Theano_map.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import theano\n",
      "import theano.tensor as T\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "theano.config.compute_test_value = 'off'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://deeplearning.net/software/theano/cifarSC2011/advanced_theano.html"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Scan"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X=T.scalar('X')\n",
      "V=T.vector('V')\n",
      "\n",
      "def f(x,y):\n",
      "     return x*y\n",
      "\n",
      "fm,_=theano.map(f,\n",
      "                sequences=np.array([3, 4, 5]), \n",
      "                non_sequences=X\n",
      "                )\n",
      "\n",
      "func = theano.function(inputs=[X], outputs=fm) \n",
      "print func(4) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 12.  16.  20.]\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u3055\u3059\u304c\u306bTensor\u306fnp.array\u306b\u3067\u304d\u306a\u3044"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X=T.scalar('X')\n",
      "Y=T.scalar('Y')\n",
      "\n",
      "def f(x,y):\n",
      "     return x*y\n",
      "\n",
      "fm,_=theano.map(f,\n",
      "                sequences=np.array([Y]*5),\n",
      "                non_sequences=X\n",
      "                )\n",
      "\n",
      "func = theano.function(inputs=[X,Y], outputs=fm) \n",
      "print func([4,3]) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AsTensorError",
       "evalue": "('Cannot convert [Y Y Y Y Y] to TensorType', <type 'numpy.ndarray'>)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAsTensorError\u001b[0m                             Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-42-cb3fda7a2ac2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m fm,_=theano.map(f,\n\u001b[0;32m      8\u001b[0m                 \u001b[0msequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m                 \u001b[0mnon_sequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m                 )\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\theano\\scan_module\\scan_views.pyc\u001b[0m in \u001b[0;36mmap\u001b[1;34m(fn, sequences, non_sequences, truncate_gradient, go_backwards, mode, name)\u001b[0m\n\u001b[0;32m     66\u001b[0m                      \u001b[0mgo_backwards\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgo_backwards\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m                      \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 68\u001b[1;33m                      name=name)\n\u001b[0m\u001b[0;32m     69\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\theano\\scan_module\\scan.pyc\u001b[0m in \u001b[0;36mscan\u001b[1;34m(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile)\u001b[0m\n\u001b[0;32m    461\u001b[0m                 \u001b[1;31m# each frame by the corresponding slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m                 \u001b[0mactual_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mseq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmintap\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 463\u001b[1;33m                 \u001b[0m_seq_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_tensor_variable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    464\u001b[0m                 \u001b[0m_seq_val_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_seq_val\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmintap\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m                 \u001b[0mnw_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_seq_val_slice\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\theano\\tensor\\basic.pyc\u001b[0m in \u001b[0;36mas_tensor_variable\u001b[1;34m(x, name, ndim)\u001b[0m\n\u001b[0;32m    190\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m             \u001b[0mstr_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 192\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mAsTensorError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot convert %s to TensorType\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mstr_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m \u001b[1;31m# this has a different name, because _as_tensor_variable is the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mAsTensorError\u001b[0m: ('Cannot convert [Y Y Y Y Y] to TensorType', <type 'numpy.ndarray'>)"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def fgrad(a,b):\n",
      "     return (a * b + 1, theano.grad(a * b + 1, b))\n",
      "\n",
      "gs, _ = theano.map(fgrad, \n",
      "                   sequences=np.array([3, 4, 5]), \n",
      "                   non_sequences=X) \n",
      "\n",
      "func = theano.function(inputs=[X], outputs=gs) \n",
      "print func(1) \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[array([ 4.,  5.,  6.]), array([ 3.,  4.,  5.])]\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def power_of_2(p, m):\n",
      "    return p*2, theano.scan_module.until(p*2 > m)\n",
      "\n",
      "max_value = T.scalar()\n",
      "values, _ = theano.scan(power_of_2,\n",
      "                        outputs_info = T.constant(1.),\n",
      "                        non_sequences = max_value,\n",
      "                        n_steps = 1024)\n",
      "\n",
      "f = theano.function([max_value], values)\n",
      "print f(45)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  2.   4.   8.  16.  32.  64.]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Reduce"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "https://groups.google.com/forum/#!topic/theano-users/SCKFg-UltQo"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res = reduce(lambda x,aux: aux + x,   [theano.shared(1),theano.shared(2),theano.shared(3)] )\n",
      "print res"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Elemwise{add,no_inplace}.0\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "seq=[theano.shared(1),theano.shared(2),theano.shared(3)]\n",
      "output, _ = theano.reduce(fn=lambda layer,aux : aux + layer.get_output(input,*args, **kwargs),\n",
      "                          outputs_info=T.matrix(\"outut\",dtype=theano.config.floatX),\n",
      "                          sequences=np.array([1,2,3])\n",
      "                          )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'TensorVariable' object has no attribute 'get_output'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-29-72ec4a9359a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m output, _ = theano.reduce(fn=lambda layer,aux : aux + layer.get_output(input,*args, **kwargs),\n\u001b[0;32m      3\u001b[0m                           \u001b[0moutputs_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"outut\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloatX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m                           \u001b[0msequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m                           )\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\theano\\scan_module\\scan_views.pyc\u001b[0m in \u001b[0;36mreduce\u001b[1;34m(fn, sequences, outputs_info, non_sequences, go_backwards, mode, name)\u001b[0m\n\u001b[0;32m    109\u001b[0m                      \u001b[0mtruncate_gradient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m                      \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m                      name=name)\n\u001b[0m\u001b[0;32m    112\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrval\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrval\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32mC:\\Python27\\lib\\site-packages\\theano\\scan_module\\scan.pyc\u001b[0m in \u001b[0;36mscan\u001b[1;34m(fn, sequences, outputs_info, non_sequences, n_steps, truncate_gradient, go_backwards, mode, name, profile)\u001b[0m\n\u001b[0;32m    730\u001b[0m     \u001b[1;31m# and outputs that needs to be separated\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m     \u001b[0mcondition\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mupdates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscan_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_updates_and_outputs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcondition\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    734\u001b[0m         \u001b[0mas_while\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-29-72ec4a9359a1>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(layer, aux)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mseq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshared\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m output, _ = theano.reduce(fn=lambda layer,aux : aux + layer.get_output(input,*args, **kwargs),\n\u001b[0m\u001b[0;32m      3\u001b[0m                           \u001b[0moutputs_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"outut\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtheano\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloatX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                           \u001b[0msequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                           )\n",
        "\u001b[1;31mAttributeError\u001b[0m: 'TensorVariable' object has no attribute 'get_output'"
       ]
      }
     ],
     "prompt_number": 29
    }
   ],
   "metadata": {}
  }
 ]
}