fashion-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 785 entries, label to pixel784\n",
      "dtypes: int64(785)\n",
      "memory usage: 359.3 MB\n"
     ]
    }
   ],
   "source": [
    "train_data= pd.read_csv('fashion-mnist_train.csv')\n",
    "test_data = pd.read_csv('fashion-mnist_test.csv')\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
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       "3       0  ...         3         0         0         0         0         1   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
       "0         0         0         0         0  \n",
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       "2         0         0         0         0  \n",
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       "\n",
       "[5 rows x 785 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
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       "\n",
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       "3       0  ...       137       126       140         0       133       224   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel781  pixel782  pixel783  pixel784  \n",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>mean</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.035333</td>\n",
       "      <td>0.101933</td>\n",
       "      <td>0.247967</td>\n",
       "      <td>0.411467</td>\n",
       "      <td>0.805767</td>\n",
       "      <td>2.198283</td>\n",
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       "      <td>34.625400</td>\n",
       "      <td>23.300683</td>\n",
       "      <td>16.588267</td>\n",
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       "      <td>22.814817</td>\n",
       "      <td>17.911483</td>\n",
       "      <td>8.520633</td>\n",
       "      <td>2.753300</td>\n",
       "      <td>0.855517</td>\n",
       "      <td>0.07025</td>\n",
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       "      <th>std</th>\n",
       "      <td>2.872305</td>\n",
       "      <td>0.094689</td>\n",
       "      <td>0.271011</td>\n",
       "      <td>1.222324</td>\n",
       "      <td>2.452871</td>\n",
       "      <td>4.306912</td>\n",
       "      <td>5.836188</td>\n",
       "      <td>8.215169</td>\n",
       "      <td>14.093378</td>\n",
       "      <td>23.819481</td>\n",
       "      <td>...</td>\n",
       "      <td>57.545242</td>\n",
       "      <td>48.854427</td>\n",
       "      <td>41.979611</td>\n",
       "      <td>43.966032</td>\n",
       "      <td>51.830477</td>\n",
       "      <td>45.149388</td>\n",
       "      <td>29.614859</td>\n",
       "      <td>17.397652</td>\n",
       "      <td>9.356960</td>\n",
       "      <td>2.12587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>170.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              label        pixel1        pixel2        pixel3        pixel4  \\\n",
       "count  60000.000000  60000.000000  60000.000000  60000.000000  60000.000000   \n",
       "mean       4.500000      0.000900      0.006150      0.035333      0.101933   \n",
       "std        2.872305      0.094689      0.271011      1.222324      2.452871   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        2.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        4.500000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        7.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max        9.000000     16.000000     36.000000    226.000000    164.000000   \n",
       "\n",
       "             pixel5        pixel6        pixel7        pixel8        pixel9  \\\n",
       "count  60000.000000  60000.000000  60000.000000  60000.000000  60000.000000   \n",
       "mean       0.247967      0.411467      0.805767      2.198283      5.682000   \n",
       "std        4.306912      5.836188      8.215169     14.093378     23.819481   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max      227.000000    230.000000    224.000000    255.000000    254.000000   \n",
       "\n",
       "       ...      pixel775      pixel776      pixel777      pixel778  \\\n",
       "count  ...  60000.000000  60000.000000  60000.000000  60000.000000   \n",
       "mean   ...     34.625400     23.300683     16.588267     17.869433   \n",
       "std    ...     57.545242     48.854427     41.979611     43.966032   \n",
       "min    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "50%    ...      0.000000      0.000000      0.000000      0.000000   \n",
       "75%    ...     58.000000      9.000000      0.000000      0.000000   \n",
       "max    ...    255.000000    255.000000    255.000000    255.000000   \n",
       "\n",
       "           pixel779      pixel780      pixel781      pixel782      pixel783  \\\n",
       "count  60000.000000  60000.000000  60000.000000  60000.000000  60000.000000   \n",
       "mean      22.814817     17.911483      8.520633      2.753300      0.855517   \n",
       "std       51.830477     45.149388     29.614859     17.397652      9.356960   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max      255.000000    255.000000    255.000000    255.000000    255.000000   \n",
       "\n",
       "          pixel784  \n",
       "count  60000.00000  \n",
       "mean       0.07025  \n",
       "std        2.12587  \n",
       "min        0.00000  \n",
       "25%        0.00000  \n",
       "50%        0.00000  \n",
       "75%        0.00000  \n",
       "max      170.00000  \n",
       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labels\n",
    "Each training and test example is assigned to one of the following labels:\n",
    "\n",
    "| Label | Description |\n",
    "| --- | --- |\n",
    "| 0 | T-shirt/top |\n",
    "| 1 | Trouser |\n",
    "| 2 | Pullover |\n",
    "| 3 | Dress |\n",
    "| 4 | Coat |\n",
    "| 5 | Sandal |\n",
    "| 6 | Shirt |\n",
    "| 7 | Sneaker |\n",
    "| 8 | Bag |\n",
    "| 9 | Ankle boot |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# splitting data into features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train_data.iloc[:, 1:]\n",
    "train_y = train_data.iloc[:, 0]\n",
    "\n",
    "test_x = test_data.iloc[:, 1:]\n",
    "test_y = test_data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (60000,) \n",
      "\n",
      "(10000, 784) (10000,) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train_x.shape, train_y.shape, '\\n')\n",
    "print(test_x.shape, test_y.shape, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing and plotting images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 9 6 0 3 4 5 8 7 1]\n"
     ]
    }
   ],
   "source": [
    "print(train_y.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_dict = {0:'T-shirt/top',1:'Trouser',2:'Pullover',3:'Dress',4:'Coat',5:'Sandal',6:'Shirt',7:'Sneaker',8:'Bag',\n",
    "              9:'Ankel boot'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that there are a total of 10 types of fashion items in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(train_x)\n",
    "y = np.array(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = np.array(test_x).reshape(-1,28,28,1)\n",
    "y_test = np.array(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(784,)\n",
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(X))\n",
    "print(np.shape(X[0]))\n",
    "print(np.shape(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting images\n",
    "images = np.array([X[i].reshape(28,-1) for i in range(len(X))])\n",
    "fig,axes = plt.subplots(4, 4, figsize=(15, 10), sharey = False)\n",
    "fig.tight_layout()\n",
    "axes = axes.ravel()\n",
    "for i in range(16):\n",
    "    axes[i].imshow(images[i], cmap = 'gray_r')\n",
    "    axes[i].set_xlabel(label_dict[y[i]])\n",
    "    axes[i].set_yticklabels([])\n",
    "    axes[i].set_xticklabels([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "X = X.reshape(-1,28,28,1)\n",
    "print(np.shape(X))\n",
    "X = X/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model = Sequential([\n",
    "    Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=(28,28,1)),\n",
    "    MaxPooling2D(pool_size=2),\n",
    "    Dropout(0.2),\n",
    "    \n",
    "    Flatten(),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.compile(\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "938/938 [==============================] - 44s 47ms/step - loss: 0.5018 - accuracy: 0.8243 - val_loss: 58.0316 - val_accuracy: 0.8393\n",
      "Epoch 2/10\n",
      "938/938 [==============================] - 42s 45ms/step - loss: 0.3381 - accuracy: 0.8820 - val_loss: 42.7083 - val_accuracy: 0.8658\n",
      "Epoch 3/10\n",
      "938/938 [==============================] - 42s 45ms/step - loss: 0.3013 - accuracy: 0.8927 - val_loss: 48.9059 - val_accuracy: 0.8535\n",
      "Epoch 4/10\n",
      "938/938 [==============================] - 42s 45ms/step - loss: 0.2800 - accuracy: 0.9002 - val_loss: 50.9560 - val_accuracy: 0.8527\n",
      "Epoch 5/10\n",
      "938/938 [==============================] - 41s 44ms/step - loss: 0.2631 - accuracy: 0.9060 - val_loss: 53.0567 - val_accuracy: 0.8484\n",
      "Epoch 6/10\n",
      "938/938 [==============================] - 45s 48ms/step - loss: 0.2483 - accuracy: 0.9110 - val_loss: 62.0130 - val_accuracy: 0.8333\n",
      "Epoch 7/10\n",
      "938/938 [==============================] - 42s 44ms/step - loss: 0.2373 - accuracy: 0.9150 - val_loss: 51.9888 - val_accuracy: 0.8611\n",
      "Epoch 8/10\n",
      "938/938 [==============================] - 45s 48ms/step - loss: 0.2248 - accuracy: 0.9183 - val_loss: 65.7640 - val_accuracy: 0.8335\n",
      "Epoch 9/10\n",
      "938/938 [==============================] - 43s 46ms/step - loss: 0.2156 - accuracy: 0.9222 - val_loss: 49.3606 - val_accuracy: 0.8711\n",
      "Epoch 10/10\n",
      "938/938 [==============================] - 42s 45ms/step - loss: 0.2059 - accuracy: 0.9253 - val_loss: 54.8489 - val_accuracy: 0.8596\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x157a6ba5828>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_model.fit(X,y,batch_size = 64 , epochs = 10 ,validation_data = (x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = x_test.reshape(10000,28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting images with predicted labels)\n",
    "fig,axes = plt.subplots(4, 4, figsize=(15, 10), sharey = False)\n",
    "# fig.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.9)\n",
    "axes = axes.ravel()\n",
    "for i in range(16):\n",
    "    axes[i].imshow(test_images[i], cmap = 'gray_r')\n",
    "    \n",
    "    predicted_values = cnn_model.predict(np.expand_dims(x_test[i],0))\n",
    "    predicted_label = np.argmax(predicted_values)\n",
    "    axes[i].set_xlabel('actual_label: '+str(label_dict[y_test[i]])+'\\n'+\n",
    "                       'predicted_label: '+str(label_dict[predicted_label]))\n",
    "    axes[i].set_yticklabels([])\n",
    "    axes[i].set_xticklabels([])"
   ]
  }
 ],
 "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": "3.6.8"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}