Fashion MNIST Image Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8da3c7ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef83b73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b06b5a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b2c7337",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Our pixel values are between 0 and 255, 0 being black and 255 being white. \n",
    "#This means we have a grayscale image as there are no color channels.\n",
    "train_images[0,4,27]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d63aa9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "090d7a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[90])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31050e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3f8f757",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),  # input layer (1)\n",
    "    keras.layers.Dense(128, activation='relu'),  # hidden layer (2)\n",
    "    keras.layers.Dense(10, activation='softmax') # output layer (3)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae104c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "842d3341",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4951 - accuracy: 0.8266\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3766 - accuracy: 0.8652\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3361 - accuracy: 0.8772\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3127 - accuracy: 0.8852\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2941 - accuracy: 0.8921\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2793 - accuracy: 0.8971\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2691 - accuracy: 0.8995\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2566 - accuracy: 0.9045\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2475 - accuracy: 0.9073\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2387 - accuracy: 0.9111\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x206c4392be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)  # we pass the data, labels and epochs and watch the magic!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a404aaf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3323 - accuracy: 0.8876\n"
     ]
    }
   ],
   "source": [
    "test_loss,test_acc=model.evaluate(test_images,test_labels,verbose=1)\n",
    "#test accuracy less than train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "71bbe224",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions=model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7dfb753b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'Pullover',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Shirt',\n",
       " 'Trouser',\n",
       " 'Coat',\n",
       " 'Shirt',\n",
       " 'Sandal',\n",
       " 'Sneaker',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Sandal',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'Pullover',\n",
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       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Sandal',\n",
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       " 'Pullover',\n",
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       " 'Coat',\n",
       " 'Ankle boot',\n",
       " 'Trouser',\n",
       " 'T-shirt/top',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'Coat',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Shirt',\n",
       " 'T-shirt/top',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'Trouser',\n",
       " 'Dress',\n",
       " 'Trouser',\n",
       " 'Dress',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Dress',\n",
       " 'Bag',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'T-shirt/top',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'Bag',\n",
       " 'Sandal',\n",
       " 'T-shirt/top',\n",
       " 'Bag',\n",
       " 'Pullover',\n",
       " 'Ankle boot',\n",
       " 'Pullover',\n",
       " 'Bag',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Coat',\n",
       " 'Pullover',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Pullover',\n",
       " 'Sandal',\n",
       " 'T-shirt/top',\n",
       " 'Ankle boot',\n",
       " 'Sandal',\n",
       " 'Dress',\n",
       " 'Shirt',\n",
       " 'Shirt',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Dress',\n",
       " 'Bag',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Ankle boot',\n",
       " 'Sneaker',\n",
       " 'Ankle boot',\n",
       " 'Coat',\n",
       " 'Bag',\n",
       " 'Ankle boot',\n",
       " 'Trouser',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Dress',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Bag',\n",
       " 'Sneaker',\n",
       " 'Pullover',\n",
       " 'Shirt',\n",
       " 'Coat',\n",
       " 'Shirt',\n",
       " 'Coat',\n",
       " 'Trouser',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Trouser',\n",
       " 'Shirt',\n",
       " 'Sandal',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Dress',\n",
       " 'Dress',\n",
       " 'Dress',\n",
       " 'Bag',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Bag',\n",
       " 'Sandal',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " 'Bag',\n",
       " 'Sneaker',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Sneaker',\n",
       " 'T-shirt/top',\n",
       " 'Bag',\n",
       " 'T-shirt/top',\n",
       " 'Ankle boot',\n",
       " 'Sneaker',\n",
       " 'Shirt',\n",
       " 'Trouser',\n",
       " 'Shirt',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Bag',\n",
       " 'T-shirt/top',\n",
       " 'Shirt',\n",
       " 'Dress',\n",
       " 'Shirt',\n",
       " 'Bag',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'Bag',\n",
       " 'Ankle boot',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'Trouser',\n",
       " 'Coat',\n",
       " 'Coat',\n",
       " 'Ankle boot',\n",
       " 'Pullover',\n",
       " 'Trouser',\n",
       " 'Sneaker',\n",
       " 'Ankle boot',\n",
       " 'Sandal',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Shirt',\n",
       " 'Ankle boot',\n",
       " 'Sandal',\n",
       " 'Dress',\n",
       " 'Bag',\n",
       " 'Coat',\n",
       " 'Pullover',\n",
       " 'Ankle boot',\n",
       " 'Pullover',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Pullover',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Coat',\n",
       " 'Ankle boot',\n",
       " 'Dress',\n",
       " 'Sandal',\n",
       " 'Bag',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'Sneaker',\n",
       " 'Pullover',\n",
       " 'Bag',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'Dress',\n",
       " 'Dress',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Sneaker',\n",
       " 'Dress',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'Shirt',\n",
       " 'Sandal',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'Shirt',\n",
       " 'Trouser',\n",
       " 'Ankle boot',\n",
       " 'Bag',\n",
       " 'Pullover',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'Ankle boot',\n",
       " 'Trouser',\n",
       " 'Bag',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Ankle boot',\n",
       " 'Bag',\n",
       " 'Trouser',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Trouser',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Sandal',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Sandal',\n",
       " 'Pullover',\n",
       " 'Pullover',\n",
       " 'Dress',\n",
       " 'Dress',\n",
       " 'Trouser',\n",
       " 'Trouser',\n",
       " 'Coat',\n",
       " 'Ankle boot',\n",
       " 'Coat',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " 'Shirt',\n",
       " 'Bag',\n",
       " 'Ankle boot',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Trouser',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'Dress',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Sandal',\n",
       " 'Sandal',\n",
       " 'Trouser',\n",
       " 'Sandal',\n",
       " 'Sneaker',\n",
       " 'Bag',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " 'Dress',\n",
       " 'Shirt',\n",
       " 'Shirt',\n",
       " 'T-shirt/top',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Sandal',\n",
       " 'Sandal',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'T-shirt/top',\n",
       " 'Ankle boot',\n",
       " 'Dress',\n",
       " 'Sneaker',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Sneaker',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Coat',\n",
       " 'Bag',\n",
       " 'T-shirt/top',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Coat',\n",
       " 'Dress',\n",
       " 'Shirt',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Ankle boot',\n",
       " 'Trouser',\n",
       " 'Bag',\n",
       " 'Trouser',\n",
       " 'Sneaker',\n",
       " 'Sandal',\n",
       " 'Sandal',\n",
       " 'Shirt',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Trouser',\n",
       " 'T-shirt/top',\n",
       " 'Sandal',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'T-shirt/top',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Sandal',\n",
       " 'T-shirt/top',\n",
       " 'Pullover',\n",
       " 'Sandal',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Bag',\n",
       " 'Pullover',\n",
       " 'Shirt',\n",
       " 'Coat',\n",
       " 'Shirt',\n",
       " 'Pullover',\n",
       " 'Ankle boot',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " 'Trouser',\n",
       " 'Coat',\n",
       " 'Coat',\n",
       " 'T-shirt/top',\n",
       " 'Sandal',\n",
       " 'T-shirt/top',\n",
       " 'Ankle boot',\n",
       " 'Ankle boot',\n",
       " 'Bag',\n",
       " 'Bag',\n",
       " 'Dress',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'Coat',\n",
       " 'Shirt',\n",
       " 'Shirt',\n",
       " 'Dress',\n",
       " 'Pullover',\n",
       " 'Dress',\n",
       " 'Sandal',\n",
       " 'Pullover',\n",
       " 'Ankle boot',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Sneaker',\n",
       " 'Sneaker',\n",
       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds=[]\n",
    "for j in range(len(predictions)):\n",
    "    idx=predictions[j].argmax()\n",
    "    preds.append(class_names[idx]) #returns index of the element that has max value\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73811320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pick a number: 5\n",
      "1/1 [==============================] - 0s 48ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLOR = 'white'\n",
    "plt.rcParams['text.color'] = COLOR\n",
    "plt.rcParams['axes.labelcolor'] = COLOR\n",
    "\n",
    "def predict(model, image, correct_label):\n",
    "    class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "    prediction = model.predict(np.array([image]))\n",
    "    predicted_class = class_names[np.argmax(prediction)]\n",
    "\n",
    "    show_image(image, class_names[correct_label], predicted_class)\n",
    "\n",
    "\n",
    "def show_image(img, label, guess):\n",
    "    plt.figure()\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "    plt.title(\"Expected: \" + label)\n",
    "    plt.xlabel(\"Guess: \" + guess)\n",
    "    plt.legend\n",
    "    plt.colorbar()\n",
    "    plt.grid(False)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def get_number():\n",
    "    while True:\n",
    "        num = input(\"Pick a number: \")\n",
    "        if num.isdigit():\n",
    "            num = int(num)\n",
    "        if 0 <= num <= 1000:\n",
    "            return int(num)\n",
    "        else:\n",
    "            print(\"Try again...\")\n",
    "\n",
    "num = get_number()\n",
    "image = test_images[num]\n",
    "label = test_labels[num]\n",
    "predict(model, image, label)\n"
   ]
  }
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