Saving -and-Loading-Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving and Loading Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)\n",
    "tf.set_random_seed(101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Network Example\n",
    "\n",
    "Let's work on a regression example, we are trying to solve a very simple equation:\n",
    "\n",
    "y = mx + b\n",
    "\n",
    "y will be the y_labels and x is the x_data. We are trying to figure out the slope and the intercept for the line that best fits our data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Artifical Data (Some Made Up Regression Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04919588, 1.32311387, 0.8076449 , 2.3478983 , 5.00027539,\n",
       "       6.55724614, 6.08756533, 8.95861702, 9.55352047, 9.06981686])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_label = np.linspace(0,10,10) + np.random.uniform(-1.5,1.5,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c509bc8198>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GueZjX0rcyENbJN0n6WXbLzVe+8fGHPjoDp+VdKhxYPMzSZ8uuZ62iYhx24clndT8FWIvqgtGoNp+XNJHJK22fVbSw5K+LOlfbX9G87/w/nLR92HkKQCkpZO7YgAAGRDsAJAYgh0AEkOwA0BiCHYASAzBDgCJIdgBIDEEOwAk5n8B8G6BWy5pfNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_data,y_label,'*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Variables **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.68530633, 0.51786747])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = tf.Variable(0.39)\n",
    "b = tf.Variable(0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = tf.reduce_mean(y_label - (m*x_data+b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)\n",
    "train = optimizer.minimize(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________\n",
    "_____________\n",
    "________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Session and Run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    sess.run(init)\n",
    "    \n",
    "    epochs = 100\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        sess.run(train)\n",
    "\n",
    "    # Fetch Back Results\n",
    "    final_slope , final_intercept = sess.run([m,b])\n",
    "    \n",
    "    # ONCE YOU ARE DONE\n",
    "    # GO AHEAD AND SAVE IT!\n",
    "    # Make sure to provide a directory for it to make or go to. May get errors otherwise\n",
    "    #saver.save(sess,'models/my_first_model.ckpt')\n",
    "    saver.save(sess,'new_models/my_second_model.ckpt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c509cbb5f8>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test = np.linspace(-1,11,10)\n",
    "y_pred_plot = final_slope*x_test + final_intercept\n",
    "\n",
    "plt.plot(x_test,y_pred_plot,'r')\n",
    "\n",
    "plt.plot(x_data,y_label,'*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from new_models/my_second_model.ckpt\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    \n",
    "    # Restore the model\n",
    "    saver.restore(sess,'new_models/my_second_model.ckpt')\n",
    "    \n",
    "\n",
    "    # Fetch Back Results\n",
    "    restored_slope , restored_intercept = sess.run([m,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c509e09518>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_test = np.linspace(-1,11,10)\n",
    "y_pred_plot = restored_slope*x_test + restored_intercept\n",
    "\n",
    "plt.plot(x_test,y_pred_plot,'r')\n",
    "\n",
    "plt.plot(x_data,y_label,'*')"
   ]
  }
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