Show different convolution results between numpy and theano

understand_dnn_conv.ipynb

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   "source": [
    "import theano\n",
    "import numpy\n",
    "from theano.sandbox.cuda import dnn\n",
    "import theano.tensor as T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the input image x0:"
   ]
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   "execution_count": 3,
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   "outputs": [],
   "source": [
    "x0 = numpy.array([[[[  7.61323881,   0.        ,   0.        ,   0.        ,\n",
    "            0.        ,   0.        ],\n",
    "         [ 25.58142853,   0.        ,   0.        ,   0.        ,\n",
    "            0.        ,   0.        ],\n",
    "         [  7.51445341,   0.        ,   0.        ,   0.        ,\n",
    "            0.        ,   0.        ],\n",
    "         [  0.        ,  12.74498367,   4.96315479,   0.        ,\n",
    "            0.        ,   0.        ],\n",
    "         [  0.        ,   0.        ,   0.        ,   0.        ,\n",
    "            0.        ,   0.        ],\n",
    "         [  0.        ,   0.        ,   0.        ,   0.        ,\n",
    "            0.        ,   0.        ]]]], dtype='float32')"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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     "data": {
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       "(1, 1, 6, 6)"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "x0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the convolution kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w0 = numpy.array([[[[-0.0015835 , -0.00088091,  0.00226375,  0.00378434,  0.00032208,\n",
    "          -0.00396959],\n",
    "         [-0.000179  ,  0.00030951,  0.00113849,  0.00012536, -0.00017198,\n",
    "          -0.00318825],\n",
    "         [-0.00263921, -0.00383847, -0.00225416, -0.00250589, -0.00149073,\n",
    "          -0.00287099],\n",
    "         [-0.00149283, -0.00312137, -0.00431571, -0.00394508, -0.00165113,\n",
    "          -0.0012118 ],\n",
    "         [-0.00167376, -0.00169753, -0.00373235, -0.00337372, -0.00025546,\n",
    "           0.00072154],\n",
    "         [-0.00141197, -0.00099017, -0.00091934, -0.00226817, -0.0024105 ,\n",
    "          -0.00333713]]]], dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 6, 6)"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "w0.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caculate the convolution with theano and cudnn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[-0.04749081]]]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = T.tensor4('input')\n",
    "W = T.tensor4('W')\n",
    "conv_out = dnn.dnn_conv(img=X, kerns=W)\n",
    "convolution = theano.function([X, W], conv_out)\n",
    "numpy.array(convolution(x0, w0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate convolutoin with numpy (note the result is different):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.097668208"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.sum(x0 * w0)"
   ]
  }
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