classification-generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run \"RBM.ipynb\"\n",
    "\n",
    "class Classification_Generation(RBM):\n",
    "    # n_k is the number of classes\n",
    "    def __init__(self, n_x, n_h, n_k, n_d=1, W=None, b=None, c=None, k=1, model_file=None):\n",
    "        self.n_x = n_x\n",
    "        self.n_k = n_k\n",
    "        self.n_d = n_d  \n",
    "        # label is represented with n_k*n_d dots, where label i is the i-th group of n_d dots\n",
    "        # np.zeros(n_k*n_d)[i*n_d:+n_d] = 1\n",
    "        super().__init__(n_x+n_k*n_d, n_h, W, b, c, k)\n",
    "        return\n",
    "        \n",
    "    def convert_input(self, X, Label):\n",
    "        n_sample = X.shape[0]\n",
    "        n_k = self.n_k; n_d = self.n_d; n_x = self.n_x\n",
    "        assert X.shape == (n_sample, n_x)\n",
    "\n",
    "        if Label is not None:\n",
    "            Label = Label.flatten()\n",
    "            #attach label to data\n",
    "            Y = np.zeros(shape=(n_sample, n_k))\n",
    "            Index = np.arange(n_sample)\n",
    "            Y[Index, Label] = 1\n",
    "            Y = Y.repeat(n_d, axis=1)\n",
    "        else:\n",
    "            Y = np.zeros(shape=(n_sample, n_k*n_d))\n",
    "\n",
    "        V = np.append(X, Y, axis=1)\n",
    "        assert V.shape == (n_sample, self.n_v)        \n",
    "        return V\n",
    "\n",
    "    def train(self, X, Label, learning=0.01):\n",
    "        V = self.convert_input(X, Label)\n",
    "        self.contrastive_divergence(V, learning)\n",
    "        return\n",
    "        \n",
    "\n",
    "    def classify(self, x):\n",
    "        #set_trace()\n",
    "        x = x.reshape(1,-1)\n",
    "        v = self.convert_input(x, None)\n",
    "        vp, vs = self.reconstruct(v)\n",
    "\n",
    "        n_k = self.n_k; n_d = self.n_d; n_x = self.n_x\n",
    "        result = vp[0, n_x:]\n",
    "        result = result.reshape(n_k, -1).sum(axis=1) # collapse the k*d dots into k sum dots.\n",
    "        pred = np.argmax(result)\n",
    "\n",
    "        return vp[0, :n_x], pred\n",
    "    \n",
    "    def generate(self, label, k_cd, init_w, w):\n",
    "        # this is a very simple generation algorithm with single-layer RBM\n",
    "        n_x = self.n_x; n_k = self.n_k; n_d = self.n_d\n",
    "\n",
    "        classes = np.zeros(n_k)\n",
    "        classes[label] = init_w\n",
    "        classes = classes.repeat(n_d)\n",
    "        \n",
    "        np.random.seed(1234)\n",
    "    #    v = np.append( np.random.binomial(1, 0.5, n_x), classes)\n",
    "    #    v = np.append( np.zeros(n_x), classes)\n",
    "        #v = np.append( np.random.uniform(0, 0.8, n_x), classes)\n",
    "        v = np.append( np.full(n_x, 0.1), classes)\n",
    "        vp = v.reshape(1,-1)\n",
    "\n",
    "        classes = np.zeros(n_k)\n",
    "        classes[label] = w\n",
    "        classes = classes.repeat(n_d)\n",
    "\n",
    "        for i in range(k_cd):\n",
    "            vp, vs = self.reconstruct(vp)\n",
    "            vp[0, n_x:] = classes\n",
    "\n",
    "        return vp[0, :n_x]\n",
    "            \n",
    "    def save_model(self, save_file):\n",
    "        dict = {'n_x':self.n_x, 'n_h':self.n_h, 'n_k':self.n_k, 'n_d':self.n_d, 'W':self.W, 'b':self.b, 'c':self.c}\n",
    "        save_file += \".(\"+ str(self.n_x) + \"+\" + str(self.n_k*self.n_d) + \")x\" + str(self.n_h)\n",
    "        with open(save_file, 'wb') as f:\n",
    "            pickle.dump(dict, f, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "\n",
    "        return\n",
    "\n",
    "    @classmethod\n",
    "    def load_model(cls, load_file):\n",
    "        with open(load_file, 'rb') as f:\n",
    "            m = pickle.load(f)\n",
    "\n",
    "        rbm = cls(m['n_x'], m['n_h'], m['n_k'], m['n_d'], m['W'], m['b'], m['c'])\n",
    "        return rbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# testing of the RBM Classification_Generation code above\n",
    "\n",
    "%run \"mnist.ipynb\"\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import Grid\n",
    "import math\n",
    "\n",
    "class MNIST_RBM_CLASSIFY:\n",
    "    def __init__(self, n_h, n_d=1, load_file=None, folder=\"../convolution-network\"):\n",
    "        self.rbm_done = False\n",
    "        d = 28\n",
    "        n_x = d*d; n_k=10\n",
    "\n",
    "        #set_trace()\n",
    "        if load_file is None:\n",
    "            self.rbm = Classification_Generation(n_x, n_h, n_k, n_d)\n",
    "        else:\n",
    "            self.rbm = Classification_Generation.load_model(load_file)\n",
    "            self.rbm_done = True\n",
    "            \n",
    "        self.train_input = MnistInput(\"train\", folder)\n",
    "        self.test_input = MnistInput(\"test\", folder)\n",
    "\n",
    "        return\n",
    "\n",
    "    def train(self, train_size=-1, n_epoch=100, batch_size=10, learning=0.1):\n",
    "        if self.rbm_done: return\n",
    "        \n",
    "        n_ep = 0\n",
    "        n_sample = 0 \n",
    "        X = []\n",
    "        Y = []\n",
    "        batch_size = batch_size if batch_size > 0 else 100\n",
    "        n_epoch = n_epoch if n_epoch > 0 else 1\n",
    "        startrate = learning\n",
    "        for i in range(n_epoch):\n",
    "            learning = startrate * math.pow(0.1, i//50) \n",
    "            for x, y in self.train_input.read(train_size):\n",
    "                n_sample += 1\n",
    "                X.append(x)\n",
    "                Y.append(y)\n",
    "                if n_sample >= batch_size:\n",
    "                    X = np.array(X).reshape(batch_size, -1) > 30\n",
    "                    X = X * 1 # make bool into number\n",
    "                    self.rbm.train(X, np.array(Y), learning)\n",
    "                    n_sample = 0\n",
    "                    X = []\n",
    "                    Y = []                \n",
    "        \n",
    "        self.rbm.save_model(\"mnist_rbm_classify.epochs\"+str(n_epoch))\n",
    "        return\n",
    "\n",
    "    def classify(self, test_size=-1, output_size=50):\n",
    "        d = 28\n",
    "        n_x = self.rbm.n_x\n",
    "        n_k = self.rbm.n_k\n",
    "        n_total=0; n_correct = 0; n_output = 0\n",
    "        X=[]; Y=[]; Recon=[]; Preds=[]\n",
    "        for x, y in self.test_input.read(test_size):\n",
    "            n_total += 1\n",
    "            reco, pred = self.rbm.classify(x/255)\n",
    "            if y == pred: n_correct += 1 \n",
    "            if n_output < output_size:\n",
    "                Preds.append(pred)\n",
    "                Recon.append(reco)\n",
    "                X.append(x)\n",
    "                Y.append(y)\n",
    "                n_output += 1\n",
    "\n",
    "\n",
    "        accuracy = n_correct/n_total\n",
    "        print(\"Accuracy: {}\".format(accuracy))\n",
    "\n",
    "        # output\n",
    "        ncols = 10\n",
    "        nrows = int(output_size/5)\n",
    "        fig = plt.figure(figsize=(ncols, int(nrows*2)), dpi=100)\n",
    "        grid = Grid(fig, rect=111, nrows_ncols=(nrows,ncols))\n",
    "\n",
    "        for i, ax in enumerate(grid):\n",
    "            j = i//2\n",
    "            if i%2 == 0:\n",
    "                ax.imshow(X[j].reshape(d,d), cmap=mpl.cm.Greys)\n",
    "                ax.set_title(\"Orig: {}\".format(Y[j]), y=0)\n",
    "            else:\n",
    "                ax.imshow(Recon[j].reshape(d,d), cmap=mpl.cm.Greys)\n",
    "                ax.set_title(\"Reco: {}\".format(Preds[j]), y=0)\n",
    "\n",
    "            ax.set_axis_off()\n",
    "\n",
    "        fig.suptitle('Original and reconstructed digits side by side')\n",
    "        fig.tight_layout()\n",
    "        fig.subplots_adjust(top=0.98)    \n",
    "        plt.show()        \n",
    "        return\n",
    "\n",
    "    def generate(self, n_reco=50, init_w=30, w=30):\n",
    "        # 50,30,30 is for mnist_rbm_classify.(784+10)x85.epochs100\n",
    "        digits = []\n",
    "        for i in range( self.rbm.n_k ):\n",
    "            digit = self.rbm.generate(i, n_reco, init_w, w)\n",
    "            digits.append(digit)\n",
    "        \n",
    "        # output\n",
    "        d = 28\n",
    "        ncols = self.rbm.n_k\n",
    "        nrows = 1\n",
    "        fig = plt.figure(figsize=(ncols, int(nrows*2)), dpi=100)\n",
    "        grid = Grid(fig, rect=111, nrows_ncols=(nrows,ncols))\n",
    "\n",
    "        for i, ax in enumerate(grid):\n",
    "            ax.imshow(digits[i].reshape(d,d), cmap=mpl.cm.Greys)\n",
    "            ax.set_title(i)\n",
    "            ax.set_axis_off()\n",
    "\n",
    "        fig.suptitle('Generated digit images from numbers')\n",
    "        fig.tight_layout()\n",
    "        fig.subplots_adjust(top=0.7)    \n",
    "        plt.show()        \n",
    "        return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAADICAYAAAD4I/J0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb9b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist = None\n",
    "if __name__ == \"__main__\" and '__file__' not in globals():\n",
    "    np.seterr(all='raise')\n",
    "    plt.close('all')\n",
    "    mnist = MNIST_RBM_CLASSIFY(None, n_d=2, load_file=\"trained_models/mnist_rbm_classify.epochs10.(784+20)x85\")\n",
    "    #mnist = MNIST_RBM_CLASSIFY(n_h=28*3+1, n_d=2)\n",
    "    mnist.train(train_size=-1, n_epoch=10, batch_size=10, learning=0.1)\n",
    "    mnist.generate(20,50,8)  #mnist_rbm_classify.epochs10.(784+10)x85\n",
    "    #mnist.classify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9166\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAewCAYAAAA83IgSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAR/T/oKnCQdQWihwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168da5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist = None\n",
    "if __name__ == \"__main__\" and '__file__' not in globals():\n",
    "    np.seterr(all='raise')\n",
    "    mnist = MNIST_RBM_CLASSIFY(None, load_file=\"trained_models/mnist_rbm_classify.(784+10)x561.epochs100\")\n",
    "    #mnist = MNIST_RBM_CLASSIFY(n_h=28*20+1)\n",
    "    mnist.train(train_size=10000, n_epoch=1, batch_size=10, learning=0.1)\n",
    "    plt.close('all')\n",
    "    #mnist.generate(2,1,1)\n",
    "    mnist.classify()"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}