Kannnada MNIST.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the data :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(r'C:\\Users\\vamsi\\Desktop\\Kannada-MNIST\\train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv(r'C:\\Users\\vamsi\\Desktop\\Kannada-MNIST\\test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "val=pd.read_csv(r'C:\\Users\\vamsi\\Desktop\\Kannada-MNIST\\Dig-MNIST.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4998</th>\n",
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       "      <th>4999</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  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",
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       "4        4       0       0       0       0       0       0       0       0   \n",
       "...    ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "4995  4995       0       0       0       0       0       0       0       0   \n",
       "4996  4996       0       0       0       0       0       0       0       0   \n",
       "4997  4997       0       0       0       0       0       0       0       0   \n",
       "4998  4998       0       0       0       0       0       0       0       0   \n",
       "4999  4999       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",
       "4995       0  ...         0         0         0         0         0         0   \n",
       "4996       0  ...         0         0         0         0         0         0   \n",
       "4997       0  ...         0         0         0         0         0         0   \n",
       "4998       0  ...         0         0         0         0         0         0   \n",
       "4999       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",
       "4995         0         0         0         0  \n",
       "4996         0         0         0         0  \n",
       "4997         0         0         0         0  \n",
       "4998         0         0         0         0  \n",
       "4999         0         0         0         0  \n",
       "\n",
       "[5000 rows x 785 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>10240 rows × 785 columns</p>\n",
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      ],
      "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",
       "10235      5       0       0       0       0       0       0       0       0   \n",
       "10236      6       0       0       0       0       0       0       0       0   \n",
       "10237      7       0       0       0       0       0       0       0       0   \n",
       "10238      8       0       0       0       0       0       0       0       0   \n",
       "10239      9       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0           0  ...         0         0         0         0         0   \n",
       "1           0  ...         0         0         0         0         0   \n",
       "2           0  ...         0         0         0         0         0   \n",
       "3           0  ...         0         0         0         0         0   \n",
       "4           0  ...         0         0         0         0         0   \n",
       "...       ...  ...       ...       ...       ...       ...       ...   \n",
       "10235       0  ...         0         0         0         0         0   \n",
       "10236       0  ...         0         0         0         0         0   \n",
       "10237       0  ...         0         0         0         0         0   \n",
       "10238       0  ...         0         0         0         0         0   \n",
       "10239       0  ...         0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0             0         0         0         0         0  \n",
       "1             0         0         0         0         0  \n",
       "2             0         0         0         0         0  \n",
       "3             0         0         0         0         0  \n",
       "4             0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "10235         0         0         0         0         0  \n",
       "10236         0         0         0         0         0  \n",
       "10237         0         0         0         0         0  \n",
       "10238         0         0         0         0         0  \n",
       "10239         0         0         0         0         0  \n",
       "\n",
       "[10240 rows x 785 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data preprocessing :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrain=np.array(train['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrainpics=train.drop(['label'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0           0       0       0       0       0       0       0       0       0   \n",
       "1           0       0       0       0       0       0       0       0       0   \n",
       "2           0       0       0       0       0       0       0       0       0   \n",
       "3           0       0       0       0       0       0       0       0       0   \n",
       "4           0       0       0       0       0       0       0       0       0   \n",
       "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "59995       0       0       0       0       0       0       0       0       0   \n",
       "59996       0       0       0       0       0       0       0       0       0   \n",
       "59997       0       0       0       0       0       0       0       0       0   \n",
       "59998       0       0       0       0       0       0       0       0       0   \n",
       "59999       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0           0  ...         0         0         0         0         0   \n",
       "1           0  ...         0         0         0         0         0   \n",
       "2           0  ...         0         0         0         0         0   \n",
       "3           0  ...         0         0         0         0         0   \n",
       "4           0  ...         0         0         0         0         0   \n",
       "...       ...  ...       ...       ...       ...       ...       ...   \n",
       "59995       0  ...         0         0         0         0         0   \n",
       "59996       0  ...         0         0         0         0         0   \n",
       "59997       0  ...         0         0         0         0         0   \n",
       "59998       0  ...         0         0         0         0         0   \n",
       "59999       0  ...         0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0             0         0         0         0         0  \n",
       "1             0         0         0         0         0  \n",
       "2             0         0         0         0         0  \n",
       "3             0         0         0         0         0  \n",
       "4             0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "59995         0         0         0         0         0  \n",
       "59996         0         0         0         0         0  \n",
       "59997         0         0         0         0         0  \n",
       "59998         0         0         0         0         0  \n",
       "59999         0         0         0         0         0  \n",
       "\n",
       "[60000 rows x 784 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrainpics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrainpics=np.array(xtrainpics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrainpics=xtrainpics.reshape(60000,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "xvalpics=val.drop(['label'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "yval=val['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10240, 784)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xvalpics=np.array(xvalpics)\n",
    "xvalpics.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xvalpics=xvalpics.reshape(10240,28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 7, 8, 9], dtype=int64)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yval=np.array(yval)\n",
    "yval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "testpics=test.drop(['id'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Building the model :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,MaxPooling2D,BatchNormalization,Dropout,Flatten\n",
    "from keras.layers import Convolution2D as Conv2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 26, 26, 132)       1320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 13, 13, 132)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 13, 13, 132)       528       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 11, 11, 164)       194996    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 5, 5, 164)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 5, 5, 164)         656       \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 3, 3, 164)         242228    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 1, 1, 164)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 1, 1, 164)         656       \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 1, 1, 196)         289492    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 1, 1, 196)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 1, 1, 196)         784       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 1, 1, 196)         0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 196)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 200)               39400     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                2010      \n",
      "=================================================================\n",
      "Total params: 772,070\n",
      "Trainable params: 770,758\n",
      "Non-trainable params: 1,312\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(132, kernel_size = (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(164, kernel_size=(3,3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(164, kernel_size=(3,3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Conv2D(196, kernel_size=(3,3), activation='relu',padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2),padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(200, activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "# Output Layer\n",
    "model.add(Dense(10,activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training the model :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10240 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 22s 372us/step - loss: 0.0299 - acc: 0.9908 - val_loss: 1.3190 - val_acc: 0.6841\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 24s 395us/step - loss: 0.0211 - acc: 0.9937 - val_loss: 1.2606 - val_acc: 0.7009\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 23s 389us/step - loss: 0.0169 - acc: 0.9949 - val_loss: 1.2872 - val_acc: 0.7031\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 23s 391us/step - loss: 0.0130 - acc: 0.9961 - val_loss: 1.3489 - val_acc: 0.6954\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 23s 379us/step - loss: 0.0104 - acc: 0.9968 - val_loss: 1.4406 - val_acc: 0.6911\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 24s 398us/step - loss: 0.0070 - acc: 0.9980 - val_loss: 1.4235 - val_acc: 0.6985\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 23s 386us/step - loss: 0.0078 - acc: 0.9979 - val_loss: 1.4597 - val_acc: 0.6974\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 24s 396us/step - loss: 0.0057 - acc: 0.9984 - val_loss: 1.3976 - val_acc: 0.7180\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 24s 397us/step - loss: 0.0058 - acc: 0.9983 - val_loss: 1.6320 - val_acc: 0.6846\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 23s 383us/step - loss: 0.0045 - acc: 0.9987 - val_loss: 1.3825 - val_acc: 0.7239\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b25bb7c288>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrainpics,ytrain,epochs=10,batch_size=2000,validation_data=(xvalpics,yval))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the model :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\vamsi\\\\Desktop\\\\Kaggle2']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(model,r'C:\\Users\\vamsi\\Desktop\\Kaggle2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "testpics=np.array(testpics)\n",
    "testpics=testpics.reshape(5000,28,28,1)\n",
    "sol=model.predict(testpics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "solutions=[]\n",
    "for i in range(len(sol)):\n",
    "    solutions.append(sol[i].argmax())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "solutions=pd.DataFrame(solutions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 40,
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   ],
   "source": [
    "solutions.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving the predictions :\n",
    "==="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "solutions.to_csv(r'C:\\Users\\vamsi\\Desktop\\solution.csv')"
   ]
  }
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