MNIST_ANN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe276d1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec4daef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test)=keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e81d6f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8b9ccdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e25f5244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e9dd2c4fd0>"
      ]
     },
     "execution_count": 37,
     "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[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1ba4dd67",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train/255\n",
    "X_test=X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b1293249",
   "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": 40,
   "id": "4fd867c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff165e33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 2s 842us/step - loss: 0.4669 - accuracy: 0.8776\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 838us/step - loss: 0.3038 - accuracy: 0.9148\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 2s 850us/step - loss: 0.2834 - accuracy: 0.9203\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 832us/step - loss: 0.2728 - accuracy: 0.9234\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 838us/step - loss: 0.2664 - accuracy: 0.9257\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 845us/step - loss: 0.2622 - accuracy: 0.9273\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 832us/step - loss: 0.2584 - accuracy: 0.9283\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 839us/step - loss: 0.2555 - accuracy: 0.9289\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 847us/step - loss: 0.2531 - accuracy: 0.9303\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 874us/step - loss: 0.2510 - accuracy: 0.9309\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e9dc466130>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape=(784,),activation=\"sigmoid\")\n",
    "])\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=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bab39171",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step - loss: 0.2611 - accuracy: 0.9278\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2610945403575897, 0.9277999997138977]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d1686a30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e9dd09d460>"
      ]
     },
     "execution_count": 44,
     "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_test[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f2dc0161",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test_flattened)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ab708be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_label = [np.argmax(i) for i in y_pred]\n",
    "y_pred_label[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "107cd71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.15321813e-05, 1.62124369e-09, 1.88418812e-06, 2.63541937e-04,\n",
       "       9.08612847e-01, 9.90509987e-04, 1.05095175e-07, 9.17119622e-01,\n",
       "       3.39780450e-01, 9.96356606e-01], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a30068b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_pred[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d62a5eca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 960,    0,    1,    2,    1,    5,    7,    2,    1,    1],\n",
       "       [   0, 1113,    4,    2,    0,    1,    3,    2,   10,    0],\n",
       "       [   3,    8,  929,   16,    8,    4,   10,   10,   41,    3],\n",
       "       [   2,    0,   18,  928,    1,   20,    2,    9,   24,    6],\n",
       "       [   1,    1,    5,    2,  923,    0,    9,    4,    9,   28],\n",
       "       [   7,    2,    1,   38,    9,  772,   14,    8,   35,    6],\n",
       "       [  10,    3,    9,    1,    7,   13,  912,    1,    2,    0],\n",
       "       [   1,    6,   20,    8,   10,    0,    0,  952,    2,   29],\n",
       "       [   5,    9,    6,   20,    9,   19,    8,   10,  881,    7],\n",
       "       [   9,    7,    1,    9,   32,    6,    0,   26,   11,  908]])>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_pred_label)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e0178602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'actual')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "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(\"actual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0848a019",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2769 - accuracy: 0.9197\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1303 - accuracy: 0.9622\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0902 - accuracy: 0.9734: 0s - loss: 0.0\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0683 - accuracy: 0.9793\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0541 - accuracy: 0.9837\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0441 - accuracy: 0.9865\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0345 - accuracy: 0.9894\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0292 - accuracy: 0.9909\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0229 - accuracy: 0.9931\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0194 - accuracy: 0.9938\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1e9d7438d90>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(100, input_shape=(784,),activation=\"relu\"),\n",
    "    keras.layers.Dense(10,activation=\"sigmoid\")\n",
    "])\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=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f8de9938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 950us/step - loss: 0.0880 - accuracy: 0.9745\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08795201033353806, 0.9745000004768372]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7a632402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(X_test_flattened)\n",
    "y_pred_label = [np.argmax(i) for i in y_pred]\n",
    "y_pred_label[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "11273a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 970,    0,    0,    0,    1,    1,    2,    2,    2,    2],\n",
       "       [   2, 1121,    3,    0,    0,    1,    2,    0,    6,    0],\n",
       "       [   8,    1,  990,    6,    4,    0,    3,    8,   12,    0],\n",
       "       [   1,    0,    2,  990,    0,    1,    0,    4,   10,    2],\n",
       "       [   1,    1,    1,    0,  968,    0,    3,    0,    2,    6],\n",
       "       [   2,    1,    0,   13,    3,  857,    7,    1,    7,    1],\n",
       "       [   8,    2,    0,    0,    4,    5,  933,    0,    6,    0],\n",
       "       [   2,    6,    9,    2,    3,    0,    0,  987,    6,   13],\n",
       "       [   8,    1,    2,    2,    2,    3,    0,    2,  950,    4],\n",
       "       [   2,    2,    0,    2,   11,    5,    0,    3,    5,  979]])>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_pred_label)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "34c44b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'actual')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "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(\"actual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9ccc48b",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}