model_2-3_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #2\n",
      "  (fname, cnt))\n",
      "/home/ubuntu/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/matplotlib/__init__.py:962: UserWarning: Duplicate key in file \"/home/ubuntu/.config/matplotlib/matplotlibrc\", line #3\n",
      "  (fname, cnt))\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import model_generator as mg\n",
    "inIter = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = mg.Model([300, 200, 40, 1], 'Model_2-3', \n",
    "                 dataFilename = 'Model_2-3/T_'+inIter+'.h5', \n",
    "                 paramFileName = 'Parameters_M1_100epochs.pkl', r_discount=.90, c_discount=1)\n",
    "mg.saveHP(model)\n",
    "model.setData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num minibatches:1213\n",
      "\tEpoch time:15 seconds\n",
      "Cost after epoch 0: 0.612859\n",
      "\tDev error (no dropout): 0.557216\n",
      "Cost after epoch 1: 0.569023\n",
      "\tDev error (no dropout): 0.548788\n",
      "Cost after epoch 2: 0.562614\n",
      "\tDev error (no dropout): 0.544124\n",
      "Cost after epoch 3: 0.559711\n",
      "\tDev error (no dropout): 0.542014\n",
      "Cost after epoch 4: 0.557090\n",
      "\tDev error (no dropout): 0.539794\n",
      "Cost after epoch 5: 0.554889\n",
      "\tDev error (no dropout): 0.538811\n",
      "Cost after epoch 6: 0.554064\n",
      "\tDev error (no dropout): 0.537461\n",
      "Cost after epoch 7: 0.552149\n",
      "\tDev error (no dropout): 0.536307\n",
      "Cost after epoch 8: 0.552080\n",
      "\tDev error (no dropout): 0.535490\n",
      "Cost after epoch 9: 0.550819\n",
      "\tDev error (no dropout): 0.535049\n",
      "\tAve epoch time: 15.591476\n",
      "\t100 epochs will take 0:25:59.147589\n",
      "Cost after epoch 10: 0.549949\n",
      "\tDev error (no dropout): 0.533844\n",
      "Cost after epoch 20: 0.544685\n",
      "\tDev error (no dropout): 0.531413\n",
      "Cost after epoch 30: 0.541778\n",
      "\tDev error (no dropout): 0.530296\n",
      "Cost after epoch 40: 0.539889\n",
      "\tDev error (no dropout): 0.528492\n",
      "Parameters have been trained!\n",
      "Train cost (No dropout): 0.5300538\n",
      "Test cost (No dropout): 0.52722067\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8afffbec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.TrainModel(num_epochs=50, minibatch_size=2048, learning_rate=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('git add .')\n",
    "os.system('git commit -m \"Model_2-3_'+str(model.modelIter)+' trained\"')\n",
    "os.system('git pull')\n",
    "os.system('git push origin master')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VS Greedy: 54.000%\tVS Random: 92.000%\n",
      "Standard metric: 368.00\n"
     ]
    }
   ],
   "source": [
    "model.TestModel(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VS Greedy: 56.155%\tVS Random: 95.465%\n",
      "Standard metric: 198.84\n"
     ]
    }
   ],
   "source": [
    "model.TestModel(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zsyMultigame as multi\n",
    "import os\n",
    "outIter = '1'\n",
    "multi.simulate('Model_2-3/params_Model_2-3_'+outIter+'.pkl', os.path.join('Model_2-3','T_'+outIter+'.h5'),\n",
    "               numGames=100000, poolsize = 20, exploration_prob = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.system('git add .')\n",
    "os.system('git commit -m \"Model_2-3_'+outIter+' simulated\"')\n",
    "os.system('git pull')\n",
    "os.system('git push origin master')"
   ]
  }
 ],
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