notebooks/lstm_from_scatch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_f = tf.Variable([1.])\n",
    "w_i = tf.Variable([1.])\n",
    "w_o = tf.Variable([1.])\n",
    "w_c = tf.Variable([1.])\n",
    "u_f = tf.Variable([1.])\n",
    "u_i = tf.Variable([1.])\n",
    "u_o = tf.Variable([1.])\n",
    "u_c = tf.Variable([1.])\n",
    "b_f = tf.Variable([1.])\n",
    "b_i = tf.Variable([1.])\n",
    "b_o = tf.Variable([1.])\n",
    "b_c = tf.Variable([1.])\n",
    "x_t = tf.constant([23.])\n",
    "h_t_1 = tf.constant([1.])\n",
    "c_t_1 = tf.constant([1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lstm_cell(x_t,h_t_1, c_t_1):  \n",
    "    f_t = tf.sigmoid(tf.tensordot(w_f,x_t,1) + tf.tensordot(u_f,h_t_1,1) + b_f)\n",
    "    i_t = tf.sigmoid(tf.tensordot(w_i,x_t,1) + tf.tensordot(u_i,h_t_1,1) + b_i)\n",
    "    o_t = tf.sigmoid(tf.tensordot(w_o,x_t,1) + tf.tensordot(u_o,h_t_1,1) + b_o)\n",
    "    tf.tensordot(w_c,x_t,1)\n",
    "    tf.tensordot(u_c,h_t_1,1)\n",
    "    temp = tf.sigmoid(tf.tensordot(w_c,x_t,1) + tf.tensordot(u_c,h_t_1,1) + b_c)\n",
    "    tf.tensordot(i_t,temp,1)\n",
    "    c_t = [tf.tensordot(f_t,c_t_1,1) + tf.tensordot(i_t,temp,1)]\n",
    "    h_t = [tf.tensordot(o_t, tf.sigmoid(c_t),1)]\n",
    "    return (h_t, c_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_t_1 = tf.constant([0.5])\n",
    "x_t_2 = tf.constant([0.7])\n",
    "h_t_1 = tf.constant([1.])\n",
    "c_t_1 = tf.constant([1.])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def step():\n",
    "    (h_t, c_t) = lstm_cell(x_t_1, h_t_1, c_t_1)\n",
    "    (h_t_p1, c_t_p1) = lstm_cell(x_t_2, h_t, c_t)\n",
    "    return (h_t, h_t_p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss(x_t_1,x_t_2,h_t_1, h_t_2):\n",
    "    return (tf.square(x_t_1-h_t_1)+tf.square(x_t_2-h_t_2))/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    with tf.GradientTape() as t:\n",
    "        lr = 0.1\n",
    "        (h_t_1, h_t_2) = step()\n",
    "        current_loss = loss(x_t_1,x_t_2,h_t_1, h_t_2)\n",
    "        print(f\"Loss: {current_loss}\")\n",
    "        g_w_f,g_w_i,g_w_o,g_w_c,g_u_f,g_u_i,g_u_o,g_u_c,g_b_f,g_b_i,g_b_o,g_b_c = t.gradient(current_loss, [w_f,w_i,w_o,w_c,u_f,u_i,u_o,u_c,b_f,b_i,b_o,b_c])\n",
    "        w_f.assign_sub(lr * g_w_f)\n",
    "        w_i.assign_sub(lr * g_w_i)\n",
    "        w_o.assign_sub(lr * g_w_o)\n",
    "        w_c.assign_sub(lr * g_w_c)\n",
    "        u_f.assign_sub(lr * g_u_f)\n",
    "        u_i.assign_sub(lr * g_u_i)\n",
    "        u_o.assign_sub(lr * g_u_o)\n",
    "        u_c.assign_sub(lr * g_u_c)\n",
    "        b_f.assign_sub(lr * g_b_f)\n",
    "        b_i.assign_sub(lr * g_b_i)\n",
    "        b_o.assign_sub(lr * g_b_o)\n",
    "        b_c.assign_sub(lr * g_b_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Loss: [7.453874e-08]\n",
      "Loss: [7.442504e-08]\n",
      "Loss: [7.43845e-08]\n",
      "Loss: [7.434253e-08]\n",
      "Loss: [7.426007e-08]\n",
      "Loss: [7.4177656e-08]\n",
      "Loss: [7.408589e-08]\n",
      "Loss: [7.404401e-08]\n",
      "Loss: [7.3971115e-08]\n",
      "Loss: [7.384989e-08]\n",
      "Loss: [7.381888e-08]\n",
      "Loss: [7.3736715e-08]\n",
      "Loss: [7.366539e-08]\n",
      "Loss: [7.3603466e-08]\n",
      "Loss: [7.352142e-08]\n",
      "Loss: [7.345956e-08]\n",
      "Loss: [7.335746e-08]\n",
      "Loss: [7.330644e-08]\n",
      "Loss: [7.3224555e-08]\n",
      "Loss: [7.313338e-08]\n",
      "Loss: [7.3051595e-08]\n",
      "Loss: [7.3010014e-08]\n",
      "Loss: [7.292971e-08]\n",
      "Loss: [7.28681e-08]\n",
      "Loss: [7.279719e-08]\n",
      "Loss: [7.271559e-08]\n",
      "Loss: [7.2634045e-08]\n",
      "Loss: [7.257256e-08]\n",
      "Loss: [7.2521814e-08]\n",
      "Loss: [7.242037e-08]\n",
      "Loss: [7.2319e-08]\n",
      "Loss: [7.2268335e-08]\n",
      "Loss: [7.2187035e-08]\n",
      "Loss: [7.213642e-08]\n",
      "Loss: [7.204592e-08]\n",
      "Loss: [7.198469e-08]\n",
      "Loss: [7.192348e-08]\n",
      "Loss: [7.185304e-08]\n",
      "Loss: [7.177197e-08]\n",
      "Loss: [7.166041e-08]\n",
      "Loss: [7.1640514e-08]\n",
      "Loss: [7.155957e-08]\n",
      "Loss: [7.14588e-08]\n",
      "Loss: [7.1408444e-08]\n",
      "Loss: [7.1367346e-08]\n",
      "Loss: [7.126671e-08]\n",
      "Loss: [7.119658e-08]\n",
      "Loss: [7.112649e-08]\n",
      "Loss: [7.108547e-08]\n",
      "Loss: [7.094543e-08]\n",
      "Loss: [7.0895254e-08]\n",
      "Loss: [7.083452e-08]\n",
      "Loss: [7.076461e-08]\n",
      "Loss: [7.068416e-08]\n",
      "Loss: [7.063407e-08]\n",
      "Loss: [7.05537e-08]\n",
      "Loss: [7.050367e-08]\n",
      "Loss: [7.0403644e-08]\n",
      "Loss: [7.034312e-08]\n",
      "Loss: [7.0293154e-08]\n",
      "Loss: [7.021298e-08]\n",
      "Loss: [7.011316e-08]\n",
      "Loss: [7.0013414e-08]\n",
      "Loss: [6.996357e-08]\n",
      "Loss: [6.991374e-08]\n",
      "Loss: [6.980364e-08]\n",
      "Loss: [6.9773506e-08]\n",
      "Loss: [6.968449e-08]\n",
      "Loss: [6.96138e-08]\n",
      "Loss: [6.9544484e-08]\n",
      "Loss: [6.9476556e-08]\n",
      "Loss: [6.941643e-08]\n",
      "Loss: [6.935633e-08]\n",
      "Loss: [6.9287154e-08]\n",
      "Loss: [6.9218004e-08]\n",
      "Loss: [6.9148896e-08]\n",
      "Loss: [6.908891e-08]\n",
      "Loss: [6.901986e-08]\n",
      "Loss: [6.8940416e-08]\n",
      "Loss: [6.8890955e-08]\n",
      "Loss: [6.8822004e-08]\n",
      "Loss: [6.874267e-08]\n",
      "Loss: [6.866338e-08]\n",
      "Loss: [6.857508e-08]\n",
      "Loss: [6.855562e-08]\n",
      "Loss: [6.8466036e-08]\n",
      "Loss: [6.838691e-08]\n",
      "Loss: [6.833765e-08]\n",
      "Loss: [6.825994e-08]\n",
      "Loss: [6.8219755e-08]\n",
      "Loss: [6.8160176e-08]\n",
      "Loss: [6.804244e-08]\n",
      "Loss: [6.8003665e-08]\n",
      "Loss: [6.791579e-08]\n",
      "Loss: [6.784599e-08]\n",
      "Loss: [6.779692e-08]\n",
      "Loss: [6.774787e-08]\n",
      "Loss: [6.7679494e-08]\n"
     ]
    }
   ],
   "source": [
    "for i in range(10000):\n",
    "    train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<tf.Tensor: id=1611592, shape=(), dtype=float32, numpy=0.5312667>],\n",
       " [<tf.Tensor: id=1611781, shape=(), dtype=float32, numpy=0.6583782>])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=144347, shape=(3,), dtype=float32, numpy=array([0.5571425, 0.5572855, 0.5572855], dtype=float32)>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(3,) dtype=float32, numpy=array([0.0767175, 0.0767175, 0.0767175], dtype=float32)>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: id=297, shape=(3,), dtype=float32, numpy=array([0.37139067, 0.55708605, 0.74278134], dtype=float32)>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_norm[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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