digit_identifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "\n",
      "Bad key text.latex.unicode in file C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 112 ('text.latex.unicode : False # use \"ucs\" and \"inputenc\" LaTeX packages for handling')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.0/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key savefig.frameon in file C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 423 ('savefig.frameon : True')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.0/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key pgf.debug in file C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 444 ('pgf.debug           : False')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.0/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key verbose.level in file C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 475 ('verbose.level  : silent      # one of silent, helpful, debug, debug-annoying')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.0/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key verbose.fileo in file C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 476 ('verbose.fileo  : sys.stdout  # a log filename, sys.stdout or sys.stderr')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.3.0/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Shivam Shandilya\\anaconda3\\envs\\deepl\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2 \n",
    "from keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Activation\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from keras.utils import np_utils\n",
    "from keras import backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed=7\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train,y_train),(X_test,y_test)=mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0], 28, 28,1).astype('float32')\n",
    "X_test = X_test.reshape(X_test.shape[0],  28, 28,1).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =X_train/255\n",
    "X_test =X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np_utils.to_categorical(y_train)\n",
    "y_test = np_utils.to_categorical(y_test)\n",
    "num_classes = y_test.shape[1]\n",
    "num_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Conv2D(32, (5, 5), input_shape=(28, 28,1), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(16, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "model.add(Dense(64, activation='relu'))\n",
    "\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 24, 24, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 10, 10, 16)        4624      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 5, 5, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 400)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               51328     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 65,690\n",
      "Trainable params: 65,690\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 30s 496us/sample - loss: 0.3712 - accuracy: 0.8853 - val_loss: 0.0768 - val_accuracy: 0.9744\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 28s 463us/sample - loss: 0.0928 - accuracy: 0.9712 - val_loss: 0.0476 - val_accuracy: 0.9840\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 30s 498us/sample - loss: 0.0673 - accuracy: 0.9785 - val_loss: 0.0392 - val_accuracy: 0.9873\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 31s 511us/sample - loss: 0.0541 - accuracy: 0.9828 - val_loss: 0.0382 - val_accuracy: 0.9865\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 31s 516us/sample - loss: 0.0459 - accuracy: 0.9859 - val_loss: 0.0311 - val_accuracy: 0.9891\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 31s 518us/sample - loss: 0.0423 - accuracy: 0.9871 - val_loss: 0.0318 - val_accuracy: 0.9892\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 31s 523us/sample - loss: 0.0362 - accuracy: 0.9884 - val_loss: 0.0280 - val_accuracy: 0.9901\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 32s 527us/sample - loss: 0.0326 - accuracy: 0.9894 - val_loss: 0.0272 - val_accuracy: 0.9901\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 32s 537us/sample - loss: 0.0296 - accuracy: 0.9906 - val_loss: 0.0281 - val_accuracy: 0.9900\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 32s 534us/sample - loss: 0.0283 - accuracy: 0.9905 - val_loss: 0.0284 - val_accuracy: 0.9903\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2125de7dd88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Large CNN Error: 0.97%\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"Large CNN Error: %.2f%%\" % (100-scores[1]*100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.02841670204218244, 0.9903]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction of model: 2\n",
      "Actual class:[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2126036bcc8>"
      ]
     },
     "execution_count": 21,
     "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": [
    "image = (X_test[1]).reshape(1,28,28,1) \n",
    "model_pred = model.predict_classes(image, verbose = 0)\n",
    "print('Prediction of model: {}'.format(model_pred[0]))\n",
    "print('Actual class:{}'.format(y_test[1]))\n",
    "plt.imshow(image.reshape(28,28,1),cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images shape: (4, 28, 28, 1)\n",
      "Predicted digit: 2\n",
      "Predicted digit: 1\n",
      "Predicted digit: 0\n",
      "Predicted digit: 4\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_images = X_test[1:5]\n",
    "test_images = test_images.reshape(test_images.shape[0], 28, 28,1)\n",
    "print (\"Test images shape: {}\".format(test_images.shape))\n",
    "\n",
    "for i, test_image in enumerate(test_images, start=1):\n",
    "    org_image = test_image\n",
    "    test_image = test_image.reshape(1,28,28,1)\n",
    "    prediction = model.predict_classes(test_image, verbose=0)\n",
    "\n",
    "    print (\"Predicted digit: {}\".format(prediction[0]))\n",
    "    plt.subplot(220+i)\n",
    "    plt.axis('off')\n",
    "    plt.title(\"Predicted digit: {}\".format(prediction[0]))\n",
    "    plt.imshow(org_image, cmap=plt.get_cmap('gray'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model.save_weights(\"model.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"shi.txt\",\"w\") as file:\n",
    "    file.write(\"biatch\")"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}