Handwritten digits 28X28.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 10000\n"
     ]
    }
   ],
   "source": [
    "(X_train,y_train), (X_test,y_test) = keras.datasets.mnist.load_data()\n",
    "print(len(X_train),len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAN0klEQVR4nO3df6zV9X3H8dcLuICgpiCFUkTpqCT7key6XHWp1bHYGdd0QdNqRtKOJc3wj5LUpH/oyBZdmmW2qdpmW0hQWGlibWzUyR+2FYmpM2soV0sEd93sHCrC7sXRBqyIwH3vj/tlu8V7P+fee358z73v5yMh55zv53vOefkVXvf7Pd/P/R5HhADkNavuAADqRQkAyVECQHKUAJAcJQAkRwkAydVSArZvsv3vtn9u+646MpTYPmh7v+19tvu7IM9220O2D4xattj2LtuvVreLuizfPbbfqrbhPtufrjHfStvP2h6w/bLtL1fLu2IbFvJ1ZBu60/MEbM+W9B+S/kjSIUl7Ja2PiH/raJAC2wcl9UXE23VnkSTb10t6R9J3IuJ3qmVfl3QsIu6tinRRRNzZRfnukfRORHyjjkyj2V4uaXlEvGj7IkkvSLpZ0p+rC7ZhId9t6sA2rGNP4GpJP4+I1yLifUnfk7SuhhzTRkQ8J+nYeYvXSdpR3d+hkb80tRgnX9eIiCMR8WJ1/4SkAUkr1CXbsJCvI+oogRWS3hz1+JA6+B88QSHpadsv2N5Yd5hxLIuII9LIXyJJS2vOM5ZNtl+qDhdqO1wZzfYqSVdK2qMu3Ibn5ZM6sA3rKAGPsazb5i5fGxG/J+mPJX2p2t3F5GyRtFpSr6Qjku6rNY0k2xdKekzSHRFxvO485xsjX0e2YR0lcEjSylGPL5V0uIYc44qIw9XtkKQnNHII020Gq2PJc8eUQzXn+TURMRgRZyNiWNKDqnkb2u7RyD+whyPi8Wpx12zDsfJ1ahvWUQJ7JV1h+2O250r6U0k7a8gxJtsLqw9nZHuhpBslHSg/qxY7JW2o7m+Q9GSNWT7g3D+uyi2qcRvatqRtkgYi4v5RQ12xDcfL16lt2PGzA5JUner4pqTZkrZHxN92PMQ4bP+GRn76S9IcSd+tO5/tRyStlbRE0qCkuyX9s6RHJV0m6Q1Jt0ZELR/OjZNvrUZ2Y0PSQUm3nzv+riHfJyX9i6T9koarxZs1ctxd+zYs5FuvDmzDWkoAQPdgxiCQHCUAJEcJAMlRAkBylACQXK0l0MVTciWRr1ndnK+bs0mdzVf3nkBX/48Q+ZrVzfm6OZvUwXx1lwCAmjU1Wcj2TZK+pZGZfw9FxL2l9ed6XszXwv97fFqn1KN5U37/diNfc7o5Xzdnk1qf7z39Su/HqbF+eW/qJTCVi4Nc7MVxjW+Y0vsBmLo9sVvH49iYJdDM4QAXBwFmgGZKYDpcHARAA3OaeO6ELg5SnerYKEnztaCJtwPQDs3sCUzo4iARsTUi+iKir5s/iAGyaqYEuvriIAAmZsqHAxFxxvYmST/S/18c5OWWJQPQEc18JqCIeErSUy3KAqAGzBgEkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOSa+mpyYDr51eeuKY5/7etbiuNfve3PiuPRf2DSmbpBUyVg+6CkE5LOSjoTEX2tCAWgc1qxJ/CHEfF2C14HQA34TABIrtkSCElP237B9sZWBALQWc0eDlwbEYdtL5W0y/YrEfHc6BWqctgoSfO1oMm3A9BqTe0JRMTh6nZI0hOSrh5jna0R0RcRfT2a18zbAWiDKZeA7YW2Lzp3X9KNkqbnORIgsWYOB5ZJesL2udf5bkT8sCWp2uTkug/sqPz6+CWzi+OLt/+klXHQYUN95Z95Xz34Jx1K0l2mXAIR8Zqk321hFgA14BQhkBwlACRHCQDJUQJAcpQAkBwlACSX6noCh68vd96C1b8sv8D21mVBG8wqz/OIy04Wx29Y+kpxfLc/MelI0wF7AkBylACQHCUAJEcJAMlRAkBylACQHCUAJJdqnsDffOb7xfGvDdzYoSRoh9mrLy+Ov/IH5YkevT/9fHH8o3v3TzrTdMCeAJAcJQAkRwkAyVECQHKUAJAcJQAkRwkAyaWaJ9DjM3VHQBvNeejdpp5/8j8vblGS6YU9ASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkptR8wSGP9lbHL9u/vOdCYJarFr4P009f+UzZ1uUZHppuCdge7vtIdsHRi1bbHuX7Ver20XtjQmgXSZyOPBtSTedt+wuSbsj4gpJu6vHAKahhiUQEc9JOnbe4nWSdlT3d0i6ubWxAHTKVD8YXBYRRySpul3aukgAOqntHwza3ihpoyTN14J2vx2ASZrqnsCg7eWSVN0OjbdiRGyNiL6I6OvRvCm+HYB2mWoJ7JS0obq/QdKTrYkDoNMaHg7YfkTSWklLbB+SdLekeyU9avuLkt6QdGs7Q07U65+5oDi+dDaHI9PZnFWXFcc/t3hnU69/wX/9ojg+U2cRNCyBiFg/ztANLc4CoAZMGwaSowSA5CgBIDlKAEiOEgCSowSA5GbU9QTmfPxEU89/75UPtSYI2uLNby4sjl87b7g4vu34peU3+OXxyUaaEdgTAJKjBIDkKAEgOUoASI4SAJKjBIDkKAEguRk1T6BZS/vL55lRNnvJJcXxwc+uKY4vvu1QcfzHa7Y1SDC/OLrlH28uji8d/NcGrz8zsScAJEcJAMlRAkBylACQHCUAJEcJAMlRAkByzBMY5eTicieWf5u9ecPXXVkcj9kujr/5qfI3PL3/0dPF8Vlzy1fWf/q6vy+O95Tj6b/PlvP99Wu3FMePDZfncSyYVc6/bE/5ehNRHJ252BMAkqMEgOQoASA5SgBIjhIAkqMEgOQoASC5GTVP4NR7PcXx4QZngv9p8wPF8Z2beicbaVLuvOSh4vgslU/En4z3i+OHz5bPo//D0bXF8U89c0dx/EM/m1scX/70YHHcr5evJ3B04ILi+LLZ5XkQsXd/cTyrhnsCtrfbHrJ9YNSye2y/ZXtf9efT7Y0JoF0mcjjwbUk3jbH8gYjorf481dpYADqlYQlExHOSjnUgC4AaNPPB4CbbL1WHC4talghAR021BLZIWi2pV9IRSfeNt6Ltjbb7bfef1qkpvh2AdplSCUTEYEScjYhhSQ9Kurqw7taI6IuIvh6Vf4sMQOdNqQRsLx/18BZJB8ZbF0B3azhPwPYjktZKWmL7kKS7Ja213auRX8E+KOn29kWcuI9//mfF8d/+u03F8ZVXvdXKOJP27FD5uvxHf3BpcfySl8vnyef+cG+DBOXnr1F/g+eXlWcpSG/d+Yni+FXzflIc/947KyaZCNIESiAi1o+xuNG3QACYJpg2DCRHCQDJUQJAcpQAkBwlACRHCQDJzajrCTTysb8sn2fudsv1Rt0R2mrB9Uebev5fPfvZ4vga/bSp15+p2BMAkqMEgOQoASA5SgBIjhIAkqMEgOQoASC5VPMEMLNd/mT5eyUwNvYEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjusJYNqY7fLPrF+s6SmOf+QHrUwzczTcE7C90vaztgdsv2z7y9XyxbZ32X61ul3U/rgAWm0ihwNnJH0lIn5T0u9L+pLt35J0l6TdEXGFpN3VYwDTTMMSiIgjEfFidf+EpAFJKyStk7SjWm2HpJvblBFAG03qg0HbqyRdKWmPpGURcUQaKQpJS1ueDkDbTbgEbF8o6TFJd0TE8Uk8b6Ptftv9p3VqKhkBtNGESsB2j0YK4OGIeLxaPGh7eTW+XNLQWM+NiK0R0RcRfT2a14rMAFpoImcHLGmbpIGIuH/U0E5JG6r7GyQ92fp4ANptIvMErpX0BUn7be+rlm2WdK+kR21/UdIbkm5tS0KgcjaGyysw9W1KGpZARDwvyeMM39DaOAA6je4EkqMEgOQoASA5SgBIjhIAkqMEgOS4ngBmjHeverfuCNMSewJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACTHPAFMG42+dwBTw1YFkqMEgOQoASA5SgBIjhIAkqMEgOQoASA55gmga5x65sPF8bO9Db53AFPCngCQHCUAJEcJAMlRAkBylACQHCUAJEcJAMk5Isor2CslfUfSRyQNS9oaEd+yfY+kv5B0tFp1c0Q8VXqti704rjHfZg502p7YreNxzGONTWSy0BlJX4mIF21fJOkF27uqsQci4hutCgqg8xqWQEQckXSkun/C9oCkFe0OBqAzJvWZgO1Vkq6UtKdatMn2S7a3217U6nAA2m/CJWD7QkmPSbojIo5L2iJptaRejewp3DfO8zba7rfdf1qnmk8MoKUmVAK2ezRSAA9HxOOSFBGDEXE2IoYlPSjp6rGeGxFbI6IvIvp6NK9VuQG0SMMSsG1J2yQNRMT9o5YvH7XaLZIOtD4egHabyNmBayV9QdJ+2/uqZZslrbfdKykkHZR0exvyAWiziZwdeF7SWOcXi3MCAEwPzBgEkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASA5SgBIjhIAkqMEgOQoASC5ht870NI3s49Ken3UoiWS3u5YgMkjX3O6OV83Z5Nan+/yiPjwWAMdLYEPvLndHxF9tQVogHzN6eZ83ZxN6mw+DgeA5CgBILm6S2Brze/fCPma0835ujmb1MF8tX4mAKB+de8JAKgZJQAkRwkAyVECQHKUAJDc/wJm7bdAglEuGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[2])\n",
    "y_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling\n",
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flattened = X_train.reshape(len(X_train),28*28)\n",
    "X_test_flattened = X_test.reshape(len(X_test),28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 1s 486us/step - loss: 0.4675 - accuracy: 0.8783\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 1s 480us/step - loss: 0.3041 - accuracy: 0.9150\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 1s 470us/step - loss: 0.2838 - accuracy: 0.9206\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 1s 458us/step - loss: 0.2734 - accuracy: 0.9238\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 1s 460us/step - loss: 0.2666 - accuracy: 0.9256\n",
      "313/313 [==============================] - 0s 374us/step - loss: 0.2679 - accuracy: 0.9256\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.26790180802345276, 0.925599992275238]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape = (784,),activation = 'sigmoid')\n",
    "])\n",
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    "    )\n",
    "model.fit(X_train_flattened,y_train,epochs =5)\n",
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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,
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "np.argmax(y_predicted[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted = [7, 2, 1, 0, 4] and true values = [7 2 1 0 4]\n"
     ]
    }
   ],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
    "print(\"Predicted = {} and true values = {}\".format(y_predicted_labels[:5],y_test[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = tf.math.confusion_matrix(labels = y_test, predictions = y_predicted_labels)\n",
    "import seaborn as sns\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(cm, annot = True, fmt = 'd')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True value');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1988 - accuracy: 0.9421\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0805 - accuracy: 0.9756\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0519 - accuracy: 0.9831\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0373 - accuracy: 0.9883\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0275 - accuracy: 0.9912\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 784).\n",
      "313/313 [==============================] - 0s 595us/step - loss: 0.0649 - accuracy: 0.9806\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0648718997836113, 0.9805999994277954]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape = (28,28)),\n",
    "    keras.layers.Dense(500, input_shape = (784,),activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'sigmoid')\n",
    "])\n",
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    "    )\n",
    "model.fit(X_train,y_train,epochs =5)\n",
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
    "cm = tf.math.confusion_matrix(labels = y_test, predictions = y_predicted_labels)\n",
    "import seaborn as sns\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(cm, annot = True, fmt = 'd')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True value');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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