Evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch_autoneb import suggest, config, find_minimum, neb, to_simple_graph, visualise\n",
    "import torch\n",
    "from torch import optim\n",
    "import main\n",
    "import pickle\n",
    "import numpy as np\n",
    "import os\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import yaml\n",
    "import random\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace this path with your own project directory\n",
    "project_directory = \"tmp/project_copy/\"\n",
    "assert os.path.isdir(project_directory), \"Project directory was not found!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Model has 274042 dimensions.\n"
     ]
    }
   ],
   "source": [
    "# Load config\n",
    "on_device = False\n",
    "config_file = os.path.join(project_directory, \"config.yaml\")\n",
    "model, minima_count, min_config, lex_config = main.read_config_file(config_file, on_device)\n",
    "print(f\"Model has {model.number_of_dimensions} dimensions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataModel(\n",
       "  (model): CompareModel(\n",
       "    (model): DenseNet(\n",
       "      (conv1): Conv2d(3, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (dense1): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (bn1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(24, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (bn1): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(36, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (bn1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (bn1): BatchNorm2d(60, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(60, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (bn1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(72, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (bn1): BatchNorm2d(84, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(84, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (6): Bottleneck(\n",
       "          (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (7): Bottleneck(\n",
       "          (bn1): BatchNorm2d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(108, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (8): Bottleneck(\n",
       "          (bn1): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (9): Bottleneck(\n",
       "          (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(132, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (10): Bottleneck(\n",
       "          (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (11): Bottleneck(\n",
       "          (bn1): BatchNorm2d(156, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(156, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (12): Bottleneck(\n",
       "          (bn1): BatchNorm2d(168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(168, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (13): Bottleneck(\n",
       "          (bn1): BatchNorm2d(180, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(180, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (14): Bottleneck(\n",
       "          (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (15): Bottleneck(\n",
       "          (bn1): BatchNorm2d(204, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(204, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (trans1): Transition(\n",
       "        (bn1): BatchNorm2d(216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv1): Conv2d(216, 108, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (dense2): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (bn1): BatchNorm2d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(108, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (bn1): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(120, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (bn1): BatchNorm2d(132, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(132, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(144, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (bn1): BatchNorm2d(156, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(156, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (bn1): BatchNorm2d(168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(168, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (6): Bottleneck(\n",
       "          (bn1): BatchNorm2d(180, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(180, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (7): Bottleneck(\n",
       "          (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (8): Bottleneck(\n",
       "          (bn1): BatchNorm2d(204, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(204, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (9): Bottleneck(\n",
       "          (bn1): BatchNorm2d(216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(216, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (10): Bottleneck(\n",
       "          (bn1): BatchNorm2d(228, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(228, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (11): Bottleneck(\n",
       "          (bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(240, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (12): Bottleneck(\n",
       "          (bn1): BatchNorm2d(252, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(252, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (13): Bottleneck(\n",
       "          (bn1): BatchNorm2d(264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(264, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (14): Bottleneck(\n",
       "          (bn1): BatchNorm2d(276, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(276, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (15): Bottleneck(\n",
       "          (bn1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (trans2): Transition(\n",
       "        (bn1): BatchNorm2d(300, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (conv1): Conv2d(300, 150, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      )\n",
       "      (dense3): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (bn1): BatchNorm2d(150, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(150, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (bn1): BatchNorm2d(162, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(162, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (bn1): BatchNorm2d(174, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(174, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (bn1): BatchNorm2d(186, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(186, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (bn1): BatchNorm2d(198, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(198, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (bn1): BatchNorm2d(210, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(210, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (6): Bottleneck(\n",
       "          (bn1): BatchNorm2d(222, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(222, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (7): Bottleneck(\n",
       "          (bn1): BatchNorm2d(234, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(234, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (8): Bottleneck(\n",
       "          (bn1): BatchNorm2d(246, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(246, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (9): Bottleneck(\n",
       "          (bn1): BatchNorm2d(258, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(258, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (10): Bottleneck(\n",
       "          (bn1): BatchNorm2d(270, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(270, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (11): Bottleneck(\n",
       "          (bn1): BatchNorm2d(282, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(282, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (12): Bottleneck(\n",
       "          (bn1): BatchNorm2d(294, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(294, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (13): Bottleneck(\n",
       "          (bn1): BatchNorm2d(306, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(306, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (14): Bottleneck(\n",
       "          (bn1): BatchNorm2d(318, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(318, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (15): Bottleneck(\n",
       "          (bn1): BatchNorm2d(330, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv1): Conv2d(330, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(48, 12, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (bn1): BatchNorm2d(342, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (linear_out): Linear(in_features=342, out_features=100, bias=True)\n",
       "    )\n",
       "    (loss): NLLLoss()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "ename": "EOFError",
     "evalue": "Ran out of input",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-141-19836063d325>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Load graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproject_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"graph.p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mgraph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEOFError\u001b[0m: Ran out of input"
     ]
    }
   ],
   "source": [
    "# Load graph\n",
    "with open(os.path.join(project_directory, \"graph.p\"), \"rb\") as file:\n",
    "    graph = pickle.load(file)\n",
    "len(graph.nodes), len(graph.edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_graph = to_simple_graph(graph, lex_config.weight_key)#, lex_config.auto_neb_config.cycle_count)\n",
    "len(simple_graph.edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connectivity Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise.draw_connectivity_graph(simple_graph, lex_config.value_key, lex_config.weight_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minimum Spanning Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mst = nx.minimum_spanning_tree(simple_graph, lex_config.weight_key)\n",
    "visualise.draw_connectivity_graph(mst, lex_config.value_key, lex_config.weight_key, nx.spring_layout(mst))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_values = torch.Tensor([mst.nodes[node][lex_config.value_key] for node in mst.nodes])\n",
    "saddle_values = torch.Tensor([mst.get_edge_data(*edge)[lex_config.weight_key] for edge in mst.edges])\n",
    "\n",
    "print(\"Averages over minimum spanning tree:\")\n",
    "print(f\"Minima:  {node_values.mean().item():.4f} ± {node_values.std().item():.4f} ({lex_config.value_key})\")\n",
    "print(f\"Saddles: {saddle_values.mean().item():.4f} ± {saddle_values.std().item():.4f} ({lex_config.weight_key})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loss along MST paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for edge in mst.edges:\n",
    "    data = mst.get_edge_data(*edge)[lex_config.weight_key.replace(\"saddle\", \"dense\")]\n",
    "    lengths = mst.get_edge_data(*edge)[\"lengths\"]\n",
    "    visualise.plot_dense(data, lengths, True)\n",
    "plt.ylabel(lex_config.weight_key.replace(\"saddle_\", \"\").replace(\"_\", \" \").title())\n",
    "plt.xticks([0, 1], [\"$\\\\theta_1$\", \"$\\\\theta_2$\"])\n",
    "plt.xlim(-0.05, 1.05)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}