CNNANNCompare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da439095",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix , classification_report,accuracy_score\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e6e088b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pull the Dataset\n",
    "(X_train, y_train), (X_test,y_test) = datasets.cifar10.load_data()\n",
    "\n",
    "\n",
    "#y_train is a 2D array, for our classification having 1D array is good enough. so we will convert this to now 1D array\n",
    "y_train = y_train.reshape(-1,)\n",
    "y_test = y_test.reshape(-1,)\n",
    "\n",
    "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]\n",
    "\n",
    "def plot_sample(X, y, index):\n",
    "    plt.figure(figsize = (5,2))\n",
    "    plt.imshow(X[index])\n",
    "    plt.xlabel(classes[y[index]])\n",
    "\n",
    "plot_sample(X_train, y_train, 8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ac39080c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizing the training data\n",
    "X_train = X_train / 255.0\n",
    "#Normalizing the testing data to test it out later\n",
    "X_test = X_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9f34c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 72s 46ms/step - loss: 1.8141 - accuracy: 0.3544\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 75s 48ms/step - loss: 1.6251 - accuracy: 0.4261\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 76s 49ms/step - loss: 1.5427 - accuracy: 0.4557\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 80s 51ms/step - loss: 1.4817 - accuracy: 0.4791\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 86s 55ms/step - loss: 1.4328 - accuracy: 0.4960\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 92s 59ms/step - loss: 1.3870 - accuracy: 0.5121\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 92s 59ms/step - loss: 1.3505 - accuracy: 0.5251\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 94s 60ms/step - loss: 1.3162 - accuracy: 0.5359\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 98s 63ms/step - loss: 1.2820 - accuracy: 0.5481\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 101s 64ms/step - loss: 1.2520 - accuracy: 0.5619\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2e47d0cd850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a ANN\n",
    "ann = models.Sequential([\n",
    "        layers.Flatten(input_shape=(32,32,3)),\n",
    "        layers.Dense(3000, activation='relu'),\n",
    "        layers.Dense(1000, activation='relu'),\n",
    "        layers.Dense(10, activation='softmax')    \n",
    "    ])\n",
    "\n",
    "ann.compile(optimizer='SGD',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "ann.fit(X_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8006a3f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 4s 12ms/step\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.35      0.47      1000\n",
      "           1       0.44      0.82      0.57      1000\n",
      "           2       0.53      0.12      0.20      1000\n",
      "           3       0.30      0.51      0.37      1000\n",
      "           4       0.51      0.35      0.41      1000\n",
      "           5       0.50      0.21      0.30      1000\n",
      "           6       0.41      0.76      0.53      1000\n",
      "           7       0.84      0.35      0.49      1000\n",
      "           8       0.45      0.80      0.57      1000\n",
      "           9       0.62      0.33      0.43      1000\n",
      "\n",
      "    accuracy                           0.46     10000\n",
      "   macro avg       0.53      0.46      0.44     10000\n",
      "weighted avg       0.53      0.46      0.44     10000\n",
      "\n",
      "Test Accuracy: 45.97%\n"
     ]
    }
   ],
   "source": [
    "y_pred = ann.predict(X_test)\n",
    "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
    "\n",
    "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))\n",
    "print(\"Test Accuracy: {}%\".format(accuracy_score(y_test,y_pred_classes)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6c530c10",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "#Now let us build a convolutional neural network to train our images\n",
    "cnn = models.Sequential([\n",
    "    # First Conv layer\n",
    "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    # Second Conv layer\n",
    "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    # Fully Connected classifier\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8b63ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1563/1563 [==============================] - 26s 16ms/step - loss: 1.4902 - accuracy: 0.4644\n",
      "Epoch 2/5\n",
      "1563/1563 [==============================] - 33s 21ms/step - loss: 1.1369 - accuracy: 0.6005\n",
      "Epoch 3/5\n",
      "1563/1563 [==============================] - 29s 19ms/step - loss: 0.9911 - accuracy: 0.6557\n",
      "Epoch 4/5\n",
      "1563/1563 [==============================] - 30s 19ms/step - loss: 0.8995 - accuracy: 0.6863\n",
      "Epoch 5/5\n",
      "1563/1563 [==============================] - 30s 19ms/step - loss: 0.8316 - accuracy: 0.7113\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2e403dda070>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "cnn.fit(X_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7cd4fec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 6ms/step - loss: 0.9279 - accuracy: 0.6830\n",
      "313/313 [==============================] - 2s 6ms/step\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.70      0.74      1000\n",
      "           1       0.82      0.81      0.82      1000\n",
      "           2       0.65      0.46      0.54      1000\n",
      "           3       0.46      0.59      0.52      1000\n",
      "           4       0.63      0.57      0.60      1000\n",
      "           5       0.62      0.55      0.58      1000\n",
      "           6       0.70      0.80      0.75      1000\n",
      "           7       0.64      0.81      0.72      1000\n",
      "           8       0.77      0.81      0.79      1000\n",
      "           9       0.82      0.73      0.77      1000\n",
      "\n",
      "    accuracy                           0.68     10000\n",
      "   macro avg       0.69      0.68      0.68     10000\n",
      "weighted avg       0.69      0.68      0.68     10000\n",
      "\n",
      "Test Accuracy: 68.30000000000001%\n"
     ]
    }
   ],
   "source": [
    "cnn.evaluate(X_test,y_test)\n",
    "\n",
    "y_pred = cnn.predict(X_test)\n",
    "y_pred_classes = [np.argmax(element) for element in y_pred]\n",
    "\n",
    "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))\n",
    "print(\"Test Accuracy: {}%\".format(accuracy_score(y_test,y_pred_classes)*100))"
   ]
  }
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