Karem BenChikha
Created September 11, 2020 © MIT

Sample Image Classification with Tensorflow

Simple Neural Networks Model to predict classes of clothing images.

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Sample Image Classification with Tensorflow

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jupyter notebokk

Python
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "number of images to train:  60000\nnumber of images to test:  10000\n"
    }
   ],
   "source": [
    "fashionMist = keras.datasets.fashion_mnist\n",
    "(trainImages,trainLabes),(testImages,testLabels) = fashionMist.load_data()\n",
    "classNames = ['T-shirt/top','Trouser','Pullover','Dress','Coast','Sandal','Shirt','Sneaker','Bag','Ankle boot']\n",
    "trainImages = trainImages / 255.0\n",
    "testImages = testImages / 255.0\n",
    "print(\"number of images to train: \",len(trainLabes))\n",
    "print(\"number of images to test: \",len(testLabels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of an Image to train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(trainImages[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set of Images with Correct Lables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x720 with 25 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(trainImages[i],cmap= plt.cm.binary)\n",
    "    plt.xlabel(classNames[trainLabes[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating Layers for the Neural Network Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(128,activation=tensorflow.nn.relu),\n",
    "    keras.layers.Dense(10,activation=tensorflow.nn.softmax)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Traint the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.4988 - accuracy: 0.8230\nEpoch 2/10\n1875/1875 [==============================] - 5s 3ms/step - loss: 0.3749 - accuracy: 0.8651\nEpoch 3/10\n1875/1875 [==============================] - 5s 3ms/step - loss: 0.3383 - accuracy: 0.8784\nEpoch 4/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.3114 - accuracy: 0.8846\nEpoch 5/10\n1875/1875 [==============================] - 5s 2ms/step - loss: 0.2941 - accuracy: 0.8909\nEpoch 6/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.2791 - accuracy: 0.8961\nEpoch 7/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.2667 - accuracy: 0.9007\nEpoch 8/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.2565 - accuracy: 0.9044\nEpoch 9/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.2465 - accuracy: 0.9074\nEpoch 10/10\n1875/1875 [==============================] - 4s 2ms/step - loss: 0.2352 - accuracy: 0.9122\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<tensorflow.python.keras.callbacks.History at 0x7f40cb584b80>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "model.fit(trainImages,trainLabes,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict the Class of the TestImages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(testImages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Some Display Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotImage(i,predictionsArray,trueLabel,img):\n",
    "    predictionsArray,trueLabel,img = predictionsArray[i],trueLabel[i],img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(img,cmap=plt.cm.binary)\n",
    "    predictedLabel = numpy.argmax(predictionsArray)\n",
    "    if predictedLabel == trueLabel:  \n",
    "        color = \"blue\"\n",
    "    else:\n",
    "        color = \"red\"\n",
    "    plt.xlabel(\"{} {:2.0f}% ({})\".format(classNames[predictedLabel],100*numpy.max(predictionsArray),classNames[trueLabel]),color=color)\n",
    "\n",
    "def plotValueArray(i,predictionsArray,trueLabel):\n",
    "    predictionsArray,trueLabel= predictionsArray[i],trueLabel[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10),predictionsArray,color=\"#777777\")\n",
    "    plt.ylim([0,1])\n",
    "    predictedLabel = numpy.argmax(predictionsArray)\n",
    "    thisplot[predictedLabel].set_color('red')\n",
    "    thisplot[trueLabel].set_color('green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Examples of Tested Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'prediction' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-dd348336069d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplotImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestImages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplotValueArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'prediction' is not defined"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plotImage(i,predictions,testLabels,testImages)\n",
    "plt.subplot(1,2,2)\n",
    "plotValueArray(i,predictions,testLabels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'prediction' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-e0b0e666134a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplotImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestImages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplotValueArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'prediction' is not defined"
     ]
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plotImage(i,predictions,testLabels,testImages)\n",
    "plt.subplot(1,2,2)\n",
    "plotValueArray(i,predictions,testLabels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "NameError",
     "evalue": "name 'prediction' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-354153856c63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumImages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumRows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnumCols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mplotImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestImages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumRows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnumCols\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mplotValueArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtestLabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'prediction' is not defined"
     ]
    }
   ],
   "source": [
    "numRows =  5\n",
    "numCols = 5\n",
    "numImages = numCols * numRows\n",
    "plt.figure(figsize=(2*2*numCols,2*numRows))\n",
    "for i in range(numImages):\n",
    "    plt.subplot(numRows,2*numCols,2*i+1)\n",
    "    plotImage(i,predictions,testLabels,testImages)\n",
    "    plt.subplot(numRows,2*numCols,2*i+2)\n",
    "    plotValueArray(i,predictions,testLabels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

Credits

Karem BenChikha

Karem BenChikha

11 projects • 2 followers
I am an Automation Engineer with over 4-years of experience in Embedded Systems & Electronics and 2-years as an IoT and Cloud developer.

Comments