{ "cells": [ { "cell_type": "markdown", "id": "ec813b28", "metadata": {}, "source": [ "# Initial Preprocessing of German Traffic Sign Dataset\n", "The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011.\n", "J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel,\n", "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition, Neural Networks, Volume 32,2012,Pages 323-332, ISSN 0893-6080,\n", "https://doi.org/10.1016/j.neunet.2012.02.016." ] }, { "cell_type": "markdown", "id": "58ae4666", "metadata": {}, "source": [ "This dataset is publicly available - see Kaggle for a description of the dataset.\n", "There are over 40 classes but we are going to use a subset of these - 10, in fact.\n", "The images are of varying quality - often with low contrast. The images are in colour and are of varying size.\n", "To fit in with the datasets we've worked on (MNIST and variants) we aim to do the following:-\n", "1) Download the database\n", "2) Select a subset of classes from the training set.\n", "3) Re-size the images to 28x28.\n", "4) Grey-scale the images.\n", "5) Store them as png images. \n", "6) Load greyscale images, flatten them to a 1D 784 array and store as csv file.\n", "7) Next we test this by loading the csv file, creating training, test and validation subsets and apply them to a Random forest. We evaluate it with a confusion matrix and provide accuracy, recall, precision and F1-scores.\n", "8) We repeat this with a tensor flow/keras deep neural network.\n", "\n", "There are other pre-processing tasks we could apply. The are discussed at the end of this notebook.\n", "\n" ] }, { "cell_type": "markdown", "id": "7c62c8a0", "metadata": {}, "source": [ "## Step 1:\n", "\n", "I assume you have downloaded the dataset and extracted it from the achive." ] }, { "cell_type": "markdown", "id": "883e10d6", "metadata": {}, "source": [ "# Step 2: \n", "\n", "After invidually examining some of the classes, choose ten classes (to fit in with our work with the MNIST digit, fashion and CIFAR10 datasets). We choose classes '1', '2', '3', '4', '5', '7', '8', '9', '14', '17'.\n", "Examine the folder structure. " ] }, { "cell_type": "markdown", "id": "d44df44a", "metadata": {}, "source": [ "# Steps 3, 4 and 5\n", "\n", "We will be extracting images from some subfolders so it's important to understand how this is done.\n", "Install Pillow. Pillow is a Python module that allows you to manipulate images in various ways, such as resizing, cropping, filtering, and adding text.\n", "We will also use the OS module. The OS module in Python provides functions for interacting with the operating system. OS comes under Python’s standard utility modules so you should not need to install it.\n", "\n", "3) Load, re-size and greyscale the images to 28x28.\n", "4) Flatten them to a 1D 784 array and,\n", "5) store them as png's." ] }, { "cell_type": "code", "execution_count": 2, "id": "65d411bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\gordon\\\\Downloads\\\\GTSRBDataset\\\\Train'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import PIL\n", "import os\n", "import numpy as np\n", "from PIL import Image\n", "f = r\"C:\\Users\\gordon\\Downloads\\GTSRBDataset\\Train\"\n", "#select 10 classes to be used - the numbering follows the GTSRBD folder names.\n", "classID=['1', '2', '3', '4', '5', '7', '8', '9', '14', '17']\n", "classID[9]\n", "f" ] }, { "cell_type": "code", "execution_count": 3, "id": "712869db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dir not made- already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n", "dir not made - already exists?\n" ] } ], "source": [ "a=0\n", "#sefine base dir - change as required\n", "path = r\"C:\\Users\\gordon\\Downloads\\GTSRBDataset\"\n", "subdir=\"gs\\\\train\"\n", "\n", "\n", "# create your subdirectory\n", "try:\n", " os.mkdir(os.path.join(path, subdir))\n", "except:\n", " print(\"dir not made- already exists?\")\n", "\n", "\n", "\n", "for i in classID:\n", " try:\n", " classDir = path+'\\\\'+subdir+'\\\\'+i\n", " classIDlist = os.listdir(f+'\\\\'+ i)\n", " os.mkdir(classDir)\n", " except:\n", " print(\"dir not made - already exists?\")\n", " \n", " for file in classIDlist:\n", " try:\n", " img = Image.open(f+'\\\\'+i+'\\\\'+file).convert('L') #grey scale coversion here\n", " img = img.resize((28,28)) #resize\n", " img.save(classDir+'\\\\'+file)\n", " \n", " except IOError:\n", " a=a+1\n", " print (\"Wrong path provided\",classDir+'\\\\'+file)" ] }, { "cell_type": "code", "execution_count": 4, "id": "c5d68cdc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[180 179 179 169 161 167 179 174 186 201 164 146 146 148 163 163 150 150\n", " 153 155 153 155 154 152 150 149 150 154 182 173 155 140 137 154 179 173\n", " 188 202 163 144 145 145 149 154 152 153 152 154 150 151 153 152 148 148\n", " 148 149 181 173 153 142 138 153 178 173 186 202 160 145 160 168 154 151\n", " 149 149 151 150 150 151 153 156 163 166 168 166 183 175 152 141 140 156\n", " 177 175 189 197 159 151 163 163 161 162 158 158 156 150 149 153 155 159\n", " 190 206 209 211 183 176 160 155 164 178 183 190 195 181 156 151 148 145\n", " 146 147 151 157 164 159 153 153 154 157 178 191 199 204 183 184 190 196\n", " 203 203 188 183 167 147 139 139 138 138 139 138 140 140 144 151 157 157\n", " 152 152 151 152 155 155 190 193 204 205 201 195 174 153 140 136 135 138\n", " 144 149 149 148 145 142 141 143 145 153 158 150 147 150 152 151 191 188\n", " 183 175 168 164 151 138 137 139 146 159 168 173 177 175 171 164 153 145\n", " 142 144 153 153 148 148 152 151 185 174 153 142 146 148 139 136 141 156\n", " 170 176 175 175 178 181 179 178 175 162 147 143 143 154 150 151 154 152\n", " 182 172 150 145 150 142 138 142 157 173 174 174 174 176 177 177 174 167\n", " 170 176 165 149 142 146 154 153 157 155 182 174 153 149 147 138 141 152\n", " 165 155 142 144 156 171 178 164 144 138 142 159 176 161 143 144 154 156\n", " 156 155 182 175 155 152 142 139 145 162 154 137 147 148 138 159 173 151\n", " 146 162 157 144 168 175 149 143 148 157 156 155 182 174 157 150 139 139\n", " 151 167 156 155 172 170 144 150 169 147 157 180 173 144 159 178 157 144\n", " 147 156 155 152 183 176 161 149 139 138 154 168 170 172 171 162 142 156\n", " 170 144 158 181 173 145 159 178 159 145 149 156 157 155 182 176 158 147\n", " 139 138 156 170 173 173 157 135 137 163 169 144 155 179 174 148 156 178\n", " 161 144 145 158 159 156 182 171 158 149 139 138 154 173 172 173 168 161\n", " 142 149 167 145 151 176 176 148 153 174 159 144 147 160 158 154 181 172\n", " 158 151 140 138 151 170 172 172 176 175 152 142 167 147 149 175 174 148\n", " 153 172 153 143 149 163 162 159 181 173 155 154 145 139 147 167 161 147\n", " 160 163 142 149 173 157 141 156 153 142 165 167 147 144 155 172 177 178\n", " 180 173 155 152 148 140 143 157 166 150 138 135 140 165 177 173 155 144\n", " 145 159 171 154 141 144 159 171 175 175 176 169 154 150 152 143 140 144\n", " 161 168 159 157 168 177 178 178 174 172 176 176 159 141 141 148 160 162\n", " 159 159 175 171 157 153 159 151 138 138 146 160 170 176 177 180 179 177\n", " 177 177 175 161 146 143 145 154 156 152 148 148 176 177 181 186 186 174\n", " 149 141 142 143 151 164 173 180 180 177 173 165 156 149 144 148 159 162\n", " 160 157 150 148 183 188 192 191 186 180 167 153 143 139 139 144 151 158\n", " 156 152 150 147 145 146 152 156 157 153 150 147 147 147 175 167 158 151\n", " 149 158 168 164 163 148 142 144 145 147 146 145 146 149 149 153 155 155\n", " 153 155 156 152 147 147 165 152 142 142 144 157 167 171 217 207 163 153\n", " 151 152 150 152 154 159 161 157 153 153 151 151 149 144 144 148 165 154\n", " 148 147 148 161 167 173 238 240 171 153 157 166 172 163 154 156 166 158\n", " 152 148 147 147 147 144 145 146 167 155 147 146 148 161 167 171 238 242\n", " 167 146 149 166 182 162 147 152 165 156 151 149 147 147 149 148 145 146\n", " 168 152 142 139 141 155 165 169 234 238 163 142 148 164 183 162 146 150\n", " 164 154 152 149 146 144 146 147 143 145]\n" ] } ], "source": [ "#check an example image can be loaded\n", "#example of how to flatted the numpy 2D array into one dimension\n", "path = r\"C:\\Users\\gordon\\Downloads\\GTSRBDataset\"\n", "subdir=\"gs\\\\train\"\n", "classDir = path+'\\\\'+subdir+'\\\\'+\"1\"\n", "img = Image.open(classDir +'\\\\'+\"00001_00000_00000.png\")\n", "value = np.asarray(img.getdata(), dtype=int).reshape((img.size[1], img.size[0]))\n", "value = value.flatten()\n", "print(value)\n", " " ] }, { "cell_type": "markdown", "id": "be668d60", "metadata": {}, "source": [ "## Step 6\n", "Load resized, grey-scaled images and convert to a single csv file. Each image is one row consisting of:-\n", "colum1=label, cols 2 to 785 each contain a pixel value in the range 0-255.\n", "We will also use the csv module. The csv module implements classes to read and write tabular data in CSV format.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "87bd976d", "metadata": {}, "outputs": [], "source": [ "import csv\n", "a=0\n", "path = r\"C:\\Users\\gordon\\Downloads\\GTSRBDataset\"\n", "subdir=\"gs\\\\train\"\n", "#10 classes used\n", "classID=['1', '2', '3', '4', '5', '7', '8', '9', '14', '17']\n", "mylist=[]\n", "heading=np.array(mylist) #first row of our csv will be headings of feature names\n", "heading=np.append(heading,'label') #first col will hold class label, remaining cols will contain pixel info (ints in range 0-255)\n", "for i in range(1,28*28+1): #create the heading\n", " heading=np.append(heading, 'pixel'+ str(i))\n", "\n", " \n", "with open(path+'\\\\'+ subdir +'\\\\'+\"img_pixels1.csv\",'w', newline='') as f:\n", " writer = csv.writer(f)\n", " writer.writerow(heading) #write heading row as first row\n", " for i in classID: #iterate through training class folders reading images\n", " classDir = path+'\\\\'+subdir+'\\\\'+i\n", " classIDlist = os.listdir(classDir)\n", " \n", " \n", " for file in classIDlist:\n", " img = Image.open(classDir +'\\\\'+ file)\n", " value = np.asarray(img.getdata(), dtype=int).reshape((img.size[1], img.size[0]))\n", " value = value.flatten() #flatten to 1D\n", " value= np.append (int(i),value) #append to value\n", " writer.writerow(value) #append to csv\n", "f.close()\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "2934b4dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['00001_00000_00000.png',\n", " '00001_00000_00001.png',\n", " '00001_00000_00002.png',\n", " '00001_00000_00003.png',\n", " '00001_00000_00004.png',\n", " '00001_00000_00005.png',\n", " '00001_00000_00006.png',\n", " '00001_00000_00007.png',\n", " '00001_00000_00008.png',\n", " '00001_00000_00009.png',\n", " '00001_00000_00010.png',\n", " '00001_00000_00011.png',\n", " '00001_00000_00012.png',\n", " '00001_00000_00013.png',\n", " 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os.listdir(f+'\\\\'+ '1')\n", "classIDList" ] }, { "cell_type": "markdown", "id": "d5153bac", "metadata": {}, "source": [ "## Step 7\n", "Next we load the csv file, creating training, test and validation subsets and apply them to a random forest. We evaluate it with a confusion matrix and provide Accuracy, recall, precision and F1-scores.\n", "Notice the random forest is not concerned what names we give our class labels." ] }, { "cell_type": "code", "execution_count": 7, "id": "1b3a06f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training data points: 12744\n", "validation data points: 3186\n", "testing data points: 3186\n", "[[414 12 1 8 6 1 0 0 0 0]\n", " [ 23 378 4 6 11 6 3 2 0 0]\n", " [ 10 5 258 1 9 1 1 0 0 0]\n", " [ 13 11 0 403 0 1 3 1 0 0]\n", " [ 14 24 7 6 322 4 5 1 0 0]\n", " [ 5 7 2 8 6 269 4 0 0 0]\n", " [ 4 9 3 5 6 11 237 0 0 0]\n", " [ 2 2 6 3 1 0 0 276 0 0]\n", " [ 1 1 0 1 0 0 0 0 135 0]\n", " [ 1 0 0 0 1 0 0 0 1 204]]\n", " precision recall f1-score support\n", "\n", " 1 0.850 0.937 0.891 442\n", " 2 0.842 0.873 0.857 433\n", " 3 0.918 0.905 0.912 285\n", " 4 0.914 0.933 0.923 432\n", " 5 0.890 0.841 0.864 383\n", " 7 0.918 0.894 0.906 301\n", " 8 0.937 0.862 0.898 275\n", " 9 0.986 0.952 0.968 290\n", " 14 0.993 0.978 0.985 138\n", " 17 1.000 0.986 0.993 207\n", "\n", " accuracy 0.909 3186\n", " macro avg 0.925 0.916 0.920 3186\n", "weighted avg 0.911 0.909 0.909 3186\n", "\n", "0.9089767733835531\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn.model_selection import train_test_split\n", "\n", "path = r\"C:\\Users\\gordon\\Downloads\\GTSRBDataset\"\n", "subdir=\"gs\"\n", "\n", "df=pd.read_csv(path+'\\\\'+subdir+'\\\\'+'img_pixels.csv',delimiter=',') #read labelled training data set\n", "mtarget = df[\"label\"].values #extract labelled results\n", "y = np.array(mtarget, dtype='int') #array of class targets results\n", "values = np.array(df.values,dtype='int') #array of attribute values for training data\n", "X = values[:,1:785] #slice-> cols 1,2,3,...784 kept. cols 0 excluded.\n", "\n", "# now, let's take 20% of the training data and use that for testing\n", "(X, tX, y, ty) = train_test_split(X, y,\n", "test_size=0.2, random_state=84)\n", "# show the sizes of each data split\n", "print(\"training data points: {}\".format(len(y)))\n", "print(\"validation data points: {}\".format(len(ty)))\n", "print(\"testing data points: {}\".format(len(tX)))\n", "feature_names=list(df)\n", "rnd_clf = RandomForestClassifier( n_estimators = 10, n_jobs =-1)\n", "rnd_clf.fit( X, y)\n", "y_pred_rf = rnd_clf.predict(tX)\n", "\n", "#tree_clf = DecisionTreeClassifier(max_depth=10, random_state=42)\n", "#ty=tree_clf.fit(values, mtarget)\n", "#ty\n", "feature_names=feature_names[1:785] #slice-> cols 1,2,3 and 4 kept. cols 0 excluded.\n", "#feature_names\n", "#values\n", "#mtarget\n", "# Print the confusion matrix\n", "\n", "\n", "from sklearn import metrics\n", "\n", "print(metrics.confusion_matrix(ty, y_pred_rf))\n", "\n", "# Print the precision and recall, among other metrics\n", "print(metrics.classification_report(ty, y_pred_rf, digits=3))\n", "print(metrics.accuracy_score(ty, y_pred_rf))" ] }, { "cell_type": "markdown", "id": "f0efcfcd", "metadata": {}, "source": [ "## Step 8\n", "We use the training, test and validation subsets to train and test a deep neural network.\n", "Note the tensorflow assumes class labels are integers values so we need to map the class labels to integer values from 0 through to 9.\n", "I've done this in a clumsy manner here. There are more elegant ways in Python." ] }, { "cell_type": "code", "execution_count": 11, "id": "1e5dffe3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 5 5\n", "1 2 4 4\n", "2 3 7 7\n", "3 4 4 4\n", "4 5 5 5\n", "5 6 4 4\n", "6 7 5 5\n", "7 8 5 5\n", "8 9 9 9\n", "9 10 9 9\n", "10 11 7 7\n", "11 12 6 6\n", "12 13 4 4\n", "13 14 5 5\n", "14 15 6 6\n", "15 16 6 6\n", "16 17 2 2\n", "17 18 0 0\n", "18 19 3 3\n", "19 20 2 2\n", "20 21 4 4\n", "21 22 0 0\n", "22 23 6 6\n", "23 24 6 6\n", "24 25 3 3\n", "25 26 0 0\n", "26 27 6 6\n", "27 28 1 1\n", "28 29 9 9\n", "29 30 0 0\n", "30 31 8 8\n", "31 32 1 1\n", "32 33 7 7\n", "33 34 3 3\n", "34 35 5 5\n", "35 36 3 3\n", "36 37 3 3\n", "37 38 3 3\n", "38 39 7 7\n", "39 40 4 4\n", "40 41 4 4\n", "41 42 8 8\n", "42 43 5 5\n", "43 44 5 5\n", "44 45 3 3\n", "45 46 9 9\n", "46 47 9 9\n", "47 48 4 4\n", "48 49 0 0\n", "49 50 9 9\n", "50 51 6 6\n", "51 52 7 7\n", "52 53 5 5\n", "53 54 0 0\n", "54 55 3 3\n", 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"2001 2002 0 0\n", "2002 2003 1 1\n", "2003 2004 1 1\n", "2004 2005 9 9\n", "2005 2006 1 1\n", "2006 2007 0 0\n", "2007 2008 0 0\n", "2008 2009 6 6\n", "2009 2010 3 3\n", "2010 2011 0 0\n", "2011 2012 0 0\n", "2012 2013 3 3\n", "2013 2014 5 5\n", "2014 2015 3 3\n", "2015 2016 5 5\n", "2016 2017 3 3\n", "2017 2018 1 1\n", "2018 2019 3 3\n", "2019 2020 0 0\n", "2020 2021 6 6\n", "2021 2022 3 3\n", "2022 2023 9 9\n", "2023 2024 1 1\n", "2024 2025 6 6\n", "2025 2026 3 3\n", "2026 2027 0 0\n", "2027 2028 1 1\n", "2028 2029 9 9\n", "2029 2030 2 2\n", "2030 2031 9 9\n", "2031 2032 4 4\n", "2032 2033 3 3\n", "2033 2034 6 6\n", "2034 2035 3 3\n", "2035 2036 5 5\n", "2036 2037 0 0\n", "2037 2038 4 4\n", "2038 2039 7 7\n", "2039 2040 2 2\n", "2040 2041 3 3\n", "2041 2042 9 9\n", "2042 2043 3 3\n", "2043 2044 9 9\n", "2044 2045 2 2\n", "2045 2046 1 1\n", "2046 2047 6 6\n", "2047 2048 6 6\n", "2048 2049 0 0\n", "2049 2050 4 4\n", "2050 2051 8 8\n", "2051 2052 7 7\n", "2052 2053 0 0\n", "2053 2054 6 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"12381 12382 9 9\n", "12382 12383 2 2\n", "12383 12384 0 0\n", "12384 12385 3 3\n", "12385 12386 1 1\n", "12386 12387 3 3\n", "12387 12388 8 8\n", "12388 12389 0 0\n", "12389 12390 6 6\n", "12390 12391 7 7\n", "12391 12392 7 7\n", "12392 12393 8 8\n", "12393 12394 0 0\n", "12394 12395 7 7\n", "12395 12396 4 4\n", "12396 12397 1 1\n", "12397 12398 0 0\n", "12398 12399 6 6\n", "12399 12400 0 0\n", "12400 12401 7 7\n", "12401 12402 2 2\n", "12402 12403 6 6\n", "12403 12404 1 1\n", "12404 12405 9 9\n", "12405 12406 4 4\n", "12406 12407 8 8\n", "12407 12408 4 4\n", "12408 12409 6 6\n", "12409 12410 0 0\n", "12410 12411 0 0\n", "12411 12412 0 0\n", "12412 12413 0 0\n", "12413 12414 6 6\n", "12414 12415 4 4\n", "12415 12416 7 7\n", "12416 12417 5 5\n", "12417 12418 3 3\n", "12418 12419 1 1\n", "12419 12420 0 0\n", "12420 12421 3 3\n", "12421 12422 0 0\n", "12422 12423 3 3\n", "12423 12424 4 4\n", "12424 12425 3 3\n", "12425 12426 0 0\n", "12426 12427 3 3\n", "12427 12428 4 4\n", "12428 12429 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"12571 12572 6 6\n", "12572 12573 6 6\n", "12573 12574 6 6\n", "12574 12575 3 3\n", "12575 12576 4 4\n", "12576 12577 2 2\n", "12577 12578 6 6\n", "12578 12579 1 1\n", "12579 12580 4 4\n", "12580 12581 6 6\n", "12581 12582 1 1\n", "12582 12583 2 2\n", "12583 12584 3 3\n", "12584 12585 6 6\n", "12585 12586 6 6\n", "12586 12587 5 5\n", "12587 12588 6 6\n", "12588 12589 7 7\n", "12589 12590 6 6\n", "12590 12591 3 3\n", "12591 12592 4 4\n", "12592 12593 3 3\n", "12593 12594 9 9\n", "12594 12595 7 7\n", "12595 12596 6 6\n", "12596 12597 1 1\n", "12597 12598 0 0\n", "12598 12599 5 5\n", "12599 12600 7 7\n", "12600 12601 2 2\n", "12601 12602 0 0\n", "12602 12603 5 5\n", "12603 12604 1 1\n", "12604 12605 5 5\n", "12605 12606 4 4\n", "12606 12607 8 8\n", "12607 12608 9 9\n", "12608 12609 3 3\n", "12609 12610 4 4\n", "12610 12611 6 6\n", "12611 12612 7 7\n", "12612 12613 0 0\n", "12613 12614 1 1\n", "12614 12615 7 7\n", "12615 12616 0 0\n", "12616 12617 4 4\n", "12617 12618 9 9\n", "12618 12619 1 1\n", "12619 12620 9 9\n", "12620 12621 0 0\n", "12621 12622 9 9\n", "12622 12623 0 0\n", "12623 12624 2 2\n", "12624 12625 2 2\n", "12625 12626 8 8\n", "12626 12627 0 0\n", "12627 12628 3 3\n", "12628 12629 6 6\n", "12629 12630 3 3\n", "12630 12631 9 9\n", "12631 12632 4 4\n", "12632 12633 4 4\n", "12633 12634 7 7\n", "12634 12635 2 2\n", "12635 12636 5 5\n", "12636 12637 1 1\n", "12637 12638 1 1\n", "12638 12639 1 1\n", "12639 12640 3 3\n", "12640 12641 9 9\n", "12641 12642 1 1\n", "12642 12643 9 9\n", "12643 12644 7 7\n", "12644 12645 3 3\n", "12645 12646 3 3\n", "12646 12647 8 8\n", "12647 12648 4 4\n", "12648 12649 6 6\n", "12649 12650 9 9\n", "12650 12651 0 0\n", "12651 12652 3 3\n", "12652 12653 9 9\n", "12653 12654 9 9\n", "12654 12655 2 2\n", "12655 12656 4 4\n", "12656 12657 8 8\n", "12657 12658 9 9\n", "12658 12659 7 7\n", "12659 12660 7 7\n", "12660 12661 3 3\n", "12661 12662 1 1\n", "12662 12663 3 3\n", "12663 12664 2 2\n", "12664 12665 0 0\n", "12665 12666 0 0\n", "12666 12667 8 8\n", "12667 12668 3 3\n", "12668 12669 6 6\n", "12669 12670 1 1\n", "12670 12671 9 9\n", "12671 12672 9 9\n", "12672 12673 6 6\n", "12673 12674 2 2\n", "12674 12675 0 0\n", "12675 12676 5 5\n", "12676 12677 6 6\n", "12677 12678 2 2\n", "12678 12679 1 1\n", "12679 12680 0 0\n", "12680 12681 1 1\n", "12681 12682 3 3\n", "12682 12683 7 7\n", "12683 12684 8 8\n", "12684 12685 0 0\n", "12685 12686 9 9\n", "12686 12687 6 6\n", "12687 12688 1 1\n", "12688 12689 6 6\n", "12689 12690 3 3\n", "12690 12691 5 5\n", "12691 12692 4 4\n", "12692 12693 4 4\n", "12693 12694 0 0\n", "12694 12695 1 1\n", "12695 12696 3 3\n", "12696 12697 0 0\n", "12697 12698 6 6\n", "12698 12699 7 7\n", "12699 12700 3 3\n", "12700 12701 0 0\n", "12701 12702 5 5\n", "12702 12703 4 4\n", "12703 12704 2 2\n", "12704 12705 4 4\n", "12705 12706 3 3\n", "12706 12707 4 4\n", "12707 12708 0 0\n", "12708 12709 0 0\n", "12709 12710 1 1\n", "12710 12711 1 1\n", "12711 12712 4 4\n", "12712 12713 5 5\n", "12713 12714 6 6\n", "12714 12715 6 6\n", "12715 12716 4 4\n", "12716 12717 9 9\n", "12717 12718 4 4\n", "12718 12719 9 9\n", "12719 12720 4 4\n", "12720 12721 3 3\n", "12721 12722 1 1\n", "12722 12723 4 4\n", "12723 12724 7 7\n", "12724 12725 6 6\n", "12725 12726 3 3\n", "12726 12727 7 7\n", "12727 12728 0 0\n", "12728 12729 7 7\n", "12729 12730 1 1\n", "12730 12731 3 3\n", "12731 12732 4 4\n", "12732 12733 3 3\n", "12733 12734 8 8\n", "12734 12735 7 7\n", "12735 12736 3 3\n", "12736 12737 6 6\n", "12737 12738 1 1\n", "12738 12739 6 6\n", "12739 12740 2 2\n", "12740 12741 1 1\n", "12741 12742 7 7\n", "12742 12743 6 6\n", "12743 12744 3 3\n", "(12744,)\n" ] }, { "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, 150) 117750 \n", " \n", " dense_1 (Dense) (None, 80) 12080 \n", " \n", " dense_2 (Dense) (None, 10) 810 \n", " \n", "=================================================================\n", "Total params: 130,640\n", "Trainable params: 130,640\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "keras.__version__\n", "\n", "from sklearn.model_selection import train_test_split\n", "#need to map class names into range 0->9\n", "ID=['1', '2', '3', '4', '5', '7', '8', '9', '14', '17']\n", "yname=[-1,0,1,2,3,4,-1,5,6,7,-1,-1,-1,-1,8,-1,-1,9]\n", "#classID=['1', '2', '3', '4', '5', '7', '8', '9', '14', '17']\n", "#To be fair if I named my original class folders 0,1,2,..,8,9 then I wouldn't need to do this!! :-( \n", "zlen=y.size\n", "y2=np.empty(zlen)\n", "x=0\n", "for i in range(zlen):\n", " y2[x]=yname[int(y[i])]\n", " x=x+1\n", " print(i,' ',x, ' ',int(y2[i]),' ',yname[int(y[i])])\n", "zlen1=ty.size\n", "y3=np.empty(zlen1)\n", "x=0\n", "for i in range(zlen1):\n", " y3[x]=yname[int(ty[i])]\n", " x=x+1\n", "train_y=y2\n", "test_y=y3\n", "print (y.shape)\n", "vX = X[:1000]/255.0 #validation set\n", "X=X[1000:]/255.0 #training set\n", "#similarly for the y target labels\n", "vy=train_y[:1000] #validation set\n", "train_y=train_y[1000:] #training set\n", "model = tf.keras.Sequential()\n", "model.add(tf.keras.layers.InputLayer(input_shape=(784,)))\n", "model.add(keras.layers.Flatten(input_shape=(784,)))\n", "model.add(keras.layers.Dense(150, activation='relu'))\n", "model.add(keras.layers.Dense(80, activation='relu'))\n", "model.add(keras.layers.Dense(10, activation='softmax'))\n", "model.summary()\n", "model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=[\"accuracy\"])\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "cecb2ac9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 2.1138 - accuracy: 0.2250 - val_loss: 1.9815 - val_accuracy: 0.3030\n", "Epoch 2/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.8592 - accuracy: 0.3821 - val_loss: 1.7149 - val_accuracy: 0.4320\n", "Epoch 3/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.6123 - accuracy: 0.4618 - val_loss: 1.4852 - val_accuracy: 0.5150\n", "Epoch 4/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.4240 - accuracy: 0.5291 - val_loss: 1.3361 - val_accuracy: 0.5380\n", "Epoch 5/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.2784 - accuracy: 0.5882 - val_loss: 1.2209 - val_accuracy: 0.5880\n", "Epoch 6/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.1489 - accuracy: 0.6408 - val_loss: 1.0488 - val_accuracy: 0.6880\n", "Epoch 7/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 1.0251 - accuracy: 0.6882 - val_loss: 0.9355 - val_accuracy: 0.7320\n", "Epoch 8/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.9206 - accuracy: 0.7262 - val_loss: 0.8403 - val_accuracy: 0.7730\n", "Epoch 9/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.8281 - accuracy: 0.7641 - val_loss: 0.7570 - val_accuracy: 0.7770\n", "Epoch 10/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.7496 - accuracy: 0.7901 - val_loss: 0.6838 - val_accuracy: 0.8160\n", "Epoch 11/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.6953 - accuracy: 0.8071 - val_loss: 0.6239 - val_accuracy: 0.8330\n", "Epoch 12/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.6519 - accuracy: 0.8215 - val_loss: 0.5830 - val_accuracy: 0.8430\n", "Epoch 13/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.5984 - accuracy: 0.8380 - val_loss: 0.5691 - val_accuracy: 0.8490\n", "Epoch 14/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.5579 - accuracy: 0.8518 - val_loss: 0.7755 - val_accuracy: 0.7680\n", "Epoch 15/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.5280 - accuracy: 0.8586 - val_loss: 0.5701 - val_accuracy: 0.8200\n", "Epoch 16/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.5205 - accuracy: 0.8592 - val_loss: 0.5387 - val_accuracy: 0.8440\n", "Epoch 17/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4782 - accuracy: 0.8747 - val_loss: 0.5096 - val_accuracy: 0.8500\n", "Epoch 18/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4582 - accuracy: 0.8749 - val_loss: 0.4350 - val_accuracy: 0.8830\n", "Epoch 19/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4281 - accuracy: 0.8868 - val_loss: 0.4618 - val_accuracy: 0.8820\n", "Epoch 20/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4257 - accuracy: 0.8840 - val_loss: 0.3907 - val_accuracy: 0.9050\n", "Epoch 21/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4183 - accuracy: 0.8850 - val_loss: 0.4174 - val_accuracy: 0.8910\n", "Epoch 22/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.4088 - accuracy: 0.8884 - val_loss: 0.4348 - val_accuracy: 0.8680\n", "Epoch 23/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3777 - accuracy: 0.8969 - val_loss: 0.4401 - val_accuracy: 0.8700\n", "Epoch 24/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3734 - accuracy: 0.8992 - val_loss: 0.3909 - val_accuracy: 0.8880\n", "Epoch 25/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3702 - accuracy: 0.9022 - val_loss: 0.9385 - val_accuracy: 0.7470\n", "Epoch 26/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3510 - accuracy: 0.9089 - val_loss: 0.3456 - val_accuracy: 0.9200\n", "Epoch 27/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3378 - accuracy: 0.9087 - val_loss: 0.3624 - val_accuracy: 0.9120\n", "Epoch 28/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3178 - accuracy: 0.9161 - val_loss: 0.3474 - val_accuracy: 0.9140\n", "Epoch 29/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3137 - accuracy: 0.9170 - val_loss: 0.3397 - val_accuracy: 0.9210\n", "Epoch 30/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3153 - accuracy: 0.9149 - val_loss: 0.3405 - val_accuracy: 0.9130\n", "Epoch 31/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3069 - accuracy: 0.9171 - val_loss: 0.3434 - val_accuracy: 0.9000\n", "Epoch 32/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.3036 - accuracy: 0.9146 - val_loss: 0.3408 - val_accuracy: 0.9080\n", "Epoch 33/50\n", "367/367 [==============================] - 1s 1ms/step - loss: 0.2833 - accuracy: 0.9258 - val_loss: 0.4132 - val_accuracy: 0.8740\n", "Epoch 34/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2855 - accuracy: 0.9212 - val_loss: 0.3392 - val_accuracy: 0.9140\n", "Epoch 35/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2855 - accuracy: 0.9190 - val_loss: 0.3111 - val_accuracy: 0.9190\n", "Epoch 36/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2577 - accuracy: 0.9297 - val_loss: 0.3098 - val_accuracy: 0.9260\n", "Epoch 37/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2624 - accuracy: 0.9291 - val_loss: 0.3484 - val_accuracy: 0.9000\n", "Epoch 38/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2544 - accuracy: 0.9312 - val_loss: 0.2978 - val_accuracy: 0.9300\n", "Epoch 39/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2661 - accuracy: 0.9296 - val_loss: 0.3086 - val_accuracy: 0.9210\n", "Epoch 40/50\n", "367/367 [==============================] - 1s 1ms/step - loss: 0.2399 - accuracy: 0.9366 - val_loss: 0.3058 - val_accuracy: 0.9210\n", "Epoch 41/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2370 - accuracy: 0.9363 - val_loss: 0.2791 - val_accuracy: 0.9360\n", "Epoch 42/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2312 - accuracy: 0.9368 - val_loss: 0.3091 - val_accuracy: 0.9210\n", "Epoch 43/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2282 - accuracy: 0.9389 - val_loss: 0.2856 - val_accuracy: 0.9280\n", "Epoch 44/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2359 - accuracy: 0.9389 - val_loss: 0.3376 - val_accuracy: 0.9090\n", "Epoch 45/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2316 - accuracy: 0.9410 - val_loss: 0.2990 - val_accuracy: 0.9280\n", "Epoch 46/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2125 - accuracy: 0.9442 - val_loss: 0.2731 - val_accuracy: 0.9420\n", "Epoch 47/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2533 - accuracy: 0.9367 - val_loss: 0.3172 - val_accuracy: 0.9240\n", "Epoch 48/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.2097 - accuracy: 0.9430 - val_loss: 0.2812 - val_accuracy: 0.9350\n", "Epoch 49/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.1956 - accuracy: 0.9482 - val_loss: 0.2864 - val_accuracy: 0.9240\n", "Epoch 50/50\n", "367/367 [==============================] - 1s 2ms/step - loss: 0.1969 - accuracy: 0.9480 - val_loss: 0.2957 - val_accuracy: 0.9250\n", "100/100 [==============================] - 0s 945us/step\n", "[[435 4 0 1 1 1 0 0 0 0]\n", " [ 33 393 1 2 3 1 0 0 0 0]\n", " [ 7 3 246 0 25 0 1 2 0 1]\n", " [ 26 2 0 400 3 0 0 0 1 0]\n", " [ 9 8 4 2 352 3 0 4 0 1]\n", " [ 7 4 0 0 9 274 3 4 0 0]\n", " [ 15 8 2 13 8 12 214 2 1 0]\n", " [ 1 2 2 0 2 4 0 279 0 0]\n", " [ 0 0 0 2 0 0 0 0 131 5]\n", " [ 2 0 0 1 0 0 0 0 3 201]]\n", " precision recall f1-score support\n", "\n", " 0.0 0.813 0.984 0.890 442\n", " 1.0 0.927 0.908 0.917 433\n", " 2.0 0.965 0.863 0.911 285\n", " 3.0 0.950 0.926 0.938 432\n", " 4.0 0.873 0.919 0.896 383\n", " 5.0 0.929 0.910 0.919 301\n", " 6.0 0.982 0.778 0.868 275\n", " 7.0 0.959 0.962 0.960 290\n", " 8.0 0.963 0.949 0.956 138\n", " 9.0 0.966 0.971 0.969 207\n", "\n", " accuracy 0.918 3186\n", " macro avg 0.933 0.917 0.923 3186\n", "weighted avg 0.923 0.918 0.918 3186\n", "\n" ] } ], "source": [ "\n", "history=model.fit(X,train_y,epochs=50,validation_data=(vX,vy))\n", "\n", "X_new=X[:]\n", "#X_test.shape\n", "\n", "y_proba=model.predict(tX)\n", "y_proba\n", "\n", "y_pred=tf.argmax(y_proba, axis=1)\n", "# Print the confusion matrix\n", "from sklearn import metrics\n", "# Print the confusion matrix\n", "print(metrics.confusion_matrix(test_y, y_pred))\n", "\n", "# Print the precision and recall, among other metrics\n", "print(metrics.classification_report(test_y, y_pred, digits=3))" ] }, { "cell_type": "code", "execution_count": 13, "id": "fae4af58", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "9\n", "9\n" ] } ], "source": [ "#give it a random quick check\n", "import matplotlib.pyplot as plt\n", "plt.figure()\n", "arr = np.asarray(tX[9])\n", "B = np.reshape(arr, (-1, 28))\n", "plt.imshow(B, cmap='gray', vmin=0, vmax=255)\n", "plt.colorbar()\n", "plt.grid(False)\n", "plt.show()\n", "print (ty[9])\n", "print (y_pred_rf[9])" ] }, { "cell_type": "code", "execution_count": 14, "id": "48d27464", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2 2 9 4 2 17 4 8 1 9 9 4]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'100kmh'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_names = ['30kmh', '50kmph', '60kmph', '70kmph', '80kmph',\n", " '100kmh', '120kmph', 'no overtaking', 'stop', 'no entry']\n", "\n", "print(ty[0:12])\n", "\n", "plt.figure(figsize=(10,10))\n", "for i in range(10):\n", " plt.subplot(2,5,i+1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.grid(False)\n", " arr = np.asarray(tX[i])\n", " B = np.reshape(arr, (-1, 28))\n", " plt.imshow(B, cmap='gray', vmin=0, vmax=255)\n", " plt.xlabel(ty[i])\n", "plt.show()\n", "class_names[int(y[1]) ]" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }