facenet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from nbutil import imshow_multi\n",
    "import os\n",
    "from PIL import Image\n",
    "import urllib, cStringIO\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "MODE = 'TRAIN' # TRAIN/EVAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "attr_file = '../data/celeba/list_attr_celeba.txt'\n",
    "keys = None\n",
    "attributes_by_image = {}\n",
    "for i, line in enumerate(open(attr_file)):\n",
    "    if i == 1:\n",
    "        keys = line.split()\n",
    "    elif i > 1:\n",
    "        image = os.path.join('../data/celeba/img_align_celeba', line.split()[0])\n",
    "        values = line.split()[1:]\n",
    "        attributes_by_image[image] = {attr: val == '1' for attr, val in zip(keys, values)}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# print keys\n",
    "image_names = attributes_by_image.keys()\n",
    "attr_vector = np.zeros((len(attributes_by_image), len(keys)))\n",
    "for i, image_name in enumerate(image_names):\n",
    "    attrs = attributes_by_image[image_name]\n",
    "    attr_vector[i] = [(1 if attrs[key] else 0) for key in keys]\n",
    "\n",
    "from collections import defaultdict\n",
    "key_value_pair_counts = defaultdict(int)\n",
    "for attrs in attributes_by_image.itervalues():\n",
    "    for pair in attrs.iteritems():\n",
    "        key_value_pair_counts[pair] += 1\n",
    "n_images = float(len(image_names))\n",
    "\n",
    "rarity_vector = np.zeros((len(attributes_by_image), len(keys)))\n",
    "for i, image_name in enumerate(image_names):\n",
    "    attrs = attributes_by_image[image_name]\n",
    "    rarity_vector[i] = [n_images / key_value_pair_counts[(key, attrs[key])] for key in keys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# filename_queue = tf.train.string_input_producer(image_names, num_epochs=None)\n",
    "# attr_queue = tf.train.input_producer(tf.constant(attr_vector), num_epochs=None)\n",
    "\n",
    "def create_qs(train):\n",
    "    filename_tensor = tf.convert_to_tensor(image_names, dtype=tf.string)\n",
    "    attr_tensor = tf.convert_to_tensor(attr_vector, dtype=tf.float32)\n",
    "    rarity_tensor = tf.convert_to_tensor(rarity_vector, dtype=tf.float32)\n",
    "    filename_q, attr_q, rarity_q = tf.train.slice_input_producer([filename_tensor, attr_tensor, rarity_tensor], num_epochs=None, shuffle=True)\n",
    "\n",
    "    # reader = tf.WholeFileReader()\n",
    "    # filename, image_data = reader.read(filename_q)\n",
    "    image_255 = tf.image.decode_jpeg(tf.read_file(filename_q))\n",
    "    image = tf.reshape(tf.cast(image_255, tf.float32) / 255.0, [218, 178, 3]) # images are 178x218\n",
    "\n",
    "\n",
    "    def resize_image(image):\n",
    "        image = tf.random_crop(image, [192, 160, 3])\n",
    "        return image\n",
    "    #     image = tf.image.resize_bilinear(img_reshaped, [192, 160])\n",
    "    #     return tf.reshape(image, [192, 160, 3])\n",
    "    image = resize_image(image)\n",
    "\n",
    "    def distort_image(image):\n",
    "        noise_amt = tf.abs(tf.random_normal([], stddev=0.2))\n",
    "        distorted_image = image + tf.random_uniform([192, 160, 3], maxval=noise_amt)\n",
    "        distorted_image = tf.image.random_flip_left_right(distorted_image)\n",
    "        distorted_image = tf.image.random_brightness(distorted_image, max_delta=0.3)\n",
    "        distorted_image = tf.image.random_contrast(distorted_image, lower=0.6, upper=1.6)\n",
    "        # distorted_image = tf.image.per_image_standardization(distorted_image)\n",
    "        # distorted_image = tf.clip_by_value(distorted_image, 0, 1)\n",
    "        return distorted_image\n",
    "\n",
    "    image = distort_image(image) if train else image\n",
    "\n",
    "    if train:\n",
    "        images_batch, attrs_batch, rarity_batch = tf.train.shuffle_batch([image, attr_q, rarity_q], batch_size=BATCH_SIZE, capacity=1000, min_after_dequeue=500)\n",
    "    else:\n",
    "        images_batch, attrs_batch, rarity_batch = tf.train.batch([image, attr_q, rarity_q], batch_size=BATCH_SIZE, allow_smaller_final_batch=True)\n",
    "    return images_batch, attrs_batch, rarity_batch\n",
    "\n",
    "images_batch, attrs_batch, rarity_batch = create_qs(True)\n",
    "# images_batch_eval, attrs_batch_eval = create_qs(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Thread(Thread-4, started daemon 139726081345280)>,\n",
       " <Thread(Thread-5, started daemon 139726072952576)>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session = tf.Session()\n",
    "init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "session.run(init_op)\n",
    "tf.train.start_queue_runners(sess=session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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OkmXjucBxIceeKmPuxSZOk0s4rOQy8I61LLS9S82EmxCENCZziIDhZ74nG+MZ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3u4ueM+FgUfotC7hYFzutFnTfHGX728GIjzfLiK8AuJ6PReMkNF1HI92rhAqbAFKz9BOJIk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\nSZIknSb+C8PJgz+IxYxXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15357b5f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "images_, attrs_, rarity_ = session.run([images_batch, attrs_batch, rarity_batch])\n",
    "\n",
    "imshow_multi(images_[:5])\n",
    "# for k, val in zip(keys, attrs_[0]):\n",
    "#     print k, val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dropout_keep_prob = tf.placeholder_with_default(tf.constant(1.0), [], name='dropout_keep_prob')        \n",
    "from tensorflow.contrib.layers.python.layers import batch_norm\n",
    "\n",
    "keyed_vars = {}\n",
    "def join_keys(key1, key2):\n",
    "    if key1 is None or key2 is None:\n",
    "        return None\n",
    "    else:\n",
    "        return key1 + '.' + key2\n",
    "\n",
    "def weight_var(shape, stddev=0.1, mean=0.0, weight_decay=0, name=None, key=None):\n",
    "    initial = tf.truncated_normal(shape, mean=mean, stddev=stddev)\n",
    "    v = tf.Variable(initial, name=name)\n",
    "    if weight_decay > 0:\n",
    "        l2 = tf.nn.l2_loss(v) * weight_decay\n",
    "        tf.add_to_collection('losses', l2)\n",
    "    if key: keyed_vars[key] = v\n",
    "    return v\n",
    "\n",
    "def create_fc(input, out_size, relu=True, key=None):\n",
    "    # input_dropped = tf.nn.dropout(input, dropout_keep_prob)\n",
    "    in_size = input.get_shape()[-1].value\n",
    "    w = weight_var([in_size, out_size], name='w', key=join_keys(key, 'w'))\n",
    "    b = weight_var([out_size], mean=0.1, name='b', key=join_keys(key, 'b'))\n",
    "    x = tf.matmul(input, w)\n",
    "    return tf.nn.relu(x + b) if relu else x + b\n",
    "\n",
    "def create_conv(input, out_channels, patch_size=5, stride=1, dropout=False, key=None):\n",
    "    in_channels = input.get_shape()[-1].value\n",
    "    w = weight_var([patch_size, patch_size, in_channels, out_channels], name='w', key=join_keys(key, 'w'))\n",
    "    b = weight_var([out_channels], stddev=0, name='b', mean=0.1, key=join_keys(key, 'b'))\n",
    "    conv = tf.nn.conv2d(input, w, strides=[1,stride,stride,1], padding='SAME')\n",
    "    activation = tf.nn.relu(conv + b)\n",
    "    if dropout: activation = create_dropout(activation)\n",
    "    return activation\n",
    "\n",
    "def create_max_pool(inputs, ksize=2, stride=2):\n",
    "    return tf.nn.max_pool(inputs, ksize=[1, ksize, ksize, 1], strides=[1, stride, stride, 1], padding='SAME')\n",
    "\n",
    "def create_avg_pool(inputs, ksize=2, stride=2):\n",
    "    return tf.nn.avg_pool(inputs, ksize=[1, ksize, ksize, 1], strides=[1, stride, stride, 1], padding='SAME')\n",
    "\n",
    "def create_batch_norm(inputs):\n",
    "    return batch_norm(inputs, is_training=(MODE == 'TRAIN'), updates_collections=None)\n",
    "\n",
    "def create_dropout(inputs):\n",
    "    return tf.nn.dropout(inputs, dropout_keep_prob)\n",
    "\n",
    "def flatten_tensor(t):\n",
    "    shape = [s.value for s in t.get_shape()]\n",
    "    flat_size = 1\n",
    "    for x in shape[1:]:\n",
    "        flat_size *= x\n",
    "    return tf.reshape(t, [-1, flat_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def conv_model(image):\n",
    "    # image size: 192, 160, 3\n",
    "    conv1 = create_conv(image, 16, patch_size=3, batch_norm=True, key='conv1')\n",
    "    pool2 = create_max_pool(conv1, ksize=4, stride=4) # [48, 40, 16]\n",
    "    \n",
    "    conv3 = create_conv(pool2, 32, patch_size=3, key='conv3')\n",
    "    pool4 = create_max_pool(conv3) # [24, 40, 32]\n",
    "    \n",
    "    conv5 = create_conv(pool4, 32, patch_size=3, batch_norm=True, key='conv5')\n",
    "    pool6 = create_max_pool(conv5) # [12, 20, 32]\n",
    "    \n",
    "    conv7 = create_conv(pool6, 64, patch_size=3, key='conv7')\n",
    "    pool8 = create_max_pool(conv7) # [6, 10, 64]\n",
    "    \n",
    "    conv9 = create_conv(pool8, 32, patch_size=3, key='conv9')\n",
    "    pool10 = create_max_pool(conv9) # [3, 10, 32]\n",
    "    \n",
    "    dropout11 = create_dropout(pool10)\n",
    "    \n",
    "    fc12 = create_fc(flatten_tensor(dropout11), 64, key='fc12')\n",
    "    fc13 = create_fc(fc12, 128, key='fc13')\n",
    "    return create_fc(fc13, len(keys), relu=False, key='fc14')\n",
    "\n",
    "def big_conv_model(image):\n",
    "    # size: 192, 160, 3\n",
    "    for i, size in enumerate([16, 16, 32, 64, 64]):\n",
    "        key = 'conv' + str(i)\n",
    "        image = create_conv(image, size, patch_size=3, batch_norm=True, key=key)\n",
    "        image = create_avg_pool(image)\n",
    "    # now we have a 5x64 image:\n",
    "    image = create_dropout(image)\n",
    "    image = create_fc(flatten_tensor(image), 256, key='fc1')\n",
    "    image = create_fc(image, 256, key='fc2')\n",
    "    return create_fc(image, len(keys), relu=False, key='final')\n",
    "\n",
    "def facenet1(image):\n",
    "    # size: 192, 160, 3\n",
    "    # 64 -> 32 -> 16 -> 8 -> 4\n",
    "    image = tf.image.resize_bilinear(image, [64, 64])\n",
    "    for i, size in enumerate([16, 32, 64, 64]):\n",
    "        key = 'conv' + str(i)\n",
    "        image = create_conv(image, size, patch_size=3, key=key)\n",
    "        image = create_batch_norm(image)\n",
    "        image = create_avg_pool(image)\n",
    "    # now we have a 4x4x64 image:\n",
    "    image = create_dropout(image)\n",
    "    image = create_fc(flatten_tensor(image), 256, key='fc1')\n",
    "    image = create_fc(image, 256, key='fc2')\n",
    "    return create_fc(image, len(keys), relu=False, key='fc3')\n",
    "\n",
    "image_input = tf.placeholder_with_default(images_batch, [None, 192, 160, 3], name='image_input')\n",
    "actual_attrs = tf.placeholder_with_default(attrs_batch, [None, len(keys)], name='actual_attrs')\n",
    "rarities = tf.placeholder_with_default(rarity_batch, [None, len(keys)], name='rarities')\n",
    "\n",
    "attr_logits = facenet1(image_input)\n",
    "attr_predictions = tf.sigmoid(attr_logits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loss_by_attr = tf.nn.sigmoid_cross_entropy_with_logits(attr_logits, actual_attrs) * rarities\n",
    "loss = tf.reduce_sum(loss_by_attr)\n",
    "\n",
    "learn_rate = tf.placeholder_with_default(tf.constant(0.001), [], name='learn_rate')\n",
    "optimizer = tf.train.AdamOptimizer(learn_rate)\n",
    "global_step = global_step = tf.contrib.framework.get_or_create_global_step()\n",
    "train_step = optimizer.minimize(loss, global_step=global_step)\n",
    "\n",
    "correct_vals = tf.cast(tf.equal(tf.round(actual_attrs), tf.round(attr_predictions)), tf.float32)\n",
    "# correct_counts = tf.reduce_sum(tf.cast(tf.equal(tf.round(actual_attrs), tf.round(attr_predictions)), tf.float32), axis=0)\n",
    "scaled_correctness = correct_vals * rarities\n",
    "scaled_correctness_avg = tf.reduce_mean(scaled_correctness, axis=0) / tf.reduce_mean(rarities, axis=0)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restored from checkpoint models/face8/model.ckpt-6500\n"
     ]
    }
   ],
   "source": [
    "save_path = 'models/face8'\n",
    "\n",
    "init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\n",
    "session.run(init_op)\n",
    "\n",
    "saver = None\n",
    "if save_path:\n",
    "    if not os.path.exists(save_path):\n",
    "        os.mkdir(save_path)\n",
    "    saver = tf.train.Saver()\n",
    "    ckpt = tf.train.get_checkpoint_state(save_path)\n",
    "    if ckpt and ckpt.model_checkpoint_path:\n",
    "        saver.restore(session, ckpt.model_checkpoint_path)\n",
    "        print 'Restored from checkpoint', ckpt.model_checkpoint_path\n",
    "    else:\n",
    "        print 'Did not restore from checkpoint'\n",
    "else:\n",
    "    print 'Will not save progress'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 8301, loss: 2692.42655888, approx epochs: 2.62224394\n",
      "{'Sideburns': 0.78173071, 'Black_Hair': 0.75992817, 'Wavy_Hair': 0.6808728, 'Young': 0.67538744, 'Heavy_Makeup': 0.84682274, 'Blond_Hair': 0.86974746, 'Attractive': 0.71972358, '5_o_Clock_Shadow': 0.79024863, 'Wearing_Necktie': 0.81152815, 'Blurry': 0.64789569, 'Double_Chin': 0.78349078, 'Brown_Hair': 0.59408349, 'Mouth_Slightly_Open': 0.55986434, 'Goatee': 0.79669678, 'Bald': 0.87071013, 'Pointy_Nose': 0.6349625, 'Gray_Hair': 0.82032639, 'Pale_Skin': 0.70351642, 'Arched_Eyebrows': 0.73182422, 'Wearing_Hat': 0.84908336, 'Receding_Hairline': 0.71231997, 'Straight_Hair': 0.56051183, 'Big_Nose': 0.70043308, 'Rosy_Cheeks': 0.75905335, 'Oval_Face': 0.59301847, 'Bangs': 0.73311281, 'Male': 0.88266057, 'Mustache': 0.7912364, 'High_Cheekbones': 0.62530029, 'No_Beard': 0.81397855, 'Eyeglasses': 0.76298094, 'Bags_Under_Eyes': 0.68193656, 'Wearing_Necklace': 0.70245194, 'Wearing_Lipstick': 0.87785977, 'Big_Lips': 0.59402728, 'Narrow_Eyes': 0.53588462, 'Chubby': 0.73849136, 'Smiling': 0.62897873, 'Bushy_Eyebrows': 0.69022685, 'Wearing_Earrings': 0.70032835}\n",
      "Step: 8351, loss: 2706.96364258, approx epochs: 2.63803868726\n",
      "Step: 8401, loss: 2688.47839355, approx epochs: 2.65383343452\n",
      "{'Sideburns': 0.80244559, 'Black_Hair': 0.73811507, 'Wavy_Hair': 0.69252801, 'Young': 0.69569182, 'Heavy_Makeup': 0.84043658, 'Blond_Hair': 0.8622117, 'Attractive': 0.72273034, '5_o_Clock_Shadow': 0.7879101, 'Wearing_Necktie': 0.79611498, 'Blurry': 0.58145839, 'Double_Chin': 0.76087904, 'Brown_Hair': 0.60584646, 'Mouth_Slightly_Open': 0.57160753, 'Goatee': 0.7945295, 'Bald': 0.88152313, 'Pointy_Nose': 0.62090617, 'Gray_Hair': 0.82722163, 'Pale_Skin': 0.69763213, 'Arched_Eyebrows': 0.72850549, 'Wearing_Hat': 0.82155246, 'Receding_Hairline': 0.7138949, 'Straight_Hair': 0.56926727, 'Big_Nose': 0.69786924, 'Rosy_Cheeks': 0.75061387, 'Oval_Face': 0.57345414, 'Bangs': 0.70867676, 'Male': 0.88950926, 'Mustache': 0.78649873, 'High_Cheekbones': 0.6459024, 'No_Beard': 0.81183469, 'Eyeglasses': 0.76680464, 'Bags_Under_Eyes': 0.67863393, 'Wearing_Necklace': 0.6815418, 'Wearing_Lipstick': 0.86695886, 'Big_Lips': 0.60049444, 'Narrow_Eyes': 0.56072438, 'Chubby': 0.7605179, 'Smiling': 0.62333661, 'Bushy_Eyebrows': 0.6747458, 'Wearing_Earrings': 0.6986078}\n",
      "Step: 8451, loss: 2673.76974609, approx epochs: 2.66962818178\n",
      "Saved\n",
      "Step: 8501, loss: 2626.84212891, approx epochs: 2.68542292904\n",
      "{'Sideburns': 0.78303981, 'Black_Hair': 0.75047052, 'Wavy_Hair': 0.69713312, 'Young': 0.68512565, 'Heavy_Makeup': 0.8509717, 'Blond_Hair': 0.84800088, 'Attractive': 0.71501726, '5_o_Clock_Shadow': 0.78197598, 'Wearing_Necktie': 0.81786668, 'Blurry': 0.6376555, 'Double_Chin': 0.7618469, 'Brown_Hair': 0.6194008, 'Mouth_Slightly_Open': 0.56430542, 'Goatee': 0.80969542, 'Bald': 0.86975592, 'Pointy_Nose': 0.62549752, 'Gray_Hair': 0.84754235, 'Pale_Skin': 0.73984891, 'Arched_Eyebrows': 0.72971076, 'Wearing_Hat': 0.86314118, 'Receding_Hairline': 0.72623169, 'Straight_Hair': 0.55629772, 'Big_Nose': 0.70752507, 'Rosy_Cheeks': 0.7565552, 'Oval_Face': 0.57896298, 'Bangs': 0.71412301, 'Male': 0.88808531, 'Mustache': 0.8006956, 'High_Cheekbones': 0.6438812, 'No_Beard': 0.8096354, 'Eyeglasses': 0.76895303, 'Bags_Under_Eyes': 0.66912073, 'Wearing_Necklace': 0.69009393, 'Wearing_Lipstick': 0.87513876, 'Big_Lips': 0.59424329, 'Narrow_Eyes': 0.52753627, 'Chubby': 0.75132036, 'Smiling': 0.62990129, 'Bushy_Eyebrows': 0.67963737, 'Wearing_Earrings': 0.68386859}\n",
      "Step: 8551, loss: 2656.68692383, approx epochs: 2.7012176763\n",
      "Step: 8601, loss: 2688.40778809, approx epochs: 2.71701242356\n",
      "{'Sideburns': 0.78324282, 'Black_Hair': 0.72122222, 'Wavy_Hair': 0.68797994, 'Young': 0.68503487, 'Heavy_Makeup': 0.82931405, 'Blond_Hair': 0.849316, 'Attractive': 0.71648306, '5_o_Clock_Shadow': 0.77447772, 'Wearing_Necktie': 0.78928518, 'Blurry': 0.58747584, 'Double_Chin': 0.76832062, 'Brown_Hair': 0.61191255, 'Mouth_Slightly_Open': 0.56750304, 'Goatee': 0.78802544, 'Bald': 0.82931197, 'Pointy_Nose': 0.6206668, 'Gray_Hair': 0.85356313, 'Pale_Skin': 0.75708449, 'Arched_Eyebrows': 0.69841433, 'Wearing_Hat': 0.83336121, 'Receding_Hairline': 0.72841626, 'Straight_Hair': 0.56060815, 'Big_Nose': 0.69344795, 'Rosy_Cheeks': 0.75551814, 'Oval_Face': 0.58228701, 'Bangs': 0.71139693, 'Male': 0.8812995, 'Mustache': 0.81837392, 'High_Cheekbones': 0.63262898, 'No_Beard': 0.80745035, 'Eyeglasses': 0.7340771, 'Bags_Under_Eyes': 0.67108732, 'Wearing_Necklace': 0.70974767, 'Wearing_Lipstick': 0.85883439, 'Big_Lips': 0.58689874, 'Narrow_Eyes': 0.53502923, 'Chubby': 0.751149, 'Smiling': 0.62193084, 'Bushy_Eyebrows': 0.66227418, 'Wearing_Earrings': 0.68196428}\n",
      "Step: 8651, loss: 2630.82275391, approx epochs: 2.73280717082\n",
      "Step: 8701, loss: 2679.38907227, approx epochs: 2.74860191807\n",
      "{'Sideburns': 0.79668027, 'Black_Hair': 0.74243104, 'Wavy_Hair': 0.68543696, 'Young': 0.69755965, 'Heavy_Makeup': 0.84242177, 'Blond_Hair': 0.86756742, 'Attractive': 0.7124505, '5_o_Clock_Shadow': 0.77654356, 'Wearing_Necktie': 0.81726515, 'Blurry': 0.59775448, 'Double_Chin': 0.73994452, 'Brown_Hair': 0.61933261, 'Mouth_Slightly_Open': 0.5751335, 'Goatee': 0.78779101, 'Bald': 0.89323598, 'Pointy_Nose': 0.62872767, 'Gray_Hair': 0.81688762, 'Pale_Skin': 0.76032901, 'Arched_Eyebrows': 0.71864873, 'Wearing_Hat': 0.86605012, 'Receding_Hairline': 0.75665075, 'Straight_Hair': 0.5504424, 'Big_Nose': 0.69641554, 'Rosy_Cheeks': 0.76011878, 'Oval_Face': 0.57888681, 'Bangs': 0.71852928, 'Male': 0.88341975, 'Mustache': 0.79075074, 'High_Cheekbones': 0.63884956, 'No_Beard': 0.8105098, 'Eyeglasses': 0.76507819, 'Bags_Under_Eyes': 0.65332848, 'Wearing_Necklace': 0.66701669, 'Wearing_Lipstick': 0.86839128, 'Big_Lips': 0.59752178, 'Narrow_Eyes': 0.53053045, 'Chubby': 0.75232947, 'Smiling': 0.62878996, 'Bushy_Eyebrows': 0.66943622, 'Wearing_Earrings': 0.69429255}\n",
      "Step: 8751, loss: 2669.67641113, approx epochs: 2.76439666533\n",
      "Step: 8801, loss: 2698.95039063, approx epochs: 2.78019141259\n",
      "{'Sideburns': 0.77617264, 'Black_Hair': 0.75304312, 'Wavy_Hair': 0.67908269, 'Young': 0.6923967, 'Heavy_Makeup': 0.84395862, 'Blond_Hair': 0.86802888, 'Attractive': 0.73806542, '5_o_Clock_Shadow': 0.77164304, 'Wearing_Necktie': 0.81373113, 'Blurry': 0.5963518, 'Double_Chin': 0.73966199, 'Brown_Hair': 0.60439545, 'Mouth_Slightly_Open': 0.57384503, 'Goatee': 0.8109929, 'Bald': 0.85891175, 'Pointy_Nose': 0.63025355, 'Gray_Hair': 0.84454894, 'Pale_Skin': 0.72330183, 'Arched_Eyebrows': 0.72732133, 'Wearing_Hat': 0.84550738, 'Receding_Hairline': 0.71427161, 'Straight_Hair': 0.54616743, 'Big_Nose': 0.70712489, 'Rosy_Cheeks': 0.74534249, 'Oval_Face': 0.56968683, 'Bangs': 0.73389834, 'Male': 0.88582063, 'Mustache': 0.79898101, 'High_Cheekbones': 0.64331573, 'No_Beard': 0.80330878, 'Eyeglasses': 0.76524025, 'Bags_Under_Eyes': 0.66430247, 'Wearing_Necklace': 0.66705012, 'Wearing_Lipstick': 0.86604452, 'Big_Lips': 0.60456371, 'Narrow_Eyes': 0.54437155, 'Chubby': 0.74942797, 'Smiling': 0.62576926, 'Bushy_Eyebrows': 0.67511088, 'Wearing_Earrings': 0.68170816}\n",
      "Step: 8851, loss: 2683.80663574, approx epochs: 2.79598615985\n",
      "Step: 8901, loss: 2651.73063965, approx epochs: 2.81178090711\n",
      "{'Sideburns': 0.79628909, 'Black_Hair': 0.74237764, 'Wavy_Hair': 0.69544894, 'Young': 0.70541185, 'Heavy_Makeup': 0.84843183, 'Blond_Hair': 0.87232763, 'Attractive': 0.74720031, '5_o_Clock_Shadow': 0.76846296, 'Wearing_Necktie': 0.78965271, 'Blurry': 0.63928801, 'Double_Chin': 0.76322442, 'Brown_Hair': 0.62627208, 'Mouth_Slightly_Open': 0.58298868, 'Goatee': 0.80804855, 'Bald': 0.8625918, 'Pointy_Nose': 0.6120432, 'Gray_Hair': 0.8504405, 'Pale_Skin': 0.7372824, 'Arched_Eyebrows': 0.71510345, 'Wearing_Hat': 0.84584475, 'Receding_Hairline': 0.71964043, 'Straight_Hair': 0.5714556, 'Big_Nose': 0.67911643, 'Rosy_Cheeks': 0.76715529, 'Oval_Face': 0.60633159, 'Bangs': 0.70851761, 'Male': 0.89345306, 'Mustache': 0.79181886, 'High_Cheekbones': 0.65450072, 'No_Beard': 0.80752379, 'Eyeglasses': 0.75182825, 'Bags_Under_Eyes': 0.68078595, 'Wearing_Necklace': 0.67175603, 'Wearing_Lipstick': 0.87145209, 'Big_Lips': 0.58231992, 'Narrow_Eyes': 0.52552086, 'Chubby': 0.75731033, 'Smiling': 0.63271594, 'Bushy_Eyebrows': 0.68003881, 'Wearing_Earrings': 0.69671607}\n",
      "Step: 8951, loss: 2673.95586426, approx epochs: 2.82757565437\n",
      "Saved\n",
      "Step: 9001, loss: 2682.52388184, approx epochs: 2.84337040163\n",
      "{'Sideburns': 0.80151153, 'Black_Hair': 0.7405861, 'Wavy_Hair': 0.67617995, 'Young': 0.67603409, 'Heavy_Makeup': 0.83499253, 'Blond_Hair': 0.86998767, 'Attractive': 0.72991759, '5_o_Clock_Shadow': 0.77444512, 'Wearing_Necktie': 0.79515952, 'Blurry': 0.60262132, 'Double_Chin': 0.73469019, 'Brown_Hair': 0.61078447, 'Mouth_Slightly_Open': 0.57638037, 'Goatee': 0.80331093, 'Bald': 0.85671067, 'Pointy_Nose': 0.63199133, 'Gray_Hair': 0.81924385, 'Pale_Skin': 0.74392021, 'Arched_Eyebrows': 0.73156065, 'Wearing_Hat': 0.84895986, 'Receding_Hairline': 0.71533424, 'Straight_Hair': 0.55286735, 'Big_Nose': 0.67860574, 'Rosy_Cheeks': 0.74146789, 'Oval_Face': 0.57730365, 'Bangs': 0.71738029, 'Male': 0.88004112, 'Mustache': 0.77680963, 'High_Cheekbones': 0.64698911, 'No_Beard': 0.81285638, 'Eyeglasses': 0.76176071, 'Bags_Under_Eyes': 0.66359133, 'Wearing_Necklace': 0.68686128, 'Wearing_Lipstick': 0.86538064, 'Big_Lips': 0.59329522, 'Narrow_Eyes': 0.52304381, 'Chubby': 0.73626828, 'Smiling': 0.63836282, 'Bushy_Eyebrows': 0.67695832, 'Wearing_Earrings': 0.68317747}\n",
      "Step: 9051, loss: 2675.2799707, approx epochs: 2.85916514889\n",
      "Step: 9101, loss: 2683.35320801, approx epochs: 2.87495989615\n",
      "{'Sideburns': 0.78675592, 'Black_Hair': 0.75316536, 'Wavy_Hair': 0.6833539, 'Young': 0.70804924, 'Heavy_Makeup': 0.83743805, 'Blond_Hair': 0.86744863, 'Attractive': 0.72305512, '5_o_Clock_Shadow': 0.76373309, 'Wearing_Necktie': 0.7974512, 'Blurry': 0.56893504, 'Double_Chin': 0.77838606, 'Brown_Hair': 0.6216085, 'Mouth_Slightly_Open': 0.57877481, 'Goatee': 0.80009508, 'Bald': 0.87555295, 'Pointy_Nose': 0.63853079, 'Gray_Hair': 0.83980805, 'Pale_Skin': 0.73519033, 'Arched_Eyebrows': 0.7100485, 'Wearing_Hat': 0.84717345, 'Receding_Hairline': 0.74193108, 'Straight_Hair': 0.56589502, 'Big_Nose': 0.70182669, 'Rosy_Cheeks': 0.74855596, 'Oval_Face': 0.59986567, 'Bangs': 0.72271311, 'Male': 0.87038285, 'Mustache': 0.77317619, 'High_Cheekbones': 0.64533907, 'No_Beard': 0.79165673, 'Eyeglasses': 0.74689013, 'Bags_Under_Eyes': 0.67567724, 'Wearing_Necklace': 0.67066228, 'Wearing_Lipstick': 0.86120629, 'Big_Lips': 0.57869565, 'Narrow_Eyes': 0.51328695, 'Chubby': 0.73514104, 'Smiling': 0.6423474, 'Bushy_Eyebrows': 0.68534833, 'Wearing_Earrings': 0.69962037}\n",
      "Step: 9151, loss: 2667.20833008, approx epochs: 2.89075464341\n",
      "Step: 9201, loss: 2644.3428125, approx epochs: 2.90654939067\n",
      "{'Sideburns': 0.79839402, 'Black_Hair': 0.74772608, 'Wavy_Hair': 0.69000888, 'Young': 0.69645303, 'Heavy_Makeup': 0.83825421, 'Blond_Hair': 0.88137543, 'Attractive': 0.72592199, '5_o_Clock_Shadow': 0.77844751, 'Wearing_Necktie': 0.80987966, 'Blurry': 0.56626385, 'Double_Chin': 0.76810348, 'Brown_Hair': 0.62423509, 'Mouth_Slightly_Open': 0.57336789, 'Goatee': 0.80443734, 'Bald': 0.87234378, 'Pointy_Nose': 0.614362, 'Gray_Hair': 0.84906721, 'Pale_Skin': 0.73199761, 'Arched_Eyebrows': 0.7037974, 'Wearing_Hat': 0.87517488, 'Receding_Hairline': 0.71053833, 'Straight_Hair': 0.56943887, 'Big_Nose': 0.70334756, 'Rosy_Cheeks': 0.75928545, 'Oval_Face': 0.58502996, 'Bangs': 0.72400576, 'Male': 0.88167578, 'Mustache': 0.79509735, 'High_Cheekbones': 0.62635088, 'No_Beard': 0.81338966, 'Eyeglasses': 0.74585283, 'Bags_Under_Eyes': 0.68077815, 'Wearing_Necklace': 0.67618638, 'Wearing_Lipstick': 0.8626225, 'Big_Lips': 0.59245008, 'Narrow_Eyes': 0.52780467, 'Chubby': 0.75068098, 'Smiling': 0.62667811, 'Bushy_Eyebrows': 0.68314481, 'Wearing_Earrings': 0.66937006}\n",
      "Step: 9251, loss: 2654.96577148, approx epochs: 2.92234413793\n",
      "Step: 9301, loss: 2656.55819336, approx epochs: 2.93813888519\n",
      "{'Sideburns': 0.80195117, 'Black_Hair': 0.72813582, 'Wavy_Hair': 0.70024753, 'Young': 0.70770162, 'Heavy_Makeup': 0.82774794, 'Blond_Hair': 0.87513769, 'Attractive': 0.72264421, '5_o_Clock_Shadow': 0.77099729, 'Wearing_Necktie': 0.81141138, 'Blurry': 0.55623788, 'Double_Chin': 0.75691307, 'Brown_Hair': 0.61424595, 'Mouth_Slightly_Open': 0.59435958, 'Goatee': 0.82920593, 'Bald': 0.87345016, 'Pointy_Nose': 0.62760782, 'Gray_Hair': 0.83890557, 'Pale_Skin': 0.74852568, 'Arched_Eyebrows': 0.7071209, 'Wearing_Hat': 0.85445344, 'Receding_Hairline': 0.73460692, 'Straight_Hair': 0.58629239, 'Big_Nose': 0.69407535, 'Rosy_Cheeks': 0.74546111, 'Oval_Face': 0.565723, 'Bangs': 0.74558711, 'Male': 0.88255966, 'Mustache': 0.82672369, 'High_Cheekbones': 0.64093626, 'No_Beard': 0.82005304, 'Eyeglasses': 0.76495069, 'Bags_Under_Eyes': 0.66601586, 'Wearing_Necklace': 0.68163568, 'Wearing_Lipstick': 0.85201561, 'Big_Lips': 0.58131546, 'Narrow_Eyes': 0.50632328, 'Chubby': 0.74032813, 'Smiling': 0.6315546, 'Bushy_Eyebrows': 0.69962943, 'Wearing_Earrings': 0.67342752}\n",
      "Step: 9351, loss: 2676.18151367, approx epochs: 2.95393363245\n",
      "Step: 9401, loss: 2678.65355957, approx epochs: 2.96972837971\n",
      "{'Sideburns': 0.80335253, 'Black_Hair': 0.75324738, 'Wavy_Hair': 0.69306666, 'Young': 0.67758489, 'Heavy_Makeup': 0.83520842, 'Blond_Hair': 0.86995095, 'Attractive': 0.71683782, '5_o_Clock_Shadow': 0.76557696, 'Wearing_Necktie': 0.81759381, 'Blurry': 0.59574568, 'Double_Chin': 0.79154348, 'Brown_Hair': 0.61933589, 'Mouth_Slightly_Open': 0.57603633, 'Goatee': 0.82032275, 'Bald': 0.86123985, 'Pointy_Nose': 0.63253343, 'Gray_Hair': 0.82411164, 'Pale_Skin': 0.72880399, 'Arched_Eyebrows': 0.69589424, 'Wearing_Hat': 0.84942091, 'Receding_Hairline': 0.72835928, 'Straight_Hair': 0.57558817, 'Big_Nose': 0.71337944, 'Rosy_Cheeks': 0.74220115, 'Oval_Face': 0.58845139, 'Bangs': 0.72012007, 'Male': 0.8831594, 'Mustache': 0.80830419, 'High_Cheekbones': 0.6410045, 'No_Beard': 0.8124634, 'Eyeglasses': 0.74290842, 'Bags_Under_Eyes': 0.69964319, 'Wearing_Necklace': 0.67529178, 'Wearing_Lipstick': 0.85730231, 'Big_Lips': 0.59261221, 'Narrow_Eyes': 0.54811555, 'Chubby': 0.76864862, 'Smiling': 0.62257737, 'Bushy_Eyebrows': 0.68593687, 'Wearing_Earrings': 0.67578566}\n",
      "Step: 9451, loss: 2657.7218457, approx epochs: 2.98552312697\n",
      "Saved\n",
      "Step: 9501, loss: 2635.65616211, approx epochs: 3.00131787422\n",
      "{'Sideburns': 0.80274618, 'Black_Hair': 0.75032961, 'Wavy_Hair': 0.6864804, 'Young': 0.69734412, 'Heavy_Makeup': 0.84700429, 'Blond_Hair': 0.87159294, 'Attractive': 0.71799368, '5_o_Clock_Shadow': 0.77909338, 'Wearing_Necktie': 0.80595785, 'Blurry': 0.62706476, 'Double_Chin': 0.77837372, 'Brown_Hair': 0.60733229, 'Mouth_Slightly_Open': 0.58599931, 'Goatee': 0.82254618, 'Bald': 0.85444045, 'Pointy_Nose': 0.63087213, 'Gray_Hair': 0.82507151, 'Pale_Skin': 0.72451395, 'Arched_Eyebrows': 0.72208488, 'Wearing_Hat': 0.84512442, 'Receding_Hairline': 0.72601539, 'Straight_Hair': 0.55794936, 'Big_Nose': 0.71327078, 'Rosy_Cheeks': 0.76642615, 'Oval_Face': 0.58003473, 'Bangs': 0.70648992, 'Male': 0.8837797, 'Mustache': 0.81725538, 'High_Cheekbones': 0.64850539, 'No_Beard': 0.81731135, 'Eyeglasses': 0.7814067, 'Bags_Under_Eyes': 0.68435365, 'Wearing_Necklace': 0.65804118, 'Wearing_Lipstick': 0.86901593, 'Big_Lips': 0.5866546, 'Narrow_Eyes': 0.5335356, 'Chubby': 0.77794802, 'Smiling': 0.63269347, 'Bushy_Eyebrows': 0.69099325, 'Wearing_Earrings': 0.68114549}\n",
      "Step: 9551, loss: 2613.9277832, approx epochs: 3.01711262148\n",
      "Step: 9601, loss: 2664.30928223, approx epochs: 3.03290736874\n",
      "{'Sideburns': 0.80287063, 'Black_Hair': 0.7364769, 'Wavy_Hair': 0.68214154, 'Young': 0.69577461, 'Heavy_Makeup': 0.83093184, 'Blond_Hair': 0.86970258, 'Attractive': 0.71840209, '5_o_Clock_Shadow': 0.75685471, 'Wearing_Necktie': 0.78749496, 'Blurry': 0.5702064, 'Double_Chin': 0.7560991, 'Brown_Hair': 0.61852491, 'Mouth_Slightly_Open': 0.5560692, 'Goatee': 0.80738258, 'Bald': 0.87256736, 'Pointy_Nose': 0.63259864, 'Gray_Hair': 0.82869589, 'Pale_Skin': 0.74424827, 'Arched_Eyebrows': 0.72161102, 'Wearing_Hat': 0.82828677, 'Receding_Hairline': 0.73527956, 'Straight_Hair': 0.53703976, 'Big_Nose': 0.70003748, 'Rosy_Cheeks': 0.71869826, 'Oval_Face': 0.58822179, 'Bangs': 0.73397708, 'Male': 0.88372684, 'Mustache': 0.81630051, 'High_Cheekbones': 0.63040209, 'No_Beard': 0.80415338, 'Eyeglasses': 0.772192, 'Bags_Under_Eyes': 0.66941714, 'Wearing_Necklace': 0.66456604, 'Wearing_Lipstick': 0.85465032, 'Big_Lips': 0.58688229, 'Narrow_Eyes': 0.54135054, 'Chubby': 0.74679863, 'Smiling': 0.62664312, 'Bushy_Eyebrows': 0.66355085, 'Wearing_Earrings': 0.68026805}\n",
      "Step: 9651, loss: 2683.96475098, approx epochs: 3.048702116\n",
      "Step: 9701, loss: 2633.66667969, approx epochs: 3.06449686326\n",
      "{'Sideburns': 0.79820997, 'Black_Hair': 0.75507897, 'Wavy_Hair': 0.68597817, 'Young': 0.68751526, 'Heavy_Makeup': 0.84216005, 'Blond_Hair': 0.87219, 'Attractive': 0.72951072, '5_o_Clock_Shadow': 0.77956557, 'Wearing_Necktie': 0.81092584, 'Blurry': 0.60883087, 'Double_Chin': 0.78251451, 'Brown_Hair': 0.62184989, 'Mouth_Slightly_Open': 0.57747567, 'Goatee': 0.79223692, 'Bald': 0.87166613, 'Pointy_Nose': 0.6288904, 'Gray_Hair': 0.83703363, 'Pale_Skin': 0.75257796, 'Arched_Eyebrows': 0.73308641, 'Wearing_Hat': 0.87036633, 'Receding_Hairline': 0.73924387, 'Straight_Hair': 0.56986964, 'Big_Nose': 0.68253577, 'Rosy_Cheeks': 0.76119077, 'Oval_Face': 0.59111887, 'Bangs': 0.7184906, 'Male': 0.89046156, 'Mustache': 0.80243868, 'High_Cheekbones': 0.65047401, 'No_Beard': 0.81554711, 'Eyeglasses': 0.77132028, 'Bags_Under_Eyes': 0.68976521, 'Wearing_Necklace': 0.69211251, 'Wearing_Lipstick': 0.86684817, 'Big_Lips': 0.60196412, 'Narrow_Eyes': 0.53383684, 'Chubby': 0.77052468, 'Smiling': 0.62929839, 'Bushy_Eyebrows': 0.67714888, 'Wearing_Earrings': 0.69871068}\n",
      "Step: 9751, loss: 2640.26425293, approx epochs: 3.08029161052\n",
      "Step: 9801, loss: 2647.45580566, approx epochs: 3.09608635778\n",
      "{'Sideburns': 0.80766052, 'Black_Hair': 0.74202639, 'Wavy_Hair': 0.6849525, 'Young': 0.68561327, 'Heavy_Makeup': 0.84807879, 'Blond_Hair': 0.86964422, 'Attractive': 0.72288132, '5_o_Clock_Shadow': 0.76051462, 'Wearing_Necktie': 0.80250978, 'Blurry': 0.63272089, 'Double_Chin': 0.75651854, 'Brown_Hair': 0.62324327, 'Mouth_Slightly_Open': 0.58479196, 'Goatee': 0.79571074, 'Bald': 0.83977807, 'Pointy_Nose': 0.63784564, 'Gray_Hair': 0.83993208, 'Pale_Skin': 0.7662034, 'Arched_Eyebrows': 0.73119956, 'Wearing_Hat': 0.86047864, 'Receding_Hairline': 0.77597392, 'Straight_Hair': 0.5436945, 'Big_Nose': 0.68675405, 'Rosy_Cheeks': 0.75020379, 'Oval_Face': 0.5774442, 'Bangs': 0.70720291, 'Male': 0.89097023, 'Mustache': 0.79081321, 'High_Cheekbones': 0.65138364, 'No_Beard': 0.8086704, 'Eyeglasses': 0.76582301, 'Bags_Under_Eyes': 0.67244804, 'Wearing_Necklace': 0.69600749, 'Wearing_Lipstick': 0.87618113, 'Big_Lips': 0.59119439, 'Narrow_Eyes': 0.54180038, 'Chubby': 0.76831174, 'Smiling': 0.64670515, 'Bushy_Eyebrows': 0.69905615, 'Wearing_Earrings': 0.67985535}\n",
      "Step: 9851, loss: 2648.62226074, approx epochs: 3.11188110504\n",
      "Step: 9901, loss: 2676.77033203, approx epochs: 3.1276758523\n",
      "{'Sideburns': 0.82490176, 'Black_Hair': 0.74110407, 'Wavy_Hair': 0.68486899, 'Young': 0.67810357, 'Heavy_Makeup': 0.84118301, 'Blond_Hair': 0.87014478, 'Attractive': 0.71425515, '5_o_Clock_Shadow': 0.77008873, 'Wearing_Necktie': 0.79527617, 'Blurry': 0.65494478, 'Double_Chin': 0.74002814, 'Brown_Hair': 0.62359792, 'Mouth_Slightly_Open': 0.58091909, 'Goatee': 0.82269371, 'Bald': 0.88475931, 'Pointy_Nose': 0.63952863, 'Gray_Hair': 0.82293397, 'Pale_Skin': 0.75824648, 'Arched_Eyebrows': 0.71970439, 'Wearing_Hat': 0.85908157, 'Receding_Hairline': 0.74250567, 'Straight_Hair': 0.5633533, 'Big_Nose': 0.6867038, 'Rosy_Cheeks': 0.7657907, 'Oval_Face': 0.58258045, 'Bangs': 0.73228264, 'Male': 0.87431657, 'Mustache': 0.80717242, 'High_Cheekbones': 0.64370871, 'No_Beard': 0.81425405, 'Eyeglasses': 0.74172664, 'Bags_Under_Eyes': 0.6791929, 'Wearing_Necklace': 0.68265718, 'Wearing_Lipstick': 0.85947138, 'Big_Lips': 0.59226525, 'Narrow_Eyes': 0.53232443, 'Chubby': 0.73228037, 'Smiling': 0.64084101, 'Bushy_Eyebrows': 0.69793016, 'Wearing_Earrings': 0.68890435}\n",
      "Step: 9951, loss: 2652.43383301, approx epochs: 3.14347059956\n",
      "Saved\n",
      "Step: 10001, loss: 2646.70523438, approx epochs: 3.15926534682\n",
      "{'Sideburns': 0.78912663, 'Black_Hair': 0.7709921, 'Wavy_Hair': 0.67777151, 'Young': 0.6919992, 'Heavy_Makeup': 0.83659053, 'Blond_Hair': 0.86986601, 'Attractive': 0.71962076, '5_o_Clock_Shadow': 0.78234118, 'Wearing_Necktie': 0.82515091, 'Blurry': 0.60624838, 'Double_Chin': 0.70626581, 'Brown_Hair': 0.62015116, 'Mouth_Slightly_Open': 0.57766014, 'Goatee': 0.77955931, 'Bald': 0.87104684, 'Pointy_Nose': 0.6226536, 'Gray_Hair': 0.85656166, 'Pale_Skin': 0.76606822, 'Arched_Eyebrows': 0.72249502, 'Wearing_Hat': 0.86848336, 'Receding_Hairline': 0.74290985, 'Straight_Hair': 0.56386125, 'Big_Nose': 0.68440473, 'Rosy_Cheeks': 0.74886018, 'Oval_Face': 0.60022426, 'Bangs': 0.73614222, 'Male': 0.88809621, 'Mustache': 0.78994352, 'High_Cheekbones': 0.65932959, 'No_Beard': 0.81790602, 'Eyeglasses': 0.76942271, 'Bags_Under_Eyes': 0.69055808, 'Wearing_Necklace': 0.67378461, 'Wearing_Lipstick': 0.86829108, 'Big_Lips': 0.59399259, 'Narrow_Eyes': 0.50643229, 'Chubby': 0.73904109, 'Smiling': 0.63341069, 'Bushy_Eyebrows': 0.69568962, 'Wearing_Earrings': 0.69497442}\n",
      "Step: 10051, loss: 2672.12299316, approx epochs: 3.17506009408\n",
      "Step: 10101, loss: 2615.18077148, approx epochs: 3.19085484134\n",
      "{'Sideburns': 0.80835795, 'Black_Hair': 0.74234658, 'Wavy_Hair': 0.70058578, 'Young': 0.68489873, 'Heavy_Makeup': 0.85417128, 'Blond_Hair': 0.86950064, 'Attractive': 0.72625661, '5_o_Clock_Shadow': 0.78575647, 'Wearing_Necktie': 0.82418251, 'Blurry': 0.67008924, 'Double_Chin': 0.78227717, 'Brown_Hair': 0.60700583, 'Mouth_Slightly_Open': 0.60754049, 'Goatee': 0.81367213, 'Bald': 0.87160331, 'Pointy_Nose': 0.63786876, 'Gray_Hair': 0.83553511, 'Pale_Skin': 0.76794034, 'Arched_Eyebrows': 0.72681695, 'Wearing_Hat': 0.84857333, 'Receding_Hairline': 0.76239312, 'Straight_Hair': 0.5379141, 'Big_Nose': 0.69861841, 'Rosy_Cheeks': 0.76112264, 'Oval_Face': 0.59774679, 'Bangs': 0.7310248, 'Male': 0.89461845, 'Mustache': 0.81370163, 'High_Cheekbones': 0.65643138, 'No_Beard': 0.81773096, 'Eyeglasses': 0.77023917, 'Bags_Under_Eyes': 0.66714799, 'Wearing_Necklace': 0.68515295, 'Wearing_Lipstick': 0.87205744, 'Big_Lips': 0.59918112, 'Narrow_Eyes': 0.52399653, 'Chubby': 0.78345734, 'Smiling': 0.65060431, 'Bushy_Eyebrows': 0.6898554, 'Wearing_Earrings': 0.67504698}\n",
      "Step: 10151, loss: 2635.28823242, approx epochs: 3.2066495886\n",
      "Step: 10201, loss: 2625.98838867, approx epochs: 3.22244433586\n",
      "{'Sideburns': 0.81870764, 'Black_Hair': 0.7385214, 'Wavy_Hair': 0.69031501, 'Young': 0.69501114, 'Heavy_Makeup': 0.84906888, 'Blond_Hair': 0.87622803, 'Attractive': 0.71889848, '5_o_Clock_Shadow': 0.77522641, 'Wearing_Necktie': 0.78796494, 'Blurry': 0.58409208, 'Double_Chin': 0.74824226, 'Brown_Hair': 0.60933119, 'Mouth_Slightly_Open': 0.57394922, 'Goatee': 0.8196916, 'Bald': 0.88864207, 'Pointy_Nose': 0.61115527, 'Gray_Hair': 0.81180251, 'Pale_Skin': 0.75011504, 'Arched_Eyebrows': 0.70666999, 'Wearing_Hat': 0.84919876, 'Receding_Hairline': 0.74892539, 'Straight_Hair': 0.56331766, 'Big_Nose': 0.70723015, 'Rosy_Cheeks': 0.76430547, 'Oval_Face': 0.60595042, 'Bangs': 0.7297197, 'Male': 0.88396615, 'Mustache': 0.81649995, 'High_Cheekbones': 0.6560213, 'No_Beard': 0.81541026, 'Eyeglasses': 0.77952117, 'Bags_Under_Eyes': 0.67693627, 'Wearing_Necklace': 0.6867758, 'Wearing_Lipstick': 0.86518592, 'Big_Lips': 0.56412166, 'Narrow_Eyes': 0.54993433, 'Chubby': 0.7624166, 'Smiling': 0.6366539, 'Bushy_Eyebrows': 0.66950327, 'Wearing_Earrings': 0.69177276}\n",
      "Step: 10251, loss: 2633.43214355, approx epochs: 3.23823908311\n",
      "Step: 10301, loss: 2620.89169922, approx epochs: 3.25403383037\n",
      "{'Sideburns': 0.82084841, 'Black_Hair': 0.75568742, 'Wavy_Hair': 0.68769699, 'Young': 0.68178868, 'Heavy_Makeup': 0.84665984, 'Blond_Hair': 0.88533098, 'Attractive': 0.73020482, '5_o_Clock_Shadow': 0.78728563, 'Wearing_Necktie': 0.80752563, 'Blurry': 0.61302441, 'Double_Chin': 0.75895292, 'Brown_Hair': 0.60863674, 'Mouth_Slightly_Open': 0.57395875, 'Goatee': 0.80519867, 'Bald': 0.88690346, 'Pointy_Nose': 0.62707299, 'Gray_Hair': 0.82834417, 'Pale_Skin': 0.79887009, 'Arched_Eyebrows': 0.7227186, 'Wearing_Hat': 0.85805923, 'Receding_Hairline': 0.74505746, 'Straight_Hair': 0.56831968, 'Big_Nose': 0.70158297, 'Rosy_Cheeks': 0.72797579, 'Oval_Face': 0.58138663, 'Bangs': 0.73029256, 'Male': 0.89006615, 'Mustache': 0.79824334, 'High_Cheekbones': 0.65812796, 'No_Beard': 0.81384116, 'Eyeglasses': 0.79259038, 'Bags_Under_Eyes': 0.66735804, 'Wearing_Necklace': 0.69260132, 'Wearing_Lipstick': 0.87210131, 'Big_Lips': 0.57182574, 'Narrow_Eyes': 0.52996463, 'Chubby': 0.75268227, 'Smiling': 0.63411051, 'Bushy_Eyebrows': 0.68386352, 'Wearing_Earrings': 0.70275742}\n",
      "Step: 10351, loss: 2641.91376953, approx epochs: 3.26982857763\n"
     ]
    },
    {
     "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-16-36fa210108aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0mstep_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled_correctness_avg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0mrolling_acc_sum\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0macc_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mprev_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_\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/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    764\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    765\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 766\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    767\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    768\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\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/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    962\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 964\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    965\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\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[1;32m   1013\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1014\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1015\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1019\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1020\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\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/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1001\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1002\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1003\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1005\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if MODE == 'TRAIN':\n",
    "    feed = {dropout_keep_prob: 0.5}\n",
    "    prev_losses = []\n",
    "    rolling_acc_sum = 0\n",
    "    \n",
    "    while True:\n",
    "        step_, loss_, _, acc_ = session.run([global_step, loss, train_step, scaled_correctness_avg], feed_dict=feed)\n",
    "        rolling_acc_sum += acc_\n",
    "        prev_losses.append(loss_)\n",
    "        \n",
    "        # update learn rate:\n",
    "        feed[learn_rate] = 0.01\n",
    "        if step_ > 1000:\n",
    "            feed[learn_rate] = 0.0001\n",
    "        # if step_ > 3000:\n",
    "        #     feed[learn_rate] = 0.0001\n",
    "        # if step_ > 10000:\n",
    "        #     feed[learn_rate] = 0.00001\n",
    "        \n",
    "        if step_ % 50 == 1:\n",
    "            avg_loss = sum(prev_losses) / len(prev_losses)\n",
    "            accuracies = rolling_acc_sum / len(prev_losses)\n",
    "            prev_losses = []\n",
    "            rolling_acc_sum = 0\n",
    "            print 'Step: {}, loss: {}, approx epochs: {}'.format(step_, avg_loss, step_ * BATCH_SIZE / float(len(image_names)))\n",
    "            if step_ % 100 == 1 and accuracies is not 0:\n",
    "                print {key: val for key, val in zip(keys, list(accuracies))}\n",
    "        if step_ % 500 == 0 and saver:\n",
    "            saver.save(session, save_path + '/model.ckpt', global_step=step_)\n",
    "            print 'Saved'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "\n",
    "def load_image(url):\n",
    "    file = cStringIO.StringIO(urllib.urlopen(url).read())\n",
    "    return Image.open(file)\n",
    "\n",
    "def detect_face(pil_image):\n",
    "    cvimg = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)\n",
    "    gray = cv2.cvtColor(cvimg, cv2.COLOR_BGR2GRAY)\n",
    "    faces = faceCascade.detectMultiScale(\n",
    "        gray,\n",
    "        scaleFactor=1.1,\n",
    "        minNeighbors=5,\n",
    "        minSize=(30, 30),\n",
    "        flags = cv2.cv.CV_HAAR_SCALE_IMAGE\n",
    "    )\n",
    "    faces = list(faces)\n",
    "    faces.sort(key=lambda(x,y,w,h): w*h, reverse=True)\n",
    "    if len(faces):\n",
    "        return faces[0]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def crop_to_face(pil_image):\n",
    "    target_size = (160, 192)\n",
    "    face = detect_face(pil_image)\n",
    "    if face is not None:\n",
    "        center_x = face[0] + face[2] / 2\n",
    "        center_y = face[1] + face[3] / 2\n",
    "        width = int(round(face[2] / 0.65))\n",
    "        height = width * target_size[1] / target_size[0]\n",
    "        # height = min(pil_image.size[1], height)\n",
    "        x = center_x - width/2\n",
    "        y = center_y - height/2\n",
    "        cropped = pil_image.crop((x, y, x + width, y + height))\n",
    "        return cropped.resize(target_size, Image.ANTIALIAS)\n",
    "    else:\n",
    "        return pil_image\n",
    "\n",
    "def eval_pic(pil_image):\n",
    "    img = np.array(crop_to_face(pil_image))\n",
    "    imshow_multi([img])\n",
    "    normalized = session.run(tf.image.per_image_standardization(img))\n",
    "    predictions_ = session.run(attr_predictions, {image_input: np.array([img])})\n",
    "    for key, pred in zip(keys, predictions_[0]):\n",
    "        print key, round(pred * 10) / 10.0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# if MODE == 'EVAL':\n",
    "#     correct_sum = 0\n",
    "#     # compute baseline accuracy of a model that just predicts the most likely value for each attribute\n",
    "#     correct_sum_baseline = 0\n",
    "#     most_likely = np.round(attr_vector.sum(axis=0, dtype=float) / len(image_names))\n",
    "#     correct_counts_baseline = tf.reduce_sum(tf.cast(tf.equal(actual_attrs, most_likely), 1.0), axis=0)\n",
    "    \n",
    "#     known_total_images = len(image_names)\n",
    "#     stop_at_count = BATCH_SIZE * 300\n",
    "#     images_processed = 0\n",
    "    \n",
    "#     i = 0\n",
    "#     while True:\n",
    "#         correct_, correct_baseline_, preds_ = session.run([correct_counts, correct_counts_baseline, attr_predictions])\n",
    "#         # print preds_\n",
    "#         correct_sum += correct_\n",
    "#         correct_sum_baseline += correct_baseline_\n",
    "#         images_processed += BATCH_SIZE\n",
    "#         # this assumes all batches are BATCH_SIZE (the last one might be smaller) but that's okay\n",
    "#         i += 1\n",
    "#         if i % 20 == 0:\n",
    "#             print str(images_processed / float(stop_at_count) * 100) + '% done'\n",
    "#         if images_processed >= stop_at_count: break\n",
    "    \n",
    "#     for key in keys:\n",
    "#         i = keys.index(key)\n",
    "#         correct = correct_sum[i]\n",
    "#         baseline_correct = correct_sum_baseline[i]\n",
    "#         print key, correct / float(stop_at_count)\n",
    "#         # print ' baseline:', baseline_correct / float(stop_at_count)\n",
    "#         # print ''\n",
    "\n",
    "if MODE == 'EVAL':\n",
    "    if True:\n",
    "        meryl_streep = 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/4b/Meryl_Streep_February_2016.jpg/220px-Meryl_Streep_February_2016.jpg'\n",
    "        obama = 'http://viralcypher.com/wp-content/uploads/2016/11/obama.jpg'\n",
    "        cpax = 'https://news.brown.edu/files/styles/vertical/public/article_images/Paxson72a_3.jpg?itok=aLoYkVWa'\n",
    "        n8 = 'https://avatars3.githubusercontent.com/u/3059249?v=3&s=460'\n",
    "        justin = 'https://scontent.fhan4-1.fna.fbcdn.net/v/t1.0-9/15895316_10207891864551393_3694897809127225461_n.jpg?oh=6946c7c4e072126072ce92bce9dfaa9e&oe=58DBF90E'\n",
    "        beyonce = 'http://factmag-images.s3.amazonaws.com/wp-content/uploads/2013/12/beyonce-121313.jpg'\n",
    "        \n",
    "        url = beyonce\n",
    "        eval_pic(load_image(url))\n",
    "    if False:\n",
    "        images_, predictions_ = session.run([image_input, attr_predictions])\n",
    "        for x in range(1):\n",
    "            img = np.clip(images_[x]/2 + 0.5, 0, 1)\n",
    "            imshow_multi([img])\n",
    "            for key, pred in zip(keys, predictions_[x]):\n",
    "                print key, round(pred * 10) / 10.0\n",
    "    \n",
    "    if False:\n",
    "        correctness_sum = 0\n",
    "        n_batches = 100\n",
    "\n",
    "        for i in xrange(n_batches):\n",
    "            correctness_sum += session.run(scaled_correctness_avg)\n",
    "            if i % 20 == 0:\n",
    "                print str(i / float(n_batches) * 100) + '% done'\n",
    "\n",
    "        for key, score in zip(keys, correctness_sum / n_batches):\n",
    "            print key, score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "out_dir = 'models/facenet-weights'\n",
    "\n",
    "if out_dir:\n",
    "    var_keys = keyed_vars.keys()\n",
    "    var_values = session.run([keyed_vars[k] for k in var_keys])\n",
    "    import struct\n",
    "\n",
    "    for key, val in zip(var_keys, var_values):\n",
    "        name = key + '.' + '_'.join(map(str, val.shape))\n",
    "        weights = val.astype(np.float32)\n",
    "        if key.startswith('conv'):\n",
    "            if len(weights.shape) == 4:\n",
    "                weights = weights.transpose([3,2,0,1])\n",
    "        elif key.startswith('fc'):\n",
    "            weights = weights.transpose()\n",
    "        \n",
    "        bytes = weights.tobytes()\n",
    "        open(out_dir + '/' + name + '.weight', 'w').write(bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batchnorm_vals = defaultdict(lambda: [None,None,None]) # (mean, variance,beta)\n",
    "\n",
    "for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):\n",
    "    if v.name.startswith('BatchNorm'):\n",
    "        # print v.name\n",
    "        params = batchnorm_vals[v.name.split('/')[0]]\n",
    "        val = session.run(v)\n",
    "        # print val.shape\n",
    "        if '/moving_mean' in v.name:\n",
    "            params[0] = val\n",
    "        elif '/moving_variance' in v.name:\n",
    "            params[1] = val\n",
    "        elif 'beta' in v.name:\n",
    "            params[2] = val\n",
    "\n",
    "for key, vals in batchnorm_vals.iteritems():\n",
    "    data = np.array(vals).astype(np.float32).tobytes()\n",
    "    # print len(data)\n",
    "    open(out_dir + '/' + key, 'w').write(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for v in batchnorm_vals['BatchNorm'][1]:\n",
    "    print v\n",
    "print batchnorm_vals.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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