{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Importing Packages\n",
    "from tensorflow import keras \n",
    "import numpy as np           \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 3us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 25s 1us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 4s 1us/step\n"
     ]
    }
   ],
   "source": [
    "#Importing Keras Dataset\n",
    "data = keras.datasets.fashion_mnist\n",
    "\n",
    "#train and test data segregation\n",
    "(train_images, train_labels), (test_images, test_labels) = data.load_data()\n",
    "\n",
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle Boot\"]\n",
    "\n",
    "train_images = train_images/255.0\n",
    "test_images = test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\vinay\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 8s 132us/sample - loss: 0.4980 - acc: 0.8253\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 8s 127us/sample - loss: 0.3717 - acc: 0.8662\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 6s 93us/sample - loss: 0.3341 - acc: 0.8787\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 8s 130us/sample - loss: 0.3126 - acc: 0.8852\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.2928 - acc: 0.8929\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x25f99983fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a object of model class\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128, activation=\"relu\"),\n",
    "    keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "#Compile the model with MSE loss and Adam optimizer\n",
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "#fitting the model\n",
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 79us/sample - loss: 0.3884 - acc: 0.8587\n",
      "Accuracy:  0.8587\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sZplw2M0y4bCbZcJhN8uEw26WicFcsnkmcBswHdgDLIqIr0qaDPwvYDbFZZv/ICI2Dl1Th6/TTjstWb/jjjuS9dSliUePHt1Qm9ph9uzZyfrGjem30zvvvJOs7969e3+bNKwNZsu+C/iziPgAcCpwpaR5wOeAByJiLvBAed/MulRl2CNiTUQ8Xt7eAjwDHAmcDywuJ1sMXDBUjTSz5u3Xd3ZJs4ETgUeBIyJiDRQfCMC0VjfOzFpn0GGXNA5YAlwdEfUvLvbu+RZK6pXU29fX10gbzawFBhV2SaMogv7tiPhB+fA6STPK+gxg/UDzRsSiiOiJiJ6pU6e2os1m1oDKsEsS8C3gmYj4ck3pbuCS8vYlwF2tb56ZtcpghrieBvwR8JSkZeVjnweuB74n6TLgFeDjQ9PE4e+qq65K1u+8885kPXVp402bNiXnffHFF5P1OXPmJOvNGD9+fLJedcnlPXv2JOuTJk3a7zYNZ5Vhj4ifAapTPqu1zTGzoeJf0JllwmE3y4TDbpYJh90sEw67WSYcdrNM+FTSXeDII49M1idOnJisp05FvWPHjuS8VaeaHsp+9qrht7t27UrWq4a4Vr323HjLbpYJh90sEw67WSYcdrNMOOxmmXDYzTLhsJtlwv3sbRARyXpxfpD6PvrRjybrS5YsqVur6su+6670OUcuvvjiZL0Z48aNS9ZXr16drFet16rx7rnxlt0sEw67WSYcdrNMOOxmmXDYzTLhsJtlwmE3y4T72dugqr93xIgRyfr8+fOT9dR55ceOHZucd9WqVcn6UJowYUKyXjUeveq88Bs2bNjvNg1n3rKbZcJhN8uEw26WCYfdLBMOu1kmHHazTDjsZpmo7GeXNBO4DZgO7AEWRcRXJV0LfBLoKyf9fETcM1QNPZClrp8+GB/5yEeS9dR556uuz7527dpk/YknnkjWjz/++GQ95bDDDkvWt23blqyPGjUqWa86335uBvOjml3An0XE45LGA0sl3V/WvhIRfzN0zTOzVqkMe0SsAdaUt7dIegZIX8LEzLrOfu1fSpoNnAg8Wj50laQnJd0sacDfLkpaKKlXUm9fX99Ak5hZGww67JLGAUuAqyNiM/AN4L3ACRRb/hsGmi8iFkVET0T0TJ06tQVNNrNGDCrskkZRBP3bEfEDgIhYFxG7I2IP8E3glKFrppk1qzLsKk59+i3gmYj4cs3jM2omuxBY3vrmmVmrDOZo/GnAHwFPSVpWPvZ5YIGkE4AAVgKfGpIWDgNVp4pu1tFHH123tmzZsro1qO6+uv/++5P1ZrretmzZkqxv37694ecGWLduXVPzDzeDORr/M2Cgd6v71M0OIP4FnVkmHHazTDjsZplw2M0y4bCbZcJhN8uETyU9DFxzzTV1a9OnT0/OW9XPfsYZZzTUpsG46KKLkvUjjjgiWa8awnrWWWftd5uGM2/ZzTLhsJtlwmE3y4TDbpYJh90sEw67WSYcdrNMKCLatzCpD3i55qEpwOtta8D+6da2dWu7wG1rVCvbNisiBjz/W1vD/q6FS70R0dOxBiR0a9u6tV3gtjWqXW3zbrxZJhx2s0x0OuyLOrz8lG5tW7e2C9y2RrWlbR39zm5m7dPpLbuZtYnDbpaJjoRd0rmSnpO0QtLnOtGGeiStlPSUpGWSejvclpslrZe0vOaxyZLul/Sr8t8Br7HXobZdK+m1ct0tkzS/Q22bKekhSc9IelrSn5SPd3TdJdrVlvXW9u/skkYAzwMfBVYBvwAWRMQv29qQOiStBHoiouM/wJB0OrAVuC0iPlg+9tfAhoi4vvygnBQRf94lbbsW2Nrpy3iXVyuaUXuZceAC4FI6uO4S7foD2rDeOrFlPwVYEREvRsQO4LvA+R1oR9eLiJ8AG/o9fD6wuLy9mOLN0nZ12tYVImJNRDxe3t4C7L3MeEfXXaJdbdGJsB8JvFpzfxXddb33AO6TtFTSwk43ZgBHRMQaKN48wLQOt6e/yst4t1O/y4x3zbpr5PLnzepE2Ae6lFQ39f+dFhEnAecBV5a7qzY4g7qMd7sMcJnxrtDo5c+b1YmwrwJm1tw/CljdgXYMKCJWl/+uB35I912Ket3eK+iW/67vcHv+VTddxnugy4zTBeuuk5c/70TYfwHMlXSMpNHAxcDdHWjHu0g6tDxwgqRDgXPovktR3w1cUt6+BLirg23ZR7dcxrveZcbp8Lrr+OXPI6Ltf8B8iiPyLwDXdKINddo1B3ii/Hu6020DvkOxW7eTYo/oMuBw4AHgV+W/k7uobbcDTwFPUgRrRofa9hGKr4ZPAsvKv/mdXneJdrVlvfnnsmaZ8C/ozDLhsJtlwmE3y4TDbpYJh90sEw57l5F0oaSQ9P5BTHuppPc0sawzJf19on54zUistf1GZo1udLnWGQ5791kA/Izix0ZVLgUaDnuViHgjIk6IiBOAG4Gv7L0fxSAmoPixiKS2vZck+VLjDXDYu0j5m+nTKH6gcnG/2mfLcfZPSLpe0seAHuDb5ZZ2bDkWf0o5fY+kh8vbp0j6Z0n/Uv77vha09VhJyyXdCDwOzJD078s2Lpf0pXK6kZI21cx3saSbam4vL1/TQzXTf1nSY+XAkMvLx8+W9GNJ3wX+pdn258ifkN3lAuDeiHhe0gZJJ0Ux9vm8svYbEbFN0uSI2CDpKuAzEdELUPwac0DPAqdHxC5JZwNfAv5d7QSSeoBPR8Tl+9HeecB/jIhPSzoK+CLFB9CbwI8l/R5wb2L+LwBnRsQ6SRPLxxYC6yPiFEkHA49Iuq+snQrMi4hX9qONVnLYu8sC4H+Ut79b3n8cOBu4JSK2AUTE/o4jnwAsljSX4ueao/pPUH5g7E/QAV6IiF+Ut38DeDDKk35IugM4nXTYfw7cJun7wN7BKucAH5C0d89mAjC3vP1/HfTGOexdQtLhwG8DH5QUwAggJH2WYljwYH7XvIv//9VsTM3jfwk8FBEXluOoH25Rs9+quV1vt2JPv1ptuz5J8SHxe8ATkn69nPaKiHig9knKPZLa5dl+8nf27vExilM8zYqI2RExE3iJYvDEfcAnJB0CxbnUynm2AONrnmMlcHJ5u3Y3fQLwWnn70iFpPTwC/FZ5BH8kxTGHf4pi2OZGSXPLg3gX1swzJyIeAf4LsJHiJCb/CFyx9yCcpPdJGjtEbc6Kw949FlCMn6+1BPjDiLiXYjRUr6RlwGfK+q3AjXsP0AH/DfiqpJ8Cu2ue56+B6yT9nGKP4V3KA3o3Ndr4iFgF/FeKvYZlwCMR8Q9l+c8pducfoBght9dXJD1FMeLrxxGxHPhbilFpy1SczPIbeA+0JTzqzSwT3rKbZcJhN8uEw26WCYfdLBMOu1kmHHazTDjsZpn4f3j+mP7mW+LjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model evaluation\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(\"Accuracy: \", test_acc)\n",
    "\n",
    "prediction = model.predict(test_images)\n",
    "#displaying predictions\n",
    "for i in range(7):\n",
    "    plt.grid(False)\n",
    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(\"Actual: \" + class_names[test_labels[i]])\n",
    "    plt.title(\"Prediction \" + class_names[np.argmax(prediction[i])])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}