A2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "28e115ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "698405de",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8beef4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(x_train.shape[0],28*28)\n",
    "x_test = x_test.reshape(x_test.shape[0],28*28)\n",
    "x_train = x_train.astype(\"float32\")/255.0\n",
    "x_test = x_test.astype('float32')/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "03ba0f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "235fb42e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()#categorical to binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9a63fb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = lb.fit_transform(y_train)\n",
    "y_test = lb.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a96cc61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2191b129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_9 (Dense)             (None, 128)               100480    \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 109,386\n",
      "Trainable params: 109,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(128,input_shape=(784,),activation=\"sigmoid\")) #imp\n",
    "model.add(Dense(64,activation=\"sigmoid\"))\n",
    "model.add(Dense(10,activation=\"softmax\"))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "742d79d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7d28f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGD(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f17856f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 2.2854 - accuracy: 0.1755 - val_loss: 2.2532 - val_accuracy: 0.1901\n",
      "Epoch 2/10\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 2.2279 - accuracy: 0.3529 - val_loss: 2.1970 - val_accuracy: 0.3769\n",
      "Epoch 3/10\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 2.1639 - accuracy: 0.4735 - val_loss: 2.1205 - val_accuracy: 0.4977\n",
      "Epoch 4/10\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 2.0730 - accuracy: 0.5450 - val_loss: 2.0092 - val_accuracy: 0.5692\n",
      "Epoch 5/10\n",
      "469/469 [==============================] - 2s 3ms/step - loss: 1.9427 - accuracy: 0.5886 - val_loss: 1.8536 - val_accuracy: 0.6030\n",
      "Epoch 6/10\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 1.7710 - accuracy: 0.6208 - val_loss: 1.6635 - val_accuracy: 0.6658\n",
      "Epoch 7/10\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 1.5782 - accuracy: 0.6597 - val_loss: 1.4680 - val_accuracy: 0.6918\n",
      "Epoch 8/10\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 1.3933 - accuracy: 0.6975 - val_loss: 1.2936 - val_accuracy: 0.7128\n",
      "Epoch 9/10\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 1.2337 - accuracy: 0.7247 - val_loss: 1.1489 - val_accuracy: 0.7533\n",
      "Epoch 10/10\n",
      "469/469 [==============================] - 1s 3ms/step - loss: 1.1032 - accuracy: 0.7526 - val_loss: 1.0324 - val_accuracy: 0.7743\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=sgd,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
    "H = model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=10,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "35a9bb41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79/79 [==============================] - 0s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_test,batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fe69457e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8b8d7b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.96      0.86       980\n",
      "           1       0.77      0.99      0.87      1135\n",
      "           2       0.85      0.72      0.78      1032\n",
      "           3       0.66      0.82      0.73      1010\n",
      "           4       0.72      0.91      0.80       982\n",
      "           5       0.88      0.40      0.55       892\n",
      "           6       0.83      0.91      0.87       958\n",
      "           7       0.78      0.87      0.82      1028\n",
      "           8       0.79      0.51      0.62       974\n",
      "           9       0.81      0.59      0.68      1009\n",
      "\n",
      "    accuracy                           0.77     10000\n",
      "   macro avg       0.79      0.77      0.76     10000\n",
      "weighted avg       0.79      0.77      0.76     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test.argmax(axis=1),predictions.argmax(axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "31a78d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2cd8692f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f001d546e50>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(0,10),H.history[\"loss\"],label='train_loss')\n",
    "plt.plot(np.arange(0,10),H.history[\"val_loss\"],label='val_loss')\n",
    "plt.plot(np.arange(0,10),H.history[\"accuracy\"],label='accuracy')\n",
    "plt.plot(np.arange(0,10),H.history[\"val_accuracy\"],label='val_accuracy')\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be888f5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1062349b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1198d9b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}