L03/code/vectorization-example.ipynb

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    "STAT 453: Deep Learning (Spring 2021)  \n",
    "\n",
    "Instructor: Sebastian Raschka (sraschka@wisc.edu)  \n",
    "Course website: http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2021/  \n",
    "GitHub repository: https://github.com/rasbt/stat453-deep-learning-ss21"
   ]
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   "source": [
    "# L03 - Vectorization Example"
   ]
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     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.8.2\n",
      "IPython 7.18.1\n",
      "\n",
      "numpy 1.18.5\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
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   "source": [
    "def forloop(x, w):\n",
    "    z = 0.\n",
    "    for i in range(len(x)):\n",
    "        z += x[i] * w[i]\n",
    "    return z\n",
    "\n",
    "\n",
    "def listcomprehension(x, w):\n",
    "    return sum(x_i*w_i for x_i, w_i in zip(x, w))\n",
    "\n",
    "\n",
    "def vectorized(x, w):\n",
    "    return x_vec.dot(w_vec)\n",
    "\n",
    "\n",
    "x, w = np.random.rand(100000), np.random.rand(100000)\n",
    "x_vec, w_vec = np.array(x), np.array(w)"
   ]
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     "text": [
      "38.7 ms ± 2.34 ms per loop (mean ± std. dev. of 100 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 100 -n 10 forloop(x, w)"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32.3 ms ± 1.81 ms per loop (mean ± std. dev. of 100 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 100 -n 10 listcomprehension(x, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 13.50 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "14.5 µs ± 7.73 µs per loop (mean ± std. dev. of 100 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 100 -n 10 vectorized(x_vec, w_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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