gymmarket_notebook-dqn-main-result.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "\"\"\"Much of this code from the excellent https://github.com/higgsfield/RL-Adventure\"\"\"\n",
    "import math, random\n",
    "import gym\n",
    "from gym import spaces, logger\n",
    "from gym.utils import seeding\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.autograd as autograd \n",
    "import torch.nn.functional as F\n",
    "\n",
    "import time\n",
    "import os\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import sys\n",
    "if \"../../\" not in sys.path:\n",
    "  sys.path.append(\"../../\") \n",
    "\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "\n",
    "import collections\n",
    "import torch.nn as nn\n",
    "\n",
    "from  collections  import deque\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "USE_CUDA = False\n",
    "Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(*args, **kwargs)\n"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "def save_model(model_name, net, optimizer):\n",
    "\n",
    "    # Print model's state_dict\n",
    "    print(\"Model's state_dict:\")\n",
    "    for param_tensor in net.state_dict():\n",
    "        print(param_tensor, \"\\t\", net.state_dict()[param_tensor].size())\n",
    "\n",
    "    # Print optimizer's state_dict\n",
    "    print(\"Optimizer's state_dict:\")\n",
    "    for var_name in optimizer.state_dict():\n",
    "        print(var_name, \"\\t\", optimizer.state_dict()[var_name])\n",
    "\n",
    "    save_path = os.path.join(os.getcwd(), \"models\", model_name.split(\",\")[0])\n",
    "    torch.save(net.state_dict(), save_path)\n",
    "\n",
    "    with open(save_path + \"_information.txt\", \"a\") as f:\n",
    "        for l in model_name.split(\",\"):\n",
    "            f.write(l + \"\\n\")\n",
    "\n",
    "\n",
    "def load_model(net, path):\n",
    "    net.load_state_dict(torch.load(path))\n",
    "    net.eval()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "class ReplayBuffer(object):\n",
    "    def __init__(self, capacity):\n",
    "        self.buffer = deque(maxlen=capacity)\n",
    "    \n",
    "    def push(self, state, action, reward, next_state, done):\n",
    "        state      = np.expand_dims(state, 0)\n",
    "        next_state = np.expand_dims(next_state, 0)\n",
    "            \n",
    "        self.buffer.append((state, action, reward, next_state, done))\n",
    "    \n",
    "    def sample(self, batch_size):\n",
    "        state, action, reward, next_state, done = zip(*random.sample(self.buffer, batch_size))\n",
    "        return np.concatenate(state), action, reward, np.concatenate(next_state), done\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.buffer)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "epsilon_start = 1.0\n",
    "epsilon_final = 0.01\n",
    "epsilon_decay = 500\n",
    "\n",
    "epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(-1. \n",
    "                                                                * frame_idx / epsilon_decay)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "class Market(gym.Env):\n",
    "    def __init__(self, kappa, episodes, time_periods, mu, r, sigma, inv_range):\n",
    "\n",
    "        self.episodes = episodes\n",
    "        self.time_periods = time_periods\n",
    "        self.epi = 0\n",
    "        self.t = 0\n",
    "\n",
    "        self.inv_range = inv_range\n",
    "\n",
    "        self.mu = mu\n",
    "        self.r = r\n",
    "        self.sigma = sigma\n",
    "\n",
    "        self._start_wealth = 100.0\n",
    "        self.kappa = kappa\n",
    "\n",
    "        self.action_space = spaces.Discrete(len(inv_range))\n",
    "        self.observation_space = spaces.Box(0,120, np.array([2]))\n",
    "        \n",
    "        self.seed()\n",
    "        self.viewer = None\n",
    "        self.state = None\n",
    "\n",
    "        \n",
    "    def seed(self, seed=None):\n",
    "        self.np_random, seed = seeding.np_random(seed)\n",
    "        return [seed]\n",
    "    \n",
    "    def reset(self):\n",
    "        self.S, self.B, self.dS, self.dB = self.generate_price_series()\n",
    "        self.wealth = self._start_wealth\n",
    "        #self.state = (int(self.S[self.t,self.epi]*100),int(self._start_wealth/10))\n",
    "        \n",
    "        self.state = (self.S[self.t,self.epi],self._start_wealth)\n",
    "        return np.array(self.state)\n",
    "\n",
    "    \n",
    "    def step(self, action):\n",
    "        \n",
    "        assert self.action_space.contains(action), \"%r (%s) invalid\"%(action, type(action))\n",
    "        price_state, wealth_state = self.state\n",
    "        \n",
    "        prop = self.inv_range[action]\n",
    "\n",
    "        discount = 0.99\n",
    "\n",
    "        if self.t < self.time_periods:\n",
    "            NB = (1-prop)*self.wealth/self.B[self.t]\n",
    "            NS = prop*self.wealth/self.S[self.t, self.epi]\n",
    "\n",
    "            dX = NB*self.dB[self.t] +NS*self.dS[self.t,self.epi]\n",
    "            reward = dX - (self.kappa/2)*(dX**2)  \n",
    "            self.wealth += dX\n",
    "\n",
    "            done = False\n",
    "\n",
    "            #self.wealth_state = int(self.wealth/10)\n",
    "            self.wealth_state = self.wealth\n",
    "            self.t += 1\n",
    "            \n",
    "            #new_state = (int(self.S[self.t,self.epi]*100), self.wealth_state)\n",
    "            new_state = (self.S[self.t,self.epi], self.wealth_state)\n",
    "            self.state = new_state\n",
    "            final_wealth = 0.0\n",
    "            \n",
    "        else:\n",
    "            #reached the end of episode...\n",
    "            self.t = 0\n",
    "            self.epi = 0 #+=1\n",
    "\n",
    "            reward = 0.0\n",
    "            dX = 0.0\n",
    "            final_wealth = self.wealth\n",
    "            done = True\n",
    "\n",
    "            _ = self.reset()\n",
    "\n",
    "        return np.array(self.state), reward, done, final_wealth\n",
    "\n",
    "    def generate_price_series(self):\n",
    "        I = 1 #self.episodes\n",
    "        M = self.time_periods\n",
    "\n",
    "        S0 = 1\n",
    "        B0 = 1\n",
    "        T = 1.0\n",
    "        dt = T/M\n",
    "\n",
    "        mu = self.mu\n",
    "        r = self.r\n",
    "        sigma = self.sigma\n",
    "\n",
    "        S = np.zeros((M+1,I))\n",
    "        dS = np.zeros((M,I))\n",
    "        dB = np.zeros(M)\n",
    "\n",
    "        B = np.zeros(M+1)\n",
    "        B[0] = B0\n",
    "        S[0] = S0\n",
    "\n",
    "        for t in range(1, M+1):\n",
    "            z = np.random.standard_normal(I)\n",
    "            #df = 10\n",
    "            #z = np.random.standard_t(df,I)\n",
    "            S[t] = S[t-1]*np.exp((mu-0.5*sigma**2)*dt + sigma*math.sqrt(dt)*z)\n",
    "            B[t] = B[t-1]*np.exp(r*dt)\n",
    "\n",
    "        for t in range(1,M):\n",
    "            dS[t] = S[t+1] - S[t]\n",
    "            dB[t] = B[t+1] - B[t]\n",
    "\n",
    "        # print(f\"{S=}\")\n",
    "        # print(f\"{B=}\")\n",
    "        # print(f\"{dS=}\")\n",
    "        # print(f\"{dB=}\")\n",
    "        return S, B, dS, dB"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "kappa = 0.008\n",
    "episodes = 3000000\n",
    "time_periods = 20\n",
    "mu =0.10\n",
    "rf = 0.02\n",
    "sigma = 0.20"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "episodes = episodes #500k worked well? as did 1m\n",
    "\n",
    "utes = 15\n",
    "u_star = np.linspace(0,5, utes)\n",
    "\n",
    "Mark = Market(kappa, episodes, time_periods, mu, rf, sigma, u_star) #parameters from earlier"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "env = Mark\n",
    "\n",
    "for _ in range(1):\n",
    "    for i in range(1):\n",
    "        if i == 0:\n",
    "            states = env.reset()\n",
    "        else:\n",
    "            states, rewards, dones, info = env.step(actions)\n",
    "\n",
    "        # actions = np.random.uniform(low=0, high=1, size=(n_paths,))\n",
    "        actions = np.ones((1,)) * 1.2"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "class DQN(nn.Module):\n",
    "    def __init__(self, num_inputs, num_actions):\n",
    "        super(DQN, self).__init__()\n",
    "        \n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Linear(env.observation_space.shape[0], 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(128, env.action_space.n)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.layers(x)\n",
    "    \n",
    "    def act(self, state, epsilon):\n",
    "        if random.random() > epsilon:\n",
    "            state   = Variable(torch.FloatTensor(state).unsqueeze(0), volatile=True)\n",
    "            q_value = self.forward(state)\n",
    "            action  = q_value.max(1)[1].data[0].item()\n",
    "        else:\n",
    "            action = random.randrange(env.action_space.n)\n",
    "        return action"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "model = DQN(env.observation_space.shape[0], env.action_space.n)\n",
    "\n",
    "if USE_CUDA:\n",
    "    model = model.cuda()\n",
    "    \n",
    "optimizer = optim.Adam(model.parameters())\n",
    "\n",
    "replay_buffer = ReplayBuffer(1000)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "def compute_td_loss(batch_size):\n",
    "    state, action, reward, next_state, done = replay_buffer.sample(batch_size)\n",
    "    print(state, action, reward, next_state, done)\n",
    "\n",
    "    state      = Variable(torch.FloatTensor(np.float32(state)))\n",
    "    next_state = Variable(torch.FloatTensor(np.float32(next_state)), volatile=True)\n",
    "    action     = Variable(torch.LongTensor(action))\n",
    "    reward     = Variable(torch.FloatTensor(reward))\n",
    "    done       = Variable(torch.FloatTensor(done))\n",
    "\n",
    "    \n",
    "    q_values      = model(state)\n",
    "    next_q_values = model(next_state)\n",
    "\n",
    "    q_value          = q_values.gather(1, action.unsqueeze(1)).squeeze(1)\n",
    "    next_q_value     = next_q_values.max(1)[0]\n",
    "    expected_q_value = reward + gamma * next_q_value * (1 - done)\n",
    "    \n",
    "    loss = (q_value - Variable(expected_q_value.data)).pow(2).mean()\n",
    "        \n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    return loss"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "def plot(frame_idx, rewards, losses):\n",
    "    clear_output(True)\n",
    "    plt.figure(figsize=(20,5))\n",
    "    plt.subplot(131)\n",
    "    plt.title('frame %s. reward: %s' % (frame_idx, np.mean(rewards[-10:])))\n",
    "    plt.plot(rewards)\n",
    "    plt.subplot(132)\n",
    "    plt.title('loss')\n",
    "    plt.plot(losses)\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "num_frames = 1000000\n",
    "num_frames = 100\n",
    "batch_size = 32\n",
    "gamma      = 0.99\n",
    "\n",
    "MODEL_NAME = str(\n",
    "    f\"model-{int(time.time())}\")\n",
    "    \n",
    "losses = []\n",
    "all_rewards = []\n",
    "episode_reward = 0\n",
    "\n",
    "state = env.reset()\n",
    "\n",
    "for frame_idx in range(1, num_frames + 1):\n",
    "    epsilon = epsilon_by_frame(frame_idx)\n",
    "    \n",
    "    action = model.act(state, epsilon) #norm\n",
    "    #print(action)\n",
    "    \n",
    "    next_state, reward, done, _ = env.step(action)\n",
    "    replay_buffer.push(state, action, reward, next_state, done)\n",
    "    \n",
    "    state = next_state\n",
    "    episode_reward += reward\n",
    "    \n",
    "    if done:\n",
    "        state = env.reset()\n",
    "        all_rewards.append(episode_reward)\n",
    "        episode_reward = 0\n",
    "        \n",
    "    if len(replay_buffer) > batch_size:\n",
    "        #print('here')\n",
    "        loss = compute_td_loss(batch_size)\n",
    "        # losses.append(loss.data[0])\n",
    "        losses.append(loss.data)\n",
    "        \n",
    "    if frame_idx % 400 == 0:\n",
    "        plot(frame_idx, all_rewards\n",
    "                         ,np.convolve(losses,np.ones((1000,))/1000,mode='valid'))\n",
    "        \n",
    "        #plt.plot(np.convolve(utilities_test_best, np.ones((10000,))/10000, mode='valid'), label='Merton Optimal')\n",
    "        #plt.plot(np.convolve(utilities_test_rand, np.ones((10000,))/10000, mode='valid'), label='Random agent')\n",
    "        #plt.plot(np.convolve(utilities_test, np.ones((10000,))/10000, mode='valid'), label='Trained agent')\n",
    "\n",
    "save_model(MODEL_NAME, model, optimizer)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      " [  0.96629733 133.71234942]\n",
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      " [  0.81481606 100.        ]\n",
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      " [  0.77016481  70.22175709]] (7, 8, 0, 7, 8, 4, 8, 13, 8, 14, 5, 4, 12, 11, 6, 5, 2, 3, 7, 8, 0, 6, 5, 9, 3, 14, 9, 9, 12, 9, 6, 9) (0.0, -1.7245100922212326, 0.08451156314226158, 0.0, -11.687674041029691, 13.25876113151738, -1.9169740627545466, 27.17803964647645, 6.806680252016057, 0.0, -7.168835214927191, 0.24379168406421087, -16.463373613198595, 12.30720220928971, 17.449627305130097, 0.8394555537284573, 5.589372395353571, 14.188697116408685, 20.234759881589635, -26.615241355729985, 0.10000997664749016, -3.5286015504396815, -11.324343801354757, 18.27736370499267, 1.3656053286055243, -12.365691779793615, 2.0150378670336653, 27.267216332850893, 19.011982124973876, -23.708629299517707, -3.969944788290957, -2.9310849219937314) [[  0.89567901 100.        ]\n",
      " [  0.9828156  117.89680532]\n",
      " [  0.8028908   84.58242841]\n",
      " [  0.95303173 100.        ]\n",
      " [  0.7503767   69.91520105]\n",
      " [  1.13306453 150.81960484]\n",
      " [  1.18330905 140.1162371 ]\n",
      " [  1.19194425 160.29237179]\n",
      " [  1.0212263  107.1028893 ]\n",
      " [  1.         100.        ]\n",
      " [  0.93849821 126.73807616]\n",
      " [  0.75243529  70.15923094]\n",
      " [  0.78596787  84.49788826]\n",
      " [  0.92594313 112.98125403]\n",
      " [  1.05676696 136.77143996]\n",
      " [  0.7754514   72.75743864]\n",
      " [  1.18810699 142.0187332 ]\n",
      " [  1.04292804 141.83891456]\n",
      " [  1.24635661 170.69183518]\n",
      " [  0.9811705  117.57803442]\n",
      " [  0.99618639 100.10005002]\n",
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      " [  0.96629733 133.71234942]\n",
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      " [  0.77016481  70.22175709]\n",
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      "[[  0.81481606 100.        ]\n",
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      " [  0.89567901 100.        ]\n",
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      " [  0.78790287  81.10227279]\n",
      " [  1.18330905 140.1162371 ]\n",
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      " [  0.7754514   72.75743864]\n",
      " [  0.78596787  84.49788826]\n",
      " [  1.         100.        ]\n",
      " [  0.8028908   84.58242841]\n",
      " [  1.         100.        ]\n",
      " [  1.12259689 136.2984754 ]\n",
      " [  1.05676696 136.77143996]\n",
      " [  0.92594313 112.98125403]\n",
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      " [  0.98712101 119.60958101]\n",
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     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-13-ff25002be534>:37: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.\n",
      "  Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(*args, **kwargs)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      " [  1.26306199 172.37480435]\n",
      " [  1.4066644  243.40661938]\n",
      " [  1.13164693 109.25211075]\n",
      " [  1.00550341  68.33207046]\n",
      " [  0.89567901 100.        ]\n",
      " [  0.98096777  96.53274246]\n",
      " [  0.99302959  90.51286736]\n",
      " [  1.19194425 160.29237179]\n",
      " [  1.20568092 149.15442349]\n",
      " [  1.24635661 170.69183518]\n",
      " [  0.97596121  90.42239974]\n",
      " [  1.14096677 124.06464908]\n",
      " [  1.17978772 135.81054089]\n",
      " [  1.05916309 103.19022536]\n",
      " [  1.10505425 111.77869817]\n",
      " [  0.78790287  81.10227279]\n",
      " [  1.06958089 102.27705938]\n",
      " [  1.13385733 122.60523941]\n",
      " [  1.16390701 122.48269545]] (12, 4, 9, 1, 0, 0, 4, 9, 11, 13, 7, 9, 12, 13, 8, 0, 11, 11, 3, 7, 14, 9, 2, 0, 1, 13, 13, 13, 8, 8, 6, 0) (-6.955073507800211, 9.552229296339624, 11.614497564792934, 0.1298300162356767, 0.10218413732462439, 0.10000997664749016, 4.026559949353994, 2.0150378670336653, 31.40368932209484, 15.677003140552507, -4.9753358944487305, -23.708629299517707, 3.7540185566035724, 32.53785282730977, 45.80120863643488, 0.10925896314999409, 4.1980243862919355, 12.30720220928971, 5.514758220118414, 13.796076779205293, -12.365691779793615, -23.199989314697316, 1.6716396203093107, 0.09043488844747669, 1.0708324349950584, 12.63164579230922, 9.388093911550644, -39.48555437383685, -11.687674041029691, 9.140514232375724, 1.2819322596739857, 0.12248388913082402) [[  1.01117035  91.31458765]\n",
      " [  1.2123918  172.1703016 ]\n",
      " [  1.14096677 124.06464908]\n",
      " [  1.03509588  77.23270961]\n",
      " [  1.06958089 102.27705938]\n",
      " [  0.99618639 100.10005002]\n",
      " [  1.16215551 162.22221456]\n",
      " [  0.98712101 119.60958101]\n",
      " [  1.07467534 136.82926977]\n",
      " [  1.13164693 109.25211075]\n",
      " [  1.08824928 111.04281454]\n",
      " [  0.95724228  85.29635905]\n",
      " [  1.4066644  243.40661938]\n",
      " [  1.32473912 210.82691706]\n",
      " [  1.5297247  303.79471768]\n",
      " [  1.11272853 109.3614175 ]\n",
      " [  1.02225086  72.60306027]\n",
      " [  0.92594313 112.98125403]\n",
      " [  1.03454591 102.17483344]\n",
      " [  1.0579393  105.16804055]\n",
      " [  1.17533731 148.48439342]\n",
      " [  1.15276256 127.78162093]\n",
      " [  1.26306199 172.37480435]\n",
      " [  0.99302959  90.51286736]\n",
      " [  1.16660543 125.14010796]\n",
      " [  1.20568092 149.15442349]\n",
      " [  1.08159331 112.96012286]\n",
      " [  1.03208596  77.1028121 ]\n",
      " [  0.7503767   69.91520105]\n",
      " [  1.10505425 111.77869817]\n",
      " [  1.14002352 123.89381336]\n",
      " [  1.13385733 122.60523941]] (False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False)\n",
      "Model's state_dict:\n",
      "layers.0.weight \t torch.Size([128, 2])\n",
      "layers.0.bias \t torch.Size([128])\n",
      "layers.2.weight \t torch.Size([128, 128])\n",
      "layers.2.bias \t torch.Size([128])\n",
      "layers.4.weight \t torch.Size([15, 128])\n",
      "layers.4.bias \t torch.Size([15])\n",
      "Optimizer's state_dict:\n",
      "state \t {0: {'step': 78, 'exp_avg': tensor([[-7.5454e+01, -9.4120e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.8781e+01, -9.8352e+03],\n",
      "        [-7.2452e+01, -8.9367e+03],\n",
      "        [-7.4541e+01, -9.2746e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.3007e+01, -7.8368e+03],\n",
      "        [-7.6934e+01, -9.5029e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.8730e+01, -9.7750e+03],\n",
      "        [-8.8532e+01, -1.0968e+04],\n",
      "        [-8.1470e+01, -1.0099e+04],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.8627e+01, -8.5949e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.5435e+01, -9.3086e+03],\n",
      "        [-8.1649e+01, -1.0210e+04],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.2664e+01, -7.8018e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.0251e+01, -9.8387e+03],\n",
      "        [-6.7787e+01, -8.3465e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.2804e+01, -1.0272e+04],\n",
      "        [-7.9563e+01, -9.7366e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.0746e+01, -8.7504e+03],\n",
      "        [-7.8836e+01, -9.7665e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.2912e+01, -1.0345e+04],\n",
      "        [-7.5682e+01, -9.4284e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.5602e+01, -9.4193e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.6778e+01, -8.3365e+03],\n",
      "        [-7.7076e+01, -9.5374e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.0676e+01, -7.4734e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.2472e+01, -9.0744e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.8828e+01, -8.5465e+03],\n",
      "        [-7.1953e+01, -8.9532e+03],\n",
      "        [-8.3635e+01, -1.0334e+04],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.8074e+01, -8.4666e+03],\n",
      "        [-7.7224e+01, -9.6152e+03],\n",
      "        [-7.4750e+01, -9.3286e+03],\n",
      "        [-7.3260e+01, -9.0525e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.8078e+01, -9.6850e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.7137e+01, -1.0689e+04],\n",
      "        [-8.0117e+01, -9.9581e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.3894e+01, -9.1531e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.0147e+01, -8.7284e+03],\n",
      "        [-6.2239e+01, -7.7405e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.0440e+01, -9.9512e+03],\n",
      "        [-7.0964e+01, -8.8445e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.0516e+01, -8.7409e+03],\n",
      "        [-5.6724e+01, -7.0714e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.2984e+01, -8.9518e+03],\n",
      "        [-7.2410e+01, -8.9990e+03],\n",
      "        [-7.1388e+01, -8.8631e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.0836e+01, -8.9115e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.0033e+01, -9.9174e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.2786e+01, -1.0343e+04],\n",
      "        [-8.5241e+01, -1.0578e+04],\n",
      "        [-7.4658e+01, -9.3302e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-6.6395e+01, -8.3101e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-8.0015e+01, -9.9263e+03],\n",
      "        [ 1.8558e-04,  5.9758e-02],\n",
      "        [-6.6777e+01, -8.2797e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.8674e+01, -9.7651e+03],\n",
      "        [-8.3067e+01, -1.0196e+04],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.4090e+01, -9.2745e+03],\n",
      "        [-6.8065e+01, -8.3939e+03],\n",
      "        [-6.4076e+01, -7.9806e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [-7.1012e+01, -8.7328e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00],\n",
      "        [ 0.0000e+00,  0.0000e+00]]), 'exp_avg_sq': tensor([[1.2795e+02, 2.0651e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.3969e+02, 2.2619e+06],\n",
      "        [1.1684e+02, 1.8483e+06],\n",
      "        [1.2485e+02, 2.0144e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [8.6863e+01, 1.4029e+06],\n",
      "        [1.3305e+02, 2.1130e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.3823e+02, 2.2189e+06],\n",
      "        [1.7809e+02, 2.8407e+06],\n",
      "        [1.4981e+02, 2.4024e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.0384e+02, 1.6902e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.2837e+02, 2.0318e+06],\n",
      "        [1.4960e+02, 2.4279e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [8.6510e+01, 1.3989e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.4405e+02, 2.2602e+06],\n",
      "        [1.0111e+02, 1.5958e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.5401e+02, 2.4626e+06],\n",
      "        [1.4295e+02, 2.2328e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1189e+02, 1.7789e+06],\n",
      "        [1.3975e+02, 2.2315e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.5413e+02, 2.4941e+06],\n",
      "        [1.2871e+02, 2.0753e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.2765e+02, 2.0567e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [9.9876e+01, 1.6226e+06],\n",
      "        [1.3316e+02, 2.1264e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [8.1060e+01, 1.2858e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1704e+02, 1.9032e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.0500e+02, 1.6854e+06],\n",
      "        [1.1444e+02, 1.8442e+06],\n",
      "        [1.5769e+02, 2.5055e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.0333e+02, 1.6621e+06],\n",
      "        [1.3322e+02, 2.1445e+06],\n",
      "        [1.2435e+02, 2.0188e+06],\n",
      "        [1.2029e+02, 1.9157e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.3635e+02, 2.1868e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.7153e+02, 2.6863e+06],\n",
      "        [1.4415e+02, 2.3168e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.2247e+02, 1.9568e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.0925e+02, 1.7639e+06],\n",
      "        [8.6135e+01, 1.3856e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.4413e+02, 2.2945e+06],\n",
      "        [1.1300e+02, 1.8269e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1061e+02, 1.7675e+06],\n",
      "        [7.1418e+01, 1.1554e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1788e+02, 1.8470e+06],\n",
      "        [1.1748e+02, 1.8869e+06],\n",
      "        [1.1271e+02, 1.8159e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1286e+02, 1.8573e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.4342e+02, 2.3000e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.5512e+02, 2.5180e+06],\n",
      "        [1.6341e+02, 2.6143e+06],\n",
      "        [1.2446e+02, 2.0278e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [9.7810e+01, 1.5946e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.4422e+02, 2.3055e+06],\n",
      "        [4.1815e-04, 1.8037e+01],\n",
      "        [9.8577e+01, 1.5773e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.3752e+02, 2.2061e+06],\n",
      "        [1.5401e+02, 2.4195e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.2238e+02, 1.9949e+06],\n",
      "        [1.0200e+02, 1.6206e+06],\n",
      "        [9.1586e+01, 1.4780e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [1.1212e+02, 1.7696e+06],\n",
      "        [0.0000e+00, 0.0000e+00],\n",
      "        [0.0000e+00, 0.0000e+00]])}, 1: {'step': 78, 'exp_avg': tensor([-6.7232e+01,  0.0000e+00, -7.0108e+01, -6.4923e+01, -6.6471e+01,\n",
      "         0.0000e+00,  0.0000e+00, -5.6187e+01, -6.8768e+01,  0.0000e+00,\n",
      "        -7.0219e+01, -7.9066e+01, -7.2773e+01,  0.0000e+00,  0.0000e+00,\n",
      "        -6.1110e+01,  0.0000e+00,  0.0000e+00, -6.7502e+01, -7.2616e+01,\n",
      "         0.0000e+00,  0.0000e+00,  0.0000e+00, -5.5918e+01,  0.0000e+00,\n",
      "        -7.2022e+01, -6.0797e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "        -7.3946e+01, -7.1486e+01,  0.0000e+00, -6.3255e+01, -7.0398e+01,\n",
      "         0.0000e+00, -7.3843e+01, -6.7500e+01,  0.0000e+00,  0.0000e+00,\n",
      "        -6.7305e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "        -5.9514e+01, -6.8946e+01,  0.0000e+00, -5.4315e+01,  0.0000e+00,\n",
      "        -6.4447e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00, -6.1446e+01,\n",
      "        -6.4256e+01, -7.4824e+01,  0.0000e+00, -6.0730e+01, -6.8820e+01,\n",
      "        -6.6447e+01, -6.5507e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "        -6.9648e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00, -7.8231e+01,\n",
      "        -7.1546e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "         0.0000e+00, -6.6082e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "         0.0000e+00,  0.0000e+00, -6.2523e+01, -5.5533e+01,  0.0000e+00,\n",
      "         0.0000e+00, -7.1974e+01, -6.3189e+01,  0.0000e+00,  0.0000e+00,\n",
      "         0.0000e+00, -6.2942e+01, -5.0529e+01,  0.0000e+00, -6.5457e+01,\n",
      "        -6.4614e+01, -6.3665e+01,  0.0000e+00, -6.2855e+01,  0.0000e+00,\n",
      "        -7.1447e+01,  0.0000e+00, -7.3690e+01, -7.6146e+01, -6.6405e+01,\n",
      "         0.0000e+00,  0.0000e+00, -5.9105e+01,  0.0000e+00,  0.0000e+00,\n",
      "        -7.1507e+01,  4.4594e-05, -5.9638e+01,  0.0000e+00,  0.0000e+00,\n",
      "        -7.0280e+01, -7.4528e+01,  0.0000e+00,  0.0000e+00, -6.5835e+01,\n",
      "        -6.0915e+01, -5.7236e+01,  0.0000e+00,  0.0000e+00,  0.0000e+00,\n",
      "        -6.3648e+01,  0.0000e+00,  0.0000e+00]), 'exp_avg_sq': tensor([9.9664e+01, 0.0000e+00, 1.0848e+02, 9.2080e+01, 9.7293e+01, 0.0000e+00,\n",
      "        0.0000e+00, 6.7683e+01, 1.0423e+02, 0.0000e+00, 1.0785e+02, 1.3940e+02,\n",
      "        1.1714e+02, 0.0000e+00, 0.0000e+00, 8.0767e+01, 0.0000e+00, 0.0000e+00,\n",
      "        1.0078e+02, 1.1609e+02, 0.0000e+00, 0.0000e+00, 0.0000e+00, 6.7458e+01,\n",
      "        0.0000e+00, 1.1380e+02, 7.9788e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
      "        1.2055e+02, 1.1313e+02, 0.0000e+00, 8.7703e+01, 1.0929e+02, 0.0000e+00,\n",
      "        1.1996e+02, 1.0038e+02, 0.0000e+00, 0.0000e+00, 9.9241e+01, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 0.0000e+00, 7.7633e+01, 1.0444e+02, 0.0000e+00,\n",
      "        6.3572e+01, 0.0000e+00, 9.0805e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
      "        8.2026e+01, 8.9563e+01, 1.2380e+02, 0.0000e+00, 8.0569e+01, 1.0382e+02,\n",
      "        9.6256e+01, 9.4254e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0635e+02,\n",
      "        0.0000e+00, 0.0000e+00, 0.0000e+00, 1.3570e+02, 1.1278e+02, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.6013e+01, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 8.5011e+01, 6.7132e+01,\n",
      "        0.0000e+00, 0.0000e+00, 1.1325e+02, 8.7756e+01, 0.0000e+00, 0.0000e+00,\n",
      "        0.0000e+00, 8.6416e+01, 5.5443e+01, 0.0000e+00, 9.3048e+01, 9.1702e+01,\n",
      "        8.7779e+01, 0.0000e+00, 8.7031e+01, 0.0000e+00, 1.1203e+02, 0.0000e+00,\n",
      "        1.2048e+02, 1.2799e+02, 9.6455e+01, 0.0000e+00, 0.0000e+00, 7.5945e+01,\n",
      "        0.0000e+00, 0.0000e+00, 1.1308e+02, 2.5614e-04, 7.7091e+01, 0.0000e+00,\n",
      "        0.0000e+00, 1.0772e+02, 1.2173e+02, 0.0000e+00, 0.0000e+00, 9.4727e+01,\n",
      "        8.0079e+01, 7.1524e+01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 8.8261e+01,\n",
      "        0.0000e+00, 0.0000e+00])}, 2: {'step': 78, 'exp_avg': tensor([[ 1.7764e-03,  0.0000e+00,  8.7651e-04,  ...,  4.2011e-04,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        [ 5.7054e-03,  0.0000e+00,  2.8275e-03,  ...,  1.3226e-03,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        [ 1.9802e-01,  0.0000e+00,  1.0405e-01,  ...,  4.8134e-02,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        ...,\n",
      "        [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  ...,  0.0000e+00,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        [-1.2493e+03,  0.0000e+00, -7.4626e+02,  ..., -4.4298e+02,\n",
      "          0.0000e+00,  0.0000e+00],\n",
      "        [-1.0973e+03,  0.0000e+00, -6.5630e+02,  ..., -3.9048e+02,\n",
      "          0.0000e+00,  0.0000e+00]]), 'exp_avg_sq': tensor([[6.2420e-01, 0.0000e+00, 1.5196e-01,  ..., 3.4954e-02, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [1.9396e+00, 0.0000e+00, 4.7674e-01,  ..., 1.0423e-01, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [3.3495e+01, 0.0000e+00, 8.9641e+00,  ..., 1.8876e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        ...,\n",
      "        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [4.0469e+04, 0.0000e+00, 1.4235e+04,  ..., 4.9078e+03, 0.0000e+00,\n",
      "         0.0000e+00],\n",
      "        [3.1242e+04, 0.0000e+00, 1.1036e+04,  ..., 3.8330e+03, 0.0000e+00,\n",
      "         0.0000e+00]])}, 3: {'step': 78, 'exp_avg': tensor([ 2.7152e-05,  8.4383e-05,  1.3731e-03,  1.1845e-05,  1.7510e-05,\n",
      "         0.0000e+00, -2.8481e+01, -2.7071e+01, -2.8613e+01,  0.0000e+00,\n",
      "        -3.0292e+01, -1.9882e+01,  0.0000e+00,  0.0000e+00,  1.5695e-04,\n",
      "         0.0000e+00,  0.0000e+00, -1.7844e+01, -2.1235e+01,  0.0000e+00,\n",
      "         1.9830e-04,  0.0000e+00, -2.2539e+01,  0.0000e+00,  0.0000e+00,\n",
      "        -4.9468e+00, -1.8913e+01,  0.0000e+00, -2.4664e+01,  0.0000e+00,\n",
      "         0.0000e+00,  0.0000e+00,  1.0405e-04,  2.7779e-05, -2.4286e+01,\n",
      "        -3.1167e+01,  0.0000e+00, -2.1999e+01, -2.0660e+01,  7.1188e-05,\n",
      "         0.0000e+00, -1.7003e+01,  2.8956e-04,  0.0000e+00,  3.1078e-05,\n",
      "         0.0000e+00,  2.2799e-05,  0.0000e+00,  0.0000e+00,  3.8860e-04,\n",
      "         0.0000e+00, -2.9883e-03, -2.0425e+01,  0.0000e+00,  0.0000e+00,\n",
      "         1.8098e-03,  0.0000e+00,  0.0000e+00, -2.2699e+01,  0.0000e+00,\n",
      "         0.0000e+00,  0.0000e+00, -2.3607e+01, -2.2499e+01,  1.2291e-05,\n",
      "        -1.8201e+01, -1.8362e+01, -1.8081e+01,  0.0000e+00,  0.0000e+00,\n",
      "         3.5256e-02, -2.3307e+01,  9.0312e-06,  0.0000e+00, -1.8813e+01,\n",
      "         0.0000e+00,  0.0000e+00, -2.3889e+01,  7.2192e-06,  0.0000e+00,\n",
      "         1.0268e-03,  0.0000e+00,  4.0582e-04,  2.3179e-04, -2.3007e+01,\n",
      "        -2.2389e+01,  0.0000e+00,  2.5694e-05,  0.0000e+00,  0.0000e+00,\n",
      "         5.1233e-04, -1.7447e+01,  0.0000e+00,  0.0000e+00,  1.1753e-05,\n",
      "         0.0000e+00, -2.2523e+01,  4.8671e-05,  0.0000e+00,  4.2435e-05,\n",
      "        -1.9792e-03,  0.0000e+00, -1.8553e+01,  0.0000e+00,  0.0000e+00,\n",
      "         0.0000e+00, -1.7023e+01,  5.6887e-05,  7.1275e-04,  0.0000e+00,\n",
      "         0.0000e+00,  0.0000e+00, -2.5469e+01,  0.0000e+00,  0.0000e+00,\n",
      "         0.0000e+00, -2.4283e+01,  4.0092e-04,  0.0000e+00, -1.7997e+01,\n",
      "         0.0000e+00,  0.0000e+00,  4.4857e-04,  0.0000e+00, -1.9467e+01,\n",
      "         0.0000e+00, -1.6842e+01, -1.3713e+01]), 'exp_avg_sq': tensor([1.9140e-04, 4.5089e-04, 7.2595e-03, 1.4464e-04, 9.7787e-05, 0.0000e+00,\n",
      "        1.9625e+01, 1.8426e+01, 2.2152e+01, 0.0000e+00, 2.2238e+01, 1.0351e+01,\n",
      "        0.0000e+00, 0.0000e+00, 1.6503e-03, 0.0000e+00, 0.0000e+00, 8.2290e+00,\n",
      "        1.1452e+01, 0.0000e+00, 1.6362e-03, 0.0000e+00, 1.1869e+01, 0.0000e+00,\n",
      "        0.0000e+00, 8.7331e-01, 8.9482e+00, 0.0000e+00, 1.5196e+01, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 8.5170e-04, 2.4429e-04, 1.3955e+01, 2.3332e+01,\n",
      "        0.0000e+00, 1.2372e+01, 1.1076e+01, 1.0204e-03, 0.0000e+00, 6.9163e+00,\n",
      "        3.3941e-03, 0.0000e+00, 4.6825e-04, 0.0000e+00, 6.3494e-04, 0.0000e+00,\n",
      "        0.0000e+00, 4.3919e-03, 0.0000e+00, 1.4795e-02, 1.1193e+01, 0.0000e+00,\n",
      "        0.0000e+00, 6.7148e-03, 0.0000e+00, 0.0000e+00, 1.1929e+01, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 1.2758e+01, 1.2412e+01, 1.5573e-04, 9.8825e+00,\n",
      "        8.4208e+00, 7.5338e+00, 0.0000e+00, 0.0000e+00, 8.2071e-04, 1.3604e+01,\n",
      "        3.8850e-05, 0.0000e+00, 9.1073e+00, 0.0000e+00, 0.0000e+00, 1.5815e+01,\n",
      "        3.0476e-05, 0.0000e+00, 4.9779e-03, 0.0000e+00, 2.1516e-03, 1.2273e-03,\n",
      "        1.2540e+01, 1.1522e+01, 0.0000e+00, 2.0397e-04, 0.0000e+00, 0.0000e+00,\n",
      "        4.5250e-03, 7.4763e+00, 0.0000e+00, 0.0000e+00, 4.3479e-05, 0.0000e+00,\n",
      "        1.3323e+01, 3.3419e-04, 0.0000e+00, 8.4191e-04, 1.3142e-03, 0.0000e+00,\n",
      "        8.1139e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 7.2317e+00, 8.9596e-04,\n",
      "        7.7004e-03, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.6397e+01, 0.0000e+00,\n",
      "        0.0000e+00, 0.0000e+00, 1.4428e+01, 2.0620e-03, 0.0000e+00, 9.9869e+00,\n",
      "        0.0000e+00, 0.0000e+00, 3.8931e-03, 0.0000e+00, 1.0024e+01, 0.0000e+00,\n",
      "        7.1233e+00, 4.9641e+00])}, 4: {'step': 78, 'exp_avg': tensor([[-3.5481e-04, -1.6544e-03, -3.8866e-01,  ...,  0.0000e+00,\n",
      "         -5.5628e+03, -4.0008e+03],\n",
      "        [ 0.0000e+00,  0.0000e+00, -1.8231e+00,  ...,  0.0000e+00,\n",
      "         -2.0197e+04, -1.4680e+04],\n",
      "        [-6.5178e-04, -2.7406e-03, -1.3977e-01,  ...,  0.0000e+00,\n",
      "         -2.3825e+03, -1.7225e+03],\n",
      "        ...,\n",
      "        [-9.5346e-04, -3.7079e-03, -5.7201e-01,  ...,  0.0000e+00,\n",
      "         -8.3147e+03, -6.0461e+03],\n",
      "        [-2.7983e-03, -1.0636e-02, -4.5734e-01,  ...,  0.0000e+00,\n",
      "         -3.9155e+03, -2.8536e+03],\n",
      "        [ 5.6056e-04,  3.9654e-05, -1.7994e-01,  ...,  0.0000e+00,\n",
      "         -3.1201e+03, -2.3010e+03]]), 'exp_avg_sq': tensor([[3.3197e-02, 2.2095e-01, 2.4609e+02,  ..., 0.0000e+00, 1.3463e+06,\n",
      "         6.4489e+05],\n",
      "        [0.0000e+00, 0.0000e+00, 2.9514e+03,  ..., 0.0000e+00, 1.4014e+07,\n",
      "         7.0855e+06],\n",
      "        [9.8444e-02, 8.0540e-01, 2.5746e+01,  ..., 0.0000e+00, 2.0130e+05,\n",
      "         9.7836e+04],\n",
      "        ...,\n",
      "        [2.6814e-01, 1.5701e+00, 3.3581e+02,  ..., 0.0000e+00, 2.6158e+06,\n",
      "         1.3156e+06],\n",
      "        [1.9931e+00, 1.5328e+01, 2.2063e+02,  ..., 0.0000e+00, 7.5721e+05,\n",
      "         3.7908e+05],\n",
      "        [1.6851e-01, 7.6556e-01, 6.6280e+01,  ..., 0.0000e+00, 1.1409e+06,\n",
      "         5.9608e+05]])}, 5: {'step': 78, 'exp_avg': tensor([-16.2241, -58.5939,  -7.3782,  -8.2380, -10.7808,  -1.4850,  -9.7549,\n",
      "        -58.4104, -33.2913, -11.5975, -30.7120, -30.6814, -22.0120, -11.9947,\n",
      "         -7.1787]), 'exp_avg_sq': tensor([ 11.6116, 119.3004,   2.8985,   2.7059,   6.3735,  17.8177,   4.1852,\n",
      "        135.3201,  38.1819,  22.6498,  32.0674,  28.4984,  24.4387,  11.6541,\n",
      "         12.8899])}}\n",
      "param_groups \t [{'lr': 0.001, 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False, 'params': [0, 1, 2, 3, 4, 5]}]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "episodes = 100001\n",
    "wealth = 100.0\n",
    "utilities_mod = []\n",
    "rewards_mod = []\n",
    "step_rewards = []\n",
    "rsum = 0\n",
    "wealth_episodes = []\n",
    "number_of_actions = utes\n",
    "\n",
    "Mark1 = Market(kappa, episodes, time_periods, mu, rf, sigma, u_star)\n",
    "\n",
    "\n",
    "for i_episode in range(episodes-1):\n",
    "\n",
    "    if i_episode%10000 ==0:\n",
    "        print(i_episode)\n",
    "        \n",
    "    state = Mark1.reset() \n",
    "    action = model(Variable(torch.FloatTensor(np.float32(state)))).argmax().item()\n",
    "\n",
    "    while True:    \n",
    "        new_state , reward, done, final_wealth = Mark.step(action) #(prop, wealth))\n",
    "        action = model(Variable(torch.FloatTensor(np.float32(new_state)))).argmax().item()\n",
    "        rsum += reward\n",
    "        state = new_state\n",
    "\n",
    "        #print(new_state)\n",
    "        #print(model(Variable(torch.FloatTensor(np.float32(new_state)))))\n",
    "        step_rewards.append(reward)\n",
    "\n",
    "        if done:\n",
    "            wealth_episodes.append(final_wealth)\n",
    "            utilities_mod.append(np.log(final_wealth))\n",
    "            rewards_mod.append(rsum)\n",
    "            rsum = 0\n",
    "            #wealth = 100.0\n",
    "            #print(i_episode, final_wealth)\n",
    "            break \n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "0\n",
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
      "80000\n",
      "90000\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "utilities_test = utilities_mod\n",
    "rewards_test = rewards_mod\n",
    "step_rew_test = step_rewards\n",
    "wealth_test = wealth_episodes"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "from lib.sim_prices import make_prices\n",
    "from lib.execute_strat import execute_strat\n",
    "from lib.graphs import make_baseline_graphs, make_agent_graphs, plot_sample_paths, plot_disc_utility, \\\n",
    "                        plot_mv_equiv, plot_const_step"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "#episodes = 99##3000000\n",
    "#Q, utilities_mod, rewards_mod, wealth_episodes = train_agent(kappa, episodes)\n",
    "\n",
    "def make_agent_graphs_new(rewards_test_best, rewards_test_rand, rewards_test,\n",
    "                      utilities_test_best, utilities_test_rand, utilities_test,\n",
    "                     wealth_test_rand, wealth_test_best, wealth_test):\n",
    "\n",
    "    block_utilities_test = np.mean(np.array(utilities_test).reshape(1000,-1),0)\n",
    "    block_utilities_test_rand = np.mean(np.array(utilities_test_rand).reshape(1000,-1),0)\n",
    "    block_utilities_test_best = np.mean(np.array(utilities_test_best).reshape(1000,-1),0)\n",
    "\n",
    "\n",
    "    block_wealth_test_rand = np.mean(np.array(wealth_test_rand).reshape(1000,-1),0)\n",
    "    block_wealth_test_best = np.mean(np.array(wealth_test_best).reshape(1000,-1),0)\n",
    "    block_wealth_test = np.mean(np.array(wealth_test).reshape(1000,-1),0)\n",
    "\n",
    "\n",
    "    block_rewards_test = np.mean(np.array(rewards_test).reshape(1000,-1),0)\n",
    "    block_rewards_test_rand = np.mean(np.array(rewards_test_rand).reshape(1000,-1),0)\n",
    "    block_rewards_test_best = np.mean(np.array(rewards_test_best).reshape(1000,-1),0)\n",
    "\n",
    "    #Calculate sharpe ratios instead of longitudinally do it at the end blocks of 1000 again\n",
    "\n",
    "    #mu - rf/ sigma\n",
    "    wr = np.array(wealth_test_rand).reshape(100,-1)\n",
    "    wr_sharpe = (wr.mean(axis=0)/100-1)/(wr.std(axis=0)/100)\n",
    "\n",
    "    wt = np.array(wealth_test).reshape(100,-1)\n",
    "    wt_sharpe = (wt.mean(axis=0)/100-1)/(wt.std(axis=0)/100)\n",
    "\n",
    "    wb = np.array(wealth_test_best).reshape(100,-1)\n",
    "    wb_sharpe = (wb.mean(axis=0)/100-1)/(wb.std(axis=0)/100)\n",
    "\n",
    "\n",
    "    sns.distplot(block_rewards_test_best, label=\"Merton optimal\")\n",
    "    sns.distplot(block_rewards_test, label=\"Trained Agent\")\n",
    "    sns.distplot(block_rewards_test_rand, label=\"Random agent\")\n",
    "    plt.title('Distribution of Final rewards Merton v Trained Agent v Random (per 1000 episodes)')\n",
    "    plt.xlabel('Episode Rewards')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.violinplot([block_rewards_test_rand, block_rewards_test, block_rewards_test_best])\n",
    "    plt.xticks([1,2,3], [\"Random Agent\", \"Trained Agent\", \"Merton Optimal\"], rotation=60, size=12)\n",
    "    plt.ylabel(\"Test Rewards\", size=12)\n",
    "    plt.xlabel(\"Agent\", size=12)\n",
    "    ax = plt.gca()\n",
    "    ax.set_axis_bgcolor('white')\n",
    "    ax.grid(0)\n",
    "    plt.title(\"Violin plot of Rewards - Random v Merton v Agent\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    sns.distplot(block_utilities_test_best, label=\"Merton optimal\")\n",
    "    sns.distplot(block_utilities_test, label=\"Trained Agent\")\n",
    "    sns.distplot(block_utilities_test_rand, label=\"Random agent\")\n",
    "    plt.title('Distribution of Final Utilities Merton v Trained Agent v Random (per 1000 episodes)')\n",
    "    plt.xlabel('Episode Utilities')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.violinplot([block_utilities_test_rand, block_utilities_test, block_utilities_test_best])\n",
    "    plt.xticks([1,2,3], [\"Random Agent\", \"Trained Agent\", \"Merton Optimal\"], rotation=60, size=12)\n",
    "    plt.ylabel(\"Test Utility\", size=12)\n",
    "    plt.xlabel(\"Agent\", size=12)\n",
    "    ax = plt.gca()\n",
    "    ax.set_axis_bgcolor('white')\n",
    "    ax.grid(0)\n",
    "    plt.title(\"Violin plot of utilities - Random v Merton v Agent\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(np.convolve(utilities_test_best, np.ones((10000,))/10000, mode='valid'), label='Merton Optimal')\n",
    "    plt.plot(np.convolve(utilities_test_rand, np.ones((10000,))/10000, mode='valid'), label='Random agent')\n",
    "    plt.plot(np.convolve(utilities_test, np.ones((10000,))/10000, mode='valid'), label='Trained agent')\n",
    "    plt.title('Moving average 10,000 episode utilities')\n",
    "    plt.ylabel('Utility')\n",
    "    plt.xlabel('Episode')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.violinplot([block_wealth_test_rand, block_wealth_test, block_wealth_test_best])\n",
    "    plt.xticks([1,2,3], [\"Random Agent\", \"Trained Agent\", \"Merton Optimal\"], rotation=60, size=12)\n",
    "    plt.ylabel(\"Test Wealth\", size=12)\n",
    "    plt.xlabel(\"Agent\", size=12)\n",
    "    ax = plt.gca()\n",
    "    ax.set_axis_bgcolor('white')\n",
    "    ax.grid(0)\n",
    "    plt.title(\"Violin plot of Wealth - Random v Merton v Agent\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    sns.distplot(block_wealth_test_best, label=\"Merton optimal\")\n",
    "    sns.distplot(block_wealth_test, label=\"Trained Agent\")\n",
    "    sns.distplot(block_wealth_test_rand, label=\"Random agent\")\n",
    "    plt.title('Distribution of Final Wealth Merton v Trained Agent v Random (per 1000 episodes)')\n",
    "    plt.xlabel('Episode Wealth')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    plt.violinplot([wr_sharpe, wt_sharpe, wb_sharpe])\n",
    "    plt.xticks([1,2,3], [\"Random Agent\", \"Trained Agent\", \"Merton Optimal\"], rotation=60, size=12)\n",
    "    plt.ylabel(\"Test Sharpe\", size=12)\n",
    "    plt.xlabel(\"Agent\", size=12)\n",
    "    ax = plt.gca()\n",
    "    ax.set_axis_bgcolor('white')\n",
    "    ax.grid(0)\n",
    "    plt.title(\"Violin plot of Sharpe ratios (M period) - Random v Merton v Agent\")\n",
    "    plt.show()\n",
    "\n",
    "    sns.distplot(wb_sharpe, label=\"Merton optimal\")\n",
    "    sns.distplot(wt_sharpe, label=\"Trained Agent\")\n",
    "    sns.distplot(wr_sharpe, label=\"Random agent\")\n",
    "    plt.title('Distribution of Final Sharpe ratios  Merton v Trained Agent v Random (per 1000 episodes)')\n",
    "    plt.xlabel('Episode Sharpe')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    return block_utilities_test_rand, block_utilities_test, block_utilities_test_best, \\\n",
    "           block_rewards_test_rand, block_rewards_test, block_rewards_test_best, \\\n",
    "           block_wealth_test_rand, block_wealth_test, block_wealth_test_best, \\\n",
    "           wr_sharpe, wt_sharpe, wb_sharpe"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "wealth = 100.0\r\n",
    "\r\n",
    "merton_ratio = (mu-rf)/sigma**2\r\n",
    "best_action = np.argmin(np.abs(u_star-merton_ratio))\r\n",
    "#temp change to execute strat\r\n",
    "utilities_test_rand, rewards_test_rand, step_rew_rand, wealth_test_rand = execute_strat(kappa,mu, \r\n",
    "                                                                      rf, sigma, utes,u_star,best_action, 'Random',\r\n",
    "                                                                      episodes=100001,\r\n",
    "                                                                      time_periods=time_periods, wealth=wealth)\r\n",
    "utilities_test_best, rewards_test_best, step_rew_best, wealth_test_best = execute_strat(kappa, mu, rf, sigma, \r\n",
    "                                                                      utes, u_star, best_action,'Merton', \r\n",
    "                                                                      episodes=100001,\r\n",
    "                                                                      time_periods=time_periods, wealth=wealth)"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "#utilities_test, rewards_test, step_rew_test, wealth_test = execute_strat(kappa, mu, rf, sigma, utes, u_star,best_action, \r\n",
    "#                                                            'Agent', q_values=Q, \r\n",
    "#                                                            time_periods=time_periods, wealth=wealth)\r\n",
    "\r\n",
    "results = make_agent_graphs_new(rewards_test_best, rewards_test_rand, rewards_test,\r\n",
    "                  utilities_test_best, utilities_test_rand, utilities_test,\r\n",
    "                         wealth_test_rand, wealth_test_best, wealth_test)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2985ff60>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/johngoodacre/anaconda/envs/pytorch/lib/python3.6/site-packages/ipykernel_launcher.py:49: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a27b6b080>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c125ba8>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/johngoodacre/anaconda/envs/pytorch/lib/python3.6/site-packages/ipykernel_launcher.py:69: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1baa4b00>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cabac50>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/johngoodacre/anaconda/envs/pytorch/lib/python3.6/site-packages/ipykernel_launcher.py:88: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c236a58>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c481da0>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/Users/johngoodacre/anaconda/envs/pytorch/lib/python3.6/site-packages/ipykernel_launcher.py:108: MatplotlibDeprecationWarning: The set_axis_bgcolor function was deprecated in version 2.0. Use set_facecolor instead.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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DMIwhmGIwDMOogGKKMhlMMRiGYVRCevSCKQbDMIxKSJFeMMVgGIYxFmlSCmCKwTAMozJSpB1MMRiGYYxFipQCBNuoxzBigzVwMUajiEUlGYYxDjZu7WHj1t6wxTACxSmFfCEdysEsBsNgcrP2N37yFmpqsjbzTzKePsjnC+SyuXBlmQLMYjCMSVL0HA1Gcind3cF8IVQ5pgpTDIYxCba7FkwvpIK0uJJMMRjGJMh7M8h0DBfppVRAzywGwzDGJC0DheHI59MxBTDFYBiTYNAbKNJUkjmNlEJV0zIRsKikCggrxt2iXKJPfvtAYZoh0ZSikmyNwZgsG7f2srHd4tuTjFkM6SJvFsPkEJEscDmwH9AHLFXVZb7tLwC+CmSAtcDpqhrJUXSiM/ezP30rmWzGZv4JJl9Ix0CRdkp63yyGyXMS0KCqhwIXABeXNohIBvg2cLaqHg7cAuwSoCzhkY7nKLUMWlRSOvBucMEUw6QpDfio6n3Awb5tzwE2Ae8VkT8Ds1RVA5TFMAJhe5RKOsaL1GMWw+RpAdp9r/MiUnJdzQYOA74JHAO8TESODlAWwwiEtAwUaacUlVRIyWJSkIqhA2j2X0tVB72/NwHLVPXfqjqAsywOLj+BYUSd0hpDmipvphlzJU2eu4HjAUTkEOBR37YngSYR2cN7/RLgXwHKEgrpeITSjT/hKS2DRtrwW4VpsRCDzGO4CThWRO7BRR6dLSKnAk2qepWInAP8yFuIvkdVfxugLIYRCOWDRjabCVEaIwj8IaoWrjpJVLUAnFv29uO+7X8AXhjU9Q1jKhgsGzRqayw1KGn47/HAYDoUgz3FhjEJ/INGWsolpI2+gfz2v4vFdNxnUwyGMUEKheKQNYa0zCbTRl9/fsjr3rLXScQUQ8CkY6kqnfQPDh0g/DNLIzn09A0Ofd07OMKeycEUg2FMkPKZZPlrIxl0lymC7t6BkCSZOkwxGMYEKR8wymeWRvzJF4p0lSmCbd39IUkzdZhiCBxzJiWVrp6hA0ZnzwDFlGTGpoWOrr4dfsLdvYMMDCbbOjTFECQ2RiSWYrFIR9fQmWOhUNxBWRjxZuu2vhHeT7bVYIohQIrb/2Mkjc6egWHDFreMMJAY8aNQKLKlY/j7ubmjZ4qlmVpMMRjGBNi4dfiBYXN7r7mTEkJ7V9+IOQvtXf2JdieZYggYGyKSRz5fYNMInfn6BvK0dybbzZAWNmwZxSoojrE95phiMIxxsn5Lz6gF89Zu6ppCaYwg6O0fHHF9ocT6Ld2JtQ5NMRjGOMgXiqwZY+Dv6OrfYWHaiBfrNnePuU//QIHNHZHsRjxpTDEESjJnE2lm7aYuBisofbFi3bYpkMYIgsF8oWI30ZqNybQOTTEESEKtzNTSP5CveCDo6hlgU3tyfdBJZt3m7op7a3T3DtLembxINFMMAWPKITksX9sxrmY8y9duS0UlziSRzxfGvUa0akNnQNKEhykGw6iArdv6RoxpH4nBwQIr1ydv0Egy6zZ3D6mYWwmd3QOJsxpMMQRI0fdfI77k8wWeXtM+oWPXb+5ORW2dJDCYL4wZWDASSZsAmGIIkqK5kpLAyvWd9A9M3CX01Op26wcdA9Zu6hq3tVCiq2dgzPDWOGGKIUCKZi3Enm3d/RWFLo5Gb18+kX7oJDGYL7B20+Tuc5LusSmGgCjNEE01xJdCochTqyfmQipnzaauVNTxjytrNnZN2qrr6hlgy7Zk5DWYYgiI7dEophliy6oNnfT2VakeThGeWt2R2EzZODMwWJi0VVhiVULWGkwxBESp/2+Rog0GMaS7d2DCC5Ej0dUzULUByKgeazdN3loo0d07yJYEZEObYggIfz9gaxIfL4rFIk+v6QjE2nML2cmtyhk3BgbzVVfWKzd0xn4yWBPUiUUkC1wO7Af0AUtVdZlv+3uBpcAG7623qqoGJc9U449i6R/IU1ebC1EaYzxs3NpLZ3cw6wGFQpHlazvYc+eZgZzfGB+rq7C2UE5P7yCbO3ppa22s6nmnksAUA3AS0KCqh4rIIcDFwIm+7QcBZ6jqQwHKEBp9/c/2/+0byNMUoixG5QzmC4HXOdrS0Ud7Zx+tTfWBXscYnb6BPOsDcu2tXN/JzOYGstlMIOcPmiBdSYcDtwCo6n3AwWXbDwI+LCJ/EZEPByhHKPT254f924g2q9Z3TkkZi/GW1zCqz4p12wLLM+rrz7N+S3zXk4JUDC2AP9YvLyJ+C+V64FzgaOBwETkhQFmmnF6fxdDTNzjKnkZU6O4dYN0U/Zh7+6rv2zYqZ1t3P5tHaLZULVZt6Ixtl7cgFUMH0Oy/lqoOAohIBvi6qm5U1X7gt8ABAcoypeQLxSFWgimGeLB8zbYpDS+O88ARZ6qZnzIa+XyR5WvjWX49SMVwN3A8gLfG8KhvWwvwTxFp8pTE0UBi1hp6egeGDDA9fYPkzW0QaTa190x5TaNCocgzMR044kxV81PGYHN7byzDV4NUDDcBvSJyD/A14L0icqqIvEVV24GPAH8E7gL+paq/C1CWKaWzpyyipegpCyOS5POF0AboTe29VmRvCmnv7Jvy5jpPrm6PXYhyYFFJqlrArSH4edy3/TrguqCuHyY7KAZgW/cATdPqQpDGGIuVGzpDzTV5enUHz92tLbYRLHGhfyDPE6uCdyGVk88XWbZyK3vtMis299gS3AJg2zD9fm1WGE06I5CN3NM3OO7mMMb4KBSK/HfF1orasgZBZ/cAy9d2hHLtiWCKocr09A0OO/vs6OqPfTZk0ti+CBmB27JqQ6cFKQTIk6vb6RrGkp9KNmzpic0EwBRDlRmpk1OhUBzWxWSEx6oNnfT0RmMwLhbhiZVbLbchAFau3xZ4aGqlPLNuWywqsJpiqDJbR2nxl6RGHnGno6u/6kXyJkt372CiavpHgQ1beli9IUL3uQhPrGyP/CRxzMVnEZkOfA7YC/g/4CLgA6pq2TllDOYLdAyzvlBiy7Zedp7XPOJ2Y2oYGCzwxMqtkXAhlbNmYxct0+usXEYVaO/s46kJtmQNkkKhyH+e2cI+u86ioS7IqkQTpxKL4RtAL7AT0A/MBa4MUqi4snVb36iDTW9f3pq1hEyxWOSJVVsjXfH2iZXxC2+MGt29A/x3RTSVP8DgYIH/PrN1SsqvTIRKFMNBqvohYEBVu3BWw0HBihVPNrb3jLnPpoj4OtPKyvWddHRGO0JsMF/gvytsvWGiDHiDbtS/v56+QZat3BrJoJRKFEP51CUDRFPNhUjfQH5UN1KJjVt7IvkgpIHNHb1Tntw0Ubp64hXeGBWKxSLLVmylLyYWV0dnf+DVfCdCJYrhLyLyOaBRRF4G3Aj8KVCpYsiGLd0Vma0DgwVbhA6B7t4BngwhuWkybNjSE1hZ6KSyYt222OUMrd3UzeaIlc2oRDF8EBgAunA9FRT4QJBCxY18ocj6LWO7kUqEnVCVNvIxds0sX9tBZ8wGurBo7+xj7aZ4/raeiljZjDGXxL3qp58Uka8AeYtG2pFNW3vGlVHZ0dVPZ88ATY21AUpllHhqdQd9Me2JUSzCspVbee5us6mtsejykRjMF2JnEfrJ54s8ubqdvXaZFbYoQAUWg4jsKSL3AluADhG5Q0QWBS9aPCgUihOKPV+1Pnp+xSSyYUtP5Mz08dI/UODpCIZdRolVIde7qgYdnf1sqiCAZSqoZApyDfADYDquXPZvgKsDlClWrN/SPaEHsr2zv6LFamPi9A3kE7OAu6WjLzKDRtTo7RtMjHt2xbptkXB5VqIYmlX1MlXtU9VuVf0asDBoweLAwGCelesnnqm6fG2HRSgFyIq10fiRVYvla7eRj2jce5is2dQV2XyF8dI/UKgo7D1oKlEMT4nIC0svRGRv4KngRIoPK9Z1Tmrg6elNzkwnanR298fehVTO4GAhcmU8wmZgMM/GreEPpNUkCoX2KsnHnosLWf0bMIhLblvrvUZVDwxQvsjS3tlXlQdy5fpOZrU0UFebq4JURonVMclXGC/rNnezoG06uZwtRINLGE2a0d3bl6ezuz/U/i2VKIZPYAltQ8gXijy9ujq+60KhyNNrOnjO4plVOZ/hGrIkNVckny+yqaOXuTOnhS1KJEiaVVhiU0dv5BXDF9NqFYzEynXbqppZuXVbHxu29DBnZmPVzplmkl52ZHO7KQZwIapRr1I6UcIu21KJPdorIgsClyQmdHT1B7Iu8My6jkgluMSZkXpiJIWO7v7IFl+bSrZ19ydm0bmckRp+TRWVWAwNwHIReRrYHoKTRisiXyjyVEBJNPm86yYmEUlwiSuFQjF2JRHGTdG1ipzRnO7S3J3dybQWSnT29DOzuSGUa1eiGKz8hcfK9dV1IZXT3tnPxq09zJ5hLqWJ0t07kLjFyOHo7OlPvWLoSngJ+66egdAUw5iuJFW9A3gQeAz4N/AfUrgY3dndz7opqMOyfG1H7DM4w6QrIq06gybs/sVRIOnfQZifr5KSGJ8ENgErcPkLTwPfDFasaFEsFnlqzdRk0ObzxUiW4Y0LaWmElBYFOBK9fYPk88k2Dbt6wrvHlbiSzgaWAF8BzgeOAY4b6yARyQKXA/sBfcBSVV02zH5XAZtV9YLKxZ5a1m/pmdKm8Ru39jB3ZmOo4WpxJemzyBKDgwUGBvPU1qQz/yWp0Uh+BvMFevsGaaif+vaflUQlbVDVlThX0vNV9RpgzwqOOwloUNVDgQtwJbuHICJvBfatXNypZzBfYNUkyl5MlOVrzWoYL4VCkZ6+9Mykw5xRhk3iAww8toW0wF6JYhgQkSW4tYXDRSQHVJKNdThwC4Cq3gcc7N8oIocBLyLi/aPXbOwKJTSwq2eALQlN3gmK3v7BVCw8l+juS/6seSTSoxjC+ZyVKIYvAt/DVVX9X2A5cFcFx7UA/tjOvIjUAHh5EZ8E3jEuaaeYgcFCqLWMVq7vtCJ74yBN1gJAd0rXGQYG8/T2pSPnp6MrnJycSqKSfgkcraqdwP7AicCbKjh3B9Dsv5aqlp7k1wGzgd/h3EynishZ45B7Sli7qSvU6pw9fYNsSWhphyBI20DZmzJFWCJN5er7Bwqh9K8ec1VDROYAbxKRuUDGe/s04H1jHHo38CrgJyJyCPBoaYOqXgJc4p3/LGAvb+0iMgzmw7UWSqza4IrsGWMTlwbw1aI3pl3pJksaFp79dHb3U986tblNlSx3Xw90A/9gfAnoNwHHisg9OIVytoicCjSp6lXjlnSKWbe5OxK1/Ht6B9myrTe0RJc4MTCQrvyPQqFIvlAkl82MvXOC6E7Zont37yBtrVN7zUoUw0JV3Xu8J1bVAnBu2duPD7PfNeM9d9AM5guRqIleYvWGLlMMFVBI4XpMIYWKoac/XYohjLWzShaf14rIjMAliRBrN3VFKnnGIpQMw1EsFlNXQDCMSggjWgwi8lXvz37gARG5Fdju3FPVsdYYYkn/QJ61U1D6YrysWL+NGc31ZDLpmh2Oh9qalDWvyUBNLl3PQ7FIYiuqjkQYkYmjuZJKvpQHvH+pYOX6ybXrDIrevjzrt/Qwb5bV4R+J+rp0ZQHX1+ZSN1HIZjNkMqQqXyWXnfoJz4iKQVU/Xv6eiLSo6tQUDQqBbd39ke4fu3L9Nma11Ke2DMJYNDXWsi5sIaaQpsbasEUIhfq6XGryGCCcCc+IqkhEmkXkGhF5iff6OmCLiPxHRHadMgmniELB9UOIMvm8awNqDE/L9PpnA6pTQFrLbjc1pquGWBgTgNFslK/g1hQeF5FXAq8AdgfeBXx1lONiyaoNnbGYhWzp6GNTe3StmjCprcnSMj0dg0Y2m6G1KZ2KobUpHfe4REsIn3c0xXAY8FZV3QC8Evi5qj6tqrcAe02JdFPEtu5+1myMTnjqWDy9xtqAjkRaeiHPammgJpeyxXaPGc0N5FKy6N48rY6GumhVVx30chHAKYk/T4E8U85gvsATK7eGLca4yOeLPLm63eooDcPM5nrqapM/YM5vS4cCHI5cNpOaLodzZ4XzOUf7BeVFpEVEFuJ6KvwRwHudmJx23PDjAAAgAElEQVR0N/uOX1x0R2d/JEp2RI1MJsP8tulhixEorU11TGtI58JziQVt00l6QFZ9XS60cjijKYZvAQ8BdwI3quoaETkeuBW4YiqEC5pN7T1sbo9v4tiKddtS07FsPMydOS3ROQ07zWkKW4TQqavNMSfhbsOd5jSFFo484q9HVa8G3gh8ADjDe3sB8HVVvXwKZAuU/oF87CN8ikV4clV7JPMuwiSbzbAwoYNna1OddfbzWDg7uVZDQ32OttbwyuCMuqrhNdjxv746WHGmjuVrOyJV9mKidPcOsnZTV2IHwokyZ0YjazZ2xtJNOBqL5jaPvVNKqKvNMW/WtEhWKpgsi+Y2h5q8mFx7exS2butjS0dy+hys3thFb8oKi41FEq2GmS31TE9pUttILJjdRDZhRQSnNdSEXmo/dYqhUCiyfG28XUjlFApFVq6b+r7UUWfOjMZElcmwtYUdqa3JJq5MzE5zw7/PYyoGEZk/zHsSjDjBs3FrD30JbHCyuaM3dQ1MxiKTySRmMJ3V0pD6SKSRWDB7emKshmkNNZEosT9addUW789bReRwni02UAv8GnhOwLJVnUKhyOqNyZ1Zr97QyXMWzwxbjEjR1trAqg2dsZ8MRGEWGVVqcs5qiFOS6khEZSIzmsXwM2ArsC/QDmzxXq/H16YzTmzZ1pu4xUg/W7f1hdLUI8okwWqY1dpAY/3UZ7/Giflt02IfodRQn4tM/avRwlWPBeqAH+CshDqgVlVzqnrKFMlXVZIYvVDOekt624G21oZYrzUsnJ3shL1qUFsT/7yGhbPDy1soZ9Q1BlUdVNUzgEWqmgdOEJGP+NxMsaGnb5CuFPjgN7b3WF5DGZlMhoWz42k12NpC5cxvmxbb6rr1teFlOQ9HJYvPlwMfE5G9cNnQewOxy2dIS0XSfL5Ie2dyQnGrxewZ8bQaFs4xa6FSGupqmBPTGkoL50Qr7LaScNUXAucCJwPfV9U3ArHrx7B1W3oGy62mGHYgjmsNZi2Mn4VzmmK31lBfl2P2jOhYC1CZYsh6bqRjgD9478VKLQ8MFujuTc+ibEdnf9giRJK21gYa6mNiNWQsEmki1NfmYldEcZf5LZFZWyhRiWJ4UkR+BewJ/ElErgX+GaxY1SVtheb6BvIM5pMbfTVR4mQ1tLVYJNJEWTinKTal12c010cmEslPJU/eWcBrgfeoar+I/BW4ZqyDRCQLXI4r2d0HLFXVZb7tpwAXAEXgh6r6jXFLXyFpDOHs6Ruk2Yqt7cCslgZWN3TRE2UL0qyFSZHLZth1YSu6fEvYooxKNpthyYJoxvGMqVZVtRN4BDhKROqA+733xuIkoEFVD8UpgItLG0QkB3wB5546FDhPRGZPQP6KSOPsOY2fuRIymQw7R7wQ3dyZ00Lp2pUkWpvqmTMz2h7vXea3UFcbTddmJVFJZwA/BD4CzARuFpE3VXDuw4FbYHuV1oNLG7w1i71VtR1oA3JAYI7xVDY6S+NnrpAZzfWRtaay2Qw7WSRSVVg8vyWy7rhZrQ2RVlyVOOLeAxwCdKjqOuAg4P0VHNeCy5gukReR7XdJVQdF5DXAP4A/AYHlsye5actI5FLaD7hSdp4fTathwezp1NZEcxYZN3LZDLsvao1UGCi4KKRdI+pCKlHJ6JH3ZvYAqOpyoBIHbQfg//VlVXXIcar6c2AnXFb1GQREGs3yxrhE34REU2NtqI1QhqO2Jhu7iJqoM62hll0XRmcQzmYz7LnzjMhP3CqRbquI7IvnnBCR1+PqJo3F3cDx3jGH4Kuv5PWS/rOI1KtqAWctBOYUT1sN+/ranM06K2Dnec2Rmk0unt9MLkLyJIW21kbmRqQ095IFLbHITalkKv1u4KfAbiKyAjeAv7qC424CjhWRe3CJ6meLyKlAk6peJSI/BO4UkQHc4vYPJvQJKqC2JkvL9Do6utIR3z8rYjPhqFJXm2PhnOmR6GXRPK2Ottbo+pzjzuJ5zXT1DIRaFmfOzEZmxyQze7Sy2/Wq2qeqj4nIfrhSGDngMVUdc4T1LIFzy95+3Lf9KuCqiYk9fubMbEyNYoiaiyTKzJ81nU1be0MNac5kYJcF0VzzSApZb73hn09sCqWWWGNDDYvnR8elNRajWQz3AgeCWygmpqW2S8xqaWBlbSd9A/Guyz8WM1vqY2GqRoVsNsOuC1t47KnNocmwYPZ0u2dTQENdDbsubOGJle1j71xFMhnYfafWWLkJR1tjiM+nqIBMJsOieQlPGspEp9FHnGiaVhdae8jG+prYVn6NI22tjcxsmdpM40Vzm2On+EezGBpE5ABGUBCq+rdgRAqOttZGNmzpSaxLaf6sabF7AKPConnNbO3sm9pObxnYdafohVMmnSULWujo2kg+H7xLaVpDjSsHHjNGUwy74bq4DffUFr3tsWPJwpbQ/IxB0lCfY6eIZ/RGmVw2w+47tfLY05unLDlwpzlNNKUsYi4K1NbkWDS3meVrOgK/1pKFrZErkFcJoymGx1T1gCmTZIpoqKthyYIWnlw1tX7GIHE+zBmx8mFGkaZpdew0p4lV64OPUpreWGud2UJk7sxG1m3uorcvOAuxrbUhtoo/2lkWATF7RmOk09HHy+L5LanL1QiKhbOn0zQt2O+yFCETx5lkUgi6ZlYm49yTcWU0xXDnlEkRAovntzCtIf4Z0W2tDaEtnCaRTCbD7otmkMsFN2jvurAlldn4UWNmS0NgY8DsGY3UR7RAXiWMqBhU9d1TKchUk8tm2HPxTGoinpo+GtMaaliysDVsMRJHfW0usHLIba0NlsgWIRYGEcWXCei8U0h8R8UqUF+bY4+dZ8QyMLcml2XPxTNtXSEg2lobq55BXlebZZeIF09LGzOb66ve1a+tpSHW1gKkXDEAtEyvY3HcfIEZ2GPnGbF/+KLOkgUt1FSxMu+uC1tjbaEmkUwmU/XChQsSEFRgTykwv216bGqYgKv70jI9mv0EkkRNLlu18shzZjbS2hS9Fo4GzG5trNoEoLWpLhG5RKYYPFzVw+gvCLa1Nlhp5ilkZkvDpHvy1uSy7Bw3qzRFZLMZ5lcpgCMpv01TDB7ZbIY9dg42GmWyNNbbYnMYLJ7fzGQiSxfNazIXUsSZO2vapDPQpzXUJMYqtKfVhyuyFc2BtxT7bovNU09DXc2E6/k31OeYEyM3ZVqpyWUnnduUhLWFEqYYypjVEs1erIvnxa8QV5JYOLtpQjPKneY0WSJbTJjfNn3CEYp1tVlmtSSn3L0phmFYPK85UhE/M5rrI9OBKq3U1ox/RtlQn0vUYJF06mtzzGqe2P2a3zY9URMAUwzDkMtl2W1RNFxKuVwmsGQrY3zMmzVtXDPK+bOSNVikgfkTcAdls5nEuQtNMYxAc4g1+v3sMr+FughZL2mmoa6GGRUuLmazGeukF0OaGmvHXXdszsxGcgkLLkjWp6kyi+Y2hepSam2qi1V+RRqodGbY1tqQuMEiLYx3QjhvZvgTyGpjT+4o5HJZliwMx42TzWYsNDWCtDbVVxTSbAo9vsxqaag4bL1leh0N9dHPfxovphjGoLWpPhSXQNjWijE82WxmzAXl2ppsbOvwG+4ez66w0GEUIxirgSmGClg8v7o1c8ZiemNtJNY3jOEZSzHMammwReeYU8lkMJvNMGOCUUxRxxRDBdTWZNll/tSUNMhkXL1+G1iiS/O0ulFzGiZbQsMIn6ZpdWNa7DOa6hObcBqYc0xEssDlwH5AH7BUVZf5tr8BeA8wCDwKnKeqhaDkmSxtrY1sau9l67a+QK+z05wmS2SLONlshpbpdcM+C9lshuZpVuAwCcxoqWfdpu4Rt89sSe4EIEiL4SSgQVUPBS4ALi5tEJFG4CLgKFV9MdAKnBCgLFVhyYKWQGspNTbUJKYIV9IZqbpt07TaSdfcMaLBqKHJGRJTF2k4glQMhwO3AKjqfcDBvm19wGGqWlLHNUBvgLJUhbraHLvMDyhKKQO7LWy1QSUmjKQYWsxaSAzN0+pGLJ44vaE20YURg/xkLUC773VeRGoAVLWgqusAROSdQBNwe4CyVI3ZMxppbar+j39B2/RxJ9YY4dFYXzOsEm8yxZAYstkMzSNNABLeDyVIxdAB+Fdss6o6WHohIlkR+QpwLHCKqhYDlKWqLKnyzL6hPsdOMe8RmzYymQzTh1kLmh6Dnh5G5YxkAZpimDh3A8cDiMghuAVmP1cCDcBJPpdSLKivzbFobvUG8l3NhRRLpjUOVQL1dTnLdk4Yw1qAGRKfpxLk9OYm4FgRuQdXeuxsETkV5zZ6EDgHuAv4g4gAfENVbwpQnqoyb9Y0NrX30tUzMKnzzJnZaFEsMaWxLOO1/LURf6Y31u5QOLGxvibxE4DAnmQv9PTcsrcf9/0d6282k8mwy4IWHnty04TPkctlWDTXWj7GlfI494Y6y1RPGrlsZgeFn3RrAWI+OIdNU2PtpGri7DSnidopzKg2qkt9mSKwKrjJpFwRpCFIxEalSbJo7sQ6e9XX5ZibwKqMaaKuxhRDGihPOB0u6CBpmGKYJHW1OeZOoJDWRBWKER2y2cyQe2jWXzIZEmmWScdakj3JVWDBOPsBN9bXWMvHhFDrW4RMcsJTmmn0WQgj5a8kDXuSq0BtTXZcrf0WzLaWj0kh6yuRktSCamknl82Q8UKT0mAtgCmGqlFpmeyamqxZCwnCrwxMMSSX0jxumikGYzw01NdUVCpjzozGVJiiacF/L+2+JpeShZ/Ebm3DYYqhilQSujoel5MRfbI+l6C5B5NL6damJVfFFEMVmdHcMOqscXpjbWpmHGnBrIR0UFIMaQlJNsVQRXLZzKjupCQ39kgrpXWFTHndBCNRlO5vWiLP0vEpp5AZTSMvLI/a+MOIJdstBtMLySaTLlehKYYq0zKCxVBTk7WWnQnEIpHSQYZ06X5TDFWmvjY3bBPx5mmmFJJIybWQpkEjrZjFYEyK4YpsNTVaae0kksvaTygtpEgvmGIIgvIGLgDTrLNXIqnxMp/TNGgYycdGqwCYVr+jxWCKIdqcc9FtEzquUISOrn4ykzjH1R87bkLHGVNLmpS/jVYBUJ4Ek8tlqK1JR/xz2shkoK210dYYUkF67rIphgCor8sNeYYa6uxrjjo2azfGIj1qwdYYAiGTyVDvsxCGi1IyDMOIKqYYAsKfOl9Xa1+zYcSeFJkMNmIFRK1PGaSlvophGMnAFENA+Ns8WstHwzDihI1YAeFXBmkpvGUYRjIILFxGRLLA5cB+QB+wVFWXle0zDbgdOEdVHw9KljCozT3rPjKLwTCMOBHkiHUS0KCqhwIXABf7N4rIwcCdwO4ByhAaNb5ewGYxGIYRJ4IcsQ4HbgFQ1fuAg8u21wMnA4myFErU+KyEnCkGw4g/xbAFmDqCHLFagHbf67yIbHddqerdqroiwOuHijWJNwwjrgSpGDqAZv+1VHUwwOtFimfLMZtSMAwjXgSpGO4GjgcQkUOARwO8VuTYvq5gesEwjJgRZBGfm4BjReQe3PB4toicCjSp6lUBXjcSZLf3AjYMIwmkaIkhOMWgqgXg3LK3d1hoVtUjg5IhbMyNZBhGHLFwmSDJpKuGu2EYycAUQ4CYM8kwjDhijQICxtSCYUSHiXbZ29jeC8WJHw/x6vlhiiFAMhlMMxhGApjd2mCLz0a1MK1gGFEiTrP2MLE1hoAx1WAYRtwwxRAg5koyDCOOmGIIHNMMhmHEi0yxGJslldAEnXAkw9YeIMPsGQ0TOt78oYZhVIFxz05t8TlAZs9oDFsEwzCMcWMWg2EYRrIZt8VgawyGYRjGEEwxGIZhGEMwxWAYhmEMwRSDYRiGMQRTDIZhGMYQTDEYhmEYQzDFYBiGYQzBFINhGIYxhDhlPlvRIcMwjCnALAbDMAxjCKYYDMMwjCGYYjAMwzCGYIrBMAzDGIIpBsMwDGMIphgMwzCMIZhiMAzDMIZgisEwQkRE6rz/W55Ogonb/TXFEEHEcZ6IzBSR13rv2b1KECJSaug9XUQagVyY8hjBISKLgM+KyC5hy1IpNthEkzpgP+Ae4DQAVS1A/GYexo6ISA3wPyKyCXgCeJGqDopIxttmJItW4NXAT0XkHBGZGbZAYxGnns+pQkSOB34C3Ac8CfxAVe8UkQZV7Q1XOqMaiMhVwBuAFcAHVPW3IYtkBIiIvBl4L7AS+DJwp6r2hSvV8JhiiCjezHEPoBZ4MyDAo8BhwOmq+mSI4hlVQET2BbYBJwMfAe4HbgQOUtV3himbUR1EJFuy9r3XOeBjwNnA7cDlqvr3sOQbCXMlRQjvoUFE9gJOBY4FHgfeB3wXmAE8aEohvpTWikTkYGBPYIuqfg3nOtwCfBy419vH3IYxpqQURGQXEfmJiFwN/B/wOeDFwFzgwjBlHAmzGCKIiDyE8z23Ac3Axap6g7et9LANmYkY0UdEMqpaFJH9gJ8CXcC+wEWqeqG3zzRV7Q5RTKPKiMivAcVZ/fOAvwI3AHcDDaraJSI5Vc2HKOYQzGKICD5r4WTgr6r6vzhz87vAO0XkQS+qoQjPLkYb8UFVS7OwU3HK/gDgZcBpIrJGRM5Q1W6zFJKDiLwEqFHV84FHcOuGewPXAu9V1S6AKCkFMMUQGVQ1LyL1wLnAP733nsE9QG8Dfqqqy32DixEjSoO9iLwMmAn8CUBV/6yqewLfAA713rN7nBwWA8+IyP5AAXefPwk8DFwF0XQZWmhctNgJaAe+KCJtwGdUtQd4VET+DTsuZhnxwDfYHwYsBZaIyCmqus3b/oXSvlFzKxiT4jac+2gQt45UC5yC8wp0RPX3bBZDyPhnC96i8rm43IUXAXeJyNnetkHv/5F7iIzRKbvHnwHmA3lgrYicX76/KYX44gsuyImIAHlV/SpuwrcY+DDwClX9nHdIJK1DW3yOCCJyGvAC3MziZ8Aq4ESconiVRSLFE1+wQDPwepxVuEFVLxeRY3GLkI+o6pFhymlUB1+AwaXA83HrCdcB5wOvAXYG7lbVv0bZMjSLIUR8C86vBU7HRS6cBSzHDSBXA8eq6pNR9EMaFVGaeX0DeA5wOHCMiMwCnlDVWcBbwMqexB2fUliEs/jPxIWnvgB4DKhX1a+r6l8h2pahPYgh4nsw3oPLiMziZhdF4LfAHFVd7e1rpl0M8QaK/YE9VPWDuHv8dZz18Glvt2XevuYmjDG+3+gewE9U9WlV/QNwHG5icLHnXoo8phhCxKuN04IrezENZzVcqKprgL8DsSm6ZYzKXsBvROSdQLuq3omLZV8gIi2mEOKPz/p/BfAl4KMicqaIzFLVXlW9AhBV1ThY/6YYQqBsMbIDl8j2IHCLqrZ7rqUWbwAxYkiZW+hW4Cicn7lU6uL1gHqRKZEfKIzR8Vn/nwe+AHwR5wn4qogc4SUudnj7Rt76N8UQLq/xEmDOxdVPeYuIPAC8HRfrvH0mYsQLXzXct+IiUr4JTAe+LSK/Ag7GKQoAUwwxplQRV0ReDixX1Z974ccnAgO4pLaDQhRx3FhU0hRTikQQkT2Ay3Axzj8G7gKeAZ6Hq4e0MUQxjUngi0Q6DhdAcB/wNeAhnq2mulxVl0U5MsUYG89VtNlLTr0Yl6PwXeBKL0EVEXmhqj4QppzjxRRDSIjIL3Ap8s24xeZZwM3An1V1bZiyGRPHpxQW45TC47hZ43OB/wBfV9UnwpTRqB4i8ifgdcBWoBF4Iy6JsQv4I/DLUu2rUtRSSKKOC1MMISAiJ+Lq7x/uvd4TN6Ocg1MOX1PV9hBFNCaJiFwD/FNVv+LlMLwAuBTYBFwPXAEU4zJQGMMjIvNwin8jcL6qftWLPDodeCHwc1W9MkwZJ4KtMYTDALBSRBpFpE5V/4tbU+jChbYdG6p0xqTw3Ap53H1GVbd5YYt3AP8GDsTFtJtSiCki0ioih6nqOpy1fxLwJhH5D7BYVT+Oa8bzC2//WK0jmWKYQnwPxyO4UNQLgWYvIebjuAXoq4H9QxHQmBSl++t15foV8DIROVJElohIE66S6idwyYsHhyepUQX2xuUl/AW3nvArVX0e8G3gOhF5GPi7pzhiEYnkx1xJISEiBwFX4tYYHsQp6XNxXZ3OUdVHQxTPmCQiMhf4ELAPUI8rdfI779/3VfWQEMUzqoCIHILry/408B3gm1748TxcNvtXvCKYscMUQ8CISC1wDtAAPIBTAM/4IhZ2BTqAI3BupEFr6xgvRGRv4BBc57WZwOOqusXbtjtu7ehpnIvwHcDHVPX2cKQ1qoWINOLasq7ChR4vwOUwHIVbJ1wW1eqpY2GKIWBEZCmu7vovcPVT/ouLaV7m7bIRtxD5FFAHPKSqAyGIakwQEXkMl938BC4U9Xm40OM+4F+4+3op0AQ0q+pvQhLVmCS+qLMGXGLqIlwv9jm4vIUzcBVVXxSimJPG+jEEzy24SKM7cNmuGdys4jicQjgAV23RQlTjy/tw6wc34xR+BlfS5NfAbOA/qvpQeOIZ1cI3+78Sd28PwSmGz6vqJSJyGdACLvGtVC4/bpjFMAWIyBG4EMVP43yRDwNLVfU+3z6W6BRDvAXnWbhkxT1wLsHdcEXU9vH2qVPVfrvH8caXnHoALpP9aFXtE5EP4gJHTkmKi9AUQ8D4SvG+EdfDuRtYp6rniEituY2Sg4j8GNiMUw6fVdXr4zxrNIZHRK7G1bn6kk9ZvB/oV9VLw5avGli4asCUwtRU9TpcFuwrcI14IKLdm4zx4atndSXwKmCGql4Pz3beM+KNr3rqLrje3B8RkRf7LMDn48rbJAKzGKYAn9XQBHwLN7M4J2y5jInjy1kolr3/Uly/hU/hyiHYDywBeBFIzbjJdAbXgOe9uOCCe4Alpd90nEpfjIQphilGRF4I/B54seUqxJPSmoH3dylKpZS8mMUp/z1V9ajQhDSqhoi8DTgaV9bkYVzk2TXAFlx57RNxa0qnhyVjtTFXUpUZKfW9VJ/fq7K4l6o+Grc0eWM7XxCRMzz/cilKJaOqRVXNq+pb8PouWNn0eCMi7+HZCMJDgZuAWlyvhQ2q+jrgGOB5IrJGRKaHJmwVMYshIETkKKAHWK+qT4Ytj1EdPB/zebim7huB60o9fL3tFlCQELzuin8BXqOqy3zv746zGP6hqu/w3ssCL1DV+8OQtdqYYqgiPrfCu3H+x404/+Mfgb+o6oZQBTSqgteY5RLgSFzW6324Mhf/DVMuo7qIyAeA3VT1bd7Av70artdl8ThVfUtcs5tHw1xJVcRTCo3Aa3Ald5cCOVyN9vNF5NAw5TMmR6lTF/BqYFfg/cANuH7dnxeRd3v330gGK4Bt/jd8rsHHgEUi0pg0pQCmGKqGr8fvfFynrg5VfVhV3w78CBfOZuZZjPGFni4FLlHVm3EuhRtx1XKtMF6y6AVOFpHnqGrBsxZK64IfAh5Q1Z6y/t6JIHEfKAx8LqSDcCUwXgv8UUReA6CqN+KyIu8b7TxGtPEFCzwKnCIiC1V1UFXvxdXA+lFSB4o0oqq/wJVP/5iIvN5bWG703Ej7qeqF3q6Jm/DZGkMVEZHLccXwfoGrungcsBa4VFXvT0J8swEi8iLgw7g+3Z3AdOBMVd0vVMGMquGb7D0fV0J7IbA7rnz6/cDPVPWOpJY5McUwSXzJay/HtfO7UFWfEJHZuCqbZwD/VtUvhyqoMWF893hvXMji04B4fzfg2nV+X1XvTepAkWa8daP9cF35pqnqn0MWKXBMMVQJEbkA+BxwJ3BiqWez151traoOmsUQP3y1cF4MfBZXWvt/cTPIDNBliiCZeK7D7HD3N+m/ZVMMk6D84RCRNp6tl/MZVb0oNOGMqiIitwMXAXOBE3CTgD8AB6jq+jBlM6rDSIN9Gq1A68cwQXw+yGbcDHIvoB3nj9wVuFVEjlPVI8KU05g8IrIzLnTxfty6wqmq+l8R+TdOUZhiiDl+pSAip+HWjZ5W1dtKSiHpVoIfi56YOKUH5Ou4Vn7gGnfcjOvgNBtXZtvKIsSf9bh7+wRws6cUDsC5k5aNeqQRFzIAIvIR4KPA8cAZIvIZEdkfdiyYmGRMMUwAz1ooeoPD/qp6uqp+ANeI5yHgTG/XpwHSZoYmAX8dK1Xtw8Wt3w8cKSKXAl8FvqGqvab4441nCRREZCYukvAAXJ/223HtOz8uImeFKOKUY66kCeCrprk73oxRROpVdbOIXAl8WURaVLUjVEGNCeFzE84F3oWrefV7nDLYGZfV/ilV/ROY4o87PkvgeKALV/piE3CtiPwdl+n+t7DkCwOzGMaJiCwVkT29h+n3wAIRea03qwQXsvofVe2w6qnxpKyv777A/+DyFvYF7gbeV1IKdo+TgVcwbz9gCfBtETkWQFUfwXXjeyRE8aYci0oaB17U0SXAIlxG5Ndxs4nvAI8A63CLkf9TyoBNYh2VJFNqxSkiAnxNVY/33l8KvA7ni75QVe8JU06j+nguwaOBl+N+x08BP1fVf4QqWAiYxTA+2nGuhd8Db8LVZl+Pq4/0HVyDlqWeUsiZUogfvnpIZwCbvTLbqOp3cF277scWnBOBr13nYSLyUdyawhxcAMntOAvxueFJGB5mMVSIL9FpIXAdzudYxBXHWwZ8S1X/FaaMxuQQkTcDP8GVPfgacBjwa+DnwOOqus23b2pCF5OIL5s9C/wV5zacB7wM2ICLKFyCq1qQT9v9NsUwTkTkWuARVb3Y80segHMv1QEnqOoToQpoTAgRmYcrdHi5iJwN/BDYB/g4UA/cCtxiPReShYh8Alisqku919Nw9/oHqnplqMKFiLmSxoGI1AIdwCwAVe3w6qbcAXzVlEJ8UdV1wHdEZF9cuPH1wHxVPQW4Fngzzu9sJABfb40O4PHS+6rajbv3B4QhV1Qwi2GciMhLgY/gXA6PAstx7f+OVNVVaTM5k0B5kICILAbegEtcfAa4FBdp1jfCKYyY4K0rzFXVNZ51sB/OOj1FzrEAAAg4SURBVPwicJOqrheR+4EPq+of0hpAYophDHxrCzsDM3GK4CzgINzC1BbgNlX9UlofoqQgIqcCL8BVSv27iByICz8+BHidqq4KVUBj0ngTu68A/wLaVPVVInIGLhrpKNxkb42qvjlEMUPHFMMo+BaoGnFRCnsC/8QVU9uEa/vXWerlbNZC/PAp/pOAzwAP4lxJP8bVvaoDdlfVB+3+JgMReSXOStgInKeqvxeRWcAeuJpYm1S1P80TPVtjqIwLgL+r6jzgXuBq4B3AnJJSgHTVUkkKvqzlD+DWEX6Ky3BegPM/vxsv69Xub7zxlS5pAv6Ecx99XkRuAlp4tv3uAAxJdEwdphhGwGctzAMKOGWAqn4MeClu4PhQiCIaVUJE9gFWeq1XDwJ+C7wTN3isS/MAkRS833Pes/7PBd6jqlfjJgNP4QJIPg2stwmAKYYR8T0c78NVW3yriLR55uUKVX0VXrE8K6IWP7z49VKkWT/QJiKH4GaOD+N+G5tU9Vveflb6IhmcCtSo6jMAqvowcCFwBHCEVyMr9TXkTDEMQ1llzQ/h+i28BOeXPEpEmrzNXd4+VkQtfpQU/2W4ZjvH4BTCG3CTgW/iendvr6YbipRGVfCs/+m4KKRFInKFiBzkbetQ1VWqusx7PTjaudKALT6XUdaw4yTv7XuAzbgw1bcBvwHONYUQb7x6SBcB71XVld57J+PqX21Q1Q+GKZ9RfTzl8ArgWGAabv3oZ6q6IlTBIoZZDDtScjFcgPM5fhC4CjhNVT8NHAz8qpQmH56YxkTw3IEv9l6eB5yCq71f4mZVPRsXcLDd5WTEE189pOd6Ge3fBFbiSmA8CByDUxKGD3voffgWqFqB1+Ji2u8B8sCZIvI74GhV/XWYchqT4lJc3X1U9d3AG4F3i8jfROQQVe31thX8/zfiic+q/y6uCOZJuOCRdcAVwBdwmc62juTDFIMPnx/5VFyiSw63MHkBLlJlGqDD7G/EAG9xeRdV/aj3OquqP1TVWcAfgHtE5LpQhTSqRmmgF5H346yDPwL/wVkLVwPvUNW/eGUw7PfswxTD8PwZt6bwfKAV1+t3JbBCVR8IUzBjUnwN6BWRJq/vQqHkKlLV84HFwI1gs8eEkPHuYwF3Xz8F3KCq7cDvgN3CFC7KpD4sC0BEZgAnAIcD1+D6Nl8N9OKUw0G4kNXTvP1TmxEZV0TkTNxEqB/Xhe0KEfmJ18KxlAG9EjcBsNljzBGRBaq6xvv7MeAGYIuqirfLybiINPs9D4NFJQEi8gOgEaco21X1DO/9nXENeLYBqqoftbII8cNbgLwbF0DwhKckPgCsxWU5/1FVe8KU0ageIvIh4PO4e3yJqg6IyDtxvRZ2wdU7K6rqySGKGWlS70ry/M7P8corfxwYEJGFnqthBS5q5QLgY94h5mKIH23ANaWy6Kp6La4717249qw/EpFdQ5TPqC5bvP9/GVguIm/ErSt8CrfY/HUsOXVUUm8xiMjNwPWqeq2IHI8rnvZbXOTCepwr6URV/U2IYhpVwltTyJSiVbzWnV8BzlfV5aEKZ1QNEXkNriNbAVcc8SncYvNfQxUsJqRaMYjIOcDxqnqKt0j1L+AuXOOOdTjrYJOq3hKimEYAePc7U9aHwXzNMUdE6rzKqEtwyYv3eF35vohzLf1YVU8LVcgYkHbF8BgwHXgOLtlpn5HqsNvaQjIxZZBcRGQ+8CXgDs8jsBg4SFVvst/z6KQ9Kul5uCSX0sLjvrDd3ZBT1YHSjvYQJRNTCslARF4LvAYXfdQKPK6qD4jINcBJIvIvVX0Q15HPGINUWwwlPLPzCuAw4NOq+pVwJTIMo1I8t6DiGu3cB/wceBMu/2gVrrT2v4BXWhe+yjDF4ENEXgVcAswBDlbVx8c4xDCMCCAie+D6LLwV+DZwPrA/Lgz9GFx46qfDkzBemGIYBhF5L3CZqvaHLYthGJUjIkcCFwMzgA+paimTPVvKdDf34diYYhgFe4gMI354a4RvweUfZXC9VFZZmfzKMcVgGEYiEZGZuKZLnzHrf3yYYjAMI/GY9T8+TDEYhmEYQ0h9rSTDMAxjKKYYDMMwjCGYYjAMwzCGYIrBMMoQkVoRWS0igRZPFJHbRGR2kNcwjIlgisEwduRk4BHgIBHZO8DrHBvguQ1jwqS9iJ5hDMd5wPXAMuA9uDILiMgFwDm4jn53Aiep6hIRqQO+iOvhkQP+DrxLVTtE5Glcu9iX4XpK36CqHxSR73nX+qOIHO81hTKMSGAWg2H4EJF9gEOAnwDXAm8UkTYReTlwFvACXA/wZt9hFwCDuJLO+wGrcZ3CSjSp6ktwRRrfKSK7qurZ3rajTCkYUcMsBsMYytuA36rqZmCziDyFsxjmAT9V1a0AInIZzgoAOAFXm+dYEQGow3X/K/FLAFVdJSLrgVm4jmKGEUlMMRiGh4hMB84Aej0XEEAL8Haca8nf79tfdycHvFtVb/bO0wQ0+Lb3+P4uYn3DjYhjriTDeJbTgI3AQlVdoqpLgN2AJvj/9u5YpaEYDMPwu9i1l/EtjoWCt9HBCxAKgpMOvQAHN4euvYfu7kIXcRTiJL0IN6FDAjXbGQKCvM92IJBkyUfyw394B1ZJ5m3sDfWQB3gB7pLMWgO3HfA0Yb4f4GLg+qUhDAbp7BZ4/t2Fsz0dbalF6B1wSPJG/UvYdxv2CHxRi84f1BvBw4T59sBrkstRG5BGsFeSNEGSBXBVStm273tgWUq5/tuVSeNZY5Cm+QQ2SdbUJ6Qjtee/9O94Y5AkdawxSJI6BoMkqWMwSJI6BoMkqWMwSJI6J9S3i08scQ7vAAAAAElFTkSuQmCC",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c682940>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cffaa20>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "source": [
    "for r in results:\r\n",
    "    print(np.mean(r))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "4.648910868039739\n",
      "4.698714400983782\n",
      "4.6987540983223\n",
      "3.957626339721907\n",
      "10.49922155925723\n",
      "10.493630569356531\n",
      "123.36834964248281\n",
      "119.77257810824398\n",
      "119.74807342228542\n",
      "0.30980748633664634\n",
      "0.3831496341981187\n",
      "0.3837782093203221\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "for r in results:\n",
    "    print(np.std(r))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.016385984467360684\n",
      "0.013013503294523717\n",
      "0.013255459031532833\n",
      "1.8240680435014167\n",
      "1.4019249182296356\n",
      "1.4837798424665614\n",
      "2.2410487046868277\n",
      "1.5692730678262694\n",
      "1.6197137043232774\n",
      "0.07819374990426393\n",
      "0.0833678517946322\n",
      "0.083853755480452\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "source": [
    "#looking at actions\r\n",
    "\r\n",
    "episodes_act = 10001\r\n",
    "#wealth_act = 100.0\r\n",
    "#utilities_mod_act = []\r\n",
    "#rewards_mod_act = []\r\n",
    "#step_rewards_act = []\r\n",
    "#rsum_act = 0\r\n",
    "#wealth_episodes_act = []\r\n",
    "#number_of_actions_act = utes\r\n",
    "\r\n",
    "Mark1_act = Market(kappa, episodes_act, time_periods, mu, rf, sigma, u_star)\r\n",
    "\r\n",
    "action_list = []\r\n",
    "\r\n",
    "for i_episode in range(episodes_act-1):\r\n",
    "\r\n",
    "    if i_episode%10000 ==0:\r\n",
    "        print(i_episode)\r\n",
    "        \r\n",
    "    state = Mark1_act.reset() \r\n",
    "    action = model(Variable(torch.FloatTensor(np.float32(state)))).argmax().item()\r\n",
    "\r\n",
    "    while True:    \r\n",
    "        new_state , reward, done, final_wealth = Mark.step(action) #(prop, wealth))\r\n",
    "        action = model(Variable(torch.FloatTensor(np.float32(new_state)))).argmax().item()\r\n",
    "        action_list.append(action)\r\n",
    "        \r\n",
    "        #print(state,action)\r\n",
    "        #print(model(Variable(torch.FloatTensor(np.float32(new_state)))))\r\n",
    "        \r\n",
    "        \r\n",
    "        #rsum_act += reward\r\n",
    "        state = new_state\r\n",
    "\r\n",
    "        #print(new_state)\r\n",
    "        #print(model(Variable(torch.FloatTensor(np.float32(new_state)))))\r\n",
    "        #step_rewards_act.append(reward)\r\n",
    "\r\n",
    "        if done:\r\n",
    "            #wealth_episodes_act.append(final_wealth)\r\n",
    "            #utilities_mod_act.append(np.log(final_wealth))\r\n",
    "            #rewards_mod_act.append(rsum)\r\n",
    "            #rsum_act = 0\r\n",
    "            #wealth = 100.0\r\n",
    "            #print(i_episode, final_wealth)\r\n",
    "            break "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\n",
      "0\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "source": [
    "plt.hist(u_star[action_list], range=(0,5))\r\n",
    "plt.title('DeepQ learner - Investment choices')\r\n",
    "plt.xlabel('Investment proportion - up to 5x leverage')\r\n",
    "plt.ylabel('Action Count')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a2ff3cf28>"
      ]
     },
     "metadata": {},
     "execution_count": 71
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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BxylBo3OQtNb0ZQOkD2rhwiUjb0HV1dVJd/fiUZefiCZjm9ulncd5Mr7PafPIy/ZnPH/NdYOk7erj3YAFlN7KzpJWlTQD2Ax4AJgP7Fnz7gHMs70IWCZpY0kdwGxgXs07W9IUSRsAU2w/On7NioiI8eyZHAGcI+lp4NfA+2wvknQ2JShMAY63vVTSucDFkm6l9Dz2r9s4HLgcmEqZJ7kDQNI84La6jSPHsU0REQF09PT0tLsObdHdvXjUDU+3eHI45FM3tmW/c47btS37hcn5PqfNIy7b0V96LlqMiIjGEkwiIqKxBJOIiGgswSQiIhobVTCR9LznuiIRETFxDRlMJN3W5/kUyjUiERERwCDXmUi6EZhVHz/T8tJy4BtjW62IiJhIBgwmtncFkHSW7aPHr0oRETHRDOcK+I9I2gtYm7LcOwC2LxmzWkVExIQynGByOfBS4EGg96rxHiDBJCIigOEFk81tbzrmNYmIiAlrOD8NflDSi8a8JhERMWENp2eyOmBJD1BuRAU8O0EfERExnGDyT2Nei4iImNCGE0wm5xr1ERExbMMJJqe0PF4F2JxyM6vvjkmNIiJiwhkymNh+Q+tzSS8DPjtmNYqIiAlnxLfttf0TScP6qbCk7YFP254laUvgHGAF8BTwbtu/kXQWsBPQe9uvvSm36r0MWKemH2S7W9IOwFmUJV3m2j6l7uckYK+afoztO0faroiIGL0hg4mkL/PsvEkHsBnwwDDKHQscCDxZk84CPmj7XkmHAR8FPgRsDcy2/WhL2Q8B99s+WdJ+wAnA0cB5wL7Aj4HrJG1V67QLsD2wPnAVsO1Q9YuIiOfOcHomN7c87gG+BvznMMo9DOwDXFqf72f7Vy37XVpXIH4F8CVJ6wIX2p5D6amcXvNeD5woaU1guu2HASTdAOxO6eXMtd0D/EzSNEldtrsHq9zMmaszbdrUYTSjf11dnaMuO1FNxja3Q7uPc7v33w5pc3PDmTO5WNKrKCsITwO+b3vZMMpdJWnDlue/ApC0I/AB4PXA8ylDX2cCU4GbJN0FrAk8UYsuBmbUtEUtu1gMbES59uWxPukzgEGDycKFS4ZqwoC6ujrp7l48dMaVyGRsc7u08zhPxvc5bR552f4M534mBwLXAC+jrNH1dUmHjKYSkt5FGaraq/YclgBn2V5iezFwI7AFJWj01rgTeLxP2nDSIyJinAxnmOvDwHa2HwOQ9EnK0NeckexI0gHAYcAs27+ryZsAX61zH1Mow1sXUybe9wTuBPYA5tleJGmZpI0pcyazKT9bXg6cLukM4CXAlNb5l4iIGHvDCSZTewMJgO1H+9wsa0iSpgJnAz+j9GwAbrF9kqRLgduBp4FLbP9A0k+AiyXdSvll1/51U4dTVjGeSpknuaNufx5wGyUgHTmSukVERHMdPT2DX+Au6TLgUeDCmvR3wAtsHzjGdRtT3d2LR31lf8ZYJ4dDPnVjW/Y757j2LXs3Gd/ntHnEZTv6Sx/OqsGHUn4xNQe4iNKDeP+oahERESul4QxzPWX7owCS1rH92zGuU0RETDAD9kwkvUDSLcA7W5LPlfRdSWuPfdUiImKiGGyY6yzgW5SLFHu9A/gO8LmxrFREREwsgw1zvdr2Aa0J9SrzU+qNsiIiIoDhTcD3Z8VzWouIiJjQBgsmj0jas2+ipDcxxFIlERExuQw2zHUscGNdUPEOyuq821KuTN9jHOoWERETxIA9E9sGtgF+AbyZEkAeAba0fe+41C4iIiaEQa8zqSv9fnyc6hIRERPUaCfgIyIi/ijBJCIiGkswiYiIxoZzD/iDgTOAmTWpA+ixPfp73kZExEplOAs9fpxyQ6tc9R4REf0azjDX/yaQRETEYIbTM1kg6UpgLrC0N9H2JWNWq4iImFCGE0xmAIuB17ak9QBDBhNJ2wOftj1L0sspN9fqAR4AjrT9jKRDKfeGXw6cavtaSasBl1HuBb8YOMh2t6QdKKsZL6fctveUup+TgL1q+jG27xxGuyIi4jkyZDCx/R5JqwCq+R+wvXyocpKOBQ4EnqxJZwIn2L5Z0nnA3pJuA46iXGm/KnCrpG8DRwD32z5Z0n7ACcDRwHnAvsCPgeskbUX5QcAuwPbA+sBVlGVfIiJinAzn11xbU76gH6PMsawr6e227xii6MPAPsCl9fnWwC318fXAX1FWH55v+yngKUkPAZsDOwGnt+Q9UdKawHTbD9d63QDsTrml8Ny6PP7PJE2T1GV70MUoZ85cnWnTRv+DtK6uzlGXnagmY5vbod3Hud37b4e0ubnhDHOdDbyrN3jUoaZzgO0GK2T7KkkbtiR11C98KENXM4A1gSda8vSX3pq2qE/ejSjzOI/1s41Bg8nChUsGe3lQXV2ddHcvHnX5iWgytrld2nmcJ+P7nDaPvGx/hvNrrjVaeyG2b6cMSY3UMy2PO4HHKcGhc4j0keRtTY+IiHEynGDyO0l79z6R9Db+tCcwXPdImlUf7wHMA+4Edpa0qqQZwGaUyfn5lKXu/5jX9iJgmaSNJXUAs+s25gOzJU2RtAEwxfajo6hfRESM0nCGuQ4DLpU0hzLZ/RBlYn2kPgycL+l5wIPAlbZXSDqbEhSmAMfbXirpXOBiSbcCy4D96zYOBy4HplLmSXqH3uYBt9VtHDmKukVERAMdPT09Q+cCJD2fcta/UgwudncvHl7D+5Ex1snhkE/d2Jb9zjlu17bsFybn+5w2j7hsR3/pA/ZMJH3J9vsk3US5NqQ3HQDb7fvER0TEn5XBhrm+WP89eRzqERERE9iAwcT2gvrwHbY/2PqapIt59pqRiIiY5AYb5rqAch3HNpJe2fLSKpTrOCIiIoDBh7lOBTakrIV1MuWXXFDWv3pwTGsVERETyoDXmdh+xPbNlKVNXm37FsrPgmfTsnpwRETEcC5avBx4UX28uJa5dODsEREx2QznosWX2n4rQL0K/QRJ945ttSIiYiIZTs+kR9Kre59I2hR4euyqFBERE81weiYfAb4t6Rf1eRdwwNhVKSIiJpoheya2/xPYgHLDqm8Av6TcYyQiIgIY3s2xXkZZ7PE9wFrAJ4G3jnG9IiJiAhnsosW3U1bpfQ1wNWVo63zbnxinukVExAQxWM/kKuBrwGttPwQg6ZlB8kdExCQ1WDDZHDgYuFXSI8BXhsgfERGT1GBXwD9g+yPAXwCnAbOAdSVdJ2nPgcpFRMTkM2RPw/YK4BrgGkldlLssngb8x0h3JulgSm8Hyn3ktwReC1wL/Kimn2v7q5IOpUz8LwdOtX2tpNWAy4B1KFfjH2S7W9IOlDXEllPuwHjKSOsWERGjN6JhK9vdwJn1b8RsXwRcBCDpC8AcYGvgTNuf6c0naT3gKGAbStC5VdK3KT9Pvt/2yZL2A04AjgbOA/YFfgxcJ2kr2/eMpo4RETFyw7kC/jknaRvglba/RAkme0n6rqQLJXUC2wHzbT9l+wnKApObUxad/FbdzPXA7pLWBKbbfth2D3ADsPt4tykiYjJr14T6x4Deoag7gQtsL5B0PHAScC/wREv+xZR7qKzZkt6atqhP3o2GqsDMmaszbdrUUTegq6tz1GUnqsnY5nZo93Fu9/7bIW1ubtyDiaS1ANm+qSZdbfvx3sfAOcB3gdaWdgKPU4JG5yBpremDWrhwyWibQFdXJ93di0ddfiKajG1ul3Ye58n4PqfNIy/bn3YMc70e+E7L8xskbVcf7wYsoPRWdpa0qqQZwGbAA8B8oPeXZHsA8+pKxsskbSypg3K/lXnj0I6IiKjaMcwlykR5ryOAcyQ9DfwaeJ/tRZLOpgSFKcDxtpdKOhe4WNKtwDJg/7qNwyn3XZlK+TXXHePUloiIoA3BxPY/93l+N/C6fvKdD5zfJ20J8M5+8t4O7PDc1jQiIoarLb/mioiIlUuCSURENJZgEhERjSWYREREYwkmERHRWIJJREQ0lmASERGNJZhERERjCSYREdFYgklERDSWYBIREY0lmERERGMJJhER0ViCSURENJZgEhERjSWYREREYwkmERHR2LjfaVHS3cCi+vQnwCeBi4Aeyn3ej7T9jKRDgcOA5cCptq+VtBpwGbAOsBg4yHa3pB2As2reubZPGc82RURMduPaM5G0KtBhe1b9ew9wJnCC7Z2BDmBvSesBR1Fu5zsbOE3SdMr94u+veS8BTqibPo9yP/idgO0lbTWe7YqImOzGu2eyBbC6pLl13x8DtgZuqa9fD/wVsAKYb/sp4ClJDwGbU4LF6S15T5S0JjDd9sMAkm4AdgfuGawiM2euzrRpU0fdkK6uzlGXnagmY5vbod3Hud37b4e0ubnxDiZLgDOAC4BXUAJCh+2e+vpiYAawJvBES7n+0lvTFvXJu9FQFVm4cMmoG9HV1Ul39+JRl5+IJmOb26Wdx3kyvs9p88jL9me8g8kPgYdq8PihpMcoPZNencDjlODQOUT6UHkjImKcjPevuQ4BPgMg6cWUXsVcSbPq63sA84A7gZ0lrSppBrAZZXJ+PrBna17bi4BlkjaW1EGZY5k3Tu2JiAjGv2dyIXCRpFspv946BHgUOF/S84AHgSttr5B0NiUoTAGOt71U0rnAxbX8MsqkO8DhwOXAVMqvue4Y11ZFRExy4xpMbLcGgFa79JP3fOD8PmlLgHf2k/d2YIfnqJoRETFCuWgxIiIaSzCJiIjGEkwiIqKxBJOIiGgswSQiIhpLMImIiMYSTCIiorEEk4iIaCzBJCIiGkswiYiIxhJMIiKisQSTiIhoLMEkIiIaSzCJiIjGEkwiIqKxBJOIiGgswSQiIhob1zstSloFmANsCEwHTgV+DlwL/KhmO9f2VyUdChwGLAdOtX2tpNWAy4B1gMXAQba7Je0AnFXzzrV9yjg2KyJi0hvvnskBwGO2dwbeBHwe2Bo40/as+vdVSesBRwGvA2YDp0maDhwB3F/LXwKcULd7HuV2wDsB20vaalxbFRExyY1rzwT4GnBlfdxB6UlsDUjS3pTeyTHAdsB8208BT0l6CNicEixOr+WvB06UtCYw3fbDlA3dAOwO3DNYRWbOXJ1p06aOuiFdXZ2jLjtRTcY2t0O7j3O7998OaXNz4xpMbP8eQFInJaicQBnuusD2AknHAycB9wJPtBRdDMwA1mxJb01b1CfvRkPVZeHCJaNuR1dXJ93di0ddfiKajG1ul3Ye58n4PqfNIy/bn3GfgJe0PnATcKntK4CrbS+oL18NbEUJDq017gQe75PeX1prekREjJNxDSaS1gXmAh+1Pacm3yBpu/p4N2ABcCews6RVJc0ANgMeAOYDe9a8ewDzbC8ClknaWFIHZY5l3vi0KCIiYPznTD4GzKTMdZxY0z4EfFbS08CvgffZXiTpbEpQmAIcb3uppHOBiyXdCiyjTLoDHA5cDkyl/JrrjvFrUkREjPecydHA0f289Lp+8p4PnN8nbQnwzn7y3g7s8BxVMyIiRigXLUZERGMJJhER0ViCSURENJZgEhERjSWYREREYwkmERHRWIJJREQ0lmASERGNJZhERERjCSYREdFYgklERDSWYBIREY0lmERERGMJJhER0ViCSURENJZgEhERjY33nRbHjKQpwL8AWwBPAe+1/VB7axURMTmsTD2TtwGr2n4tcBzwmTbXJyJi0liZgslOwLfgj7fx3aa91YmImDw6enp62l2H54SkC4CrbF9fn/8M2Mj28vbWLCJi5bcy9UwWAZ0tz6ckkEREjI+VKZjMB/YEkLQDcH97qxMRMXmsNL/mAq4G3ijpe0AH8J421yciYtJYaeZMIiKifVamYa6IiGiTBJOIiGgswSQiIhpbmSbgx9xkXbJF0vbAp23PanddxpqkVYA5wIbAdOBU299oa6XGmKSpwPmAgB7gcNsPtLdW40PSOsAC4I22/6fd9Rlrku6mXEYB8BPbz9kPlRJMRuaPS7bUnx9/Bti7zXUaU5KOBQ4Enmx3XcbJAcBjtg+UtDZwL7BSBxPgLQC2XydpFvBJVvLPNfzxxOGLwB/aXZfxIGlVoGOsTgrH77BVAAAI4UlEQVQzzDUyk3HJloeBfdpdiXH0NeDE+rgDWOkvfLX978D76tOXAo+3sTrj6QzgPOCX7a7IONkCWF3SXEk31hPi50yCycisCTzR8nyFpJW6d2f7KuDpdtdjvNj+ve3FkjqBK4ET2l2n8WB7uaSLgXOAy9tdn7Em6WCg2/YN7a7LOFpCCaCzgcOBy5/L768Ek5HJki2TgKT1gZuAS21f0e76jBfbBwGbAOdLen676zPGDqFc5HwzsCVwiaT12lulMfdD4DLbPbZ/CDwGvOi52vhKfVY9BuZTxpf/LUu2rJwkrQvMBT5g+zvtrs94kHQg8BLbp1HOXp+pfyst26/vfVwDyuG2f92+Go2LQ4BXA++X9GLKSMuvnquNJ5iMTJZsWfl9DJgJnCipd+5kD9sr8yTt14EvS/ousApwzEre3snqQuAiSbdSfrV3yHM5spLlVCIiorHMmURERGMJJhER0ViCSURENJZgEhERjSWYREREYwkmAYCkcflZn6Sbxnj720n69FjuYzQkvUzShfXxNpIuaHedBjKaY1iX5/iBpHvr3/bDLHdRvRo9JrhcZxLjbdYYb/8vgXXHeB+j8VJgYwDbdwHvbW91BjWiYyipA9gU2CArQkxeCSbxJ+qqsR+jXAm9GeUq//2BTwG/tH1GzXclcAVlVYAvAutTrpr+B9v/KWk34HTKxVELgb8BPl7L3mF7e0m/Br4J7Ey5EvdfgKOAlwAH275F0suBc4EX1Dp90PY9ki6irJO2dc1/CuWi0k8Aa0g63vYnW9p1MGXByrUpX5TfBD4M7FLrORV4ADiCshz7FrU9Z9i+ZKDytnskfYyy2vAKytXzx9bj8S3gUWApsA6wkaQvUBaTPNn2LEmbAF+q230SOMr2f/XXPttfHsH7eHPdx82SNgRutr1h3e4zlCuhZwD/aPvSlnJrtR5D4DTgc8BulPfyUtt9ey29S9dfX5d0P9/25yW9lbKy9qtrG24GdrD9iwHq/G7gGMqIyQLgSMoClJvY/kDNcwZlYcYvAV8AXkV57z5t+yv1fToIeCHlPbqCst7YGpT34DO2z5Y0A7gEeDnw41q/twM/B/6ZctIzFbjI9meHPuKRYa7oz47AByjBZAPKwnCXAvsB1EUQdwSuA84C5tjeGngr8MX6+gmUJSq2ofynfo3towBs9w6BrAtca3vT+vzttncGTqZ8qQBcDBxr+zWUL5Z/bann+pRA9BbKl/7jlID1jdZA0mJbYF/glcAOlC8PKOtR7VrXpjqZsgT9q4BdgZMlbT5QeUl71nZvDWxF+XI6vOYXcIDt3SlB8i7bR/ap02XA2bY3B/4euFLS9P7a1097RusllPdvV+CM1jWp+jmGh9d6bA5sB+wraa8+25sJfIdyi4bdgMMlvbHeB+Z7wPHAl4GPDBJIXgkcCuxoe0vgt8BHKO/32yRNrT2gdwBfoXy+FtTP3euB4yVt1NK+rWx/jNIDPNX2tsAbKMvrU9to26+knIj0vseH1uPwmtrevSXtPJyDOtklmER/HrD9C9vPAA8Ca9u+B1i19hTeTgkCTwG7A5+QdC9wPWU5jo0p9wC5WtLngQdtzx1gX9fXf38K3NjyeKakNShf4F+u27+Ccsb8gppvru0eSo9i7WG06xu2f2N7GeVLateabtu9q0HvSll2AtuPAtfw7NBcf+V3Bb5i+w91iGcO5QsV4Le2HxmoMrV9L7f99bq/24HfUYLQaNo3XF+2/XT9Yp9PubXCQHalnJ2vsL2EsqLwbq0ZbN9m+922n6zH7EJgz/ry0cDfAb+23Xoi0NcbgFcAt9f3em9gU9u/pdxT5g2UwPpD27+ifO4Or3m/CzyfEuQB7m4Zbvsw5XP7D5RAskZNfyPlBKl32PH7NX134K11u3dQAtOrB6l3VBnmiv4sbXncQ1mHDMpZ9LsoZ7W9Qx1TKWf1vwOoC8j9xva9kr4JvBk4XdKV/fUW6hdzr77j7VOBpfVMlbr9l1C+cP9YzzrUNJx2tW5/SsvzP/RJb9XBs/9P+is/WP6h1reawrPHtr/yg7ZP0n8AL65P97Tdel+O1vdtlT5FBzoOA9VxoPr11mMnYHrLwpgdPHvbgnUpw3+bSppeT0D6MxX4t97eaw20vfvp/dwtq4978x9g++6af13K5+Jv+dPj/m+UYdZvUk4A9qvpK/ppW+92j+0N8JJeyOS5MVwj6ZnESFxO+U/9CmBeTbsReD+ApL+knOGtLukOoNP254DPAq+p+Yd9D5jaW/iRpAPq9t9IOQsdzHIGPknaQ9KMese5v+HZXlGrGyln0r1fJG+jjPUPVP5G4G8krVbb9R7K8vVD1sv2IuBhSfvU/e0ArEfpiQzJ9p62t6x/fW/w9CjPnqm/rc9rfy2pQ9JLge159r3sr643AgfVYabVKV/Wfdu3FvDPklatQ5wHUXqlU4GLKL2TW4B/HKQ5N1OGDdepw1nn8uxQ5zWUoazZlEUpe+t1BICkF1E+dxv0s903Ah+3fQ1lfqz3NsXfpswFIunVlLmXnrrdQyWtUgParfUYxRASTGLYbP+c8iV1ZR1+AfggsIOk7wNfBQ60vZgyiX+RpAWUuY6Tav5rgPvqF/Jw/C3w3rr904B3tey7P3fW+nyqn9d+C/wHcB/wzQFujPQJYG1J91MC1yd7z377K2/7WuBa4C7gB5QhunP62e6DwFqSLu2TfgBwVN3f54F9+vTWRut0ylLjdwOr9Xlt9Vrf64D32X6sz+utx/CLwC8obb6HMtR3dWvmegyuq68voMyh3UYZYvpNPcv/GLCfBri7n+37KHMXN1KO4xTKjz6oKxjPB+60/fta5BRgNUkP1DLH2n64n02fDNxaj8Ns4BHgZcCpwMvr5+oTwK8pPZrzgB/VttxFGRK8ub86x5/KqsExKdRf+cyyfXA7yv+5qL/mutn2RW2uSlvV3u5PbM+XtAGl57RxnSeMUcicSURMRv8DnFeHvFYAhyWQNJOeSURENJY5k4iIaCzBJCIiGkswiYiIxhJMIiKisQSTiIho7P8Dk4JuyIW5MesAAAAASUVORK5CYII=",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3049eac8>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "u_star[6]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2.142857142857143"
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "source": [
    "#helper function to give keras like model summary\r\n",
    "import torch\r\n",
    "import torch.nn as nn\r\n",
    "from torch.autograd import Variable\r\n",
    "\r\n",
    "from collections import OrderedDict\r\n",
    "\r\n",
    "\r\n",
    "def summary(model, input_size, device=\"cuda\"):\r\n",
    "        def register_hook(module):\r\n",
    "            def hook(module, input, output):\r\n",
    "                class_name = str(module.__class__).split('.')[-1].split(\"'\")[0]\r\n",
    "                module_idx = len(summary)\r\n",
    "\r\n",
    "                m_key = '%s-%i' % (class_name, module_idx+1)\r\n",
    "                summary[m_key] = OrderedDict()\r\n",
    "                summary[m_key]['input_shape'] = list(input[0].size())\r\n",
    "                summary[m_key]['input_shape'][0] = -1\r\n",
    "                if isinstance(output, (list,tuple)):\r\n",
    "                    summary[m_key]['output_shape'] = [[-1] + list(o.size())[1:] for o in output]\r\n",
    "                else:\r\n",
    "                    summary[m_key]['output_shape'] = list(output.size())\r\n",
    "                    summary[m_key]['output_shape'][0] = -1\r\n",
    "\r\n",
    "                params = 0\r\n",
    "                if hasattr(module, 'weight') and hasattr(module.weight, 'size'):\r\n",
    "                    params += torch.prod(torch.LongTensor(list(module.weight.size())))\r\n",
    "                    summary[m_key]['trainable'] = module.weight.requires_grad\r\n",
    "                if hasattr(module, 'bias') and hasattr(module.bias, 'size'):\r\n",
    "                    params +=  torch.prod(torch.LongTensor(list(module.bias.size())))\r\n",
    "                summary[m_key]['nb_params'] = params\r\n",
    "                \r\n",
    "            if (not isinstance(module, nn.Sequential) and \r\n",
    "               not isinstance(module, nn.ModuleList) and \r\n",
    "               not (module == model)):\r\n",
    "                hooks.append(module.register_forward_hook(hook))\r\n",
    "                \r\n",
    "        device = device.lower()\r\n",
    "        assert device in [\"cuda\", \"cpu\"], \"Input device is not valid, please specify 'cuda' or 'cpu'\"\r\n",
    "\r\n",
    "        if device == \"cuda\" and torch.cuda.is_available():\r\n",
    "            dtype = torch.cuda.FloatTensor\r\n",
    "        else:\r\n",
    "            dtype = torch.FloatTensor\r\n",
    "        \r\n",
    "        # check if there are multiple inputs to the network\r\n",
    "        if isinstance(input_size[0], (list, tuple)):\r\n",
    "            x = [Variable(torch.rand(2,*in_size)).type(dtype) for in_size in input_size]\r\n",
    "        else:\r\n",
    "            x = Variable(torch.rand(2,*input_size)).type(dtype)\r\n",
    "            \r\n",
    "            \r\n",
    "        # print(type(x[0]))\r\n",
    "        # create properties\r\n",
    "        summary = OrderedDict()\r\n",
    "        hooks = []\r\n",
    "        # register hook\r\n",
    "        model.apply(register_hook)\r\n",
    "        # make a forward pass\r\n",
    "        # print(x.shape)\r\n",
    "        model(x)\r\n",
    "        # remove these hooks\r\n",
    "        for h in hooks:\r\n",
    "            h.remove()\r\n",
    "\r\n",
    "        print('----------------------------------------------------------------')\r\n",
    "        line_new = '{:>20}  {:>25} {:>15}'.format('Layer (type)', 'Output Shape', 'Param #')\r\n",
    "        print(line_new)\r\n",
    "        print('================================================================')\r\n",
    "        total_params = 0\r\n",
    "        trainable_params = 0\r\n",
    "        for layer in summary:\r\n",
    "            # input_shape, output_shape, trainable, nb_params\r\n",
    "            line_new = '{:>20}  {:>25} {:>15}'.format(layer, str(summary[layer]['output_shape']), '{0:,}'.format(summary[layer]['nb_params']))\r\n",
    "            total_params += summary[layer]['nb_params']\r\n",
    "            if 'trainable' in summary[layer]:\r\n",
    "                if summary[layer]['trainable'] == True:\r\n",
    "                    trainable_params += summary[layer]['nb_params']\r\n",
    "            print(line_new)\r\n",
    "        print('================================================================')\r\n",
    "        print('Total params: {0:,}'.format(total_params))\r\n",
    "        print('Trainable params: {0:,}'.format(trainable_params))\r\n",
    "        print('Non-trainable params: {0:,}'.format(total_params - trainable_params))\r\n",
    "        print('----------------------------------------------------------------')\r\n",
    "        # return summary"
   ],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "source": [
    "from torchvision import models\n",
    "\n",
    "summary(model,[2,])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [-1, 128]             384\n",
      "              ReLU-2                  [-1, 128]               0\n",
      "            Linear-3                  [-1, 128]          16,512\n",
      "              ReLU-4                  [-1, 128]               0\n",
      "            Linear-5                   [-1, 15]           1,935\n",
      "================================================================\n",
      "Total params: 18,831\n",
      "Trainable params: 18,831\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {
    "collapsed": true
   }
  }
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