V3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "274d6e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c67c794",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = open('names.txt', 'r').read().splitlines()\n",
    "# train_words = words[:25000]     WORDS CUT INTO 80/10/10\n",
    "# dev_words = words[25001: 28000]\n",
    "# test_words = words[28001: 32033]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b65b3815",
   "metadata": {},
   "outputs": [],
   "source": [
    "chars = sorted(list(set(''.join(words))))\n",
    "i2s = {p+1:l for p, l in enumerate(chars)}\n",
    "i2s[0] = '.'\n",
    "\n",
    "\n",
    "s2i = {}\n",
    "\n",
    "for i, l in i2s.items():\n",
    "    s2i[l] = i\n",
    "\n",
    "# s2i = {s:i for i, s in i2s.items()} same thing one line\n",
    "\n",
    "    \n",
    "# build the vocabulary of characters and mappings to/from integers\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c98ea3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0,  0,  0],\n",
      "        [ 0,  0,  5],\n",
      "        [ 0,  5, 13],\n",
      "        [ 5, 13, 13],\n",
      "        [13, 13,  1],\n",
      "        [ 0,  0,  0],\n",
      "        [ 0,  0, 15],\n",
      "        [ 0, 15, 12],\n",
      "        [15, 12,  9],\n",
      "        [12,  9, 22],\n",
      "        [ 9, 22,  9],\n",
      "        [22,  9,  1]]) tensor([ 5, 13, 13,  1,  0, 15, 12,  9, 22,  9,  1,  0])\n"
     ]
    }
   ],
   "source": [
    "block_size = 3\n",
    "X, Y = [], [] # X is input, Y is labels\n",
    "for w in words[:2]:\n",
    "    \n",
    "    context = [0] * block_size\n",
    "    for ch in w + '.':\n",
    "#         print(w)\n",
    "        ix = s2i[ch]\n",
    "        X.append(context) # current running context\n",
    "        Y.append(ix)\n",
    "#         print(''.join(i2s[i] for i in context), '--->', i2s[ix])\n",
    "        context = context[1:] + [ix] # move it up one \n",
    "        \n",
    "X = torch.tensor(X) # [n, 3] 3 leading characters of however many inputs\n",
    "Y = torch.tensor(Y)  # [n] this is the next letter after the 3, and what we are trying to predict \n",
    "print(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "db94b969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([12, 3, 2])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2 = torch.Generator().manual_seed(214748364)\n",
    "\n",
    "C = torch.randn((27, 2), generator=g2) # 27 x 2\n",
    "V = F.one_hot(torch.tensor(5), num_classes=27).float() # 27 x 1\n",
    "A = V @ C\n",
    "emb = C[X]\n",
    "emb.shape   # 3 arrays of [32, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "124e22d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3])\n",
      "torch.Size([32, 3, 2])\n",
      "tensor([-0.7303,  1.1832])\n",
      "tensor([-0.7303,  1.1832])\n"
     ]
    }
   ],
   "source": [
    "# PyTorch Indexing\n",
    "\n",
    "print(X.shape)\n",
    "print(C[X].shape)\n",
    "print(C[X][13,2])\n",
    "print(C[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d9145a41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating first hidden layer of neurons\n",
    "\n",
    "W1 = torch.randn((6, 100), generator=g2)\n",
    "b1 = torch.randn(100, generator=g2)\n",
    "\n",
    "# we want to do eeb @ W1 + b1 but because of dimensions wont work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "21baeb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 6])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate all the pieces of embedded matrix, which is the weights at each place of letter in matrix X\n",
    "# N = torch.cat((emb[:, 0, :], emb[:, 1], emb[:, 2]), 1)\n",
    "# N.shape     These lines do the same thing, but below is more flexible at \n",
    "\n",
    "Z = torch.cat(torch.unbind(emb, 1), 1)\n",
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fd235e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 6])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = torch.cat(torch.unbind(emb, 1), 1) # uneffiecent and creates lots of memory\n",
    "Z.shape\n",
    "\n",
    "# Q = torch.unbind(emb, 1)  this breaks the [32, 3, 2] matrix into a tuple of 3 [32, 2] arrays\n",
    "# Q[0].shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f5a91e0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([18])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0,  1],\n",
       "         [ 2,  3],\n",
       "         [ 4,  5]],\n",
       "\n",
       "        [[ 6,  7],\n",
       "         [ 8,  9],\n",
       "         [10, 11]],\n",
       "\n",
       "        [[12, 13],\n",
       "         [14, 15],\n",
       "         [16, 17]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## practice with a.view\n",
    "\n",
    "a = torch.arange(18)\n",
    "print(a.shape)\n",
    "a.view(3,3,2) # must multiply to same numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ecb9165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.7437,  0.9111,  0.4172,  ..., -0.8906,  0.0780, -0.1556],\n",
       "        [-0.9579,  0.9981,  0.9558,  ..., -0.7515,  0.7401,  0.9818],\n",
       "        [ 0.5338,  0.9274,  0.1247,  ...,  0.9116,  0.4257,  0.8823],\n",
       "        ...,\n",
       "        [-0.9653,  0.9928,  0.9868,  ...,  0.8227, -0.8342,  0.9816],\n",
       "        [-0.4200,  0.9607,  0.2070,  ...,  0.8971,  0.9916,  0.2514],\n",
       "        [-0.9407,  0.9780, -0.9071,  ...,  0.9787, -0.9759, -0.2924]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = torch.tanh(emb.view(-1, 6) @ W1 + b1) # this is creating a \n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "70a27d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(words):\n",
    "    block_size = 3\n",
    "    X, Y = [], []\n",
    "    \n",
    "    for w in words:\n",
    "        context = [0] * block_size\n",
    "        for ch in w + '.':\n",
    "            ix = s2i[ch] \n",
    "            Y.append(ix)\n",
    "            X.append(context)\n",
    "            context = context[1:] + [ix]\n",
    "            \n",
    "    X = torch.tensor(X)\n",
    "    Y = torch.tensor(Y)\n",
    "    return X, Y\n",
    "\n",
    "import random\n",
    "random.seed(42)\n",
    "random.shuffle(words)\n",
    "\n",
    "n1 = int(.8*len(words))\n",
    "n2 = int(.9*len(words))\n",
    "\n",
    "Xtr, Ytr = build_dataset(words[:n1]) # train_words\n",
    "Xdev, Ydev = build_dataset(words[n1:n2]) # dev_words\n",
    "Xte, Yte = build_dataset(words[n2:]) # test_words\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3627c80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lre = torch.linspace(-3, 0, 1000)\n",
    "lrs = 10**lre\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "babdffbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11897\n"
     ]
    }
   ],
   "source": [
    "g = torch.Generator().manual_seed(214748364)\n",
    "\n",
    "C = torch.randn((27, 10), generator=g2) # 27 x n_embeddings this is a 2d vector of all the next possible characters\n",
    "\n",
    "W1 = torch.randn((30, 200), generator=g2) # weights (block_size(3) x n_embeddings (10))  x N_neurons\n",
    "b1 = torch.randn(200, generator=g2)\n",
    "\n",
    "W2 = torch.randn((200,27), generator=g2) # creating second hidden layer which will take in the 100 transformed matrix and connect it to 27 output neurons\n",
    "b2 = torch.randn(27 , generator = g2) # bias \n",
    "\n",
    "\n",
    "params = [C, W1, W2, b1, b2]\n",
    "num_params = sum(p.nelement() for p in params)\n",
    "print(num_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8d6b69d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(2.4713, grad_fn=<NllLossBackward0>)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iters = 10000\n",
    "lri = []\n",
    "lossi = []\n",
    "\n",
    "for p in params:\n",
    "    p.requires_grad = True\n",
    "\n",
    "for i in range(iters):\n",
    "    # Mini Sample\n",
    "    ix = torch.randint(0, Xtr.shape[0], (32,)) # this is a sample from 0 to size of X\n",
    "\n",
    "    emb = C[Xtr[ix]]\n",
    "    h = torch.tanh(emb.view(-1, 30) @ W1 + b1)\n",
    "    logits = h @ W2 + b2 # logits which are outputs\n",
    "    loss = F.cross_entropy(logits, Ytr[ix])\n",
    "    # counts = logits.exp()  OLD WAY TO CALCULATE LOSS ==> F.cross_entropy is better\n",
    "    # prob = counts / counts.sum(1, keepdim=True)\n",
    "    # loss = -prob[torch.arange(32), Y].log().mean() # this plucks the spot of probabilities following each Y\n",
    "    # print(loss)\n",
    "    # prob[0].sum()  == 1 #(normalized)\n",
    "\n",
    "    # backward pass\n",
    "    for p in params:\n",
    "        p.grad = None\n",
    "\n",
    "    # BACK PROPOGATE\n",
    "    loss.backward()\n",
    "    \n",
    "    \n",
    "    lr = .1 # using minimum found on the graph\n",
    "    for p in params:\n",
    "        p.data += -lr * p.grad\n",
    "        \n",
    "    # track stats\n",
    "    lossi.append(loss.item())\n",
    "    final = loss\n",
    "\n",
    "x_plot = torch.arange(iters)\n",
    "plt.plot(x_plot, lossi)\n",
    "print(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d1463124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffd3bd50dc0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lri, lossi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a944c663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.4322, grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = C[Xdev]  # the network has not been trained on these, yet still performs decent\n",
    "h = torch.tanh(emb.view(-1,30) @ W1 + b1)\n",
    "logits = h @ W2 + b2\n",
    "loss = F.cross_entropy(logits, Ydev)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "eaaba8be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.scatter(C[:,0].data, C[:,1].data, s=200)\n",
    "for i in range(C.shape[0]):\n",
    "    plt.text(C[i, 0].item(), C[i, 1].item(), i2s[i], ha=\"center\", va=\"center\", color=\"white\")\n",
    "plt.grid('minor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "838f3a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kull.\n",
      "audiquel.\n",
      "iva.\n",
      "tan.\n",
      "nvelian.\n",
      "anjrleett.\n",
      "muer.\n",
      "rardo.\n",
      "dington.\n",
      "vatriel.\n",
      "jaylan.\n",
      "noelton.\n",
      "losen.\n",
      "pomit.\n",
      "vien.\n",
      "yiasthadis\n",
      "aloe.\n",
      "tkher.\n",
      "sayjelynn.\n",
      "nef.\n"
     ]
    }
   ],
   "source": [
    "# sample from the model\n",
    "g = torch.Generator().manual_seed(214748364 + 1)\n",
    "\n",
    "for _ in range(20):\n",
    "    \n",
    "    out = []\n",
    "    context = [0] * block_size # initialize with all ...\n",
    "    count = 0\n",
    "    while True:\n",
    "        emb = C[torch.tensor([context])] # (1,block_size,d)\n",
    "        h = torch.tanh(emb.view(1, -1) @ W1 + b1)\n",
    "        logits = h @ W2 + b2\n",
    "        probs = F.softmax(logits, dim=1)\n",
    "        ix = torch.multinomial(probs, num_samples=1, generator=g).item()\n",
    "        context = context[1:] + [ix]\n",
    "        out.append(ix)\n",
    "        count +=1 \n",
    "        if ix == 0:\n",
    "            break\n",
    "        if count == 10:\n",
    "            break\n",
    "    \n",
    "    print(''.join(itos[i] for i in out))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c0c799",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}