Handwritten Digit Classification.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7797917c-8a2b-49c6-8b74-18d54b811a28",
   "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": 2,
   "id": "f7cc33a0-c9e0-4eab-b264-a6be348d74b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 1us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8066c6a-e053-46b0-bd0a-607f166c5aa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b360247-b51a-4be9-a362-52bcb3346ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e91cb0d8-f488-484e-9a40-4ea1e14a8941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8ee3624d-ba97-4d45-bec9-291b4783ef06",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5133b869-ad9a-4081-ae92-46b587c3a95c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2193b5a5-b8e0-4531-a036-e37f305929fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a0cd6890-46fc-4eaf-9d8c-3014df936e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=X_train/255\n",
    "X_test=X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "72680c74-9bbd-49ce-bab7-857add56bad2",
   "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": 28,
   "id": "b113e188-47f4-4604-91e7-6513bc6755d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "63c0a051-634e-43db-836e-aad124f5730f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_flattened.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fd9d2ec5-e7ab-4f33-ab3a-f003339201b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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       "       0.4745098 , 0.99607843, 0.99607843, 0.85882353, 0.15686275,\n",
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       "       0.        , 0.        , 0.        , 0.4745098 , 0.99607843,\n",
       "       0.81176471, 0.07058824, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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       "       0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_flattened[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "708b5588-01ee-48cc-a948-e48fce06ef2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 434us/step - accuracy: 0.8132 - loss: 0.7198\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 438us/step - accuracy: 0.9139 - loss: 0.3097\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 436us/step - accuracy: 0.9198 - loss: 0.2837\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 434us/step - accuracy: 0.9234 - loss: 0.2740\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 437us/step - accuracy: 0.9267 - loss: 0.2633\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x18b31863f50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model= keras.Sequential([\n",
    "    keras.layers.Dense(10,input_shape=(784,),activation='sigmoid')\n",
    "]) #stack of layers in neural network, each element will be treated as a layer. \n",
    "#Dense means all the layers are connected to each other. 10 is number of outputs, input is 784\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy']\n",
    "             )#compile neural network with arguments, sparse means output variable is integer, if it is one hot encoded no need to use sparse\n",
    "model.fit(X_train_flattened, y_train, epochs=5)  #epochs is number of iterations for which neural network will run the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1c6ef7dc-d18c-413d-822a-073e4c740309",
   "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 399us/step - accuracy: 0.9130 - loss: 0.3055\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.27149322628974915, 0.9243999719619751]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cef39fe1-28a1-4e6d-98de-da19a426cd1c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x18b2d83c830>"
      ]
     },
     "execution_count": 34,
     "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": 46,
   "id": "10cd491f-ef6e-43ec-aa07-4dafd2c614b2",
   "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 426us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2.64406037e-02, 3.05247710e-07, 6.12620860e-02, 9.23009753e-01,\n",
       "       2.77840416e-03, 1.06543966e-01, 1.37496522e-06, 9.99719620e-01,\n",
       "       8.28435272e-02, 5.83799779e-01], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted=model.predict(X_test_flattened)\n",
    "y_predicted[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2e226613-e455-4308-a3d6-829a68b3e557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predicted[0])  #So, np.argmax(predictions) is particularly useful after model.predict() when you want to convert the predicted probabilities into actual class labels for classification tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "92d323c6-33e0-48c1-92ce-993b311d556f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]  #finds label class for all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5ced10d3-6271-48b2-82c3-6f2758e90b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 962,    0,    2,    2,    0,    5,    6,    2,    1,    0],\n",
       "       [   0, 1110,    3,    2,    1,    1,    4,    2,   12,    0],\n",
       "       [   6,   10,  925,   13,   10,    2,   12,    9,   42,    3],\n",
       "       [   5,    0,   26,  896,    2,   32,    2,   11,   30,    6],\n",
       "       [   1,    1,    6,    0,  933,    0,    9,    3,   11,   18],\n",
       "       [  11,    3,    4,   21,   13,  781,   15,    5,   35,    4],\n",
       "       [  13,    3,    7,    0,    8,   11,  910,    2,    4,    0],\n",
       "       [   1,    5,   24,    2,   10,    0,    0,  956,    3,   27],\n",
       "       [   5,    8,    7,   14,    9,   16,    9,   13,  888,    5],\n",
       "       [  10,    6,    1,    8,   51,    7,    0,   29,   14,  883]])>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm= tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed9e649b-15f6-4175-aa98-ddcf46b2aade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 54,
     "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": "code",
   "execution_count": 56,
   "id": "d21eb971-6f7a-4275-ba46-f1f137323d9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 676us/step - accuracy: 0.8723 - loss: 0.4564\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 696us/step - accuracy: 0.9627 - loss: 0.1295\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 731us/step - accuracy: 0.9743 - loss: 0.0886\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 713us/step - accuracy: 0.9793 - loss: 0.0681\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 710us/step - accuracy: 0.9844 - loss: 0.0509\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x18b2e96a4b0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model= keras.Sequential([\n",
    "    keras.layers.Dense(100,input_shape=(784,),activation='relu'), #for hidden layer with 100 neurons\n",
    "    keras.layers.Dense(10,activation='sigmoid')\n",
    "]) \n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy']\n",
    "             )#compile neural network with arguments, sparse means output variable is integer, if it is one hot encoded no need to use sparse\n",
    "model.fit(X_train_flattened, y_train, epochs=5)  #epochs is number of iterations for which neural network will run the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "73b72488-f5cb-456c-b309-54ebc9d7791d",
   "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 461us/step - accuracy: 0.9709 - loss: 0.0901\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07942081987857819, 0.9750999808311462]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "713cfcee-7009-49ba-bd0e-0414b7d647bc",
   "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 562us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 59,
     "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_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",
    "\n",
    "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')"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}