mx_net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mxnet as mx\n",
    "import numpy as np\n",
    "from mxnet.gluon import nn\n",
    "from mxnet import metric\n",
    "from mxnet.gluon import loss\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "?mx.nd.ones\n",
    "# nd arrays = tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "MXNetError",
     "evalue": "Cannot find argument 'device', Possible Arguments:\n----------------\nshape : Shape(tuple), optional, default=[]\n    The shape of the output\nctx : string, optional, default=''\n    Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls.\ndtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32'\n    Target data type.\n, in operator _ones(name=\"\", ctx=\"cpu(0)\", shape=\"(1000,)\", dtype=\"float32\", device=\"cpu(0)\")",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMXNetError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-2eaf56a62920>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/ndarray.py\u001b[0m in \u001b[0;36mones\u001b[0;34m(shape, ctx, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   2435\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmx_real_t\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m     \u001b[0;31m# pylint: disable= no-member, protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_internal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2438\u001b[0m     \u001b[0;31m# pylint: enable= no-member, protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mxnet/ndarray/register.py\u001b[0m in \u001b[0;36m_ones\u001b[0;34m(shape, ctx, dtype, out, name, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mxnet/_ctypes/ndarray.py\u001b[0m in \u001b[0;36m_imperative_invoke\u001b[0;34m(handle, ndargs, keys, vals, out)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mc_str_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0mc_str_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         ctypes.byref(out_stypes)))\n\u001b[0m\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0moriginal_output\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/mxnet/base.py\u001b[0m in \u001b[0;36mcheck_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m    251\u001b[0m     \"\"\"\n\u001b[1;32m    252\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMXNetError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_LIB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMXGetLastError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMXNetError\u001b[0m: Cannot find argument 'device', Possible Arguments:\n----------------\nshape : Shape(tuple), optional, default=[]\n    The shape of the output\nctx : string, optional, default=''\n    Context of output, in format [cpu|gpu|cpu_pinned](n).Only used for imperative calls.\ndtype : {'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32'\n    Target data type.\n, in operator _ones(name=\"\", ctx=\"cpu(0)\", shape=\"(1000,)\", dtype=\"float32\", device=\"cpu(0)\")"
     ]
    }
   ],
   "source": [
    "mx.nd.ones(shape=(1000,), device=mx.cpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = metric.Accuracy()\n",
    "?accuracy.update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('accuracy', 1.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = mx.nd.array([1,2,3])\n",
    "predictions = mx.nd.array([1,2,3])\n",
    "accuracy.update(labels=labels, preds=predictions)\n",
    "accuracy.get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0.01,1,0.001)\n",
    "plt.plot(x, -np.log(x))\n",
    "plt.title('Log loss when true value label = 1')\n",
    "plt.xlabel('precited probability')\n",
    "o = plt.ylabel('loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[0.01165623 0.03168492 0.08612854 0.23412167 0.6364086 ]]\n",
       "<NDArray 1x5 @cpu(0)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mx.nd.array([[1,2,3,4,5]]).softmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[1.]\n",
       "<NDArray 1 @cpu(0)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mx.nd.array([[1,2,3,4,5]]).softmax().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}