diff --git a/.gitignore b/.gitignore
index 500d049..6fb5f00 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 events.out.*
 /optuna_result/
 /runs/
+/logs/
 
 # Created by https://www.gitignore.io/api/code,linux,macos,python,windows,jupyternotebook,jupyternotebooks
 # Edit at https://www.gitignore.io/?templates=code,linux,macos,python,windows,jupyternotebook,jupyternotebooks
diff --git a/anp-rnn_1d_regression.ipynb b/anp-rnn_1d_regression.ipynb
new file mode 100644
index 0000000..eeac9e5
--- /dev/null
+++ b/anp-rnn_1d_regression.ipynb
@@ -0,0 +1,698 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:16.219722Z",
+     "start_time": "2020-02-15T01:37:15.839731Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "%reload_ext autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.400940Z",
+     "start_time": "2020-02-15T01:37:16.223520Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
+      "`%matplotlib` prevents importing * from pylab and numpy\n",
+      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib as plt\n",
+    "import collections\n",
+    "from tqdm.auto import tqdm\n",
+    "from tensorboardX import SummaryWriter\n",
+    "\n",
+    "import math\n",
+    "%pylab inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.739638Z",
+     "start_time": "2020-02-15T01:37:17.405045Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n",
+    "from torch.utils.data import TensorDataset, DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.750739Z",
+     "start_time": "2020-02-15T01:37:17.742317Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from src.data.gp_curves import GPCurvesReader\n",
+    "from src.models.model import LatentModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Util"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.760279Z",
+     "start_time": "2020-02-15T01:37:17.753680Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def set_seed(n=42):\n",
+    "    np.random.seed(n)\n",
+    "    torch.manual_seed(n)\n",
+    "#     torch.backends.cudnn.deterministic = True\n",
+    "#     torch.backends.cudnn.benchmark = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.775195Z",
+     "start_time": "2020-02-15T01:37:17.763344Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_functions(target_x, target_y, context_x, context_y, pred_y, std):\n",
+    "    \"\"\"Plots the predicted mean and variance and the context points.\n",
+    "  \n",
+    "  Args: \n",
+    "    target_x: An array of shape [B,num_targets,1] that contains the\n",
+    "        x values of the target points.\n",
+    "    target_y: An array of shape [B,num_targets,1] that contains the\n",
+    "        y values of the target points.\n",
+    "    context_x: An array of shape [B,num_contexts,1] that contains \n",
+    "        the x values of the context points.\n",
+    "    context_y: An array of shape [B,num_contexts,1] that contains \n",
+    "        the y values of the context points.\n",
+    "    pred_y: An array of shape [B,num_targets,1] that contains the\n",
+    "        predicted means of the y values at the target points in target_x.\n",
+    "    std: An array of shape [B,num_targets,1] that contains the\n",
+    "        predicted std dev of the y values at the target points in target_x.\n",
+    "      \"\"\"\n",
+    "    # Plot everything\n",
+    "    plt.plot(target_x[0], pred_y[0], 'b', linewidth=2)\n",
+    "    plt.plot(target_x[0], target_y[0], 'k:', linewidth=2)\n",
+    "    plt.plot(context_x[0], context_y[0], 'ko', markersize=10)\n",
+    "    plt.fill_between(\n",
+    "          target_x[0, :, 0],\n",
+    "          pred_y[0, :, 0] - std[0, :, 0],\n",
+    "          pred_y[0, :, 0] + std[0, :, 0],\n",
+    "          alpha=0.2,\n",
+    "          facecolor='#65c9f7',\n",
+    "          interpolate=True)\n",
+    "\n",
+    "    # Make the plot pretty\n",
+    "    plt.yticks([-2, 0, 2], fontsize=16)\n",
+    "    plt.xticks([-2, 0, 2], fontsize=16)\n",
+    "    plt.ylim([-2, 2])\n",
+    "    plt.grid('off')\n",
+    "    ax = plt.gca()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.796927Z",
+     "start_time": "2020-02-15T01:37:17.777547Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def test(model, dataset_test, writer=None, plot=False, global_step=None, seed=42, title=''):\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        data_test = dataset_test.generate_curves()\n",
+    "\n",
+    "        (context_x, context_y), target_x = data_test.query\n",
+    "        target_y = data_test.target_y\n",
+    "\n",
+    "        context_x = context_x.cuda()\n",
+    "        context_y = context_y.cuda()\n",
+    "        target_x = target_x.cuda()\n",
+    "        target_y = target_y.cuda()\n",
+    "\n",
+    "        y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x, target_y)\n",
+    "\n",
+    "        # Lets order the result, for plotting\n",
+    "        target_inds = torch.argsort(target_x[0, :, 0])\n",
+    "        target_x = target_x[:, target_inds, :]\n",
+    "        target_y = target_y[:, target_inds, :]\n",
+    "        context_inds = torch.argsort(context_x[0, :, 0])\n",
+    "        context_y = context_y[:, context_inds, :]\n",
+    "        context_x = context_x[:, context_inds, :]\n",
+    "        y_pred = y_pred[:, target_inds, :]\n",
+    "        y_std = y_std[:, target_inds, :]\n",
+    "        \n",
+    "        if writer is not None:\n",
+    "            writer.add_scalar('val/loss', loss, global_step=global_step)\n",
+    "            writer.add_scalar('val/y_std', y_std.mean(), global_step=global_step)\n",
+    "            writer.add_scalar('val/mse_loss', mse_loss, global_step=global_step)\n",
+    "            writer.add_scalar('val/kl', kl.mean(), global_step=global_step)\n",
+    "        \n",
+    "        if plot:\n",
+    "            set_seed(seed)\n",
+    "            fig = plt.figure()\n",
+    "            plt.title(f\"{title}. Iter {global_step}\")            \n",
+    "            plot_functions(target_x.detach().cpu().numpy(),\n",
+    "                           target_y.detach().cpu().numpy(),\n",
+    "                           context_x.detach().cpu().numpy(),\n",
+    "                           context_y.detach().cpu().numpy(),\n",
+    "                           y_pred.detach().cpu().numpy(),\n",
+    "                           y_std.detach().cpu().numpy())\n",
+    "            \n",
+    "            writer.add_figure('test', fig, global_step=global_step, close=False)\n",
+    "            plt.show()\n",
+    "            \n",
+    "    return y_pred, kl, loss, y_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.839053Z",
+     "start_time": "2020-02-15T01:37:17.823647Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "MAX_CONTEXT_POINTS = 50\n",
+    "random_kernel_parameters = True\n",
+    "\n",
+    "dataset_train = GPCurvesReader(\n",
+    "    batch_size=16,\n",
+    "    max_num_context=MAX_CONTEXT_POINTS,\n",
+    "    random_kernel_parameters=random_kernel_parameters,\n",
+    "    sequential=True,\n",
+    ")\n",
+    "\n",
+    "dataset_test = GPCurvesReader(\n",
+    "    batch_size=1,\n",
+    "    max_num_context=MAX_CONTEXT_POINTS,\n",
+    "    testing=True,\n",
+    "    random_kernel_parameters=random_kernel_parameters,\n",
+    "    sequential=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Replicate ANP paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:37:17.857781Z",
+     "start_time": "2020-02-15T01:37:17.845425Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "epochs = 100000\n",
+    "PLOT_AFTER = 10000\n",
+    "PRINT_AFTER = 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "runs/Feb15_09-37-17_mjcdesktop-anp_1d_reg\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dfd10f911bde4d9b8efc5aac94863c29",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(FloatProgress(value=0.0, max=100000.0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train: 0 loss= 0.8772 val_loss= 1.351\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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T4B+j/sKQofE3CmrXjiy+m/tRqc/t2LGDH1euYOumjSya/R6tWh3CY08+xeRJrzLj/feDr4leJ53EOf3O46D6dfc61pAhQ3h7ylt0an8o3839iPXr1rFP7dqkpaWRWjuFGdOn8sXs92nWvHkUftuaYdHCBfz1jtu54qqBnHNuv4oLVMHga66hbno6tmNzmf/P1WF5UvgfRLOyssLKy0h27u4BHnLO3Vpi+z3Arc65ct90KtO5O2vWLE7s1o212bFr6inN1NcnMWLQFeTmlnd1F71OzXCsWp5Jny5HlDrZqCLpGRl8+VPgvb9D4wYhfVIoWS6efDf3I9p27lrm81s2beLXdb/Q+rB2Ze4Tqv99+D7XXXI+nU/oytMvvc6v635h4edz6HPm2cF98vPzy5x05JzDEqCN6B93jeLRv9/HiFF3c/0td8S6OtWiUR0jtVZ4/zfhdu5GcjjnZkq/si/rk0BCWrU8k5uuvoJdO+Nn9c6iQy67dWxTqdAvOWmt7/kXh1w2UYYxbt2yhZfHjws+brDfftUS+gAtWweGfJgZu3bupNHvGhcL/U9mzuCsbl1Y/v2yUssPveJizuzaudiSBvHopjvHMHHaDK4ZfnPUzrkjK4v7R93KT2vXVOtxo7lkdCSD/ysC7fwltQO+juB5a5R4m6VZcg2Zyio5aW3g0OEhl3UuP+7H+Ofl5XFp31O5ZfAgnn/ysWo/fpOmBzP14/k8NXEyqWlpxZ5zzjH2vrtYumgBb7/2SqnlF34+hyUL51OvXkZw29P/eogzu3Yus0xNMXLYYMY/8W/Wrl4FwLFdu1c4ibI63Xv7X3hy7N8Zc0vor+mK5Ofnc27vExlz6wi2bdtWbcctSySD/y2gi5m1LNxgZs0J9FZFcdhGbMXTLM3ybjAfqrLugdCsZSt6nHJayMdxLr4XLatVqxaXXzOYlq3b0Ou0MyJyjoNbtCy1ucbMeHbSFEaMupvrbgq0tObl5TFl0svBO8tN/2wRE6fNoEWRwQYL5nzKkoXz2b17V3DbZx/N4syunXnqnw9G5HcoKi8vj21btwYfb9qwgb49jqP30cXnwXy9dDF33jSUe2+7KeJ1Ks31t9zBsV17BP9tq8PCuZ/xxby5THt9UpUnnYUiksH/NLASeNPMzjKzMwmM8lkDPBnB89Yo8TJLc9XyTK46v2+lmnUKVXQPhNF/f5g6dVJLKVm2mtocFoqzLxjA9Dlf0KRp9CerZ9Svz/W33EFycqArbfGCeVx/+UUMveLi4PPHdu1e7OZFDzw+jonTZtCtd5/gtkXz5rJk4Xx+WrM6uG3Lpk2c2bUzY24dUam6bfz1VxbM/Yw1q1YGt818bzrtDsxgxNWXB7fV33dfvlm6mB+++6bYG8LwkaMZ/8Y0xo77b6XOX1WNm/yeidM+4A9HHl1tx+x07PFM/XQBf/v3U8FloyMpYsHvAjNzewLLgP8CLwArgJ7OucRoxA1B3RBvBh/L1TsLm3e+/6ZyLXDJKSlcMui6Cu+B0KxlKx5/8dUyl7coT01qDivLunW/cNHpJwWbIIAqrRNTnf43432Sk5M558KyFwssfDP4bWgkXDLwWiZOm8FFfx4U3Pbl4oUsWTifhZ/PKVZ+0AXncMcN17F1y5bgtrH33sWV553J6hXLg9ueeXQs5/Y6gdcn/vYp98DGB7F71y62bvqt+69WrVq8+t5s5i//mXoZvzVJndCjN916nxKVgAzFr+vWVbrsrl2/fbpq3/EIuvU+pTqqVKGIrtXjnFvtnDvXOZfhnKvnnOvrnFsZyXPWNKHewrH7SbEZ1VPeEsGhCmcRutLucRCKmtIcVp5xTz3Fp7M/5P47otfRGKrrb7mDud+vpWef08MqVy8jg2O7di+2rPFRfzyWidNmcNOou4Pbtm7ezHtvv8mkF8YXu9iZ87/ZzJg+lTUrVwS3tWrTlo5HdWLf/fYPbmtzWHuW/LiJV96bVez8HY/qxAGNGtXIEUjOOe6742aOb9eCb75cEnb5H9espkubpjx491+J1OjKsmg9/ggbOHQ4r734fLlt5klJSQwfOTp6lSoilM7nshQdjhrOhKuS9zhoUS85pBd+1rbKdzZHww03DqdJy9bccvf9sa7KXpKSkti/YfVMOkqrW5dju3Yvti21bl0mTpvBLz+tDTYvAQy5+Tb+PHgoh3boGNzW7+LL6HfxZcXKJycnk1G/frXUL1rMjN27dpGbk8O8Tz7msCK/Yyjef/sttmzaxOqVy6P+xhaxcfxVlUjj+Ge+N73cWZpjn36ePmcFhuJl79jBxg2/0rRZ86jULZzx9SVdMug6rhoyrMqzbMOpQ1rduuzMzqZuejp9z7+YgUOHx3SW7/p1v9Cw0YGYWYXj+CXxbNu6ldUrMoPr94dr0by57H9AQw5uERwDE/fj+KVARbdwLAx95xwjh13HaccdxewP3o1K3cKZUVtSVe5pXFTf8y8O+Yone8eOGjPi58svFnLKMR3519/uicn5JfYy6tcvFvrhXkgfeUznYqEfLQr+KCls3vjyp81l3gx+z549bN++jZw9e2h80O8jXqctmzaF3PlcUpt21bfU9MChwyvVxhnrET+rVyxn65bNLF7wObm5MVgLXGqURfPmck6vE4r1Z5Tmuccf5euli6NUq9Ip+GuQ2rVr89TEybw1e26xzrSqXJWX5dEH7uPYQ5vRtfcpYY+wgcDQzOrSrGWrKi0xuTM7m4EXnB318D/t7H5MeOtdnnjxtWLt2uKn//vr7Sz6fA4Tnnmi2Pb8IkvSbt+2jTG3Dqf/yd3YtGFDtKsYpOCvYcysWOh/PPMDTmzfihnT367W82zetJGd2dk0ObhZ2LMebxw5muO69ajW+qRX8pNHoWVffxWVZp9PZ88sNjTxuO49ozLhRmq+Jye+xrUjbmHEX8cAsGbVSs444RjO6tYluE+9jAzatv8Dw26/k/0OOCBWVVXw13RvvPwimzZuYPGC8Dq6S9q0YQMrfvg++Hj4yLt4+Z2Z3HHvA+XeOrKoNu068OLUD7jh1lFVqktpQh32Wp5IN/vM+/RjLj/ndC48vTcb1q+PyDkkftVv0IBb7roveCHQsNGBfPvlEr79cgm7du4M7vfc5Le56vobY1VNQMFf4z3w2DP889n/csNtv4VtuO3hi+bNpedRhzH0iouDC0HVTU+n8wmBEShldT5fMug6Zi9ZxsqsPFZm5fHe54ur/Uq/UCg3rQ9FJCd6HfaHw2nX8Qh6nHxqTK/WJD7USU3ltRkfs2j1r9RJ/W3Gek24X4KCv4ZLSkrirP4XBafWZ+/YwQWn9gqO+gnl5uVtDmtPWlpd6jdowLYisyqLCqXzOZIquml9qCI50Su9Xj1efPt9xox9tMzljkWKOvzoY0ivVy/W1diLeqTizAvjnmTux7PZvHEDuTk5DLnswmLzA7K2b+el58bx8vhneWLiJHr1OZ266elM/vATDmx8UI2cAVmo8JPHM48+zOsvTWDH9ixS66axe9cu8sIYNVOd6x699uLzrMrMZPiou4DAPAKReKfgjzNXDhnGnt27aX/4kVw74LxSl1rIzQ28CVxzYT8+WPAlzVq24nc1/C5fhUrO6oXfJsCFuqxEda179OOa1dw6eBA5OTmc0LM3fzz+xGo5rkis6fNqnElKSmLwX27jg2lTKlxqIT8/v8YvbBaKwk8CRUc7lee4rtXTD9Gk6cE8+NRz3HbP/yn0JaEo+ONUKOv85+Xl1viFzULVrGUrnn7p9b1uOlKazidULaSLri555nkXcPWw2Kz7LhIpCv44FS/r/Fen8jqAC28AM+Tm27lyyG9D5ZxzIXWAF+778H1306fz4RXOvhSJZwr+OBUP6/xHQkXrHt1UMHkG4Ocf19KtYxtOPqZj8FaShev8vDDuSbp1bEPzIm8Emcu+4+OZH7Du559YsrBq8yZEajIFf5wKZcJTyRueJ4pQh57+ffRIVq9Yzu7du/ZqFgvOhSiy4NufTjyGPw++gWdfm8Lp55wXrV9HJOoU/HEqlAlP4dwgJRHVSU0Nebx94YJvIwZdTotWrSNcM5HYUvDHqVDau8O9QUqieevVicUWyApFPNziUaSqFPxxrKL27tJueO6TyqxqGg+3eBSpKk3ginOlTXiSgLrp6ZW6u1gijYQSKY2u+CVhVXbFz0QbCSVSkoJfElZlVvxM1JFQIkUp+CVhVWbFT99HQokfFPyS0Ep2gENgddKSq5RqJJT4RMEvCa/ohK+VWbnMXrKMAQOv1Ugo8ZZG9Yh3NBJKfKcrfhERzyj4RUQ8k3BNPUnA/vvU3NsLSuJKtsq99mrw3TAlBlKi8HpIuOA3M9Irf69ukUpLMkiPxl+tSBWpqUdExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERzyj4RUQ8o+AXEfGMgl9ExDMKfhERz0Qk+M2sjZn908yWmFmWmf1sZm+Z2eGROJ+IiIQuUlf8JwM9gPHAn4DrgIbAHDM7OkLnFBGRECRH6LgvAf92zrnCDWb2IbASuAG4NELnFRGRCkQk+J1zG0rZttXMlgFNInFOEREJTdQ6d81sP6AD8E20zikiInuLVFNPaR4BDHi4rB3MbBAwqOBhlpl9F+Y5DgD2+rQhEiV6/UmsFH3tNatoZyvSDF/2Tma9gfdDOPls51z3UsrfBtwHXOmcezaE41SKmc13znWK1PFFyqPXn8RKuK+9UK/4PwUOC2G/7FIqdA2B0B8ZydAXEZHQhBT8zrls4NtwD25mlwCPAQ865+4Nt7yIiFS/iHXumtnZwH+AZ5xzN0XqPCU8FaXziJRGrz+JlbBeeyG18YfLzLoC7wFfAdcD+UWe3u2cW1TtJxURkZBEalRPT6A2cBTwSYnnVgHNI3ReERGpQESu+EVEpOZKyNU5tUicRIuZNTWzSWa21cy2mdlkMzs41vWSxGZm/czsNTNbZWY7zew7M7vfzOqFVD4Rr/jNbAiBiWDjgYVAA+Bm4AjgBOfcghhWTxKEmaUBi4HdwEjAAfcAaUBH59yOGFZPEpiZzQFWA28Ca4EjgdEERl8e55zLL7t04gb/AcDGEovE1SewSNwU55wWiZMqM7MbgIeAts65Hwq2tQC+B252zj0Uy/pJ4jKzhs65X0tsu5TAxW4v59yH5ZVPyKYe59wGV+IdzTm3FdAicVKdzgTmFIY+gHNuBYEBDWfFrFaS8EqGfoF5Bd8rzLiEDP7SaJE4iYD2wJelbP8KaBfluoh0K/heYcZ5E/yEsEicSJj2AzaXsn0TsG+U6yIeM7MmwN3AB865+RXtHxfBb2a9zcyF8DWrjPK3ARcBQ4p+LBcRiXdmlk6gkzcXuCKUMtFclrkqtEic1ESbKf3KvqxPAiLVysxSgSlAS6Cbc25tKOXiIvi1SJzUUF8RaOcvqR3wdZTrIp4xsxRgEtAJOMk5tzTUsnHR1FMZMVokTvzyFtDFzFoWbjCz5sDxBc+JRISZJQEvEFgep69zbk5Y5RN0HL8WiZOIM7O6BCZw7eS3CVxjgHoEJnBlxbB6ksDM7HHgGuBe4O0ST6+tqMknUYN/NHBnGU+vcs41j15tJJEVLM8wFjiJwKixGcAw59zKWNZLEpuZraTsWyze5ZwbXW75RAx+EREpW8K28YuISOkU/CIinlHwi4h4RsEvIuIZBb+IiGcU/CIinlHwi4h4RsEvIuKZ/wdoUpWKOLPZHwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "writer = SummaryWriter(comment='-anp_1d_reg')\n",
+    "print(writer.logdir)\n",
+    "\n",
+    "hparams = dict(\n",
+    "    x_dim=1,\n",
+    "    y_dim=1,\n",
+    "    hidden_dim=128,\n",
+    "    latent_dim=128,\n",
+    "    latent_enc_self_attn_type=\"uniform\",\n",
+    "    det_enc_self_attn_type=\"uniform\",\n",
+    "    det_enc_cross_attn_type=\"multihead\",\n",
+    "    use_rnn=True)\n",
+    "model = LatentModel(**hparams).cuda()\n",
+    "\n",
+    "optim = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
+    "\n",
+    "for n_iter in tqdm(range(epochs)):\n",
+    "    model.train()\n",
+    "\n",
+    "    data_train = dataset_train.generate_curves()\n",
+    "\n",
+    "    (context_x, context_y), target_x = data_train.query\n",
+    "    target_y = data_train.target_y\n",
+    "\n",
+    "    context_x = context_x.cuda()\n",
+    "    context_y = context_y.cuda()\n",
+    "    target_x = target_x.cuda()\n",
+    "    target_y = target_y.cuda()\n",
+    "\n",
+    "    optim.zero_grad()\n",
+    "    y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x,\n",
+    "                                              target_y)\n",
+    "    loss.backward()\n",
+    "    optim.step()\n",
+    "\n",
+    "    writer.add_scalar('train/loss', loss, n_iter)\n",
+    "    writer.add_scalar('train/mse_loss', mse_loss, n_iter)\n",
+    "    writer.add_scalar('train/y_std', y_std.mean(), n_iter)\n",
+    "    writer.add_scalar('train/kl', kl.mean(), n_iter)\n",
+    "\n",
+    "    if n_iter % PRINT_AFTER == 0:\n",
+    "        y_pred, kl, val_loss, y_std = test(\n",
+    "            model, dataset_test, writer, plot=False, global_step=n_iter)\n",
+    "        print(\n",
+    "            f\"train: {n_iter} loss={loss.item(): 4.4g} val_loss={val_loss.item(): 4.4g}\"\n",
+    "        )\n",
+    "\n",
+    "    if n_iter % PLOT_AFTER == 0:\n",
+    "        test(model, dataset_test, writer, plot=True, global_step=n_iter, title='test')\n",
+    "        \n",
+    "        test(model, dataset_train, writer, plot=True, global_step=n_iter, seed=n_iter, title='train')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T01:35:16.595569Z",
+     "start_time": "2020-02-15T01:35:16.586714Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    set_seed(i)\n",
+    "    test(model, dataset_test, writer, plot=True, global_step=i, seed=i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    test(model, dataset_train, writer, plot=True, global_step=i, seed=i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Final validation\n",
+    "loss = torch.stack([test(model, dataset_test)[2] for _ in tqdm(range(100))]).mean().cpu()\n",
+    "print('val loss (n=100)', loss)\n",
+    "writer.add_hparams(hparams, dict(val_loss=loss))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T03:26:03.797075Z",
+     "start_time": "2020-02-01T03:19:32.800Z"
+    }
+   },
+   "source": [
+    "## Improved\n",
+    "\n",
+    "These have no been extensively tested:\n",
+    "\n",
+    "- use torches ~50% faster multiheaded attention\n",
+    "- calc loss in log domain for stability (and untested changes to clipping)\n",
+    "- dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T07:27:59.578047Z",
+     "start_time": "2020-02-01T07:27:59.573762Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "writer = SummaryWriter(comment='-anp_1d_impr')\n",
+    "print(writer.logdir)\n",
+    "                 \n",
+    "hparams = dict(\n",
+    "    x_dim=1,\n",
+    "    y_dim=1,\n",
+    "    hidden_dim=128,\n",
+    "    latent_dim=128,\n",
+    "    latent_enc_self_attn_type=\"ptmultihead\",\n",
+    "    det_enc_self_attn_type=\"ptmultihead\",\n",
+    "    det_enc_cross_attn_type=\"ptmultihead\",\n",
+    "    use_rnn=True\n",
+    "    min_std=1e-3,\n",
+    "    use_lvar=True,\n",
+    "    use_self_attn=True,\n",
+    "#     attention_dropout=0.3,\n",
+    "#     dropout=0.3,\n",
+    "#     batchnorm=True,\n",
+    ")\n",
+    "model = LatentModel(**hparams).cuda()\n",
+    "\n",
+    "optim = torch.optim.AdamW(model.parameters(), lr=1e-4)\n",
+    "\n",
+    "for n_iter in tqdm(range(epochs)):\n",
+    "    model.train()\n",
+    "\n",
+    "    data_train = dataset_train.generate_curves()\n",
+    "\n",
+    "    (context_x, context_y), target_x = data_train.query\n",
+    "    target_y = data_train.target_y\n",
+    "\n",
+    "    context_x = context_x.cuda()\n",
+    "    context_y = context_y.cuda()\n",
+    "    target_x = target_x.cuda()\n",
+    "    target_y = target_y.cuda()\n",
+    "\n",
+    "    optim.zero_grad()\n",
+    "    y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x,\n",
+    "                                              target_y)\n",
+    "    loss.backward()\n",
+    "    optim.step()\n",
+    "\n",
+    "    writer.add_scalar('train/loss', loss, n_iter)\n",
+    "    writer.add_scalar('train/mse_loss', mse_loss, n_iter)\n",
+    "    writer.add_scalar('train/y_std', y_std.mean(), n_iter)\n",
+    "    writer.add_scalar('train/kl', kl.mean(), n_iter)\n",
+    "\n",
+    "    if n_iter % PRINT_AFTER == 0:\n",
+    "        y_pred, kl, val_loss, y_std = test(\n",
+    "            model, dataset_test, writer, plot=False, global_step=n_iter)\n",
+    "        print(\n",
+    "            f\"train: {n_iter} loss={loss.item(): 4.4g} val_loss={val_loss.item(): 4.4g}\"\n",
+    "        )\n",
+    "\n",
+    "    if n_iter % PLOT_AFTER == 0:\n",
+    "        test(model, dataset_test, writer, plot=True, global_step=n_iter)\n",
+    "        test(model, dataset_train, writer, plot=True, global_step=n_iter, seed=n_iter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    test(model, dataset_test, writer, plot=True, global_step=i, seed=i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    test(model, dataset_train, writer, plot=True, global_step=i, seed=i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Final validation\n",
+    "loss = torch.stack([test(model, dataset_test)[2] for _ in tqdm(range(100))]).mean().cpu()\n",
+    "print('val loss (n=100)', loss)\n",
+    "writer.add_hparams(hparams, dict(val_loss=loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2020-02-15T01:37:15.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "(hparams, dict(val_loss=loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "jup3.7.2",
+   "language": "python",
+   "name": "jup3.7.2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/anp_1d_regression.ipynb b/anp_1d_regression.ipynb
deleted file mode 100644
index c97c69b..0000000
--- a/anp_1d_regression.ipynb
+++ /dev/null
@@ -1,1710 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.259765Z",
-     "start_time": "2020-02-01T13:01:09.093578Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "%reload_ext autoreload"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.323260Z",
-     "start_time": "2020-02-01T13:01:09.272918Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib as plt\n",
-    "import collections\n",
-    "from tqdm.auto import tqdm\n",
-    "from tensorboardX import SummaryWriter\n",
-    "\n",
-    "import math\n",
-    "%pylab inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.360522Z",
-     "start_time": "2020-02-01T13:01:09.336892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from torch import nn\n",
-    "import torch.nn.functional as F\n",
-    "from torch.utils.data import TensorDataset, DataLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.398406Z",
-     "start_time": "2020-02-01T13:01:09.370332Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from src.data.gp_curves import GPCurvesReader\n",
-    "from src.models.model import LatentModel"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Util"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.444284Z",
-     "start_time": "2020-02-01T13:01:09.416961Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def set_seed(n=42):\n",
-    "    np.random.seed(n)\n",
-    "    torch.manual_seed(n)\n",
-    "#     torch.backends.cudnn.deterministic = True\n",
-    "#     torch.backends.cudnn.benchmark = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.540329Z",
-     "start_time": "2020-02-01T13:01:09.453535Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def plot_functions(target_x, target_y, context_x, context_y, pred_y, std):\n",
-    "    \"\"\"Plots the predicted mean and variance and the context points.\n",
-    "  \n",
-    "  Args: \n",
-    "    target_x: An array of shape [B,num_targets,1] that contains the\n",
-    "        x values of the target points.\n",
-    "    target_y: An array of shape [B,num_targets,1] that contains the\n",
-    "        y values of the target points.\n",
-    "    context_x: An array of shape [B,num_contexts,1] that contains \n",
-    "        the x values of the context points.\n",
-    "    context_y: An array of shape [B,num_contexts,1] that contains \n",
-    "        the y values of the context points.\n",
-    "    pred_y: An array of shape [B,num_targets,1] that contains the\n",
-    "        predicted means of the y values at the target points in target_x.\n",
-    "    std: An array of shape [B,num_targets,1] that contains the\n",
-    "        predicted std dev of the y values at the target points in target_x.\n",
-    "      \"\"\"\n",
-    "    # Plot everything\n",
-    "    plt.plot(target_x[0], pred_y[0], 'b', linewidth=2)\n",
-    "    plt.plot(target_x[0], target_y[0], 'k:', linewidth=2)\n",
-    "    plt.plot(context_x[0], context_y[0], 'ko', markersize=10)\n",
-    "    plt.fill_between(\n",
-    "          target_x[0, :, 0],\n",
-    "          pred_y[0, :, 0] - std[0, :, 0],\n",
-    "          pred_y[0, :, 0] + std[0, :, 0],\n",
-    "          alpha=0.2,\n",
-    "          facecolor='#65c9f7',\n",
-    "          interpolate=True)\n",
-    "\n",
-    "    # Make the plot pretty\n",
-    "    plt.yticks([-2, 0, 2], fontsize=16)\n",
-    "    plt.xticks([-2, 0, 2], fontsize=16)\n",
-    "    plt.ylim([-2, 2])\n",
-    "    plt.grid('off')\n",
-    "    ax = plt.gca()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.608076Z",
-     "start_time": "2020-02-01T13:01:09.562004Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def test(model, dataset_test, writer=None, plot=False, global_step=None, seed=42):\n",
-    "    model.eval()\n",
-    "    with torch.no_grad():\n",
-    "        data_test = dataset_test.generate_curves()\n",
-    "\n",
-    "        (context_x, context_y), target_x = data_test.query\n",
-    "        target_y = data_test.target_y\n",
-    "\n",
-    "        context_x = context_x.cuda()\n",
-    "        context_y = context_y.cuda()\n",
-    "        target_x = target_x.cuda()\n",
-    "        target_y = target_y.cuda()\n",
-    "\n",
-    "        y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x, target_y)\n",
-    "\n",
-    "        # Lets order the result, for plotting\n",
-    "        target_inds = torch.argsort(target_x[0, :, 0])\n",
-    "        target_x = target_x[:, target_inds, :]\n",
-    "        target_y = target_y[:, target_inds, :]\n",
-    "        context_inds = torch.argsort(context_x[0, :, 0])\n",
-    "        context_y = context_y[:, context_inds, :]\n",
-    "        context_x = context_x[:, context_inds, :]\n",
-    "        y_pred = y_pred[:, target_inds, :]\n",
-    "        y_std = y_std[:, target_inds, :]\n",
-    "        \n",
-    "        if writer is not None:\n",
-    "            writer.add_scalar('val/loss', loss, global_step=global_step)\n",
-    "            writer.add_scalar('val/y_std', y_std.mean(), global_step=global_step)\n",
-    "            writer.add_scalar('val/mse_loss', mse_loss, global_step=global_step)\n",
-    "            writer.add_scalar('val/kl', kl.mean(), global_step=global_step)\n",
-    "        \n",
-    "        if plot:\n",
-    "            set_seed(seed)\n",
-    "            fig = plt.figure()\n",
-    "            plt.title(f\"Iter {global_step}\")            \n",
-    "            plot_functions(target_x.detach().cpu().numpy(),\n",
-    "                           target_y.detach().cpu().numpy(),\n",
-    "                           context_x.detach().cpu().numpy(),\n",
-    "                           context_y.detach().cpu().numpy(),\n",
-    "                           y_pred.detach().cpu().numpy(),\n",
-    "                           y_std.detach().cpu().numpy())\n",
-    "            \n",
-    "            writer.add_figure('test', fig, global_step=global_step, close=False)\n",
-    "            plt.show()\n",
-    "            \n",
-    "    return y_pred, kl, loss, y_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Init"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.641395Z",
-     "start_time": "2020-02-01T13:01:09.616554Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "MAX_CONTEXT_POINTS = 50 \n",
-    "random_kernel_parameters=True \n",
-    "\n",
-    "dataset_train = GPCurvesReader(\n",
-    "    batch_size=16, max_num_context=MAX_CONTEXT_POINTS, random_kernel_parameters=random_kernel_parameters)\n",
-    "\n",
-    "dataset_test = GPCurvesReader(\n",
-    "    batch_size=1, max_num_context=MAX_CONTEXT_POINTS, testing=True, random_kernel_parameters=random_kernel_parameters)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Replicate ANP paper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:01:09.665487Z",
-     "start_time": "2020-02-01T13:01:09.643995Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "epochs = 100000\n",
-    "PLOT_AFTER = 10000\n",
-    "PRINT_AFTER = 1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:26:35.086938Z",
-     "start_time": "2020-02-01T13:01:09.668425Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "runs/Feb01_21-01-09_mjcdesktop-anp_1d_reg\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7ab8d71f56ff46f8b5e012c78d883bec",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=100000.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 0 loss= 0.9901 val_loss= 1.203\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 1000 loss= 0.2283 val_loss=-0.5778\n",
-      "train: 2000 loss= 0.141 val_loss= 0.2598\n",
-      "train: 3000 loss=-0.02194 val_loss= 1.127\n",
-      "train: 4000 loss= 0.1633 val_loss= 0.2397\n",
-      "train: 5000 loss=-0.2981 val_loss= 1.3\n",
-      "train: 6000 loss=-0.2146 val_loss=-0.7948\n",
-      "train: 7000 loss= 0.4765 val_loss=-0.4249\n",
-      "train: 8000 loss=-0.4619 val_loss= 0.3319\n",
-      "train: 9000 loss= 0.2651 val_loss= 0.0496\n",
-      "train: 10000 loss=-0.3873 val_loss= 0.4204\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 11000 loss=-0.6022 val_loss=-0.1339\n",
-      "train: 12000 loss=-0.7479 val_loss= 0.1351\n",
-      "train: 13000 loss=-0.8724 val_loss=-0.7456\n",
-      "train: 14000 loss=-0.319 val_loss=-0.617\n",
-      "train: 15000 loss=-0.6649 val_loss=-1.035\n",
-      "train: 16000 loss=-0.4514 val_loss=-0.1827\n",
-      "train: 17000 loss=-0.5549 val_loss= 0.06608\n",
-      "train: 18000 loss=-0.7057 val_loss=-1.228\n",
-      "train: 19000 loss=-0.4386 val_loss=-0.3513\n",
-      "train: 20000 loss=-0.6165 val_loss=-0.6527\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 21000 loss=-0.2358 val_loss=-0.1649\n",
-      "train: 22000 loss=-0.7763 val_loss=-0.8249\n",
-      "train: 23000 loss=-0.3989 val_loss= 0.07137\n",
-      "train: 24000 loss= 0.183 val_loss=-0.3059\n",
-      "train: 25000 loss=-1.005 val_loss= 0.5692\n",
-      "train: 26000 loss=-0.6361 val_loss=-1.653\n",
-      "train: 27000 loss=-0.7043 val_loss= 0.9915\n",
-      "train: 28000 loss=-1.054 val_loss=-1.052\n",
-      "train: 29000 loss=-0.5186 val_loss=-0.3443\n",
-      "train: 30000 loss=-0.4902 val_loss=-0.03693\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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JwsKn4HT6Xr2azGaPkSRCwAO9IW6bpO3dIUi5n4MH5nJXE43BvQR7dhTdZqORQtPfepMLDj3z9aLTmNADzJ81k4/efZv7Wjbj1rrV+eCtyYDxUMjsaJqF4GA7s5bGZgk9QPkKMGuJxB4iWblEMOtt3/dKc2OodpFCoSg6ZUrsf/gBHuwKDofGI49L/u8VaSgKB8Bms3FXh/xdCDWuhtlLJet+ctPzYYnFAmu/ENzXUmNIb8He3YW3u6iRQr5o1LgJVapV5/V3Z2W4TewkX7yIZjIV4C4a9pBw+g4cxJofPEfd1G8EU2fp4j35RcGB/b7vejGjqsL58+dZvnw5y5cvL4BNCoXCKGXKjZOSAvd3kQTZjvHOwqoIAdeEmQ1FmGiahsPp5KJTD4l0eEk+cjgcHPjtNJ8tqcbc9yAtVfdX3NVJMnSE5ObmoBXgMWrURiE0DiUWrfLmkcN/UbNWbb7Z+DX9OndACOFjbgsW6yA+/fodopsYm+PZJwSL5wra3yv5YLH3z2URUM2usXPnTpo0aUKDBg3Yt2+f8Q9UAig3jiLQKDeOD4KDYeVnMOzJA1n+4hCDCTqhoaFoQhBmEVQL1ihn9exw/nnbdzSsHM64kb3438uSrXsljz0hsQVL1scJurfXaFZPMGaY4OsvjYVtGrUxJKzoyUY1a9UG4PY2bfl8yw/YgoO9jtc0C7MWDzcs9ACjnpdYgyRrv/C9undIfZO5bt263HPPPXTu3Nn4RAqFwjBlSuwBgoJyrqoLm8gTbhFUtglyF8msVacu6enpJCdfREpJpSrw/GuSb/ZJYp6WVKshOXVCsGSeYGAPjVvrCV75P8Hfh/Kf34iN3mrOFJboJk29hEJasFrtzFm2lNbtCxYKWbEy9Mj4cc6c5nsTO9mlN0z/4osveO211wo0l0KhMEaZE/vcFCWRx2bSBd+UTa8qVKrE3uPnWf3t9hy9XitUgtHjJd/ul8RtczPqeTcNb5RcOC+Y9bagVbTgse6C7d8X3sb8as4UhcxQyJZ3DUKIcEBD08Lp0X8Q67bvoo2XfQxvDBkuMZkkny2BfXu8j73oVA3JFYpAU+bFvladKBYuWeaxmJiRHrNWTVDJJnL8oELzKeELevRO/Ubw5BhY/a3k8y1uHuyrb+iujxN0a6cxcpDgbLbe296SjcwWC8F2u9eaM0UhNQXmTK/LhjUzkDKB21o62fHXOV6f+U6R5qtZG/oNBJdL8MxQgbf9Z5eE1Iw9kqNHj7J58+ZCz6tQKDxT5sXeboKu997NU089hdVqzdEKz1MPV09YNcFVQYWLqY9uAlPel3z/u+TJ0bove/kiQYdmgk3rLo1r074T81bG0eHe+wkNC0cIzXDNmcKybw90vkPw8QcCi0Xyv5fdLPxCElneP/cf/aKkZm3Jr3sEc3xULL7olOzfv58aNWrQu3fvQjdMVygUninzYh+ZsdH6xhtvkJqayoULF7z2cM0Pu1lkbdou/fhD7m/VnE8+/MCwHVdVhFEvSNb9JLm1he7Xf6SrxvMjBJn5Xj999w2rV8TSvf8jHEp0sPfYOSZMLdoKOz9WLIaurQUHfhPUvU6yYpNkyPCCRRH5IiQUXpqqu2c+nCFIT89/7EUn1LvuOq6//nqaNWvG+fPn/WeIQqEo22JvN5GjDaEQwqdv3BvhFkGkRZBw/jy7f/6JA78ZCCTPRa06sChO8uwENxaLZP4swT3/Eez8CUJCQql7XX0aNb6p0DYa4eP3YcRAjfR0Qd/HJKu2ShrdGJi5WreDetfrpRTWfeF9bLJLsG/fPlasWEH58n56vVAoFEAZF/tQiy70P/74I05n3p6ohSHCKujbswefrv+GYU//r1D3MJlg6AhYuVlybX3JwQOCB+4U7N39JJ+s3suDfR7yi62emPU2vDBK/2d/bqKbV96SBButEVcIhIDej+qr++WLvLvCkpwSzZ+vFgqFIosy+z/LJCDYJPjrr79o1aoV1157rd86INWvczUdW7agYqVKRbpPw2j4fKtk6EiJ2QyxCwStbxTMnAp+ejZl4XbDpHGCV/5P/yefMM3NoKf8O0d+3NcdTCbJ5nVw5lT+4xxusvr1Hjp0qEhF2vxFfHw8MTGXehaHh4czbdo0Q5VIFYrSRJkV+/SEs+zdu5fy5ctTpUoVWrVq5dcOSHZzRhx+Ee9js8GzL0lmL9tPm46JJF8UTHpBo0trwa+/+MVUTv4LQ/sIZk4RmEySN95z03+Qf+5thAqVoGVbcDoFq2K9j01yQrNmzahTpw6//OKnH0AhiYuLIzo6mtmzZ5OYUes/MTGR1atXe61EqlCURsqs2H++7BPuvfdeVq9ezbhx45gxY4Zf77927VoGPvIQv/2wJU/iVWEYN+p+Nn0Vwavv7KP61ZK9uwT33SGY9or3jU1ffLYU7rpZsPYLQWiY5MNPJd39m5tliK69dFfOisXef1jJTkmdqCgqVarEyZMni8M0j8THx9OtWzeSk5PzvGG4XC6Sk5Pp1q2bWuErLhvKpNhrAspHRnL48GGWLVvGww8/jN3uX8f0li1bWLBgAStil1IlV+JVQUlPTycsPILQsDC69avLVz9IHhoscToFb72qi/4vOwt2z4tJ8Mzjgv8O0EhMENzZUbLmB0mrtoW3syi0uweC7ZLdPwuO/ZP/ODcw/YMPOXHiBB07diw2+7LjcrmYPHkyaWlpXsc5HA6mTZtWTFYpFEWjzIl9cnIyCz6ex/XXX0+7du0ICgrKkenqL/r06cPYsWOZMGECFk1QxSawFfKnabVaWbX1R3YcPonFYiE0TA9ZXBznplYdyW/7BF1aC14dK0i84Pt+e3bAvbcLls0XBNkkE992M2eZpEbRe6XnQAChZkGY2bc7K9hO1oNm7SrvY13mIH+YVygSExOpWbMms2fPxuVyeR3rcDiYP39+MVmmUBSNMif2E158kXlz5xIREcHatWtZtGhRQOZp0KABEyZM4KqrrgLArAkqZxRQK+wPNXdYaPM7YM02ycAnJW43vP+moHW0YO5MSPAQhn7sH3h+hKBrG8GhPwXXNZB8vlnSZwA+G4kUlFCzoFqwnmxWPkhQOdj3BO07666ctV94H5vq1humlwSJiYkcP37ccPSWvzb9FYpAU+bEPlN8L168WCLzh1sE1e2CCItx0feWLRpsh7GvSlZslDS9TW+ROP4ZjZtr6/H5Q3oLRgwUdL1T8J/6ei9YKeHRGMlnWyTXNfTP58pEAyrZdJE3Z9ussGr6Ct8bd3XUo3J++AbOn81/nMPhoGXLltSpU6fYM2mrVavGxo0bDW/m+3PTX6EIJGVO7MeMGcOqVato0qQANXkLyYkTJ5g4cSKvvPJKjuOaEERaBdXsghCz7/t0vfM/3NWkIfF//J7vmMZNYdlayXufuLmtpURK2Ldbb4q+YrFg548CixXufVDy1Q+Sca9LbLaifsKcWDWoEiwIzmeDIsLq/Rcqopz+tuJyCdavyX+cxWLhjz9+59ChQxw/frxoRheC1q1b079/f0PF6XJXS1UoSitlqnlJJsXVXOLw4cPUrl2bypUr8++//+Y7LtmpNwP31EbQ7XbTqGokyRcvsvufM0RERhqaO+E8HDwAR/+G5GSoVgNuvBnCwgv7abwTYoarrMLn/sf5dL1tYn58/L6e1NWhs+T9RfmP2/3zT1xXvRL1rqlVrIlWhw8fxuVykZKayi233OK1L7DdbmfPnj2GS24oFEZRzUtKGTVr1mT06NFMnDjRq7vBnuHfDrfkFUpN09jx1wnitu00LPQAEZFw0y1w74N67fjb2wRO6EPNggpBmqGN7jCLvnGbH23v0b9u/hq89YC/8eZbqFCjdrFn1D733HNERUWx6dtt+VYiNZlMPqulKhSlDSX2RUDTNCZNmsSAAQN8ipIm9EJq1YIFuTXfFhxM/UbRAbS04Aj02kIVggpW8dMkBCFefPfVakB0E0lqimDrBu/3uuiUuIr5zTMyMpKaNWtSvlrNrFr/vR8dRGh4eNbDzuVysXDhQp/VUhWK0oQS+2LGogmqBAvsBen1XczYTVDdLqho07wKd36E+6g118FAVE78H78z4X9P89obUws8f1GYPn06e+MP06L1XXzwFkx6oS5de73D3mPnOJToZOjwp2nevDnVq1cvVrsUiqKixL6I7Ny5k/vuu4+QkJCs2ikxMTFeMys1oQtpOatg4nOjGTnoYf4sRAXNQGA3QUWbhqkIsZoWTRDk5Ter/b3613WrITXV85gzp04y+51pxC5biruYVveTJk3i9ddf5/CJM3y5AiY+p/HlSkGfewT79+pjxkx4jZdeeZV6NzZV3bUUlxVK7ItAXFwct912G6tWrSI5OTmrdsrs2bMN1U4JtwjWfr6c5YsW+K0qZ1GwahS6SUtuvL0R1L0eGjWWJJwTrF7uecy19Rsy+sWJPDH6/0gspnpokydPZsyYMSRdTOO1Fy7Zn5oieLiL4MhfeplsCSQ4JKfSlNgrLh+U2BeSzNopnlLqHQ6H4dopyz/9lGlvv039Bg0CZaohTAIqBgk0P2VfeQs5FQL6D9KFcv4sz/NFli9PzKgxtL27M4nF0KNWSslzzz3HwCeG8+O3lfn7kOCaupJ9/7ppfodej//xfnqdIikl+/fu4dPYWBK9RB4pFKUJJfaFZMqUKT5L8BqpnXLTTTcx/MknqRFqNhSTHwjMAirbciZJFRVNCIK97Evc1x0iykl2/eS77o9L6tUwA4kQgseG/Zexr01h8Vzd8AHDJCGh8P4iSY1aenG6L1fAqZMn6dT8JmL692TSG5OVO0dxWaDEvpAsWLDAkNgbrZ2iCT28MbP1YXFh06BqsMjR0ctfhHpx5QTbyaq++fEHnsclnD/P58sW89nST0h0Bl5QEx2Sgwdg21ZBsF3Spad+PCISYkbJLFsrVa7M46PGMHPBUrr3fzTgDyKFwh8osS8kRmuieBv3zDPPMGPGjBylHcItggp+8pv7IswsqGTzn+smN8EmvFYD7fuYLqCfL/NcPuGPX/fy1KN9mfHGazjckOopK81P7Nn/O99s3cLcmacB6NwNwsPJ2mju0hNCQiU7fhD8e9zGmBcn0vH+B4gsX165chSXBUrsC0lha6dkdj4KCwtj8uTJPPHEE4wcOTKHbz8kQ4QDKfkRFr2AWSAqgmYihF4jKD+uqQst20rSUgXLFuQ9X/+GG+ncrScd738AgAsBFNVJr79Oz45tiF3wMQC9HpFEWgVVgjWsGthD4M6Mistbt1YEyPrZOSSkBPBBpFD4AyX2haRfv34Frp2SvfNR9hX/Rx99lCd6J9iUser2v+l64/RicheFmvHa3OWhwZc2anNXFA4NC+OduZ8wcux4AFJcgamG6XBLalxTl5q1biI5uRXXNZDcfKtuO0D5jJ/V3V30ub/Zoov9z9u+Y9yop1gVu0St7hWlHiX2hWTUqFGGxH7EiBGA985H+UXv2Ex66eCiNEbJjd1Ese4LCOG9GmabDnD1NZK/Dwm++NT3/RID4B+/4ICYUWOoHfUzcAu9HpGEWS65t4JMeqmL1u31Biy/7Q/nyGH4/de9zHt/BuvjvgjYg0ih8BdK7AtJVFQUsbGx2O32PKJvsVjy1E6ZMmUK6T76C3qK3rFmNEbx4g0xTHBG+YPixluUkckEw57WRfKdSYLcJYZSU1M5eOAPTme0KEzycxim0y1JckqO/AVbNwisQZKuvfLaHGGBEDu0aa///d7bBVWrt2HMS6/Sf9DjGbb5zSyFwu8osS8CnTp1Ys+ePQwePJjw8PCsDNrBgwezZ8+eHLVT5s6d6zNxKr/oHbOmu3QKUbkgixCzHkcfSB99flg07w+rrr2h+tWSP3/XQxuz89rYMdx5U31WLlkIgFtCsvcGUgUi0Qk7f/qBxfP0FmB3d4GKV+kP2exoQhBshhHPSSpXSSHhnODZJ66lz6Ojubl5C0CvbqpQlFaU2BeRqKgopk+fztatW6lSpQr169dn+vTpOaohxsfHk5KSYuh++UXvmDWhx8IXUKsFus/ZaNXKQBHsxXCrFR4fqQvljMl685VMrr4mipq1r8FkurTU9pd/3OmWXEh38+iD9zJjciXgGPf3kNjzsTXYJKhXH2Z9+BM33So5cZejZkgAACAASURBVFzw+vhLYx0S0tRGraKUosTeT1x11VUcO3aMw4cP5zk3ZcoUw/fxFuWTKfhGXTqmjGSpMH/4gIqItwQrgG79oGJlyf5fBJvWXjo+YNhTbN37J4/GPJl1LM0N6X7wjyc44Py5c1SpGgWyBmHhVWnROn+3U+ZnCApy8/oMiaZJFs89xgdvzWPrhnWAvomsUJRGlNj7iapVq3Lo0CGOHDmS59yCBR7iCvPBV+cjc0bVTF/iGaRBFZsgyJ+7u0UgSPP+y2azwaCnLq3uAQ4fjGfs8GE0qhrJNWFmGlWNZOzwYRw+GF/kejnpGb76yPLlua/HNuBP2t4NoTbyTTDThMCS8SHq1Yf7e4DLtY6Jzw1g4ez3ARWCqSi9KLH3E5qmUbt2bczmvMvCgjSlzoze8TqXEFSyaVQIyhupo6FH21QJ1vxa/qCoCCGw+XhA9Rmgl1DY/r3gg7fi6Ni8MYvnzSEpMREpJUmJiSyeN4eOzRvzxZdfFrrWvZSSM9mKmK35TAAaHe/P34WTSVC2n+lDQyTQEmtQF+64qwMA6SXYLF2h8IYS+wBw+vRpXNmCxo0mYAUHBxeo81GIWVA9WFAxSBBpEVxl1Zude+qIVRrIr3dtJqFh0H8gQDyvvdCDlORknLnCVJ0OBynJyTzerwe7f/NeZC4/zjt0UQbYt/sse3YI7CGSVm3B7qM+UfbSzY2bQq0615CetoJadQZlHU9VrhxFKUSJvZ+5+eabqVixIgcPHsw6ZiQBy2w2M2DAgALPJ4TAbhZEWAWhlsCVPvAHwQYKvT00VCLEFNwu33WH3npzWoFr3Sc5ZFYmbmpKCve3vhqIpmXbFELteaNwchOU7e1ECN2VA7ByyaXrlCtHURpRYu9nKlWqRGhoKEePHs06ZiQBy2q1GnLhXM6Ysvm886NSZTCZFgDexd7pcLB88QIuFMB3n+qSnEm/JMR//r4ft8sEmOn8oM3nmwfo/vzso7r0lEAaq5fv48/fDmXMg6qEqSh1KLH3M0uWLOHChQs5OsMXNAGrLGMzsI/gdBnb47iYmMQFhzTkI3e68zYbqVCpCW73GazWWFq3x+eeQibZX57q1IMq1aaSknwDE5+bDoAbPWJIoShNKLH3M+HZGlNnpyAJWGUZI4JqdI8jJCwUCTk2W/PjTLok85mQGeXT8oZIwIbL3ZhXxw7j6F/G9gBMIme9n5Ztrwfq8tfB8KxjKgRTUdpQYh8AkpKSWLduHV9//XXWMafTyfr163nxxRdJSEjA5XKRkJCQJwGrrGNE7Lv07Ium+djjsFjo2ksviJ/q9l4R84JDZm2ablx7KconPS0RkLicepRP4xtv9NlKEvREtauy1Rd6+oX7gQMcPTKelGT9mPLbK0obSuwDwJYtW2jfvj0vvvhi1rEPPviAIUOG0KZNmxK0rOTRhPdm5ACDnhqJNch3kbmBTwzP+vu5dOkxezXNJTmX4ac/fDCemH75R/kYbSUJYDdfCiWtVAVubKqXav5mo37MoUIwFaUMJfYBoHnz5jRr1ow77rgj61iLFi1o1qwZ48ePLznDSgk2HxuhtepEMXPhUswWO5BT9M1mC8F2O+8uWEqtOjnfiE6nyRyx90635HQ2F8+st6f6pZVkJtlDXNvdLQEHq5df6sKiXDmK0oQS+wBQvnx5tm3bxsSJE7OONW7cmO+++46uXbuWoGWlA1/ZvwBt2ndi+fpdmMyDgHCE0AgND6f3gEGs2baLNu3z7nE4JRxP0ROmzqRJjqdKstcmW7lkYZ4VfW4K0koy2HQpqc1m+xQoR9zKp7MqdypXjqI0ocQ+gJw5c4aXXnqJtWv1Yi+aVrLFyEoLQSbhtaFJJtFNohgQMx1IoENnJ3uPnWPC1HfyrOizozcn10sh5PaiXPRDK8ncZPbZbd6yDnCRtLRj7Nqun1MhmIrShBL7ALJixQrGjRvHwIED1X/6XNgN1uwZ9JTEGiRZ8/k5pr3yPvNnzSz0nCGFbCXpjcy3lPo3RNO93zFgLd9s0I9J9M1jhaI0oMQ+gPTo0YNOnTrxzDPPqBV9Lrw1NMlOpSrQoz/Aed56NYYZb7xa6Dm79OyL2VywVpK+CMpw5ZhMJu7sWBmA77dc+rdWpRMUpQUl9gEkPDycL7/8kieffNL34CsMm8l4bf4hIySaVhMhHuG+7jGFfksa9NRIcm/45iZ7K0mjZL6lNLtd//v2bU5SU/Xvld9eUVpQYq8oMYzW2a9ZC7r0MiPlR6Sm/K/Qb0nBIVEglgF2NC3nLnFRMpkza/5o2lmC7a1wpNdjxw+6yKsQTEVpQYm9osQINWNooxZgyH91wVw6H86cKtx8s98WOB13c3ub3fQdOITQ8HCEHzKZbRm1+iPKlUPK34DDfLXqr6zzqnSCojSgxF5RYmhCEG7Ql3NdQ2jZNpHUlF289VrBSxufPQ0LZuvfj3mpDhOmvsPeY+dIdTiLnMkshCDIpH99ZtxXwGn2/1In67xy5ShKA0rsFSVKmMX46r7WNe8DTVg89y2SLxZsno/eFSRfFLRuL9FMu9iwZjUnj/3js6SxUTIrZnbrG40Q5dn1E6RmtB1Wm7SK0oASe0WJoglBhEHffev21xFku570tKrMn2V8joTzMPc9/fsnR0uWzJ3DgG73sfbz5YWw2DOZIZgR5aBBNKSnC37eph9zSf/0zFUoioISe0WJE2bGUBP1uzrdw3uf7AOeY8Zkwbkzxu4/dyYkXhC0aCW5uTlEXXs9Ldu2p1H9+kWyOztmTW8En3D+PFI+CfRWIZiKUoUSe0WJI4SgfJDB1X07uL2N5MJ5wTuv+77mzCmY9bY+7qln9dX1w0OH8fHKOO7p2L7wRnvAZhLYQ0I4sH8OsJitG7PXyVEre0XJosReUSqwmYy5c4SA0S86gGQ+fh/27fE+fsZkQVKioFU7SfNLdemwaPi9IbvNpIdwvjBpBkKsZe/OEC5mVF5Ic1HgFooKhT9RYq8oNURYwOrjN/KtVyfQrW0Yt/5nFk6nYPTjgvxqm/3zNyzI8O2PflEX2uNH/+Hokb8NtSAsKJn9afsPfoToJm1xuWxsz/DbS1QIpqJkUWKvKDUIIShv9S7CEZHlSE9Pp+GN/1CjlmTfbsGrYwWeFs1TJwjS0wX395A0jNaPfTjjLf5T/xpmTJnkd/uz99jNfItQfntFaUGJvaJUEWQS2L2UQH6w70PsPX6eca9PYvJ7EotF8uEMwYiBIkc45oY1sHyRwGqVjHo+55MgLDycZk1vDoz9miA1NRWH40PgFb7fculcilO5cRQlhxJ7Rakj0svqPiw8nNCwMEBfPb8zTxJsl6xcIujSWrD5a5jxBsT01+8xYqzk6msuXf/cxDf4/egp7rzzzoDYHqTppaw/fn8o8Dy/7Egm8YJ+ziFV6QRFyaHEXlHqsGjCUIMTgI73weebJVHXSv7YL3i4i8YbL2qkpgh6PiwZMjzvNeE2CyaTwQkKSJAJrFYrjzz+JFWrv4jb7eSn7y6dV64cRUmhxF5RKgn3Epnz0bvv0KVNCzasWQ1Avfrw+RbJ4P/qot+qnWTBKjevTZdo2X7D09LSEBjrlFVYLJremGXsq5Pp2nssEMG2rdn89mplryghlNgrSiU2k8g3MuffY/+w66cf2LPj56xjIaHwf69I1u+QzFshub2NHqaZnZEDH6Jpnaqsy+gcFigyG6rf1lIX9ux+e7WyV5QUBltIKBTFT7hF5GgYnkn3/o/Sun0nGtxwY4Hud+TwX5w6eZJKlSr5y0SPWDVBUrqTyHK/YzIdYd/ujiSch4hIvXSCwy2x+DnGX6HwhRJ7RaklxCxIcEgcueLT6153PXWvu77A91u1ZRvuU0eoXr26nyz0TJAJzp89S+c7bkDTQnC7E/jxW0G7e/TzKS6yQjQViuJC/copSjWRBoukGSHUrFG7dm0sFu/dqopKkAZXVazIzc1bUDuqPXAhV7y98tsrih8l9opSjd3s2Xf/2dJFjH/6vxw5/Jeh+0gpDfe9LSpaRnLVp19v5eVpsUC5PH571YC+cLilJNUlVemJQqDEXlHq8VQzZ+WShcx9bzr7du80dI8+97Sle9f7OXr0qL/N80hQhk++STOwWiX7f7lUpVOVTig4UkrOp0v+SZacSJUcTZacS5eqdHQBUGKvKPV4Wt0/2Och/vfyJK6t39Dn9S6Xi50/bmPVqlWEhoYGyMqcZEbkWIPcNIj+G4Afvrl0XkXleMbp1gU8+8rdJSUnUyUJDknmUTdwwSE5niI5neZWyWoGCKjYCyFqCiFihRAJQogLQojlQoirAzmnomySO6v23gd7MGT409Spd63PazVNY+fu3axevZqIiIhAmZiDIJNe2/6GauX4dc8NgJtvN136DKrkcU7cUhftDxYupf+jj7H6m5/4ft8BHuzdB1tQEFXsJmqHmqhXzsZ/B/Tn8MFLrSkvOuF4iiTRoX6m3giYF1MIYQc2AGnAw+hvry8DG4UQ0VLKAjaWU1zJBJsEVk2SXgj3R6hFUPvaa7n+Wt8PBn9h0QTlykUSFh6BwyE4c+oEWzdUyTqf7tZXrKbcyQBXIE635GSaHnX1zYavWfrxR5hMFj79ZB7paWk5xjocDj5b+glxn33K+4s+pU17vUG8GzibLklxScpbhd/LV5cFArllNQioA1wnpfwTQAixBzgADAGmBnBuRRkkwiI4lRF373a7+WXnzxw+GM993Xt5vS7MYFNzfxOkwdc/78MWHMZNtQR/xQuO/CWpWVs/n+KE0MAGBpV6jh7/l+2/xSOFRuyCeUSUL0/M08/y3rQ3cLvy93Wlp6XxeJ/ufPXjbmrVudQoPsWlr/IjrHoHNKEeplkE0o1zH7AtU+gBpJSHgG+B+wM4r6KMkt13L4Sgzz1teerRvpw9fTrfa6wajPrvk7z00kucOWOwj6GfsJkEoWFhmM3QoqV+bOuGS+eTr3BXjtPl4v6uD/Bgu1Z0bdOChXPeZ8cP2zh/9qxXoc8kLS2V2dPfzHPcDZxLlxxLkSrMNRuBFPuGwF4Px/cBDQI4r6IMUy7Ddy+E4K5O93J3lwdJTU3Jd3zK2ZO89957TJgwAZcBAfEnQdn+d91+p+5/+mZDdr+97sq5Etm1axc//XqARx5/knr1G/Lp+m/oeF9Xho54ms+XLTJ0DyklKxYvyPe8U8KJVEmS8uUDIAIV7yuESAemSimfzXX8ZeBZKWUeF5IQYjAwGKBy5co3L168uFBzJyUlFVvUhaL4cUowEnwhAE262LlzJwcPHqRHjx5+mb8gv18XklMZ+78x/PPPv5w7e5zQMBdLP/2WzKKbZgFXont55KhR7Nyxg7HjxnP7HS1zuFs6tr3TcB6CEII1X2/wOc4k9D+XC0XRsDZt2vwspWya+3ipKpcgpfwA+ACgadOmsnXr1oW6z6ZNmyjstYrSj9Mt+TdV4usN/SqrINQiuOuuu/w6f0F+v06kuDl56jHOnT1J1RoHOf5PXdKtLWmc8V/RqkHV4CsrAjrJIenQtQe///47XR8ZylUVK+Y4HxIaSlJioqF7hYSFcV2zlobGhpoF5a2Xhx8/EBoWyN+yc0A5D8fLZ5xTKAqFWRNUDBIIwOl08u3G9bjdOcN0gjQ9CqeksZoEs5as4Kf4Y7Rpr28kbl1/6Xy6G9KuIL9yslNyJl3S8b6uxH2/M4/QA3Tp2deQIAsh6Nqrn+G5k5ySU2lXbvZtIMV+H7rfPjcNgF8DOK/iCiDIJKhkE3Rt04K+ndszqGeXrFd/iwYVbYIjR47wf//3f6xcubLk7NQguklTKlauzB136vZt3ZBTyJKcJWFZ8ZPslJxMdSOlpHZU3RxRNNkZ9NRIrEFBPu9nDQqiddsOjB0+jEZVI7kmzEyjqpGMHT4sRxx+dlJccDL1yhT8QIr950BzIUSdzANCiNrAfzLOKRRFwmYSjH/heXr2fYghw57CLPT6N5VtApMQ7Nixg1dffZX333+/BG289H2LVqBpkh0/QFI2L8VFZ9kXnzSX5HSaZO+uHdxQrRxPPdo337G16kTx3iexXgVf0zTSUlMZ2q87i+fNISkxESklSYmJLJ43h47NG7NxbZxnW9z6xu2VtjkeSLGfBfwFfCaEuF8IcR/wGXAEKLn/fYoyxQNd7mfR/Ll0v6c9NULMVAjSshKV6tWrx7hx4+jdu3eJ2acJgUXA+29O5qGut3Fdw4M4nSJH6QQJJDpKzMSAk5k0JQGzxULd6xvgdHj/wG3ad2LdT7/QpVdfzNmqlJotFu7r3otufR8GwJGenudeToeDlORkYvr1yHeFn34FCn7AxD4jQ/ZO4A9gPrAQOATcKaVMCtS8iiuPTP+uw+HIEcXRoEEDxo8fz0MPPVRSpgG6y+m7zRvYvf1H6tTdA8DW9TldOYlOWWYrYZ5Jk1nRU/UbRTPiuXG0vaezz+tq1Ynizdkf8+e5VP5KcvFXkos/z6Xy9kcLMZnNOR4CnkhJTmZQr675Cr7DfWW5dAIaBiCl/FtK+aCUMlxKGSal7CKl/CuQcyquTA4ePEiLFi146623iImJITw8HE3TCA8PJyYmhvh4z//hi4MgDR4fOYaPPl3FA31uB3ImV4Hewaos+u6THJLtO3dyz3+aMu2VFwFo1bYDD/TuX6j7nTsDm7+Gz5Yu9vl2APDHr/u8unTS3XjshlYWKVWhlwpFYVm5ciXbt29n586daJqGI0MIEhMTmT17NvPmzSM2NpZOnToVu202EzS/oxUADgeEhEri/xAc+0dSrcalcQkOSWgZSvF3S8k5h+SLT5eyb/dOwiMivY6XEhIvQLAdci/aD+yHV54TbF4HUgrAuHMg06WzZtsuj5vCKS44ny7zFNsra1xZAb6KMsstt9yC1WrF5XJlCX0mDoeD5ORkunXrViIrfLMmshJ6LBa4Tdd9Nq/LOa6sre4THHry27Mvvcqabbt4ftIUj+OkhBWL4faGgujqGg0qCfrcI1j7Bfx9CMY/I+jYXLBprcBigRubSqBgCUcOh8NjaYVLtkqSnWV7ha/EXlEmWLRokU+ft8PhYNq0acVkUU7M0smyBXN5c+JL3NVRt3P9mrwryQRH2fAhO9ySC9nKFFzf6AaPDeITzsGAboIRAzWO/i2wBUucTvhus2BwL42WN2jMnSmQEvoNlGz7Q/LZJknvR/uimYxXkXM6HF5LK4DuzinLzVCU2CvKBAsWLMizos+Nw+Fg/vz5xWRRTkIsZsY//V/enPgiTZqdBeDbjZC7rI9Llo3InHPpumh6ewD/8zd0aSPY+JUgsrzk9Zlufj0h2XVE8txEN1HXSipUlLRsK/niW8nLb0rKV9CvHTpiJEFBBSsZejHRu+tHom/YltVGKErsFWWCpCRjPlyj4/xNsFnw8NAn+O//XqBSZUmjxpKUZMH3W/OOveC4vEMC01ySlIyac3NnTqdH+9Zs/OrLHGMSL8Bj3QSH/hTUv0GyaqukR3/QNIiIhEFPwfodku2HJB+vlDS4IecctepE8e6CpQTb7YbtCgnz7fpxZRRPK4uCr8ReUSYwWjSqpArkmTXB/734CiOeG0e5q67izo768Q1xeV05esu94rXPn5zP5r5Z+8Vn/PjdVhIvXMg65nTCk48Ifv9VEHWtZHGcpGatgs/Tpn0n1mzbxbUNfLemNJsthksrOCX8mypxlDHBV2KvKBP069cPi4+4a4vFQv/+hQv58we2bGUX296tC0ncZ3qETm4uOC7P1WWqS+bor/vB4uW8/dFC2nS4GwC3G54frm+2lisv+TBW4iNIxyu16kQxa/EKnyt8l8tC207DDd/XlSH4ZcmHr8ReUSYYNWqUIbEfMWJEMVmUF5PbwR+/7mPL+rXccBPUvU5y+qRg41eex5+/DOuwJ6TntDksPJz7uvciLDwctxtGxwgWzRUE2STvL5LUqpPPjQpAdpdO7kQrTTMBVqSMZVAvQa+7jdfRcUs4kSLLTKE6JfaKMkFUVBSxsbHY7fY8om+xWLDb7cTGxhIV5bn4VnGQcPoEnVveykP3d2Lz12vo+bAuIks/9hzffdHJZeVKSHNJUrMVH829OfvqWEHsAkGwXfJRrOTW//hv7kyXTu9HBxEaHo4QGqHh4fQdOIS4bXtp0cpFetoNbNtSsDo6bvRN27Ig+ErsFWWGTp06sWfPHgYPHpwjg3bw4MHs2bOnRBKqslOrZk1envwmjwx9gpCQULr2ArNZsvErOHHc8zUJl9HqPrutJ44f464mDXl3yiQAPn4fZr0tMJsls5ZIWrT2//y16kQxYeo77D12jkOJDvYeO8eEqe8QHCz5bnNnIBUoeB0dN3Aq7fLeNAcl9ooyRlRUFNOnTychIQGXy0VCQgLTp08v0RV9dh4bNJjxk9/ilha3U6EStLsHXC7BJx9e3qv7i85LETgAqz5dysEDv7Prpx/YukFPjAJ4bYbk9jbFa9vsd6b5zEr2lXTlkpd/WQUl9gpFMZK95DHAw0N1AVk4G9LSPF9T2lf3aS7JmVxC2G/gUD75Yh2PPfECTw8RuN2CJ0dLuuVf2ThgrFyy0GfCnZGkq1TXpfyByxEl9gpFMWLT4Nzp03y/ZROJFy7Q7HZoEC05fUqwKtbzNaV5de+Seven3NbZbDZatL6TJfNu4sRxQZNmkuHPlcxnuGgwt8JX0hXoUVKXa1kFJfYKRTEihGBonwfpffdd/PzDdwgBA4bp4jH7bUGu7opZlNa4+zNpeXsBnzl1CoC1X8DyRXoJhMnvyawm68VNiMHcCk0L5aKB58LlWlZBib1CUcy0+M9/uOnW5ll/79wNqlST/LZP8PWXnq9Jcpa+uPvcfnrQV9Ftb27I2OEjGDtcr+o25kVJnXolYGAGXXr29Vn7Hiy4XO3o2TGRs6e9j5TAqcuw8YkSe4WimBk8aCANoxvzxEO9uCbMzM21I6lxdQwQz9uv6UW/PJFYiipiOt2Ssx42LH/8bivnzp7hq1XbOPmvhRubSh4eWgIGZmPQUyN95mBYrSbgC/bu6s+DbSV/+OiS7ZS64F9OReuU2CsUxUhcXBw3N74xT9/UXdvnANHs3bWGdas9X5tUSipiSqlvyHryOLVp34n5n8Vz9vS7CAETpko0P6qMSUCwSW8qbxRvSVdmi4Vgu52X35xO5aqVKH9VPQ79aaJzS8H8WeT74AW9l+3lFKGjxF6hKCbi4+Pp1q0bycnJefumOh1AMtCNl8YcJD097/WlpWZOgoMcyVOHD8YzdvilzNRHHmiMyzWHex+MJ7qJ7/tpQKRFUMMuqBWiUcUm8kQtBZugkk1Qw65RyaZRLVijWrAgwiIwG+g5kl/SVe9HB7Fm2y56PPQosV9vYt7Kx+jeX5KWKnh+hMagXoLdP+f8fNkzb1Nc5IlEKq2I0tr3smnTpnL79u2FunbTpk20bt3avwYpFBkU9vcrJiaG2bNn+yjFbAEGM/a1txn4RN6zAqgWLDBrJdNVKc0l+Tf1kmZsXBtHTL8eOByOXA8wC7ZgCzMXLqVN+/yT2awaVAzy/HmSHBKnhBAzWHx83otOvX5+ej4b3AVl8dzTvDDqPdLTbgZ6YDI5cLkufT6zxYLFYuHdBfrni7QIIvzY6aooGiaE+FlK2TT3cbWyVyiKCSM19/UMz/m89arwuFEoKbmaOW4pc7gtDh+MJ6ZfD1I8vKmAg9QU75mpVg0q2/J/cIVaBJFW4VPoAULMgqrZVvtFeRZKKZk7sy3paeMQ4gEgOYfQQ97M2/OXQUimEnuFopgwXks/icQEwZsT88+qLYlaLQkOfWMyk1lvTzXUMMZTZqpJ6Ct6zc/9di2a/oCoHiyItIhCCZwQgtEvvkJoWDgmk4HuZxNfZOzwYVQuH1Fqmtx7Qom9QlFMGK2lbw8JRdMkC+eQb1TI+WyZnPHx8cTExOSoB+RvsUlz5WwzCHpmat4VfU48ZaaahfcVvT/QhO5WqWYXBBcivv/OjvcgpczYS8kfp8PBysULc2y4Zza5j46OJi7Oc4G1kkCJvUJRTBipuW+2WHiwbz/6DNBr5kz4n+dQzFS3Xjs+Li6O6OhoZs+eTWKAxEZKyRkPZQIKk5lq1aBKsDHXjD8wCUElm0aFoEtN342SbCTDKoPcD72SbnLvCSX2CkUxYbTm/sAnhjPiOUlYhGTresGmtZ7H7v7tz6zontzuFH+Kzdl0cHjY+DSamZrZDjDTdWPys+vGkA1mQbVgQbhFYHR2o5/PGyXZ5D43SuwVimLCW819s1mP9353wVJq1Yniqorw32f11fSEZ4XHblbT3zTmMy+K2FxwSJLy2Xhs3a4vevRQ/pgtl9oB5hd1U1xoQlDOqot+mNm36BvLvPVO9ib3xeFu84YSe4WiGMldc18IQWhYOL0H6PHe2cMUHxoCtaMkBw8IFszKe6+VSxYaEvtMsSkoiQ6Zb5XHQ3/Ct5tG4UvsM99UylkFQQX1owQIsyYoHySobtdX+vlhJPPWCBcuXODaa6+lbt26zJw5M2DuNl8osVcoipnsNffdbjfHzpxnwtR3qFUnZ819qxWem6iL7dRXBKdO5LyPUZ+58SigS6S6JGfzEfrDB6HvvYJzZ+vS4IZl2ILtmHJVOcvMTH13wVKurRvlVVRLClO2lb7VgxL6yrwtCAcOHPB4vDh9+0rsFYoSJsQMTkc6yRcv5jnX9m5o00GSmCB4dWxOwbSHhBi6vxCCffv2GbbHlSuePjt/H4KeHQXH/hE0vU2ybF1HvvphF30eG+IxM7VN+05cFVT6hD47Fk1QxabH5+fGW+bt/T36YDIXfeUPxePbV2KvUJQwr7z8MtdVCOXjD97Nc04IGP+GxBokWb5IsG3rpXPXN4r2eW8hBC6XixkzZhi256yHssUACefgkQcE/x4T3HKbZO5ySUho/u0Aa9WJItJSfJE3RUEIPT6/ik0QlEsV8/t8I8eOx2r1n9gX1t1mFCX2CkUJU6FCBdxuybQYdAAADP9JREFUN2dOnfR4vlYdGPa0rr7PjxBZdXOmfjCXoCCb13sHBwfTo0cP3njjDUO2nE+XJLvyHnc4IKa/4OABwXUNJB9+KgkNgwfb3kH3dq04euTvPNdYNQj3jxYWG0EmQZVgjco2gc2HOuZw8+Ra4RdmxV8Yd1tBUGKvUJQwDz/8MMnJybz6+uR8xwwZoW/WHvhNMOtt/VitOlG8tyjWo0/ZYrFgt9uJjY1lyZIlhBhw+VxwSI8tEKWEF0YKvt0kqFBRMidWEhYOqamp7PrpB3b8+D1XVaiY57qrgoTP3q+lFZtJUDlYL8pm95KUleXmGTCIkNBwdEkNx2waBIaDPHWMJt0VFiX2CkUJY7fbsdlsRFjg+NF/PPZLtdngpan68SkvXWRgj8f5edt3+fqU+w0YxK7du+nU6VJ0T1paGidOnMhzb9CF3lPkjZTw9muw6COBxfonN98WQ8dmevXHptdUoWuvfkyb/TG24OAc10VYBNbLwH3jiyCToKJNF/389pgz3Tz7/j3H/pMO7ux4nrS0GYBx8bZYLPTv398/RueDEnuFopTQqUN7bruuFvt27/R4vuVdMGS4xO3+lK+//ICXnv0f4NmnPG7KO9ir1eFiRoerVV+sJiwsjEGDh5CcUSEy2an/OZnq9ij0TieMGyWY9oqGEF8iuJH1cTnr8K9c+gljhg1i49pLoYNWDSIuM/eNL3T3jh6f741gO7y/SNK9vwT64Ss0NROLxcKIESOKbqgXlNgrFKWEa665hvDwcKa/PtFj7XSAZ8ZLmjS7HRiD2/WU1+YaTqk31ziaIgmtEYXT6eT0+QucStNX8afS9D+5WwsCpCTD0D6Cjz8QWCx/YrF0Jz3dQx3+XNUfNaDCZey+8YYm9Pj8yjbvpRcsFnj9XcnAJ0ZgROxtNhuxsbFERUX5HFsUlNgrFKWEO++8E4fDwfq4L3KsnhfPm0PH5o3ZuDYOsxlmfFyHsPBX+WVndz5fZuzetaPqsufoWV6b/n6+jTgyuZAA/e4TfP2lILK8pE2HKbil7+StOdPfpJLt8oi+KQo2kx6bH+pllS8EjH0titHjlwJ28hP9666/nm3btuVwtwUKJfYKRSkgPj6exx57jJSUlDxZsZmr54E9urBu9edUqSZ5/jV9ST/u6bzJVp7QNI3t276lY/PGeVoiZn+YpKXBkN6Cn7cJqtWQxK6VfLfZWHXLlYsXlJos2UCjCcFVQXqopqeErExinu7Eyk27qF5zEKBv4AoRTv0bYpi7/A/Wbd9HvYa+Q2j9YnOxzKJQKLwyZcoUn6UPXE4ng3p25cTxY3TvDy3bSs6fFQzsKUg47/3+3hqNZLli+vbgsW4H+X6LoGJlyZI1krrXBzZT93InyKQ3TSlvzb92fuOmUXzz6zvMXnqORo2dSJnA/l9m8MgD9eh1j2D995LTiSkBt1WJvUJRCjDWxQqCgmxUqVYdIXS/cM3akt3bBf3vE5w5lf91RhqNpKQ4+Gbjm4RFSOaukNSsnTGnzXssfyaBDh0szYRZBFWD8w/TFELPhl61VfL5Fjd9BkhCwyTfbf6T+1o2o2JEaI7iaEePHvW7jUrsFYpSgNFVcXq2TuRVqsHiOMnV10j27BB0uk3w0btw/mze64w0GgEHQswndq0k07Nw+GA8Dk/dz3NhNpsDHjpY2jFrephm1WBBiNlzlL0QEN0EJr4tmTTjS8zmxvD/7d1/bF1lHcfx9/fedu26DhDYYtJMkEWJQABxUfEXKuAfJKIzYoxhRLNlIQYFDSGSkAgq+pebPwoaf2DmsoTFYWBoTJgQJMEscWocTEWNMkP8ASiplrLZtY9/nHaMy73tWXfuve193q+kaXpuz3meP04+59zzPOf7sA/S9EuKo23atKny4miGvbQIlL0rnq0NP2tkDezak7joTYmn/xHcdlONN74muGFj8OBP4Nmn4eE9MP7f8ksinn3ui399+2tbioQqod1TB5eKZbXg9IEaI0PBqcuKlbJmg7YvYHkdnjn4Z2689kMcOTLxsv0nJyc5dOhQ5cXRDHtpESi7itVsbfhjrX4l/OCBxJ3bp3n7pYnJ/8G9O4ONV9VYd1aNj66vUfYFn8aLyb07dzB15Mi8+/X397d96uBSU49gZX+xUtaaFTXOWFFjZKjG6sEad41umfebVtXF0Qx7aRE4nlWsmqnX4Yr1sP2+xCOPJz59yzTnX5RYeVLi3AsS5114NfX68V9Myg7OHj58uNT/qVBmjKbq4miGvbQIzLWK1Wydm2/OrGI1nzVnwCc/A7sfSTz2t8SPH03c8f1PsWxg7rCfnp7mnh3bXjL/fvnQUKn+5zw4uxBlx2iqnOFk2EuLROMqVrOzMzZv3sz+/fu56sorFnzsuRbimJWmp5l4/vmj8+93bvsuhw8dolabOyY6Udel15S9OFZ5ETXspUXk2FWspqamGBsbY3R0lLVr17K8HqwaKL9gdqNmRdOGhoePrjLVWIBtcnKSqakppqebrDZ+jE7Udek1ZcZoqr6IGvbSEjLUV8znXugqf41F0z7w4auJee7c6/U6fX19LR8vdaKuS68pO0ZT5UXUsJeWmP5asHqeYlxzGarDqoFise0y8++npqYYHBxs+XipE3Vdes18YzTtKI7WV9mRJHVMXy1YPQj/fCEx90OWQgAr+oKT+jlaqGyI8rNtJiYmGB0dZXR0dMF91kvNjtFs3bqV7du3Mz4+zvDwMBs2bODiiy+u/CLqnb20RC2rFTXWG9dMbTTcF4wMFYW7GitSdmOgUC9qNUYzMjJSeVuGvbSE9deKNVNXDRQld5fXi8c0w33Byf1FVcbTBoJ6i7dguzFQqO4w7KUeMNRXhPrqwRqrBmucNhCcsizmLTncjYFCdYdhL2WszMtczrbpDYa9lLn5XuZytk1vcDaOpKMDhc626V3e2UtSBgx7ScqAYS9JGTDsJSkDhr0kZcCwl6QMGPaSlAHDXpIyYNhLUgYMe0nKgGEvSRkw7CUpA4a9JGXAsJekDBj2kpQBw16SMmDYS1IGDHtJyoBhL0kZMOwlKQOGvSRlwLCXpAwY9pKUAcNekjJg2EtSBgx7ScqAYS9JGTDsJSkDhr0kZcCwl6QMGPaSlAHDXpIyYNhLUgYMe0nKgGEvSRkw7CUpA4a9JGXAsJekDBj2kpQBw16SMmDYS1IGDHtJykBbwj4iXhsRX42I/RExHhF/j4jdEXFBO9qTJM2tXXf27wHeBWwD3gt8HFgF7I2IN7SpTUlSC31tOu7dwB0ppTS7ISIeAp4ErgeuaVO7kqQm2hL2KaVnm2wbi4g/ACPtaFOS1FrHBmgj4lTgPOB3nWpTklRo12OcZr4OBPCVVv8QEZuBzTN/jkfEEwts63TgZd8upIp4fqndTuQcO6PZxjjmsXpLEXEZsKdEIz9LKb2zyf43A18ENqaU7ipxnBMSEftSSuva3Y7y5PmldmvHOVb2zv7nwOtK/N9E44aIuJYi6G/pRNBLkl6uVNinlCaA3x/vwSNiA3An8OWU0u3Hu78kqRptG6CNiPXA94DvpJRubFc7LXyrw+0pL55farfKz7FSz+yP+6AR7wAeAA4AnwCmj/n4cErp15U3KklqqV2zcd4NDAAXAY82fHYQOLNN7UqSmmjLnb0kaXHp6aqXFmRTVSJiTUTsioixiPhPRPwwIl7V7X6pN0TEByPinog4GBEvRMQTEfGliFhZWRu9fGcfEddRvKS1DfgVcApwE3Ah8LaU0i+72D0tERExBPwGOAzcAiTgC8AQcH5K6fkudk89ICL2An8F7gOeAl4P3EoxC/ItKaXp1nuXbKPHw/504F8NBdlOpijIdn9KyYJsmldEXA9sAc5OKf1pZturgT8CN6WUtnSzf1r6ImJVSumZhm3XUNyoXppSeuhE2+jpxzgppWdTw9UspTQGWJBNx+NKYO9s0AOklP5CMfngfV3rlXpGY9DP+MXM70qyqqfDvhkLsmkBzgUeb7L9AHBOh/uifFwy87uSrMou7ClRkE1qcCrwXJPt/wZe0eG+KAMRMQJ8DvhpSmlfFcdcUmEfEZdFRCrx83CL/W8GPgJcd+xXcklaLCJimGKg9gjwsaqO28kSx1WwIJu64Tma38G3uuOXFiQilgP3A2cBl6SUnqrq2Esq7C3Ipi45QPHcvtE5wG873Bf1qIjoB3YB64DLU0qPVXn8JfUYZyG6XJBNvWE38OaIOGt2Q0ScCbx15jPphEREDdhBUWrm/SmlvZW30ePz7C3IphMWESsoXqp6gRdfqvo8sJLiparxLnZPPSAivgFcC9wO/Kjh46eqeJzT62F/K/DZFh8fTCmd2bneaCmbKY2wFbicYjbXg8ANKaUnu9kv9YaIeJIWywkCt6WUbj3hNno57CVJhZ5/Zi9JMuwlKQuGvSRlwLCXpAwY9pKUAcNekjJg2EtSBgx7ScrA/wFsvymFfbZilAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 31000 loss=-1.075 val_loss=-0.7518\n",
-      "train: 32000 loss=-0.7325 val_loss=-1.555\n",
-      "train: 33000 loss=-0.8459 val_loss=-0.272\n",
-      "train: 34000 loss=-0.6167 val_loss=-0.4969\n",
-      "train: 35000 loss=-0.5839 val_loss=-1.162\n",
-      "train: 36000 loss=-0.9839 val_loss=-1.722\n",
-      "train: 37000 loss=-0.7776 val_loss= 0.1143\n",
-      "train: 38000 loss=-0.7039 val_loss=-0.9417\n",
-      "train: 39000 loss=-0.6321 val_loss=-1.303\n",
-      "train: 40000 loss=-0.8522 val_loss=-0.1387\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 41000 loss=-1.062 val_loss=-0.05956\n",
-      "train: 42000 loss=-1.14 val_loss=-0.5668\n",
-      "train: 43000 loss=-0.7403 val_loss= 0.5438\n",
-      "train: 44000 loss=-0.9275 val_loss=-0.1802\n",
-      "train: 45000 loss=-0.8797 val_loss=-0.154\n",
-      "train: 46000 loss=-1.049 val_loss=-0.1875\n",
-      "train: 47000 loss=-1.019 val_loss=-1.753\n",
-      "train: 48000 loss=-1.31 val_loss=-0.4702\n",
-      "train: 49000 loss=-0.7908 val_loss=-0.814\n",
-      "train: 50000 loss=-1.019 val_loss=-0.3179\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 51000 loss=-0.796 val_loss=-0.3768\n",
-      "train: 52000 loss=-1.034 val_loss=-0.8259\n",
-      "train: 53000 loss=-0.5959 val_loss= 0.806\n",
-      "train: 54000 loss=-0.7591 val_loss= 0.3678\n",
-      "train: 55000 loss=-0.9368 val_loss=-0.1205\n",
-      "train: 56000 loss=-1.392 val_loss=-1.612\n",
-      "train: 57000 loss=-1.235 val_loss=-1.785\n",
-      "train: 58000 loss=-1.055 val_loss= 0.6461\n",
-      "train: 59000 loss=-1.266 val_loss= 0.6836\n",
-      "train: 60000 loss=-0.2273 val_loss=-0.9401\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 61000 loss=-0.6909 val_loss=-0.2672\n",
-      "train: 62000 loss=-1.219 val_loss=-1.08\n",
-      "train: 63000 loss=-0.6516 val_loss=-0.1455\n",
-      "train: 64000 loss=-1.158 val_loss=-1.255\n",
-      "train: 65000 loss=-1.053 val_loss= 0.9945\n",
-      "train: 66000 loss=-1.304 val_loss=-1.819\n",
-      "train: 67000 loss=-0.7711 val_loss= 0.2355\n",
-      "train: 68000 loss=-1.272 val_loss=-1.704\n",
-      "train: 69000 loss=-1.325 val_loss=-0.1996\n",
-      "train: 70000 loss=-0.832 val_loss= 1.172\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 71000 loss=-1.294 val_loss=-0.5367\n",
-      "train: 72000 loss=-0.9753 val_loss=-2.084\n",
-      "train: 73000 loss=-1.193 val_loss=-0.9146\n",
-      "train: 74000 loss=-1.119 val_loss=-0.4217\n",
-      "train: 75000 loss=-1.286 val_loss=-0.9784\n",
-      "train: 76000 loss=-0.7448 val_loss=-0.8317\n",
-      "train: 77000 loss=-1.421 val_loss=-1.213\n",
-      "train: 78000 loss=-0.6679 val_loss=-1.24\n",
-      "train: 79000 loss=-1.619 val_loss=-1.49\n",
-      "train: 80000 loss=-1.173 val_loss=-0.7005\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 81000 loss=-1.338 val_loss= 0.01664\n",
-      "train: 82000 loss=-1.147 val_loss=-2.089\n",
-      "train: 83000 loss=-0.6598 val_loss=-0.5354\n",
-      "train: 84000 loss=-1.014 val_loss=-1.317\n",
-      "train: 85000 loss=-1.322 val_loss=-0.8638\n",
-      "train: 86000 loss=-0.9507 val_loss=-0.6958\n",
-      "train: 87000 loss=-1.516 val_loss=-0.1163\n",
-      "train: 88000 loss=-1.21 val_loss=-2.103\n",
-      "train: 89000 loss=-0.8139 val_loss=-1.801\n",
-      "train: 90000 loss=-1.155 val_loss=-1.002\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 91000 loss=-0.9485 val_loss=-1.883\n",
-      "train: 92000 loss=-0.7806 val_loss=-0.5944\n",
-      "train: 93000 loss=-1.427 val_loss=-1.137\n",
-      "train: 94000 loss=-1.471 val_loss= 0.6587\n",
-      "train: 95000 loss=-1.312 val_loss=-1.141\n",
-      "train: 96000 loss=-1.19 val_loss=-0.7824\n",
-      "train: 97000 loss=-0.6071 val_loss=-0.6734\n",
-      "train: 98000 loss=-1.387 val_loss=-0.2842\n",
-      "train: 99000 loss=-1.024 val_loss= 0.01523\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "writer = SummaryWriter(comment='-anp_1d_reg')\n",
-    "print(writer.logdir)\n",
-    "\n",
-    "hparams = dict(\n",
-    "    x_dim=1,\n",
-    "    y_dim=1,\n",
-    "    hidden_dim=128,\n",
-    "    latent_dim=128,\n",
-    "    latent_enc_self_attn_type=\"multihead\",\n",
-    "    det_enc_self_attn_type=\"multihead\",\n",
-    "    det_enc_cross_attn_type=\"multihead\")\n",
-    "model = LatentModel(**hparams).cuda()\n",
-    "\n",
-    "optim = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
-    "\n",
-    "for n_iter in tqdm(range(epochs)):\n",
-    "    model.train()\n",
-    "\n",
-    "    data_train = dataset_train.generate_curves()\n",
-    "\n",
-    "    (context_x, context_y), target_x = data_train.query\n",
-    "    target_y = data_train.target_y\n",
-    "\n",
-    "    context_x = context_x.cuda()\n",
-    "    context_y = context_y.cuda()\n",
-    "    target_x = target_x.cuda()\n",
-    "    target_y = target_y.cuda()\n",
-    "\n",
-    "    optim.zero_grad()\n",
-    "    y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x,\n",
-    "                                              target_y)\n",
-    "    loss.backward()\n",
-    "    optim.step()\n",
-    "\n",
-    "    writer.add_scalar('train/loss', loss, n_iter)\n",
-    "    writer.add_scalar('train/mse_loss', mse_loss, n_iter)\n",
-    "    writer.add_scalar('train/y_std', y_std.mean(), n_iter)\n",
-    "    writer.add_scalar('train/kl', kl.mean(), n_iter)\n",
-    "\n",
-    "    if n_iter % PRINT_AFTER == 0:\n",
-    "        y_pred, kl, val_loss, y_std = test(\n",
-    "            model, dataset_test, writer, plot=False, global_step=n_iter)\n",
-    "        print(\n",
-    "            f\"train: {n_iter} loss={loss.item(): 4.4g} val_loss={val_loss.item(): 4.4g}\"\n",
-    "        )\n",
-    "\n",
-    "    if n_iter % PLOT_AFTER == 0:\n",
-    "        test(model, dataset_test, writer, plot=True, global_step=n_iter)\n",
-    "        \n",
-    "        test(model, dataset_train, writer, plot=True, global_step=n_iter, seed=n_iter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T12:41:13.300719Z",
-     "start_time": "2020-02-01T12:41:12.865025Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:26:36.856203Z",
-     "start_time": "2020-02-01T15:26:35.090212Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    set_seed(i)\n",
-    "    test(model, dataset_test, writer, plot=True, global_step=i, seed=i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:26:37.977575Z",
-     "start_time": "2020-02-01T15:26:36.866251Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    test(model, dataset_train, writer, plot=True, global_step=i, seed=i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:26:58.558046Z",
-     "start_time": "2020-02-01T15:26:37.981507Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a851f5254664951a99d41785630d676",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "val loss (n=100) tensor(-0.8646)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Final validation\n",
-    "loss = torch.stack([test(model, dataset_test)[2] for _ in tqdm(range(100))]).mean().cpu()\n",
-    "print('val loss (n=100)', loss)\n",
-    "writer.add_hparams(hparams, dict(val_loss=loss))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T03:26:03.797075Z",
-     "start_time": "2020-02-01T03:19:32.800Z"
-    }
-   },
-   "source": [
-    "## Improved\n",
-    "\n",
-    "These have no been extensively tested:\n",
-    "\n",
-    "- use torches ~50% faster multiheaded attention\n",
-    "- calc loss in log domain for stability (and untested changes to clipping)\n",
-    "- dropout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T07:27:59.578047Z",
-     "start_time": "2020-02-01T07:27:59.573762Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T19:06:43.265129Z",
-     "start_time": "2020-02-01T15:26:58.561968Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "runs/Feb01_23-26-58_mjcdesktop-anp_1d_impr\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98d420578cd340c6ba91496c588ae3ab",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=100000.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 0 loss= 1.66 val_loss= 1.457\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 1000 loss= 0.2346 val_loss=-0.6747\n",
-      "train: 2000 loss= 0.1635 val_loss= 0.315\n",
-      "train: 3000 loss= 0.06675 val_loss= 1.185\n",
-      "train: 4000 loss= 0.155 val_loss=-0.04905\n",
-      "train: 5000 loss=-0.1739 val_loss= 1.384\n",
-      "train: 6000 loss= 0.06595 val_loss=-0.3293\n",
-      "train: 7000 loss= 0.9722 val_loss=-0.1167\n",
-      "train: 8000 loss=-0.1602 val_loss= 0.5385\n",
-      "train: 9000 loss= 0.5204 val_loss= 0.2312\n",
-      "train: 10000 loss=-0.1201 val_loss= 0.5715\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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C5/fv0ZL0+nwEAoGaEGyuaDTKdw7tRywa5Z8FG9n2v62ccMSAZtXVGMN/SyJcOvZMXn/l780qUzusly15gScefbhVRzQZwSBL3voXV04cz9o1H/LzW+7g/Isn8/+uvoK0tHTuX/iXPd6/4uWlDbbAvV4vJw4fyTtvvUHprhJ8fh8GQyQS3ivco1Gnn7myAirij5WVUFUBVZVO11Jdj943jeUvzyMWTe65A2MMky6/kttmz60ZBurxNDxOxFqnjqUl8aUUdmyH7V/B9m2w/Svj/PyV0yLfshG2b2t6eGOPnpZ+A+OB3h/6DbA14d67L/jj50/fem0FM6+/ms8/+bhFn/PcCydy17wFLSoD4DPQI83w9n4W9suAdGvtiXXWvwZgrT25sfIK+44TtZbyCBx0QBdKk9Cy9/n9nHX+BLKyuzTYkmyJWCzGO2++QY9eBzLo8CMAyO3dtVl19Xq9fPJVKUP69aC8DaN+WsPn93PRjy5n+KjR/GT8GF55dwvBUC8qyi1lZREiYT9VdQJ504ZCXn/1Lj74Vz7VVaX4AyEGDppEzmEz8PlyqKhwQjvx/sRjRbkTkpUVEA43f2z3boXAYKBtJ97r4/Fkc/hR/6XgsyGkZxxF/4EvEI16iYQhGoVIBCJhKCuDshKIRFpWf5/PcuBBcFAfJ7h794WD+lj6xoO9b38IZja9nbt/extzbr+lVZ/R5/dTsKP+oz+PAb8Bv8fg9zgBn3hMnEBvS4Z1RNh/CTxvrf1pnfX3AeOttT0bK6+wb3+VUecilfIoFO/cye2/uI5nn1rYaL+x1+ec9ok2MnqkoW6aZLpp+rSm+7iNAWu5+sabuOd3d9Axg/rSgMr4vlsTwi1njCU9A9LTIT0DMoKQFv85LQ0aaljv2L6UTz8eTywWBpI9DHQT8BPgdZze3sQIlpnA34BXgAMACKRZQiHIzILMEHTrDgf0hB494YCelgN6Os97Hgh9+kGPXhAfN9Bqb722govPPLVN29hQFsXvAb9xQj2xeJtxYVUqwj6VJ2i7AzvqWf810K2+AsaYKcAUgAMPPJCVK1e2aselpaWtLusGmzZt4umnn+bVV1+loqKCjIwMRp56KmPHT+Cgg/oAsODPj/KX/AU1o20a4vP5mHrVz7gvby6RcHiP7g2v14vP7+f/brqZsi838fanX1G8009xcXzZ6WfXLj+VFV4qKxOLh6oqL+GwIRbbvUSjhlgMYlFDNGacC22swcac2AyHpxGNLKDRULIZdO32Ggvn5QJ345xUa29VQJi0NA+BQIy0tBhpaVECac7PgUDUeUyLkZ5YH4jFX9/9WlqgdpkY6enRmu0F0qJ7lPH7bYMjPxoXZPPmh3hm0V9Y9sorbR5em2CMh7n3bebDD84if8HbXHjxsyx58QHKyorZVbwdgD/OWU7//r3JCDr1bxYLO79wlrb65dTL2ryNgndeb3XZVGRYKlv21cBsa+0v6qy/HfiFtbbRPzRq2adGQ2PRwRmWd9lV01n6/LNUVVWy6Ysirv3VLO6/83cNjtxI9LcXrStk3ty7eOaJfMrLSgkEQvTPmUTXbjPY9r8cNhY1flIsSZ8OGIcT+LU/mz++LMIZ8wwwFZhH8lusjQukpfHptvJWhm/HS5xHqK6ubvRoriHGGM6feAl/fOARwDn/4vF4GP29b3NAj54cPPAQ0jMymPn7OYBzIvnhe2bz3FMLmz26KRkGhNp2aBAIBJo1D1FD9rdunK3Ac+rG2Xc0Z9x8wjW/vJkRp49myNDjGxy5ce6E6WzZlMPqtw1r3odPP4FdOxtOse4HWLr3gO494ID4Y7fukJllychw+lEzgs6jPwBeD3i8ziG51xd/9DrrPB6n79PUWjCwZWMhixbexSsv5lNRXkowM8RpZ03igkun07d/Ts17N20oZPLZQ6isSH6fdEMSffa3zZ7bbvtMhcT34S+PPUplE8Nd62poNM6XmzeRGcoiKzu7Zl1jJ6hrNzRSoa1hP3HiRPLz81tdfn8L++VAwFo7rM76lfH96gRtO4pay5Qrp7HgkXlN9sGPOvNsHqgzMgSgeCe89gq89qrh3bec8cl1de1uOfwoOOxIGDDI0n8gHDyg+SfF2lNDYZIq7XHuor3VbgiU7iqhsfMggbS0Zo2zrygv56F77iTvd3c0ep1HMn6fHhJ96YZAvE894AFvIyOEmpKWlsaaNWvaNEX0/hb204E/AodZa9fF1w3AGXr5C2vtnY2VV9gnRzhm2RWGsojl6GaOWEmMRQcn4Jc8Cy8+Y1j1xp4jI0JZlm9/B477nmXIcXD40dCzV8NXBu6LEmG1+PEFlKdgXDm0T0t0X1G0rpA5v57FC4ufrvkD6vcHOHPseGb838xmBfMH/1rN2Sd9p8n3+fx+Tj/rXHbu2M5/3nuX8nq6eYrWFTL79pm8+OyiWvXxM3bceGbNmsXhhw6q2d6mTZvo0aMHaWlp5ObmsmbNmhZ9dmMM6enpLF68uM3TcuxvYZ+Jc5q9gt0XVd0GZOFcVNXo/yyFfdvUHlmTMDDL18yx6B4efzFM/sOGV5ZAdZWT3l6v5bgT4NQfWL57EhxxNPg60bypqWjp+/0BLv7RZVz+s+kMzMmpGX8T73UCw57rDJj4msTzxM/UeV/t5y1RM8tkgzNO2pppHmrPPFl3eodUOrR7BuHq6laV9fp8YC1er5fqRraRlpbGs88+y+jRo3n99dc55ZRTiEaj3H333RxyyCGMGTOmyX0Fg0EqKysJhUJMnjyZGTNmJOWmL/vVaBxrbZkx5hSc6RIew/lOLsOZLiE1TSiXs9YJ911hS3U9F5NmhkLNatkbT4iLfuAcxhpjOXG45dwLLaN+AF27J7vW+44Rp43mpVX/SVpLv765ePYPjf/5qD1xW+JuTYnpJGrfFKT2e6It/CPRlj+2iRPHTV34VlVVxdixY3nvvfdYtWpVzfuvu+46du7cyaxZs5g5c2aD5WfNmpW0uebbQ0rbZfE5cM5P5T4EIjFLaQRKI7be/1SPP/IQxTt3csbZY1n8xGPYRqcV8BOLTqZrd8sPr7BMuAQO6puyqu/Fg9Nq9cRbvInH2q3e+lq8zV1HzXZqPU/s28BBRw/ihPvzePSBPF5aupQJ48dTVVXVoitma8+yuf8FfdM8xuBp8nBi7zfErK05Skio/XWtXSIUCrXLVB2VlZVcfvnlvPnmm1x66aU89dRT7Nixg8zMTG6++WaGDRvGNddcw0cffVRTJjc3l7vvvjspd49qV4lLlve15dhjj7WttWLFilaX3ZcUFBTYK6+80mZlZVljjM3KyrJXXnmlXbZs2R7rQ1lZdtJlV9iX3/3QvlOw0a4vjdoHn1hsvz9ylL13wZP2p9Ovt4D93X0P2/SMjMS9IRpYgvaKGZ/Zj7ZE7frS5C1FpVG7qTxqt1ZE7fbKmC2ujtnScMxWRGK2Ohqz0Viso3/d9SooKLDTpk2zoVBoj99TKBSy06ZNs8uWLbPTpk2z2dnZ1uPx2OzsbDtt2jRbUFDQ0VXfr1155ZXW7wywT/ni9/s7+uPupS0ZBqy29WRqh4d6Q4vbw37JkiU2GAzu9YX3eDx7PCYWn99v/YGA9fl89sW33rP3PLrQAvb6m2+zgw4/0l594012fWnUPvrMCzYjGLQeb93/SH7r9wftQ0+80OZg31DmhPqOqpgtC8dsOLpvBnlrdYbv176uoKDABoPBdgl7j8fT0R93L6kIe92pah/U2P1HEzM71p3hMRIOE66uJhKJcN8ff8uYcRcw5LjvcM6Ei/j7O+9z7a9mUVUFm74YTXaX94lFpwDZgIe09Gwm/uRyXn3vP5w2puWjCHwGQj5DjzRDnwxD36CHXukeugYMQZ/B1/Qxv8gecnJyWLRoEcFgEL9/z7s61X3eVqFQKKnb21d1orEUnUdb7j/q8XjI7tIFYwzPLn8TYwwV5fD4o/DQXYatWwwwiEMOncu0n9/D2eN3z/DXEukeyPAZMrzOGGWRZBs9ejQffPABc+bM4bHHHqu5SczkyZPZuXMnTz/9dJtv3WiMYfLkyUmq8b5NLfsOVN/9Un9yxVTmL1jQ6i9xLBbjb4udG1Ts/Npwz29h2FGG2270sHWL4YhcS978GK+stpx/ccuCPuCB7gFD36DhwAwP2X6joJeUysnJIS8vj+LiYqLRKMXFxeTl5TFr1qyktPDT09OZMWNGEmq671PLvoO88OISLpgwfo85akpKSpq8wrU5ykpKmXmd4enHoKLcCeNvHWu56gbLyNENz3LYkEwfZPkMaV4Fu+wbEt08Dc3z1BxpaWksXry4U46Yqo9a9u0gErOURyw7qy3/q4zxj48+Z8KE8fX2ySfjYh5rQ8x/0FBRbjh5lGXh32I8t9Iy6szmB70Bsv1OH3yPNI+CXvY5iW6eKVOm1BwdZ2Rk4PP58DVytV8gEGDixImsWbMmKTeg318o7JMoZi1V8StXd1RbtlbG2FAeY1OF5asqS3HYUhGFB++e3ea+xob5MWYSYy+2vLQqxvxnLSeOaNkUBlk+Q5+goVtAJ1dl31a3m6e8vJy1a9fy05/+dI/u0WnTplFQUIC1lqqqKvLz813Tok9QN04LRWLOhUsRC5EYhK2NPzb/UvLnmrghSFv4/H7+8vfpHHN8y69rz/RBV78CXvZviT8AeXl5HV2VfYrCHqdFvvsy71qPOJd9R2LUBHwytORGznXv22qMF2ujgBeofaMQP4E0Z7KtY45vWYslwwtdA4aAQl6k0+p0YW+tJQaUhG3NRE67J3WqM6cH7TexU23NnaMmmBli+KhLeHVJPtXVpUAIaycDY8kMLSZcnU843Pr7uQY80C1gSFd/vEin1+nCPobTvfJ1dQekeDOde8HEJu+Xaowfay9hyXP3AvcC0KWb5bSz4PyLLcefOByPp3U3wfB7nO6aoE8hL+IWnS7s9weXX30tix9f0GjYW+unovxaevdxAv70MZbjTmzdBVAJPuN012Qq5EVcR2Hfjqqq4NOPY7y3qh/pGd+kovzfOPdArX0U4sfj8XPeRU/zwysGkjuktTeL3s1roIvfEPI5VwyKiPt0qrAvLCzkj3feyYL586moqGi3mxPXZS1s3QJr18Daj2DtR4ZPPoTP195FLFYAnAv8EwhhzHHAe1hbTUZGiHMvnMQVM1rW994Qn3HGyivkRaTThP3SpUv3upqutKSEJ+f/icWPL0j6LeHCYfhqKxStc5b16wxFhbA+/ry8rG64fgxcjzEBRp/7GwbmfEi/Ads5f+L329Q1U580jxPy6pMXkYROEfa1Z4msKxK/xdzUSRMavTlxJAIlxc49VxPLrvjjV1th6xbD/750WuxfbobtX4G1DYdpl27OjbePzIUjci1H5B7BP5b/hj4HH8jYi0LAUcn6+IBzxWumz5DlR0MoRWQvnSLsmzNLZGVFmEvPu4tDj8yjogzKy6G8bHfAl5a0LCCNsfToZTl4IAw4BPofYhmQA/0PcZ5Ho19hjKF7jx41ZY457vpWfb7GBDzO9MKZPucOQiIi9ekUYZ+fn99k2FsbZn1hPusL7633dWMs2V0huwt06bp7ye4KPXtBr96WA3vDgb2h1zegR6+9R8ZUV1dTVVlJVnY2k8+exJjxF3Lq6DF8vvZjjsgdTJeuXZPyeX0Ggj6nL16zTopIc3SKsC9t5hWpxpRy/8IYwUwIBiE9CF26OIGeld3y2SDruuLi83n91ZfJW/Akb7++ks0bN/L0gkdZ/fab9O0/gH+sKWz1thMBH/SiSclEpMU6Rdg39+bEPp+P086K4amV6kXrCvnDLbN57qmFlJWWtngEj7WW555aSNdu3dm6ZQuRSITjvjeMPzzwJ4rWrWPE6aO5/qc/5tbZLbsAygBpXsjw6gYhItJ2nSLsJ02axLx585rsygmHq1n32acMOuJIAFa8vJSpkyYQjp/EBWcEz8I/PUj+vAfAGEJNhP+ihfP5+RU/IeewI/j7O++za+dOuvfowXkXTqp5zyurP2zW5wh4IN1rSPc6I2rUBy8iydIppji+7rrrmrxrTSAQ4IZbbq8J+qJ1hUydNIGK8vK9rmR17tkLWFszfPOM7w5hxctLa95z+y+v59Ybr+W7w04G4Iprf47P59vjhMYKBN8AAAc+SURBVGxjPDi39uviN/RKN/QLGnpneOgWMGR4jYJeRJKqU4R9Yzcn9vn8ZASDPPjkM0y9/pdUV1fzyot/5VfXXtXsOeUj4TAV5eVMnTSBonWFRKNR0tLSyX/4frw+HwU7qxg/6YcNlvcYJ9izfIYDAobeGYZ+mR4OzHBuyq1wF5FU6xRhD7vvWnPZ5ZcTDAYxxkMoO5uLfnw5L636T80FVW+9tpzLLziPf6xY1uI55SvKy5l41ijC1dWsX1fA4GOPY8fX2/H5fHhwumGCXueCpu4Bw4Hpzv1a+wWdYO+eZgj5NZWwiLS/TtFnn5CTk8PcvDzOPX88h3/npHrfM2zEqRzUtx+bN25o1T42flHEy4ufYP6CxwimB/AaZ6SMWuYisi/rVGEPzigWr3G6TBL5axKLARPws+KNt/h27hGUlZW1ah/Djj+WAzLTklVlEZGU6zTdOAkeY/Aa6J7m3EO1W8DQNWDoEjBk+w1ZfsNhA/pyySWXNHlStz7Z2dkcc8wxKai5iEjqdLqwb67mjOCpy+/3M3ny5BTVSEQkdVwb9o2N4GmI3+9nxowZKa6ZiEjyuTbsYfcInilTppCdnV0z53vdud/9fj/BYJBFixaRk9N+8+KLiCSLq8MenBZ+Xl4excXFxGIxCgoKmDp1KtnZ2Xg8HrKzs5kyZQoffPABo0cnbz58EZH21OlG47RVIvzz8vI6uioiIknj+pa9iIgbKOxFRFxAYS8i4gIKexERF1DYi4i4gMJeRMQFFPYiIi6gsBcRcQGFvYiICyjsRURcQGEvIuICCnsRERdQ2IuIuIDCXkTEBRT2IiIuoLAXEXEBhb2IiAso7EVEXEBhLyLiAgp7EREXUNiLiLiAwl5ExAUU9iIiLqCwFxFxAYW9iIgLKOxFRFxAYS8i4gIKexERF1DYi4i4gMJeRMQFFPYiIi6gsBcRcQGFvYiICyjsRURcQGEvIuICCnsRERdQ2IuIuIDCXkTEBRT2IiIuoLAXEXEBhb2IiAso7EVEXEBhLyLiAgp7EREXUNiLiLiAwl5ExAUU9iIiLqCwFxFxAYW9iIgLKOxFRFxAYS8i4gIKexERF1DYi4i4gMJeRMQFFPYiIi6gsBcRcQGFvYiICyjsRURcQGEvIuICCnsRERdQ2IuIuIDCXkTEBRT2IiIukJKwN8YcZoy52xjzgTGm1BizxRjzV2PMt1KxPxERaVyqWvanASOA+cAYYCrQE1hljDk2RfsUEZEG+FK03SeBe621NrHCGLMcWA9cA1ySov2KiEg9UhL21tpt9awrNsZ8BvRJxT5FRKRh7XaC1hjTHcgFPmmvfYqIiCNV3Tj1mQsY4K6G3mCMmQJMiT8tNcZ82sp99QD2OroQSRJ9vyTV2vId61/fSlOrW71BxphTgVeasZPXrLXD6yn/S+DXwE+stY80YzttYoxZba0dmur9iDvp+yWplorvWHNb9m8BRzbjfeV1VxhjrsAJ+pvaI+hFRGRvzQp7a205sLalGzfGTAbuA+601t7R0vIiIpIcKTtBa4w5D3gUmGetvT5V+2nAQ+28P3EXfb8k1ZL+HWtWn32LN2rMScDLwBrgZ0Cs1stV1tp/J32nIiLSoFSNxjkFSAO+DbxZ57UiYECK9isiIvVIScteRET2LZ161ktNyCbJYozpZ4xZZIwpNsbsMsY8Y4w5uKPrJZ2DMWacMWaxMabIGFNhjPnUGPMbY0xW0vbRmVv2xpircC7Smg/8C+gK3AAMAYZZa9/rwOrJfsIYEwTeB6qAmwAL3A4EgcHW2rIOrJ50AsaYVcAXwPPARuAY4BacUZAnWGtjDZdu5j46edj3ALbXmZCtC86EbH+z1mpCNmmSMeYaYDZwuLW2IL5uIPA5cIO1dnZH1k/2f8aYntbar+qsuwSnoTrSWru8rfvo1N041tptts5fM2ttMaAJ2aQlzgZWJYIewFr7X5zBB+d0WK2k06gb9HHvxh+TklWdOuzrownZpBWOBj6qZ/0a4Kh2rou4x8nxx6RklevCnmZMyCZSR3dgRz3rvwa6tXNdxAWMMX2AW4FXrbWrk7HN/SrsjTGnGmNsM5aVDZT/JXAxcFXtQ3IRkX2FMSaEc6I2AvwoWdttzymOk0ETsklH2EH9LfiGWvwirWKMyQD+BhwCnGyt3Zisbe9XYa8J2aSDrMHpt6/rKODjdq6LdFLGGD+wCBgKjLLWfpjM7e9X3Tit0cETsknn8Ffgu8aYQxIrjDEDgBPjr4m0iTHGAyzEmWrmXGvtqqTvo5OPs9eEbNJmxphMnIuqKth9UdVtQBbORVWlHVg96QSMMfcDVwB3AC/UeXljMrpzOnvY3wLMbODlImvtgParjezP4lMjzAFG4YzmWgZMt9au78h6SedgjFlPA7cTBGZZa29p8z46c9iLiIij0/fZi4iIwl5ExBUU9iIiLqCwFxFxAYW9iIgLKOxFRFxAYS8i4gIKexERF/j/ke7o/V55JUsAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 11000 loss=-0.246 val_loss= 0.2482\n",
-      "train: 12000 loss=-0.3654 val_loss= 0.8571\n",
-      "train: 13000 loss=-0.3429 val_loss=-0.3089\n",
-      "train: 14000 loss= 0.1667 val_loss=-0.5312\n",
-      "train: 15000 loss=-0.05928 val_loss= 0.236\n",
-      "train: 16000 loss=-0.03568 val_loss= 0.002041\n",
-      "train: 17000 loss=-0.06225 val_loss= 0.1638\n",
-      "train: 18000 loss=-0.2034 val_loss=-0.5371\n",
-      "train: 19000 loss=-0.06715 val_loss= 0.2504\n",
-      "train: 20000 loss=-0.1886 val_loss=-0.2158\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 21000 loss=-0.1663 val_loss=-0.3638\n",
-      "train: 22000 loss=-0.3555 val_loss=-0.02034\n",
-      "train: 23000 loss=-0.003735 val_loss= 0.4405\n",
-      "train: 24000 loss=-0.01453 val_loss= 0.02991\n",
-      "train: 25000 loss=-0.5452 val_loss= 0.5273\n",
-      "train: 26000 loss=-0.4023 val_loss=-1.654\n",
-      "train: 27000 loss=-0.3546 val_loss= 1.217\n",
-      "train: 28000 loss=-0.8688 val_loss=-1.209\n",
-      "train: 29000 loss=-0.3726 val_loss=-0.247\n",
-      "train: 30000 loss=-0.3504 val_loss=-0.1419\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 31000 loss=-1.07 val_loss=-1.122\n",
-      "train: 32000 loss=-0.7569 val_loss=-1.905\n",
-      "train: 33000 loss=-0.8855 val_loss=-0.1284\n",
-      "train: 34000 loss=-0.744 val_loss=-0.7068\n",
-      "train: 35000 loss=-0.6671 val_loss=-1.456\n",
-      "train: 36000 loss=-1.196 val_loss=-2.031\n",
-      "train: 37000 loss=-1.076 val_loss=-0.02586\n",
-      "train: 38000 loss=-0.9341 val_loss=-1.009\n",
-      "train: 39000 loss=-0.6252 val_loss=-1.563\n",
-      "train: 40000 loss=-0.9949 val_loss=-0.7833\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 41000 loss=-1.376 val_loss=-0.1559\n",
-      "train: 42000 loss=-1.452 val_loss=-1.198\n",
-      "train: 43000 loss=-0.8674 val_loss= 0.3678\n",
-      "train: 44000 loss=-1.239 val_loss=-0.7432\n",
-      "train: 45000 loss=-0.9778 val_loss=-1.205\n",
-      "train: 46000 loss=-1.355 val_loss=-1.026\n",
-      "train: 47000 loss=-1.237 val_loss=-1.892\n",
-      "train: 48000 loss=-1.548 val_loss=-1.017\n",
-      "train: 49000 loss=-1.155 val_loss=-1.373\n",
-      "train: 50000 loss=-1.267 val_loss=-0.9196\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 51000 loss=-1.049 val_loss=-0.8146\n",
-      "train: 52000 loss=-1.309 val_loss=-1.071\n",
-      "train: 53000 loss=-0.9399 val_loss= 0.8218\n",
-      "train: 54000 loss=-1.08 val_loss=-0.05891\n",
-      "train: 55000 loss=-1.319 val_loss=-0.7948\n",
-      "train: 56000 loss=-1.765 val_loss=-1.974\n",
-      "train: 57000 loss=-1.632 val_loss=-2.255\n",
-      "train: 58000 loss=-1.441 val_loss= 0.002214\n",
-      "train: 59000 loss=-1.627 val_loss= 0.1506\n",
-      "train: 60000 loss=-0.7349 val_loss=-1.395\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 61000 loss=-1.107 val_loss=-1.13\n",
-      "train: 62000 loss=-1.726 val_loss=-1.811\n",
-      "train: 63000 loss=-1.163 val_loss=-0.176\n",
-      "train: 64000 loss=-1.494 val_loss=-1.848\n",
-      "train: 65000 loss=-1.399 val_loss= 0.7774\n",
-      "train: 66000 loss=-1.596 val_loss=-1.886\n",
-      "train: 67000 loss=-0.9347 val_loss=-0.5104\n",
-      "train: 68000 loss=-1.317 val_loss=-1.838\n",
-      "train: 69000 loss=-1.682 val_loss=-0.4937\n",
-      "train: 70000 loss=-0.9635 val_loss= 0.3594\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 71000 loss=-1.66 val_loss=-1.048\n",
-      "train: 72000 loss=-1.506 val_loss=-2.102\n",
-      "train: 73000 loss=-1.588 val_loss=-1.467\n",
-      "train: 74000 loss=-1.632 val_loss=-0.766\n",
-      "train: 75000 loss=-1.667 val_loss=-1.177\n",
-      "train: 76000 loss=-1.106 val_loss=-1.301\n",
-      "train: 77000 loss=-1.89 val_loss=-1.436\n",
-      "train: 78000 loss=-1.159 val_loss=-2.02\n",
-      "train: 79000 loss=-2.078 val_loss=-1.704\n",
-      "train: 80000 loss=-1.68 val_loss=-1.441\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 81000 loss=-1.63 val_loss=-0.6517\n",
-      "train: 82000 loss=-1.532 val_loss=-2.119\n",
-      "train: 83000 loss=-1.252 val_loss=-1.494\n",
-      "train: 84000 loss=-1.372 val_loss=-1.631\n",
-      "train: 85000 loss=-1.611 val_loss=-1.697\n",
-      "train: 86000 loss=-1.321 val_loss=-1.236\n",
-      "train: 87000 loss=-1.923 val_loss=-0.7022\n",
-      "train: 88000 loss=-1.625 val_loss=-2.153\n",
-      "train: 89000 loss=-1.087 val_loss=-2.136\n",
-      "train: 90000 loss=-1.476 val_loss=-1.422\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train: 91000 loss=-1.484 val_loss=-2.06\n",
-      "train: 92000 loss=-1.242 val_loss=-1.477\n",
-      "train: 93000 loss=-1.907 val_loss=-1.68\n",
-      "train: 94000 loss=-1.732 val_loss= 0.3016\n",
-      "train: 95000 loss=-1.754 val_loss=-1.611\n",
-      "train: 96000 loss=-1.7 val_loss=-1.685\n",
-      "train: 97000 loss=-1.062 val_loss=-1.34\n",
-      "train: 98000 loss=-1.787 val_loss=-0.3952\n",
-      "train: 99000 loss=-1.542 val_loss=-0.3366\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "writer = SummaryWriter(comment='-anp_1d_impr')\n",
-    "print(writer.logdir)\n",
-    "                 \n",
-    "hparams = dict(\n",
-    "    x_dim=1,\n",
-    "    y_dim=1,\n",
-    "    hidden_dim=128,\n",
-    "    latent_dim=128,\n",
-    "    latent_enc_self_attn_type=\"ptmultihead\",\n",
-    "    det_enc_self_attn_type=\"ptmultihead\",\n",
-    "    det_enc_cross_attn_type=\"ptmultihead\",\n",
-    "    min_std=1e-3,\n",
-    "    use_lvar=True,\n",
-    "    use_self_attn=True,\n",
-    "#     attention_dropout=0.3,\n",
-    "#     dropout=0.3,\n",
-    "#     batchnorm=True,\n",
-    ")\n",
-    "model = LatentModel(**hparams).cuda()\n",
-    "\n",
-    "optim = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
-    "\n",
-    "for n_iter in tqdm(range(epochs)):\n",
-    "    model.train()\n",
-    "\n",
-    "    data_train = dataset_train.generate_curves()\n",
-    "\n",
-    "    (context_x, context_y), target_x = data_train.query\n",
-    "    target_y = data_train.target_y\n",
-    "\n",
-    "    context_x = context_x.cuda()\n",
-    "    context_y = context_y.cuda()\n",
-    "    target_x = target_x.cuda()\n",
-    "    target_y = target_y.cuda()\n",
-    "\n",
-    "    optim.zero_grad()\n",
-    "    y_pred, kl, loss, mse_loss, y_std = model(context_x, context_y, target_x,\n",
-    "                                              target_y)\n",
-    "    loss.backward()\n",
-    "    optim.step()\n",
-    "\n",
-    "    writer.add_scalar('train/loss', loss, n_iter)\n",
-    "    writer.add_scalar('train/mse_loss', mse_loss, n_iter)\n",
-    "    writer.add_scalar('train/y_std', y_std.mean(), n_iter)\n",
-    "    writer.add_scalar('train/kl', kl.mean(), n_iter)\n",
-    "\n",
-    "    if n_iter % PRINT_AFTER == 0:\n",
-    "        y_pred, kl, val_loss, y_std = test(\n",
-    "            model, dataset_test, writer, plot=False, global_step=n_iter)\n",
-    "        print(\n",
-    "            f\"train: {n_iter} loss={loss.item(): 4.4g} val_loss={val_loss.item(): 4.4g}\"\n",
-    "        )\n",
-    "\n",
-    "    if n_iter % PLOT_AFTER == 0:\n",
-    "        test(model, dataset_test, writer, plot=True, global_step=n_iter)\n",
-    "        test(model, dataset_train, writer, plot=True, global_step=n_iter, seed=n_iter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T19:06:44.533260Z",
-     "start_time": "2020-02-01T19:06:43.268317Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    test(model, dataset_test, writer, plot=True, global_step=i, seed=i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T19:06:45.383632Z",
-     "start_time": "2020-02-01T19:06:44.538238Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(3):\n",
-    "    test(model, dataset_train, writer, plot=True, global_step=i, seed=i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T19:07:01.809655Z",
-     "start_time": "2020-02-01T19:06:45.393091Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "064356d256e145aebd6329c600a6e896",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "val loss (n=100) tensor(-1.3316)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Final validation\n",
-    "loss = torch.stack([test(model, dataset_test)[2] for _ in tqdm(range(100))]).mean().cpu()\n",
-    "print('val loss (n=100)', loss)\n",
-    "writer.add_hparams(hparams, dict(val_loss=loss))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T19:07:01.839570Z",
-     "start_time": "2020-02-01T19:07:01.820100Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "({'x_dim': 1,\n",
-       "  'y_dim': 1,\n",
-       "  'hidden_dim': 128,\n",
-       "  'latent_dim': 128,\n",
-       "  'latent_enc_self_attn_type': 'ptmultihead',\n",
-       "  'det_enc_self_attn_type': 'ptmultihead',\n",
-       "  'det_enc_cross_attn_type': 'ptmultihead',\n",
-       "  'min_std': 0.001,\n",
-       "  'use_lvar': True,\n",
-       "  'use_self_attn': True},\n",
-       " {'val_loss': tensor(-1.3316)})"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(hparams, dict(val_loss=loss))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "jup3.7.2",
-   "language": "python",
-   "name": "jup3.7.2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.2"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/anp_rnn.png.jpeg b/docs/anp_rnn.png.jpeg
new file mode 100644
index 0000000..6f6293d
Binary files /dev/null and b/docs/anp_rnn.png.jpeg differ
diff --git a/smartmeters-anp-optuna.ipynb b/smartmeters-ANP-RNN.ipynb
similarity index 99%
rename from smartmeters-anp-optuna.ipynb
rename to smartmeters-ANP-RNN.ipynb
index 6d4267f..9db1ec3 100644
--- a/smartmeters-anp-optuna.ipynb
+++ b/smartmeters-ANP-RNN.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# This notebook uses pytorch lightning & optuna & an ANP\n",
+    "# This notebook uses pytorch lightning & optuna & a Recurrent Attentive Neural Process for Sequential Data (RANPfSQ)\n",
     "\n",
     "This notebook trains an Attentional Neural Network on timeseries data from smartmeters.\n",
     "\n",
@@ -12,16 +12,18 @@
     "\n",
     "It also pushes results to the tensorboard hyperparameter dashboard for examination.\n",
     "\n",
-    "- https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py"
+    "- https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py\n",
+    "\n",
+    "- similar to https://arxiv.org/abs/1910.09323"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:40.853741Z",
-     "start_time": "2020-02-01T23:50:40.605164Z"
+     "end_time": "2020-02-14T23:25:52.621775Z",
+     "start_time": "2020-02-14T23:25:52.478625Z"
     }
    },
    "outputs": [],
@@ -47,27 +49,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:41.047601Z",
-     "start_time": "2020-02-01T23:50:40.856693Z"
+     "end_time": "2020-02-14T23:25:53.015332Z",
+     "start_time": "2020-02-14T23:25:52.896419Z"
     }
    },
    "outputs": [],
    "source": [
     "import logging\n",
     "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
-    "logger = logging.getLogger(\"smartmeters.ipynb\")"
+    "logger = logging.getLogger(\"RANP.ipynb\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:41.241478Z",
-     "start_time": "2020-02-01T23:50:41.051645Z"
+     "end_time": "2020-02-14T23:25:53.953136Z",
+     "start_time": "2020-02-14T23:25:53.838224Z"
     }
    },
    "outputs": [],
@@ -79,11 +81,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:41.646148Z",
-     "start_time": "2020-02-01T23:50:41.244739Z"
+     "end_time": "2020-02-14T23:25:54.381080Z",
+     "start_time": "2020-02-14T23:25:54.090473Z"
     }
    },
    "outputs": [],
@@ -97,11 +99,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:41.841504Z",
-     "start_time": "2020-02-01T23:50:41.649371Z"
+     "end_time": "2020-02-14T23:25:54.491404Z",
+     "start_time": "2020-02-14T23:25:54.384568Z"
     }
    },
    "outputs": [],
@@ -120,11 +122,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:55.817293Z",
-     "start_time": "2020-02-01T23:50:41.844946Z"
+     "end_time": "2020-02-14T23:26:05.180504Z",
+     "start_time": "2020-02-14T23:25:55.792803Z"
     }
    },
    "outputs": [],
@@ -134,21 +136,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:57.443443Z",
-     "start_time": "2020-02-01T23:50:55.819906Z"
+     "end_time": "2020-02-14T23:26:06.021835Z",
+     "start_time": "2020-02-14T23:26:05.183449Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f2bbc697be0>"
+       "<matplotlib.legend.Legend at 0x7fb08889a7b8>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -177,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hyperparam search"
+    "# Train helpers"
    ]
   },
   {
@@ -194,11 +196,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:57.626386Z",
-     "start_time": "2020-02-01T23:50:57.458162Z"
+     "end_time": "2020-02-14T23:26:24.063601Z",
+     "start_time": "2020-02-14T23:26:23.941776Z"
     }
    },
    "outputs": [
@@ -206,16 +208,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/anp3\n"
+      "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/ranp\n"
      ]
     }
    ],
    "source": [
     "PERCENT_TEST_EXAMPLES = 0.3\n",
-    "EPOCHS = 2\n",
+    "EPOCHS = 5\n",
     "DIR = Path(os.getcwd())\n",
-    "MODEL_DIR = DIR/ 'optuna_result'/ 'anp3'\n",
-    "name = 'anp2' # study name\n",
+    "MODEL_DIR = DIR/ 'optuna_result'/ 'ranp'\n",
+    "name = 'ranp' # study name\n",
     "MODEL_DIR.mkdir(parents=True, exist_ok=True)\n",
     "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")"
    ]
@@ -229,11 +231,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:57.829632Z",
-     "start_time": "2020-02-01T23:50:57.630024Z"
+     "end_time": "2020-02-14T23:26:25.041624Z",
+     "start_time": "2020-02-14T23:26:24.921709Z"
     }
    },
    "outputs": [],
@@ -331,232 +333,15 @@
     "    return model.logger.metrics[-1]['val_loss']\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-01-27T04:27:28.582523Z",
-     "start_time": "2020-01-27T04:27:28.445326Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:50:58.148356Z",
-     "start_time": "2020-02-01T23:50:57.832731Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[I 2020-02-02 07:50:58,105] Using an existing study with name 'anp2' instead of creating a new one.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import argparse \n",
-    "\n",
-    "parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n",
-    "parser.add_argument('--pruning', '-p', action='store_true',\n",
-    "                    help='Activate the pruning feature. `MedianPruner` stops unpromising '\n",
-    "                         'trials at the early stages of training.')\n",
-    "args = parser.parse_args(['-p'])\n",
-    "\n",
-    "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n",
-    "\n",
-    "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name=name, load_if_exists=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:51:05.489281Z",
-     "start_time": "2020-02-01T23:50:58.152081Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-d34fa1166ee6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                 self._optimize_sequential(func, n_trials, timeout, catch, callbacks,\n\u001b[0;32m--> 302\u001b[0;31m                                           gc_after_trial, None)\n\u001b[0m\u001b[1;32m    303\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0mtime_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)\u001b[0m\n\u001b[1;32m    536\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial_and_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial_and_callbacks\u001b[0;34m(self, func, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# type: (...) -> None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m         \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m             message = 'Setting status of trial#{} as {}. {}'.format(trial_number,\n",
-      "\u001b[0;32m<ipython-input-10-ffc699c21a4b>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;31m# see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m     \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_sugg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'trial'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'params'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-10-ffc699c21a4b>\u001b[0m in \u001b[0;36madd_sugg\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"batchnorm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_self_attn\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_lvar\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_deterministic_path\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36msuggest_categorical\u001b[0;34m(self, name, choices)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m         \u001b[0mchoices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCategoricalDistribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36m_suggest\u001b[0;34m(self, name, distribution)\u001b[0m\n\u001b[1;32m    486\u001b[0m                 self.study, trial, name, distribution)\n\u001b[1;32m    487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_new_param_or_get_existing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_new_param_or_get_existing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36m_set_new_param_or_get_existing\u001b[0;34m(self, name, param_value, distribution)\u001b[0m\n\u001b[1;32m    493\u001b[0m         \u001b[0mparam_value_in_internal_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_internal_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         set_success = self.storage.set_trial_param(self._trial_id, name,\n\u001b[0;32m--> 495\u001b[0;31m                                                    param_value_in_internal_repr, distribution)\n\u001b[0m\u001b[1;32m    496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mset_success\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m             \u001b[0mparam_value_in_internal_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/storage.py\u001b[0m in \u001b[0;36mset_trial_param\u001b[0;34m(self, trial_id, param_name, param_value_internal, distribution)\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m         \u001b[0mcommit_success\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_commit_with_integrity_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcommit_success\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/storage.py\u001b[0m in \u001b[0;36m_commit_with_integrity_check\u001b[0;34m(session)\u001b[0m\n\u001b[1;32m    860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m             \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIntegrityError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m             \u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/orm/session.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1034\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0msa_exc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No transaction is begun.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransaction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1037\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/orm/session.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnested\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCOMMITTED\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1725\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This transaction is inactive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1727\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1728\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_do_commit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1756\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1757\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1758\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_commit_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_commit_impl\u001b[0;34m(self, autocommit)\u001b[0m\n\u001b[1;32m    755\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_dbapi_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    758\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m             if (\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1476\u001b[0m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1478\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb, cause)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_commit_impl\u001b[0;34m(self, autocommit)\u001b[0m\n\u001b[1;32m    753\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"COMMIT\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    756\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_dbapi_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_commit\u001b[0;34m(self, dbapi_connection)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m         \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_close\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "study.optimize(objective, n_trials=200, timeout=pd.Timedelta('2d').total_seconds())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T22:48:16.540204Z",
-     "start_time": "2020-02-01T22:47:55.013103Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:51:05.495029Z",
-     "start_time": "2020-02-01T23:50:40.900Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "print('Number of finished trials: {}'.format(len(study.trials)))\n",
-    "\n",
-    "print('Best trial:')\n",
-    "trial = study.best_trial\n",
-    "\n",
-    "print('  Value: {}'.format(trial.value))\n",
-    "\n",
-    "print('  Params: ')\n",
-    "for key, value in trial.params.items():\n",
-    "    print('    {}: {}'.format(key, value))\n",
-    "\n",
-    "# shutil.rmtree(MODEL_DIR)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-01-27T07:45:29.794794Z",
-     "start_time": "2020-01-27T07:45:29.733450Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-01-27T07:35:33.470251Z",
-     "start_time": "2020-01-27T07:34:47.800Z"
-    }
-   },
-   "source": [
-    "# View\n",
-    "\n",
-    "TODO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:51:05.496375Z",
-     "start_time": "2020-02-01T23:50:40.900Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n",
-    "df.sort_values('value')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:51:05.497893Z",
-     "start_time": "2020-02-01T23:50:40.900Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df.sort_values('value').head(17).T"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test\n",
+    "# Train\n",
     "\n",
     "TODO test best"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T07:57:53.966541Z",
-     "start_time": "2020-02-01T07:57:53.843765Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "trial = study.best_trial"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:11:02.346022Z",
-     "start_time": "2020-02-02T00:11:02.190493Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# try:\n",
-    "#     trial = study.best_trial\n",
-    "# except ValueError as e:\n",
-    "#     logging.warning(f\"Could not load best trial as: '{e}'\")\n",
-    "#     trial = study.get_trials(1)[0]\n",
-    "# trial.params"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -598,7 +383,7 @@
     "                                 \n",
     "                                })\n",
     "trial = add_sugg(trial)\n",
-    "trial.number = 1002"
+    "trial.number = 1"
    ]
   },
   {
@@ -877,7 +662,8 @@
     "ExecuteTime": {
      "end_time": "2020-02-02T00:11:02.179447Z",
      "start_time": "2020-02-02T00:10:04.499779Z"
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
     {
@@ -942,6 +728,190 @@
     "trainer.test(model)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-01-27T04:27:28.582523Z",
+     "start_time": "2020-01-27T04:27:28.445326Z"
+    }
+   },
+   "source": [
+    "# Hyperparam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-14T23:26:14.699258Z",
+     "start_time": "2020-02-14T23:26:14.485044Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[I 2020-02-15 07:26:14,685] Using an existing study with name 'anp2' instead of creating a new one.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse \n",
+    "\n",
+    "parser = argparse.ArgumentParser(description='PyTorch Lightning example.')\n",
+    "parser.add_argument('--pruning', '-p', action='store_true',\n",
+    "                    help='Activate the pruning feature. `MedianPruner` stops unpromising '\n",
+    "                         'trials at the early stages of training.')\n",
+    "args = parser.parse_args(['-p'])\n",
+    "\n",
+    "pruner = optuna.pruners.MedianPruner() if args.pruning else optuna.pruners.NopPruner()\n",
+    "\n",
+    "study = optuna.create_study(direction='minimize', pruner=pruner, storage=f'sqlite:///optuna_result/{name}.db', study_name=name, load_if_exists=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T23:51:05.489281Z",
+     "start_time": "2020-02-01T23:50:58.152081Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-d34fa1166ee6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstudy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trials\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimedelta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_seconds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m    300\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                 self._optimize_sequential(func, n_trials, timeout, catch, callbacks,\n\u001b[0;32m--> 302\u001b[0;31m                                           gc_after_trial, None)\n\u001b[0m\u001b[1;32m    303\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m                 \u001b[0mtime_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(self, func, n_trials, timeout, catch, callbacks, gc_after_trial, time_start)\u001b[0m\n\u001b[1;32m    536\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 538\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial_and_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    539\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial_and_callbacks\u001b[0;34m(self, func, catch, callbacks, gc_after_trial)\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# type: (...) -> None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m         \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgc_after_trial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0mfrozen_trial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_storage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/study.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(self, func, catch, gc_after_trial)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrialPruned\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m             message = 'Setting status of trial#{} as {}. {}'.format(trial_number,\n",
+      "\u001b[0;32m<ipython-input-10-ffc699c21a4b>\u001b[0m in \u001b[0;36mobjective\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;31m# see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m     \u001b[0mtrial\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madd_sugg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'trial'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'params'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-10-ffc699c21a4b>\u001b[0m in \u001b[0;36madd_sugg\u001b[0;34m(trial)\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"batchnorm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_self_attn\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_lvar\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0mtrial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuggest_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"use_deterministic_path\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36msuggest_categorical\u001b[0;34m(self, name, choices)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m         \u001b[0mchoices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_suggest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCategoricalDistribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchoices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36m_suggest\u001b[0;34m(self, name, distribution)\u001b[0m\n\u001b[1;32m    486\u001b[0m                 self.study, trial, name, distribution)\n\u001b[1;32m    487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_new_param_or_get_existing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_new_param_or_get_existing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/trial.py\u001b[0m in \u001b[0;36m_set_new_param_or_get_existing\u001b[0;34m(self, name, param_value, distribution)\u001b[0m\n\u001b[1;32m    493\u001b[0m         \u001b[0mparam_value_in_internal_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistribution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_internal_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m         set_success = self.storage.set_trial_param(self._trial_id, name,\n\u001b[0;32m--> 495\u001b[0;31m                                                    param_value_in_internal_repr, distribution)\n\u001b[0m\u001b[1;32m    496\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mset_success\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m             \u001b[0mparam_value_in_internal_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_trial_param\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_trial_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/storage.py\u001b[0m in \u001b[0;36mset_trial_param\u001b[0;34m(self, trial_id, param_name, param_value_internal, distribution)\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m         \u001b[0mcommit_success\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_commit_with_integrity_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcommit_success\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/optuna/storages/rdb/storage.py\u001b[0m in \u001b[0;36m_commit_with_integrity_check\u001b[0;34m(session)\u001b[0m\n\u001b[1;32m    860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m             \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIntegrityError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    864\u001b[0m             \u001b[0mlogger\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_logger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/orm/session.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1034\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0msa_exc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No transaction is begun.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1036\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransaction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1037\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/orm/session.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnested\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_connections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m                 \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCOMMITTED\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36mcommit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1725\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidRequestError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"This transaction is inactive\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1727\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1728\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_do_commit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1756\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_do_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1757\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1758\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_commit_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_commit_impl\u001b[0;34m(self, autocommit)\u001b[0m\n\u001b[1;32m    755\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    756\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_dbapi_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    758\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    759\u001b[0m             if (\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1476\u001b[0m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from_cause\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlalchemy_exception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1477\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1478\u001b[0;31m                 \u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/util/compat.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb, cause)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/base.py\u001b[0m in \u001b[0;36m_commit_impl\u001b[0;34m(self, autocommit)\u001b[0m\n\u001b[1;32m    753\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"COMMIT\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    756\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_dbapi_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sqlalchemy/engine/default.py\u001b[0m in \u001b[0;36mdo_commit\u001b[0;34m(self, dbapi_connection)\u001b[0m\n\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_commit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m         \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_close\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdbapi_connection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "study.optimize(objective, n_trials=200, timeout=pd.Timedelta('2d').total_seconds())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T22:48:16.540204Z",
+     "start_time": "2020-02-01T22:47:55.013103Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T23:51:05.495029Z",
+     "start_time": "2020-02-01T23:50:40.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "print('Number of finished trials: {}'.format(len(study.trials)))\n",
+    "\n",
+    "print('Best trial:')\n",
+    "trial = study.best_trial\n",
+    "\n",
+    "print('  Value: {}'.format(trial.value))\n",
+    "\n",
+    "print('  Params: ')\n",
+    "for key, value in trial.params.items():\n",
+    "    print('    {}: {}'.format(key, value))\n",
+    "\n",
+    "# shutil.rmtree(MODEL_DIR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-01-27T07:45:29.794794Z",
+     "start_time": "2020-01-27T07:45:29.733450Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-01-27T07:35:33.470251Z",
+     "start_time": "2020-01-27T07:34:47.800Z"
+    }
+   },
+   "source": [
+    "## View\n",
+    "\n",
+    "TODO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T23:51:05.496375Z",
+     "start_time": "2020-02-01T23:50:40.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df = study.trials_dataframe(attrs=('number', 'value', 'params', 'state'))\n",
+    "df.sort_values('value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-01T23:51:05.497893Z",
+     "start_time": "2020-02-01T23:50:40.900Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df.sort_values('value').head(17).T"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/smartmeters.ipynb b/smartmeters.ipynb
deleted file mode 100644
index 187f7a3..0000000
--- a/smartmeters.ipynb
+++ /dev/null
@@ -1,1357 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:34.276106Z",
-     "start_time": "2020-02-01T23:17:33.008415Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
-      "`%matplotlib` prevents importing * from pylab and numpy\n",
-      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib as plt\n",
-    "import collections\n",
-    "from pathlib import Path\n",
-    "from tqdm.auto import tqdm\n",
-    "\n",
-    "import math\n",
-    "%pylab inline\n",
-    "%reload_ext autoreload\n",
-    "%autoreload 2\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:34.312645Z",
-     "start_time": "2020-02-01T23:17:34.280204Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
-    "logger = logging.getLogger(\"smartmeters.ipynb\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:35.202966Z",
-     "start_time": "2020-02-01T23:17:34.316106Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from torch import nn\n",
-    "import torch.nn.functional as F"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:35.447327Z",
-     "start_time": "2020-02-01T23:17:35.205979Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from src.models.model import LatentModel\n",
-    "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n",
-    "from src.plot import plot_from_loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:35.491551Z",
-     "start_time": "2020-02-01T23:17:35.450770Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Params\n",
-    "device='cuda'\n",
-    "batch_size=32\n",
-    "num_workers=3\n",
-    "use_logy=False\n",
-    "num_context, num_extra_target = 24*2*3, 24*2\n",
-    "esp=1e-4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Load kaggle smart meter data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:49.983327Z",
-     "start_time": "2020-02-01T23:17:35.498148Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df_train, df_test = get_smartmeter_df()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:50.657749Z",
-     "start_time": "2020-02-01T23:17:49.986153Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f2c58321b70>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_train['energy(kWh/hh)'].plot(label='train')\n",
-    "df_test['energy(kWh/hh)'].plot(label='test')\n",
-    "plt.title('energy(kWh/hh)')\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-11-02T05:13:17.870138Z",
-     "start_time": "2019-11-02T05:13:17.836065Z"
-    }
-   },
-   "source": [
-    "# Dataloaders"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:50.687611Z",
-     "start_time": "2020-02-01T23:17:50.660781Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_train = SmartMeterDataSet(df_train, num_context, num_extra_target)\n",
-    "data_test = SmartMeterDataSet(df_test, num_context, num_extra_target)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2019-11-02T05:19:43.988134Z",
-     "start_time": "2019-11-02T05:19:43.959496Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:50.719384Z",
-     "start_time": "2020-02-01T23:17:50.690025Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "loader_train = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, \n",
-    "    collate_fn=collate_fns(num_context, num_extra_target, sample=True),\n",
-    "    num_workers=num_workers,\n",
-    ")\n",
-    "\n",
-    "loader_test = torch.utils.data.DataLoader(data_test, batch_size=batch_size, shuffle=False, \n",
-    "    collate_fn=collate_fns(num_context, num_extra_target, sample=False)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:55.083142Z",
-     "start_time": "2020-02-01T23:17:50.721942Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(torch.Size([32, 120, 17]), torch.Size([32, 120, 1]))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x, y, _, _ = next(iter(loader_train))\n",
-    "x.shape, y.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:17:59.782194Z",
-     "start_time": "2020-02-01T23:17:55.088484Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from src.models.model import LatentModel\n",
-    "model = LatentModel(\n",
-    "    x_dim=x.shape[-1], \n",
-    "    y_dim=y.shape[-1], \n",
-    "#     hidden_dim=64,\n",
-    "#     latent_dim=64,\n",
-    "#     dropout=0.,\n",
-    "#     min_std=1e-3,\n",
-    "#     attention_dropout=0,\n",
-    "#     num_heads=8,\n",
-    "#     n_latent_encoder_layers=3,\n",
-    "#     n_det_encoder_layers=3,\n",
-    "#     n_decoder_layers=3,\n",
-    "#     attention_layers=2,\n",
-    "#     latent_enc_self_attn_type=\"ptmultihead\", \n",
-    "#     det_enc_self_attn_type=\"ptmultihead\",\n",
-    "#     det_enc_cross_attn_type=\"ptmultihead\",\n",
-    "#     use_self_attn=True,\n",
-    "#     batchnorm=False,\n",
-    "#     use_lvar=True,\n",
-    "#     use_deterministic_path=False\n",
-    "    \n",
-    "\n",
-    "    hidden_dim=128,\n",
-    "    latent_dim=128,\n",
-    "    latent_enc_self_attn_type=\"ptmultihead\",\n",
-    "    det_enc_self_attn_type=\"ptmultihead\",\n",
-    "    det_enc_cross_attn_type=\"ptmultihead\",\n",
-    "    min_std=1e-3,\n",
-    "    use_lvar=True,\n",
-    "    use_self_attn=True,\n",
-    ").to(device)\n",
-    "\n",
-    "optim = torch.optim.AdamW(model.parameters(), lr=3e-4)\n",
-    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=4)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:18:04.805320Z",
-     "start_time": "2020-02-01T23:17:59.784574Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=======================================================================================================\n",
-      "                                                   Kernel Shape  \\\n",
-      "Layer                                                             \n",
-      "0__latent_encoder.Linear__input_layer                 [18, 128]   \n",
-      "1__latent_encoder._encoder.0.Linear_linear           [128, 128]   \n",
-      "2__latent_encoder._encoder.0.ReLU_act                         -   \n",
-      "3__latent_encoder._encoder.0.Dropout2d_dropout                -   \n",
-      "4__latent_encoder._encoder.1.Linear_linear           [128, 128]   \n",
-      "5__latent_encoder._encoder.1.ReLU_act                         -   \n",
-      "6__latent_encoder._encoder.1.Dropout2d_dropout                -   \n",
-      "7__latent_encoder._encoder.2.Linear_linear           [128, 128]   \n",
-      "8__latent_encoder._encoder.2.ReLU_act                         -   \n",
-      "9__latent_encoder._encoder.2.Dropout2d_dropout                -   \n",
-      "10__latent_encoder.Linear__penultimate_layer         [128, 128]   \n",
-      "11__latent_encoder.Linear__mean                      [128, 128]   \n",
-      "12__latent_encoder.Linear__log_var                   [128, 128]   \n",
-      "13__latent_encoder.Linear__input_layer                [18, 128]   \n",
-      "14__latent_encoder._encoder.0.Linear_linear          [128, 128]   \n",
-      "15__latent_encoder._encoder.0.ReLU_act                        -   \n",
-      "16__latent_encoder._encoder.0.Dropout2d_dropout               -   \n",
-      "17__latent_encoder._encoder.1.Linear_linear          [128, 128]   \n",
-      "18__latent_encoder._encoder.1.ReLU_act                        -   \n",
-      "19__latent_encoder._encoder.1.Dropout2d_dropout               -   \n",
-      "20__latent_encoder._encoder.2.Linear_linear          [128, 128]   \n",
-      "21__latent_encoder._encoder.2.ReLU_act                        -   \n",
-      "22__latent_encoder._encoder.2.Dropout2d_dropout               -   \n",
-      "23__latent_encoder.Linear__penultimate_layer         [128, 128]   \n",
-      "24__latent_encoder.Linear__mean                      [128, 128]   \n",
-      "25__latent_encoder.Linear__log_var                   [128, 128]   \n",
-      "26__deterministic_encoder.Linear__input_layer         [18, 128]   \n",
-      "27__deterministic_encoder._d_encoder.0.Linear_l...   [128, 128]   \n",
-      "28__deterministic_encoder._d_encoder.0.ReLU_act               -   \n",
-      "29__deterministic_encoder._d_encoder.0.Dropout2...            -   \n",
-      "30__deterministic_encoder._d_encoder.1.Linear_l...   [128, 128]   \n",
-      "31__deterministic_encoder._d_encoder.1.ReLU_act               -   \n",
-      "32__deterministic_encoder._d_encoder.1.Dropout2...            -   \n",
-      "33__deterministic_encoder._d_encoder.2.Linear_l...   [128, 128]   \n",
-      "34__deterministic_encoder._d_encoder.2.ReLU_act               -   \n",
-      "35__deterministic_encoder._d_encoder.2.Dropout2...            -   \n",
-      "36__deterministic_encoder._cross_attention.batc...    [17, 128]   \n",
-      "37__deterministic_encoder._cross_attention.batc...            -   \n",
-      "38__deterministic_encoder._cross_attention.batc...            -   \n",
-      "39__deterministic_encoder._cross_attention.batc...            -   \n",
-      "40__deterministic_encoder._cross_attention.batc...   [128, 128]   \n",
-      "41__deterministic_encoder._cross_attention.batc...    [17, 128]   \n",
-      "42__deterministic_encoder._cross_attention.batc...            -   \n",
-      "43__deterministic_encoder._cross_attention.batc...            -   \n",
-      "44__deterministic_encoder._cross_attention.batc...            -   \n",
-      "45__deterministic_encoder._cross_attention.batc...   [128, 128]   \n",
-      "46__decoder.Linear__target_transform                  [17, 128]   \n",
-      "47__decoder._decoder.0.Linear_linear                 [384, 384]   \n",
-      "48__decoder._decoder.0.ReLU_act                               -   \n",
-      "49__decoder._decoder.0.Dropout2d_dropout                      -   \n",
-      "50__decoder._decoder.1.Linear_linear                 [384, 384]   \n",
-      "51__decoder._decoder.1.ReLU_act                               -   \n",
-      "52__decoder._decoder.1.Dropout2d_dropout                      -   \n",
-      "53__decoder._decoder.2.Linear_linear                 [384, 384]   \n",
-      "54__decoder._decoder.2.ReLU_act                               -   \n",
-      "55__decoder._decoder.2.Dropout2d_dropout                      -   \n",
-      "56__decoder.Linear__mean                               [384, 1]   \n",
-      "57__decoder.Linear__std                                [384, 1]   \n",
-      "\n",
-      "                                                         Output Shape  \\\n",
-      "Layer                                                                   \n",
-      "0__latent_encoder.Linear__input_layer                  [32, 120, 128]   \n",
-      "1__latent_encoder._encoder.0.Linear_linear             [32, 120, 128]   \n",
-      "2__latent_encoder._encoder.0.ReLU_act                  [32, 120, 128]   \n",
-      "3__latent_encoder._encoder.0.Dropout2d_dropout      [32, 128, 120, 1]   \n",
-      "4__latent_encoder._encoder.1.Linear_linear             [32, 120, 128]   \n",
-      "5__latent_encoder._encoder.1.ReLU_act                  [32, 120, 128]   \n",
-      "6__latent_encoder._encoder.1.Dropout2d_dropout      [32, 128, 120, 1]   \n",
-      "7__latent_encoder._encoder.2.Linear_linear             [32, 120, 128]   \n",
-      "8__latent_encoder._encoder.2.ReLU_act                  [32, 120, 128]   \n",
-      "9__latent_encoder._encoder.2.Dropout2d_dropout      [32, 128, 120, 1]   \n",
-      "10__latent_encoder.Linear__penultimate_layer                [32, 128]   \n",
-      "11__latent_encoder.Linear__mean                             [32, 128]   \n",
-      "12__latent_encoder.Linear__log_var                          [32, 128]   \n",
-      "13__latent_encoder.Linear__input_layer                 [32, 147, 128]   \n",
-      "14__latent_encoder._encoder.0.Linear_linear            [32, 147, 128]   \n",
-      "15__latent_encoder._encoder.0.ReLU_act                 [32, 147, 128]   \n",
-      "16__latent_encoder._encoder.0.Dropout2d_dropout     [32, 128, 147, 1]   \n",
-      "17__latent_encoder._encoder.1.Linear_linear            [32, 147, 128]   \n",
-      "18__latent_encoder._encoder.1.ReLU_act                 [32, 147, 128]   \n",
-      "19__latent_encoder._encoder.1.Dropout2d_dropout     [32, 128, 147, 1]   \n",
-      "20__latent_encoder._encoder.2.Linear_linear            [32, 147, 128]   \n",
-      "21__latent_encoder._encoder.2.ReLU_act                 [32, 147, 128]   \n",
-      "22__latent_encoder._encoder.2.Dropout2d_dropout     [32, 128, 147, 1]   \n",
-      "23__latent_encoder.Linear__penultimate_layer                [32, 128]   \n",
-      "24__latent_encoder.Linear__mean                             [32, 128]   \n",
-      "25__latent_encoder.Linear__log_var                          [32, 128]   \n",
-      "26__deterministic_encoder.Linear__input_layer          [32, 120, 128]   \n",
-      "27__deterministic_encoder._d_encoder.0.Linear_l...     [32, 120, 128]   \n",
-      "28__deterministic_encoder._d_encoder.0.ReLU_act        [32, 120, 128]   \n",
-      "29__deterministic_encoder._d_encoder.0.Dropout2...  [32, 128, 120, 1]   \n",
-      "30__deterministic_encoder._d_encoder.1.Linear_l...     [32, 120, 128]   \n",
-      "31__deterministic_encoder._d_encoder.1.ReLU_act        [32, 120, 128]   \n",
-      "32__deterministic_encoder._d_encoder.1.Dropout2...  [32, 128, 120, 1]   \n",
-      "33__deterministic_encoder._d_encoder.2.Linear_l...     [32, 120, 128]   \n",
-      "34__deterministic_encoder._d_encoder.2.ReLU_act        [32, 120, 128]   \n",
-      "35__deterministic_encoder._d_encoder.2.Dropout2...  [32, 128, 120, 1]   \n",
-      "36__deterministic_encoder._cross_attention.batc...     [32, 120, 128]   \n",
-      "37__deterministic_encoder._cross_attention.batc...     [32, 120, 128]   \n",
-      "38__deterministic_encoder._cross_attention.batc...  [32, 128, 120, 1]   \n",
-      "39__deterministic_encoder._cross_attention.batc...     [32, 120, 128]   \n",
-      "40__deterministic_encoder._cross_attention.batc...     [32, 120, 128]   \n",
-      "41__deterministic_encoder._cross_attention.batc...     [32, 147, 128]   \n",
-      "42__deterministic_encoder._cross_attention.batc...     [32, 147, 128]   \n",
-      "43__deterministic_encoder._cross_attention.batc...  [32, 128, 147, 1]   \n",
-      "44__deterministic_encoder._cross_attention.batc...     [32, 147, 128]   \n",
-      "45__deterministic_encoder._cross_attention.batc...     [32, 147, 128]   \n",
-      "46__decoder.Linear__target_transform                   [32, 147, 128]   \n",
-      "47__decoder._decoder.0.Linear_linear                   [32, 147, 384]   \n",
-      "48__decoder._decoder.0.ReLU_act                        [32, 147, 384]   \n",
-      "49__decoder._decoder.0.Dropout2d_dropout            [32, 384, 147, 1]   \n",
-      "50__decoder._decoder.1.Linear_linear                   [32, 147, 384]   \n",
-      "51__decoder._decoder.1.ReLU_act                        [32, 147, 384]   \n",
-      "52__decoder._decoder.1.Dropout2d_dropout            [32, 384, 147, 1]   \n",
-      "53__decoder._decoder.2.Linear_linear                   [32, 147, 384]   \n",
-      "54__decoder._decoder.2.ReLU_act                        [32, 147, 384]   \n",
-      "55__decoder._decoder.2.Dropout2d_dropout            [32, 384, 147, 1]   \n",
-      "56__decoder.Linear__mean                                 [32, 147, 1]   \n",
-      "57__decoder.Linear__std                                  [32, 147, 1]   \n",
-      "\n",
-      "                                                      Params Mult-Adds  \n",
-      "Layer                                                                   \n",
-      "0__latent_encoder.Linear__input_layer                 2.432k    2.304k  \n",
-      "1__latent_encoder._encoder.0.Linear_linear           16.384k   16.384k  \n",
-      "2__latent_encoder._encoder.0.ReLU_act                      -         -  \n",
-      "3__latent_encoder._encoder.0.Dropout2d_dropout             -         -  \n",
-      "4__latent_encoder._encoder.1.Linear_linear           16.384k   16.384k  \n",
-      "5__latent_encoder._encoder.1.ReLU_act                      -         -  \n",
-      "6__latent_encoder._encoder.1.Dropout2d_dropout             -         -  \n",
-      "7__latent_encoder._encoder.2.Linear_linear           16.384k   16.384k  \n",
-      "8__latent_encoder._encoder.2.ReLU_act                      -         -  \n",
-      "9__latent_encoder._encoder.2.Dropout2d_dropout             -         -  \n",
-      "10__latent_encoder.Linear__penultimate_layer         16.512k   16.384k  \n",
-      "11__latent_encoder.Linear__mean                      16.512k   16.384k  \n",
-      "12__latent_encoder.Linear__log_var                   16.512k   16.384k  \n",
-      "13__latent_encoder.Linear__input_layer                     -    2.304k  \n",
-      "14__latent_encoder._encoder.0.Linear_linear                -   16.384k  \n",
-      "15__latent_encoder._encoder.0.ReLU_act                     -         -  \n",
-      "16__latent_encoder._encoder.0.Dropout2d_dropout            -         -  \n",
-      "17__latent_encoder._encoder.1.Linear_linear                -   16.384k  \n",
-      "18__latent_encoder._encoder.1.ReLU_act                     -         -  \n",
-      "19__latent_encoder._encoder.1.Dropout2d_dropout            -         -  \n",
-      "20__latent_encoder._encoder.2.Linear_linear                -   16.384k  \n",
-      "21__latent_encoder._encoder.2.ReLU_act                     -         -  \n",
-      "22__latent_encoder._encoder.2.Dropout2d_dropout            -         -  \n",
-      "23__latent_encoder.Linear__penultimate_layer               -   16.384k  \n",
-      "24__latent_encoder.Linear__mean                            -   16.384k  \n",
-      "25__latent_encoder.Linear__log_var                         -   16.384k  \n",
-      "26__deterministic_encoder.Linear__input_layer         2.432k    2.304k  \n",
-      "27__deterministic_encoder._d_encoder.0.Linear_l...   16.384k   16.384k  \n",
-      "28__deterministic_encoder._d_encoder.0.ReLU_act            -         -  \n",
-      "29__deterministic_encoder._d_encoder.0.Dropout2...         -         -  \n",
-      "30__deterministic_encoder._d_encoder.1.Linear_l...   16.384k   16.384k  \n",
-      "31__deterministic_encoder._d_encoder.1.ReLU_act            -         -  \n",
-      "32__deterministic_encoder._d_encoder.1.Dropout2...         -         -  \n",
-      "33__deterministic_encoder._d_encoder.2.Linear_l...   16.384k   16.384k  \n",
-      "34__deterministic_encoder._d_encoder.2.ReLU_act            -         -  \n",
-      "35__deterministic_encoder._d_encoder.2.Dropout2...         -         -  \n",
-      "36__deterministic_encoder._cross_attention.batc...    2.176k    2.176k  \n",
-      "37__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "38__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "39__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "40__deterministic_encoder._cross_attention.batc...   16.512k   16.384k  \n",
-      "41__deterministic_encoder._cross_attention.batc...    2.176k    2.176k  \n",
-      "42__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "43__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "44__deterministic_encoder._cross_attention.batc...         -         -  \n",
-      "45__deterministic_encoder._cross_attention.batc...   16.512k   16.384k  \n",
-      "46__decoder.Linear__target_transform                  2.304k    2.176k  \n",
-      "47__decoder._decoder.0.Linear_linear                147.456k  147.456k  \n",
-      "48__decoder._decoder.0.ReLU_act                            -         -  \n",
-      "49__decoder._decoder.0.Dropout2d_dropout                   -         -  \n",
-      "50__decoder._decoder.1.Linear_linear                147.456k  147.456k  \n",
-      "51__decoder._decoder.1.ReLU_act                            -         -  \n",
-      "52__decoder._decoder.1.Dropout2d_dropout                   -         -  \n",
-      "53__decoder._decoder.2.Linear_linear                147.456k  147.456k  \n",
-      "54__decoder._decoder.2.ReLU_act                            -         -  \n",
-      "55__decoder._decoder.2.Dropout2d_dropout                   -         -  \n",
-      "56__decoder.Linear__mean                               385.0     384.0  \n",
-      "57__decoder.Linear__std                                385.0     384.0  \n",
-      "-------------------------------------------------------------------------------------------------------\n",
-      "                        Totals\n",
-      "Total params          635.522k\n",
-      "Trainable params      635.522k\n",
-      "Non-trainable params       0.0\n",
-      "Mult-Adds             735.104k\n",
-      "=======================================================================================================\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torchsummaryX import summary\n",
-    "data = next(iter(loader_train))\n",
-    "data = [d.to(device) for d in data]\n",
-    "context_x, context_y, target_x, target_y = data\n",
-    "with torch.no_grad():\n",
-    "    model.eval()\n",
-    "    summary(model, context_x, context_y, target_x, target_y)\n",
-    "    dist, log_p, kl, loss_mse, loss = model(context_x, context_y, target_x, target_y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T23:18:05.390820Z",
-     "start_time": "2020-02-01T23:18:04.808392Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor(1.5802, device='cuda:0')"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Lets choose a challenging time to forecast\n",
-    "vis_i = 670 # 290 # 1600\n",
-    "plot_from_loader(loader_test, model, i=vis_i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:23:58.470241Z",
-     "start_time": "2020-02-01T15:23:51.044449Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T13:17:08.536646Z",
-     "start_time": "2020-02-01T13:17:08.499131Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:50.714867Z",
-     "start_time": "2020-02-01T23:51:57.092912Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "355668580ac14e419513d9db59b5bca0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=30.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9438424b69f946b992e4530867964347",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=1098.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2c593c9b70>\n",
-      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2c593c9b70>\n",
-      "Traceback (most recent call last):\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 926, in __del__\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 926, in __del__\n",
-      "    self._shutdown_workers()\n",
-      "    self._shutdown_workers()\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 906, in _shutdown_workers\n",
-      "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f2c593c9b70>\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 906, in _shutdown_workers\n",
-      "    w.join()\n",
-      "    w.join()\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/process.py\", line 138, in join\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/process.py\", line 138, in join\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 926, in __del__\n",
-      "    assert self._parent_pid == os.getpid(), 'can only join a child process'\n",
-      "    assert self._parent_pid == os.getpid(), 'can only join a child process'\n",
-      "    self._shutdown_workers()\n",
-      "AssertionError: can only join a child process\n",
-      "AssertionError: can only join a child process\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\", line 906, in _shutdown_workers\n",
-      "    w.join()\n",
-      "  File \"/home/wassname/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/process.py\", line 138, in join\n",
-      "    assert self._parent_pid == os.getpid(), 'can only join a child process'\n",
-      "AssertionError: can only join a child process\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0, i=0, loss: -1.272, test: 0.3633, grad_norm:  9, std:  0.085\n",
-      "0, i=100, loss: -1.099, test: 0.3568, grad_norm:  3.8, std:  0.1\n",
-      "0, i=200, loss: -1.136, test: 0.3577, grad_norm:  3.5, std:  0.095\n",
-      "0, i=300, loss: -1.091, test: 0.3527, grad_norm:  14, std:  0.1\n",
-      "0, i=400, loss: -1.194, test: 0.3533, grad_norm:  4.1, std:  0.095\n",
-      "0, i=500, loss: -1.114, test: 0.3531, grad_norm:  4.8, std:  0.087\n",
-      "0, i=600, loss: -1.262, test: 0.353, grad_norm:  2.4, std:  0.088\n",
-      "0, i=700, loss: -1.135, test: 0.3529, grad_norm:  9.4, std:  0.11\n",
-      "0, i=800, loss: -1.196, test: 0.3532, grad_norm:  2, std:  0.09\n",
-      "0, i=900, loss: -0.949, test: 0.3533, grad_norm:  9.6, std:  0.11\n",
-      "0, i=1000, loss: -1.097, test: 0.3537, grad_norm:  11, std:  0.11\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 0, loss: -1.663, loss_test:0.3539, kl=0.00054, grad_norm:  20\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b69fc19caab948d2ab6d3dac1a923b61",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=1098.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1, i=0, loss: -1.216, test: 0.3539, grad_norm:  11, std:  0.095\n",
-      "1, i=100, loss: -1.102, test: 0.3536, grad_norm:  1.8, std:  0.1\n",
-      "1, i=200, loss: -1.291, test: 0.3537, grad_norm:  3.1, std:  0.086\n",
-      "1, i=300, loss: -1.243, test: 0.3532, grad_norm:  6.7, std:  0.093\n",
-      "1, i=400, loss: -1.104, test: 0.3537, grad_norm:  4.1, std:  0.1\n",
-      "1, i=500, loss: -1.242, test: 0.3536, grad_norm:  14, std:  0.084\n",
-      "1, i=600, loss: -1.309, test: 0.3537, grad_norm:  3.9, std:  0.089\n",
-      "1, i=700, loss: -1.076, test: 0.3541, grad_norm:  3.1, std:  0.11\n",
-      "1, i=800, loss: -1.265, test: 0.3546, grad_norm:  6, std:  0.085\n",
-      "1, i=900, loss: -1.317, test: 0.3546, grad_norm:  18, std:  0.082\n",
-      "1, i=1000, loss: -1.214, test: 0.355, grad_norm:  2.4, std:  0.089\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 1, loss: -1.267, loss_test:0.3556, kl=0.00034, grad_norm:  12\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e4127d41b608466e9ab0dca0b6fafd8c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=1098.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2, i=0, loss: -1.268, test: 0.3556, grad_norm:  4.5, std:  0.087\n",
-      "2, i=100, loss: -1.015, test: 0.3555, grad_norm:  4.6, std:  0.11\n",
-      "2, i=200, loss: -1.177, test: 0.355, grad_norm:  2.6, std:  0.092\n",
-      "2, i=300, loss: -1.001, test: 0.3549, grad_norm:  11, std:  0.11\n",
-      "2, i=400, loss: -1.087, test: 0.3553, grad_norm:  3.7, std:  0.11\n",
-      "2, i=500, loss: -1.475, test: 0.3553, grad_norm:  21, std:  0.076\n",
-      "2, i=600, loss: -1.247, test: 0.3553, grad_norm:  2.9, std:  0.088\n",
-      "2, i=700, loss: -1.151, test: 0.3556, grad_norm:  9.6, std:  0.095\n",
-      "2, i=800, loss: -1.336, test: 0.356, grad_norm:  3.3, std:  0.076\n",
-      "2, i=900, loss: -0.8478, test: 0.3558, grad_norm:  9.6, std:  0.12\n",
-      "2, i=1000, loss: -1.278, test: 0.356, grad_norm:  5, std:  0.082\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "epoch: 2, loss: -1.282, loss_test:0.3562, kl=0.00047, grad_norm:  12\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d52e23e62ef04705bac54f99082c0152",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(FloatProgress(value=0.0, max=1098.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3, i=0, loss: -0.9849, test: 0.3562, grad_norm:  5, std:  0.13\n",
-      "3, i=100, loss: -1.028, test: 0.3557, grad_norm:  5.7, std:  0.11\n",
-      "3, i=200, loss: -1.109, test: 0.3556, grad_norm:  3.6, std:  0.095\n",
-      "3, i=300, loss: -1.17, test: 0.3556, grad_norm:  3.6, std:  0.1\n",
-      "3, i=400, loss: -1.23, test: 0.3557, grad_norm:  4.3, std:  0.086\n",
-      "3, i=500, loss: -1.006, test: 0.3557, grad_norm:  3.7, std:  0.1\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-323fc9b33e82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mpbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'batch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpbar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m             \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    802\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    803\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 804\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    805\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    806\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    770\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 771\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    772\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    722\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 724\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    725\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    111\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         \u001b[0;31m# unserialize the data after having released the lock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_ForkingPickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mqsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/torch/multiprocessing/reductions.py\u001b[0m in \u001b[0;36mrebuild_storage_fd\u001b[0;34m(cls, df, size)\u001b[0m\n\u001b[1;32m    292\u001b[0m                     \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m         \u001b[0mfd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    295\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage_from_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/resource_sharer.py\u001b[0m in \u001b[0;36mdetach\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     56\u001b[0m             \u001b[0;34m'''Get the fd.  This should only be called once.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m_resource_sharer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_handle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/reduction.py\u001b[0m in \u001b[0;36mrecv_handle\u001b[0;34m(conn)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;34m'''Receive a handle over a local connection.'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileno\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAF_UNIX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mrecvfds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mDupFd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.7.2/lib/python3.7/multiprocessing/reduction.py\u001b[0m in \u001b[0;36mrecvfds\u001b[0;34m(sock, size)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mbytes_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitemsize\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mancdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maddr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecvmsg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCMSG_SPACE\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mancdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mEOFError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 30\n",
-    "grad_clip = 40\n",
-    "vis_i = 670\n",
-    "history = []\n",
-    "for epoch in tqdm(range(epochs), unit='epoch'):\n",
-    "\n",
-    "    pbar = tqdm(loader_train, unit='batch')\n",
-    "    for i, data in enumerate(pbar):\n",
-    "        model.train()\n",
-    "        assert all(torch.isfinite(d).all() for d in data)\n",
-    "        data = [d.to(device) for d in data]\n",
-    "        context_x, context_y, target_x, target_y = data\n",
-    "\n",
-    "        optim.zero_grad()\n",
-    "        y_pred, kl, loss, loss_mse, y_std = model(context_x, context_y, target_x, target_y)\n",
-    "\n",
-    "        # These attentive NP models work great when you need uncertainty, but still seem a bit unstable. \n",
-    "        # Sometimes they produce nans, especically if the data is not fully normalized. \n",
-    "        assert torch.isfinite(y_pred.sum())\n",
-    "        if not torch.isfinite(loss):\n",
-    "            logger.error(\"loss is not finite\")\n",
-    "            continue\n",
-    "        loss.backward()\n",
-    "        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)\n",
-    "        if not np.isfinite(grad_norm):\n",
-    "            logger.error(\"grad_norm is not finite\")\n",
-    "            continue\n",
-    "        optim.step()\n",
-    "\n",
-    "        if i%100==0:\n",
-    "            model.eval()\n",
-    "            test_loss = plot_from_loader(loader_test, model, i=vis_i, plot=False)\n",
-    "            pbar.set_description(f\"{epoch}, loss: {loss.item():4.4g}, test: {test_loss.item():4.4g}\")\n",
-    "            print(f\"{epoch}, i={i}, loss: {loss.item():4.4g}, test: {test_loss.item():4.4g}, grad_norm: {grad_norm: 2.2g}, std: {y_std.mean().item(): 2.2g}\")\n",
-    "            \n",
-    "            history.append(dict(\n",
-    "                n=epoch*len(loader_train) + i,\n",
-    "                epoch=epoch,\n",
-    "                i=i,\n",
-    "                loss=loss.item(),\n",
-    "                kl=kl.mean().item(),\n",
-    "                test_loss=test_loss.item(),\n",
-    "                y_std=y_std.mean().item(),\n",
-    "                grad_norm=grad_norm,\n",
-    "            ))\n",
-    "            scheduler.step(loss.item())\n",
-    "        \n",
-    "    test_loss = plot_from_loader(loader_train, model, i=vis_i, title=f'epoch={epoch}, train,')\n",
-    "    plt.show()\n",
-    "    model.eval()\n",
-    "    test_loss = plot_from_loader(loader_test, model, i=vis_i, title=f'epoch={epoch}, test,')\n",
-    "    plt.show()\n",
-    "    print(f\"epoch: {epoch}, loss: {loss.item():4.4g}, loss_test:{test_loss.item():4.4g}, kl={kl.mean().item():2.2g}, grad_norm: {grad_norm: 2.2g}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:52.671019Z",
-     "start_time": "2020-02-02T00:49:52.096187Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NJpOJY8eOsWXLFpKTk8nPzyckJKTKY8jOzqZPnz4EBQUhhODo0aMkJSUREBDAhAkTqtxvTWAIAgMDg8uGoqIiAgMDAWjXrh333XcfPXv2dOtYX19f2rRpw+rVq/nLX/5yUUIAIDo62s51dfDgwXzwwQd07979ovqtCQyvIQMDg8sCKSUdO3akTZs2JCUl4U7w6cqVK+nevTudOnWq0bHl5kJ2NrRsWaOncYkrryFjRWBgYHBZsGvXrjKVTpMmTSpsL6Vk0qRJ5ObmcuzYMVq3bl3hMZXBbDbj4+NDVhZYAotp1gz8vPCpaxiLDQwMLguuuuoqTp06xa+//oqPj3q0ZWVlceTIEU2vnYKCAoYPH86gQYPshMC6deu4//77+fjjjy9qPKNGjSIysiXLlm2ktBRKS+Hjj79h8eLFXLhw4aL6rm68UDYZGBgYVB4/Pz9atGhht61169ZlrqShoaF2+0JCQli1apVTP4cPH+bjjz8mKCiI++67r8rjOXo0hdOnTxEc3Khs2+zZz5Ca+n8MHDiQLl26VLnv6sZYEdQiJqe8qgYGtUtSUhIxMTH4+PgQExNDUlKSp4dUo0RHRxMTE0Nubq7bxwwaNIgPPviAyZMnX9S516z5g+XLjxIdfWXZtgEDxjBhwpSLNkRXN4axuBbZvh1atVJ6QgOD2iYpKYm4uDjy8/PLtoWEhJCYmEhsbKwHR1Y9fP7556xbt44777yTYcOGVdg+NzeXwMBA/P39a2Q8u3bB+fPO27t2BTdMGNWOkWLCC8jPhwsX4M8/IS3N06MxqIvEx8fbCQGA/Px84uPjPTSi6mXTpk0kJiayb98+t9rPmDGDoKAgFi1aVO1jMZmUl5AWBQXVfrqLxhAEtYRtPZ3Dh6GkxHNjMaibHDt2rFLbLzXGjRvHokWLGDx4sFvtc3JykFI6eRgVFRXx1VdfsWLFiiqPZePGHcyceTcrV75pt91kKuXEiTNkZGRUue+awBAE1URFuldbQVBSAsnJtTxAgzpPVFRUpbZfavTt25eHH36Ybt26lW2bN28eXbt25ZNPPnFq/69//YvCwkJGjx5tt72oqIjbb7+dKVOmVHkse/YcZuPGz9i7d7Pd9lWr3qFfv+a88sorVe67JjC8hqoBR91ramoqcXFxAMTGxlJYqAJKbDl7Fq64AtzIiWVgUC0kJCRo2ggSEi7fMiCZmZns3buX48ePa+4PCAhw2tagQQNGjx5NWFhYWSxAZZASYmL6MmPGCho3tjcIhoU1o1Gj8BqzS1QVw1hcDcTExJCamuq0PTo6mpSUFFJSICXF+bjevcHLnAcMLnOSkpKIj48vC6CaOXMmkyZN8vSwqoVvvvkGX19fbrjhBoKDgwGl9jp//jytW7emcePGtTKOEydcr/h9fWHgwFoZih2GsbiGcaV7NZng5EnN3TjY7QwMapy7776bhx9+mM6dO3P8+HEmT5582biRTp48mREjRnDexlUnKiqK7t27OwmBI0eO0LdvX6ZOnVqtYygogKNHXbcxmbzPRmgIgmrAle719Gn9Lz0vrwYHZWCgQVpaGs899xz79u0ri7ZNTU1l/PjxhIeHX9ICYfjw4QwfPtytmf+RI0f47bff2L17t+Z+k8lEZmZmpesSHD6sHvQ7d25k3bok0tKcNQUA1VTuoNqoUUEghLhJCHFACJEshHhOY3+UEOJ/Qog/hBC7hRD6uWK9mISEBKcAkeDgYBISEtBRTQLGisCg9vH396dBgwaa+zIzM4mLi7tkhcHHH3/M2rVrCQoKKtt24sQJZsyYwVtvvWXXtk+fPvz888/MnTtXs6/Ro0cTHh7O999/7/b5TSY4d069X736n8yZM97JWFxcXMRjj11Hnz7dNHrwHDUmCIQQvsBC4GagMzBOCNHZodnfUUXtrwbGAtXv0FsLxMbGkpiYSHR0NKDS2U6bNo2RI2PRqlO9fn0SY8fG0KVL3YjuNPAemjdv7jLKVi+uwMtS47jNuXPneOWVV1i6dKnd9gYNGtC/f3/69euneVyTJk1o1KhRpQrNnz8PZrN637XrAAYPHkurVh3s2vj7B3Dw4A7+/HOPU0yHJ6kxY7EQoh8wS0p5o+X/5wGklHNt2rwHHJFSvmZp/4aU8jpX/XqjsdiWpUuX8tJLL3H8+HEiI6OYMCGBoUPLozbXr09i/vw4ioouz+hOA+9Hz7nBihACs/WJZuHIEWjbtqZHVnVKSkooKSkhODjYrjzluXPnePfdd4mKimLixIlu9yeldKvMpS0HDsDp0xW327//N9q0acjw4R3w9fWt1DkuBk8Zi1sCtoqRE5ZttswCxgshTgDfAo9rdSSEiBNCbBNCbEu3dcj3MhYsWMBjjz3GsWPHkFJy8mQq8+fHsX59+Yx/yZJ4OyEAl1d0p4F3c/z4cZ544gmXuW60bF4XLnhnRKyVX375hXr16jkFkzVu3JgXX3zRTghIKZk4cSJLly7VnfFXRghs3LiRW2+9lTffdM8Nt3PnPkRGXlGrQqAiPG0sHgd8KKVsBYwAlgkhnMYkpUyUUvaSUvZyp9iEp3jqqacocPi1FBXls2RJ+UP+7NnLO7rTGykuNgzzVl5++WWmTZvGPffco5mzXy+uID/fu21a+fn5BAYG6to/bNm2bRsffvghzz77bKVn/VpkZWXxn//8h927fy3blpFxioKCPN2i9d4mVGtSEJwEbCs9tLJss2USsBJASvkrEASE1+CYahSTTnpR24d/RMTlHd3pjWRkqJcBhIaGEh0dzR133EFGRgaffPIJ0dHRCCGIjo7WVFGaTFBU5N3C9Oabb6awsJAvvvjCaV9aWhpr1qwh2eLc37lzZ5YuXcrs2bM1A8oAfvrpJwYOHMjTTz/t8rx79uyhT58+LFz4JXFxrwFqxTF2bDQjRtSnpKTY6Zhff/2GBQueZdOmLU77PEVNCoKtQAchRBshRADKGLzaoc0xYAiAEOJKlCDwXt1PBViNxY7YPvwnT04gMNB+WX65R3d6mvR0yMz09Ci8g/nz55OSksItt9wCKEeHlJQUzGYzKSkpmnYq60rAmwWBFT+N8l9z5szhtttu4/PPPwegXr16TJw4kYcffli3n4KCAn7++Wd27dql2yYlJYUePXowcuTtdOp0K23aqPoCxcWFNGrUlNDQxgQEBDodt3Xr96xY8TqbNv1W2Y9XY9RYigkpZakQ4jFgLeALLJVS7hNCzAa2SSlXA08B7wsh/gZIYIK81EKdbdAK4Q8MDGHy5PKH/NChsZw7d4ZVqxaQnn6ciIgoXnstwTAU1xAlJZCVVf7eyyL7vQKz2UxiYiJ79+7lnXfecUqpcCkJAi0GDBjAvn37aNWqldvH9O7dmw0bNhAZGanb5uDBg4SHh9O4cTt8fcsfpYGBwXz++Snd4/r1u5UmTVrQpYtLv5haxUgxUc3YhvBHREQxebK915CUkpEjG5GXl0PDhuEsXryNrl2j8aJiRZcVaWnwf/+n3l9xBTRv7tnxeCthYWFkZWUhhCAqKoqEhPLJydGjkJoKPj4qNYI35sd6/fXXWb9+PdOmTeOmm27Sbffkk0/StGlTHnroIbfqGlfEyZNFbN2aw65dGzl+/CC33jqFRo3cs2M2aaJqE9QWRoqJWiArK4srrriCxMREUlJS+OMPMytWpNgJAYCcnHOEhalEVNnZGeTmZhmqixrEtvZDXb/GixYtwtfXFyGEXfxKUlISeZbpvpSyLGmidb91RWA2e19ErJWdO3eybt06Ml18yefPn2fhwoXMnDlT155XWUpLA2nUqCkrVszjX/+K5/jxg24fm5NTLUOoFozso9VEXl4eBw4cICcnh5ISWLr0HTZt+prJkxPo3LlvWbuGDZuwbNlBDh/ejZ+fPy1aKOfsrCzPVC26nDl7tlwtBN7nqVGbJCUl8dRTT5XFB9hmyI2Pj6fEIQ+K1aU5NjbWzlsoLw8s+dy8ihdffJH777+fri6m2BcuXGDJkiWcPn2aiIiICvtcsGAB6enpvPjii07ZQr///nsGDRpEfr7aPmTIOHr0uIGwsAi2b/+BxMRn6dPnZh544GWnfgsL8zlyZDdms5lrrrkOm0Boj2GohqqJkpISkpOTKS0tJSioK3fddQO7dm0EoFmz6DI7wZIl8Zw966w2qu1l4uVOaSn8/rtyHbVSrx5ce63nxuRJXGXItca9OCKEwGQys2lTecRs69bQrl1Nj7b6eeaZZ5g/fz7z5s2r0BPIStOmTcnIyOD06dM0t9EpZmVlERYWRrt27Vix4hC5ufa6su+++4jXXpvAsGHjeeGFZYDKRnrqlFKtHT26h0mTuhEdfSVbt+6ntjziXamGjBVBNeHv78+VV15JQQEkJCTx55/lHgFnzqTy6qsTEUJQWlpctm3+fDUjGzo0tk7PVmuC5GR7IQAqT3xdxVWG3KioKE0hERUVRUZGuRAAOHNGRRh7o53AFZ06daJhw4ZOcT6uePLJJzGbzQQG2nv+nD59mo4dOxIWFkZBgfOFuO66kSxa9Bv16oVy4AC88QYcOqT2Pf009O/fnE6detGqVUdycqg1QeAKY0VQzRw8CNdfH8OZM/oh/LaEhTXjiy/SEAKuv/7S+4HVFrZGeEdjpiPnzoFWUsngYOjTp4YH6qW4WhHoFax5771E2rePdbILeKr4uiveeOMNgoKCmDx5stODG9SK3dfXt9JFZlyRm1vKtm1qLl1cXMSZM6mYTKXExKiUaj/+CK+9piYkgYEqFiMyEj7+WNUkAAgKgr599c5QvRjG4lrgwIEDTJ8+nZUrP9KNHtbi/PkzgJqtGqsCbawV4FJTUzWNmbaYTEoYa3GJzXmqlYSEBKfgKWv8ijVpYv369QGIjIwkMTGRAQOchQDYG+C9AZPJxLPPPstjjz2mGyns7+9frUIAoLi4XKGyZ8/P3HdfJ956S8UmHDsGCQlKCNxyC3z5pRICp07Bhg3lfRQW6he5r00MQVBNHDp0iHnz5vHdd5/pRg9r0bhxue7Rm0P4PUl8fLxTpka9/EwpKfqeLXVZEMTGxpbV8tWKIo6NjeXChQuYzZIvvzxJq1ax6OWly8jwrsIqxcXFzJw5k0cffVQ3UrgqZGRksHXrVo4dO8Zvv/3G6tWrOXXqFKWlpYD97zUiojWRkW0JD2/JDz8sZ8GCDZjNMHgwPPWUWo2OG6fafvqpuhellJjNZs6cqbYhVxlDEFQTnTp14tVXX2X48Hs1o4e1CAwM4eGH55f9b6wItHGl37YlL0+VCdSjLgsCUL7277zzDocPH9aNIk5Ph9Wrk7jnnhgGDfJh7NgYu6SJoK6jN7k+BgcHM3PmTP7xj39Ua78LFiygd+/efPDBByxcuJBRo0axcOFC/P396devHxculBtPWrfuSFLSYWbM+JQff1zOjh2NABg2rFzdO3w4NGigMrk+91wcw4cHsnPnBtLT7e0wnsAQBNVEhw4dmD79Wa6//h6GDo3l6acTadhQ3wrk4+PL008n2sUZGCsCbfTyMPn4+Niph5KTXT/s67ogGDRoEI8//jht2rTR3J+UlESHDk1ISBjPmTNKDWd1anAUBnXhXm3Xrh09e/YkPDycq6++GlDpKkCtSAsLtR+fPXpMAXoQHGyiZ8/y7QEB0NKSf7m4uBmlpSXk5GRSUqJqGXgSQxBUI7YZbYcOjeWrr84SH/8J/v72xit//xCeeGIhzZpFc+TInrLtdeHHVRW0KsCB0g3HxcXxyCOPEB0dw9VXa89gDSomKSmJKVPiyMk557TPMYMueNfqddeuXezZs6fSZSUrYsKECWzfvp1HH32Uv/3tb3z33XcEBgYyadIk/v3vzzRTbhQXF2Iy3QrAddf54qipaqZiSRky5HnWri3ghhvuAjxf+McQBNVESkoKGzf+THq6vW5i6NBYrrtuBhANCCCawMBE/P0jmDp1IB9/PLusrSEItImNjWXMmDGaxr78/HwWL17MsWOuZ7BQt1cEeXl5JCUlsWnTJs39zz8fT0GB/g3o6ADhTffqs88+S7du3SpVVrIqDBkyhPT0dJYsWUJgYEcspoIy5s+P48Yb6/PRRwcAFTPgiFUQZGeHEBBQHknmomhcrWAIgmpi2bJl3HTTQNasec9u+6pV74d6tRAAACAASURBVPDLL/8EjlGvXmsiIxPIzY3l999706XLdbRq1bGsbUmJCoSqpuj3y4bi4mI+//xzp6pZVhxdoLVmsKpdjQzvkiAlJYXx48czZcoUp33FxXDihGtPN0cHCG8SBFFRUXTs2JFOnTrVSP8mk4mMjAx8fX1p0KABubnKK8iRa64ZSmDgPygsvILQUOjd27mNVRA4Gog9LQiMgLJqIjIykl69rqN583L96/r1SSxaNA2zWT3Z8/KOUVKigsh++imWJUs246iu3bJFCQIjpkCRlJTE888/T2FhIUII3UIfjmi58NZlQRAQEMDYsWPtImStHD+uHvR6sS+OGXRBCY/SUtDI+lzrJCYm1ki/Z8+epWfPnpw8qcqotGnThiNHjnD4sPa9lJ9/N0VF4OcneekloZmKwyoIjh7NYdasyURFdeKBB16msFD97j1VtMxYEVQTkyZNYuXKzYwY8UDZtiVL4suEgJXi4nyCg+Mxm2HxYud+SkuNmAIr1viB48dVxVO9NAhaaLnw1mVB0KFDB5YvX85bb71lt72kRPm263m6hYY2cXJqsOJNq4KaIDQ0tEwINGzYkPDwcLu05rZICStWqPdPPino0UO7T6sgyMjwZePGz9ix48eyfZ5cFVQozy2lI7sDkUABsFdKebamB3Yp4pjSQC+wrLBQbd+xQ38WUFAALsrK1gm04gcAfH19MZvNREVFMWLECD766COXNSCs1GVBoMeJE+oetD7o9XJhaZGfD6GhtTVSbYqKimokWAwgKCiI1NRUwsPDCQkJobS0lIwM7fvo8GF1LRs2BBdZsMsEQVZWCH//+3KaNy8vZpWXp473BLpXTwjRTgiRCCQDr6LqCz8CrBdCbBFCTNSqL1yXcRQEQUHabo8REVE0bmyitBTuvru/ZpvLfbblDnrxA2azuayi1qJFi3jvvcSy1N6NGzfXncFC3RUG58+fd8qzk59vH3cxdGgsK1ak8OOPZt5992fS00/yzTfvA3DggHNhGm+4R999913q16/P3Llza6T/qKioMo81Pz8/3ZKnG1V+Sa6/3rV6p359NcErLBT06jWWLl36le3z5IrA1YP8FeAToJ2U8kYp5Xgp5Z1Sym7AbUBD4N7aGOSlwD333MOAAY3ZsuVbQEVfFhQkAPbTeutstVUr9US6cEG7RLOhGtKPH7But+aU79ChW1mqjrvumuZyFltXBcHDDz9MSEgI//73vwG1Cti3T98xISPjFImJz7J69WL27IGHHoJ//tO+jTcIgpMnT1JQUOBW0fqLxWRSeawckbI8bcRf/uK6DyHKVwVnHfQqnqwApysIpJTjpJQ/aZWOlFKelVK+LaX8yFXnQoibhBAHhBDJQojnNPa/JYTYaXkdFEJoaN8uDbKyssjJOV+2RFVVsWKJiUmkWTNVHLxZs+iy2WpUlJo2TJnyuWZ/hiDQjh8ICQlh4sSJtG7dmhEjRgDw++87yvZ36NATA2dMJhP+/v40taS6TE52/eBp0aINd9/9FLfeGleWwG/vXvs23nCPvvXWW2RlZXHffffVSP+JiYkIIQgICODrr3/QjAC2VQvp2QZssZZC2LDhd1ateocLF1Q0mVfbCACEENcBMbbtpZQfV3CML7AQGAacALYKIVZLKffb9PE3m/aPA1dXZvDexFdffc26dXkEBanEXdbEZ/36xRIX5zxDbd1aGTlPn9YuousNsy1PY02B4Jh19Nprr2XWrFllWSbbtevOxImziYnpwjXXDHHq5623VOTmiy/WzRVBUlISW7dupbS0lIkTJzJr1hzatnVdI7tRo6Zl6U9eeUVtO3HC3lPIW6qVNaxBxfqvv/4KqOylKSn5NG5svz83V2UYBbUacMfrx7oiWLt2A5mZz9K16wAaNAjDZFLq5WpMl+Q27hiLlwHtgJ2AdSEpAZeCAOgNJEspj1j6WQGMAvbrtB8HvOjGmL0SH58gQkPLA0SsgkDPtdlaR9viEONEUZHKP1IDNrBLitjYWKecOKWlpSQnJxMdrQxtbdr04L77tKdiaWmwerV6f/BgxUv3yw2r55XVmH7s2DEeeSSOp57CpQrNlqNH1V+TSXkYWTV2ns6PUxs88MADtGnTBj+/FrRr189un8kEL7ygVletWsGECe71afXgbd78egYMeIR69coFmacmKu6sCHoBnbVURBXQErB9zJ0ANLPBCyGigTbAj1r7LwVsDcVSKuMaQMeO2u2tgmDv3rOkpxfTtGkrpzYFBaqqloE9fn5+bNmyhSFDhnDsmPJweeCBl+nW7XoKCnJp06ZLWVvLhA6A7dth8mQPDNiDaHleFRaqgLuKBEFaWippaSc5dqwfKipeBVJZBYE3rK5iY2MpLS0lMTGxRlYGAwcO5JprBrJ1q/Pn3bcP9uyBsDCYN0/9dQfriqBJk748+aR9MQJPCVd35pt7AecolOplLPC5lFLTdCWEiBNCbBNCbEtPT6/hoVSNJ598lLlz7yc7O5P0dOVrHBpaLv0dadECoJTCwnDOnNH2xjXUQ7B582YaNmzInXfeWbbNsT6BSisxhXHjYpg+/Ua743/5pfy91o/5ckfP88qdmhmvvno/f/vbFEpLy2M1HFNTe3pV8NVXX7Fy5coacR+1cuGC9n1z5Ij626eP/u9cC6uNQKuug9cJAiHEGiHEaiAc2C+EWCuEWG19udH3SaC1zf+tLNu0GAss1+tISpkopewlpezV1Bvqumnw1Vef8f33H2M2m8rUQh076kcH+/tD48ZFqK+grWYbbzDGeZqMjAxycnIotllyPfec8yy3pERl/IuIKL/l8vJg506lXvPxUTM4b0qfXBvoeV65UzOjXbvutGw5Bii/jx3liqcFwcqVK/n000+pV0NL5wMHDtCnT3tmzrzDaV9Kivqrk8xVF+tXkpoqSUs7QUbGqbJ93qgamu9inztsBToIIdqgBMBY4K+OjYQQVwBhwK+O+y4VSkpg2rT3uHAhm3r1GlaoFrLSoUM9fvsNsrIaae6va4JAqxzluHHjLD7wShAUF8PJk3qzWcHCheW30datyrjZrZv6u38//PQTjBlTCx/GS0hISGDKlCl2MQR6AXeOPP74AkJC4JNPVHnK3budBYGnV1i33HJLjfZ/9OhRUlMPk5p6WGOf+ltZQRAaaq1WJhg37mauvbYlr7/+HeA5waorCKSUGy+mYyllqRDiMWAt4AsslVLuE0LMBrZJKa2rirHAiirYILyGEyegf//by/53VxC0agW//aZvMPamKlA1jaNR01qOEmD48FiSk8vL+unlxWnWrHyWazKVG4mvu06tDvbvh/Xr65YgcPS8cidi2Bar+uOGG8oFgZTlKwRPrwhqmhtvvJFp096iXTt7Xb6U5SuCmJjK99uhgzK8BwQMICQk065fT1ChYk0IMUYIcUgIkS2EyBFCXBBCuLXAllJ+K6XsKKVsJ6VMsGybaSMEkFLOklI6xRhcKphMcNJG4fX777Btm1JFdO7s+tiAADW9+u23C5o3wKUrGiuPXjnK+++/n3ffTcJkUvrprCztvDi2s1wp4Z134I8/1OxryBDKCoT89lutfByvIjY2lpSUFHbsMLNiRYrbQgDKH3Y9eqhrmZ+PXXStJwVBRkYG7733Hv/5z39q7BxCCG6//Uk6d7YXBOfOKTVj/foQrh0T6hKrN+HNNy9k1qyVZdu9zkZgw+vAbVLKhlLKUCllAymlhzOMeA95eZCfX8i6dZ/w7bfrSEhQD6IJE6Aic0Zm5kdAPrt2NeCzz5z31yVBoGfUNJlMJCTcz4cfvlS2zVoBzjFQ78CB7dx9d2tee20fq1crO0xCgvqhWnPi1DV1G8CaNWuYPTuBbdt2Vuq4efMSOHUqBvDhuediCA1VNR5svypP3qOHDx/moYce4qWXXqq4cRWxpoZ3xFYtVJUswVZtwcGD9o9gbxYEZ6SUf9b4SC5RCgogOzuTOXPu5d13j5GTo/KQa5SDdaJXr7Z07boEUJlI9ztEWNQlQaBn1AQwm0189dVCu21Dh8byt7/9k7vumsaZM6ksX/4aBQW5pKf3Y+1a5T76wgtw1VWqvTXQR+tHfbnzxRdf8OKLf+fAgW1uH/P110l8++0cIBWQnD2byunTcUAShw6Vt/PkiiAsLIwpU6Zw22231dg59CYOVbUPWLEKgsOHoaREcu6cciHyOtWQRSU0BtgmhPi3EGKcdZtluwFKb+3n58/QobGEhqowiVGj3AsEEwLOnn0T8EHKGFassK+qdbnrX23RK0dpJSfHOdvXp5++ysqVbwDKa6h//1fw91e5dB58UOm1rVgFQV0s+jNkyCj++tfn6Njxmgrb7tkDcXHw9tvxgL2qzmTKB+L54YfybZ6crHTs2JHExET+/ve/19g59Fy4K2MfCApyjhZu0EAZjIuL4Z57bmT8+A6YTKXeZywGRtq8zweG2/wvgS9qZESXGAUFEBYWQXz8J0yapBJJuaMzXL8+ifnz4ygqst5pqWzeHMf69eURn3VpRRAbG8uaNWvKkqI5ouXueP31d9Chw9UMHjyO4OA2PPHEOkpK4oFU3n/fl/feM9GsWTSTJyfQpYu6pnVxRXDFFaNp1Wp0he2ys2HWLGtiNT3PrGMkJ6to2vbtL//JysWsCHx9oW1b9cAXQjmFHLZxPurYURmMpbwGf/8dpKWlctVV7apv8JXAlSBYB6yVUma6aFOnSUpK4pln4klLU94Y2dkJQKxbgmDJkngbIaAwm+0jPuuSIADItWTdCggIsIsb0HN3vOOOqWXvJ09O4sKFOKyzWGtBIGsNY1WhMbbOCQKz2f2slu+8o4RAt25w+nQU6enOnlnBwVEUFMDatZ4XBNnZ2eTn5xMWFkZQUFDFB1QBLUFw6JB6CeFaEDRvDi1blv8fGakcHqz3YKdOKmtpjx4zmDEjAR8fH6+0EbQGPhNCbBJCzBJC9BF65aDqIFZ3x9Ony6NbCwvHA+Fs2+ZcON0RvchO2+2X+2zLkcWLF/Pll9/w6KNvExamwi8DA0MYOfJBl54uBQVw+LCzKsNKUVE+K1aoGsZ1TRDk58Pu3T+TkrJft+YzwJo18OOPSo0xfTrExWl7Zo0dqwTyunXKkOrJycrChQuJjIxk9uzZNXYOR0GQm6tWTaWlMHKk60IyjtHGvr5KGFi57jr1d+vWEEpK1KPY6wSBlPI1KeVgYASwC3gA2CGE+FQIcZ8QolltDdIb0aueBZm8+WYc69e7FgZ6kZ3h4eXb69qKoFWrVlx99S3cdtvDjBmjZvtFRfn8979LNdubzWY+/3wBEyaMQV+VocjMVPvrmiA4d66IqVMHMmlSN902GzfC22+r9489pmaxVs+s0NAmAAQGBvP004nce28srVsrNdLBg56drPj7+xMREUGTJk1qpP/SUmcbwXvvKXVO+/bw6KP6x9arp+wAjrRqVW4/jIpS6qG8vPKcWN4YWQyAlPIC8KXlhRCiM3AzKvvojS4OvazRc3cE9fCqKKnX5MkJDjYCgBBuvbVcBVLXBIGU6kcG0Lv3zeTmZvHbb/+lS5frNNuvWfMeCxc+afnPl/LkuM6Eh0eRnl73BEFmZj5duw7AbDZr5uNJSVEutmazcnm2DdQdOjSWq68ezK5dP3Hllb1p0ULpQdq3V/ruEyc8KwieeeYZnnnmmRrpW0rnwj25uWolBBAf7zpddDOdaXJAgFp1WQXMsGFKoL799i8UFSXTtm3N1FWoCLcyNQkhWgohrhNCXI/KPbRVSllnhUBSUlKFSa60VD+2akxHX/jAwGggkZYty4VHXREESUlJNGvWDB8fwS23NGf9+iQ6duzJQw/N44MP9vL004lOx6xfn8Q///m0zRZ9IRAYGML99ysBW9e8hvz8wnjnnU384x+bnfaZTCqXfkkJ3HgjaNV2adKkBYMH31MmBKA8c+7Jk5fvPXr4sKphYcsPP6j08Fdf7dpbyN9fXxAAWMpoADB4MAhhJju7F4cPH/c+91ErQojXgM3A34FnLK+nXR50GWO1DZgqeKI4qn58fFS+Flv5YVsj9vbbU4BYuyjlumAjsF7Ps5a6fefPn2H+fKVaW78+ibFjYxg82IexY2Ps1G1axnYAHx/7yiAREVE8/XQiw4Z5j9dQUlISMTEx+Pj4EBMTQ1JSxTalquIqg+2KFaqSXkSEUgnpWQAdv4fz59V4T568fO9RrZKU1gBmV+mNGjSAXr3sH/aO2O5r3Bi6dCkEAoiImOKV7qNWRgOdpJRFNT2YSwF920A5Wl4ujRsrvWHz5uXqD4BZs+7ixIlDDB++CWhgJwgu19mWLVrXs6gon3fffYLCwjyKi1UZLKv3DygBqmdsl9LM//4nGTkyjNzcLBYv3kpYWETZSsDTgsBVTiXHAjwXi8mk7/545Ah8+KF6/8wzKlWCFuvXJzFv3mS77+Gbb+4FNnPixCKP3qMTJ05k3759LF68mJ49q69EqZTO1dcOHFCeQqGhMHCg9nFCQJcuroUAOO/v2TOEvXvh3LkI7zMW23AE0K6nWAdxZRsAQWhoeV1iW6w5yKOi7GdeKSn7OHx4F/Xrq4CpuiYI9K5nTk5m2cPHitX2AvrGduv21177L598klxm7LSuxEwmz15XvZxK8fHx1X6u/Hz48suF3HZbYz78cFbZ9tJSePXVcs+XXr30+1iyJN7pe1BhRItJSUnyqKpt7969ZSU4q5PiYvuVTm4uzJ2r3g8frm8bCAuzV//q4SgI2lqy0B854oXGYiHEu6hvPB/YKYT4AShbFUgpp+odezkTFRVFqmN1DsDfP5qSkhReesm5gLWvb3mQWVCQKkpjXRU8++yH+Pr6Ua9eC0AJgnPnICREvS539K6nHtaVgJax3XYl5pgkTAglDMxmJQz83KrWXf3oCT7XE4zKYZvOOyQklLy8bGyT+yYlqdlt8+bw0EOu+9IvYCMpLo4nI0N5EXmC5cuXk5GRQeeKsjtWEttVlNkML7+s/P+jo+H++/WPc7c4jaMgCA09AbTizz/zMZs986N3tSLYBmwHVgMvA79Y/re+6iRaqRACA0MIDlYPIK1Ecy1a2NsGYmLKUx5ceWVvOnbsSYsWQQQEqOyad9wB06bVjRXByy9r+6tbZ/KOWGf85e6N0YAytmutxGzxhnxDejmVXOVaqgyO1dvy8rIJDAwmPFxFNiUnw7Jlqu306RVPNlwXsDlWFmHrCdq3b0/fvn2pr6fXqiK2guD//k9lFA4NhTlz9FVo/v7uZyF1XDUUFe0D8snJCSErq0pDvmhcCYJs4L9Syo+0XrU1QG8jNjaWf/xjUdn/zZpF89RTiRQUqAeQo0tz8+bQziFqPCCgvEqRFR8f+0L3f/7prKe8HLnxRvVAt8YqNm3amqefTuTxxxe4TDUNShiMGJECmBk71j698pEje1i8eDrffPN+2TarIPBknYeEhAR8fe0N2iEhISQkVFwoxh20bS4FvP32I3z3XRJz56oV0e23K++XilDXWy+ONMqjgqCmsBUEtuUobYPBHGnd2r38YuC8ImjZsg2NGinVsLWWSW3jaoE8HlgohMhHrQY2A79IKffWysi8mIED7+O//72LwsI8GjVqSlaWerjUr28v7YOD1cNdyxujZUuVr2T//i1s3bqWK6/sw4sv3sTx48qlLy0NTp+2LwJyOXLmDAwZ8le+/34ZeXnZvPPOz3YPyiVL4jl7VrugyvnzquIYOIf6nzmTyr//PY9rr72RW2+dAnjHiiA2Npbs7GymTZtGUVERzZs3Z/78+dVmKNZTMZnNJt54I47SUoiMjLWk3KiYoUNj2bt3M19/vRilKVb4+gZjMiV4TBCYzWaef/55GjZsyAsvvFCtfdsKAqvWMjpau62vr/qNW22A7uDnp46z2ldat+5I377w3XdeKAiklHcCCCFigOssrweFEFGoOIIRtTFAb6OoSJWYCwoKIShIzVithTocl4YNG+o/xP381HJy795f+PDDWdxxxxP07n0TTZooP+20NGVHMJvLH2CXGyUlyh4ihCgr1WfL0KGxTqoesxn+9z8lQDZsUNeoXTs1Y7OlXbvuTJr0Cu3alRtsvEEQADzyyCM88sgjNdJ3ixZRnDqlbXMpLVXZQ8eOjSU42P0+n3xyEf379+eNN+LLKsOZTAVAPJs3A1Svt5M75Obm8vrrr1O/fn2PCoKwsMoJASuBgfauvVaDsbXeeW1T4WJGSpkC7AD+AHYCZwG3biMhxE1CiANCiGQhhGYVMiHE3UKI/UKIfUKIT90fumc4ftzZdzo9Xf11FAShFZTvCQ6Gq67qz733zqB375vLtluXoCoz4UUO2Itxx0vCbIaPPoKHH4ZNm+D11+GVV+D995XBMzJSbXPUu+7e/RPffPM+8fEjGTs2hp9+SvK4IKjJ+IHSUpVCeuJEZ5uLPcd03R9dMXFirKUynO1PP5Vdu+JqNA5CD19fX+bMmVPtQgC0BYFeAFlVzRO26iGTyUSjRqoegacEgSuvoReAfkBT4ACwBfgHECelrNBpTAjhCywEhgEngK1CiNVSyv02bToAzwP9pZTnhRBVkK21R3Gxejinpv7Jhx/Ool277owf/0JZoInjrEEr14gtwcHQuXMfOne2n85aMxZezoIgPV2pvgCklGjlMywqUgY6q/pn5kz1NygIbr1VGTpvvVXFaNjimOL7zJlU5s6Ns3gKeSYDqVb8wJQpUzh16lS1pEnYtQsuXChPYZ6QMF6zXUBAFI0aVb7/0FD417/iKSqyD0yQUrm+VncMREXUq1eP559/vtr7LS4uV9nk56u08v7+yuFDexxVO4+tIMjLy2bOnG7AWZKTPaMOdrUiuA+IBL4DkoBPpZR/uCMELPQGkqWUR6SUxcAKYJRDmynAQinleQAp5dlKjb6WOXJEzVDT00+wYcNK/vjjR7Zsgc2b1UN97Njytj4+Fc8W9Jbnl/uKwGSyn/n8+efvDB8exDPPDLdrt2iREgL16qlrGxKi3s+bpxJ+TZyo7aWlFXVcWJhPfr7nMpBqGXELCgqYPn26y6yg7mAyKSFgZciQv5Z5CdkTwtChlTdKBwSo15kzNe/66mlsVwPWj9W6tb56tqorAtsVbFBQCM2aheDjk01eXrmGoTZxlX30CtRsfhtwA/ClEOJ3IcT7QoiJbvTdEjhu8/8JyzZbOgIdhRCbhRBbhBA3VWr0tUhGhtLbA7Rt25UZM5Zz113P8u67atuECfaqoQYNKpbqwcFgMpWSnLyLPXt+BtRs9u23YwAftm2L4dNPa3/ZXdOcP2/vuVNQcIGSkiK7tB27d8Pq1eoH+OabquLYypWwfHl5+Uk99HzfzWbPZSB19bA875jUppI41hsQQvDZZyeIj/+EZs2Uey2oXFZxcZWfuVtXti1aaLuStm5dPa6vlSEzM5NffvmFI1a3nmqisobiythabLFdEQQEBLFiRQpNm6qc1hUkLqgRXNoIpJTnpJTfADNRKpzPgEHAkmo6vx/QASVoxgHvCyGcFq5CiDghxDYhxLZ0D4jL0lL7GWzjxs0ZPHgsTZsO49QpNSsd41C8syL7AKibqKiogClTejB9+o1lKo3MTFUn1mRK5ZFHPKODrUkyHKpO9uw5hLVrC5g9WxW9y8+H+fPVvr/+tby+q15qX0f0fN99fdV2TwgCvTgBX19fmjZtelE2A73CM0OHxjJvXgqhoWYghTFjYl3mz9fDOut9/nkt+0MIM2dWj+trZfjpp5/o378/06ZNq7Y+S0vL1ZVQsSCoqloItNNQWCOW9dKC1CSuahbfJoR4VQixCWUgng80AZ4C3ImhO4kqbmOllWWbLSeA1VLKEinlUeAgSjDYIaVMlFL2klL2aqqlC6hhTpxQukNHrJO8Dh2cI1XdeWAFBUFwcH3at+9Bp07X8v77LzipNAoKaib9gKeQEjIdat4JIQgICKJ+/Ybk5qrcN8ePqx/geG1Vt0uUUdP+gRUSEkJ4uHpgeUIQ6NVkNplMSCnLcg5VRRhYCrtpMn8+5ORA375QVUcl6708fryK+QgPb4kQAuVAmMjIkbXvNVS/fn369u1bbVHFRUWwY4eqs2ClIkFwMXFsWqkorMLBqwQBMAFIB6YDzaWUA6WUz0kpv5ZSujMt3wp0EEK0EUIEAGNRUcq2fIVaDSCECEepiqp3rXeRmExKENiSnLyTdeuS2L1bmTQcJ3vBwcqtrCICAsDPT/D++39w661TdFUal5MOdsmSJO64QzujqMkEzz4L+/erNL4JCa5zvuthjTpu3lyl+I6OjiYxMZGmTT2XgVQFIv6Dpk2bIoRwCiqDquccclwR/PDDcu677wo+/nguf/6ptj3/fNXdkK0PvOBgdW0/++wEP/5opmnTVCDWIw+uYcOG8euvvzJnzpxq6e/0aXuVjMlUHkxWEyuC4GD77+O550aQmroD8EwgqSsbwRgp5RtAI4uxtwwhRAUZSkBKWQo8BqwF/gRWSin3CSFmCyFuszRbC2QKIfYD/wOe8bYayadOOT84fvrpC+bMGc/u3cpoYJtrJSwMrrlGeRq4w4YNSYwaFa7r5QHVl36gqlSX22NSUhJTp8Zx5kx5ec/58+NYsOBxZs26mwULdrB/v1K1LVhgX++1skycGMupUymUlpayd+9eYmNjy4SKp9xH+/XrR3p6Oh06dNA1EFdF6DsKgrS0FI4fP8CpU4GUlCjnA3dUlVo0alQ+ew0MtH94eXIGW93YapzNZmWXSktT181af8GRi1kRCGEvSM6ePU5Jicov4W0rAiszhBCDrf8IIabj7P2jiZTyWyllRyllOyllgmXbTCnlast7KaWcJqXsLKXsKqVcUZUPUVMUFSkVhSNt2lzF4MHjKCxUEsD2Od2ypfsJzZKSkpgzJ46cHH3Z5+dXfekHqoJj7hp3VBh6guP55+MpLHROOf399x+zceN3rFuncmw89JDrwh6u8PNTxuR27WDTpp/wndCL7wAAIABJREFU9/dn5MiRZfvAc4LAz8+PkSNHcsMNN1RbzqHiYueUGbff/hgffLCPDh1UhjTHFCfu4OOj7uvu3e23h4TAhQvneffdJ8jOVktlTzy4ZDW60+Xl2QvT5cvh22+VoJs9W3tSFxBwcYIA7NXHs2d/Qffuyo3cWwXBbcAcIcRAIUQC0Ac3BcGlTFER7NypbRsYNOhu/v73Tzl/Xul/bFcEFeUityU+3vnB6EiPHom17qNtS2XTJusJjmXLkjhxQnu2m59/gb59t1JYWI8uXWDQoKqNNShI5c+xem81atQIs9lMoWWt7WlB0L59e1avXs17771HQkICwQ4uJ1XJOaRlKA4JaUBMTGfS01Xiq8oKguho6NdPRbs6er4plYYfX3zxDhcueE4QjBs3joiICL799tuL7svR/2TTJvV3+nRnQQhqVdS168VH/NsKklatOtCwoVoieKUgkFJmoITBQlRcwZ2OqiJvpqpqjUOHXH8hGRlqf8OG2HliuJOP3ErFaoBowsM9JwSg8mmT9QTH9Onxut48DRtGsWVLJ3x8VHxAVYJphICePe2X2126dKGoqIhfLZXBPS0IbImNjeX9998nOtrejlGR0M/IsB+/nscQqHKL4L4gEAKuvFLlbdJTbTZsqATNo4++TXS0SvDkiQfXyZMnSU9Pp97FKOotnLWJXpKy3Ankmmu02195pXvOIBXh2IdXGouFEBeEEDlCiBwgGWXIvQuwbvN6qqLWsGLrPeDIhQvnOXRIPexsVwM+Pu7bBsC1GsDfPwRIoMjDdeEqq8LQExBnzhxj8uQE/P3tJWVAQAh5eWoW/NBD6kdWFYKDnQ3Lvr6+BNhs9LQgKC4utrMNxMbGkpKSgtlsJiUlpUIhkJoKe/eWP+BtPbBsy0necktD4uNHceiQOpc7gsBaXasilVxEhLrP77zzCZpZGnviwbVhwwZOnjxJH8ckU5UgJwe2b7c3Emdmqs8TGoqmq22jRu6nm66IevXKM5b++OO/SU5WExZvMxY3kFKG2ryCpJT1rdtrc5BVRW92+uyzrj0zCgtdpyqeMeN24uNV2Wbb52Fl1EKg71IYGtqEu+9OBGI9LggSEhLwd5BurlQYegIiIiKKoUNjmTZtMSEh6vYJCgqhfftESktjueqqIwwblqF5rDu4MzG0CgJPpaF+7bXX8PPz4+WXXy7btmDBAiZNmsRxB2OU1krW2uT0aZWsb98+FZxnjT+xGuHz83P45ZfVZGcvJySk4oe7EHDFFe494Pz9y1N6eHIG6+vrS2RkJEGVWYI7cPCgfUQ2lNsE9YrtWJPDVQe2BuOtW9dy9OhWwMsCyixZR3URCh17unegNzs9deqYZnFqKzkVrHf8/QPw9e0KVN0+AGpGmJiYWKYeaNYsmvj4T/j66wz69lWzQy0bRW0SGxvLBx98UObu2Lp1a5cqDL3CPdY6AjfddD9vvvkDISEN6NdvAocOxQJm9u4dxJkzKVUep54gmDp1Ktdffz1HjhwpW615akWQn5+PlNLONrBq1SqWLl3KYes0H/2V7Hffla9k9+wpD8zTSqmhiKdtW9d58oVQaZQrY5xv3hyOHz/I+fMpwKXrNaQ1bleCoGnTqntf6WG1EwwadDc9evQDvEwQAPOEEKuEEPcJIboIISKEEFFCiMFCiJdR9QmquJCvHVzNTq3BIidP4lQVqCJBMG/e91x99cOWc5Rvr6wggHL1wNmzZlasUMVVVq58k7ffngR4XhCAGmOzZs0QQrB582aXKgyrcGvWrFy4OVYO69ChJ2vWZHHllQst7o3JDB7cn8aN3az1p4GeINi2bRubNm3i5MmTHlcNzZ07l5KSEqZOLa/yOnXqVN577z06dCiPo9RbyVrrNYN9Dir9cpKpBAS4VoN27Oh+iUUrjRvDDz8sY8+e74HaFwQHDhxg1KhRFxVDUFiIZr1l69zR8dHRpEnV1ZausAqW3r1vonv3awHPCFZX9QjuEkJ0RiUbfwBogapf/CfwLZAgpfTqGloJCQl2GR+hfHaana3K0KWlKelvm5HRcbnoSFqaWpaDfbDJRaxS7XKWpKUd5fDh3wA8rhqysmvXLho1aoSfG76xR4+mMX58PAcPbmf9+k+ZM+deliyJZ/LkBPLzc8nIOEFISCOWLHkXOEZeXhT9+iXQtGnVF5h6rnyvv/46JSUldOvWzeOCAHC6fnfeeadTG72VrN4DPyIiqqxOgCO7d8exfj2aJTwjIvSzarrCxwe6d7+GyEh/Tp2q/QdXcnIyq1evpqioqMppqPXGrLUiqF9fuSTXREbQ8HClopLSsykmXP6qLSmjL9n8BtaZ69SpT3Hu3BkaNYrg0UffLPtRWJPInTtXblCT0rUgMJtVBbGCAhg40D7oqSorAiu2QmT06Me48sr7mDPH8yuCtLQ0unbtSs+ePVm7dm2F7c1mM6+++hJ5eRcQQpT5e1uDxyIiWnP8+AF8fYMwmdQ8Ijtb7QPtB1ZF+PrqC+EBAwaUvfe0ashdoqKiSE11frDreV1NnpzA3LkTMJudP1hpqVpJOF5Xf39o377qYzSb88jKigdG8tJLUTRtmlBrbs69evVi1apVhF6EnkZP/aIlCFwVmLpY/P3VauPAgdOkp+cCHbzLa+hy4S9/iWXUqEcBGDFikuaD5uuvk4iOVoa56OgYvv9efzm9apVk507w8zvHk0/aR4dejCDw9S2fEURFdaJrV7VM9PSK4OjRo2RkZHDOlVHFhqysEu69dwbBwfWdgn6KivLJyckkKKhxmRCw3Wer+qgMISHu/VA9vSJ44IEHGD16tN2MPzMzk2+//ZYNGzaUbdOyswQH29drtiU0NBYhEnXP67iSsNoFqpK+A5QNY8aMOPLzVXLErKyq50mqCs2aNWPMmDEMHTq0yn1oCYLiYjU59PGxr09cDR6qLomIgM2bv+arr+bpjq2muewFwYMPTmDz5q8ZM2Yq11473Gm/1ePi2DFlmDt+XM1ObXPgWElJUZWxFHE0bmx/+S5GEID9rNb6I/VkoXWAPn36cOrUKe666y5uv/12PvroI6c2Vg8XIQRNm9Zj8eLpFBRoZ0LLycmksFA77bK+rts1rn6of/75J2+99RbffPONxwXBunXr+Prrr+1cSHfs2MEtt9zCSy+9VLbN0YkgOjqahIRE3dXSm2+CyTSRgADtpDi2KwkfH6XmuBgXyPj4eAoK3A8y9Ea0Zt0nTiiNQGSkvRv4xUYQV0R4ODRvHkXr1tG6Y6tpLmtBkJlZyHffLSM5eScPPvgaPXrcYLd//fok5s6938njQmt2WloKc+dCSYmgX7+zzJv3uNP5LsZGAOV2gmPHDrBmzTuA51VDPj4+tGjRgsDAQL766iu2bt1qt9/WwwVUkXRXNGwYBegb8auCK0Gwbds2pk2bRlJSkscFwaeffsqqVatobmOdbdOmDcOGDSM0NNTOXXTp0qV069aN7du3c+jQIW66SVsInD+v6jcHB8NTTzlnXbX12AKl8mjS5OI+R2WDDKublStXsmzZMv6/vfMOj7LKGvjvTHogEAgQahKQJlUExYoN1EVR14qOiopmLSCiuKjRtaKuWBZdFRELQhRdy9pB+cS2KgqKCjakhF5CDQlJSHK+P+68mclkZpKQaUne3/PMk5m3zcmd973n3nPOPSffO595HfA16vZlFvLOCRQKHA44/fSRjB1r+pyo8xEAiMibwHPAh6pav1JKYWbHDgf33fc227atJz6+ai9tzQT8dVyeo1NVeOIJ49RJT4ecnHY0a1a1qmZMTO1zDPnDUiR5eb/w4ou3AtdH3DRkMXLkSDp16kTfvn2rbPcV4eKPhIQkWrSYxK5drYiJyaa8vLoTv64kJATu2AYMGMD48eMZOnSoq9B65BTBsT6KBXfv3p0xY8ZUK2O5du1aVJV3332Xzz77jBYthvm85quv5gI57Nu3luefz+DUU8fwzTcfsHXrWtq1y+DKK6dUmUkcaA4nT/z5MMKVHPG+++7j559/ZsmSJbSpxdRm504r5bv5XFFRfdHWb7+ZinhgVlZbeGcJDRXNm7utAFG1oMyDp4CLgBWu+gS9QixTUMjNzWXYsJ7k5JxBbu4DzJlzP0uWLKjc7z/22uA5On31VVMtKy4O7rjD9wihvmYhcN+oGRm9Ofdc49eIxIzA09TjcDgQEU444QRKSko42CuGrrajwFat0jn99PGsXXsZ4CQ7O3CIqSfei6JSUtwRL0OGmP3+GDhwII8//jhOpzPiMwJ/+FKmnv6V/Px8n/fBggW5vPFGNmBs9Vu25DFv3iyuvHIKn3ziDke2aNEicFvVFl8+jAPJk3SgnHPOOYwePZpMf/mhPSgsNBF+W7a4t3mPW9avhwkTTKqJgw+G885z7wu1WciiWbMoLUxjoaoLVNUJHAqsARaIyFcicrmI1CGhQviwzBWbNxu7/9ata3nuuRwmTRpBWZkxugeyR3uOTn/+GWa4/HC33QZpaWuYPfs+Fi58zeuc+sttzQgyMw/muusewuEwo5dwdlzeph6rQ9qwYYNPh2BNo8BmzVpw1FFncN99/6V58zFAc9LS1nH++U4eemgexxxzFoMGnehXCXTtCocdZh7QgQOhTx+TA6ZPH+PwrCmlh+cK3RdfzAJyI6II8vPzeeyxx3jzzTeryedrdA2mYE95eTn79u3jjDOq13CYOTOHsrKazZoWwZgNgNuHkZpqymAmJdUuT1KwuPPOO3nllVdIq8HGpWpScpSVuSMEoXpH++abZsB19NHwr39VDSUPtVnIYuXKH7nttuE+5QsHtfIRiEgaplDNlcAPwDSMYvg4ZJLVA3/mivj4RIqLTZYuf/ZohyOmcnRaWmoqPKmC0wnHH2/s988/fwcffPBclfNSqxXYrDve9U+tTi6cs4JApp6ioiLGj6/qG/GXJgOMQr3ooqe45Za3yctbSW7uSMBBScmxLFiQS3x8Il988Rbz5r3ACSdItUI16elmnYYVEdSqlZkJ1BbvFboFBXlANt9+G/7SnytXruTGG2+ssgjKks8fGRkZvPLKK2RnV6/hsGBBrt/BjK/tInVru5pwOp08+OAaoIKjjqo5T1Ik2LrV3akWFxsTkWrVUqmFhTBvnnl/xRXVI6nCNSNISYmntHR7pazhpkZFICJvAV8AycAoVT1DVV9V1fFAmJqpbvgzV+zfX0Lz5qbH9lXOMCEhmVGjspk5M4cTT3Tw179msXZtLl26wKWXmmPat8/E6byN448/v/K8Vq2qr0Q8EBISjOMoJqactWt/JybGDF3D6SeoydSzc+fOKorCGh0mJ5tUig6HMaimp2dy9NEzePZZJ+edl8vUqdmUlRkTxt69pjN75ZWHEHHfgp6dXMuW7lrFB4pvpVbE/Pnhj25JS0vj+uuv5zwPu0MgpRsXF8e5557r8xhr1N+mTe2c7s2amcysdUmIWBu++srMbtau3VrDkcFjz549rFy5ska/lKq71KTFhg1mhu9pJpo3zyiLgQN95xEKlyLo2bMns2aZ/NfROiN43FU45gFV3eS5Q1WHhEiuetGxo/8HJD/fTP+yskw5Q8tO3bp1e1q0SOO99571SN5lRpDHH5/rEePfmyuvnMJpp5kUELGxxkwRrAUnBx8MffsWMGZMb4qKTOx+OBVBTaaelJQUysrKqqQ4MCkozKrgmTOXsnChcvbZa/jkEzNKLCnJQbV6Z/beezPwjj8oKSnixRdzOOSQ+jvp/Cm1XbvCX/qze/fuTJs2jcmTJ1duC6R09+/fz8KFC/2ajbZsyaNHj5GY8ZkbX073Qw4JTtpkb4qLTY+6d2/4bG3z5s2je/fuXGqNzPywbVt1X0B+PlVyjH37rSlCA3D22VWPtRLxBcPkWxtiYmLo1MlonUgEiNRGEbQSkbO9XieJSBAnmsGjosL/aH/MmCnk5Chvv21KIQ4f7mTu3DV88kkF99//Htu2raO83PumLuKjj/yPIFu0CO5Iq21baNkyhc6duxMbazrJcN4YU6ZMIcHP3S8iFBQU0K/fAB59tGqt4RtvfJa77voP7dtnsW0bPPOMeZgmTarAODOr4y9ia+PGtUFRrP6UmogjbIufAuFPvtjYWG666SYuvPDCgOd/++0sYAym3Dc+ne6JicGfCVicfbap/Na8efi6AlUlMzOzmqO4qMidknvfPlNPJBCvvWbqY2/fbjr8o4+uur9Hj7rnYKovVlbXaJ0RjAVmYnIOOYFngcnA/0TkkkAnisipIvK7iPwpIrf42H+ZiGwTkaWu15UH8D9UYc8eOP74qqP9Vq0y6djxZh5+OI0//jA9zLJlsGjRVt54YyaffvopmZkHA757H0+b67ZtG1i16md27zZ3XSicSTExMbz//grS082dGE4fgdPp5JFHHql0xFlZRz3TRaxbl0dOTjYvvGA60/x86NfvaI477lySkprz0UdGIR9zDCQkvOL3uywzkjfBCkP0579QLQ/bStjc3FzatGmDiCAitGnTpvJ7/cnncDiYOnUqHTp0ICXAUN44ij9A9TQmTHiyWpQQhGYmYNG3r5kF7t9fz7jpOnDBBRewZs0aHnnkkSrbV6400UGbN5tnu6aFmFa2lEsvhccfrz77tDrlcHLffcZfVFwcvDKctaU2iiAOOFhVz1HVc4A+gGJKVk72d5KIxGCqmv3Fdc6FriR23ryqqoe4XjPr/B94YVVsysrqy9SpH/HCC/spKlrD6tU9qKi4GnAQH58F5PLccw7+/e9TuO++IZSWJpOeHtjmumBBLpdd1oexYwfgdB7EggW5IbMheo7kwj1VvO6668jPz0dVKSsrIzMz02e6iJycHDZtMjHYFqpu59vIkQRIGyGMGpVdbeYWzDBEy38R48PGFI6VsLm5uVx++eVs3+6uSb19+3auuOIKcnNzfa4gHj9+Is899y1PPPEy2dnZFNSUAZG1TJt2Dcccc5bPvaG0cVuFWyKdhnrnTjOyr6gw92Kgqm1gooisxWOjR1efMcXG1n9x6IEwb56JRIxKZzHQWVU93CtsBbqo6g4gkN49HPhTVVe5SlvOJQy1jq2b4IknrufSS3tz1117KCnJRcQdb11aamz/K1bMB7pQXt6cxYvhiCNG4j0rsGyu1gK0oqI9ru/ZzcMPZ/P++6EZVUZCEfgr6+nPlr1pUx5PPpnLrl3b+c9/HuOLL95i2TITl52WZsI+/YfpKjfc8BSTJs2gY8e6lWusC06ns0pKB09CvRI2JyeH/T6GpqWlpZVKyOl0snr1GvLyKvjwwzWMHv0o5eXx3HPP32u1UK9duwwGDBhKmzYdfe4P5YygpMQY3HfvjmwS4jVr6nb8hg1mxtC+ffVIPQjOWosD4cUXZyKilJdL2EOca6MIPhWR90RkjIiMAd52bWsG7ApwXifAs+zSetc2b84RkZ9E5HUR8VkXSESyRWSxiCze5l1p2ovCQjNy/+OPJYCyZs0gRCZUc1aajNo5tDL153n77Vw++OB5zGTHzamnjmH4cKfPBWglJUXcfXdoRpXXXnsOq1cvcX1PSL6iCoHKegYy1Tz8cDZvv/0UTz11Iy+9dB9vvGG2n3KKmW77C9NNTzc23pNPNh1hbcs1Hgh1LbcZDAKtDwC3EqqoMCaNlStNNEtpKUydOpbt2zfW+B0JCclcdVXg2VMoFUF5ucknFc4R7PDhwxk0aFBlIZ/y8prrh3hjKY6sLN/7w7V2wJvzzjuXxEQzEA33LKs2iuA64AXgENfrJeA6VS1U1RPq+f3vAlmqOgCzJqF6RjNAVWeo6hBVHdK2bduAF3zrLTNyLy62Ou08VLf7OXotDzxg3i1blsP+/dV73K++eg/wP7Jdty40o8pdu/IpKzNmgXAoggkTJvgshpKTk8M//lHd+W5RUlLEO+9M5+yzr6dt23/y2WcmFnvkSLPfn+Peimxp1+7As2DWlnCvhK1pfQC4ldCvv1aNa58z5wGWL//a73kOV7mxZs1aEBcXz5QpF3PmmW18JkkMpaMYoEsX61lMxM+kK+j89NNPLF26tPL33LWrapGe2hCtigAiV/4zoCJw2fk/UdU3VHWi6/W6ehuMfbMB8Bzhd3Ztq0RVt6uq1c3NBAbXQfZqlJTAjBmBU0d40rZtJ9LSNtG6tVJR4btDz89fD/gf2YZqVPnSS3MYOPAIIPTO4tzc3Cp2bE/Wrl3LwIHG+e6PHTs2MWzYNBYtMisjJ09212kYPryq4947ssVfbdhgYtniW7c2K2GbNw/tStia8i/Fx8czZcoUNm40YY4WCxbkMmvWXX7Pi4lJ5tZbXyInZw5lZWXs3Wsm5Hv2bPeZMTfUMfDJyUmVHVe4ZgVLly5l8eLFtHOtjvOuLuiNqkkR85//uLetXm3+RpsiWLlyJRUVZpYVVYpAVcuBChFpeQDX/g7oISJdRSQeGA2843mAiHjWRzoDU/3sgCksrH0q44SEZLp06c1553UkPf03asqIaUa2VQ2KSUmhG1V269aFZs2MxyrUM4JATtP09AwKCkyHbplzvGnXLoN584yZ47zz4MQTq+73DNP1jGxJTw/fQ+d0Opk6dQ1Qwbnnrg7pSthAvoe0tDSef/55Ro1y4lGmGLBSRvjT+pmceKJRoE88MaFWGXODXV/XF+EewXbs2JHBgwdXBgDUpAhefBGmTzcJ5Ta6rG3WjMAzuZwnkVIEn3/+OXv2mLFyVCkCF3uBn0XkORF53HrVdJKqlgHjgPmYDv41VV0uIveIyBmuw64XkeUi8iNwPSaNxQEzZ04u4icAvUWLNFq2bAMI7dplMGnSDLp3H0irVu3o1WsnMIVAi3OGD3dy0UW3Vu5LT8/kX/8KbX4VK3Ih1IogUMc1dqxb0fkz81xwwc0sXWqcoiedVLvvbNvWxG+Hk9mznwdg1arQOon9zRIzMzPJz8/nxBOdLF1avWau/0GMAGs4+mgnCxbksmeP79nb1q1rK5Vrz57hmW3FxRmv5qZNNfTIIaCsDPb6LnsBwPvvw0svuT9/+aWZXa9fb9a4+PqZ4uJCb6r0x6BBg2jRwgw2wx05VJsA4Dddrzqjqh9g6ht7bvuHx/tbgVu9zzsQcnNzmTw522eESEJCMuPHT6sWYz18uJNrrnmYggL49NOjXLHEOezYYVL4XnHFfRx22KmVx1966R2cd96N7N6dT1ZWJkNCuK76/fff588/2wKHh1wR+Esr3KJFWpU2s94/9dSN7Ny5lRYt0hg/fhrff7+czZvjiI0tpXv3mp+ixMTgrsauLQ6HuTeKikJra/NVK9vySZSWumvUeuOv9nBsbAZlZcafEig4ISMjIyQF1gNRWLgF6MSiRT/Rr5/vVNnBYvHixTz//PMce+yxXHjhheze7d8/sHkz/Pvf5v0xxxgl8MUXJlttebkpPuMrRDSS/oFDDjmErl3hxx+jcEagqrOA14BvVHWW9Qq9aLUnNzeXMWPGeDiI3XgmkfNHSoqxI772mpPXXzcmjFGj/sYDD1zCWWe1qZIMLSmpGZ06ZdK/f/3rDwRi8eLFrF27DAi9j2DKlCnEeXkVLeXpzfDhTt58cwsLFypvv53P8OFOCgpMjYIuXXbUKi1Ehw7hVwIAF198AQBZWfUo1lsLnE4nd999d2VNXc+w2O3b/Xde/mZccXFmVta2bWDTZ7jSQHuSlGS6ENXQB94vXbqUp59+mo8/NrkuPc1Cn30GCxfiksUsEisuhuOOg1tvNSP95cth8WJzjD+zULhyC/nDUk5RpwhEZBSwFJjn+nyIiLwT+KzwYK3avPjiiyn3nme7UK2oVALLl3/NO+9MJy+vuisiPt4kfAPjtJs9+97KfZ7J0BwO6Ns3HFWLHMTH3w44uPnmrJCugnU6nTz77LN06tQJEaFDh8D1AV58EbKz3Xlb2rc3xw0bVvOafBGjCCJBaqqJpSwrC70W6tWrF3v27OG0006rEhYbKPrZl2P9hhtmsG+fk5gYK/uqb7NTWlpaRDKAdulifsz+/Q8P+XcdddRRTJs2jdGjRwPusNE//oC774Z77jFJ5RYuhK+/Ns/ouHFmXcBhhxkFMX26OceXWTIjAw46KOT/RkDi442pLT+/hlVxQaY2PoK7MIvDdgGo6lLAR56+8GKF6PmLdrHwfHA+/PAFHnvsGn788XMAJk48gWuuOZwdO7ZUOcesGaiqkktKinj44WyefvqyAOGowSE3N5cHH3yQ0tJNgLJjR+iLg48ZM4aff17Pp59W8PLL1VMVWCxeDLNmmVwuL764g+LiIn7+2ezr37/m70lLi5wNNpyFaXr37s0DDzzAJZe4s7CUlZlVsIHwdqz3729+hzZtzLoMX7OGpKRkpk2rPnsLB4WFuUAWRx5ZdRFiKOjTpw/XX389J598MqrGP6BqTEDWLOvBB2GqqQFPdra7NrNVHE7VmIrOOafqtTMyTPbRSMxUPfn1128A+OGHesXN1JnaGDf2q+puLydsxEtW1qZEoncmxoEDjwOULl1MfuMVK76nsHAP8fFVk6z5m36XlBQxd+4s/ve/T3jggQfCGn5oxfSHctS3cycB48H37nU/ZADvvgtDhnzPihXH4HAYu783LVqY8yoqzEMWDgemP7Zt2wR0YNmyX4HQGtN79OjBLbdUTa8VyCzkyf79pq1iY90zCKuWgKWgZ87MqSxHec89UyIyG8jNzWX16mygyJX2Oa9y/USo5SksNLb+hQvNLCA11Yz8rcig4cNh1Cj38ccdB199ZbL7XnCBe/YPJvKpFsXOwkLLlonk50NxcXg1Um0UwXIRuQiIEZEemOier0IrVs3UlB7Al29gxAgnI0Y4SUw09sOnnlrE3r27SE6uGmfnz2lnsW7dupDe8JEoDj5p0iTy8vYyevSdpKVVt90sWGAyiubnm2n1pk0r2b37IB5/fBAVFZCQsBzIxLNERadO0L27UTC//GLOa3kggchBQtVENm3ZsqOGI0PDpk0Pdg5jAAAgAElEQVS+t+/eDd9/D0OHmgVmd91lRqePPmqKq4DxD1gMH+6svK9FzAg3EuTk5FBREb4By/z584mLi2Po0KHs2WNss6+5CgVecYVxAP/97+aemzSp6ug+KcmYjnzRrVt46hLXhiOOGMLKldCnT72WVNWZ2piGxgN9gRLgFWAPcEMohaoNgRZyxcYmc+uts3yaN1JTTZEOh8PUFujT54jK1ZoWvqbf3oQyaVkkUiLMnTuX119/xkcabvj4Y5gyxSiBHj3g9tth4kRjHdy+3WQaKS0dV6XNWrQwx4qYTI5HHOGepkeKDh1Mb9qjR+hDa/Ly8vjyyy9Z58putnmz75j3tWvhb38zndSFF8Itt5gZ1E8/wYcfVlcEWVlVVwsnJ0euEwv3gOXqq6/mpJNOYtOmTRQUmBnW77+bEf3JJ5sSpi+9ZFLM16aOQFycKXcarBKewcBaAB91zmJVLVLVHFU9zJXmIUdVI5tlikAlEtNwOqvOBFq1MqOtrl3LSUvbzJYt6wiUqcJy2qWlBXZ+huqGnzJlCkle2bBCXRx86tTHmTDhSVJTq+aW37HDHYaXnW2cbZ06QUnJy64sroLD0QbVT7noom6V0VXWymKLUEZY1ZaUlCTX39DnGJ49ezbHHnss06dPp7QU/vyz+jG//ALjx5scQykpxvlZVmYGKgDPPWdmYmBMQwcfbBRB587ua4Rj0Zg/wj1gGTZsGMOGDaNDhw7s2QOLFpntgwa5O/5OnWqfObR//8gFLvgjahWBiPQUkRki8pGIfGK9wiFcIKy0AdZovk2bTsAcHI58Ro92K4G4OPMAJSXB+vXfkJHRgfPPP5+ysnVMn/53Pvxwps9O6qqrnKxfv5r58+fTybtXcxGqG96K4rGKg6ekhL44+Iknns1ZZ11b6S8pLTWx1/fdZzqoIUNMyl6Hw0RVPfpotiuLq7vAjBVd9emnuQEVbaQIp7O4Q4cOHHnkkXTt2pU//qj+nd98AzfdZNp26FATvvz443D//aZOdr9+ZgaRl2eUwPHHu2cFnTqZWYDDEdw6xHVlypQpxMaGL4fTrFmz+Oyzz0hMbEZRkWlDMLPNuiIS+VBR3xhT20MPhdf5X5tx2n+A6ZhcQL5jNCOE0+kkJiaGVavySUn5G9dfH0dGRtURQY8e7iiVtm3b0qZNG1q0aMHOnat59dWpHHnk0YwZc2XlFBzMTdKpE8TGJnLyySdz6623Mm7cuCrfHeoRutPpZOdOJ+PHG5NBqH2BnmnvVeGOO0wpPzBK9Kab3DZXX5lYLUpKinj22Rzuuiv6iplbimDTpm1s2FDqV8EHg7FjxzJ27Fg2bjThjZ7Mm2ec7hUVcOqppm1jY6tGXU2caMxxgwYZ+3dqqtvBGRtrnJupqZGdETidTubMgXnzcoC1ZGZmMGVK6B3Xe/aYgcoSk5yXoUPrfo3k5KoO42ihVSszENuxo5jy8nKftTRCQW0UQZmqPh1ySQ6Q0aNHs2IFPO2SsEcP977k5Kojpp49e2Klsc7Ly+OBBx6gffv2tGpFFUXQsmVVU4bT6WTx4sW89dZb7Nmzh4yM8NzwlnXIVCwKXRTBli1bePXVBbRs2ZV+/Y5i0SKjBJo3N7VcTzqpatm+mvI5bdoU/prAtcH6Tdeu3cDSpRtCqgjApAbxNgm9/jo8+aR573TC2LG+Qxa7dTOmIQtvc0cI3UV1YvBgJ/PmOTn44Ll89dWppKamhuR7ysrKqKioID4+nu3bjQ+lqMiYyg6kpGQo03PXhxYtTMefnT2hmu8ylNTmm94VkWtFpIOItLZeIZesDuze7R519ezp3h7oYcnMzOSWW27hsssuq1aWztOpOWXKFDIyMnjxxRdJTU1l9uzZIcub701Ozo0A7NpVu2yqB8rSpT9y++0X88IL/6CiAp591my/5BK4/PLq7ehvUZNFKJ3a9cEoglxETmfUqFEhj3vfvbtqOO7u3TDDlcR1/Hi48srax61HomJWbbAWVv766xq2bNkS+OB68OWXX5KQkMCIESPIz6/bbCApqbqPKjrNQm4fQUVFot+8aaGgNopgDHAzJmR0ieu1OJRC1ZYdO3aQm/sq33zzVTVFkJBQe/tpQkLVqkSucr3k5uZyzz33VJYL9CzWEg4sB1hhYWiN2s2atea44y5l796HueoqWLXK2KPP8l39MGBUVahNZvVh3rxcIBvVDdWK7wSbiy++mJ49W/PVV+9WbvvoI7NG4LDDzEyrLkSrIrBmrSNGnEP7EFZ737t3LzExMSQkJFNcbBztAAMGBD4vIQEGDjRmNE+iVRFY7RkomV4oqE3UUFcfr4ivLAb49ddfufji0Tz11C2sXm1GV91daWS6dPFtA7zgggto37496enpOBwOunTpQm5ubmWlsmbN3D9GTk4OpV6JfsJR69biscf+CUBiYmiD73v1GsKwYbP4449DWLXKONjHjfO/AtgzFQK4i9BnZITeqV0f/vWvHCxnnEWofs+dO3eye/fOylGdqlmAB3DGGQFO9EO0K4L09B60DOEikdNPP539+/fz6KOvUV5uwkaByiR7lvPck7g4owQSE6sXo49WRWD9zv/733d89tlnYftevz4CEfm7qj7ken+eqv7HY9/9qnpbOAQMREpKCqecci4ix/Hrr6bzT042jdnRdwlXlixZUmUKu379erKzs3n0UTjsMGeVXCORWNjlSfPmJmA81ClpCwvdI6yzz4YxY2p2QnouagKjQA87LIRCBgF/votQ/J5vvPEGn3xSSGyssZ38+KMpmN6mDRx5ZN2vF+2KIBwjWBGhoCCB1avNM9GhA5UDuIwM45OxVhZbOcGsmb6nIvBlKooWrPZct24LS5eu5LjjjgvL9waaEYz2eO+dKvpUooABAwZw553/oV8/E9FjOYozM/1HBPhKS1FUVMT99+dw6KFVV75GYmGXJ5ZpKNTZRzdt2sXy5SYg7IgjalYCMTHVj7EeyGimY8dw/p6JJCenER+fiCpY1qfTTjuwBWDRrghWrMjju+++C+l3lZeb6DZr0GLNBhITzSDQc31Fz55VzUGJiW5Zo3U2AG4Zu3YdxIgRI8L2vYEUgfh57+tzRCgqMqMAT/9AYmLgKILNmzf73O6r9nC4a91688knHwKwcWN+DUfWj9tvv47ffjPpF2qTz75r1+oO5BAFiwSV226rXnwoVL+n5wj5889Nsr6UFP9+l5qIVkVgDQiWL1/J3LlzQ/Y9Dz/8MH/5y0i+++4jfnXlY7Pu1W7dzMAvOdn49zp18t0HtG5tlHC0LSLzxK2sOtHHV/KuEBFIEaif974+R4RNm4ooLS2poghatgwcidHa21jowteo0Fq0lplp0gJ75pUPB1u2mPQEBQWhmxJUVMDu3ZlAIm3bFtY4WmrVyoy8PLOIijQMRXD++U5gBg5H6H/Pm2+ewIMPXsaGDdsqV2ZfeeWBtVNCQnTGvIM7OCM+fhCDBg0K2fcsWbKEjz/+kF27tlUqgj59jB/Ac/HiQQe5/YTetGtn1mX46QKiAkvhFxfXLkFhsAh0ew0UkT0iUgAMcL23Ptci4TCIyKki8ruI/CkitwQ47hwRURGpU82ve++9nVNOac6KFSaqpkePwGaN3NxcdvlI+GIVE/eF0+lkzZo1VFRUhC1s1GLUqJMBaN48dEl6Xnwxlz/+eBlwUFDQp1oBdItOneDoo43zDUznb+VoSU2NXpurJ0ZGJykpof89P/jgdebPf58772xZmajvtNP8Hx8oNXe0zgbAmGETE6G0tBUjRlwcsu+5/fbbmTnzXXr0OIG8PKMAunc3SsBz4Jec7H8g2LJldJuFwL2+IT+/iDfeeDfwwUHEryJQ1RhVbaGqKaoa63pvfY7zd56FiMQATwJ/AfoAF4pItbmOiKQAE4BFdRW+uLgMh6M/5eWxdOxofuRAiiAnJ8dnAZuUlJSojHTp1SsLANXQJPDPzc1l3LhsCgryAKW4eG1lAR5PkpPNSMuriBnt2xvbbG3qEEQD4UwxMW7c06SlrWDlynjatzeJ+vz5Bjp1gsMPN/eviDF5eHb+0awIHA5jmgFYujR039O3b1+OPPJ01q83USDduxvlGckUG6Gga1eIjVV27kxm4sSgVPGtFaGccB4O/Kmqq1S1FJgLnOnjuHuBfwJ1jo257rrHuekms7KkRw9zUwbS+P6iQ3bsiExa4poItbM4JyeHffuqOs9LSoqYOdOEU7ZsaZRAr16+TRPNmhkFEa1mC28sRVBUtJ/evXuH7HffuxdSUs5g+/ZU0tNN0j5fi5ibNTMPfo8eRrYBA8wrPb1q3YZoVgQAPXoYG8bnn4f2Odq3D+bPN++HDTPPR0MwSdaFuDjo1s2sQuzb99KwfW8oH+FOwDqPz+td2yoRkUOBLqr6fl0vXlxsoghWrDDzwJ49zbQqkH8g0lFAdWXvXlMJbc+e0KQi9KcYt25dS8uWxp56+OGRrSEQTCxFoOrg999/r7G63YGyY4c7gOHww90LFC1EjHI97LCqBVHi493RV+3bm05BJLL5hGpDRoYZwz300Gzy/JfxqBfTpk3juede4uuvFYcDRowgKhMbBoN+/czUcdCgv4dl9gqhVQQBEREH8ChwUy2OzRaRxSKy2MoVZJWps4pR9+1b8wMT6SigurJnj4kWCtXKYn8KsF27jIjXbg0FbtNMDD/9tIzMEJWl2rJlP599tgaomvLEolevmiNXYmKMyWXQoOqKJNoYMMBMWRyOfqxYsb/Gcpx1paKightvvJGnn/6e8nKpVK6NbTZgYQUL/f47lY7xUBNKRbAB8CxM2Nm1zSIF6Ad8KiJrgCOAd3w5jFV1hqsWwpC2rmFAYSHcdNP1rF8PLVpU0K9fzYmkIh0FVFe6dDEGUJGkGo6sO7m5uRQUVF8FlJSUzOTJU6J+FHogiLiVQe/efYkPQfHk8nLYuHE3v/xi7Hm9elXdn5ZW+yRpHTpE/2wAoG9fMw1PSzuJ2Ng4CoNcd72srIzrrruJFi0mASZjKzSMtjkQ+vUzf1esKGPTpmJCmMKpklAqgu+AHiLSVUTiMQvU3rF2qupuVW2jqlmqmgV8A5yhqrXKY1RYCMuWmY5y6ND9Phc5+SKSUUB1pX17YycoL48NaihZbm4u2dnZ7NhR1TTSqlUazz47g4kTo7dN6ounwzg3N5esrCwcjuAVXt+5E4qKYoCeOBz7ycpy7xOhUc60+vY1CnbzZrOup4ZS4nUmPj6eq69+iD17OpOQYFZmJyYGjrRqyFiKYNmy3SxevKBKivhQETJFoKplwDhgPvAr8JqqLheRe0TkALKtVGXvXkhPNxUzjzsujvj46Heq1RWHw50xM5gdVk5Ojs8V1s2bN49qxRgMLEVw9tlnM3bsWPLy8oKagG7HDti82Sjwnj3jqkRatW9fNblhY+Hdd3OBLFQdXHRRFq+/HvwkftZq4i5djAJorLMBMOt04uL2Amls26ZBn2H5IqQ+AlX9QFV7qupBqjrFte0fqvqOj2OPr+1soKLCZMhcv745SUlw2GGOqM0vXn9Mxsy1a4PXYflzEq9fH511BIKJ1THPm/dfSkpKquwLRgK6PXt8p0SPjTURQo0Na3ZZXm5CkHfsyOOee4Kb0XX37t18+aVJImRFUzVmRZCQAAcfbHJUdegwKix5nBpI4F9VCgvd9UoPP9yMEBqrIigru5VgZ8xsaNFTwcS98M23ra0+CegqKsy9+dtvJvzvoIPcTv7MzMZpyvA1uywpKWLMmDFBm8EuWLCAadNeAJqGIkhMhO7djd9l5UqTujzU+cYapCIoKIBly4yaTEr6EWicisA8QOt87qtPh+UreioxMXqjp4KJpQg6dfIdMVQfZVhYaJTBjz+amcYHH1wLmPwxIS6GFjH83Yfl5eVBNLnFkJQ0GHCnl2+Mz7sn1gr+Dz4wfpdQzwoarCL44Ye5QBbz5h3C6NFZvPdeeIrFhAtryu2P+nRYVvRUcnImIDRvnsljj0Vv9FQwsaKG/v734IcSFxSY0pQ7dyYB22jTJp+ePWHw4Iaz6K6u1OY+rO8M9qSTzqJ9exMq1KWLmQ2EsXhXRDjppO0kJuaxfj3Mnk3o/QSq2qBegwcP1rvvnqOQrJj5vQKanJysc+bM0cZCZmZmlf8v2P9rRYVqv36qoDp1qmpxcZAEj3IyM83//OabP+gdd9yhmZmZKiKamZlZ7zb9/XfV0aPN9c88U/Xnn4MjczQzZ84cTU5O9nuvWi8ROeDvWLNGNS7OtOt776lu2xbEfyBKWb++TGNjj1Mo15iYCn3vvfpfE1isfvrViHfsdX0NHjxY09J8d5KZmZn1b60oQUT8PlTBUHirV2/RhIS9Cqpvvx0EgRsI3bubux66K6B33323Llq0SPPz8+t97e++U01PN9efNk01Ly8IAjcA5syZ4zFwcQT92fz4Y9OmaWmmjZsCBQWqjzzyf3rUUXsUVG+4of7XDKQIGtyEtbwctm+PbOWwcOBvyt2qVaugmHCWLFlHSUkzRPZVJg1rClg+gj59jBH2zjvvZOjQoTz++OP1uq4qfPstbNliqpDVZoFjY8Fam3P88WcCPQl2zYc77zS/TWrqDkK0GDzqaNYMhgw5kaFDzU20fHlo01I3SEXQrFnjj3rxlw7jiSeeCMr1d+0yWRzT0vY26ggMbyxFMHfu68yaNYvExERatGhBzIGUDfOgsBAWLjTvu3X7lYkTj2Pu3Bn1lLZhceGFV/K3v00GZiDifhaLioqYMGECbdq0qXMkkSqUlpqRStu2hY02v5A3ImbNiRWCvGIFuLLrhIQGpwgqKqBTp/BVmooUlkM3IcE4dNu1C246jO3bTbKbfv3aNkFFkMspp2Rx2WWXkZ6ezlNPPcU//vGPel3XRLKZ961aLeannz5nw4bGM0OtDQkJu/nvf+8CLkG1EJNOzLB9+3a2b99ep0iiGTNmcMstdxATY5baDhzYSJML+aG4eDOff34XIuWsXWvur5AlofNnM4rWV+/eg7VjR1WYow5HYtAcfdHKyScb++gHHwT3uldeaa57ySWqZWXBvXY007VraAINfvrJ7dB8/vl1+sILC3XFihVBkjr6qa3T2POVlpYW0Fl/3HHHKaDdu+9QUH366Qj9cxHi++83KKAOx3IF1SefVK3PLUVj8xFs3AixsU4+/ngfJSXRnzOoPlhpM/LyNnHooYdyyimnBOW6y5ebeLSOHQsPqJh6Q2XDhhyCvUAP4McfzcKfjh2ha9fOnHTS8XT3VzOxEeIvbUkgtm/fHjDFxzXXXMO4cXeyaZPJgx7CSphRyYABHRg79i769jVJJ//8EzZtCo2voMEpgmJX+ZqsLFPAw7tqVmPDKk5TVhbPDz/8wM8//1zva5aUwK+/mlzBGze+Xe/rNSRKS32ba/LqkUi/tBR++sm8t2y6TcncBsEJ1PBWyBdccAEnnngXhYUOWrdunCk6AhETI9x7750ce6z5x1esMAPhYCf1gwaoCPa5arT07994CqYEwlIEDkcq3333HV9//XW9r7l3L+zdazK3HnJII8yCFoCEBP8BBaUHuI5/1y7zkIJRBN988zbPPvsIv//++wFdryESrEANT4Wyfz98/71537t340zYVxPt27tLwVr3WCiykTY4RWBpww8/vIzJky+PrDBhwFIEhYUxDBkyJCjFVFatMjOMli3hqqvOqvf1GhLdu1cPNEhMTOSRRx4h1p2IqFZYaazbt3fw3ntZQC49e8Jnn83m5psnBWX21lDwFeUGccTFmW3isRS4Xbt2pPmptmMplD179vDmm/P47juTKr1Xr6apCAoK9lBS8gFgPbcmsWGwaXCKwJoRFBfPQ3V/ZIUJA5Yi2L07eNe0Uvp27tz0Hq6MDCcwg7Zt3cWJZs6cyY033oijDnkgrBQglo27rCwPyGbTplzOO+8MJk6cSC/vqjSNGO+iT4mJmcAL9O27l0WLlJ07d/Lxxx/zyy+/sGXLFs4///wqygEgKSmpMvJv2bJljB79Fz7/3MwQ+vdvvGk6ArFmzRpuuOE0YmN/Z/9++Oij0MwIIh4FVNcXDNbOnSv0gw+26caNGw/chd5AmDrVRKKcd57qc889pxMmTKh3NMptt5lrjhwZJCEbEKNGaVBWU/tLAdKxY2ZQ5GzozJun2rKlie5r0aJqZJC/CKOzzz678vzvvvtODzvsZI2JKVZQXbgwcv9LJCktLdURI07RI454WcGsXP/oI9Xy8rpfi8aUYgIG68iRJlyvKfDRR+ZX6t9f9ZRTTlFA33///Xpd87zzzDVTUh7UrVu3BknShsFf/2r+9zfecG9799139aabbtIvv/yy1tfxlwKkPjl1GhPLlqmefnr1UN2kpCRNSUnx2XadO3euPL+4WPX557Wy82tCkbg++eIL1a5dTXuMH6+6e3fdzi8ra2ThowADBkDz5pGWIjxY6WhXroSLLrqCRx99tF4mh+JiWLHCxJ8VFHxBSlPJg+DCs1SlxYIFC3jkkUdYZBW5qAX+U4C0plOnTjgcDjIzM4NaoKUh0awZLFpUPVR33759FPixbWzY4C5pnpdnireD8Q8kBb9sd4MiNRWuuMK8f/PNupuHNm8OvD+kikBEThWR30XkTxG5xcf+q0XkZxFZKiJfikif2lz3u+/u4M47s9kSjqrOEaZdO5O7pqgI0tPPZ+LEiRxUj8K3BQXG6QTw5ptPkdjY6nvWgC9FMGrUKP75z38ybNiwasf7qmucm5vLHj8eux07trNx40ZUlbVr1wal/GVDpHlzyM+vW0ipqpKVlcW//51LXl4py5ebAUvv3kaxNFVUlaKijbRp8wNJSbBhA/z6a83rCdR1QHm5WX9Q48GheAExwEqgGxAP/Aj08Tqmhcf7M4B5NV03MbFr5bS8c+fOjXZFsSdHH22mhPfco7pp04FfZ86cOZqenqkgKpKp06c3/rbz5pJLTFvOmuXeZmXP9F7h6suWHRcXp/Hx8XVaQduYsuLWln371HWv1b6drFdCQrIedNAhKrJCQXX69Ka1+t2bH374QQHt0qWXHnqouy/wNJft379fp0+fXuW8v/71r/r999/rxo2qX38dIR8BcCQw3+PzrcCtAY6/EPiwpuuKVE1z29jqEPhi7FjzS11wQaE+/vhCXbRocZ2v4atTawpt583ll5u2fO4589mf47JZs2bqcPhOqVzXV1P1G9x33xyNi6tb2gnrFRubpKCamLhfv/460v9JZCkuLtZ27drpoYeepBdfXO7qC4wD3XLx3XHHHRoTE6M7d+5UVaM8unfvrlddla3ffluzIgilaagTVessrndtq4KIXCciK4GHgOtruqhqRZXPwUgPEO30cRnMfvxxJ9dffwL33HN/na/hKwVAU2g7b7xNQ/5SIxQWFlJRUVFt+4HQmLLi1oVx45xcffUMIBMQHI7a5zIpKzMpBA45xIGfJQdNhoSEBDZv3syMGQvo39902VaCw3XrzGB+9erVqCp//PEHAA6Hg8LCQlJSOtSqulnEncWq+qSqHgRMBm73dYyIZIvIYhFZ7Gt/Y6pD4Asrx8q6dQuIi0vg/fffrHNRcH9t1NjbzhtvRRDq/7+xZcWtCy1bwpVXOmnffg1QweWXzyIhwXvhir+akw7AwbJl3ViwoOn5WLwRETp3dg8Kf/9dKS01i8sKC4WXXnqJxYsXc/jhhwPQv39/Vq1ax9ln31Wr64dSEWwAunh87uza5o+5gM9lrqo6Q1WHqOoQX/sb+4hr4EBwOHIpLLyW/ftNYfS6FgX310atW7cOmpwNAW9FEJx7x/djlJaWFtTU4Q2RjAx3ioT9+51MmjSD9HSz6Cw9PZMzz7zah3IAKAeUvXvzmDSpaTrcvWnXDvLzvycu7k/KyoRXX/0/wCThFBEGeWTlExHWrImhtFR57bVHWblyfcBrh1IRfAf0EJGuIhIPjAbe8TxARHp4fDwNWFHXL2kKI67UVIiJqXvWTM+IF38hewUFBU3qIbMUwX7XonTfqRHqhkiM12fhmmuuIT8/v0krATDJ90aONO9ffhkyM53MnbuGTz6pYO7cNdxww1NVlINnDQOLpmjC9I3yzTevkJRkMhzGxZ1IXt6vLFr0C8XFVUOI1q41IaN5eb8ye/YjXHZZDelT/DkPgvECRgJ/YKKHclzb7gHOcL2fBiwHlgILgb41XTM1NU2Tk5MbfR0Cb6BuC5jqkh++KUW13HyzcRbfead725w5czQtLe0AncExTb5Na2LpUtUzzzTtnpmpOn++cXR6v3r1Otx2uNdAaalqTo5py7ZtVY8//ioFdOLEp/SPP1Q3bFBdtcrdpsccc5bGxd2tEDlnMar6gar2VNWDVHWKa9s/VPUd1/sJqtpXVQ9R1RNUdXlN18zKyqp05DXmOgTedOjg24ShqrRp06baqL4u+eGbkp/AmhF4lv1zOp3k5+cHPM93Kct4jAmjOk2pTWuiVSu4+mro0sUsFHv55erHLFiQy4oVP/q9RmM3/9aWuDg4/XSztmLbNsjLu5zWrdvTr98xbNgAf/xh2thiwoT/EBt7R43XjbizuK40pSIqntx66xQ/tlRT4OOKK66oogzq0hE1pYfMUgRFRdVrwPrL7JqZmcmsWbOqZMxMSUkDnsdExFSnKbVpTaSmmgJLN91kPr/8Mqz3MFnv2AFPPplDRUWJz/Obgvm3LqSlwRVXrEdkP6tXH8kdd2yka9d+Po+dPTuWffuEo48OfM0GpwiWLFnSJJfuX3KJcbR526MtSktLK+2o5eXQuXPtOqKm9pBZhYzKy83oybMEgS9/gdU+1qxBVVm/Xrn11nzASZcuU0hMbNz1s+tLSooZwA0cCKeeavwzt90G06ebmcI558CuXf4HLk3d4e5NaiocfHAK8E8AZs8uq5bJFWDNGnjvPZO1ddy4wNdscIoAaJJL95s3h+HDndXWUXiydu1avvwSvvgCLr10CrGx8QGvmUyT78cAABOISURBVJmZ2eQeMmtGUF5uOqTffnPv806l7Kt9VI0TbrnLiHnMMU4eeSTwOU0dEdN5AfztbyZlyrp18OqrJp9QUhLExvoeuGRmZtpt6UVqKjRv3pLbbhtCQkIF338fx59/Vj/umWegosKYkrKyAl+zQSoCaHqRBLGxpnZAu3b+R/rt2mVUhkUOH+6kQwf/tf0yMzOblI/FwlMRgDFLeOQ6w+l0smbNGr8+qA0bTL4ma0FPv35w1VWBz7ExZWXBdGIvvAB33w2XXAK33w5vvQWTJ1c3fSYl2TMrX8TFmdxLw4efyqhRpgt/9dWqx3z7LXzzjekzLrus5ms2WEUATc8hl5kJV101BRHfhZr37dvLv/51LWee2YYTThDWrfudmJi4ajODpmy6sAr9eCbhWrXKXfAoEPv2mWOLikxxHxE4/PDGXzc7GLRr554VNG8Ow4aZbJonnWR+k+HDq68xePJJe2blD6stzz3XmH4++cQo1PJymD8f7nD5hy+6yDjra0JUteajoghXwjnAPaptSvzwAzzzTC7PPDMB2F7Ls2KAVGAH7dpl8OijU5rsA7ZqFRx8sPENPP64e7FTixYmvbm/apXl5abt9+41D92995pz33gDevTwfY5NVQoLYfFiVyBjDSQkwJFHhl6mhsquXbB0qXmfmwszZ5r38fFuv9fIkTBxormnExPhyCNlifpZlNtgZwRNdVTbvTuMHu3k2mvz8RexUp1yoDn33lvBxx83bdNFt26QnW3eP/6420S0Z4/ppHytu1M1M4C9e83nL780f4891igQm9rRrJlZaRyIxERjbjv00PDI1FBJTaUyB5PTaZzvlhJo3x5uvBEmTXIPbGoq89kgFUFTdsilpJjO59xzAepiGlvLMcfUbprY2Ln6amOz/vNPE1VhUVxsRlk7dri3FRSYbdtdk6/SUmN7BaMIWrYMn9yNgayswG3Wq5dxJlsmPBv/HHSQMU8CjBgB//2veb3yCowa5d4H0NW/uxBogIpg8ODBTd4h16GD+ZHbtq19rHp6ujm2iaUW8klyMlx7rXn/3HOwe7d7X3k5/PyziWZZsgS+/77q/iVLjK+gRw/js2lidX3qjYgxzVmFZkRMx5+UBB072gOVupCcbNrMIinJt5Lt2BHatg18rQanCGzMj+pwQHa2/0VmnsTGxnPllVNISGjalZ4sHA4zmh8yxIz4n3226n5V40wuKKhuz54/3/w95hjbLHSgJCaatu/d2zjb+/WDoUNtX8uBkJkZeJFtQoIxJ9eErQgaILGxRhl4RloAHvne3XPCFi3SmDz5eYYPd9qzARci5jV+vGnL99+Hr76q+bzvv4fPPjO22JNPthVBfRAxtmzPWsQ+1kTZ1EB8PHSqVuXFTdeuNfsHwFYEDZbOnc2DM3y40zXaT6aiwsp7oyQkJJOTM4e3385n+HBjRrMVgcHqcDIy3AXB77+/atoDb/bvh2nTzPtLLjGdmO0fsIkGunTxHcLcvLl7/UZN2IqggZKSYpQBwMyZOZSUVE0wV1JSxMyZ7gV3Dodtf7VITXUrg9GjjZmosNBEXng6ii3Ky+Ghh0xq3y5d4PzzzYOXkhJeuW1sfBEXB337Vh35p6UZX0xtZ1m2ImjAdO1qHEZbt/qOHrK2p6TA4MH+Y+SbGikp7jBGEZg82URgrFsHN99c1TlcXAz33AMLFhgzxi23mOl4mza2KcMmekhNNT6XzEzjb+nfv27+QFsRNGAcDhNv7S9Fdbt2GbRpY46xncRVycx0t0mzZjB1qhntr1pl1hl8/bVZpn/11fD55+aYhx92lwqsKQrDxibctGtnBoeefpfaYiuCBk5sLDz0UPWsmUlJydx88xT69rVHrr5wOMziMotWreCRR8x0eutWYyaaPNnkds/IMIvPLCUQF2eb2WwaF7axoBFgranIyclh7dq1ZGRkVKZOtvFPWpqJ/Nmzx3xu29Z0+C+9ZGYBLVsaxTBmTNVRlm0WsmlsNLhcQ0OGDNHFixdHWgybRsLOnfCj/8JYPjn0UDt01KbhIRKhXEMicqqI/C4if4rILT723ygiv4jITyLyfyJS2+Q5NjZBoVWrqqsza6JdO1sJ2DQ+QqYIxJTSehL4C9AHuFBE+ngd9gMwRFUHAK8DD4VKHhsbf/ToYTr4mvD2K9jYNBZCOSM4HPhTVVepaikwFzjT8wBVXaiqVgD8N0DnEMpjY+MTERN6F2hdgIgJMbVzC9k0RkKpCDoB6zw+r3dt88dY4ENfO0QkW0QWi8jibd4Vx21sgoDDYRbl+FprER9v6u0GWspvY9OQiYqoIRG5GBgCHOdrv6rOAGa4jt0mInlB+uo2QH6QrhVKbDmDT0OR1ZYzuDRlOf36YEOpCDYAXTw+d3Ztq4KIDAdygONUtaSmi6pq0JbyiMhif170aMKWM/g0FFltOYOLLadvQmka+g7oISJdRSQeGA2843mAiAwCngHOUNWtIZTFxsbGxsYPIVMEqloGjAPmA78Cr6nqchG5R0TOcB02FWgO/EdElorIO34uZ2NjY2MTIkLqI1DVD4APvLb9w+P98FB+fy2YEeHvry22nMGnochqyxlcbDl90OBWFtvY2NjYBBc76ZyNjY1NE6dRKwIRqWV9nsgjIg0in6WIpEVahtpgt2dwEZEGkXi7oTzz0daejVIRiEhzEfkX8KGIPCMiZ0daJn+ISLKIPAnME5HxrkgqRCSqfhtXmz4GvC8i94nICZGWyRd2ewYXEUkUkaeBha5AjxNd26OxPaP+mY/W9oyqHzMYiEgnYDamgvtI4DOiO4fRjUAaMAZIxITToqoVkRTKExHpAbwFlANXANuA2yIqlH/s9gwuVwDtMIs9VwPPi0hilLVnQ3rmo7I9G50iAIqBmao6QVU3A68BS0VkQITlqkREEl1/Y4F44GVV/U1VpwLbXCPFiI8SPCgEZqjqJFX9BRMJtklEoiI3lN2ewUVEmnt+BL5W1e2q+gLwNXC/67hoqcoQ1c98Q2jPaHkwDhgR6SUi00UkCUBVtwOfehzSBegG/B4B8aogIj1FJBd4QkSGuNZaNAeO9DjsauASEekcqVGCq00rR1SqupGqeaCSgd6quj7swnlgt2dwEZHuIvIa8KKInCYiCa5dnrlZbwb+KiIHqapGovNqKM98Q2lPaOCKQESOwUwJszEmAUREVLXQ47B4YE1t0leEEtdN+wzwI/ATcJ2IjAX+CVwtIm0AVHUdMAe4KkJynga8CUwSVw0JEYlV1b0eh7Um8g+Z3Z5BxDVb+hewDPNMjQL+AbwEjBSRfgAuZfU2LlOWhjn+vKE88w2lPS0atCIAtmNsbj2By0Uk00dDDgJWAojIVRGcLh4EFKrqQ6r6BDAT+CuQBDxN1QUkf2CytUZiurgFcGLadLKINFfVMhFxeMjSB1juku8iEekZZhnBbs9g0wHYBUxR1beBe4ERQA+Mws0Rd0TOPCBYiR/rSkN55htKewINXBGo6q+Ymgd/Ah8D90A1W/BJQJqIvAFchLEnhh1VXQZkicgw16afgP8D/o5JutdaRO4UkfOBK4F9rvPCOkJQ1cXAb642nQdMd+0SD1mOAdqKyFuYTm5/OGV0yWm3Zz3xVIqqugGTAXiEx+engftdirYQuFtErsTMunaEQ0YfckbtM+8pQzS3p09UNepfmKlzC4/P4v0eSAH+BE7yOvdDzGjr3DDJ2gZI95QPcLjejwfmeOw7BHjOdU5P4FLgI8AZCTl9tGkLzKjmMI99CZhOdwlwfhjk7AAc5bUtGtuzmpxR3J5jvbbFuP5eBnzpsT0V43g9BGiFMW/MDWN7jvWzL2qeeUyNlYeB+Ghuzxr/j0gLUIuGvgOTtG4ucJdrm8PrGKvhbwDec72/0NUJHx9GWW8HfsOU3XzQUzbX++7AG8AY1+c0TFK+9mFuU19y+mvTO4CFrvenuv6eFUZZl2NG0oe6PnsqrKhoz5rkjJb2dH33EuBGP/sdwCfADR7bXgL6hbktA8rp1Z4Re+YxgQjLgH9jghS8n6GoaM9a/S+RFiBAIzfDZCd9FUjHjPB2A1mu/dVGsK73O13HPQckhknWROBBjLZv65K3CGht3RAex56MsV8eCpwPLAQyokFOr2M927QMKACmAXFhktXherg+xEydJwLNoqk9ayGnv3s0Eu35sOs7q32fl2yDMfHtZwEXuzrkPmFsT79yBpA5rM887hnpE/46ddyzloi2Z63/p0gL4KMBU11/Y4HjgViPfc8CV/g5ryVGcfwEHB1OWV3vO3i8Px54Bejr5+a4BnjcdVOEXNa6yumxv42rzX8It5we2/4NTHJ1msOirT1rI2c0tCdwMKYueApmUHUFcITX8VYHdyZwN/A5cEy0yelxbFifeS85HcDPmMFUX5ccl+ExqMI9awlrex7Q/xZpATwaLc31MH0EXA9099gnmJCwhcAhfs53AAMiJGtv1/ZYzAh1DfAI8C1mumrdEJ4j2ZholdPj/NgwPWCeco4HDnZt7w686Hp/I2bEfQPQ2bovItietZYzwu15vYecd2Ec0V8Bd2IiVS71uD8l1LIFQ06P88PyzPv43fu7tt8PPOba/jfMwsD7PZ4zR6hlC9YrKqKGROQIzNQ6H7gP44C53uMQB+bGKMVHuUswKQRU9acQi+pP1mtdMpQB3wPdVPUmzCjgRpf8qMeCJlUtj1Y5PWQsU9X/hVnOzh5y/gnsEZEYoBcwARiqroVX6nraXO/D3Z61ltNDxki0ZyfMjAnMbOUeYLiq3o0pHHWth3xKmKiPnBbheOb9/O5jXbstk+R/VPUZYDLQHteCMY2iNBw1ERXF6zGRFI+q6lyozBx5kmsl3n5VLReRbq7328QklIq3jo8SWROBElWtLDitqu+LyCTMTb7GlrNOcsZh4uv7Y6b+KzH2YxWRHqq6wpaz1nIOd/3uu1X1XutAVX1XRG7EdG7hjmNvyHKOcO37DLOK/VCXnD+7FjI2GAVgEXZF4FoFWGXkoaq/icg6j337MaYhz5WBJwFJIvISZtQ1OcpkLfY8R0QOxowg8oCNtpx1lnM/8KOIfAl8oaofiUhXTO3rkMaFN0I5D7J+d49z+2B+9zVEz+/eUOTs5tr3p4j8G7hFTD6rbphZ9epQyhkKwmoaEpE4z4b2WihS6LEvCzO68qQN0A8TlztUVT+NUlkTROQSTLTTJ6p6maqW2nIemJyqeoeqfuR6v1pVH1CTNsKW8wDkFJFYEfkrJhz7/1T1cpcys+U8sN99KcZs9SXwjqqermbxWIMibIpARMYBC8Tk4B4FxibpuRrP4303YJFr29ki0hKTtjdLVUNey7M+smISiM3H2IiftOWs92/fPpSyNTU5MZE2nwGHR/Pv3lDkFJGOqrpDVd9Q1edCKWcoCbkiEJHWYsw5pwC3YpwuY1zT50qHiogMULdzpQ/QU0Q+BM7D+AN+U9V9DUDWGFXdGkpZm5Cc5xAGe2sTkvNcTCTLDm8TjC3nAf/uZaGSL6xo6EOvYjCZAq2Y727Ai7hi1zFe9peAL4COQAawB5NWNmwrWBuSrLactpy2nE1HzrC0RQgaNxazsKaLx7bmHu8dmAUjPVyfRwDXel3j8jDdCA1CVltOW05bzqYjZyRewW7o/pj49C3AK36OORj4wM+++GDK0xhkteW05bTlbDpyRuoVbB9BPmapf29MiuCTAUQkxsML3wFToxURGSquot0iIhrCqJUGLKstpy2nLWfTkTMiBFURqOom4FVV3YmxtVlVd8oxaSIABgLxIjIVszzbOjdsqxobkqy2nLactpxNR85IEfSoIXVHobwEFIvI9a7tFS7NOww4Edihqkep6sJgy1BbGoqstpy2nLacTUfOiBBKuxMmLGuR6/0A198z8HDWRMurochqy2nLacvZdOQMW3uEocHnASWYzHxtIv0PNwZZbTltOW05m46c4XiFbEGZmOLc92E88eNUdaR6JDqLJhqKrLacwcWWM7jYcjZcrIUUobm4yF8weWxKajw4wjQUWW05g4stZ3Cx5WyYhFQR2NjY2NhEP1FRmMbGxsbGJnLYisDGxsamiWMrAhsbG5smjq0IbGxsbJo4tiKwsbGxaeLYisDGpgZEpFxElorIchH5UURuEo/qVX7OyRKRi8Ilo41NfbAVgY1NzexT1UNUtS8mR/1fgDtrOCcLsBWBTYPAXkdgY1MDIrJXVZt7fO4GfAe0ATKB2UAz1+5xqvqViHyDWbm6GpiFSYH8IHA8kAA8qarPhO2fsLEJgK0IbGxqwFsRuLbtAnoBBUCFqhaLSA9M0ZMhInI8MElVT3cdnw20U9X7RCQB+B9wnqquDus/Y2Pjg9hIC2Bj08CJA/4tIocA5UBPP8edDAwQkXNdn1sCPTAzBhubiGIrAhubOuIyDZUDWzG+gi2YoiYOoNjfacB4VZ0fFiFtbOqA7Sy2sakDItIWmA78W41dtSWwSVUrgEuAGNehBUCKx6nzgWtEJM51nZ4i0gwbmyjAnhHY2NRMkogsxZiByjDO4Udd+54C3hCRSzH57Qtd238CykXkR0xpxGmYSKLvXdWwtgFnhesfsLEJhO0strGxsWni2KYhGxsbmyaOrQhsbGxsmji2IrCxsbFp4tiKwMbGxqaJYysCGxsbmyaOrQhsbGxsmji2IrCxsbFp4tiKwMbGxqaJ8//5dDEUiFaSuwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "test_loss = plot_from_loader(loader_test, model, i=670, title=f'epoch={epoch}, train,')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T15:45:11.163568Z",
-     "start_time": "2020-02-01T15:41:56.500Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:57.474412Z",
-     "start_time": "2020-02-02T00:49:53.557661Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for vis_i in [20, 50, 60, 70]:\n",
-    "    test_loss = plot_from_loader(loader_train, model, i=vis_i, title=f'epoch={epoch}, train,')\n",
-    "    plt.show()\n",
-    "    model.eval()\n",
-    "    test_loss = plot_from_loader(loader_test, model, i=vis_i, title=f'epoch={epoch}, test,')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-01T12:20:14.488703Z",
-     "start_time": "2020-02-01T12:20:14.427953Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## History"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:57.540832Z",
-     "start_time": "2020-02-02T00:49:57.478524Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>epoch</th>\n",
-       "      <th>grad_norm</th>\n",
-       "      <th>i</th>\n",
-       "      <th>kl</th>\n",
-       "      <th>loss</th>\n",
-       "      <th>n</th>\n",
-       "      <th>test_loss</th>\n",
-       "      <th>y_std</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>9.048189</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.000529</td>\n",
-       "      <td>-1.272202</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.363311</td>\n",
-       "      <td>0.085361</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3.828949</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.000626</td>\n",
-       "      <td>-1.098765</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.356763</td>\n",
-       "      <td>0.102071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3.531951</td>\n",
-       "      <td>200</td>\n",
-       "      <td>0.000582</td>\n",
-       "      <td>-1.135993</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>0.357749</td>\n",
-       "      <td>0.095088</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   epoch  grad_norm    i        kl      loss      n  test_loss     y_std\n",
-       "0      0   9.048189    0  0.000529 -1.272202    0.0   0.363311  0.085361\n",
-       "1      0   3.828949  100  0.000626 -1.098765  100.0   0.356763  0.102071\n",
-       "2      0   3.531951  200  0.000582 -1.135993  200.0   0.357749  0.095088"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_hist = pd.DataFrame(history)\n",
-    "df_hist['n'] = df_hist['n']/(df_hist['epoch']+1)\n",
-    "df_hist.head(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:57.934350Z",
-     "start_time": "2020-02-02T00:49:57.543882Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c502857b8>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(df_hist[['loss', 'test_loss']]).plot(ylim=[-2,0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-02T00:49:59.345827Z",
-     "start_time": "2020-02-02T00:49:57.936533Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/wassname/.pyenv/versions/3.7.2/envs/jup3.7.2/lib/python3.7/site-packages/pandas/plotting/_core.py:441: UserWarning: Attempted to set non-positive bottom ylim on a log-scaled axis.\n",
-      "Invalid limit will be ignored.\n",
-      "  ax.set_ylim(self.ylim)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c5007d320>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df_hist[['y_std']].plot(ylim=[0,1e4], logy=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "file_extension": ".py",
-  "kernelspec": {
-   "display_name": "jup3.7.2",
-   "language": "python",
-   "name": "jup3.7.2"
-  },
-  "mimetype": "text/x-python",
-  "name": "python",
-  "npconvert_exporter": "python",
-  "pygments_lexer": "ipython3",
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": true
-  },
-  "version": 3
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/data/gp_curves.py b/src/data/gp_curves.py
index 451aae9..4df9f63 100644
--- a/src/data/gp_curves.py
+++ b/src/data/gp_curves.py
@@ -40,6 +40,7 @@ class GPCurvesReader(object):
         sigma_scale=1.0,
         random_kernel_parameters=True,
         testing=False,
+        sequential=False
     ):
         """Creates a regression dataset of functions sampled from a GP.
 
@@ -63,6 +64,7 @@ class GPCurvesReader(object):
         self._sigma_scale = sigma_scale
         self._random_kernel_parameters = random_kernel_parameters
         self._testing = testing
+        self._sequential = sequential
 
     def _gaussian_kernel(self, xdata, l1, sigma_f, sigma_noise=2e-2):
         """Applies the Gaussian kernel to generate curve data.
@@ -112,10 +114,10 @@ class GPCurvesReader(object):
         # If we are testing we want to have more targets and have them evenly
         # distributed in order to plot the function.
         if self._testing:
-            num_target = 400
+            num_target = num_context*2
             num_total_points = num_target
             x_values = (
-                torch.arange(-2, 2, 1.0 / 100).unsqueeze(0).repeat(self._batch_size, 1)
+                torch.linspace(-2, 2, num_target).unsqueeze(0).repeat(self._batch_size, 1)
             )
             x_values = x_values.unsqueeze(-1)
         # During training the number of target points and their x-positions are
@@ -127,6 +129,10 @@ class GPCurvesReader(object):
                 torch.rand((self._batch_size, num_total_points, self._x_size)) * 4 - 2
             )
 
+        # For sequential
+        if self._sequential:
+            x_values = x_values.sort(dim=1)[0]
+
         # Set kernel parameters
         # Either choose a set of random parameters for the mini-batch
         if self._random_kernel_parameters:
@@ -160,7 +166,6 @@ class GPCurvesReader(object):
         y_values = torch.matmul(
             cholesky, torch.randn((self._batch_size, self._y_size, num_total_points, 1))
         )
-
         # [batch_size, num_total_points, y_size]
         y_values = y_values.squeeze(3)
         y_values = y_values.permute(0, 2, 1)
@@ -172,6 +177,8 @@ class GPCurvesReader(object):
 
             # Select the observations
             idx = torch.randperm(num_target)
+            if self._sequential:
+                idx = idx.sort()[0]
             context_x = x_values[:, idx[:num_context]]
             context_y = y_values[:, idx[:num_context]]
 
diff --git a/src/models/model.py b/src/models/model.py
index c174a36..e9dd655 100644
--- a/src/models/model.py
+++ b/src/models/model.py
@@ -53,11 +53,24 @@ class LatentModel(nn.Module):
                  batchnorm=False,
                  use_lvar=False,
                  attention_layers=2,
+                 use_rnn=False,
                  **kwargs,
                 ):
 
         super(LatentModel, self).__init__()
 
+        self._use_rnn = use_rnn
+
+        if self._use_rnn:
+            self._lstm = nn.LSTM(
+                input_size=x_dim,
+                hidden_size=hidden_dim,
+                num_layers=attention_layers,
+                dropout=dropout,
+                batch_first=True
+            )
+            x_dim = hidden_dim
+
         self._latent_encoder = LatentEncoder(
             x_dim + y_dim,
             hidden_dim=hidden_dim,
@@ -106,6 +119,15 @@ class LatentModel(nn.Module):
     def forward(self, context_x, context_y, target_x, target_y=None):
         num_targets = target_x.size(1)
 
+        if self._use_rnn:
+            # see https://arxiv.org/abs/1910.09323 where x is substituted with h = RNN(x)
+            # x need to be provided as [B, T, H]
+            x = torch.cat([context_x, target_x], dim=1)
+            # h: [B, T, num_direction * H]
+            h, _ = self._lstm(x)
+            context_x = h[:, :context_x.shape[1], :]
+            target_x = h[:, context_x.shape[1]:, :]
+
         dist_prior, log_var_prior = self._latent_encoder(context_x, context_y)
 
         if target_y is not None: