えんべっでぃんぐるっくあっぷ

TensorFlow+Test.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import contrib\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "keys = tf.Variable([\"1\", \"2\", \"3\"], dtype=tf.string)\n",
    "values = tf.Variable([1, 2, 3], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = contrib.lookup.HashTable(\n",
    "    contrib.lookup.KeyValueTensorInitializer(keys, values), -1)\n",
    "out = table.lookup(names_tf)\n",
    "with tf.Session() as sess:\n",
    "    table.init.run()\n",
    "    print(out.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## これはめも\n",
    "\n",
    "https://qiita.com/kzmssk/items/ddf2c0f956a5d26e992a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
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    {
     "data": {
      "text/plain": [
       "array([28918,  3294, 28918, 28918, 19501,  4847])"
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     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "mapping_strings = tf.constant(all_words)\n",
    "table = tf.contrib.lookup.index_table_from_tensor(\n",
    "    mapping=mapping_strings, num_oov_buckets=1, default_value=-1)\n",
    "features = tf.constant([\"emerson\", \"feel\", \"and\", \"palmer\", \"deep\", \"popcorn\"])\n",
    "ids = table.lookup(features)\n",
    "tf.tables_initializer().run()\n",
    "\n",
    "ids.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('positive_example.pickle', 'rb') as f:\n",
    "    positive_example = pickle.load(f)\n",
    "with open('all_words.pickle', 'rb') as f:\n",
    "    all_words = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TOTAL_SIZE = len(all_words)\n",
    "DIM = 46\n",
    "BATCH_SIZE = 2\n",
    "np_x = np.random.rand(TOTAL_SIZE, DIM).astype(np.float32)\n",
    "x = tf.Variable(np_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[ 0.33407766  0.66344464  0.13722664  0.57478184  0.48289245  0.72210312\n",
      "   0.47872588  0.71832132  0.22690746  0.9794398   0.23006117  0.77183402\n",
      "   0.76896125  0.72980267  0.32537961  0.40896854  0.60130352  0.78982878\n",
      "   0.03451943  0.72029352  0.63087696  0.63158554  0.25121796  0.94101858\n",
      "   0.46409729  0.34709042  0.22485785  0.78404438  0.33605623  0.79317814\n",
      "   0.04439238  0.22641681  0.4303872   0.34324634  0.06531932  0.80964732\n",
      "   0.97386193  0.42601633  0.73470759  0.18472442  0.78394419  0.3850196\n",
      "   0.20360945  0.81814951  0.34891433  0.97032487]\n",
      " [ 0.36110488  0.25897187  0.23618111  0.97066313  0.38464648  0.70196772\n",
      "   0.99959099  0.47679022  0.05685244  0.98868525  0.33194125  0.50208384\n",
      "   0.75492752  0.96577746  0.40557855  0.46122807  0.76450753  0.0459351\n",
      "   0.275621    0.24587847  0.67910224  0.63183731  0.61231363  0.17298323\n",
      "   0.58105111  0.70024359  0.74448323  0.70820373  0.75346893  0.48200104\n",
      "   0.85238212  0.60653716  0.52646261  0.26115027  0.15900189  0.70963442\n",
      "   0.04609635  0.70944047  0.79803944  0.46148825  0.61928189  0.45201811\n",
      "   0.88260871  0.56540591  0.14678472  0.64773971]]\n"
     ]
    }
   ],
   "source": [
    "np_inds = np.array([0, 3])\n",
    "inds = tf.placeholder(tf.int32, [BATCH_SIZE])\n",
    "\n",
    "h = tf.nn.embedding_lookup(x, inds)\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(sess.run(h, feed_dict={inds: np_inds}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61467636,  0.40071121,  0.99041861, ...,  0.77075809,\n",
       "         0.20176312,  0.79317933],\n",
       "       [ 0.76744157,  0.52253145,  0.53163719, ...,  0.15996242,\n",
       "         0.84362042,  0.72988248],\n",
       "       [ 0.43286699,  0.63465619,  0.28215456, ...,  0.30870688,\n",
       "         0.45839456,  0.45931903],\n",
       "       ..., \n",
       "       [ 0.60267776,  0.55912673,  0.7641086 , ...,  0.53222561,\n",
       "         0.79974645,  0.65310097],\n",
       "       [ 0.87345588,  0.31659752,  0.38947991, ...,  0.60199815,\n",
       "         0.89751315,  0.31559569],\n",
       "       [ 0.56062859,  0.37648842,  0.68439686, ...,  0.83632123,\n",
       "         0.73119318,  0.5668627 ]], dtype=float32)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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