Project 2/DM_HW2_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "id": "GsCNC8gyXXPw"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "id": "fgBnZFoNXync"
   },
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "IGIx5ep2zzAE"
   },
   "outputs": [],
   "source": [
    "y_test = to_categorical(y_test)\n",
    "y_train = to_categorical(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "hIP0ELNrY33s",
    "outputId": "bf251d52-515c-4bc8-8194-0f91d156a54e"
   },
   "outputs": [],
   "source": [
    "cnn = tf.keras.models.Sequential()\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[28, 28, 1]))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n",
    "cnn.add(tf.keras.layers.Dropout(0.25))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n",
    "cnn.add(tf.keras.layers.Dropout(0.25))\n",
    "cnn.add(tf.keras.layers.Flatten())\n",
    "cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))\n",
    "cnn.add(tf.keras.layers.Dropout(0.25))\n",
    "cnn.add(tf.keras.layers.Dense(units=10, activation='softmax'))\n",
    "\n",
    "cnn.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model = cnn.fit(x_train,y_train, batch_size=32, epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "AFbF6kCF8EAs"
   },
   "outputs": [],
   "source": [
    "y_pred = cnn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "7rNNDyTa8P_E",
    "outputId": "a5663b64-dafd-464a-96f2-42f19ea22b11"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.plot(model.epoch, model.history[\"loss\"], 'g', label='Training loss')\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.plot(model.epoch, model.history[\"accuracy\"], 'g', label='Training accuracy')\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "VmOM1dw18sBj",
    "outputId": "42e36564-2a67-4af9-ae13-3fea2bcae69b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8989\n"
     ]
    }
   ],
   "source": [
    "counter = 0\n",
    "correct = 0\n",
    "for i in range(len(y_pred)):\n",
    "  if (np.argmax(y_pred[i]) == np.argmax(y_test[i])):\n",
    "    correct += 1\n",
    "  counter += 1\n",
    "print(correct/counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WcRZbkXC9IvT",
    "outputId": "2fa02a37-8de7-44e2-cdeb-d153c67ff4df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[859,   0,  18,  11,   1,   1, 105,   0,   5,   0],\n",
       "       [  0, 974,   0,  17,   4,   0,   2,   0,   3,   0],\n",
       "       [ 17,   0, 876,   9,  51,   0,  47,   0,   0,   0],\n",
       "       [ 17,   2,   8, 924,  23,   0,  25,   0,   1,   0],\n",
       "       [  2,   0, 101,  34, 801,   0,  61,   0,   1,   0],\n",
       "       [  0,   0,   0,   0,   0, 978,   0,  14,   0,   8],\n",
       "       [122,   1,  99,  21,  86,   0, 667,   0,   4,   0],\n",
       "       [  0,   0,   0,   0,   0,  10,   0, 974,   0,  16],\n",
       "       [  1,   0,   4,   5,   4,   2,   2,   0, 981,   1],\n",
       "       [  1,   0,   0,   1,   0,   4,   0,  39,   0, 955]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test.argmax(axis=1), y_pred.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s3zsAVneCACH",
    "outputId": "157f5859-d66c-4a80-e41d-efa072f9af40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     class 0       0.84      0.86      0.85      1000\n",
      "     class 1       1.00      0.97      0.99      1000\n",
      "     class 2       0.79      0.88      0.83      1000\n",
      "     class 3       0.90      0.92      0.91      1000\n",
      "     class 4       0.83      0.80      0.81      1000\n",
      "     class 5       0.98      0.98      0.98      1000\n",
      "     class 6       0.73      0.67      0.70      1000\n",
      "     class 7       0.95      0.97      0.96      1000\n",
      "     class 8       0.99      0.98      0.98      1000\n",
      "     class 9       0.97      0.95      0.96      1000\n",
      "\n",
      "    accuracy                           0.90     10000\n",
      "   macro avg       0.90      0.90      0.90     10000\n",
      "weighted avg       0.90      0.90      0.90     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_names = ['class 0', 'class 1', 'class 2', 'class 3', 'class 4', 'class 5', 'class 6', 'class 7', 'class 8', 'class 9']\n",
    "print(classification_report(y_test.argmax(axis=1), y_pred.argmax(axis=1), target_names=target_names))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "DM-HW2-2.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}