final_exp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "strange-decimal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "import os\n",
    "import cv2\n",
    "from tqdm.notebook import trange\n",
    "\n",
    "import gym\n",
    "from gym import spaces\n",
    "\n",
    "from stable_baselines3 import DQN\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "from stable_baselines3.dqn.policies import CnnPolicy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import sklearn as sk\n",
    "from sklearn import datasets\n",
    "# from stable_baselines3.common.policies import register_policy\n",
    "# register_policy(\"myPolicy\", CnnPolicy)\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "affiliated-engagement",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/vol1/idrl/idrl_env/mmd/idrl_mmd/')\n",
    "norm_models = sorted(glob('*norm*.zip'))\n",
    "models = sorted(glob('*.zip'))\n",
    "models = [model for model in models if model not in norm_models]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "regional-prior",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_svmlight_file\n",
    "from dqn import *\n",
    "import numpy as np\n",
    "from stable_baselines3 import DQN\n",
    "\n",
    "\n",
    "train = load_svmlight_file('data/mmd_mnist_torch')\n",
    "x, y = train\n",
    "x = x.todense()\n",
    "\n",
    "sortind = np.argsort(y)\n",
    "x = x[sortind, :]\n",
    "y = y[sortind]\n",
    "\n",
    "test = load_svmlight_file('data/mmd_mnist_torch.t')\n",
    "testx, testy = test\n",
    "testx = testx.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "recovered-soldier",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dqn_cnn_mnist_100_tarun.zip\n"
     ]
    },
    {
     "data": {
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       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 18.50%\n",
      "dqn_cnn_mnist_10_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea4eab81110349f2a68e74e0122863e8",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 30.57%\n",
      "dqn_cnn_mnist_1_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5aa00a4137fc4d30887afa63349cc5cf",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.06%\n",
      "dqn_cnn_mnist_25_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c7be49e44094fa1ad54109ca226d8dc",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 21.20%\n",
      "dqn_cnn_mnist_50_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dadfcd636b7e49df9bf8b00f558d4f43",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 21.78%\n",
      "dqn_cnn_mnist_5_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03d5b695f889424289dfa7c125e2e51f",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 49.40%\n",
      "dqn_cnn_mnist_667_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5a187a601484da1aff0430c9dafaa76",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 17.22%\n",
      "dqn_cnn_mnist_75_tarun.zip\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66eb774a90964c178060669600b98e84",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 23.31%\n"
     ]
    }
   ],
   "source": [
    "width, height = 64, 64\n",
    "for model_name in models:\n",
    "    print(model_name)\n",
    "    model = DQN.load(model_name)\n",
    "    \n",
    "    total_rewards = 0\n",
    "    pred_y_test = []\n",
    "    count = 0\n",
    "\n",
    "    for idx in trange(len(testx)):\n",
    "        # Generate an evaluation observation frame.\n",
    "        obs = cv2.resize(np.array(testx[idx]).astype(np.float32), (width, height), interpolation = cv2.INTER_CUBIC)\n",
    "        obs = obs.reshape(width, height, 1)\n",
    "\n",
    "        # Predict an action based on the observation.\n",
    "        action, _states = model.predict(obs)\n",
    "        pred_y_test.append(action)\n",
    "\n",
    "\n",
    "        # Score the prediction.\n",
    "        if (action.size > 1):\n",
    "            # print(action)\n",
    "            count += 1\n",
    "            pass\n",
    "        else:\n",
    "            reward = 1 if action == testy[idx] else 0\n",
    "            total_rewards += reward\n",
    "\n",
    "    print('Accuracy: {:.2f}%'.format(total_rewards / (len(testy) - count) * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "renewable-amateur",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dqn_cnn_mnist_100_tarun.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/vol1/idrl/idrl_env/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 10010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6bc2c88d22b4788a67c0f7550b785c8",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width, height = 64, 64\n",
    "for model_name in models:\n",
    "    print(model_name)\n",
    "    model = DQN.load(model_name)\n",
    "    \n",
    "    total_rewards = 0\n",
    "    pred_y_test = []\n",
    "    count = 0\n",
    "\n",
    "    for idx in trange(len(x)):\n",
    "        # Generate an evaluation observation frame.\n",
    "        obs = cv2.resize(np.array(x[idx]).astype(np.float32), (width, height), interpolation = cv2.INTER_CUBIC)\n",
    "        obs = obs.reshape(width, height, 1)\n",
    "\n",
    "        # Predict an action based on the observation.\n",
    "        action, _states = model.predict(obs)\n",
    "        pred_y_test.append(action)\n",
    "\n",
    "\n",
    "        # Score the prediction.\n",
    "        if (action.size > 1):\n",
    "            # print(action)\n",
    "            count += 1\n",
    "            pass\n",
    "        else:\n",
    "            reward = 1 if action == y[idx] else 0\n",
    "            total_rewards += reward\n",
    "\n",
    "    print('Accuracy: {:.2f}%'.format(total_rewards / (len(y) - count) * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "moderate-operations",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "protos/protos_1500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 8710     |\n",
      "|    time_elapsed     | 0        |\n",
      "|    total timesteps  | 6000     |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.19     |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 8404     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 8467     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 18000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 8343     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 8292     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 30000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 24       |\n",
      "|    fps              | 8322     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 28       |\n",
      "|    fps              | 8238     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 42000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 32       |\n",
      "|    fps              | 8283     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 36       |\n",
      "|    fps              | 1379     |\n",
      "|    time_elapsed     | 39       |\n",
      "|    total timesteps  | 54000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0271   |\n",
      "|    n_updates        | 999      |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 40       |\n",
      "|    fps              | 683      |\n",
      "|    time_elapsed     | 87       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0488   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
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       "model_id": "e8656fb5fc7f4d7d91ec092ed2d8c10a",
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 71.97%\n",
      "=============== m=protos/protos_1500.npy error=28.02990418026745 ===============\n",
      "protos/protos_2000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10456    |\n",
      "|    time_elapsed     | 0        |\n",
      "|    total timesteps  | 8000     |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.387    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 10490    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 10194    |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 10164    |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 9698     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 40000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 24       |\n",
      "|    fps              | 9618     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 28       |\n",
      "|    fps              | 1092     |\n",
      "|    time_elapsed     | 51       |\n",
      "|    total timesteps  | 56000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0147   |\n",
      "|    n_updates        | 1499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "534fab7798c34e0888e959ab17e28ae0",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.38%\n",
      "=============== m=protos/protos_2000.npy error=25.618877067312766 ===============\n",
      "protos/protos_3000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9860     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.369    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9231     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 8854     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 8905     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 710      |\n",
      "|    time_elapsed     | 84       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0215   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
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       "model_id": "ca57140943a04b45b46ba841f4c74a0d",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 75.02%\n",
      "=============== m=protos/protos_3000.npy error=24.97898276586801 ===============\n",
      "protos/protos_4500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9342     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 18000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.284    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9172     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 1443     |\n",
      "|    time_elapsed     | 37       |\n",
      "|    total timesteps  | 54000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0185   |\n",
      "|    n_updates        | 999      |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "870e3ea87cd24a53909be633562654a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.51%\n",
      "=============== m=protos/protos_4500.npy error=27.485503426462827 ===============\n",
      "protos/protos_6000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9662     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.258    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9018     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c8f44d9dbe548cebfdf63163f5533aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 75.25%\n",
      "=============== m=protos/protos_6000.npy error=24.74923450533207 ===============\n",
      "critics/crits_1500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10488    |\n",
      "|    time_elapsed     | 0        |\n",
      "|    total timesteps  | 6000     |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.25     |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9987     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 10091    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 18000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 9731     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 9408     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 30000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 24       |\n",
      "|    fps              | 9240     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 28       |\n",
      "|    fps              | 9093     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 42000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 32       |\n",
      "|    fps              | 9066     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 36       |\n",
      "|    fps              | 1594     |\n",
      "|    time_elapsed     | 33       |\n",
      "|    total timesteps  | 54000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0281   |\n",
      "|    n_updates        | 999      |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 40       |\n",
      "|    fps              | 728      |\n",
      "|    time_elapsed     | 82       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0375   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7df118331bf0451fbc851bdbf184a905",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 50.46%\n",
      "=============== m=critics/crits_1500.npy error=49.54157445463168 ===============\n",
      "critics/crits_2000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10470    |\n",
      "|    time_elapsed     | 0        |\n",
      "|    total timesteps  | 8000     |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.137    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 10213    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 10010    |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 9461     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 9247     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 40000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 24       |\n",
      "|    fps              | 9153     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 28       |\n",
      "|    fps              | 1064     |\n",
      "|    time_elapsed     | 52       |\n",
      "|    total timesteps  | 56000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0343   |\n",
      "|    n_updates        | 1499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "898003f6ed0748e28285c112ad31a868",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 55.00%\n",
      "=============== m=critics/crits_2000.npy error=44.99842221520984 ===============\n",
      "critics/crits_3000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10053    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.146    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9849     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 9404     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 9108     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 685      |\n",
      "|    time_elapsed     | 87       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0598   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8994dd680d224ab2a4240890ef041403",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 58.65%\n",
      "=============== m=critics/crits_3000.npy error=41.35021097046413 ===============\n",
      "critics/crits_4500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9891     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 18000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.184    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9289     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 1439     |\n",
      "|    time_elapsed     | 37       |\n",
      "|    total timesteps  | 54000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0165   |\n",
      "|    n_updates        | 999      |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be5ad7a8128444a5b24a6618822bb42c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 59.19%\n",
      "=============== m=critics/crits_4500.npy error=40.81138040042149 ===============\n",
      "critics/crits_6000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9003     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.141    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9086     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0464b46c2b1c4a4bb420ee98c3949f95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 60.18%\n",
      "=============== m=critics/crits_6000.npy error=39.81695771091942 ===============\n",
      "combined/combined_1500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10046    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.196    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9873     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 9336     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 9260     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 720      |\n",
      "|    time_elapsed     | 83       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0159   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "032fdf28fd884f068d85ed1a513a989d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.06%\n",
      "=============== m=combined/combined_1500.npy error=29.944216398273866 ===============\n",
      "combined/combined_2000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10120    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.247    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9214     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 9222     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45f7df3f191c43b0a9d4a2f308d374c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.29%\n",
      "=============== m=combined/combined_2000.npy error=26.708074534161483 ===============\n",
      "combined/combined_3000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 8941     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.165    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9042     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f893c7ea4f86480184b5fa1c7768e024",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.47%\n",
      "=============== m=combined/combined_3000.npy error=29.528222409435557 ===============\n",
      "combined/combined_4500.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9166     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.129    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16a8e70c1eb742b092350c62dccb4c2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.09%\n",
      "=============== m=combined/combined_4500.npy error=25.91386554621849 ===============\n",
      "combined/combined_6000.npy\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9359     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.179    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94902e3337c24a55bf5ff0b610a5daa4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.42%\n",
      "=============== m=combined/combined_6000.npy error=26.580550304557875 ===============\n"
     ]
    }
   ],
   "source": [
    "protos = sorted(glob('protos/*'))\n",
    "critics = sorted(glob('critics/*'))\n",
    "combined = sorted(glob('combined/*'))\n",
    "\n",
    "epochs = 1\n",
    "\n",
    "for testm in protos:\n",
    "    print(testm)\n",
    "    selected = np.load(testm)\n",
    "#     print(selected)\n",
    "\n",
    "    data_indices = random.sample(range(0, 60000), testm)\n",
    "\n",
    "    env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "#     env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected, :], y[selected]))])\n",
    "\n",
    "    # Grab the observation shape for generating evaluation frames.\n",
    "    width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "    test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "    test_model.learn(total_timesteps=len(x) * epochs)\n",
    "    \n",
    "    test_model.save('./models/without_norm_{}_{}.zip'.format(testm, epochs))\n",
    "\n",
    "    accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "    error = 100 - accuracy\n",
    "    print(\"=============== m={} error={} ===============\".format(testm, error))\n",
    "#     break\n",
    "\n",
    "for testm in critics:\n",
    "    print(testm)\n",
    "    selected = np.load(testm)\n",
    "#     print(selected)\n",
    "    env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected, :], y[selected]))])\n",
    "\n",
    "    # Grab the observation shape for generating evaluation frames.\n",
    "    width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "    test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "    test_model.learn(total_timesteps=len(x) * epochs)\n",
    "    \n",
    "    test_model.save('./models/without_norm_{}_{}.zip'.format(testm, epochs))\n",
    "\n",
    "    accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "    error = 100 - accuracy\n",
    "    print(\"=============== m={} error={} ===============\".format(testm, error))\n",
    "#     break\n",
    "\n",
    "for testm in combined:\n",
    "    print(testm)\n",
    "    selected = np.load(testm)\n",
    "#     print(selected)\n",
    "    env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected, :], y[selected]))])\n",
    "\n",
    "    # Grab the observation shape for generating evaluation frames.\n",
    "    width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "    test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "    test_model.learn(total_timesteps=len(x) * epochs)\n",
    "    \n",
    "    test_model.save('./models/without_norm_{}_{}.zip'.format(testm, epochs))\n",
    "\n",
    "    accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "    error = 100 - accuracy\n",
    "    print(\"=============== m={} error={} ===============\".format(testm, error))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "rolled-measurement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 8845     |\n",
      "|    time_elapsed     | 0        |\n",
      "|    total timesteps  | 8000     |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.266    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9175     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 8887     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 8847     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 8845     |\n",
      "|    time_elapsed     | 4        |\n",
      "|    total timesteps  | 40000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 24       |\n",
      "|    fps              | 8999     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 28       |\n",
      "|    fps              | 1145     |\n",
      "|    time_elapsed     | 48       |\n",
      "|    total timesteps  | 56000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.013    |\n",
      "|    n_updates        | 1499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c259670556e409f988b7fa407af1400",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.60%\n",
      "=============== m=2000 error=25.403353 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 10493    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 12000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.3      |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 10114    |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 9674     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 16       |\n",
      "|    fps              | 9287     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 20       |\n",
      "|    fps              | 755      |\n",
      "|    time_elapsed     | 79       |\n",
      "|    total timesteps  | 60000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0404   |\n",
      "|    n_updates        | 2499     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2851bfb75dae4afa8f3c788479dc0402",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 72.65%\n",
      "=============== m=3000 error=27.351862 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9129     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.358    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9265     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 9045     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "899357f3cb2a4c40a1e5fa8b7762dd69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 71.02%\n",
      "=============== m=4000 error=28.979549 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9915     |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 18000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.287    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9772     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 1408     |\n",
      "|    time_elapsed     | 38       |\n",
      "|    total timesteps  | 54000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.0385   |\n",
      "|    n_updates        | 999      |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b48721a92e934fe38ec51f3ff66f1770",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.86%\n",
      "=============== m=4500 error=25.141837 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9189     |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 24000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.225    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 9050     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b566685c7270481e9166047d423ab40c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.92%\n",
      "=============== m=6000 error=25.078898 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9421     |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 36000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.255    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3627da6f9d94805a4673a5526c77905",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 75.34%\n",
      "=============== m=9000 error=24.657101 ===============\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 9216     |\n",
      "|    time_elapsed     | 5        |\n",
      "|    total timesteps  | 48000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.283    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b65add18b699489785319c2d0bf88754",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 74.24%\n",
      "=============== m=12000 error=25.759327 ===============\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "ranges = [1500, 2000, 3000, 4500, 6000]\n",
    "ranges += [item*2 for item in ranges]\n",
    "ranges = sorted(list(set(ranges)))\n",
    "\n",
    "epochs = 1\n",
    "for testm in ranges[1:]:\n",
    "    data_indices = random.sample(range(0, 60000), testm)\n",
    "\n",
    "    env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[data_indices, :], y[data_indices]))])\n",
    "\n",
    "    # Grab the observation shape for generating evaluation frames.\n",
    "    width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "    test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "    test_model.learn(total_timesteps=len(x) * epochs)\n",
    "    \n",
    "    test_model.save('./models/without_norm_random_{}_{}.zip'.format(testm, epochs))\n",
    "\n",
    "    accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "    error = 100 - accuracy\n",
    "    print(\"=============== m=%d error=%f ===============\" % (testm, error))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "noble-hypothesis",
   "metadata": {},
   "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 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "m = [1500, 2000, 3000, 4000, 4500, 6000, 9000, 12000]\n",
    "accprotos = [71.97, 74.38, 75.02, 76.11, 72.51, 75.25, np.NaN, np.NaN]\n",
    "acccritics = [50.46, 55.00, 58.65, 56.37, 59.19, 60.18, np.NaN, np.NaN]\n",
    "acccombined = [69.14, 72.76, 70.06, 76.97, 74.84, 70.47, 74.09, 73.42]\n",
    "accrandom = [63.73, 64.60, 62.65, 61.02, 64.86, 64.92, 65.34, 64.24]\n",
    "\n",
    "# plt.plot(m, accprotos, marker='o', label='prototypes')\n",
    "# plt.plot(m, acccritics, marker='o', label='criticisms')\n",
    "# plt.plot(m, acccombined, marker='o', label='combined')\n",
    "# plt.plot(m, accrandom, marker='o', label='random')\n",
    "# # plt.plot(m, error)\n",
    "# plt.legend()\n",
    "# plt.title('Deep Q Network (Accuracy Entire Dataset - 60000 Images: 69.85)')\n",
    "# plt.ylabel('Accuracy')\n",
    "# plt.xlabel('Number of data points')\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "plt.scatter(m, accprotos, marker='o', label='prototypes')\n",
    "plt.scatter(m, acccritics, marker='^', label='criticisms')\n",
    "plt.scatter(m, acccombined, marker='x', label='combined')\n",
    "plt.scatter(m, accrandom, marker='*', label='random')\n",
    "# plt.scatter(m, 69.85, marker='o', label='total')\n",
    "plt.legend()\n",
    "plt.title('Deep Q Network (Accuracy Entire Dataset - 60000 Images: 69.85)')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Number of data points')\n",
    "plt.savefig('result_scatter_plot.jpg')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(15,15))\n",
    "\n",
    "ax[0, 0].plot(m, accprotos, marker='o', label='prototypes')\n",
    "ax[0, 0].plot(m, accrandom, marker='o', label='random')\n",
    "\n",
    "ax[1, 0].plot(m, acccritics, marker='o', label='criticisms')\n",
    "ax[1, 0].plot(m, accrandom, marker='o', label='random')\n",
    "\n",
    "ax[0, 1].plot(m, acccombined, marker='o', label='combined')\n",
    "ax[0, 1].plot(m, accrandom, marker='o', label='random')\n",
    "\n",
    "ax[1, 1].plot(m, accprotos, marker='o', label='prototypes')\n",
    "ax[1, 1].plot(m, acccritics, marker='o', label='criticisms')\n",
    "ax[1, 1].plot(m, acccombined, marker='o', label='combined')\n",
    "ax[1, 1].plot(m, accrandom, marker='o', label='random')\n",
    "\n",
    "ax[0, 0].legend(loc=\"upper right\")\n",
    "ax[0, 1].legend(loc=\"upper right\")\n",
    "ax[1, 0].legend(loc=\"lower right\")\n",
    "ax[1, 1].legend(loc=\"lower right\")\n",
    "\n",
    "ax[0, 0].set_title('Prototypes vs Random')\n",
    "ax[0, 1].set_title('Combined vs Random')\n",
    "ax[1, 0].set_title('Criticisms vs Random')\n",
    "ax[1, 1].set_title('Prototypes vs Combined vs Criticisms vs Random')\n",
    "\n",
    "ax[0, 0].set_xlabel('Number of data points')\n",
    "ax[0, 1].set_xlabel('Number of data points')\n",
    "ax[1, 0].set_xlabel('Number of data points')\n",
    "ax[1, 1].set_xlabel('Number of data points')\n",
    "\n",
    "ax[0, 0].set_ylabel('Accuracy')\n",
    "ax[0, 1].set_ylabel('Accuracy')\n",
    "ax[1, 0].set_ylabel('Accuracy')\n",
    "ax[1, 1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.suptitle('Deep Q Network (Accuracy Entire Dataset - 60000 Images: 69.85)', fontsize=16)\n",
    "fig.savefig('result_line_plot.jpg')\n",
    "fig.show()\n",
    "\n",
    "fig, ax = plt.subplots(2, 2, figsize=(15,15))\n",
    "\n",
    "ax[0, 0].scatter(m, accprotos, marker='o', label='prototypes')\n",
    "ax[0, 0].scatter(m, accrandom, marker='*', label='random')\n",
    "\n",
    "ax[1, 0].scatter(m, acccritics, marker='^', label='criticisms')\n",
    "ax[1, 0].scatter(m, accrandom, marker='*', label='random')\n",
    "\n",
    "ax[0, 1].scatter(m, acccombined, marker='x', label='combined')\n",
    "ax[0, 1].scatter(m, accrandom, marker='*', label='random')\n",
    "\n",
    "ax[1, 1].scatter(m, accprotos, marker='o', label='prototypes')\n",
    "ax[1, 1].scatter(m, acccritics, marker='^', label='criticisms')\n",
    "ax[1, 1].scatter(m, acccombined, marker='x', label='combined')\n",
    "ax[1, 1].scatter(m, accrandom, marker='*', label='random')\n",
    "\n",
    "ax[0, 0].legend(loc=\"upper right\")\n",
    "ax[0, 1].legend(loc=\"upper right\")\n",
    "ax[1, 0].legend(loc=\"lower right\")\n",
    "ax[1, 1].legend(loc=\"lower right\")\n",
    "\n",
    "ax[0, 0].set_title('Prototypes vs Random')\n",
    "ax[0, 1].set_title('Combined vs Random')\n",
    "ax[1, 0].set_title('Criticisms vs Random')\n",
    "ax[1, 1].set_title('Prototypes vs Combined vs Criticisms vs Random')\n",
    "\n",
    "ax[0, 0].set_xlabel('Number of data points')\n",
    "ax[0, 1].set_xlabel('Number of data points')\n",
    "ax[1, 0].set_xlabel('Number of data points')\n",
    "ax[1, 1].set_xlabel('Number of data points')\n",
    "\n",
    "ax[0, 0].set_ylabel('Accuracy')\n",
    "ax[0, 1].set_ylabel('Accuracy')\n",
    "ax[1, 0].set_ylabel('Accuracy')\n",
    "ax[1, 1].set_ylabel('Accuracy')\n",
    "\n",
    "fig.suptitle('Deep Q Network (Accuracy Entire Dataset - 60000 Images: 69.85)', fontsize=16)\n",
    "fig.savefig('result_scatter_all_plot.jpg')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "private-artwork",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-de8be595986d>\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[0mdata_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1500\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata_indices\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[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata_indices\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[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDummyVecEnv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mlambda\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUSPSGym\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchannels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata_indices\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[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mselected\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata_indices\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "testm = 'combined/combined_3000.npy'\n",
    "selected = np.load(testm)\n",
    "#     print(selected)\n",
    "\n",
    "data_indices = random.sample(range(0, 6000), 1500)\n",
    "print(len(x[selected[data_indices], :]), len(y[selected[data_indices]]))\n",
    "\n",
    "env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "\n",
    "# Grab the observation shape for generating evaluation frames.\n",
    "width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "test_model.learn(total_timesteps=len(x) * 1)\n",
    "\n",
    "test_model.save('./models/without_norm_{}_{}.zip'.format('protos_1500', 1))\n",
    "\n",
    "accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "error = 100 - accuracy\n",
    "print(\"=============== m={} error={} ===============\".format(1500, error))\n",
    "\n",
    "\n",
    "data_indices = random.sample(range(0, 6000), 2000)\n",
    "print(len(x[selected[data_indices], :]), len(y[selected[data_indices]]))\n",
    "\n",
    "env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "\n",
    "# Grab the observation shape for generating evaluation frames.\n",
    "width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "test_model.learn(total_timesteps=len(x) * 1)\n",
    "\n",
    "test_model.save('./models/without_norm_{}_{}.zip'.format('protos_2000', 1))\n",
    "\n",
    "accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "error = 100 - accuracy\n",
    "print(\"=============== m={} error={} ===============\".format(2000, error))\n",
    "\n",
    "\n",
    "data_indices = random.sample(range(0, 6000), 4500)\n",
    "print(len(x[selected[data_indices], :]), len(y[selected[data_indices]]))\n",
    "\n",
    "env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "\n",
    "# Grab the observation shape for generating evaluation frames.\n",
    "width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "test_model.learn(total_timesteps=len(x) * 1)\n",
    "\n",
    "test_model.save('./models/without_norm_{}_{}.zip'.format('protos_4500', 1))\n",
    "\n",
    "accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "error = 100 - accuracy\n",
    "print(\"=============== m={} error={} ===============\".format(4500, error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "southeast-nightmare",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 4000\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 11744    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.314    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 12301    |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 12542    |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "050f634c8f22497bb834ac65b719292e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 56.37%\n",
      "=============== m=4000 error=43.63040490538111 ===============\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "testm = 'critics/crits_4500.npy'\n",
    "selected = np.load(testm)\n",
    "#     print(selected)\n",
    "\n",
    "data_indices = random.sample(range(0, 4500), 4000)\n",
    "print(len(x[selected[data_indices], :]), len(y[selected[data_indices]]))\n",
    "\n",
    "env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "\n",
    "# Grab the observation shape for generating evaluation frames.\n",
    "width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "test_model.learn(total_timesteps=len(x) * 1)\n",
    "\n",
    "test_model.save('./models/without_norm_{}_{}.zip'.format('critics_4000', 1))\n",
    "\n",
    "accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "error = 100 - accuracy\n",
    "print(\"=============== m={} error={} ===============\".format(4000, error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cross-boston",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000 4000\n",
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 4        |\n",
      "|    fps              | 12552    |\n",
      "|    time_elapsed     | 1        |\n",
      "|    total timesteps  | 16000    |\n",
      "| train/              |          |\n",
      "|    learning_rate    | 0.0001   |\n",
      "|    loss             | 0.165    |\n",
      "|    n_updates        | 2500     |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 8        |\n",
      "|    fps              | 12735    |\n",
      "|    time_elapsed     | 2        |\n",
      "|    total timesteps  | 32000    |\n",
      "----------------------------------\n",
      "----------------------------------\n",
      "| rollout/            |          |\n",
      "|    exploration rate | 0.05     |\n",
      "| time/               |          |\n",
      "|    episodes         | 12       |\n",
      "|    fps              | 12809    |\n",
      "|    time_elapsed     | 3        |\n",
      "|    total timesteps  | 48000    |\n",
      "----------------------------------\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1df55499728342cc9301c4cc47338f46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 76.97%\n",
      "=============== m=4000 error=23.03157894736843 ===============\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "testm = 'combined/combined_4500.npy'\n",
    "selected = np.load(testm)\n",
    "#     print(selected)\n",
    "\n",
    "data_indices = random.sample(range(0, 4500), 4000)\n",
    "print(len(x[selected[data_indices], :]), len(y[selected[data_indices]]))\n",
    "\n",
    "env = DummyVecEnv([lambda: USPSGym(width=64, height=64, channels=1, dataset=(x[selected[data_indices], :], y[selected[data_indices]]))])\n",
    "\n",
    "# Grab the observation shape for generating evaluation frames.\n",
    "width, height = env.observation_space.shape[0], env.observation_space.shape[1]\n",
    "\n",
    "test_model = DQN(CnnPolicy, env, verbose=1)\n",
    "\n",
    "test_model.learn(total_timesteps=len(x) * 1)\n",
    "\n",
    "test_model.save('./models/without_norm_{}_{}.zip'.format('combined_4000', 1))\n",
    "\n",
    "accuracy = eval(test_model, testx, testy)\n",
    "\n",
    "error = 100 - accuracy\n",
    "print(\"=============== m={} error={} ===============\".format(4000, error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "changing-relations",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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