12 DNN Image Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6258bd32",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f838ab74",
   "metadata": {},
   "source": [
    "#### Read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dee7410a",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59f41c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68353646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8968d5b9",
   "metadata": {},
   "source": [
    "#### Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f60e953a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f84be8cd4c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.imshow(x_train[0], cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94256ce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.title(y_train[i])\n",
    "    plt.imshow(x_train[i], cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80acd340",
   "metadata": {},
   "source": [
    "#### Scale the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11c97614",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train / 255\n",
    "x_test = x_test / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ef9cf0",
   "metadata": {},
   "source": [
    "#### Flatten the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16263ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0346041a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2d0d83de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = x_train.shape[1] * x_train.shape[2]\n",
    "size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e6e1f75a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(-1,size)\n",
    "x_test = x_test.reshape(-1,size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b57139d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a2df1c1",
   "metadata": {},
   "source": [
    "#### One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5094d099",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aefd8b8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e9c412cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "954d34db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d56046e",
   "metadata": {},
   "source": [
    "#### Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "25b7715c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(512, input_shape=(size,), activation='relu'))\n",
    "\n",
    "model.add(Dense(256, activation='relu'))\n",
    "\n",
    "model.add(Dense(10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a3df8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a273715",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6000/6000 [==============================] - 42s 7ms/step - loss: 0.1830 - accuracy: 0.9439\n",
      "Epoch 2/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0905 - accuracy: 0.9724\n",
      "Epoch 3/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0645 - accuracy: 0.9802\n",
      "Epoch 4/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0518 - accuracy: 0.9846\n",
      "Epoch 5/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0445 - accuracy: 0.9863\n",
      "Epoch 6/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0380 - accuracy: 0.9893\n",
      "Epoch 7/10\n",
      "6000/6000 [==============================] - 40s 7ms/step - loss: 0.0368 - accuracy: 0.9905\n",
      "Epoch 8/10\n",
      "6000/6000 [==============================] - 40s 7ms/step - loss: 0.0354 - accuracy: 0.9904\n",
      "Epoch 9/10\n",
      "6000/6000 [==============================] - 39s 7ms/step - loss: 0.0302 - accuracy: 0.9921\n",
      "Epoch 10/10\n",
      "6000/6000 [==============================] - 40s 7ms/step - loss: 0.0263 - accuracy: 0.9931\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f84b817b760>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, batch_size=10, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c40203",
   "metadata": {},
   "source": [
    "#### Evaluate on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7da800ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 18s 2ms/step - loss: 0.1504 - accuracy: 0.9782\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.15039220452308655, 0.9782000184059143]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8e86d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train),(x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "49479f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAANgUlEQVR4nO3df6xX9X3H8ddLuIBASUFbekUiTnCtW1bY7nCb1No6LbVZwDQjJZlxicltOk10NW2N/aP+sWSszjVtbN3oSsqMpWlnTfkDNxkxUdNBuSjjZxVqsOWKUKUbWOWn7/1xD+YW7/dzL9/f+H4+km++557393zPO199cc73nO85H0eEALz7XdDpBgC0B2EHkiDsQBKEHUiCsANJjG/nyiZ4YkzSlHauEkjlmH6jE3HcI9UaCrvtxZK+LmmcpH+NiBWl10/SFF3t6xtZJYCCTbGhZq3u3Xjb4yR9U9InJV0labntq+p9PwCt1ch39oWS9kbEixFxQtL3JS1pTlsAmq2RsM+S9Mthf++v5v0W2/22B2wPnNTxBlYHoBEtPxofESsjoi8i+no0sdWrA1BDI2EflDR72N+XVvMAdKFGwr5Z0jzbl9ueIOkzktY2py0AzVb3qbeIOGX7Dkn/qaFTb6siYmfTOgPQVA2dZ4+IdZLWNakXAC3Ez2WBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSTQ0ZLPtfZKOSjot6VRE9DWjKQDN11DYKx+LiFeb8D4AWojdeCCJRsMekp6wvcV2/0gvsN1ve8D2wEkdb3B1AOrV6G78oogYtP1+Sett/ywinhr+gohYKWmlJE3zjGhwfQDq1NCWPSIGq+dDkh6TtLAZTQFovrrDbnuK7fecmZZ0o6QdzWoMQHM1shs/U9Jjts+8z/ci4j+a0hWApqs77BHxoqQPN7EXAC3EqTcgCcIOJEHYgSQIO5AEYQeSaMaFMMB5562PLCjWDy68sFi/aPFgsf4XvduL9b1vvr9m7ed/fKy4bL3YsgNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEpxnT278ZbOL9Zc/Va4fu7j8/sd6T9WsfehLPysue+TGDxXrgx8v3/ho2Z/+tGbt3vd9q7js7hMTivWfvDGvWH/wyRuK9d6nXbM2VRuLy9aLLTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJMF59uR2ffkDxfreTz3Y0Pu/cLL2tdmHPzGpuOwl49YV6/9+tHxz44e2fLRmbfMj5QGHJ/10T7F++n//r1ifp03FeiewZQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJDjP/i437nfnFutzLj9UrB84/UaxvuZI+Vz3Pz93bc3a5G3le7PPXlvu7fTze4v1eXq2WC++d91Ldq9Rt+y2V9k+ZHvHsHkzbK+3vad6nt7aNgE0aiy78d+VtPisefdI2hAR8yRtqP4G0MVGDXtEPCXp8Fmzl0haXU2vlrS0uW0BaLZ6v7PPjIgD1fQrkmbWeqHtfkn9kjRJk+tcHYBGNXw0PiJCUs07/0XEyojoi4i+Hk1sdHUA6lRv2A/a7pWk6rl82BRAx9Ub9rWSbq2mb5X04+a0A6BVRv3ObnuNpOskXWx7v6SvSFoh6Qe2b5P0kqRlrWwSZeM/UPOQiW5Zu6G47F9Ofa1Y//A3vlisz/qHnxTrc/VcsV7ybjzX3Umjhj0iltcoXd/kXgC0ED+XBZIg7EAShB1IgrADSRB2IAkucT0PXDC5/DPjXX9/ac3aogsfLS4779G7y/Wv/nexjvMHW3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSILz7F3glbv+rFj/4d/eX6xfMf6ZmrVfnCqve/LguGJ9/CW9xfqpwZfLK0DXYMsOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0l4aECX9pjmGXG1uSnt2f58x9Fi/fb37i7Wt50onysv+YMJ5Rs2T3T5pxiLvvA3xfq07208555Qv02xQUfisEeqsWUHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSS4nr0LPP75jxXrT+7/o2L99K4X6l63F/xesX7t6oFi/Zn7v1Wsz7vmczVrV965pbhsnBrlYnyck1G37LZX2T5ke8eweffZHrS9tXrc1No2ATRqLLvx35W0eIT5X4uI+dVjXXPbAtBso4Y9Ip6SdLgNvQBooUYO0N1he1u1mz+91ots99sesD1wUscbWB2ARtQb9ockXSFpvqQDkh6o9cKIWBkRfRHR16OJda4OQKPqCntEHIyI0xHxlqRvS1rY3LYANFtdYbc9/P7CN0vaUeu1ALrDqOfZba+RdJ2ki23vl/QVSdfZni8pJO2T9NnWtfju1/NE+Vx2+YrzxsRzO4v1p/9qQbE+eU35OMyLN/9LzdrVG2ufg5ek9z7M2PDNNGrYI2L5CLO/04JeALQQP5cFkiDsQBKEHUiCsANJEHYgCS5xHaNxV15Ru/ir8qUDp3/96yZ30z7RU75N9Tc2fKJY/9ynH6xZm7bvWF09oT5s2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCc6zj9Gb36x9W+Pllz5fXPbrDy8t1t+Ye6JYv+yHI47A+7ae12v3dnJq+T/x4EfL9Qc+vbpYn9NT/o3BBx+/o2btyqfLl/aiudiyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASnGcfo19s761ZW/rBPcVlb7u99jXdY/HaDW8W6xddcGFD719yy77ri/WX/25usX7l45ub2Q4awJYdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5LgPPsYzf38xpq1jx/8QnHZCz/yarG+bM6Wuno646GB62rWJu+eWFz2kqd/U6x7445ifeJbnEc/X4y6Zbc92/aTtnfZ3mn7zmr+DNvrbe+pnqe3vl0A9RrLbvwpSXdHxFWS/kTS7bavknSPpA0RMU/ShupvAF1q1LBHxIGIeLaaPippt6RZkpZIOnPPotWSlraoRwBNcE7f2W3PkbRA0iZJMyPiQFV6RdLMGsv0S+qXpEmaXHejABoz5qPxtqdKelTSXRFxZHgtIkJSjLRcRKyMiL6I6OtR+WARgNYZU9ht92go6I9ExI+q2Qdt91b1XkmHWtMigGbw0Ea58ALbGvpOfjgi7ho2/35Jr0XECtv3SJoREV8svdc0z4irXb5kEkD9NsUGHYnDI957fCzf2a+RdIuk7ba3VvPulbRC0g9s3ybpJUnLmtArgBYZNewR8YykWqMUsJkGzhP8XBZIgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkRg277dm2n7S9y/ZO23dW8++zPWh7a/W4qfXtAqjXWMZnPyXp7oh41vZ7JG2xvb6qfS0i/rF17QFolrGMz35A0oFq+qjt3ZJmtboxAM11Tt/Zbc+RtEDSpmrWHba32V5le3qNZfptD9geOKnjjXULoG5jDrvtqZIelXRXRByR9JCkKyTN19CW/4GRlouIlRHRFxF9PZrYeMcA6jKmsNvu0VDQH4mIH0lSRByMiNMR8Zakb0ta2Lo2ATRqLEfjLek7knZHxD8Nm9877GU3S9rR/PYANMtYjsZfI+kWSdttb63m3Stpue35kkLSPkmfbUF/AJpkLEfjn5HkEUrrmt8OgFbhF3RAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkHBHtW5n9K0kvDZt1saRX29bAuenW3rq1L4ne6tXM3i6LiPeNVGhr2N+xcnsgIvo61kBBt/bWrX1J9FavdvXGbjyQBGEHkuh02Fd2eP0l3dpbt/Yl0Vu92tJbR7+zA2ifTm/ZAbQJYQeS6EjYbS+2/bztvbbv6UQPtdjeZ3t7NQz1QId7WWX7kO0dw+bNsL3e9p7qecQx9jrUW1cM410YZryjn12nhz9v+3d22+MkvSDpBkn7JW2WtDwidrW1kRps75PUFxEd/wGG7WslvS7p3yLi96t5X5V0OCJWVP9QTo+IL3VJb/dJer3Tw3hXoxX1Dh9mXNJSSX+tDn52hb6WqQ2fWye27Asl7Y2IFyPihKTvS1rSgT66XkQ8JenwWbOXSFpdTa/W0P8sbVejt64QEQci4tlq+qikM8OMd/SzK/TVFp0I+yxJvxz2935113jvIekJ21ts93e6mRHMjIgD1fQrkmZ2spkRjDqMdzudNcx413x29Qx/3igO0L3Tooj4Q0mflHR7tbvalWLoO1g3nTsd0zDe7TLCMONv6+RnV+/w543qRNgHJc0e9vel1byuEBGD1fMhSY+p+4aiPnhmBN3q+VCH+3lbNw3jPdIw4+qCz66Tw593IuybJc2zfbntCZI+I2ltB/p4B9tTqgMnsj1F0o3qvqGo10q6tZq+VdKPO9jLb+mWYbxrDTOuDn92HR/+PCLa/pB0k4aOyP9c0pc70UONvn5H0v9Uj52d7k3SGg3t1p3U0LGN2yRdJGmDpD2S/kvSjC7q7WFJ2yVt01CwejvU2yIN7aJvk7S1etzU6c+u0FdbPjd+LgskwQE6IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wHYwwYPuFRjZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new = x_test[9675]\n",
    "plt.imshow(new);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2bd07207",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 23ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = new / 255\n",
    "new = new.reshape(-1,size)\n",
    "np.argmax(model.predict(new))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8aca4e60",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.10"
  }
 },
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}