpSXd3d58NAADYk6AEAABggOzatSsbNmxIY2Njsa2ioiKNjY1pb28/qGO2tramurq6uE2YMOGAfj4AAAx3ghIAAIABsmPHjuzevTs1NTV92mtqatLR0XFQx1yyZEm6urqK27PPPntAPx8AAIa7I4a6AAAAAAZeZWXlXl/lBQAA/J4ZJQAAAANkzJgxGTlyZDo7O/u0d3Z27nWh9qEYEwAA+D1BCQAAwAAZNWpUpk+fnra2tmJbb29v2traMnv27ENmTAAA4Pe8egsAAGAAtbS0ZOHChZkxY0ZmzZqVlStXZufOnWlubk6SLFiwIOPHj09ra2uS3y3W/vjjjxf//Nxzz2XTpk055phjcsopp+zXmAAAwIETlAAAAAyg+fPnZ/v27Vm6dGk6OjpSX1+ftWvXFhdj37JlSyoqfj+5f+vWrXnLW95S/LxixYqsWLEiZ599dtatW7dfYwIAAAdOUAIAADDAFi1alEWLFvX73Svhxyvq6upSKBRe05gAAMCBE5QAw1rd4vuGugQAAAAA4BAmKAEAAAAYZH6JCwAOXYISAAAAAIBDyIGEq5uXzx2ESqA8VLx6FwAAAAAAgOFJUAIAAAAAAJQtQQkAAAAAAFC2rFECAIcB76cFAAAAGBxmlAAAAAAAAGVLUAIAAAAAAJQtQQkAAAAAAFC2BCUAAAAAAEDZEpQAAAAAAABlS1ACAAAAAACULUEJAAAAAABQtgQlAAAAAABA2RKUAAAAAAAAZUtQAgAAAAAAlC1BCQAAAAAAULaOGOoCAAAAAAB4beoW31fyPpuXzx2ESuDwY0YJAAAAAABQtgQlAAAAAABA2RKUAAAAAAAAZUtQAgAAAAAAlK0DCkpWrVqVurq6VFVVpaGhIevXr99n/7vvvjtTpkxJVVVVzjjjjNx///3F715++eVceeWVOeOMM3L00Udn3LhxWbBgQbZu3XogpQEAAAAAAOy3I0rdYc2aNWlpacnq1avT0NCQlStXpqmpKU8++WTGjh27R/+HHnooF1xwQVpbW/Nnf/ZnufPOOzNv3rxs3Lgxp59+el566aVs3Lgx11xzTaZNm5Z///d/z6WXXpr3ve99+clPfjIgBwkMD3WL7xvqEgAAADybAMAwM6JQKBRK2aGhoSEzZ87MzTffnCTp7e3NhAkT8slPfjKLFy/eo//8+fOzc+fO3HvvvcW2t73tbamvr8/q1av7/Rk//vGPM2vWrPzyl7/Mm970pletqbu7O9XV1enq6sro0aNLORzgMOJhBEqzefncoS4BGETugSmVawYGjmcTYLjw3MhwVsr9b0mv3tq1a1c2bNiQxsbG3w9QUZHGxsa0t7f3u097e3uf/knS1NS01/5J0tXVlREjRuT1r399v9/39PSku7u7zwYAAAAAAFCqkoKSHTt2ZPfu3ampqenTXlNTk46Ojn736ejoKKn/b3/721x55ZW54IIL9prytLa2prq6urhNmDChlMMAAAAAAABIcoCLuQ+Wl19+Of/9v//3FAqF3HLLLXvtt2TJknR1dRW3Z5999iBWCQAAAAAADBclLeY+ZsyYjBw5Mp2dnX3aOzs7U1tb2+8+tbW1+9X/lZDkl7/8Zf75n/95n+8Mq6ysTGVlZSmlAwAAAAAA7KGkoGTUqFGZPn162traMm/evCS/W8y9ra0tixYt6nef2bNnp62tLZdddlmx7YEHHsjs2bOLn18JSX7xi1/kBz/4QU444YTSjwQAAACgRBZmBwBKCkqSpKWlJQsXLsyMGTMya9asrFy5Mjt37kxzc3OSZMGCBRk/fnxaW1uTJJdeemnOPvvsfPGLX8zcuXNz11135Sc/+Um+9rWvJfldSPLnf/7n2bhxY+69997s3r27uH7J8ccfn1GjRg3UsQIAAAAAAPRRclAyf/78bN++PUuXLk1HR0fq6+uzdu3a4oLtW7ZsSUXF75c+OfPMM3PnnXfm6quvzlVXXZXJkyfnnnvuyemnn54kee655/Ld7343SVJfX9/nZ/3gBz/IOeecc4CHBgDl7UB+O3Lz8rmDUAkAAADAoeuAFnNftGhRfvnLX6anpyePPPJIGhoait+tW7cut99+e5/+H/rQh/Lkk0+mp6cnjz76aP70T/+0+F1dXV0KhUK/m5AEAAA4HK1atSp1dXWpqqpKQ0ND1q9fv8/+d999d6ZMmZKqqqqcccYZuf/++/t8/9GPfjQjRozos82ZM2cwDwEAAMrGAQUlAAAA9G/NmjVpaWnJsmXLsnHjxkybNi1NTU3Ztm1bv/0feuihXHDBBbnwwgvz05/+NPPmzcu8efPy6KOP9uk3Z86c/PrXvy5u3/72tw/G4QAAwLAnKAEAABhAN954Yy666KI0NzfntNNOy+rVq3PUUUfltttu67f/l7/85cyZMyeXX3553vzmN+e6667LW9/61tx88819+lVWVqa2tra4HXfccfuso6enJ93d3X02AABgT4ISAACAAbJr165s2LAhjY2NxbaKioo0Njamvb29333a29v79E+SpqamPfqvW7cuY8eOzamnnpqLL744zz///D5raW1tTXV1dXGbMGHCAR4VAAAMb4ISAACAAbJjx47s3r07NTU1fdpramrS0dHR7z4dHR2v2n/OnDn5xje+kba2tnzhC1/Igw8+mPe85z3ZvXv3XmtZsmRJurq6ituzzz77Go4MAACGryOGugAAAAD27fzzzy/++YwzzsjUqVNz8sknZ926dXn3u9/d7z6VlZWprKw8WCUCAMBhy4wSAACAATJmzJiMHDkynZ2dfdo7OztTW1vb7z61tbUl9U+SSZMmZcyYMXnqqadee9EAAFDmBCUAAAADZNSoUZk+fXra2tqKbb29vWlra8vs2bP73Wf27Nl9+ifJAw88sNf+SfKrX/0qzz//fE488cSBKRwAAMqYoAQAAGAAtbS05NZbb80dd9yRn//857n44ouzc+fONDc3J0kWLFiQJUuWFPtfeumlWbt2bb74xS/miSeeyGc/+9n85Cc/yaJFi5IkL774Yi6//PI8/PDD2bx5c9ra2vL+978/p5xySpqamobkGAEAYDixRgkAAMAAmj9/frZv356lS5emo6Mj9fX1Wbt2bXHB9i1btqSi4ve/s3bmmWfmzjvvzNVXX52rrroqkydPzj333JPTTz89STJy5Mj87Gc/yx133JEXXngh48aNy7nnnpvrrrvOGiQAADAARhQKhcJQF/FadXd3p7q6Ol1dXRk9evRQlwPsh7rF9w11CUA/Ni+fO9QlAPvJPTClcs1A/zybAOXMMyDDWSn3v169BQAAAAAAlC1BCQAAAAAAULasUQIAAAAAUIYO5PWDXtfFcGRGCQAAAAAAULbMKAEAivw2EQAAAFBuzCgBAAAAAADKlqAEAAAAAAAoW4ISAAAAAACgbFmjBAAAABg2DmTNNQCgvJlRAgAAAAAAlC0zSgAAAAAA2C+lztzbvHzuIFUCA0dQAgAAABySvEYLADgYvHoLAAAAAAAoW2aUAAPCb3oBAAAAAIcjM0oAAAAAAICyJSgBAAAAAADKlldvAQCvyYG8em/z8rmDUAkAAABA6cwoAQAAAAAAypagBAAAAAAAKFuCEgAAAAAAoGxZowQAAAAYdAeyrhkA5clamBxsZpQAAAAAAABly4wSYA9+0wsAAAAAKBeCEgAAAKAkfrkKABhOBCUAwEFX6j+ueNcsAAAAMFisUQIAAAAAAJQtM0pgmDMlHgAAAICh4t+mOByYUQIAAAAAAJQtM0oAgEPegfwGknVNAAAAgP0hKAEAAAAA4LDmF+x4LQQlAAAAAACUHeEKrxCUwGHE4lcA+88NLwAAALA/BCUAAABwiBL8AwAMvgMKSlatWpUbbrghHR0dmTZtWm666abMmjVrr/3vvvvuXHPNNdm8eXMmT56cL3zhC/nTP/3T4veFQiHLli3LrbfemhdeeCFvf/vbc8stt2Ty5MkHUh4cFswOATj0+McoYKB4ZuJw4tkEAAaP58zDQ8lByZo1a9LS0pLVq1enoaEhK1euTFNTU5588smMHTt2j/4PPfRQLrjggrS2tubP/uzPcuedd2bevHnZuHFjTj/99CTJ9ddfn6985Su54447MnHixFxzzTVpamrK448/nqqqqtd+lAAAAAeJZyYAAF4L4crBN6JQKBRK2aGhoSEzZ87MzTffnCTp7e3NhAkT8slPfjKLFy/eo//8+fOzc+fO3HvvvcW2t73tbamvr8/q1atTKBQybty4fPrTn85nPvOZJElXV1dqampy++235/zzz99jzJ6envT09BQ/d3V15U1velOeffbZjB49upTDgX6dvux7Q10CAPTx6LVNQ10Ch5ju7u5MmDAhL7zwQqqrq4e6HP6LQ+GZKfHcNFx4NgEA9odnxj2V8sxU0oySXbt2ZcOGDVmyZEmxraKiIo2NjWlvb+93n/b29rS0tPRpa2pqyj333JMkeeaZZ9LR0ZHGxsbi99XV1WloaEh7e3u/N/2tra259tpr92ifMGFCKYcDAHDYqF451BVwqPrNb34jKDmEHCrPTInnJgCAcuKZce/255mppKBkx44d2b17d2pqavq019TU5Iknnuh3n46Ojn77d3R0FL9/pW1vff7QkiVL+jxI9Pb25t/+7d9ywgknZMSIEXut/5UEyW9QDTzndvA4t4PHuR0czuvgcW4Hj3M7eJzbwfHKeX388cczbty4oS6H/+JQeWZKDvy56XDj7xkOFtcaB4PrjIPFtcbBMNTXWaFQyG9+85v9emY6oMXch1plZWUqKyv7tL3+9a/f7/1Hjx7tL4BB4twOHud28Di3g8N5HTzO7eBxbgePczs4xo8fn4qKiqEug0PUa31uOtz4e4aDxbXGweA642BxrXEwDOV1tr+z70t6qhozZkxGjhyZzs7OPu2dnZ2pra3td5/a2tp99n/lf0sZEwAA4FDkmQkAAA4/JQUlo0aNyvTp09PW1lZs6+3tTVtbW2bPnt3vPrNnz+7TP0keeOCBYv+JEyemtra2T5/u7u488sgjex0TAADgUOSZCQAADj8lv3qrpaUlCxcuzIwZMzJr1qysXLkyO3fuTHNzc5JkwYIFGT9+fFpbW5Mkl156ac4+++x88YtfzNy5c3PXXXflJz/5Sb72ta8lSUaMGJHLLrssn//85zN58uRMnDgx11xzTcaNG5d58+YN3JHmd1PPly1btsf0c14753bwOLeDx7kdHM7r4HFuB49zO3ic28HhvB7aDudnpsOR/z9wsLjWOBhcZxwsrjUOhsPpOhtRKBQKpe50880354YbbkhHR0fq6+vzla98JQ0NDUmSc845J3V1dbn99tuL/e++++5cffXV2bx5cyZPnpzrr78+f/qnf1r8vlAoZNmyZfna176WF154IWeddVa++tWv5o/+6I9e+xECAAAcZJ6ZAADg8HFAQQkAAAAAAMBwUNIaJQAAAAAAAMOJoAQAAAAAAChbghIAAAAAAKBsCUoAAAAAAICyVRZByebNm3PhhRdm4sSJOfLII3PyySdn2bJl2bVrV59+P/vZz/KOd7wjVVVVmTBhQq6//vohqvjw8ld/9Vc588wzc9RRR+X1r399v31GjBixx3bXXXcd3EIPQ/tzbrds2ZK5c+fmqKOOytixY3P55ZfnP//zPw9uocNAXV3dHtfo8uXLh7qsw9KqVatSV1eXqqqqNDQ0ZP369UNd0mHvs5/97B7X55QpU4a6rMPSD3/4w7z3ve/NuHHjMmLEiNxzzz19vi8UClm6dGlOPPHEHHnkkWlsbMwvfvGLoSn2MPJq5/WjH/3oHtfwnDlzhqbYw0hra2tmzpyZY489NmPHjs28efPy5JNP9unz29/+NpdccklOOOGEHHPMMTnvvPPS2dk5RBXDoaWnpyf19fUZMWJENm3aNNTlMIzs778xwIHwPMVg2p/7Sxhoy5cvz4gRI3LZZZcNdSn7VBZByRNPPJHe3t78zd/8TR577LF86UtfyurVq3PVVVcV+3R3d+fcc8/NSSedlA0bNuSGG27IZz/72Xzta18bwsoPD7t27cqHPvShXHzxxfvs9/Wvfz2//vWvi9u8efMOToGHsVc7t7t3787cuXOza9euPPTQQ7njjjty++23Z+nSpQe50uHhc5/7XJ9r9JOf/ORQl3TYWbNmTVpaWrJs2bJs3Lgx06ZNS1NTU7Zt2zbUpR32/viP/7jP9fmjH/1oqEs6LO3cuTPTpk3LqlWr+v3++uuvz1e+8pWsXr06jzzySI4++ug0NTXlt7/97UGu9PDyauc1SebMmdPnGv72t799ECs8PD344IO55JJL8vDDD+eBBx7Iyy+/nHPPPTc7d+4s9vmLv/iL/NM//VPuvvvuPPjgg9m6dWs++MEPDmHVcOi44oorMm7cuKEug2Fof/6NAQ6E5ykG2/7cX8JA+vGPf5y/+Zu/ydSpU4e6lFdXKFPXX399YeLEicXPX/3qVwvHHXdcoaenp9h25ZVXFk499dShKO+w9PWvf71QXV3d73dJCt/5zncOaj3Dyd7O7f3331+oqKgodHR0FNtuueWWwujRo/tcy7y6k046qfClL31pqMs47M2aNatwySWXFD/v3r27MG7cuEJra+sQVnX4W7ZsWWHatGlDXcaw84f/bert7S3U1tYWbrjhhmLbCy+8UKisrCx8+9vfHoIKD0/9/Td/4cKFhfe///1DUs9wsm3btkKSwoMPPlgoFH53fb7uda8r3H333cU+P//5zwtJCu3t7UNVJhwS7r///sKUKVMKjz32WCFJ4ac//elQl8Qw94f/xgAHwvMUB9sf3l/CQPrNb35TmDx5cuGBBx4onH322YVLL710qEvap7KYUdKfrq6uHH/88cXP7e3teec735lRo0YV25qamvLkk0/m3//934eixGHnkksuyZgxYzJr1qzcdtttKRQKQ13SYa+9vT1nnHFGampqim1NTU3p7u7OY489NoSVHZ6WL1+eE044IW95y1tyww03eIVZiXbt2pUNGzaksbGx2FZRUZHGxsa0t7cPYWXDwy9+8YuMGzcukyZNyoc//OFs2bJlqEsadp555pl0dHT0uYarq6vT0NDgGh4A69aty9ixY3Pqqafm4osvzvPPPz/UJR12urq6kqR4D7thw4a8/PLLfa7ZKVOm5E1vepNrlrLW2dmZiy66KN/85jdz1FFHDXU5lIk//DcGKJXnKYbCH95fwkC65JJLMnfu3D5/rx3KjhjqAobCU089lZtuuikrVqwotnV0dGTixIl9+r3yj88dHR057rjjDmqNw83nPve5/Lf/9t9y1FFH5fvf/34+8YlP5MUXX8ynPvWpoS7tsNbR0dEnJEn6Xrfsv0996lN561vfmuOPPz4PPfRQlixZkl//+te58cYbh7q0w8aOHTuye/fufq/JJ554YoiqGh4aGhpy++2359RTT82vf/3rXHvttXnHO96RRx99NMcee+xQlzdsvPL3Zn/XsL9TX5s5c+bkgx/8YCZOnJinn346V111Vd7znvekvb09I0eOHOryDgu9vb257LLL8va3vz2nn356kt9ds6NGjdpjHTPXLOWsUCjkox/9aD7+8Y9nxowZ2bx581CXRBno798YoFSepzjY+ru/hIFy1113ZePGjfnxj3881KXst8N6RsnixYv7XST8v25/+B+T5557LnPmzMmHPvShXHTRRUNU+aHvQM7tvlxzzTV5+9vfnre85S258sorc8UVV+SGG24YxCM4dA30uWXvSjnXLS0tOeecczJ16tR8/OMfzxe/+MXcdNNN6enpGeKjgOQ973lPPvShD2Xq1KlpamrK/fffnxdeeCF///d/P9SlwX45//zz8773vS9nnHFG5s2bl3vvvTc//vGPs27duqEu7bBxySWX5NFHH81dd9011KXAkNjf+7qbbropv/nNb7JkyZKhLpnDkH9jAMqJ+0sGy7PPPptLL7003/rWt1JVVTXU5ey3w3pGyac//el89KMf3WefSZMmFf+8devWvOtd78qZZ565xyLttbW16ezs7NP2yufa2tqBKfgwUuq5LVVDQ0Ouu+669PT0pLKy8oDHORwN5Lmtra3N+vXr+7SV83X7h17LuW5oaMh//ud/ZvPmzTn11FMHobrhZ8yYMRk5cmS/f5e6HgfW61//+vzRH/1RnnrqqaEuZVh55Trt7OzMiSeeWGzv7OxMfX39EFU1PE2aNCljxozJU089lXe/+91DXc4hb9GiRbn33nvzwx/+MG984xuL7bW1tdm1a1deeOGFPrNK/L3LcLS/93X//M//nPb29j2eMWbMmJEPf/jDueOOOwaxSg53A/lvDFAqz1McTHu7v4SBsGHDhmzbti1vfetbi227d+/OD3/4w9x8883p6ek5JN8scFgHJW94wxvyhje8Yb/6Pvfcc3nXu96V6dOn5+tf/3oqKvpOppk9e3b+8i//Mi+//HJe97rXJUkeeOCBnHrqqWX52q1Szu2B2LRpU4477riyC0mSgT23s2fPzl/91V9l27ZtGTt2bJLfXbejR4/OaaedNiA/43D2Ws71pk2bUlFRUTyvvLpRo0Zl+vTpaWtry7x585L8bipvW1tbFi1aNLTFDTMvvvhinn766XzkIx8Z6lKGlYkTJ6a2tjZtbW3FYKS7uzuPPPJILr744qEtbpj51a9+leeff75PIMWeCoVCPvnJT+Y73/lO1q1bt8drYqdPn57Xve51aWtry3nnnZckefLJJ7Nly5bMnj17KEqGQbO/93Vf+cpX8vnPf774eevWrWlqasqaNWvS0NAwmCUyDAzkvzFAqTxPcTC82v0lDIR3v/vd+Zd/+Zc+bc3NzZkyZUquvPLKQzIkSQ7zoGR/PffccznnnHNy0kknZcWKFdm+fXvxu1dS+f/xP/5Hrr322lx44YW58sor8+ijj+bLX/5yvvSlLw1V2YeNLVu25N/+7d+yZcuW7N69O5s2bUqSnHLKKTnmmGPyT//0T+ns7Mzb3va2VFVV5YEHHshf//Vf5zOf+czQFn4YeLVze+655+a0007LRz7ykVx//fXp6OjI1VdfnUsuuaQsQ6gD1d7enkceeSTvete7cuyxx6a9vT1/8Rd/kf/5P/9nWQalr0VLS0sWLlyYGTNmZNasWVm5cmV27tyZ5ubmoS7tsPaZz3wm733ve3PSSSdl69atWbZsWUaOHJkLLrhgqEs77Lz44ot9ZuI888wz2bRpU44//vi86U1vymWXXZbPf/7zmTx5ciZOnJhrrrkm48aNKz6s0r99ndfjjz8+1157bc4777zU1tbm6aefzhVXXJFTTjklTU1NQ1j1oe+SSy7JnXfemX/8x3/MscceW1x3pLq6OkceeWSqq6tz4YUXpqWlJccff3xGjx6dT37yk5k9e3be9ra3DXH1MDTe9KY39fl8zDHHJElOPvlkvzHLgNmff2OAA+F5isH2aveXMBCOPfbYPda9Ofroo3PCCScc2uvhFMrA17/+9UKSfrf/6v/7//6/wllnnVWorKwsjB8/vrB8+fIhqvjwsnDhwn7P7Q9+8INCoVAo/N//+38L9fX1hWOOOaZw9NFHF6ZNm1ZYvXp1Yffu3UNb+GHg1c5toVAobN68ufCe97yncOSRRxbGjBlT+PSnP114+eWXh67ow9CGDRsKDQ0Nherq6kJVVVXhzW9+c+Gv//qvC7/97W+HurTD0k033VR405veVBg1alRh1qxZhYcffnioSzrszZ8/v3DiiScWRo0aVRg/fnxh/vz5haeeemqoyzos/eAHP+j379WFCxcWCoVCobe3t3DNNdcUampqCpWVlYV3v/vdhSeffHJoiz4M7Ou8vvTSS4Vzzz238IY3vKHwute9rnDSSScVLrrookJHR8dQl33I29v969e//vVin//4j/8ofOITnygcd9xxhaOOOqrwgQ98oPDrX/966IqGQ8wzzzxTSFL46U9/OtSlMIzs778xwIHwPMVg2p/7SxgMZ599duHSSy8d6jL2aUShUCgMSgIDAAAAAABwiPMSTQAAAAAAoGwJSgAAAAAAgLIlKAEAAAAAAMqWoAQAAAAAAChbghIAAAAAAKBsCUoAAAAAAICyJSgBAAAAAADKlqAEAAAAAAAoW4ISAAAAAACgbAlKAAAAAACAsiUoAQAAAAAAytb/DyEXnp7waOAPAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lri = []\n",
        "lossi = []\n",
        "stepi = []"
      ],
      "metadata": {
        "id": "veyUKSwJiIri"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_steps = 200000\n",
        "batch_size = 32\n",
        "lossi = []\n",
        "\n",
        "for i in range(200000):\n",
        "\n",
        "  # minibatch construct\n",
        "  ix = torch.randint(0, Xtr.shape[0], (32,))\n",
        "\n",
        "  # forward pass\n",
        "  emb = C[Xtr[ix]] # (32, 3, 10)\n",
        "  embcat = emb.view(emb.shape[0], -1)\n",
        "  hpreact = embcat @ W1 + b1 # (32, 200)\n",
        "\n",
        "  bnmeani = hpreact.mean(0, keepdim=True)\n",
        "  bnstdi = hpreact.std(0, keepdim=True)\n",
        "  hpreact = bngain * (hpreact - bnmeani) / bnstdi + bnbias\n",
        "\n",
        "  with torch.no_grad():\n",
        "    bnmean_running = 0.999 * bnmean_running + 0.001 * bnmeani\n",
        "    bnstd_running = 0.999 * bnstd_running + 0.001 * bnstdi\n",
        "\n",
        "  h = torch.tanh(hpreact)\n",
        "  logits = h @ W2 + b2 # (32, 27)\n",
        "  loss = F.cross_entropy(logits, Ytr[ix])\n",
        "  #print(loss.item())\n",
        "\n",
        "  # backward pass\n",
        "  for p in parameters:\n",
        "    p.grad = None\n",
        "  loss.backward()\n",
        "\n",
        "  # update\n",
        "  lr = 0.1 if i < 100000 else 0.01\n",
        "  for p in parameters:\n",
        "    p.data += -lr * p.grad\n",
        "\n",
        "  ## track status\n",
        "  if i % 10000 == 0:\n",
        "    print(f'{i:7d}/{max_steps:7d}: {loss.item():.4f}')\n",
        "  lossi.append(loss.log10().item())\n",
        "\n",
        "  # break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCUfEc0OdegF",
        "outputId": "ac3032c3-02e7-43e4-f264-cdbf846be75f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      0/ 200000: 3.3102\n",
            "  10000/ 200000: 2.2824\n",
            "  20000/ 200000: 2.2800\n",
            "  30000/ 200000: 2.0824\n",
            "  40000/ 200000: 2.1360\n",
            "  50000/ 200000: 2.1656\n",
            "  60000/ 200000: 2.3270\n",
            "  70000/ 200000: 2.3075\n",
            "  80000/ 200000: 2.0548\n",
            "  90000/ 200000: 2.0544\n",
            " 100000/ 200000: 2.0242\n",
            " 110000/ 200000: 2.0284\n",
            " 120000/ 200000: 1.9702\n",
            " 130000/ 200000: 2.1005\n",
            " 140000/ 200000: 2.1739\n",
            " 150000/ 200000: 2.1115\n",
            " 160000/ 200000: 1.7135\n",
            " 170000/ 200000: 1.9933\n",
            " 180000/ 200000: 1.8169\n",
            " 190000/ 200000: 2.3476\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "plt.imshow(h.abs() > 0.99, cmap='gray', interpolation='nearest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "FE_YluJDwqpj",
        "outputId": "d23a8725-79b1-419e-e2b2-fa2f80c30f0b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fdc216a6fb0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(h.view(-1).tolist(), 50);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "UA_B99TryEH_",
        "outputId": "740ceecb-01af-4ba7-d5e6-82599542e066"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(hpreact.view(-1).tolist(), 50);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "gbD2yOX1yKMU",
        "outputId": "a496707a-e058-47e5-e85a-ddb31195030a"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "-torch.tensor(1/27.0).log()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFqFdo0DjvWV",
        "outputId": "d7fbc660-b454-49d2-87c4-ddf6a1bc9251"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(3.2958)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(torch.arange(200000), torch.tensor(lossi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "tmso9zCIfGmf",
        "outputId": "38ce134a-5fdc-4850-bccc-64a40c1e7eb2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fdc23f10550>]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## calibrate the batch norm at the end of the training\n",
        "\n",
        "with torch.no_grad():\n",
        "  emb = C[Xtr]\n",
        "  embcat = emb.view(emb.shape[0], -1)\n",
        "  hpreact = embcat @ W1 + b1\n",
        "  ## measure the mean/std over the entire training set\n",
        "  bnmean = hpreact.mean(0, keepdim=True)\n",
        "  bnstd = hpreact.std(0, keepdim=True)"
      ],
      "metadata": {
        "id": "B_h_q34OKrRI"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def split_loss(split):\n",
        "  x, y = {\n",
        "    'train': (Xtr, Ytr),\n",
        "    'val': (Xdev, Ydev),\n",
        "    'test': (Xte, Yte),\n",
        "  }[split]\n",
        "  emb = C[x]\n",
        "  embcat = emb.view(emb.shape[0], -1)\n",
        "  hpreact = embcat @ W1 + b1\n",
        "  hpreact = bngain * (hpreact - bnmean_running) / bnstd_running + bnbias\n",
        "  h = torch.tanh(hpreact)\n",
        "  logits = h @ W2 + b2\n",
        "  loss = F.cross_entropy(logits, y)\n",
        "  print(split, loss.item())\n",
        "  return loss.item()\n",
        "\n",
        "split_loss('train')\n",
        "split_loss('val')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asH8-w1BKxP6",
        "outputId": "2bf5c28c-4987-4c3f-fd1f-530a4f954afe"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train 2.068892478942871\n",
            "val 2.1086833477020264\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1086833477020264"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5au8CecsLS-X"
      },
      "execution_count": 49,
      "outputs": []
    }
  ]
}