MNIST_Fashion_tensorflow-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import datasets\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "(xtrain,ytrain),(xtest,ytest)=datasets.fashion_mnist.load_data()\n",
    "print(xtrain.shape)\n",
    "print(ytrain.shape)\n",
    "print(xtest.shape)\n",
    "print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "names=['T-shirt/top','Trouser','Pullover','Dress','Coat',\n",
    "       'Sandal','Shirt','Sneaker','Bag','Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(xtrain[53000],cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'T-shirt/top'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[ytrain[53000]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28*28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "ytrain = to_categorical(ytrain)\n",
    "ytest = to_categorical(ytest)\n",
    "print(ytrain.shape)\n",
    "print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change shape of data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers,models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty neural network\n",
    "model = models.Sequential()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#add the output layer with 10 neurons and softmax activation function\n",
    "model.add(layers.Dense(10,activation=\"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile the neural network\n",
    "model.compile(loss=\"categorical_crossentropy\",metrics=[\"accuracy\"],\n",
    "             optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 1s 20us/sample - loss: 11.2070 - accuracy: 0.7001 - val_loss: 3.6895 - val_accuracy: 0.7673\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 1s 9us/sample - loss: 2.7488 - accuracy: 0.7859 - val_loss: 2.5996 - val_accuracy: 0.7779\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - ETA: 0s - loss: 1.9150 - accuracy: 0.80 - 1s 9us/sample - loss: 1.9107 - accuracy: 0.8045 - val_loss: 1.8505 - val_accuracy: 0.7992\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 1s 10us/sample - loss: 1.4961 - accuracy: 0.8176 - val_loss: 1.6875 - val_accuracy: 0.7993\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 1s 10us/sample - loss: 1.1740 - accuracy: 0.8298 - val_loss: 1.3082 - val_accuracy: 0.8145\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 1s 10us/sample - loss: 0.9846 - accuracy: 0.8377 - val_loss: 1.2354 - val_accuracy: 0.8186\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 1s 11us/sample - loss: 0.8538 - accuracy: 0.8474 - val_loss: 1.1303 - val_accuracy: 0.8202\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 1s 10us/sample - loss: 0.7603 - accuracy: 0.8507 - val_loss: 1.1442 - val_accuracy: 0.8054\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 1s 11us/sample - loss: 0.6771 - accuracy: 0.8567 - val_loss: 1.0039 - val_accuracy: 0.8283\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 1s 10us/sample - loss: 0.6293 - accuracy: 0.8599 - val_loss: 0.9803 - val_accuracy: 0.8284\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2d16736c508>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model for 10 iterations\n",
    "model.fit(xtrain,ytrain,epochs=10,batch_size=1000,verbose=True,\n",
    "         validation_data=(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(xtest[8000],cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(xtest[8000].reshape(1,28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sneaker'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[7]"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}