{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:54:23.398548Z",
     "start_time": "2018-09-30T11:54:23.169238Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.75,  0.9 ,  0.85,  0.35, -0.8 , -0.6 ,  0.8 ,  0.8 ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import h5py\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#now load this dataset \n",
    "h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
    "X = h5f['sizes'][:]\n",
    "Y = h5f['spectrum'][:]\n",
    "\n",
    "\n",
    "#create a train - test split of the dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=42)\n",
    "\n",
    "# normalize inputs \n",
    "x_test = (x_test - 50)/20 \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:45:01.118397Z",
     "start_time": "2018-09-30T11:44:59.855736Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mxnet as mx\n",
    "\n",
    "# Step1: Load the model in MXNet\n",
    "\n",
    "# Use the same prefix and epoch parameters we used in save_mxnet_model API.\n",
    "sym, arg_params, aux_params = mx.model.load_checkpoint(prefix='my_mod', epoch=0)\n",
    "\n",
    "# We use the data_names and data_shapes returned by save_mxnet_model API.\n",
    "mod = mx.mod.Module(symbol=sym, \n",
    "                    data_names=['/input_12'], \n",
    "                    context=mx.gpu(1), \n",
    "                    label_names=None)\n",
    "mod.bind(for_training=False, \n",
    "         data_shapes=[('/input_12', (1,8))], \n",
    "         label_shapes=mod._label_shapes)\n",
    "mod.set_params(arg_params, aux_params, allow_missing=True)                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:47:00.407910Z",
     "start_time": "2018-09-30T11:47:00.306416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gpu(1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = np.random.randint(30, 71, (1,8))\n",
    "#spec_ac = snlay.calc_spectrum(size, mats, lams)\n",
    "size = (size - 50.0)/20.0\n",
    "#size = np.expand_dims(size, axis = 0)\n",
    "size.shape\n",
    "#res1 = model.predict(size)\n",
    "#data_iter = mx.io.NDArrayIter(size, None, 1)\n",
    "for icv in np.arange(1000):\n",
    "    res2 = mod.predict(size)\n",
    "res2.context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-30T11:42:00.611085Z",
     "start_time": "2018-09-30T11:42:00.601881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "[[0. 0. 0. 0.]\n",
       " [0. 0. 0. 0.]\n",
       " [0. 0. 0. 0.]]\n",
       "<NDArray 3x4 @gpu(0)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mxnet as mx\n",
    "from mxnet import nd\n",
    "mx.random.seed(1)\n",
    "x = nd.empty((3, 4), ctx=mx.gpu(0))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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