Inversion.ipynb

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     "text": [
      "/usr/local/lib/python2.7/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import numpy as np\n",
    "import os \n",
    "import sys\n",
    "from PIL import Image\n",
    "from sys import stdout\n",
    "import scipy\n",
    "import scipy.misc\n",
    "from pylearn2.datasets.preprocessing import ZCA\n",
    "from pylearn2.expr.preprocessing import global_contrast_normalize\n"
   ]
  },
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from IPython import display"
   ]
  },
  {
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    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape=shape, stddev=0.1)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def mnist_imshow(img):\n",
    "    plt.imshow(img.reshape([28,28]), cmap=\"gray\")\n",
    "    plt.axis('off')\n",
    "\n",
    "def face_imshow(img):\n",
    "    plt.imshow(img.reshape([112,92]), cmap=\"gray\")\n",
    "    plt.axis('off')   \n",
    "    \n",
    "\n",
    "def one_hot_preds(preds):\n",
    "    t = np.argmax(preds, axis=1)\n",
    "    r = np.zeros(preds.shape)\n",
    "    for i in range(t.shape[0]):\n",
    "        r[i,t[i]] = 1\n",
    "    return r\n",
    "\n",
    "def one_hot_class(a):\n",
    "    b = np.zeros((len(a), np.max(a).astype(int)+1), np.float32)\n",
    "    b[np.arange(len(a)), a.astype(int)] = 1\n",
    "    return b\n",
    "\n",
    "def unpack_facedataset(path = '/home/yash/Documents/Attacks/Guillaume-Freisz-project/orl_faces'\n",
    ", sz=None):\n",
    "    \n",
    "    \"\"\"Reads the images in a given folder, resizes images on the fly if size is given.\n",
    "\n",
    "    Args:\n",
    "        path: Path to a folder with subfolders representing the subjects (persons).\n",
    "        sz: A tuple with the size Resizes \n",
    "\n",
    "    Returns:\n",
    "        A list [X,y]\n",
    "\n",
    "            X: The images, which is a Python list of numpy arrays.\n",
    "            y: The corresponding labels (the unique number of the subject, person) in a Python list.\n",
    "    \"\"\"\n",
    "    c = 0\n",
    "    X,y = [], []\n",
    "    for dirname, dirnames, filenames in os.walk(path):\n",
    "        for subdirname in dirnames:\n",
    "            subject_path = os.path.join(dirname, subdirname)\n",
    "            for filename in os.listdir(subject_path):\n",
    "                try:\n",
    "                    im = Image.open(os.path.join(subject_path, filename))\n",
    "                    im = im.convert(\"L\")\n",
    "                    # resize to given size (if given)\n",
    "                    if (sz is not None):\n",
    "                        im = im.resize(sz, Image.ANTIALIAS)\n",
    "                    X.append(np.asarray(im, dtype=np.uint8))\n",
    "                    y.append(c)\n",
    "                except IOError as err:\n",
    "                    print (\"I/O error({0}): {1}\".format(err))\n",
    "                except:\n",
    "                    print (\"Unexpected error:\", sys.exc_info()[0])\n",
    "                    raise\n",
    "            c = c+1\n",
    "    X = (np.array(X).astype(np.float32)/255).reshape(len(X), 92*112)\n",
    "    y = np.array(y).astype(np.float32)\n",
    "    X = X.reshape(40,10,112*92)\n",
    "    y = y.reshape(40,10)\n",
    "\n",
    "    train_x, test_x = X[:,0:7,:], X[:,7:10,:]\n",
    "    train_y, test_y = y[:,0:7], y[:,7:10]\n",
    "    train_x, test_x, train_y, test_y = train_x.reshape(40*7,112*92), test_x.reshape(40*3,112*92), train_y.reshape(40*7), test_y.reshape(40*3)\n",
    "    \n",
    "    return train_x, test_x, one_hot_class(train_y), one_hot_class(test_y)\n",
    "\n",
    "def normalize(img, prep, img_shape):\n",
    "    \n",
    "    img = prep.inverse(img.reshape(1, -1))[0]\n",
    "    img /= np.abs(img).max()\n",
    "    img = np.clip(img, -1., 1.)\n",
    "    img = (img + 1.) / 2.\n",
    "    img = global_contrast_normalize(img.reshape(1, -1) * 255, scale=55.)\n",
    "    img = prep._gpu_matrix_dot(img - prep.mean_, prep.P_)\n",
    "    return img.reshape(img_shape)\n",
    "\n",
    "def post_process(img, prep, img_shape):\n",
    "    # normalize without contrast_normalize and mean_subtract\n",
    "    img = prep.inverse(img.reshape(1, -1))[0]\n",
    "    img /= np.abs(img).max()\n",
    "    img = np.clip(img, -1., 1.)\n",
    "    img = (img + 1.) / 2.\n",
    "    return img.reshape(img_shape)\n"
   ]
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    "class Model:\n",
    "    def __init__(self, x, y_):\n",
    "\n",
    "        in_dim = int(x.get_shape()[1]) # 10304 for Face dataset\n",
    "        out_dim = int(y_.get_shape()[1]) # 40 for Face dataset\n",
    "        self.x = x\n",
    "        # switiching to a simple 2-layer network with relu\n",
    "        W = weight_variable([in_dim,out_dim])\n",
    "        b = bias_variable([out_dim])\n",
    "        self.y = tf.matmul(x,W) + b # output layer\n",
    "        self.probs = tf.nn.softmax(self.y)\n",
    "        self.class_inds = tf.argmax(self.probs,1)\n",
    "        self.cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=self.y))\n",
    "        \n",
    "        class_ind_correct = tf.argmax(y_,1)\n",
    "        self.class_prob = (self.probs[0, tf.cast(class_ind_correct[0],tf.int32)])\n",
    "        self.loss = tf.subtract(tf.constant(1.0), self.class_prob )\n",
    "        \n",
    "        self.grads = tf.gradients(self.cross_entropy, x)\n",
    "        \n",
    "        self.train_step = tf.train.GradientDescentOptimizer(0.1).minimize(self.cross_entropy)\n",
    "        \n",
    "        # performance metrics\n",
    "        correct_prediction = tf.equal(tf.argmax(self.y,1), tf.argmax(y_,1))\n",
    "        self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "        \n",
    "    \n",
    "    def train(self, train_x, train_y, sess, test_x, test_y,num_iters, disp_freq=50):\n",
    "        for i in range(num_iters):\n",
    "            feed_dict = {x: train_x, y_: train_y}\n",
    "            sess.run(self.train_step, feed_dict)\n",
    "            if(i%disp_freq == 0):\n",
    "                train_acc = self.test(train_x, train_y, sess)\n",
    "                test_acc = self.test(test_x, test_y, sess)\n",
    "                stdout.write(\"\\r Train Acc. : %f    Test Acc. : %f\"% (train_acc, test_acc))\n",
    "                stdout.flush()\n",
    "        stdout.write(\"\\n\")\n",
    "            \n",
    "    \n",
    "    def test(self, test_x, test_y, sess):\n",
    "        return (sess.run(self.accuracy, feed_dict={x: test_x, y_: test_y}))\n",
    "    \n",
    "    def invert(self, sess, num_iters, lam, img, pre_process, pred_cutoff= 0.99, disp_freq=1):\n",
    "        \n",
    "        probs = self.preds(img)\n",
    "        class_ind = sess.run(self.class_inds, feed_dict= {x:[img]})[0]\n",
    "        current_X = np.zeros(list(img.shape)[0]).astype(np.float32)\n",
    "        Y = (one_hot_preds(probs)).astype(np.float32)\n",
    "        best_X = np.copy(current_X)\n",
    "        best_loss = 100000.0\n",
    "        prev_losses = [100000.0]*100\n",
    "        \n",
    "        for i in range(num_iters):\n",
    "            feed_dict = {x: [current_X], y_: Y }\n",
    "            der,current_loss = sess.run([self.grads, self.loss], feed_dict)\n",
    "            current_X = np.clip(current_X - lam*(der[0][0]),0.0,1.0)\n",
    "            current_X = normalize(current_X, pre_process, current_X.shape)\n",
    "            probs = self.preds(current_X)[0]\n",
    "            \n",
    "            if current_loss < best_loss:\n",
    "                best_loss = current_loss\n",
    "                best_X = current_X\n",
    "            \n",
    "            if current_loss > 2*max(prev_losses):\n",
    "                print(\"\\n Breaking due to gradient chaos!!\")\n",
    "                break\n",
    "            \n",
    "            if pred_cutoff < probs[class_ind]:\n",
    "                print(\"\\n Above Probability Criteria!: {0}\".format(probs[class_ind]))\n",
    "                break\n",
    "                \n",
    "            if i%disp_freq ==0:\n",
    "#                 plt.close()\n",
    "#                 face_imshow(post_process(current_X, pre_process, current_X.shape))\n",
    "#                 plt.show()\n",
    "                stdout.write(\"\\r Acc: %f and Loss: %f and Best Loss: %f\" % (probs[class_ind], current_loss, best_loss))\n",
    "                stdout.flush()\n",
    "            \n",
    "            \n",
    "        stdout.write(\"\\n\")   \n",
    "        print('Loop Escape.')\n",
    "        \n",
    "        current_preds = self.preds(current_X)\n",
    "        best_preds = self.preds(best_X)\n",
    "        current_X = post_process(current_X, pre_process, current_X.shape)\n",
    "        best_X = post_process(best_X, pre_process, best_X.shape)\n",
    "        return current_X, current_preds, best_X, best_preds\n",
    "\n",
    "    \n",
    "    def preds(self, img):\n",
    "        return sess.run(self.probs, feed_dict= {x:[img]})\n",
    "\n",
    "def perform_inversion(pre_process, images):\n",
    "        for img in images:\n",
    "            face_imshow(img)\n",
    "            plt.title('Image-Class used for inversion.')\n",
    "            plt.show()\n",
    "            print('Predictions: ' + str((model.preds(img))))\n",
    "\n",
    "\n",
    "            inv_img_last, inv_img_last_p, inv_img_best, inv_img_best_p = model.invert(sess, 100, 0.1, img, pre_process = pre_process)\n",
    "\n",
    "            face_imshow(inv_img_best)\n",
    "            plt.title('Best Image after inversion.')\n",
    "            plt.show()\n",
    "            print('Predictions: ' + str(inv_img_best_p))\n",
    "            \n",
    "            face_imshow(inv_img_last)\n",
    "            plt.title('Last Iteration Image after inversion.')\n",
    "            plt.show()\n",
    "            print('Predictions: ' + str(inv_img_last_p))"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing zca of a (280, 10304) matrix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yash/Documents/pylearn2/pylearn2/datasets/preprocessing.py:1202: UserWarning: This ZCA preprocessor class is known to yield very different results on different platforms. If you plan to conduct experiments with this preprocessing on multiple machines, it is probably a good idea to do the preprocessing on a single machine and copy the preprocessed datasets to the others, rather than preprocessing the data independently in each location.\n",
      "  warnings.warn(\"This ZCA preprocessor class is known to yield very \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cov estimate took 4.1276268959 seconds\n",
      "eigh() took 520.845889091 seconds\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y  = unpack_facedataset() # 7:3 ratio for train:test \n",
    "\n",
    "# GCN and ZCA object!! \n",
    "train_x_normalized = global_contrast_normalize(train_x * 255, scale=55.)\n",
    "zca = ZCA()\n",
    "zca.fit(train_x_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Train Acc. : 1.000000    Test Acc. : 0.950000\n"
     ]
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, 112*92])\n",
    "y_ = tf.placeholder(tf.float32, shape=[None, 40])\n",
    "model = Model(x,y_)\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "model.train(train_x, train_y, sess, test_x, test_y, 250)"
   ]
  },
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   "execution_count": 89,
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MiFSBMZBcddxgMpOEZinO39jYGPkSkfyOpoBdkaqO2ke6wnyO8EBqI32ZA3NbW1ub/t+f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\ndCwQ/QfY0bFA/P/60FjhhOy7PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[9.6186727e-01 8.9160650e-04 5.0968683e-04 9.4819465e-05 1.2295325e-04\n",
      "  8.3364255e-04 5.6160969e-04 2.1705457e-03 5.3993566e-04 7.3261093e-05\n",
      "  1.5308143e-04 5.0771022e-03 1.7999168e-04 9.9314326e-05 5.3130597e-04\n",
      "  3.2523337e-03 7.0569926e-04 5.0845225e-03 1.1119379e-04 3.6973000e-04\n",
      "  3.0384228e-05 5.9488579e-05 6.1549807e-05 1.7529193e-04 9.5841923e-04\n",
      "  4.7669946e-05 6.0285610e-04 3.0562726e-03 2.8007791e-05 2.1178227e-04\n",
      "  3.1002008e-03 3.9824471e-04 1.2149358e-03 1.0971896e-03 4.7438322e-03\n",
      "  4.4044751e-05 1.5862417e-05 5.2106014e-04 3.9555840e-04 7.5423077e-06]]\n",
      " Acc: 0.780279 and Loss: 0.219348 and Best Loss: 0.018958\n",
      "Loop Escape.\n"
     ]
    },
    {
     "data": {
      "image/png": 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XXtlQwGvIXJw3Qce5bGZAuysIy3bzXy4/XSFaMh6Xu84Y5YIleluPkcHd0pHp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\nYQbUi1cozIB68QqFGfD/AXRf0IRngvvqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[8.48070323e-01 1.83040145e-04 8.26251402e-04 4.84882062e-03\n",
      "  8.99282563e-03 1.94834005e-02 5.74863880e-05 4.50945646e-03\n",
      "  3.90641345e-03 1.07725944e-04 1.34098425e-03 2.70169287e-04\n",
      "  1.19447976e-03 8.46857112e-03 3.19131614e-05 1.22810248e-04\n",
      "  1.61444885e-04 5.68191404e-04 5.27372351e-03 1.89813133e-02\n",
      "  8.97535938e-06 4.18467716e-05 1.01831474e-05 7.69841427e-05\n",
      "  1.19075715e-03 1.30150956e-03 1.33932326e-05 4.29100401e-05\n",
      "  1.05255560e-04 9.98464134e-03 7.09668850e-04 6.00086823e-05\n",
      "  5.80204511e-03 1.23550213e-04 5.03631728e-03 6.67858403e-03\n",
      "  3.56311910e-02 3.55978729e-03 2.15836242e-03 6.47963025e-05]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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cPXtW0h7jdkVq1X0x3KdXX321pbkmSzNqiNlLVl1uMxk3kRghBsG4fT67LMMY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\nPIpNQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[7.80279458e-01 1.45823555e-03 4.48816834e-04 6.23594550e-03\n",
      "  6.73397072e-03 4.12229635e-02 1.69641891e-04 8.19921610e-04\n",
      "  7.86366221e-03 4.43777331e-04 3.89155728e-04 2.48965737e-03\n",
      "  3.48211703e-04 2.06114165e-02 5.32280933e-03 1.01521355e-03\n",
      "  2.97601626e-04 1.99194602e-03 5.26485220e-03 1.30725615e-02\n",
      "  1.60991494e-05 1.51777256e-03 1.63575096e-05 6.98151707e-05\n",
      "  6.23044383e-04 5.60059038e-04 4.01743251e-04 1.24905375e-03\n",
      "  7.45704339e-04 5.52951880e-02 4.39663767e-04 3.25620029e-04\n",
      "  1.13598322e-02 9.72969574e-04 6.67311251e-03 6.21573487e-03\n",
      "  7.32892146e-03 4.97912196e-03 4.56376234e-03 1.66714410e-04]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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bWlr3saXWJSksZZ3/Zl9mzUatgrvOnfOauxwIsP8QWH9z0dz1/KStPlq6c4v5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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[7.30976753e-05 8.98442268e-01 2.59025884e-03 3.30395487e-05\n",
      "  5.99460350e-03 1.13718306e-04 9.63318889e-05 9.49211972e-05\n",
      "  7.58196547e-05 2.15914403e-03 6.45723892e-04 5.67093142e-04\n",
      "  5.15461026e-04 4.55698668e-04 2.55959196e-04 6.67475513e-04\n",
      "  8.31075304e-05 3.08035146e-06 1.75295339e-04 5.64462447e-04\n",
      "  6.96892515e-02 1.42265882e-04 8.47191259e-04 1.33609474e-02\n",
      "  8.66476548e-05 2.49416298e-05 1.50558917e-04 6.47784327e-05\n",
      "  1.98915077e-04 2.26186057e-06 1.16605515e-04 1.46371473e-04\n",
      "  7.06749197e-05 1.82905016e-04 1.16024530e-05 2.37498491e-04\n",
      "  7.30773900e-04 1.51103653e-04 3.26636909e-05 1.45297789e-04]]\n",
      " Acc: 0.795052 and Loss: 0.206251 and Best Loss: 0.163688\n",
      "Loop Escape.\n"
     ]
    },
    {
     "data": {
      "image/png": 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KE2AWjJdMt+sK8oy5DFKZPromWDrOr5RwtCgmdqaDvk1RYmBASpWhZPd+tJgm5kyhaa07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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[3.64652253e-03 8.36239100e-01 1.85445824e-04 2.91622500e-03\n",
      "  9.92486603e-04 3.89743634e-02 1.36027462e-04 1.40295329e-03\n",
      "  1.47144042e-03 1.95950721e-04 1.28852960e-04 5.34150167e-04\n",
      "  1.82969918e-04 1.21125495e-02 4.73296532e-04 6.52373477e-04\n",
      "  2.41220361e-04 1.32595061e-03 9.44355968e-04 3.69111868e-03\n",
      "  1.32050272e-05 1.10081735e-03 6.00449221e-06 1.31749257e-05\n",
      "  8.41280387e-04 6.58502613e-05 4.41039068e-04 6.99395838e-04\n",
      "  1.39727513e-03 6.62740171e-02 4.59693139e-04 1.54420617e-04\n",
      "  2.03708257e-03 1.02198368e-03 1.97069929e-03 3.98792326e-03\n",
      "  5.28282207e-03 3.81401344e-03 3.85628245e-03 1.15579627e-04]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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esVAzzHdbnx9549GrixUtIp3JYSMZN5EYIQbBuNTYcZZmTlt69dQeMlGGwze8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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[5.1289285e-03 7.9505247e-01 1.8979922e-04 3.1020194e-03 1.4982186e-03\n",
      "  6.0903467e-02 1.6957364e-04 1.8739927e-03 2.2338715e-03 2.1076367e-04\n",
      "  1.3335806e-04 6.9222634e-04 2.5072641e-04 1.9959120e-02 3.5675400e-04\n",
      "  7.7602942e-04 3.4206340e-04 1.4868316e-03 9.6724700e-04 5.2500116e-03\n",
      "  1.8935256e-05 1.8489296e-03 7.1030381e-06 1.6400278e-05 1.0992859e-03\n",
      "  6.6251327e-05 5.9245917e-04 9.7206299e-04 1.9014064e-03 6.2703140e-02\n",
      "  5.5379007e-04 1.5761175e-04 1.4682165e-03 1.4965936e-03 1.9780486e-03\n",
      "  3.2588132e-03 6.2780865e-03 6.1301314e-03 8.7115597e-03 1.6365742e-04]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WQ0uYinECN4pxCUDviNZrHcZf5n7ttdcmSb70pS8lGdsHCNjgGJdBcUlE9EdA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\nRP8BdnTMEP0H2NExQ/QfYEfHDPH/AVaJ81KpPtXYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[4.6542114e-05 9.3005435e-04 9.4548887e-01 9.2451408e-04 2.6781275e-03\n",
      "  6.6528009e-05 5.3296713e-05 2.5483110e-05 1.4801172e-04 3.0880033e-03\n",
      "  2.0058152e-04 1.7511589e-04 1.7207836e-03 3.7492984e-05 1.7085343e-04\n",
      "  1.2367956e-05 1.0934556e-04 1.6237065e-04 1.1359523e-03 3.5630160e-03\n",
      "  4.6552457e-03 5.7963545e-05 5.2498085e-03 4.2975205e-03 4.3081917e-04\n",
      "  1.3371259e-03 5.1834457e-05 6.4448759e-05 7.7291584e-04 4.8395308e-05\n",
      "  9.3220107e-05 3.2604062e-03 8.7872853e-05 6.3347031e-04 1.2803153e-04\n",
      "  3.9542071e-03 1.3232328e-03 1.2181883e-02 1.2157666e-05 6.2222086e-04]]\n",
      " Acc: 0.760800 and Loss: 0.239507 and Best Loss: 0.217075\n",
      "Loop Escape.\n"
     ]
    },
    {
     "data": {
      "image/png": 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EIazghhx37961bhAGgzNAwAX18jiGL2noNTQ8dmUmyNA0n+saNDZpvRu20QUg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\nQmEC1IdXKEyA/w/wmpboy0dquQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[4.69666943e-02 6.69498404e-04 4.56357628e-01 1.76056307e-02\n",
      "  2.92670336e-02 6.45726547e-02 1.84962628e-04 1.51447486e-02\n",
      "  1.41179590e-02 3.36435187e-04 5.60319936e-03 8.98375059e-04\n",
      "  3.73651902e-03 2.55372655e-02 1.11918831e-04 3.85199935e-04\n",
      "  5.53324760e-04 1.71597989e-03 1.95753034e-02 6.21562935e-02\n",
      "  2.88197953e-05 1.60662836e-04 3.28071910e-05 2.12797415e-04\n",
      "  3.88079486e-03 3.89307016e-03 4.34272079e-05 1.45429163e-04\n",
      "  4.09365282e-04 3.42568457e-02 2.50044116e-03 2.46389478e-04\n",
      "  1.99332032e-02 3.85812426e-04 1.72578953e-02 2.02785805e-02\n",
      "  1.11193806e-01 1.17322579e-02 7.66664883e-03 2.44332477e-04]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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aybiJxAgxCMaNilbRe4dyqNFVyYDxn5Z/nn76aUlzbTGjg3w+WdJsZ/9j+t76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\nEPniJhIjRL64icQIkS9uIjFC/D87XbbX7HMofgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[1.1338479e-03 9.4169407e-04 7.6080024e-01 2.1829898e-03 3.1298646e-03\n",
      "  7.9168141e-02 4.3747303e-04 2.3844182e-03 6.3423179e-03 1.6613076e-04\n",
      "  1.5410988e-03 1.9622820e-03 8.6336411e-05 1.6866045e-02 2.4565302e-03\n",
      "  3.0703275e-04 9.4532070e-04 3.1414661e-03 1.6793426e-02 5.0546853e-03\n",
      "  1.0742746e-05 1.4342075e-03 3.8194667e-05 2.2461943e-05 1.2427287e-03\n",
      "  1.9275460e-04 3.5282611e-04 3.4354406e-04 9.3763421e-04 4.7980331e-02\n",
      "  2.5232459e-04 1.7233993e-04 1.2222135e-02 2.3319749e-03 6.0080108e-03\n",
      "  2.6909003e-03 9.0115694e-03 5.9234230e-03 2.8460086e-03 1.4455731e-04]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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WrtcqyeHzD4vOgB0dc8RCMKDL9dlyZh+cGaCWuo54t0R26YVWUisSkBhHpDGS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\nAXZ0zBH9B9jRMUf0H2BHxxzxfwHqWF34IM4LrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions: [[1.2256796e-04 8.5394422e-04 6.0598198e-02 8.9349115e-01 7.2576324e-05\n",
      "  1.8021590e-03 1.0913976e-03 6.8033597e-04 3.0239299e-04 1.6626850e-04\n",
      "  1.0750330e-05 2.0090670e-04 7.8614130e-06 1.3043030e-05 9.5981779e-04\n",
      "  1.2500632e-05 4.9238715e-05 1.1693453e-04 1.8890733e-03 4.4010702e-04\n",
      "  1.3289675e-04 8.0873183e-04 1.7655038e-04 5.9254025e-06 7.1238442e-03\n",
      "  1.2034151e-03 4.7139315e-06 1.4470097e-03 2.8658747e-03 9.7555159e-05\n",
      "  9.1219636e-06 2.8552678e-05 7.0893875e-05 1.2937290e-04 6.1093306e-05\n",
      "  1.2857415e-02 5.0023231e-03 4.9231297e-05 7.4463233e-04 4.2996895e-03]]\n",
      " Acc: 0.826358 and Loss: 0.174841 and Best Loss: 0.053830"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-ac78bcbc324d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mperform_inversion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzca\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_x\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\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[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-86-fdb052baaf16>\u001b[0m in \u001b[0;36mperform_inversion\u001b[0;34m(pre_process, images)\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m             \u001b[0minv_img_last\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv_img_last_p\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv_img_best\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minv_img_best_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_process\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpre_process\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m             \u001b[0mface_imshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv_img_best\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-86-fdb052baaf16>\u001b[0m in \u001b[0;36minvert\u001b[0;34m(self, sess, num_iters, lam, img, pre_process, pred_cutoff, disp_freq)\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcurrent_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0mcurrent_X\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_X\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlam\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mder\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m             \u001b[0mcurrent_X\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpre_process\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m             \u001b[0mprobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurrent_X\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-60-8fb76e7bc712>\u001b[0m in \u001b[0;36mnormalize\u001b[0;34m(img, prep, img_shape)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\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[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yash/Documents/pylearn2/pylearn2/datasets/preprocessing.pyc\u001b[0m in \u001b[0;36minverse\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m   1509\u001b[0m             \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'...done inverting'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1511\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gpu_matrix_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minv_P_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/yash/Documents/pylearn2/pylearn2/datasets/preprocessing.pyc\u001b[0m in \u001b[0;36m_gpu_matrix_dot\u001b[0;34m(matrix_a, matrix_b, matrix_c)\u001b[0m\n\u001b[1;32m   1252\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmatrix_c\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mtheano_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatrix_b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\u001b[0m                 \u001b[0mmatrix_c\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtheano_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatrix_b\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    883\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 884\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    885\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "perform_inversion(zca, test_x[0::3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom sys import stdout\\nfrom time import sleep\\nimport numpy as np\\n\\na = np.array([10.2,.2,24.5,3,4,5,6,7,8,9,10,11])\\nfor i in range(1,10):\\n    stdout.write(\"\\r Hye there %d hi deer %d \" % (a,i))\\n    stdout.flush()\\n    sleep(.25)\\nstdout.write(\"\\n\") # move the cursor to the next line\\n\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "train_x, test_x, train_y, test_y  = unpack_facedataset() # 7:3 ratio for train:test \n",
    "zca = ZCA()\n",
    "zca.fit(train_x[:2])\n",
    "img = train_x[10]\n",
    "img = normalize(img)\n",
    "face_imshow(img,zca,img.shape)\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "from sys import stdout\n",
    "from time import sleep\n",
    "import numpy as np\n",
    "\n",
    "a = np.array([10.2,.2,24.5,3,4,5,6,7,8,9,10,11])\n",
    "for i in range(1,10):\n",
    "    stdout.write(\"\\r Hye there %d hi deer %d \" % (a,i))\n",
    "    stdout.flush()\n",
    "    sleep(.25)\n",
    "stdout.write(\"\\n\") # move the cursor to the next line\n",
    "\n",
    "\"\"\""
   ]
  }
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