Fashion-MNIST_Continued_fraction_of_straight_lines/notebook.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["#import mnist_reader was not included on the repo\n","#sO I MANUALLY CREATED THE FUNCTION AND MODIFIED SOME CODE. It is fairly easy and\n","# I have include the whole script on a cell and ran it.It works ."],"metadata":{"id":"Awt2tlvKEzSg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","import urllib.request\n","import gzip\n","import shutil\n","\n","# Create directories\n","os.makedirs(\"data/fashion\", exist_ok=True)\n","\n","# Base URL and files\n","base_url = \"http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/\"\n","files = [\n","    \"train-images-idx3-ubyte.gz\",\n","    \"train-labels-idx1-ubyte.gz\",\n","    \"t10k-images-idx3-ubyte.gz\",\n","    \"t10k-labels-idx1-ubyte.gz\"\n","]\n","\n","# Download and extract\n","for file in files:\n","    gz_path = f\"data/fashion/{file}\"\n","    raw_path = gz_path.replace(\".gz\", \"\")\n","\n","    # Download\n","    print(f\"Downloading {file}...\")\n","    urllib.request.urlretrieve(base_url + file, gz_path)\n","\n","    # Extract\n","    print(f\"Extracting {file}...\")\n","    with gzip.open(gz_path, 'rb') as f_in:\n","        with open(raw_path, 'wb') as f_out:\n","            shutil.copyfileobj(f_in, f_out)\n","\n","    # Remove .gz file\n","    os.remove(gz_path)\n","\n","print(\"All files downloaded and extracted to data/fashion/\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p4EtJaKBDA17","outputId":"9f86e84a-b6cd-40ad-d8a8-97b2d4f4d6a6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading train-images-idx3-ubyte.gz...\n","Extracting train-images-idx3-ubyte.gz...\n","Downloading train-labels-idx1-ubyte.gz...\n","Extracting train-labels-idx1-ubyte.gz...\n","Downloading t10k-images-idx3-ubyte.gz...\n","Extracting t10k-images-idx3-ubyte.gz...\n","Downloading t10k-labels-idx1-ubyte.gz...\n","Extracting t10k-labels-idx1-ubyte.gz...\n","All files downloaded and extracted to data/fashion/\n"]}]},{"cell_type":"code","source":["#!/usr/bin/env python\n","# coding: utf-8\n","import os\n","import urllib.request\n","import gzip\n","import shutil\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import confusion_matrix, normalized_mutual_info_score\n","import struct\n","import warnings\n","warnings.filterwarnings('ignore')\n","\n","np.random.seed(2)\n","\n","def load_mnist(path, kind='train'):\n","    labels_path = os.path.join(path, f'{kind}-labels-idx1-ubyte')\n","    images_path = os.path.join(path, f'{kind}-images-idx3-ubyte')\n","\n","    with open(labels_path, 'rb') as lbpath:\n","        _, _ = struct.unpack('>II', lbpath.read(8))\n","        labels = np.frombuffer(lbpath.read(), dtype=np.uint8)\n","\n","    with open(images_path, 'rb') as imgpath:\n","        _, _, rows, cols = struct.unpack(\">IIII\", imgpath.read(16))\n","        images = np.frombuffer(imgpath.read(), dtype=np.uint8).reshape(len(labels), rows * cols)\n","\n","    return images, labels\n","\n","\n","# ------------------ STEP 2: Load Data ------------------\n","x_train, y_train = load_mnist('data/fashion', kind='train')\n","x_test, y_test = load_mnist('data/fashion', kind='t10k')\n","\n","\n","y_train = y_train.reshape(-1, 1)\n","y_test = y_test.reshape(-1, 1)\n","\n","x_train = x_train/255.\n","x_test = x_test/255.\n","\n","# stack together for next step\n","X = np.vstack((x_train, x_test))\n","y = np.vstack((y_train, y_test))\n","\n","# one-hot encoding\n","digits = 10\n","examples = y.shape[0]\n","y = y.reshape(1, examples)\n","Y_new = np.eye(digits)[y.astype('int32')]\n","Y_new = Y_new.T.reshape(digits, examples)\n","\n","# number of training set\n","m0 = 60000\n","m_test = X.shape[0] - m0\n","X_train, X_test = X[:m0].T, X[m0:].T\n","Y_train, Y_test = Y_new[:, :m0], Y_new[:, m0:]\n","\n","# shuffle training set\n","shuffle_index = np.random.permutation(m0)\n","X_train, Y_train = X_train[:, shuffle_index], Y_train[:, shuffle_index]\n","X_train.shape,Y_train.shape\n","\n","n_f = X_train.shape[0]\n","n_x = X_train.shape[1]\n","digits = 10\n","\n","Y_hat = np.empty(shape=(digits,n_x))\n","lr = 1\n","lra = 1\n","\n","#activation\n","def nonlin(x,ap,m):\n","    xdep = (-27*(x*m)/2/ap+np.sqrt(\n","        729*((x*m)/ap)**2+108/ap**3)/2)\n","    return xdep**(-1./3)/ap-xdep**(1./3)/3\n","def gradfn_a(x,ap):\n","    return -x**3/(1+3*ap*x**2)\n","def gradfn_m(x,y,ap):\n","    return x/(1+3*ap*y**2)\n","def gradfn_wb(x,ap,m):\n","    return m/(1+3*ap*x**2)\n","\n","# training\n","permutation = np.random.permutation(X_train.shape[1])\n","\n","X_train_shuffled = X_train[:, permutation]\n","Y_train_shuffled = Y_train[:, permutation]\n","batches=100\n","batch_size = 600\n","iterc = 50\n","\n","figloss, axloss = plt.subplots()\n","figacc, axacc = plt.subplots()\n","markers = ['+','.']\n","\n","##for a,marker in [(np.ones((digits,1)),'+'),\n","##                   (5*np.ones((digits,1)),'.')]:\n","# initialization\n","w = np.random.randn(digits,n_f) * np.sqrt(1. / n_f)\n","b = np.zeros((digits,1))\n","m = b#np.random.random((digits,1)) * np.sqrt(1. / digits)\n","wl = w\n","bl = b\n","\n","L=np.empty(shape=(iterc,1))\n","Ll=np.empty(shape=(iterc,1))\n","a_track = np.empty(shape=(digits,iterc))\n","m_track = np.empty(shape=(digits,iterc))\n","acc = np.empty(shape=(iterc,1))\n","acc_l = np.empty(shape=(iterc,1))\n","a=1\n","marker='+'\n","for k in range(iterc):\n","    for j in range(batches):\n","        # get mini-batch\n","        begin = j * batch_size\n","        end = min(begin + batch_size, X_train.shape[1] - 1)\n","        X = X_train_shuffled[:, begin:end]#n_f,batch_size\n","        Y = Y_train_shuffled[:, begin:end]#digits,batch_size\n","        m_batch = end - begin\n","\n","        xinp = np.matmul(w,X)+b#digits,batch_size\n","        Yb_hat = nonlin(xinp,a,m)#digits,batch_size\n","        Y_hat[:,begin:end] = Yb_hat\n","        Y_logf = 1./(1+np.exp(-Yb_hat))\n","        L_sum = np.sum(np.multiply(Y, np.log(Y_logf)))\n","        L[k] = -(1./batch_size) * L_sum\n","        da = -1/m_batch*(Y_logf-Y)* gradfn_a(Yb_hat,a)\n","        dela = np.sum(da,axis=1,keepdims=True)\n","        a = a + lra*dela #Satisfy the constraint a>0\n","        if not np.all(a>0):\n","            a = a - lra*dela\n","            lra = lra/1.1\n","            print('Reducing lra to ...:',lra)\n","            print(lra)\n","            j=j-1\n","        else: #If a>0 is satisfied\n","            #tune the rest of parameters with step size set to unity\n","            dm = -(Y_logf-Y)* gradfn_m(xinp,nonlin(xinp,a,m),a)\n","            m = m + lr*np.mean(dm,axis=1,keepdims=True)\n","\n","            dwb1 = -1/m_batch*(Y_logf-Y)* gradfn_wb(\n","                nonlin(xinp,a,m),a,m)\n","            w = w + lr*np.dot(dwb1,X.T)\n","            b = b + lr*np.sum(dwb1,axis=1,keepdims=True)\n","            # Comparison with vanilla logistic regression with step size 1\n","            Yl = 1./(1+np.exp(-(np.matmul(wl,X)+bl)))\n","            wl = wl + lr*np.dot(-(Yl-Y),X.T)\n","            bl = bl + lr*np.sum(-(Yl-Y),axis=1,keepdims=True)\n","            Ll[k] = -(1./batch_size) * np.sum(np.multiply(Y, np.log(Yl+0.05)))\n","            a_track[:,k]=a[:,0]\n","            m_track[:,k]=m[:,0]\n","    a_f = a\n","    Yt_hat = nonlin(np.dot(w,X_test)+b,a_f,m)\n","    Yt_logf = 1./(1+np.exp(-Yt_hat))\n","    Lt = -np.sum(np.multiply(Y_test, np.log(Yt_logf)),axis=1)\n","    print('NMI:',normalized_mutual_info_score(np.argmax(Y_test,axis=0),\n","                                        np.argmax(Yt_logf,axis=0)))\n","    #Measuring accuracy on test set and comparing with vanilla log regression\n","    op=np.argmax(Y_test,axis=0)-np.argmax(Yt_logf,axis=0)\n","    Ylt = 1./(1+np.exp(-(np.dot(wl,X_test)+bl)))\n","    opl = np.argmax(Y_test,axis=0)-np.argmax(Ylt,axis=0)\n","    acc[k] = np.count_nonzero(op)\n","    acc_l[k] = np.count_nonzero(opl)\n","print('Continued fraction accuracy:',100-acc/100)\n","print('Vanilla logistic regression accuracy:',100-acc_l/100)\n","\n","#Plotting\n","fig, ax = plt.subplots()\n","ax.plot(m_track[0,:],'.k',m_track[1,:],'+k',m_track[2,:],'xk',\n","        m_track[3,:],'1k',m_track[4,:],'2k',m_track[5,:],'3k',\n","        m_track[6,:],'4k',m_track[7,:],'|k',m_track[8,:],'_k');\n","ax.plot(m_track[9,:],'o',color=[0.5,0.5,0.5])\n","ax.legend(['0','1','2','3','4','5','6','7','8','9'])\n","plt.xlabel('iterations',fontsize=15);plt.ylabel('m',fontsize=15)\n","plt.grid();#plt.savefig('m_track'+str(na))\n","\n","fig, ax = plt.subplots()\n","ax.plot(a_track[0,:],'.k',a_track[1,:],'+k',a_track[2,:],'xk',a_track[3,:],'1k',a_track[4,:],'2k',a_track[5,:],'3k',a_track[6,:],'4k',a_track[7,:],'|k',a_track[8,:],'_k');\n","ax.plot(a_track[9,:],'o',color=[0.5,0.5,0.5])\n","plt.xlabel('iterations',fontsize=15);plt.ylabel('a',fontsize=15)\n","ax.legend(['0','1','2','3','4','5','6','7','8','9'])\n","plt.grid();#plt.savefig('m_track'+str(na))\n","\n","axacc.plot(100-acc/100,marker,color='k');\n","axloss.plot(L,marker,color='k');\n","print(lra,a,m)\n","axacc.plot(100-acc_l/100,'xk')\n","axacc.legend([r'$\\sigma(y)$ form ($a_{{initial}}$=1)',\n","##              r'$\\sigma(y)$ form ($a_{{initial}}$=5)',\n","              r'$\\sigma(w^Tx)$ form'],\n","             fontsize=10);\n","axacc.set_xlabel('iterations',fontsize=15);\n","axacc.set_ylabel('% of correct predictions on test data',fontsize=15);\n","axacc.grid();\n","\n","axloss.plot(Ll,'xk')\n","axloss.legend([r'$\\sigma(y)$ form ($a_{initial}$=1)',\n","##              r'$\\sigma(y)$ form ($a_{initial}$=5)',\n","               r'$\\sigma(w^Tx)$ form'],\n","             fontsize=10);\n","axloss.set_xlabel('iterations',fontsize=15);\n","axloss.set_ylabel('Loss',fontsize=15);\n","axloss.grid();plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ngFLwkdLEI-8","outputId":"7b27e65d-4112-4f71-cef8-e2b7d49a5026"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Reducing lra to ...: 0.9090909090909091\n","0.9090909090909091\n","Reducing lra to ...: 0.8264462809917354\n","0.8264462809917354\n","Reducing lra to ...: 0.7513148009015777\n","0.7513148009015777\n","Reducing lra to ...: 0.6830134553650705\n","0.6830134553650705\n","Reducing lra to ...: 0.6209213230591549\n","0.6209213230591549\n","Reducing lra to ...: 0.5644739300537771\n","0.5644739300537771\n","Reducing lra to ...: 0.5131581182307065\n","0.5131581182307065\n","Reducing lra to ...: 0.4665073802097331\n","0.4665073802097331\n","Reducing lra to ...: 0.4240976183724846\n","0.4240976183724846\n","Reducing lra to ...: 0.3855432894295314\n","0.3855432894295314\n","Reducing lra to ...: 0.35049389948139215\n","0.35049389948139215\n","Reducing lra to ...: 0.31863081771035645\n","0.31863081771035645\n","Reducing lra to ...: 0.2896643797366877\n","0.2896643797366877\n","NMI: 0.6603173505287142\n","Reducing lra to ...: 0.2633312543060797\n","0.2633312543060797\n","Reducing lra to ...: 0.23939204936916333\n","0.23939204936916333\n","Reducing lra to ...: 0.21762913579014848\n","0.21762913579014848\n","NMI: 0.6860849956583137\n","Reducing lra to ...: 0.19784466890013497\n","0.19784466890013497\n","Reducing lra to ...: 0.17985878990921358\n","0.17985878990921358\n","NMI: 0.7003000683415458\n","NMI: 0.7097991626824867\n","Reducing lra to ...: 0.1635079908265578\n","0.1635079908265578\n","Reducing lra to ...: 0.14864362802414344\n","0.14864362802414344\n","Reducing lra to ...: 0.13513057093103947\n","0.13513057093103947\n","Reducing lra to ...: 0.12284597357367223\n","0.12284597357367223\n","NMI: 0.7311053947343792\n","Reducing lra to ...: 0.11167815779424747\n","0.11167815779424747\n","Reducing lra to ...: 0.10152559799477043\n","0.10152559799477043\n","Reducing lra to ...: 0.09229599817706402\n","0.09229599817706402\n","Reducing lra to ...: 0.08390545288824001\n","0.08390545288824001\n","Reducing lra to ...: 0.07627768444385455\n","0.07627768444385455\n","Reducing lra to ...: 0.06934334949441322\n","0.06934334949441322\n","NMI: 0.7271438172739649\n","Reducing lra to ...: 0.06303940863128474\n","0.06303940863128474\n","Reducing lra to ...: 0.057308553301167936\n","0.057308553301167936\n","Reducing lra to ...: 0.052098684819243575\n","0.052098684819243575\n","Reducing lra to ...: 0.047362440744766886\n","0.047362440744766886\n","NMI: 0.7266716455729246\n","Reducing lra to ...: 0.04305676431342444\n","0.04305676431342444\n","Reducing lra to ...: 0.03914251301220403\n","0.03914251301220403\n","Reducing lra to ...: 0.0355841027383673\n","0.0355841027383673\n","Reducing lra to ...: 0.03234918430760663\n","0.03234918430760663\n","Reducing lra to ...: 0.02940834937055148\n","0.02940834937055148\n","Reducing lra to ...: 0.026734863064137707\n","0.026734863064137707\n","NMI: 0.7284234758371558\n","Reducing lra to ...: 0.024304420967397912\n","0.024304420967397912\n","Reducing lra to ...: 0.022094928152179918\n","0.022094928152179918\n","NMI: 0.7319118331022821\n","Reducing lra to ...: 0.02008629832016356\n","0.02008629832016356\n","Reducing lra to ...: 0.01826027120014869\n","0.01826027120014869\n","NMI: 0.7313681341463633\n","Reducing lra to ...: 0.01660024654558972\n","0.01660024654558972\n","Reducing lra to ...: 0.01509113322326338\n","0.01509113322326338\n","NMI: 0.7353307842193124\n","Reducing lra to ...: 0.013719212021148525\n","0.013719212021148525\n","Reducing lra to ...: 0.01247201092831684\n","0.01247201092831684\n","NMI: 0.7335828288490795\n","Reducing lra to ...: 0.011338191753015309\n","0.011338191753015309\n","Reducing lra to ...: 0.010307447048195735\n","0.010307447048195735\n","NMI: 0.7391423796228742\n","Reducing lra to ...: 0.009370406407450666\n","0.009370406407450666\n","Reducing lra to ...: 0.008518551279500606\n","0.008518551279500606\n","NMI: 0.7402060886710863\n","Reducing lra to ...: 0.007744137526818732\n","0.007744137526818732\n","Reducing lra to ...: 0.0070401250243806645\n","0.0070401250243806645\n","NMI: 0.7420482859628902\n","Reducing lra to ...: 0.006400113658527876\n","0.006400113658527876\n","Reducing lra to ...: 0.0058182851441162505\n","0.0058182851441162505\n","NMI: 0.7391566560227111\n","Reducing lra to ...: 0.005289350131014773\n","0.005289350131014773\n","NMI: 0.7434370134552114\n","Reducing lra to ...: 0.004808500119104339\n","0.004808500119104339\n","NMI: 0.7433778684411909\n","Reducing lra to ...: 0.004371363744640307\n","0.004371363744640307\n","NMI: 0.7432639424518644\n","Reducing lra to ...: 0.003973967040582098\n","0.003973967040582098\n","NMI: 0.7437315980039393\n","Reducing lra to ...: 0.0036126973096200885\n","0.0036126973096200885\n","NMI: 0.7434082470189146\n","Reducing lra to ...: 0.0032842702814728075\n","0.0032842702814728075\n","NMI: 0.7442050542220183\n","NMI: 0.7431709192422724\n","Reducing lra to ...: 0.00298570025588437\n","0.00298570025588437\n","NMI: 0.7443365569358475\n","NMI: 0.7432855423391046\n","NMI: 0.7431555660148859\n","Reducing lra to ...: 0.0027142729598948817\n","0.0027142729598948817\n","NMI: 0.7433303677788486\n","NMI: 0.7440530267754333\n","NMI: 0.7439929798734759\n","NMI: 0.744262542992173\n","NMI: 0.7449345064063762\n","NMI: 0.7446105814614896\n","NMI: 0.7447991101027904\n","NMI: 0.7459122644211943\n","NMI: 0.7461272278678912\n","Reducing lra to ...: 0.0024675208726317103\n","0.0024675208726317103\n","NMI: 0.745208507404502\n","NMI: 0.7460758711754077\n","NMI: 0.7464331973728915\n","NMI: 0.7462108109971533\n","NMI: 0.7462053292811504\n","NMI: 0.745884006167821\n","NMI: 0.7455243805163981\n","NMI: 0.745223802560396\n","NMI: 0.7451403869239993\n","NMI: 0.7448796007535802\n","NMI: 0.7453989350554519\n","NMI: 0.7438579513160031\n","NMI: 0.7443893702466575\n","NMI: 0.7439672534106281\n","NMI: 0.7442492370888671\n","Continued fraction accuracy: [[73.78]\n"," [75.16]\n"," [79.48]\n"," [79.3 ]\n"," [81.87]\n"," [81.9 ]\n"," [81.12]\n"," [81.97]\n"," [82.21]\n"," [81.69]\n"," [82.84]\n"," [82.43]\n"," [83.39]\n"," [83.56]\n"," [83.73]\n"," [82.82]\n"," [83.97]\n"," [83.95]\n"," [83.89]\n"," [83.9 ]\n"," [83.89]\n"," [83.95]\n"," [83.96]\n"," [83.96]\n"," [83.91]\n"," [83.91]\n"," [83.96]\n"," [84.01]\n"," [83.99]\n"," [84.02]\n"," [84.11]\n"," [84.07]\n"," [84.1 ]\n"," [84.18]\n"," [84.21]\n"," [84.15]\n"," [84.2 ]\n"," [84.24]\n"," [84.21]\n"," [84.22]\n"," [84.2 ]\n"," [84.21]\n"," [84.18]\n"," [84.19]\n"," [84.16]\n"," [84.2 ]\n"," [84.11]\n"," [84.15]\n"," [84.12]\n"," [84.14]]\n","Vanilla logistic regression accuracy: [[62.4 ]\n"," [67.22]\n"," [66.71]\n"," [63.11]\n"," [62.83]\n"," [60.56]\n"," [59.41]\n"," [62.12]\n"," [66.01]\n"," [69.6 ]\n"," [73.  ]\n"," [74.7 ]\n"," [77.09]\n"," [60.73]\n"," [73.35]\n"," [71.98]\n"," [68.48]\n"," [61.99]\n"," [68.59]\n"," [66.98]\n"," [78.53]\n"," [69.09]\n"," [72.76]\n"," [72.2 ]\n"," [71.24]\n"," [69.2 ]\n"," [64.55]\n"," [72.08]\n"," [73.6 ]\n"," [69.72]\n"," [73.82]\n"," [73.51]\n"," [61.46]\n"," [69.52]\n"," [66.09]\n"," [75.98]\n"," [72.39]\n"," [70.89]\n"," [76.46]\n"," [72.98]\n"," [65.75]\n"," [74.07]\n"," [72.34]\n"," [72.41]\n"," [80.47]\n"," [67.64]\n"," [77.92]\n"," [74.33]\n"," [73.95]\n"," [63.77]]\n","0.0024675208726317103 [[0.00755102]\n"," [0.03225863]\n"," [0.00947666]\n"," [0.01075749]\n"," [0.00458351]\n"," [1.8685981 ]\n"," [0.01091596]\n"," [0.03328122]\n"," [0.03951603]\n"," [0.01153326]] [[ 0.65727923]\n"," [-3.03824113]\n"," [ 0.53554357]\n"," [ 0.89325898]\n"," [ 0.54567533]\n"," [12.46471987]\n"," [-0.46475429]\n"," [-1.96188132]\n"," [ 2.21463526]\n"," [ 1.6647341 ]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"HQRGxyYZ3jVb"},"execution_count":null,"outputs":[]}]}