gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import t3f\n",
    "from t3f import TensorTrain\n",
    "from t3f.ops import *\n",
    "import t3f.kronecker as kr\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = t3f.get_variable(name='A', initializer=t3f.random_matrix(shape=((2,2,2),(3,3,3)), tt_rank=2))\n",
    "B = t3f.get_variable(name='B', initializer=t3f.random_matrix(shape=((2,2,2),(3,3,3)), tt_rank=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "i_shapes, j_shapes = A.get_raw_shape()\n",
    "C_cores = []\n",
    "for core_idx in range(A.ndims()):\n",
    "    core_A = A.tt_cores[core_idx]\n",
    "    core_B = B.tt_cores[core_idx]\n",
    "    A_r = A.get_tt_ranks()[core_idx]\n",
    "    B_r = B.get_tt_ranks()[core_idx]\n",
    "    A_r_next = A.get_tt_ranks()[core_idx+1]\n",
    "    B_r_next = B.get_tt_ranks()[core_idx+1]\n",
    "    \n",
    "    if core_idx == 0:\n",
    "        core_C = tf.concat(3, [core_A, core_B])\n",
    "    elif core_idx == A.ndims()-1:\n",
    "        core_C = tf.concat(0, [core_A, core_B])\n",
    "    else:\n",
    "        core_C_1 = tf.concat(3, [core_A, tf.zeros((A_r, i_shapes[core_idx], j_shapes[core_idx], B_r_next))])\n",
    "        core_C_2 = tf.concat(3, [tf.zeros((B_r, i_shapes[core_idx], j_shapes[core_idx], A_r_next)), core_B])\n",
    "        core_C = tf.concat(0, [core_C_1, core_C_2])\n",
    "    C_cores.append(core_C)\n",
    "C_shape = A.get_raw_shape()\n",
    "C_ranks = [rank_A.value + rank_B.value for rank_A, rank_B in zip(A.get_tt_ranks(), B.get_tt_ranks())]\n",
    "C_ranks[0] = 1\n",
    "C_ranks[-1] = 1\n",
    "C = t3f.TensorTrain(C_cores, C_shape, C_ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.847928e-06"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(sess.run(t3f.ops.full(A))+sess.run(t3f.ops.full(B)) - sess.run(t3f.ops.full(C)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

sum.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import t3f\n",
    "from t3f import TensorTrain\n",
    "from t3f.ops import *\n",
    "import t3f.kronecker as kr\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A = t3f.get_variable(name='A', initializer=t3f.random_matrix(shape=((2,2,2),(3,3,3)), tt_rank=2))\n",
    "B = t3f.get_variable(name='B', initializer=t3f.random_matrix(shape=((2,2,2),(3,3,3)), tt_rank=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "i_shapes, j_shapes = A.get_raw_shape()\n",
    "C_cores = []\n",
    "for core_idx in range(A.ndims()):\n",
    "    core_A = A.tt_cores[core_idx]\n",
    "    core_B = B.tt_cores[core_idx]\n",
    "    A_r = A.get_tt_ranks()[core_idx]\n",
    "    B_r = B.get_tt_ranks()[core_idx]\n",
    "    A_r_next = A.get_tt_ranks()[core_idx+1]\n",
    "    B_r_next = B.get_tt_ranks()[core_idx+1]\n",
    "    \n",
    "    if core_idx == 0:\n",
    "        core_C = tf.concat(3, [core_A, core_B])\n",
    "    elif core_idx == A.ndims()-1:\n",
    "        core_C = tf.concat(0, [core_A, core_B])\n",
    "    else:\n",
    "        core_C_1 = tf.concat(3, [core_A, tf.zeros((A_r, i_shapes[core_idx], j_shapes[core_idx], B_r_next))])\n",
    "        core_C_2 = tf.concat(3, [tf.zeros((B_r, i_shapes[core_idx], j_shapes[core_idx], A_r_next)), core_B])\n",
    "        core_C = tf.concat(0, [core_C_1, core_C_2])\n",
    "    C_cores.append(core_C)\n",
    "C_shape = A.get_raw_shape()\n",
    "C_ranks = [rank_A.value + rank_B.value for rank_A, rank_B in zip(A.get_tt_ranks(), B.get_tt_ranks())]\n",
    "C_ranks[0] = 1\n",
    "C_ranks[-1] = 1\n",
    "C = t3f.TensorTrain(C_cores, C_shape, C_ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.847928e-06"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.norm(sess.run(t3f.ops.full(A))+sess.run(t3f.ops.full(B)) - sess.run(t3f.ops.full(C)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}