LOGISTIC REGRESSION USING NEURAL NETWORK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import h5py\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from lr_utils import load_dataset\n",
    "#from public_tests import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_orig,y_train,X_test_orig,y_test,classes=load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'non-cat', b'cat'], dtype='|S7')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:,25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(209, 64, 64, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=[1], is a cat picture\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=25\n",
    "plt.imshow(X_train_orig[index])\n",
    "print(\"y=\" +str(y_train[:,index])+ \", is a \"+ classes[np.squeeze(y_train[:,index])].decode('utf-8')+ \" picture\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: m_train = 209\n",
      "Number of testing examples: m_test = 50\n",
      "Height/Width of each image: num_px = 64\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_set_x shape: (209, 64, 64, 3)\n",
      "train_set_y shape: (1, 209)\n",
      "test_set_x shape: (50, 64, 64, 3)\n",
      "test_set_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "m_train=X_train_orig.shape[0]\n",
    "m_test=X_test_orig.shape[0]\n",
    "num_px=X_train_orig.shape[1]\n",
    "\n",
    "print (\"Number of training examples: m_train = \" + str(m_train))\n",
    "print (\"Number of testing examples: m_test = \" + str(m_test))\n",
    "print (\"Height/Width of each image: num_px = \" + str(num_px))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_set_x shape: \" + str(X_train_orig.shape))\n",
    "print (\"train_set_y shape: \" + str(y_train.shape))\n",
    "print (\"test_set_x shape: \" + str(X_test_orig.shape))\n",
    "print (\"test_set_y shape: \" + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Flattened trainig set(12288, 209)\n",
      "Flattened test set(12288, 50)\n"
     ]
    }
   ],
   "source": [
    "X_train_flatten=X_train_orig.reshape(X_train_orig.shape[0],-1).T\n",
    "X_test_flatten=X_test_orig.reshape(X_test_orig.shape[0],-1).T\n",
    "\n",
    "print('Flattened trainig set'+ str(X_train_flatten.shape))\n",
    "print('Flattened test set'+ str(X_test_flatten.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train_flatten/255\n",
    "X_test=X_test_flatten/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    s=1/(1+1/np.exp(z))\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initializeToZero(dim):\n",
    "    w=np.zeros((dim,1))\n",
    "    b=np.float(0)\n",
    "    return w,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def propogate(w,b,X,Y):\n",
    "    m=X.shape[1]\n",
    "    Z=np.dot(w.T,X)+b\n",
    "    A=sigmoid(Z)\n",
    "    cost=-np.sum(np.multiply(Y,np.log(A))+np.multiply(1-Y,np.log(1-A)))/m\n",
    "                 \n",
    "    dw=np.dot(X,(A-Y).T)/m\n",
    "    db=np.sum(A-Y)/m\n",
    "    \n",
    "    \n",
    "    grads={'dw':dw,\n",
    "          'db':db}\n",
    "    cost=np.squeeze(np.array(cost))\n",
    "    \n",
    "    return grads,cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(w,b,X,Y,num_iterations=1000,learning_rate=0.09,print_cost=False):\n",
    "    w = copy.deepcopy(w)\n",
    "    b = copy.deepcopy(b)\n",
    "    costs=[]\n",
    "    for i in range(num_iterations):\n",
    "        grads,cost=propogate(w,b,X,Y)\n",
    "        \n",
    "        dw=grads['dw']\n",
    "        db=grads['db']\n",
    "        \n",
    "        w=w-learning_rate*dw\n",
    "        b=b-learning_rate*db\n",
    "        \n",
    "        if i%100==0:\n",
    "            costs.append(cost)\n",
    "            \n",
    "            if print_cost:\n",
    "                print('cost at {} th iteration is {} '.format(i,cost))\n",
    "        \n",
    "    params={'w':w,\n",
    "           'b':b}\n",
    "    grads={'dw':dw,\n",
    "          'db':db}\n",
    "    return params,grads,costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(w,b,X):\n",
    "    Z=np.dot(w.T,X)+b\n",
    "    A=sigmoid(Z)\n",
    "    \n",
    "    m=X.shape[1]\n",
    "    Y_prediction=np.zeros((1,m))\n",
    "    w=w.reshape((X.shape[0],1))\n",
    "    for i in range(A.shape[1]):\n",
    "        if A[0,i]>0.5:\n",
    "            Y_prediction[0,i]=1\n",
    "        else:\n",
    "            Y_prediction[0,i]=0\n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X_train,y_train,X_test,y_test,num_iterations=1000,learning_rate=0.05,print_cost=False):\n",
    "    w,b=initializeToZero(X_train.shape[0])\n",
    "    grads,cost=propogate(w,b,X_train,y_train)\n",
    "    params,grads,costs=optimize(w,b,X_train,y_train,num_iterations=num_iterations,learning_rate=learning_rate,print_cost=print_cost)\n",
    "    \n",
    "    w=params['w']\n",
    "    b=params['b']\n",
    "    \n",
    "    Y_test_predictions=predict(w,b,X_test)\n",
    "    Y_train_predictions=predict(w,b,X_train)\n",
    "    \n",
    "    if print_cost:\n",
    "        print(\"train accuracy: {} %\".format(100 - np.mean(np.abs(Y_train_predictions - y_train)) * 100))\n",
    "        print(\"test accuracy: {} %\".format(100 - np.mean(np.abs(Y_test_predictions - y_test)) * 100))\n",
    "        \n",
    "    d={\"costs\": costs,\n",
    "       \"Y_prediction_test\": Y_test_predictions, \n",
    "         \"Y_prediction_train\" : Y_train_predictions, \n",
    "         \"w\" : w, \n",
    "         \"b\" : b,\n",
    "         \"learning_rate\" : learning_rate,\n",
    "         \"num_iterations\": num_iterations}\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost at 0 th iteration is 0.6931471805599453 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-f6df7ea182e7>:5: RuntimeWarning: divide by zero encountered in log\n",
      "  cost=-np.sum(np.multiply(Y,np.log(A))+np.multiply(1-Y,np.log(1-A)))/m\n",
      "<ipython-input-16-f6df7ea182e7>:5: RuntimeWarning: invalid value encountered in multiply\n",
      "  cost=-np.sum(np.multiply(Y,np.log(A))+np.multiply(1-Y,np.log(1-A)))/m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost at 100 th iteration is 1.0252720971937779 \n",
      "cost at 200 th iteration is 9.869147741793876 \n",
      "cost at 300 th iteration is 9.975164765940905 \n",
      "cost at 400 th iteration is 1.1283230487676839 \n",
      "cost at 500 th iteration is 1.9780734439588734 \n",
      "cost at 600 th iteration is 0.27778608154020823 \n",
      "cost at 700 th iteration is 0.025948125886281995 \n",
      "cost at 800 th iteration is 0.01702370428649015 \n",
      "cost at 900 th iteration is 0.013305392972973605 \n",
      "cost at 1000 th iteration is 0.011211687693931091 \n",
      "cost at 1100 th iteration is 0.009838992197608958 \n",
      "cost at 1200 th iteration is 0.008849985077902096 \n",
      "cost at 1300 th iteration is 0.008091829707285786 \n",
      "cost at 1400 th iteration is 0.007485495133529934 \n",
      "cost at 1500 th iteration is 0.00698569911518391 \n",
      "cost at 1600 th iteration is 0.0065643310610264075 \n",
      "cost at 1700 th iteration is 0.0062028204469807904 \n",
      "cost at 1800 th iteration is 0.005888289335294025 \n",
      "cost at 1900 th iteration is 0.005611458839906456 \n",
      "train accuracy: 100.0 %\n",
      "test accuracy: 68.0 %\n"
     ]
    }
   ],
   "source": [
    "log_regression=model(X_train,y_train,X_test,y_test,num_iterations=2000,learning_rate=0.05,print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y=[1]is a cat picture\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=1\n",
    "plt.imshow(X_test[:,index].reshape((num_px,num_px,3)))\n",
    "print(\"Y=\" + str(y_test[:,index])+ 'is a ' + classes[int(log_regression['Y_prediction_test'][0,index])].decode('utf-8')+ ' picture')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12288, 50)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1.0, your algorithm predicts a \"cat\" picture.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_image = \"Manojwaa.jpeg\"   \n",
    "\n",
    "# We preprocess the image to fit your algorithm.\n",
    "fname = my_image\n",
    "image = np.array(Image.open(fname).resize((num_px, num_px)))\n",
    "plt.imshow(image)\n",
    "image = image / 255.\n",
    "image = image.reshape((1, num_px * num_px * 3)).T\n",
    "my_predicted_image = predict(log_regression[\"w\"], log_regression[\"b\"], image)\n",
    "\n",
    "print(\"y = \" + str(np.squeeze(my_predicted_image)) + \", your algorithm predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}