tensorexample.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.client.session.InteractiveSession at 0x7f4e1c57ae80>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_history = np.load('history_predict.npz')['valid_final_result']\n",
    "test_history = np.load('history_predict.npz')['test_final_result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True]\n",
      "(array([  5,   6,   7,   8,  21,  23,  24,  39,  44,  46,  56,  60,  61,\n",
      "        62,  64,  70,  74,  99, 102, 108, 109, 110, 115, 126, 127, 129,\n",
      "       134, 135, 147, 149, 160, 161, 164, 166, 169]),)\n",
      "64\n",
      "84\n"
     ]
    }
   ],
   "source": [
    "print(test_history[:,0])\n",
    "print(np.where(np.all(test_history,axis=0)))\n",
    "print(np.where(np))\n",
    "for i in range(test_history.shape[0]):\n",
    "    print(np.sum(test_history[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"MatMul:0\", shape=(2, 4, 4), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant(np.arange(-100, 200, dtype=np.float32),\n",
    "              shape=[32, 14, 20])\n",
    "b = tf.constant(np.random.randint(0,2,size=[32,14,20],dtype=np.int32).astype(np.float32))\n",
    "#mask = tf.constant(np.random.randint(0,2,size=[2,4],dtype=np.int32).astype(np.float32))\n",
    "sequence1 = tf.constant(np.arange(-32,0, dtype=np.float32),shape=[32])\n",
    "sequence2 = tf.constant(np.arange(-100, 100, dtype=np.int32),shape=[4,32,600])\n",
    "sequence3 = tf.constant(np.arange(-100, 100, dtype=np.int32),shape=[5,32,600])\n",
    "mask = tf.constant(np.array([[1,1,1,0],[1,1,0,0]],dtype=np.float32))\n",
    "c = tf.constant(np.random.rand(2,4,3).astype(np.float32))#shape after attention\n",
    "masks=tf.stack([mask]*3,-1)\n",
    "padding = tf.fill(tf.shape(masks), float(-1e8))\n",
    "attention = tf.where(tf.equal(masks, 0.), padding, c)\n",
    "print(tf.matmul(c,tf.transpose(c,[0,2,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-100.  -99.  -98. ...  -83.  -82.  -81.]\n",
      "  [ -80.  -79.  -78. ...  -63.  -62.  -61.]\n",
      "  [ -60.  -59.  -58. ...  -43.  -42.  -41.]\n",
      "  ...\n",
      "  [ 120.  121.  122. ...  137.  138.  139.]\n",
      "  [ 140.  141.  142. ...  157.  158.  159.]\n",
      "  [ 160.  161.  162. ...  177.  178.  179.]]\n",
      "\n",
      " [[ 180.  181.  182. ...  197.  198.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]]\n",
      "[[[-100.  -99.  -98. ...  -83.  -82.  -81.]\n",
      "  [ -80.  -79.  -78. ...  -63.  -62.  -61.]\n",
      "  [ -60.  -59.  -58. ...  -43.  -42.  -41.]\n",
      "  ...\n",
      "  [ 120.  121.  122. ...  137.  138.  139.]\n",
      "  [ 140.  141.  142. ...  157.  158.  159.]\n",
      "  [ 160.  161.  162. ...  177.  178.  179.]]\n",
      "\n",
      " [[ 180.  181.  182. ...  197.  198.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]\n",
      "\n",
      " [[ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  ...\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]\n",
      "  [ 199.  199.  199. ...  199.  199.  199.]]]\n"
     ]
    }
   ],
   "source": [
    "#print(a.eval())\n",
    "b=tf.reshape(tf.transpose(a,[1,0,2]),[32*14,20])\n",
    "c=tf.reshape(a,[32*14,20])\n",
    "#print(b.eval())\n",
    "#print(c.eval())\n",
    "print(tf.transpose(tf.reshape(b,[14,32,20]),[1,0,2]).eval())#huan hui lai bi xu he huan zhi qian xing zhuang yi yang\n",
    "print(tf.reshape(c,[32,14,20]).eval())\n",
    "#print(tf.transpose(tf.reshape(c,[32,14,20]),[1,0,2]).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1. 1. 1. ... 0. 0. 1.]\n",
      "  [1. 0. 1. ... 0. 0. 0.]\n",
      "  [0. 0. 1. ... 1. 0. 0.]\n",
      "  ...\n",
      "  [1. 0. 1. ... 1. 0. 0.]\n",
      "  [0. 1. 0. ... 0. 1. 0.]\n",
      "  [0. 0. 0. ... 1. 0. 1.]]\n",
      "\n",
      " [[0. 1. 1. ... 1. 0. 1.]\n",
      "  [1. 0. 0. ... 0. 0. 0.]\n",
      "  [1. 0. 0. ... 0. 0. 0.]\n",
      "  ...\n",
      "  [0. 0. 0. ... 1. 0. 0.]\n",
      "  [1. 1. 0. ... 1. 1. 1.]\n",
      "  [0. 0. 0. ... 1. 1. 1.]]\n",
      "\n",
      " [[0. 1. 1. ... 1. 0. 1.]\n",
      "  [1. 1. 0. ... 0. 1. 0.]\n",
      "  [0. 0. 0. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [0. 0. 1. ... 0. 0. 1.]\n",
      "  [0. 0. 1. ... 0. 1. 0.]\n",
      "  [1. 0. 0. ... 0. 0. 0.]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[1. 0. 1. ... 1. 0. 0.]\n",
      "  [0. 1. 0. ... 0. 1. 0.]\n",
      "  [1. 0. 0. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [0. 0. 0. ... 0. 1. 0.]\n",
      "  [0. 1. 0. ... 1. 0. 0.]\n",
      "  [0. 0. 1. ... 0. 1. 0.]]\n",
      "\n",
      " [[1. 0. 0. ... 1. 1. 1.]\n",
      "  [1. 0. 0. ... 0. 1. 1.]\n",
      "  [1. 1. 1. ... 0. 0. 1.]\n",
      "  ...\n",
      "  [1. 0. 0. ... 1. 1. 0.]\n",
      "  [0. 1. 0. ... 1. 1. 1.]\n",
      "  [0. 0. 0. ... 1. 0. 1.]]\n",
      "\n",
      " [[0. 1. 0. ... 0. 1. 0.]\n",
      "  [0. 0. 0. ... 1. 0. 0.]\n",
      "  [0. 0. 0. ... 0. 0. 0.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 0. 0.]\n",
      "  [0. 0. 0. ... 1. 0. 1.]\n",
      "  [1. 0. 0. ... 0. 0. 1.]]]\n",
      "[ -0.4375      -0.4516129   -0.46666667  -0.4827586   -0.5\n",
      "  -0.5185185   -0.53846157  -0.56        -0.5833333   -0.6086956\n",
      "  -0.6363636   -0.6666667   -0.7         -0.7368421   -0.7777778\n",
      "  -0.8235294   -0.875       -0.93333334  -1.          -1.0769231\n",
      "  -1.1666666   -1.2727273   -1.4         -1.5555556   -1.75\n",
      "  -2.          -2.3333333   -2.8         -3.5         -4.6666665\n",
      "  -7.         -14.        ]\n"
     ]
    }
   ],
   "source": [
    "#print(tf.abs(tf.sign(b)).eval())\n",
    "#print(tf.abs(a).eval())\n",
    "print(b.eval())\n",
    "print((tf.reduce_sum(tf.reduce_max(tf.abs(b),2),1)/sequence1).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 3.0750072e-01  3.7025404e-01  5.8989849e-02]\n",
      "  [ 8.1092465e-01  4.7678506e-01  7.8572065e-01]\n",
      "  [ 1.5264922e-01  5.5301994e-01  2.8071898e-01]\n",
      "  [-1.0000000e+08 -1.0000000e+08 -1.0000000e+08]]\n",
      "\n",
      " [[ 5.5735517e-01  1.4127716e-01  4.2938459e-01]\n",
      "  [ 9.0394694e-01  4.3465996e-01  6.8084031e-01]\n",
      "  [-1.0000000e+08 -1.0000000e+08 -1.0000000e+08]\n",
      "  [-1.0000000e+08 -1.0000000e+08 -1.0000000e+08]]]\n",
      "---------------------------\n",
      "[[[0.30750072 0.37025404 0.05898985]\n",
      "  [0.81092465 0.47678506 0.78572065]\n",
      "  [0.15264922 0.55301994 0.28071898]\n",
      "  [0.2124572  0.1525188  0.43251896]]\n",
      "\n",
      " [[0.55735517 0.14127716 0.4293846 ]\n",
      "  [0.90394694 0.43465996 0.6808403 ]\n",
      "  [0.28218064 0.20750502 0.536587  ]\n",
      "  [0.955543   0.36796862 0.2597632 ]]]\n"
     ]
    }
   ],
   "source": [
    "print(attention.eval())\n",
    "print('---------------------------')\n",
    "print(c.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.37571058 0.25929824 0.26080477]\n",
      "  [0.19174616 0.33350915 0.29272607]\n",
      "  [0.43254325 0.40719265 0.4464691 ]\n",
      "  [0.         0.         0.        ]]\n",
      "\n",
      " [[0.43232435 0.4025567  0.5133915 ]\n",
      "  [0.56767565 0.59744334 0.48660848]\n",
      "  [0.         0.         0.        ]\n",
      "  [0.         0.         0.        ]]]\n",
      "---------------------------\n",
      "[[[27273714. 21354730. 20305152.]\n",
      "  [13919306. 27466432. 22790408.]\n",
      "  [31399334. 33534700. 34760188.]\n",
      "  [       0.        0.        0.]]\n",
      "\n",
      " [[18944856. 21296110. 23782714.]\n",
      "  [24876074. 31606030. 22541998.]\n",
      "  [       0.        0.        0.]\n",
      "  [       0.        0.        0.]]]\n"
     ]
    }
   ],
   "source": [
    "print(tf.nn.softmax(attention,1).eval())\n",
    "print('---------------------------')\n",
    "#print(tf.nn.softmax(c,1).eval())\n",
    "#print('---------------------------')\n",
    "print((tf.nn.softmax(c,1)*masks).eval())#mask the attention\n",
    "#print((tf.nn.softmax(attention,1)*masks)[1,:,:].eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.236068\n",
      "[[12.  2.  4.  1. 12.]\n",
      " [ 2.  4.  1. 12.  2.]\n",
      " [ 4.  1. 12.  2.  4.]\n",
      " [ 1. 12.  2.  4.  1.]]\n",
      "-------------origin--------------\n",
      "[[4.9990061e-01 2.2695453e-05 1.6769797e-04 8.3491905e-06 4.9990061e-01]\n",
      " [4.5379827e-05 3.3531408e-04 1.6694305e-05 9.9955720e-01 4.5379827e-05]\n",
      " [3.3521690e-04 1.6689466e-05 9.9926752e-01 4.5366676e-05 3.3521690e-04]\n",
      " [1.6694785e-05 9.9958593e-01 4.5381134e-05 3.3532371e-04 1.6694785e-05]]\n",
      "-----------attention----------------\n",
      "[[4.9980128e-01 5.3726708e-05 5.0272618e-04 3.9387935e-05]\n",
      " [5.3726708e-05 9.9911469e-01 6.2064682e-05 6.7035272e-04]\n",
      " [5.0272618e-04 6.2064682e-05 9.9853581e-01 6.2056853e-05]\n",
      " [3.9387935e-05 6.7035272e-04 6.2056853e-05 9.9917215e-01]]\n",
      "-----------matrix multipule----------------\n",
      "0.5002037\n",
      "------------result---------------\n"
     ]
    }
   ],
   "source": [
    "#iden=tf.eye(40,batch_shape=[32])\n",
    "iden4=tf.eye(4,)\n",
    "iden5=tf.eye(5,)\n",
    "a = tf.constant(np.array([12,2,4,1]*5, dtype=np.float32),shape=[4,5])\n",
    "print(a.eval())\n",
    "print('-------------origin--------------')\n",
    "c=tf.nn.softmax(a,1)\n",
    "print(c.eval())\n",
    "print('-----------attention----------------')\n",
    "b=tf.matmul(c,tf.transpose(c))\n",
    "bb=tf.matmul(tf.transpose(c),c)\n",
    "print(b.eval())\n",
    "print('-----------matrix multipule----------------')\n",
    "#print(bb.eval())\n",
    "#print('-----------bb----------------')\n",
    "#(iden-tf.expand_dims(tf.expand_dims(cc,-1),-1)).eval()\n",
    "c1=tf.norm(b-iden4, ord='fro', axis=(-2,-1))\n",
    "print(c1.eval())\n",
    "print('------------result---------------')\n",
    "#c2=tf.norm(bb-iden5, ord='fro', axis=(-2,-1))\n",
    "#print(c2.eval())\n",
    "#print('------------c2---------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.         1.         0.        ]\n",
      "  [0.33333334 0.33333334 0.33333334]\n",
      "  [0.40386766 0.5961324  0.        ]\n",
      "  [0.33333334 0.33333334 0.33333334]]\n",
      "\n",
      " [[1.         0.         0.        ]\n",
      "  [0.         1.         0.        ]\n",
      "  [0.41355765 0.         0.5864423 ]\n",
      "  [1.         0.         0.        ]]]\n",
      "---------------------------\n",
      "[[[0.22932205 0.38388658 0.38679132]\n",
      "  [0.30755463 0.27381158 0.4186338 ]\n",
      "  [0.2781362  0.41054538 0.31131846]\n",
      "  [0.4675371  0.2780771  0.25438577]]\n",
      "\n",
      " [[0.23888281 0.40872332 0.35239387]\n",
      "  [0.42417446 0.3178231  0.25800246]\n",
      "  [0.25891495 0.37393266 0.3671524 ]\n",
      "  [0.35517532 0.25223944 0.39258522]]]\n"
     ]
    }
   ],
   "source": [
    "print(tf.nn.softmax(attention).eval())\n",
    "print('---------------------------')\n",
    "print(tf.nn.softmax(c).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.18106501 0.69628495 0.7038231 ]\n",
      "  [0.32330215 0.2070896  0.6316459 ]\n",
      "  [0.4961527  0.88552827 0.60885817]\n",
      "  [0.7650095  0.2454293  0.1563826 ]]\n",
      "\n",
      " [[0.32351276 0.8605782  0.71228915]\n",
      "  [0.7896852  0.50103533 0.2925095 ]\n",
      "  [0.14227057 0.5098466  0.4915479 ]\n",
      "  [0.3729046  0.03067185 0.47304666]]]\n",
      "---------------------------------------\n",
      "[[[0.22932205 0.38388658 0.38679132]\n",
      "  [0.30755463 0.27381158 0.4186338 ]\n",
      "  [0.2781362  0.41054538 0.31131846]\n",
      "  [0.4675371  0.2780771  0.25438577]]\n",
      "\n",
      " [[0.23888281 0.40872332 0.35239387]\n",
      "  [0.42417446 0.3178231  0.25800246]\n",
      "  [0.25891495 0.37393266 0.3671524 ]\n",
      "  [0.35517532 0.25223944 0.39258522]]]\n"
     ]
    }
   ],
   "source": [
    "#print(tf.nn.softmax(a,-1).eval())\n",
    "print(c.eval())\n",
    "print('---------------------------------------')\n",
    "print(tf.nn.softmax(c,-1).eval())\n",
    "#print(tf.transpose(tf.nn.softmax(a,-1),[1,0,2,3]).eval())\n",
    "#print(tf.transpose(tf.transpose(tf.nn.softmax(a,-1),[1,0,2,3]),[1,0,2,3]).eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1]\n",
      "  [ 2  3]\n",
      "  [ 4  5]\n",
      "  [ 6  7]\n",
      "  [ 8  9]]\n",
      "\n",
      " [[10 11]\n",
      "  [12 13]\n",
      "  [14 15]\n",
      "  [16 17]\n",
      "  [18 19]]\n",
      "\n",
      " [[20 21]\n",
      "  [22 23]\n",
      "  [24 25]\n",
      "  [26 27]\n",
      "  [28 29]]\n",
      "\n",
      " [[30 31]\n",
      "  [32 33]\n",
      "  [34 35]\n",
      "  [36 37]\n",
      "  [38 39]]]\n",
      "-----aaaa----\n",
      "[[[ 0  1]\n",
      "  [10 11]\n",
      "  [20 21]\n",
      "  [30 31]]\n",
      "\n",
      " [[ 2  3]\n",
      "  [12 13]\n",
      "  [22 23]\n",
      "  [32 33]]\n",
      "\n",
      " [[ 4  5]\n",
      "  [14 15]\n",
      "  [24 25]\n",
      "  [34 35]]\n",
      "\n",
      " [[ 6  7]\n",
      "  [16 17]\n",
      "  [26 27]\n",
      "  [36 37]]\n",
      "\n",
      " [[ 8  9]\n",
      "  [18 19]\n",
      "  [28 29]\n",
      "  [38 39]]]\n",
      "----transpose-----\n",
      "[[[ 0  1]\n",
      "  [ 2  3]\n",
      "  [ 4  5]\n",
      "  [ 6  7]]\n",
      "\n",
      " [[ 8  9]\n",
      "  [10 11]\n",
      "  [12 13]\n",
      "  [14 15]]\n",
      "\n",
      " [[16 17]\n",
      "  [18 19]\n",
      "  [20 21]\n",
      "  [22 23]]\n",
      "\n",
      " [[24 25]\n",
      "  [26 27]\n",
      "  [28 29]\n",
      "  [30 31]]\n",
      "\n",
      " [[32 33]\n",
      "  [34 35]\n",
      "  [36 37]\n",
      "  [38 39]]]\n",
      "-----reshape----\n"
     ]
    }
   ],
   "source": [
    "print(a.eval())\n",
    "print('-----aaaa----')\n",
    "print(tf.transpose(a,[1,0,2]).eval())\n",
    "print('----transpose-----')\n",
    "print(tf.reshape(a,[5,4,2]).eval())\n",
    "print('-----reshape----')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]\n",
      "\n",
      " [[1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]\n",
      "\n",
      " [[1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]\n",
      "\n",
      " [[1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]\n",
      "\n",
      " [[1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  ...\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]\n",
      "  [1. 1. 1. ... 1. 1. 1.]]]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'b' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-3a17aa2b1eeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#b=tf.where(tf.equal(b, 0), padding, tf.cast(b,tf.float32))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#tf.nn.softmax(b, 1).eval()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
     ]
    }
   ],
   "source": [
    "#b=tf.expand_dims(b,-1)\n",
    "print(tf.abs(tf.sign(a)).eval())\n",
    "#padding.eval()\n",
    "#b=tf.where(tf.equal(b, 0), padding, tf.cast(b,tf.float32))\n",
    "#tf.nn.softmax(b, 1).eval()\n",
    "d=tf.expand_dims(tf.argmax(tf.nn.softmax(b, 1),1),-1).eval()\n",
    "c.eval()\n",
    "tf.equal(d,tf.cast(c,tf.int64))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[-100  -99  -98 ...   -3   -2   -1]\n",
      "   [   0    1    2 ...   97   98   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]\n",
      "\n",
      "\n",
      " [[[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]\n",
      "\n",
      "\n",
      " [[[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]\n",
      "\n",
      "\n",
      " [[[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]\n",
      "\n",
      "\n",
      " [[[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]\n",
      "\n",
      "  [[  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   ...\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]\n",
      "   [  99   99   99 ...   99   99   99]]]]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39]\n"
     ]
    }
   ],
   "source": [
    "output=[]\n",
    "print(sequence.eval())\n",
    "for i,j in enumerate(tf.unstack(sequence)) :\n",
    "    output.append(i)\n",
    "print(tf.stack(output).eval())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor 'mul:0' shape=(32, 40, 600) dtype=int32>,\n",
       " array([[[-10000,  -9999,  -9998, ...,  -9403,  -9402,  -9401],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         ...,\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]],\n",
       " \n",
       "        [[  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         ...,\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]],\n",
       " \n",
       "        [[     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         ...,\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         ...,\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]],\n",
       " \n",
       "        [[  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         ...,\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]],\n",
       " \n",
       "        [[  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         ...,\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999],\n",
       "         [     0,      0,      0, ...,      0,      0,      0],\n",
       "         [  9999,   9999,   9999, ...,   9999,   9999,   9999]]],\n",
       "       dtype=int32))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c,c.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'range:0' shape=(5,) dtype=int32>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, 10])\n",
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.range(tf.shape(x)[0]), {x: np.zeros((7,10))})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'map_7/TensorArrayStack/TensorArrayGatherV3:0' shape=(?, 10) dtype=float32>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def aa(x):\n",
    "    print(x)\n",
    "tf.map_fn(lambda y: y, x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected EOF while parsing (<ipython-input-10-976888b5443b>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-976888b5443b>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    print(i.read\u001b[0m\n\u001b[0m                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
     ]
    }
   ],
   "source": [
    "TensorArr = tf.TensorArray(tf.int32, 1, dynamic_size=True, infer_shape=False)\n",
    "x_array = TensorArr.unstack(sequence)\n",
    "for i in enumeratx_array:\n",
    "    print(i.read(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}