Handwritten digit with CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d4a20bbf",
   "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": 115,
   "id": "60457f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "47fb997e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "42dfaf5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9b5de882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "abbf6a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4c9dc846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2654c045280>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S7pR0oqQZ6ttTuGWQ1803s1Vmtmq/3ivfMYC6GlIImFm7+gLgXnd/SJLcfae797h7r6TFks4a6LXuvsjdu9y9q10d9eobQJ0M5eqASbpL0iZ3v7Xf8sn9nnaJpA31bw9Aow3l6sA5kv5c0nozW1ssu0HSXDObIcklbZV0ZQP6A9BgQ7k68GNJNkDp8fq3A6DZGDEIJEcIAMkRAkByhACQHCEAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByhACQHCEAJEcIAMkRAkBy5u7NW5nZLyW91G/RREmvNa2B4aO/clq5v1buTap/fx9192MGKjQ1BH5t5War3L2rsgZqoL9yWrm/Vu5Nam5/HA4AyRECQHJVh8CiitdfC/2V08r9tXJvUhP7q/ScAIDqVb0nAKBihACQHCEAJEcIAMkRAkBy/wcHzkrmVL86owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_train[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "08d42575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "00a548c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9, 2], dtype=uint8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:6]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5b965525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e874e1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "91ed7fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255\n",
    "x_test = x_test/255"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529ad050",
   "metadata": {},
   "source": [
    "### Now let's build CNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5ddcaedb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(-1,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "54e8fcad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "bc3859b0",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "        [[0.],\n",
       "         [0.],\n",
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       "\n",
       "\n",
       "       [[[0.],\n",
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       "         [0.],\n",
       "         ...,\n",
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       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
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       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.],\n",
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       "         [0.],\n",
       "         ...,\n",
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       "         [0.],\n",
       "         [0.]],\n",
       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
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       "\n",
       "        [[0.],\n",
       "         [0.],\n",
       "         [0.],\n",
       "         ...,\n",
       "         [0.],\n",
       "         [0.],\n",
       "         [0.]]]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "dae3ffc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = keras.models.Sequential([\n",
    "    #CNN \n",
    "    keras.layers.Conv2D(filters=32, kernel_size = (3,3),activation='relu', input_shape = (28,28,1)),\n",
    "    keras.layers.MaxPool2D((2,2)),\n",
    "    \n",
    "    ##Dense\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(64,activation = 'relu'),\n",
    "    keras.layers.Dense(10, activation = 'sigmoid')\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "dbcd186d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.compile(optimizer= 'adam', loss= 'sparse_categorical_crossentropy', metrics= ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "0ed6b2c1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.1884 - accuracy: 0.94471s - loss: 0.1917 - accuracy: 0. -\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 38s 20ms/step - loss: 0.0664 - accuracy: 0.9800\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 37s 20ms/step - loss: 0.0466 - accuracy: 0.9857\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 39s 21ms/step - loss: 0.0351 - accuracy: 0.98900s - loss: 0.0352 - accuracy\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 38s 20ms/step - loss: 0.0252 - accuracy: 0.9918\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2654c13f490>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.fit(x_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ab0e4a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 7ms/step - loss: 0.0500 - accuracy: 0.9838\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.05000826716423035, 0.9837999939918518]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn.evaluate(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "b389b1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cnn.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "7e59097e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = [np.argmax(element) for element in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "32a64006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.99      0.98      0.99      1032\n",
      "           3       0.99      0.98      0.98      1010\n",
      "           4       0.98      0.99      0.99       982\n",
      "           5       0.96      0.99      0.98       892\n",
      "           6       0.99      0.98      0.99       958\n",
      "           7       0.98      0.98      0.98      1028\n",
      "           8       0.99      0.98      0.98       974\n",
      "           9       0.97      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix , classification_report\n",
    "print(\"Classification Report: \\n\", classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "90adf3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 2, 1, 0, 4, 1, 4, 9, 5, 9]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "a049ff36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  }
 ],
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