tf_conv.ipynb

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   "source": [
    "Convolution in tensorflow is actually cross-correlation. \n",
    "First execute 2D convolution in tensorflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b93b7ad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from scipy.signal import convolve\n",
    "\n",
    "X = np.random.randint(0,10, (7,6,5))\n",
    "F = np.random.randint(0,10, (4,3,2))\n",
    "\n",
    "Xt = tf.constant(X, dtype=tf.float32)\n",
    "Xt = tf.reshape(Xt, [1]+Xt.shape+[1])\n",
    "Ft = tf.constant(F, dtype=tf.float32)\n",
    "Ft = tf.reshape(Ft, Ft.shape+[1,1])\n",
    "\n",
    "Zt = tf.nn.convolution(Xt, Ft, padding='VALID')\n",
    "Zt = tf.squeeze(Zt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42922357",
   "metadata": {},
   "source": [
    "Then flip the filter and do convolution in Scipy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efe274c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = convolve(X, np.flip(F), 'valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a3ed3f",
   "metadata": {},
   "source": [
    "They give the same result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f4d00b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(np.linalg.norm(Z - Zt.numpy()))"
   ]
  }
 ],
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