2. learning_lstm_gan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 학습 데이터셋 생성"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 라이브러리 임포트"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.init as init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_index = 1;\n",
    "if task_index == 1:\n",
    "    robot_data = pd.read_csv(\"./1. preprocessing_Data/task1/train_data_68_0.05.csv\", encoding='CP949')\n",
    "elif task_index == 2:\n",
    "    robot_data = pd.read_csv(\"./1. preprocessing_Data/task2/train_data_64_0.05.csv\", encoding='CP949')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp_x_1</th>\n",
       "      <th>temp_x_2</th>\n",
       "      <th>temp_x_3</th>\n",
       "      <th>temp_x_4</th>\n",
       "      <th>temp_x_5</th>\n",
       "      <th>temp_x_6</th>\n",
       "      <th>temp_x_7</th>\n",
       "      <th>temp_x_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.180</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.794</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.456</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.194</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.209</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.956</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.213</td>\n",
       "      <td>0.342</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.953</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.212</td>\n",
       "      <td>0.379</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.954</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>0.174</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>0.163</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.243</td>\n",
       "      <td>0.836</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.143</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.874</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.127</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.143</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>751 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     temp_x_1  temp_x_2  temp_x_3  temp_x_4  temp_x_5  temp_x_6  temp_x_7  \\\n",
       "0       0.180     0.292     0.794     0.108     0.456     1.000     0.385   \n",
       "1       0.194     0.293     0.769     0.136     0.450     0.978     0.401   \n",
       "2       0.209     0.310     0.754     0.169     0.460     0.956     0.399   \n",
       "3       0.213     0.342     0.758     0.185     0.490     0.953     0.379   \n",
       "4       0.212     0.379     0.767     0.200     0.524     0.954     0.359   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "746     0.174     0.069     0.798     0.110     0.249     0.854     0.260   \n",
       "747     0.163     0.067     0.835     0.132     0.243     0.836     0.272   \n",
       "748     0.143     0.067     0.874     0.114     0.250     0.852     0.261   \n",
       "749     0.127     0.067     0.900     0.087     0.259     0.876     0.246   \n",
       "750     0.143     0.068     0.873     0.115     0.251     0.852     0.261   \n",
       "\n",
       "     temp_x_8  \n",
       "0       0.978  \n",
       "1       0.989  \n",
       "2       0.981  \n",
       "3       0.991  \n",
       "4       0.987  \n",
       "..        ...  \n",
       "746     0.966  \n",
       "747     0.972  \n",
       "748     0.972  \n",
       "749     0.972  \n",
       "750     0.966  \n",
       "\n",
       "[751 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "robot_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1 = robot_data[[\"temp_x_1\"]].squeeze().to_numpy()\n",
    "data_2 = robot_data[[\"temp_x_2\"]].squeeze().to_numpy()\n",
    "data_3 = robot_data[[\"temp_x_3\"]].squeeze().to_numpy()\n",
    "data_4 = robot_data[[\"temp_x_4\"]].squeeze().to_numpy()\n",
    "data_5 = robot_data[[\"temp_x_5\"]].squeeze().to_numpy()\n",
    "data_6 = robot_data[[\"temp_x_6\"]].squeeze().to_numpy()\n",
    "data_7 = robot_data[[\"temp_x_7\"]].squeeze().to_numpy()\n",
    "data_8 = robot_data[[\"temp_x_8\"]].squeeze().to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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"
    }
   ],
   "source": [
    "plt.plot(data_1)\n",
    "plt.plot(data_2)\n",
    "plt.plot(data_3)\n",
    "plt.plot(data_4)\n",
    "plt.plot(data_5)\n",
    "plt.plot(data_6)\n",
    "plt.plot(data_7)\n",
    "plt.plot(data_8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_len = 5\n",
    "data_num = np.size(data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(data_num-time_len):\n",
    "    if i == 0:\n",
    "        input_1_temp = data_1[i:i+time_len]\n",
    "        target_1_temp = data_1[i+1:i+time_len+1]\n",
    "        \n",
    "        input_2_temp = data_2[i:i+time_len]\n",
    "        target_2_temp = data_2[i+1:i+time_len+1]\n",
    "        \n",
    "        input_3_temp = data_3[i:i+time_len]\n",
    "        target_3_temp = data_3[i+1:i+time_len+1]\n",
    "        \n",
    "        input_4_temp = data_4[i:i+time_len]\n",
    "        target_4_temp = data_4[i+1:i+time_len+1]\n",
    "        \n",
    "        input_5_temp = data_5[i:i+time_len]\n",
    "        target_5_temp = data_5[i+1:i+time_len+1]\n",
    "        \n",
    "        input_6_temp = data_6[i:i+time_len]\n",
    "        target_6_temp = data_6[i+1:i+time_len+1]\n",
    "        \n",
    "        input_7_temp = data_7[i:i+time_len]\n",
    "        target_7_temp = data_7[i+1:i+time_len+1]\n",
    "        \n",
    "        input_8_temp = data_8[i:i+time_len]\n",
    "        target_8_temp = data_8[i+1:i+time_len+1]\n",
    "    else:\n",
    "        input_1_temp = np.vstack([input_1_temp, data_1[i:i+time_len]])\n",
    "        target_1_temp = np.vstack([target_1_temp, data_1[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_2_temp = np.vstack([input_2_temp, data_2[i:i+time_len]])\n",
    "        target_2_temp = np.vstack([target_2_temp, data_2[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_3_temp = np.vstack([input_3_temp, data_3[i:i+time_len]])\n",
    "        target_3_temp = np.vstack([target_3_temp, data_3[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_4_temp = np.vstack([input_4_temp, data_4[i:i+time_len]])\n",
    "        target_4_temp = np.vstack([target_4_temp, data_4[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_5_temp = np.vstack([input_5_temp, data_5[i:i+time_len]])\n",
    "        target_5_temp = np.vstack([target_5_temp, data_5[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_6_temp = np.vstack([input_6_temp, data_6[i:i+time_len]])\n",
    "        target_6_temp = np.vstack([target_6_temp, data_6[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_7_temp = np.vstack([input_7_temp, data_7[i:i+time_len]])\n",
    "        target_7_temp = np.vstack([target_7_temp, data_7[i+1:i+time_len+1]])\n",
    "        \n",
    "        input_8_temp = np.vstack([input_8_temp, data_8[i:i+time_len]])\n",
    "        target_8_temp = np.vstack([target_8_temp, data_8[i+1:i+time_len+1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "746\n"
     ]
    }
   ],
   "source": [
    "data_num = np.shape(target_1_temp)[0]\n",
    "print(data_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(746, 5)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(input_1_temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numpy 2 torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input\n",
    "input_1 = torch.from_numpy(input_1_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_2 = torch.from_numpy(input_2_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_3 = torch.from_numpy(input_3_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_4 = torch.from_numpy(input_4_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_5 = torch.from_numpy(input_5_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_6 = torch.from_numpy(input_6_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_7 = torch.from_numpy(input_7_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "input_8 = torch.from_numpy(input_8_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "\n",
    "# target\n",
    "target_1 = torch.from_numpy(target_1_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_2 = torch.from_numpy(target_2_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_3 = torch.from_numpy(target_3_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_4 = torch.from_numpy(target_4_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_5 = torch.from_numpy(target_5_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_6 = torch.from_numpy(target_6_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_7 = torch.from_numpy(target_7_temp).unsqueeze(2).type_as(torch.FloatTensor()) \n",
    "target_8 = torch.from_numpy(target_8_temp).unsqueeze(2).type_as(torch.FloatTensor()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_num = int(data_num*(5/5))\n",
    "# test_num = int(data_num/5)\n",
    "# training_num, test_num, training_num + test_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input = np.concatenate((input_1[0:training_num],\n",
    "                                 input_2[0:training_num],\n",
    "                                 input_3[0:training_num],\n",
    "                                 input_4[0:training_num],\n",
    "                                 input_5[0:training_num],\n",
    "                                 input_6[0:training_num],\n",
    "                                 input_7[0:training_num],\n",
    "                                 input_8[0:training_num]\n",
    "                                ),2)\n",
    "\n",
    "training_target = np.concatenate((target_1[0:training_num],\n",
    "                                  target_2[0:training_num],\n",
    "                                  target_3[0:training_num],\n",
    "                                  target_4[0:training_num],\n",
    "                                  target_5[0:training_num],\n",
    "                                  target_6[0:training_num],\n",
    "                                  target_7[0:training_num],\n",
    "                                  target_8[0:training_num]\n",
    "                                 ),2)\n",
    "training_input = torch.from_numpy(training_input).type_as(torch.FloatTensor())\n",
    "training_target = torch.from_numpy(training_target).type_as(torch.FloatTensor())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([746, 5, 8])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(training_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "batch_size = training_num\n",
    "seq_len = time_len\n",
    "num_layers = 5\n",
    "input_size = 8\n",
    "hidden_size = 2048\n",
    "lr = 0.01\n",
    "num_epochs = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTMGenerator(nn.Module):\n",
    "    def __init__(self, in_dim, out_dim, n_layers=1, hidden_dim=256):\n",
    "        super().__init__()\n",
    "        self.n_layers = n_layers\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.out_dim = out_dim\n",
    "\n",
    "        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layers, batch_first=True)\n",
    "        self.linear = nn.Sequential(nn.Linear(hidden_dim, out_dim), nn.Tanh())\n",
    "\n",
    "    def forward(self, input):\n",
    "        batch_size, seq_len = input.size(0), input.size(1)\n",
    "        h_0 = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device)\n",
    "        c_0 = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device)\n",
    "\n",
    "        recurrent_features, _ = self.lstm(input, (h_0, c_0))\n",
    "        outputs = self.linear(recurrent_features.contiguous().view(batch_size*seq_len, self.hidden_dim))\n",
    "        outputs = outputs.view(batch_size, seq_len, self.out_dim)\n",
    "        return outputs\n",
    "\n",
    "\n",
    "class LSTMDiscriminator(nn.Module):\n",
    "    def __init__(self, in_dim, n_layers=1, hidden_dim=256):\n",
    "        super().__init__()\n",
    "        self.n_layers = n_layers\n",
    "        self.hidden_dim = hidden_dim\n",
    "\n",
    "        self.lstm = nn.LSTM(in_dim, hidden_dim, n_layers, batch_first=True)\n",
    "        self.linear = nn.Sequential(nn.Linear(hidden_dim, 1), nn.Sigmoid())\n",
    "\n",
    "    def forward(self, input):\n",
    "        batch_size, seq_len = input.size(0), input.size(1)\n",
    "        h_0 = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device)\n",
    "        c_0 = torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device)\n",
    "\n",
    "        recurrent_features, _ = self.lstm(input, (h_0, c_0))\n",
    "        outputs = self.linear(recurrent_features.contiguous().view(batch_size*seq_len, self.hidden_dim))\n",
    "        outputs = outputs.view(batch_size, seq_len, 1)\n",
    "        return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "# gpu가 사용 가능한 경우에는 device를 gpu로 설정하고 불가능하면 cpu로 설정합니다.\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "\n",
    "netD = LSTMDiscriminator(input_size*2, num_layers, hidden_size).to(device)\n",
    "netG = LSTMGenerator(input_size, input_size, num_layers , hidden_size).to(device)\n",
    "\n",
    "criterion = nn.BCELoss().to(device)\n",
    "# delta_criterion = nn.MSELoss().to(device)\n",
    "\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "# setup optimizer\n",
    "optimizerD = optim.Adam(netD.parameters(), lr=lr)\n",
    "optimizerG = optim.Adam(netG.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([746, 5, 16])\n",
      "torch.Size([746, 5, 1])\n",
      "torch.Size([746, 5, 1])\n",
      "tensor(0.6937, grad_fn=<BinaryCrossEntropyBackward>)\n",
      "torch.Size([746, 5, 8])\n",
      "torch.Size([746, 5, 1])\n",
      "tensor(0.6926, grad_fn=<BinaryCrossEntropyBackward>)\n"
     ]
    }
   ],
   "source": [
    "input = training_input[0*batch_size:(0+1)*batch_size].to(device)\n",
    "target = training_target[0*batch_size:(0+1)*batch_size].to(device)\n",
    "print(np.shape(torch.cat((input, target), 2)))\n",
    "\n",
    "label = torch.full((batch_size, seq_len, 1), real_label, device=device)\n",
    "label = label.type(torch.FloatTensor)\n",
    "print(np.shape(label))\n",
    "\n",
    "real = torch.cat((input, target), 2)\n",
    "output = netD(real)\n",
    "output = output.type(torch.FloatTensor)\n",
    "print(np.shape(output))\n",
    "\n",
    "errD_real = criterion(output, label)\n",
    "print(errD_real)\n",
    "\n",
    "fakeG = netG(input)\n",
    "label.fill_(fake_label) \n",
    "print(np.shape(fakeG))\n",
    "\n",
    "fake = torch.cat((input, fakeG.detach()), 2)\n",
    "output = netD(fake)\n",
    "output = output.type(torch.FloatTensor)\n",
    "print(np.shape(output))\n",
    "\n",
    "errD_fake = criterion(output, label)\n",
    "print(errD_fake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 868.00 MiB (GPU 0; 8.00 GiB total capacity; 5.37 GiB already allocated; 30.38 MiB free; 5.85 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_284/1892503435.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[0merrG\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[0merrG\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m         \u001b[0mD_G_z2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\py38\\lib\\site-packages\\torch\\_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m    253\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m                 inputs=inputs)\n\u001b[1;32m--> 255\u001b[1;33m         \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\py38\\lib\\site-packages\\torch\\autograd\\__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    146\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m     Variable._execution_engine.run_backward(\n\u001b[0m\u001b[0;32m    148\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n",
      "\u001b[1;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 868.00 MiB (GPU 0; 8.00 GiB total capacity; 5.37 GiB already allocated; 30.38 MiB free; 5.85 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "loss_arr =[]\n",
    "training_data_num = np.shape(training_input)[0]\n",
    "\n",
    "for i in range(num_epochs):      \n",
    "    \n",
    "    for j in range(0, np.int64(training_data_num/batch_size)):\n",
    "        input = training_input[0*batch_size:(0+1)*batch_size].to(device)\n",
    "        target = training_target[0*batch_size:(0+1)*batch_size].to(device)   \n",
    "        #Train with real data\n",
    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(x)))\n",
    "        # log(1-D(x+1))\n",
    "        \n",
    "        netD.zero_grad()\n",
    "        real = torch.cat((input, target), 2)\n",
    "        label = torch.full((batch_size, seq_len, 1), real_label, device=device)\n",
    "        label = label.type(torch.FloatTensor)\n",
    "        \n",
    "        output = netD(real)\n",
    "        output = output.type(torch.FloatTensor)\n",
    "        \n",
    "        errD_real = criterion(output, label)\n",
    "        errD_real.backward()\n",
    "        D_x = output.mean().item()\n",
    "        \n",
    "        # Train with fake data\n",
    "        # log(1-D(G(x)))\n",
    "        fakeG = netG(input)\n",
    "        label.fill_(fake_label)\n",
    "        \n",
    "        output = netD(torch.cat((input, fakeG.detach()), 2))\n",
    "        output = output.type(torch.FloatTensor)\n",
    "        \n",
    "        errD_fake = criterion(output, label)\n",
    "        errD_fake.backward()\n",
    "        D_G_z1 = output.mean().item()\n",
    "        errD = errD_real + errD_fake\n",
    "        optimizerD.step()\n",
    "        \n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        netG.zero_grad()\n",
    "        label.fill_(real_label)\n",
    "        \n",
    "        output = netD(torch.cat((input, fakeG.detach()), 2))\n",
    "        output = output.type(torch.FloatTensor)\n",
    "        errG = criterion(output, label)\n",
    "        errG.backward()\n",
    "        D_G_z2 = output.mean().item()\n",
    "\n",
    "        optimizerG.step()\n",
    "        \n",
    "    if i % 100 == 0:\n",
    "        print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f' \n",
    "              % (i, num_epochs, j, np.int64(training_data_num/batch_size),\n",
    "                 errD.item(), errG.item(), D_x, D_G_z1, D_G_z2), end='\\n')\n",
    "        '''\n",
    "        with torch.no_grad():\n",
    "            test_data_num = np.shape(training_input)[0]\n",
    "            test_total =[]\n",
    "\n",
    "            for j in range(0, np.int64(training_data_num/batch_size)):\n",
    "                netG.zero_grad()\n",
    "                input = training_input[j*batch_size:(j+1)*batch_size].to(device)\n",
    "\n",
    "                output = netG(input)\n",
    "\n",
    "                test = output.cpu().detach().numpy()\n",
    "                test = test.squeeze()\n",
    "\n",
    "                if j == 0:\n",
    "                    test_total = test\n",
    "                else:\n",
    "                    test_total = np.vstack([test_total, test])\n",
    "\n",
    "            plt.plot(training_target[:,time_len-1,:], color='r', linewidth=3.0)\n",
    "            plt.plot(test_total[:,time_len-1,:], color='k', linewidth=1.0)\n",
    "            plt.show()\n",
    "        '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(loss_arr,'-b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "torch.set_printoptions(precision=3)\n",
    "\n",
    "test_total =[]\n",
    "# input = test_input[1].reshape(1, time_len, 12).to(device)\n",
    "input = training_input[0].reshape(1,time_len, input_size).to(device)\n",
    "input = input.cpu().detach().numpy() # + np.random.normal(0, 1, input.shape)\n",
    "input = torch.from_numpy(input).type_as(torch.FloatTensor()).to(device)\n",
    "\n",
    "for j in range(0,training_num): # range(0, 1000):#len(test_input)):\n",
    "    netG.zero_grad()\n",
    "    # hidden = torch.zeros(num_layers, 1, hidden_size).to(device) \n",
    "    # cell = torch.zeros(num_layers, 1, hidden_size).to(device) \n",
    "    \n",
    "    # output, hidden, cell = rnn(input,hidden,cell)\n",
    "    output = netG(input)\n",
    "    test = output.cpu().detach().numpy()\n",
    "    test = test #  + np.random.normal(0, .005, test.shape)\n",
    "    # input = test_input[j].reshape(1, time_len, input_size).to(device) \n",
    "    input = training_input[j].reshape(1, time_len, input_size).to(device) \n",
    "    # test1= input.cpu().detach().numpy()\n",
    "    # test = np.round(test,3)\n",
    "    # input = torch.from_numpy(test).type_as(torch.FloatTensor()).to(device)\n",
    "    # input = \n",
    "    # input = output\n",
    "    test = test.squeeze()\n",
    "    \n",
    "    # if np.linalg.norm(test - test1) < 1e-2:\n",
    "        # print(np.linalg.norm(test - test1))\n",
    "        # input = torch.from_numpy(np.random.normal(test,0.05)).float().reshape(1, 20, 7).to(device)\n",
    "    # print(np.linalg.norm(test - test1) < 1e-4)\n",
    "    \n",
    "    # numpy.random.normal(2*X+2,20)\n",
    "    \n",
    "    if j == 0:\n",
    "        test_total = test[time_len-1,:]# test[time_len-1,:]\n",
    "        # test_total = test[0,:]\n",
    "    else:\n",
    "        test_total = np.vstack([test_total, test[time_len-1,:]])\n",
    "        \n",
    "    # input = test_input[j*batch_size:(j+1)*batch_size].to(device)\n",
    "\n",
    "# print(test_total)\n",
    "# print(np.shape(test[19,:]))\n",
    "fig = plt.figure()\n",
    "# ax = plt.axes(projection='3d')\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "\n",
    "# ax.plot3D(data_1, data_2, data_3, '-.k')\n",
    "ax.plot3D(training_input[:,time_len-1,0], training_input[:,time_len-1,1],training_input[:,time_len-1,2], '-.k')\n",
    "ax.plot3D(test_total[:,0],test_total[:,1], test_total[:,2],'-b')\n",
    "# ax.plot3D(exp_state[:, 0], exp_state[:, 1], exp_state[:, 2], '--b')\n",
    "# ax.set_xlim(.2, .4)\n",
    "# ax.set_ylim(-.3, .3)\n",
    "# ax.set_zlim(.45, .85)\n",
    "#ax = plt.gca()\n",
    "ax.xaxis.pane.fill = False\n",
    "ax.yaxis.pane.fill = False\n",
    "ax.zaxis.pane.fill = False\n",
    "\n",
    "# Now set color to white (or whatever is \"invisible\")\n",
    "ax.xaxis.pane.set_edgecolor('w')\n",
    "ax.yaxis.pane.set_edgecolor('w')\n",
    "ax.zaxis.pane.set_edgecolor('w')\n",
    "\n",
    "# Bonus: To get rid of the grid as well:\n",
    "# ax.grid(False)\n",
    "fig = plt.figure()\n",
    "for i in range(1,7):\n",
    "    ax = fig.add_subplot(610+i)\n",
    "    ax.plot(training_input[:,time_len-1,i-1], '-k')\n",
    "    ax.plot(test_total[:,i-1], '-.b')\n"
   ]
  }
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