Handwritten Character recognition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.layers import Flatten,Dense\n",
    "from tensorflow.keras import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train),(x_test,y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1c921061cd0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(x_train[0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1c92188a1f0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(x_train[1])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255.0\n",
    "x_test = x_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1c921917f40>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(x_train[1])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Flatten(input_shape = (28,28)))\n",
    "model.add(Dense(128,activation = \"relu\"))\n",
    "model.add(Dense(10,activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = \"adam\",loss = \"sparse_categorical_crossentropy\",metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4338 - accuracy: 0.8757\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1228 - accuracy: 0.9635\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0755 - accuracy: 0.9776\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0560 - accuracy: 0.9828\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0416 - accuracy: 0.9870: 1s -\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0310 - accuracy: 0.9907\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0267 - accuracy: 0.9922\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0203 - accuracy: 0.9940\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0175 - accuracy: 0.9946\n",
      "Epoch 10/10\n",
      "1269/1875 [===================>..........] - ETA: 1s - loss: 0.0147 - accuracy: 0.9957"
     ]
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss,test_acc = model.evaluate(x_test,y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}