DL_A2_43217.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tPIKfVcI7cXn"
   },
   "source": [
    "Name: **Megha Dandapat**<br>\n",
    "Div: **BE10**<br>\n",
    "Roll no: **43217**<br>\n",
    "Title: **Assignment 2: Implementing Feedforward neural networks with Keras and TensorFlow**<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "hcpVc-38-OFf"
   },
   "outputs": [],
   "source": [
    "#installations\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.metrics import classification_report\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "LUZLOYG8-Yz2"
   },
   "outputs": [],
   "source": [
    "#grabbing the mnist dataset\n",
    "((X_train, Y_train), (X_test, Y_test)) = mnist.load_data()\n",
    "X_train = X_train.reshape((X_train.shape[0], 28 * 28 * 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], 28 * 28 * 1))\n",
    "X_train = X_train.astype(\"float32\") / 255.0\n",
    "X_test = X_test.astype(\"float32\") / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "rGDSwm8e-i44"
   },
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "Y_train = lb.fit_transform(Y_train)\n",
    "Y_test = lb.transform(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "lxBbB-yA-s_g"
   },
   "outputs": [],
   "source": [
    "#building the model\n",
    "model = Sequential()\n",
    "model.add(Dense(128, input_shape=(784,), activation=\"sigmoid\"))\n",
    "model.add(Dense(64, activation=\"sigmoid\"))\n",
    "model.add(Dense(10, activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xKc03Mvz-wrz",
    "outputId": "b0544424-d139-48f9-c1bc-1f6f5d659645"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 1.0797 - accuracy: 0.7650 - val_loss: 1.0090 - val_accuracy: 0.7840\n",
      "Epoch 2/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.9786 - accuracy: 0.7835 - val_loss: 0.9160 - val_accuracy: 0.7936\n",
      "Epoch 3/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.8928 - accuracy: 0.7976 - val_loss: 0.8388 - val_accuracy: 0.8105\n",
      "Epoch 4/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.8213 - accuracy: 0.8115 - val_loss: 0.7733 - val_accuracy: 0.8229\n",
      "Epoch 5/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7616 - accuracy: 0.8216 - val_loss: 0.7192 - val_accuracy: 0.8328\n",
      "Epoch 6/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.7120 - accuracy: 0.8304 - val_loss: 0.6744 - val_accuracy: 0.8416\n",
      "Epoch 7/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.6702 - accuracy: 0.8385 - val_loss: 0.6363 - val_accuracy: 0.8472\n",
      "Epoch 8/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.6347 - accuracy: 0.8458 - val_loss: 0.6036 - val_accuracy: 0.8529\n",
      "Epoch 9/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.6043 - accuracy: 0.8515 - val_loss: 0.5757 - val_accuracy: 0.8586\n",
      "Epoch 10/10\n",
      "469/469 [==============================] - 1s 2ms/step - loss: 0.5779 - accuracy: 0.8561 - val_loss: 0.5514 - val_accuracy: 0.8634\n"
     ]
    }
   ],
   "source": [
    "sgd = SGD(0.01)\n",
    "epochs=10\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=sgd,metrics=[\"accuracy\"])\n",
    "H = model.fit(X_train, Y_train, validation_data=(X_test, Y_test),epochs=epochs, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mhq0Upb3-8O3",
    "outputId": "eba335a5-13da-4ebd-dafa-1c137c15ff89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 0s 1ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.93       980\n",
      "           1       0.92      0.98      0.95      1135\n",
      "           2       0.89      0.83      0.86      1032\n",
      "           3       0.82      0.87      0.85      1010\n",
      "           4       0.82      0.86      0.84       982\n",
      "           5       0.84      0.69      0.76       892\n",
      "           6       0.87      0.91      0.89       958\n",
      "           7       0.88      0.90      0.89      1028\n",
      "           8       0.85      0.81      0.83       974\n",
      "           9       0.82      0.79      0.81      1009\n",
      "\n",
      "    accuracy                           0.86     10000\n",
      "   macro avg       0.86      0.86      0.86     10000\n",
      "weighted avg       0.86      0.86      0.86     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#making the predictions\n",
    "predictions = model.predict(X_test, batch_size=128)\n",
    "print(classification_report(Y_test.argmax(axis=1),predictions.argmax(axis=1),target_names=[str(x) for x in lb.classes_]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "id": "DN7oqCII_Crw",
    "outputId": "6946110c-590d-4825-dabe-e030fefa519c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8b5a8a15b0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "#plotting the training loss and accuracy\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, epochs), H.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(np.arange(0, epochs), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(np.arange(0, epochs), H.history[\"accuracy\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(0, epochs), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}