handwritten_digits_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handwritten digits predictions by building Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Made by Udayan Sharma 09/06/2024\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train,Y_train),(X_test,Y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cd01a75180>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1], dtype=uint8)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flatten = X_train.reshape(len(X_train),28*28)\n",
    "X_test_flatten = X_test.reshape(len(X_test),28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flatten.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\udaya_z6clw8x\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1ms/step - accuracy: 0.8108 - loss: 0.7225\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9142 - loss: 0.3085\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9200 - loss: 0.2909\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9236 - loss: 0.2764\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9269 - loss: 0.2610\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1cd01aefd90>"
      ]
     },
     "execution_count": 72,
     "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_flatten,Y_train,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 772us/step - accuracy: 0.9162 - loss: 0.3021\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.26633796095848083, 0.9262999892234802]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flatten, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cd04860df0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 887us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.1481415e-02, 3.5698309e-07, 4.2413402e-02, 9.5788479e-01,\n",
       "       1.9420140e-03, 1.2202961e-01, 2.1816306e-06, 9.9983764e-01,\n",
       "       1.0441542e-01, 7.6382905e-01], dtype=float32)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_flatten)\n",
    "y_pred[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 2, 1, 0, 4]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_labels = [np.argmax(i) for i in y_pred]\n",
    "y_pred_labels[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4], dtype=uint8)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 963,    0,    0,    2,    0,    5,    6,    3,    1,    0],\n",
       "       [   0, 1107,    4,    2,    0,    1,    4,    2,   15,    0],\n",
       "       [   7,    6,  929,   16,    9,    4,   12,   11,   34,    4],\n",
       "       [   5,    0,   22,  912,    0,   29,    2,   12,   18,   10],\n",
       "       [   2,    1,    3,    2,  912,    0,    9,    4,    9,   40],\n",
       "       [  11,    2,    4,   25,   10,  779,   17,    5,   31,    8],\n",
       "       [  14,    3,    8,    1,    7,    9,  913,    2,    1,    0],\n",
       "       [   1,    5,   22,    4,    5,    0,    0,  952,    2,   37],\n",
       "       [  10,    5,    7,   18,    9,   23,   11,   13,  870,    8],\n",
       "       [  11,    6,    1,    7,   23,    7,    0,   23,    5,  926]])>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = tf.math.confusion_matrix(labels = Y_test, predictions=y_pred_labels)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\udaya_z6clw8x\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\core\\dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - accuracy: 0.8712 - loss: 0.4551\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9617 - loss: 0.1322\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9760 - loss: 0.0854\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9799 - loss: 0.0658\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9848 - loss: 0.0493\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1cd0d773cd0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "\n",
    "    keras.layers.Dense(100,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_flatten,Y_train,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.9723 - loss: 0.0907\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07758794724941254, 0.9753999710083008]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flatten,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_flatten)\n",
    "y_pred_labels = [np.argmax(i) for i in y_pred]\n",
    "cm = tf.math.confusion_matrix(labels = Y_test, predictions=y_pred_labels)\n",
    "\n",
    "\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without flattening datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8707 - loss: 0.4626\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9604 - loss: 0.1371\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9744 - loss: 0.0877\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9810 - loss: 0.0655\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9858 - loss: 0.0481\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9880 - loss: 0.0394\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 1ms/step - accuracy: 0.9896 - loss: 0.0324\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9920 - loss: 0.0267\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9940 - loss: 0.0214\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - accuracy: 0.9947 - loss: 0.0180\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1cd0e2cf9a0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)), #flatten your X_train and test without using it above\n",
    "    keras.layers.Dense(100,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",
    "    )\n",
    "model.fit(X_train,Y_train,epochs=10)"
   ]
  }
 ],
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