Kannada_Digits_MNIST.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/Kannada-MNIST/Dig-MNIST.csv\n",
      "/kaggle/input/Kannada-MNIST/train.csv\n",
      "/kaggle/input/Kannada-MNIST/test.csv\n",
      "/kaggle/input/Kannada-MNIST/sample_submission.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Flatten, Dense, Activation, Dropout\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "execution_count": 3,
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   "source": [
    "train= pd.read_csv('/kaggle/input/Kannada-MNIST/train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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   "source": [
    "test= pd.read_csv(\"/kaggle/input/Kannada-MNIST/test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      0       0       0       0       0       0       0       0       0   \n",
       "1      1       0       0       0       0       0       0       0       0   \n",
       "2      2       0       0       0       0       0       0       0       0   \n",
       "3      3       0       0       0       0       0       0       0       0   \n",
       "4      4       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Val= pd.read_csv(\"/kaggle/input/Kannada-MNIST/Dig-MNIST.csv\")\n",
    "Val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train= train.drop(['label'],axis=1).values\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train=train['label'].values\n",
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X_train.reshape(X_train.shape[0],28,28,1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10240, 28, 28, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val= Val.drop(['label'],axis=1).values\n",
    "X_val= X_val.reshape(X_val.shape[0],28,28,1)\n",
    "X_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_val= Val['label'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Pixel Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_val= X_train/255.0, X_val/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1, X_valid1, Y_train1, Y_valid1 = train_test_split(X_train, Y_train, test_size = 0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_d = ImageDataGenerator(rescale = 1./255.,\n",
    "                                   rotation_range = 10,\n",
    "                                   width_shift_range = 0.25,\n",
    "                                   height_shift_range = 0.25,\n",
    "                                   shear_range = 0.1,\n",
    "                                   zoom_range = 0.25,\n",
    "                                   horizontal_flip = False)\n",
    "valid_d = ImageDataGenerator(rescale=1./255) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_decay(epoch):\n",
    "    return initial_learningrate * 0.99 ** epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='val_loss', verbose=1, patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_learningrate=2e-3\n",
    "batch_size = 1000\n",
    "epochs = 10\n",
    "input_shape = (28, 28, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 28, 28, 128)       1280      \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 14, 14, 256)       295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 7, 7, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 7, 7, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 7, 7, 128)         295040    \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 4, 4, 64)          73792     \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 4, 4, 64)          256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 4, 4, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 2, 2, 64)          36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_13 (Batc (None, 2, 2, 64)          256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)   (None, 2, 2, 64)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 1, 1, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 1, 1, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 709,578\n",
      "Trainable params: 708,298\n",
      "Non-trainable params: 1,280\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model= Sequential()\n",
    "model.add(Conv2D(128,(3,3),input_shape=(28,28,1),activation='relu',padding='same'))\n",
    "model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer=\"uniform\"))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same')) \n",
    "\n",
    "model.add(Conv2D(256,(3,3),padding='same',activation='relu')) \n",
    "model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer=\"uniform\"))\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(128,(3,3),padding='same',activation='relu')) \n",
    "model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer=\"uniform\"))\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),padding='same',activation='relu')) \n",
    "model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer=\"uniform\"))\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Conv2D(64,(3,3),padding='same',activation='relu')) \n",
    "model.add(BatchNormalization(momentum=0.9, epsilon=1e-5, gamma_initializer=\"uniform\"))\n",
    "model.add(LeakyReLU(alpha=0.1))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(Flatten())  \n",
    "model.add(Dense(64,activation='relu')) \n",
    "model.add(Dense(10,activation='softmax'))\n",
    "\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 39s - loss: 1.3351 - accuracy: 0.5432 - val_loss: 2.7779 - val_accuracy: 0.2031\n",
      "Epoch 2/10\n",
      " - 38s - loss: 0.1984 - accuracy: 0.9408 - val_loss: 0.9322 - val_accuracy: 0.8469\n",
      "Epoch 3/10\n",
      " - 37s - loss: 0.1398 - accuracy: 0.9587 - val_loss: 0.0036 - val_accuracy: 0.9969\n",
      "Epoch 4/10\n",
      " - 38s - loss: 0.1120 - accuracy: 0.9663 - val_loss: 0.0044 - val_accuracy: 0.9875\n",
      "Epoch 5/10\n",
      " - 37s - loss: 0.0986 - accuracy: 0.9703 - val_loss: 0.0015 - val_accuracy: 0.9937\n",
      "Epoch 6/10\n",
      " - 38s - loss: 0.0937 - accuracy: 0.9719 - val_loss: 0.0062 - val_accuracy: 0.9969\n",
      "Epoch 7/10\n",
      " - 38s - loss: 0.0868 - accuracy: 0.9739 - val_loss: 0.0043 - val_accuracy: 0.9906\n",
      "Epoch 8/10\n",
      " - 37s - loss: 0.0800 - accuracy: 0.9756 - val_loss: 0.0183 - val_accuracy: 0.9906\n",
      "Epoch 9/10\n",
      " - 38s - loss: 0.0791 - accuracy: 0.9762 - val_loss: 1.3811e-04 - val_accuracy: 0.9906\n",
      "Epoch 10/10\n",
      " - 38s - loss: 0.0748 - accuracy: 0.9769 - val_loss: 7.3991e-04 - val_accuracy: 0.9906\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "model1 = model.fit_generator(\n",
    "      train_d.flow(X_train,Y_train, batch_size=batch_size),\n",
    "      steps_per_epoch=100,\n",
    "      epochs=epochs,\n",
    "      callbacks=[LearningRateScheduler(lr_decay),es           \n",
    "               ],\n",
    "      validation_data=valid_d.flow(X_valid1,Y_valid1),\n",
    "      validation_steps=10,  \n",
    "      verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.87353515625"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_Val=model.predict_classes(X_val/255)\n",
    "accuracy_score(preds_Val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 28, 28, 1)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test= test.drop(['id'],axis=1).values\n",
    "X_test = X_test.reshape(X_test.shape[0],28,28,1)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_classes(X_test/255.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label\n",
       "0   0      2\n",
       "1   1      8\n",
       "2   2      2\n",
       "3   3      6\n",
       "4   4      2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')\n",
    "submission['label'] = predictions\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission.csv\",index=False)"
   ]
  }
 ],
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