main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc6cfe93",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a025c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d934697",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('fashion-mnist_train.csv')\n",
    "test = pd.read_csv('fashion-mnist_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf4a3353",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
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       "      <th>pixel7</th>\n",
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       "      <th>pixel9</th>\n",
       "      <th>...</th>\n",
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       "      <th>pixel777</th>\n",
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       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
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       "      <th>pixel784</th>\n",
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       "  </thead>\n",
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       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0      2       0       0       0       0       0       0       0       0   \n",
       "1      9       0       0       0       0       0       0       0       0   \n",
       "2      6       0       0       0       0       0       0       0       5   \n",
       "3      0       0       0       0       1       2       0       0       0   \n",
       "4      3       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0        30        43         0   \n",
       "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",
       "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d51c615",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = np.array(train, dtype = 'float32')\n",
    "test_data = np.array(test, dtype = 'float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f85a3c",
   "metadata": {},
   "source": [
    "Rescaling the pixel intensity values between 0-1 by dividing the sample by 255 (Since grayscale values ranges from 0-255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a52452a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_data[:,1:]/255\n",
    "\n",
    "y_train = train_data[:,0]\n",
    "\n",
    "x_test= test_data[:,1:]/255\n",
    "\n",
    "y_test=test_data[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63a3bd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 785)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cfbb92",
   "metadata": {},
   "source": [
    "The labels corresponds to the following array index of the class_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b25cdcae",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/Top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1fa7817",
   "metadata": {},
   "source": [
    "Creating a model using Sequential API "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ab655d82",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28,28]),\n",
    "    keras.layers.Dense(300,activation='relu'),\n",
    "    keras.layers.Dense(100,activation='relu'),\n",
    "    keras.layers.Dense(10,activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0192c2b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d94a7bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "             optimizer = 'sgd',\n",
    "             metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb14300f",
   "metadata": {},
   "source": [
    "TRAINING the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91064dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 784).\n",
      "WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 784).\n",
      "1676/1688 [============================>.] - ETA: 0s - loss: 0.7559 - accuracy: 0.7530WARNING:tensorflow:Model was constructed with shape (None, 28, 28) for input KerasTensor(type_spec=TensorSpec(shape=(None, 28, 28), dtype=tf.float32, name='flatten_input'), name='flatten_input', description=\"created by layer 'flatten_input'\"), but it was called on an input with incompatible shape (None, 784).\n",
      "1688/1688 [==============================] - 4s 2ms/step - loss: 0.7545 - accuracy: 0.7534 - val_loss: 0.5556 - val_accuracy: 0.8077\n",
      "Epoch 2/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.4971 - accuracy: 0.8266 - val_loss: 0.5200 - val_accuracy: 0.8253\n",
      "Epoch 3/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.4524 - accuracy: 0.8416 - val_loss: 0.4516 - val_accuracy: 0.8418\n",
      "Epoch 4/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.4213 - accuracy: 0.8519 - val_loss: 0.4399 - val_accuracy: 0.8460\n",
      "Epoch 5/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.4014 - accuracy: 0.8592 - val_loss: 0.4198 - val_accuracy: 0.8542\n",
      "Epoch 6/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3853 - accuracy: 0.8646 - val_loss: 0.4178 - val_accuracy: 0.8528\n",
      "Epoch 7/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3710 - accuracy: 0.8693 - val_loss: 0.3950 - val_accuracy: 0.8635\n",
      "Epoch 8/30\n",
      "1688/1688 [==============================] - 2s 1ms/step - loss: 0.3584 - accuracy: 0.8734 - val_loss: 0.4415 - val_accuracy: 0.8388\n",
      "Epoch 9/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3485 - accuracy: 0.8766 - val_loss: 0.3946 - val_accuracy: 0.8607\n",
      "Epoch 10/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3399 - accuracy: 0.8778 - val_loss: 0.3802 - val_accuracy: 0.8677\n",
      "Epoch 11/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3303 - accuracy: 0.8831 - val_loss: 0.3720 - val_accuracy: 0.8683\n",
      "Epoch 12/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3234 - accuracy: 0.8843 - val_loss: 0.3670 - val_accuracy: 0.8722\n",
      "Epoch 13/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3159 - accuracy: 0.8873 - val_loss: 0.3903 - val_accuracy: 0.8605\n",
      "Epoch 14/30\n",
      "1688/1688 [==============================] - 2s 1ms/step - loss: 0.3096 - accuracy: 0.8903 - val_loss: 0.3691 - val_accuracy: 0.8678\n",
      "Epoch 15/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.3013 - accuracy: 0.8915 - val_loss: 0.3939 - val_accuracy: 0.8623\n",
      "Epoch 16/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2963 - accuracy: 0.8946 - val_loss: 0.3540 - val_accuracy: 0.8760\n",
      "Epoch 17/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2896 - accuracy: 0.8957 - val_loss: 0.3633 - val_accuracy: 0.8703\n",
      "Epoch 18/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2853 - accuracy: 0.8972 - val_loss: 0.3723 - val_accuracy: 0.8680\n",
      "Epoch 19/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2787 - accuracy: 0.8988 - val_loss: 0.3367 - val_accuracy: 0.8773\n",
      "Epoch 20/30\n",
      "1688/1688 [==============================] - 2s 1ms/step - loss: 0.2745 - accuracy: 0.9015 - val_loss: 0.3347 - val_accuracy: 0.8810\n",
      "Epoch 21/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2698 - accuracy: 0.9025 - val_loss: 0.3423 - val_accuracy: 0.8785\n",
      "Epoch 22/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2661 - accuracy: 0.9030 - val_loss: 0.3462 - val_accuracy: 0.8750\n",
      "Epoch 23/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2610 - accuracy: 0.9062 - val_loss: 0.3610 - val_accuracy: 0.8713\n",
      "Epoch 24/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2559 - accuracy: 0.9079 - val_loss: 0.3672 - val_accuracy: 0.8690\n",
      "Epoch 25/30\n",
      "1688/1688 [==============================] - 3s 1ms/step - loss: 0.2523 - accuracy: 0.9085 - val_loss: 0.3457 - val_accuracy: 0.8807\n",
      "Epoch 26/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2481 - accuracy: 0.9102 - val_loss: 0.3306 - val_accuracy: 0.8837\n",
      "Epoch 27/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2443 - accuracy: 0.9121 - val_loss: 0.3218 - val_accuracy: 0.8850\n",
      "Epoch 28/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2403 - accuracy: 0.9123 - val_loss: 0.3225 - val_accuracy: 0.8817\n",
      "Epoch 29/30\n",
      "1688/1688 [==============================] - 3s 2ms/step - loss: 0.2366 - accuracy: 0.9131 - val_loss: 0.3207 - val_accuracy: 0.8885\n",
      "Epoch 30/30\n",
      "1688/1688 [==============================] - 3s 1ms/step - loss: 0.2322 - accuracy: 0.9162 - val_loss: 0.3210 - val_accuracy: 0.8868\n"
     ]
    }
   ],
   "source": [
    "logs = model.fit(x_train, y_train, epochs=30, validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e12ded4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step - loss: 0.3034 - accuracy: 0.8897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3033735752105713, 0.8896999955177307]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "442d9ea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(logs.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0920392c",
   "metadata": {},
   "source": [
    "We can infer from the graph that the validation accuracy is on gradual inclination and the loss is declining steadily as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9dad06c",
   "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": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}