MHWGAN_demo.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6bCNJ1Ve4G73"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['font.family'] = 'serif'\n",
        "plt.rcParams['font.serif'] = ['Times New Roman'] + plt.rcParams['font.serif']\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "import matplotlib.dates as mdates\n",
        "from models import *\n",
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "EMedwqDh7vhJ"
      },
      "outputs": [],
      "source": [
        "font_style = {\n",
        "  'size': 26,\n",
        "  'weight': 'bold'\n",
        "}\n",
        "label_style = {\n",
        "  'labelsize': 26,\n",
        "  'labelweight': 'bold',\n",
        "  'linewidth': 3\n",
        "}\n",
        "title_style = {\n",
        "  'labelsize': 26,\n",
        "  'labelweight': 'bold',\n",
        "  'linewidth': 3\n",
        "}\n",
        "data_style = {'linewidth': 3}\n",
        "legend_style = {\n",
        "  'labelspacing': 0.3,\n",
        "  'edgecolor':'none',\n",
        "  'facecolor':'none',\n",
        "  'fontsize':18,\n",
        "  'loc': 'upper right'\n",
        "}\n",
        "plt.rc('font', **font_style)\n",
        "plt.rc('axes', **label_style)\n",
        "plt.rc('lines', **data_style)\n",
        "plt.rc('legend', **legend_style)\n",
        "\n",
        "def sliding_windows(x, y, seq_length):\n",
        "    x_windows = []\n",
        "    y_windows = []\n",
        "    for i in range(seq_length, x.shape[0]):\n",
        "        x_windows.append(x[i-seq_length:i, :])\n",
        "        y_windows.append(y[i-seq_length:i+1])\n",
        "    return np.array(x_windows), np.array(y_windows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuMAs3QHhmX7",
        "outputId": "953a5eb7-c40e-4f42-b056-9a459d1933aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train_x: (2696, 57) train_y: (2696,)\n",
            "Training the VAE model------------------\n",
            "Epoch: 1/200, Loss: 471.7551\n",
            "Epoch: 2/200, Loss: 418.2120\n",
            "Epoch: 3/200, Loss: 383.1627\n",
            "Epoch: 4/200, Loss: 361.6456\n",
            "Epoch: 5/200, Loss: 344.3212\n",
            "Epoch: 6/200, Loss: 322.2314\n",
            "Epoch: 7/200, Loss: 300.0597\n",
            "Epoch: 8/200, Loss: 281.6479\n",
            "Epoch: 9/200, Loss: 247.0028\n",
            "Epoch: 10/200, Loss: 226.8614\n",
            "Epoch: 11/200, Loss: 199.3712\n",
            "Epoch: 12/200, Loss: 194.0804\n",
            "Epoch: 13/200, Loss: 166.0189\n",
            "Epoch: 14/200, Loss: 147.9232\n",
            "Epoch: 15/200, Loss: 147.4622\n",
            "Epoch: 16/200, Loss: 121.6584\n",
            "Epoch: 17/200, Loss: 108.2281\n",
            "Epoch: 18/200, Loss: 110.9136\n",
            "Epoch: 19/200, Loss: 101.8580\n",
            "Epoch: 20/200, Loss: 77.3268\n",
            "Epoch: 21/200, Loss: 72.4049\n",
            "Epoch: 22/200, Loss: 91.9259\n",
            "Epoch: 23/200, Loss: 86.8232\n",
            "Epoch: 24/200, Loss: 59.9636\n",
            "Epoch: 25/200, Loss: 54.2047\n",
            "Epoch: 26/200, Loss: 68.1670\n",
            "Epoch: 27/200, Loss: 78.4759\n",
            "Epoch: 28/200, Loss: 70.4801\n",
            "Epoch: 29/200, Loss: 58.4050\n",
            "Epoch: 30/200, Loss: 48.3444\n",
            "Epoch: 31/200, Loss: 39.4641\n",
            "Epoch: 32/200, Loss: 33.2979\n",
            "Epoch: 33/200, Loss: 45.0919\n",
            "Epoch: 34/200, Loss: 47.8406\n",
            "Epoch: 35/200, Loss: 43.2216\n",
            "Epoch: 36/200, Loss: 29.9044\n",
            "Epoch: 37/200, Loss: 25.2107\n",
            "Epoch: 38/200, Loss: 20.2170\n",
            "Epoch: 39/200, Loss: 22.2932\n",
            "Epoch: 40/200, Loss: 22.9117\n",
            "Epoch: 41/200, Loss: 22.9576\n",
            "Epoch: 42/200, Loss: 21.1304\n",
            "Epoch: 43/200, Loss: 19.2566\n",
            "Epoch: 44/200, Loss: 16.8989\n",
            "Epoch: 45/200, Loss: 17.1835\n",
            "Epoch: 46/200, Loss: 13.8763\n",
            "Epoch: 47/200, Loss: 17.7073\n",
            "Epoch: 48/200, Loss: 17.6737\n",
            "Epoch: 49/200, Loss: 15.9013\n",
            "Epoch: 50/200, Loss: 15.6242\n",
            "Epoch: 51/200, Loss: 18.3082\n",
            "Epoch: 52/200, Loss: 20.4368\n",
            "Epoch: 53/200, Loss: 17.3649\n",
            "Epoch: 54/200, Loss: 19.8809\n",
            "Epoch: 55/200, Loss: 21.7424\n",
            "Epoch: 56/200, Loss: 13.1570\n",
            "Epoch: 57/200, Loss: 20.3606\n",
            "Epoch: 58/200, Loss: 20.6508\n",
            "Epoch: 59/200, Loss: 21.2895\n",
            "Epoch: 60/200, Loss: 26.7700\n",
            "Epoch: 61/200, Loss: 29.8948\n",
            "Epoch: 62/200, Loss: 28.9215\n",
            "Epoch: 63/200, Loss: 27.5464\n",
            "Epoch: 64/200, Loss: 29.4940\n",
            "Epoch: 65/200, Loss: 32.2605\n",
            "Epoch: 66/200, Loss: 28.7088\n",
            "Epoch: 67/200, Loss: 33.0990\n",
            "Epoch: 68/200, Loss: 29.8962\n",
            "Epoch: 69/200, Loss: 31.0318\n",
            "Epoch: 70/200, Loss: 37.0109\n",
            "Epoch: 71/200, Loss: 38.3812\n",
            "Epoch: 72/200, Loss: 34.8935\n",
            "Epoch: 73/200, Loss: 35.9384\n",
            "Epoch: 74/200, Loss: 32.2142\n",
            "Epoch: 75/200, Loss: 40.2300\n",
            "Epoch: 76/200, Loss: 39.7692\n",
            "Epoch: 77/200, Loss: 39.4995\n",
            "Epoch: 78/200, Loss: 36.4808\n",
            "Epoch: 79/200, Loss: 34.3203\n",
            "Epoch: 80/200, Loss: 39.4080\n",
            "Epoch: 81/200, Loss: 33.4786\n",
            "Epoch: 82/200, Loss: 37.2203\n",
            "Epoch: 83/200, Loss: 35.5157\n",
            "Epoch: 84/200, Loss: 36.2862\n",
            "Epoch: 85/200, Loss: 36.4031\n",
            "Epoch: 86/200, Loss: 35.9881\n",
            "Epoch: 87/200, Loss: 34.8332\n",
            "Epoch: 88/200, Loss: 38.1406\n",
            "Epoch: 89/200, Loss: 37.9241\n",
            "Epoch: 90/200, Loss: 34.1239\n",
            "Epoch: 91/200, Loss: 34.6700\n",
            "Epoch: 92/200, Loss: 39.7154\n",
            "Epoch: 93/200, Loss: 35.5476\n",
            "Epoch: 94/200, Loss: 30.3297\n",
            "Epoch: 95/200, Loss: 33.6076\n",
            "Epoch: 96/200, Loss: 36.7634\n",
            "Epoch: 97/200, Loss: 37.2890\n",
            "Epoch: 98/200, Loss: 37.9413\n",
            "Epoch: 99/200, Loss: 38.5291\n",
            "Epoch: 100/200, Loss: 34.4675\n",
            "Epoch: 101/200, Loss: 38.1154\n",
            "Epoch: 102/200, Loss: 37.3708\n",
            "Epoch: 103/200, Loss: 40.5262\n",
            "Epoch: 104/200, Loss: 39.8841\n",
            "Epoch: 105/200, Loss: 43.1809\n",
            "Epoch: 106/200, Loss: 40.3871\n",
            "Epoch: 107/200, Loss: 43.9180\n",
            "Epoch: 108/200, Loss: 44.9508\n",
            "Epoch: 109/200, Loss: 43.4046\n",
            "Epoch: 110/200, Loss: 43.8545\n",
            "Epoch: 111/200, Loss: 45.7978\n",
            "Epoch: 112/200, Loss: 45.2929\n",
            "Epoch: 113/200, Loss: 44.4496\n",
            "Epoch: 114/200, Loss: 42.0125\n",
            "Epoch: 115/200, Loss: 45.6325\n",
            "Epoch: 116/200, Loss: 42.3568\n",
            "Epoch: 117/200, Loss: 44.1786\n",
            "Epoch: 118/200, Loss: 47.5944\n",
            "Epoch: 119/200, Loss: 39.1648\n",
            "Epoch: 120/200, Loss: 48.1272\n",
            "Epoch: 121/200, Loss: 48.8882\n",
            "Epoch: 122/200, Loss: 45.2555\n",
            "Epoch: 123/200, Loss: 47.5116\n",
            "Epoch: 124/200, Loss: 48.0304\n",
            "Epoch: 125/200, Loss: 48.8425\n",
            "Epoch: 126/200, Loss: 48.6317\n",
            "Epoch: 127/200, Loss: 50.1343\n",
            "Epoch: 128/200, Loss: 49.0805\n",
            "Epoch: 129/200, Loss: 52.8445\n",
            "Epoch: 130/200, Loss: 52.1279\n",
            "Epoch: 131/200, Loss: 54.1388\n",
            "Epoch: 132/200, Loss: 52.1443\n",
            "Epoch: 133/200, Loss: 55.5860\n",
            "Epoch: 134/200, Loss: 53.9732\n",
            "Epoch: 135/200, Loss: 48.9642\n",
            "Epoch: 136/200, Loss: 52.6922\n",
            "Epoch: 137/200, Loss: 47.3475\n",
            "Epoch: 138/200, Loss: 50.7502\n",
            "Epoch: 139/200, Loss: 53.4622\n",
            "Epoch: 140/200, Loss: 45.8431\n",
            "Epoch: 141/200, Loss: 51.6264\n",
            "Epoch: 142/200, Loss: 47.5059\n",
            "Epoch: 143/200, Loss: 47.8134\n",
            "Epoch: 144/200, Loss: 48.6484\n",
            "Epoch: 145/200, Loss: 50.2429\n",
            "Epoch: 146/200, Loss: 45.8984\n",
            "Epoch: 147/200, Loss: 46.2967\n",
            "Epoch: 148/200, Loss: 46.0646\n",
            "Epoch: 149/200, Loss: 46.1174\n",
            "Epoch: 150/200, Loss: 44.2365\n",
            "Epoch: 151/200, Loss: 46.5601\n",
            "Epoch: 152/200, Loss: 43.9973\n",
            "Epoch: 153/200, Loss: 46.5414\n",
            "Epoch: 154/200, Loss: 46.2083\n",
            "Epoch: 155/200, Loss: 41.1868\n",
            "Epoch: 156/200, Loss: 42.1583\n",
            "Epoch: 157/200, Loss: 41.2516\n",
            "Epoch: 158/200, Loss: 41.2325\n",
            "Epoch: 159/200, Loss: 40.2722\n",
            "Epoch: 160/200, Loss: 37.6332\n",
            "Epoch: 161/200, Loss: 35.9790\n",
            "Epoch: 162/200, Loss: 34.3580\n",
            "Epoch: 163/200, Loss: 31.9684\n",
            "Epoch: 164/200, Loss: 32.5507\n",
            "Epoch: 165/200, Loss: 29.2350\n",
            "Epoch: 166/200, Loss: 28.4963\n",
            "Epoch: 167/200, Loss: 27.6210\n",
            "Epoch: 168/200, Loss: 27.3868\n",
            "Epoch: 169/200, Loss: 25.3322\n",
            "Epoch: 170/200, Loss: 26.0662\n",
            "Epoch: 171/200, Loss: 26.6924\n",
            "Epoch: 172/200, Loss: 23.1189\n",
            "Epoch: 173/200, Loss: 25.9327\n",
            "Epoch: 174/200, Loss: 26.7085\n",
            "Epoch: 175/200, Loss: 30.2505\n",
            "Epoch: 176/200, Loss: 26.5004\n",
            "Epoch: 177/200, Loss: 29.2462\n",
            "Epoch: 178/200, Loss: 28.4325\n",
            "Epoch: 179/200, Loss: 32.8426\n",
            "Epoch: 180/200, Loss: 28.9176\n",
            "Epoch: 181/200, Loss: 28.6742\n",
            "Epoch: 182/200, Loss: 28.7931\n",
            "Epoch: 183/200, Loss: 33.5903\n",
            "Epoch: 184/200, Loss: 31.6957\n",
            "Epoch: 185/200, Loss: 32.8183\n",
            "Epoch: 186/200, Loss: 33.0102\n",
            "Epoch: 187/200, Loss: 35.3970\n",
            "Epoch: 188/200, Loss: 33.1151\n",
            "Epoch: 189/200, Loss: 37.9987\n",
            "Epoch: 190/200, Loss: 35.7397\n",
            "Epoch: 191/200, Loss: 37.8429\n",
            "Epoch: 192/200, Loss: 39.1373\n",
            "Epoch: 193/200, Loss: 38.9605\n",
            "Epoch: 194/200, Loss: 41.4970\n",
            "Epoch: 195/200, Loss: 42.0612\n",
            "Epoch: 196/200, Loss: 46.0393\n",
            "Epoch: 197/200, Loss: 43.1988\n",
            "Epoch: 198/200, Loss: 47.3433\n",
            "Epoch: 199/200, Loss: 47.3764\n",
            "Epoch: 200/200, Loss: 47.4043\n",
            "train_x: (2686, 10, 67), train_y: (2686, 11, 1)\n"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv('./assets/data.csv', index_col = \"Date\")\n",
        "data['y'] = data['Close']\n",
        "\n",
        "data_x = data.iloc[:, :-1].values\n",
        "data_y = data.iloc[:, -1].values\n",
        "\n",
        "train_x = data_x[:-330, :]\n",
        "train_y = data_y[:-330]\n",
        "\n",
        "print(f'train_x: {train_x.shape} train_y: {train_y.shape}')\n",
        "\n",
        "x_scaler = RobustScaler()\n",
        "y_scaler = RobustScaler()\n",
        "\n",
        "train_x = x_scaler.fit_transform(train_x)\n",
        "train_y = y_scaler.fit_transform(train_y.reshape(-1, 1))\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "vae = VAE([train_x.shape[1], 400, 400, 400, 400, 10], 10, use_cuda=1)\n",
        "vae = vae.to(device)\n",
        "num_epochs = 200\n",
        "learning_rate = 0.0001\n",
        "optimizer = torch.optim.Adam(vae.parameters(), lr=learning_rate)\n",
        "\n",
        "\n",
        "train_loader = DataLoader(TensorDataset(torch.from_numpy(train_x).float(), torch.from_numpy(train_y).float()),\n",
        "                          batch_size=128, shuffle=True)\n",
        "\n",
        "\n",
        "print('Training the VAE model------------------')\n",
        "history = np.zeros(num_epochs)\n",
        "for epoch in range(num_epochs):\n",
        "    total_loss = 0\n",
        "    for x, _ in train_loader:\n",
        "        x = x.to(device)\n",
        "        x_hat, z, mu, log_var = vae(x)\n",
        "        loss = vae.criterion(x_hat, x, mu, log_var)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    history[epoch] = total_loss\n",
        "    print(f'Epoch: {epoch + 1}/{num_epochs}, Loss: {total_loss:.4f}')\n",
        "\n",
        "vae.eval()\n",
        "with torch.no_grad():\n",
        "    _, _, mu, _ = vae(torch.from_numpy(train_x).float().to(vae.device))\n",
        "    mu = mu.cpu().numpy()\n",
        "\n",
        "train_x = np.concatenate((train_x, mu), axis=1)\n",
        "\n",
        "seq_length = 10\n",
        "train_x, train_y = sliding_windows(train_x, train_y, seq_length)\n",
        "print(f'train_x: {train_x.shape}, train_y: {train_y.shape}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sr_yIKEJiA7o",
        "outputId": "93a260c4-0859-45fd-fcad-68e92527c3c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Basic for 1000 epochs\n",
            "[1/1000] LossD: -0.00052 LossG: -0.19722\n",
            "[2/1000] LossD: -0.00099 LossG: -0.20664\n",
            "[3/1000] LossD: -0.00144 LossG: -0.21548\n",
            "[4/1000] LossD: -0.00189 LossG: -0.22359\n",
            "[5/1000] LossD: -0.00232 LossG: -0.23163\n",
            "[6/1000] LossD: -0.00274 LossG: -0.23945\n",
            "[7/1000] LossD: -0.00315 LossG: -0.24736\n",
            "[8/1000] LossD: -0.00353 LossG: -0.25535\n",
            "[9/1000] LossD: -0.00392 LossG: -0.26314\n",
            "[10/1000] LossD: -0.00430 LossG: -0.27092\n",
            "[11/1000] LossD: -0.00468 LossG: -0.27869\n",
            "[12/1000] LossD: -0.00506 LossG: -0.28640\n",
            "[13/1000] LossD: -0.00548 LossG: -0.29398\n",
            "[14/1000] LossD: -0.00589 LossG: -0.30118\n",
            "[15/1000] LossD: -0.00630 LossG: -0.30876\n",
            "[16/1000] LossD: -0.00672 LossG: -0.31645\n",
            "[17/1000] LossD: -0.00715 LossG: -0.32364\n",
            "[18/1000] LossD: -0.00759 LossG: -0.33112\n",
            "[19/1000] LossD: -0.00804 LossG: -0.33855\n",
            "[20/1000] LossD: -0.00850 LossG: -0.34616\n",
            "[21/1000] LossD: -0.00897 LossG: -0.35382\n",
            "[22/1000] LossD: -0.00948 LossG: -0.36108\n",
            "[23/1000] LossD: -0.00999 LossG: -0.36760\n",
            "[24/1000] LossD: -0.01053 LossG: -0.37450\n",
            "[25/1000] LossD: -0.01108 LossG: -0.38125\n",
            "[26/1000] LossD: -0.01165 LossG: -0.38759\n",
            "[27/1000] LossD: -0.01226 LossG: -0.39419\n",
            "[28/1000] LossD: -0.01288 LossG: -0.40099\n",
            "[29/1000] LossD: -0.01352 LossG: -0.40767\n",
            "[30/1000] LossD: -0.01421 LossG: -0.41417\n",
            "[31/1000] LossD: -0.01492 LossG: -0.42092\n",
            "[32/1000] LossD: -0.01565 LossG: -0.42789\n",
            "[33/1000] LossD: -0.01642 LossG: -0.43467\n",
            "[34/1000] LossD: -0.01723 LossG: -0.44236\n",
            "[35/1000] LossD: -0.01812 LossG: -0.45110\n",
            "[36/1000] LossD: -0.01924 LossG: -0.46025\n",
            "[37/1000] LossD: -0.02038 LossG: -0.47014\n",
            "[38/1000] LossD: -0.02139 LossG: -0.47963\n",
            "[39/1000] LossD: -0.02243 LossG: -0.48857\n",
            "[40/1000] LossD: -0.02348 LossG: -0.49642\n",
            "[41/1000] LossD: -0.02457 LossG: -0.50441\n",
            "[42/1000] LossD: -0.02573 LossG: -0.51221\n",
            "[43/1000] LossD: -0.02699 LossG: -0.52028\n",
            "[44/1000] LossD: -0.02822 LossG: -0.52815\n",
            "[45/1000] LossD: -0.02953 LossG: -0.53631\n",
            "[46/1000] LossD: -0.03091 LossG: -0.54438\n",
            "[47/1000] LossD: -0.03234 LossG: -0.55299\n",
            "[48/1000] LossD: -0.03383 LossG: -0.56098\n",
            "[49/1000] LossD: -0.03538 LossG: -0.56959\n",
            "[50/1000] LossD: -0.03702 LossG: -0.57762\n",
            "[51/1000] LossD: -0.03873 LossG: -0.58581\n",
            "[52/1000] LossD: -0.04050 LossG: -0.59405\n",
            "[53/1000] LossD: -0.04236 LossG: -0.60234\n",
            "[54/1000] LossD: -0.04432 LossG: -0.61084\n",
            "[55/1000] LossD: -0.04634 LossG: -0.61924\n",
            "[56/1000] LossD: -0.04846 LossG: -0.62770\n",
            "[57/1000] LossD: -0.05066 LossG: -0.63618\n",
            "[58/1000] LossD: -0.05295 LossG: -0.64430\n",
            "[59/1000] LossD: -0.05535 LossG: -0.65314\n",
            "[60/1000] LossD: -0.05788 LossG: -0.66233\n",
            "[61/1000] LossD: -0.06054 LossG: -0.67189\n",
            "[62/1000] LossD: -0.06323 LossG: -0.68082\n",
            "[63/1000] LossD: -0.06600 LossG: -0.68988\n",
            "[64/1000] LossD: -0.06889 LossG: -0.69871\n",
            "[65/1000] LossD: -0.07192 LossG: -0.70804\n",
            "[66/1000] LossD: -0.07497 LossG: -0.71689\n",
            "[67/1000] LossD: -0.07806 LossG: -0.72611\n",
            "[68/1000] LossD: -0.08121 LossG: -0.73510\n",
            "[69/1000] LossD: -0.08439 LossG: -0.74428\n",
            "[70/1000] LossD: -0.08747 LossG: -0.75349\n",
            "[71/1000] LossD: -0.09049 LossG: -0.76337\n",
            "[72/1000] LossD: -0.09348 LossG: -0.77350\n",
            "[73/1000] LossD: -0.09613 LossG: -0.78461\n",
            "[74/1000] LossD: -0.09851 LossG: -0.79611\n",
            "[75/1000] LossD: -0.10027 LossG: -0.80795\n",
            "[76/1000] LossD: -0.10113 LossG: -0.82053\n",
            "[77/1000] LossD: -0.10089 LossG: -0.83376\n",
            "[78/1000] LossD: -0.09869 LossG: -0.84847\n",
            "[79/1000] LossD: -0.09426 LossG: -0.86421\n",
            "[80/1000] LossD: -0.08606 LossG: -0.88201\n",
            "[81/1000] LossD: -0.07273 LossG: -0.90203\n",
            "[82/1000] LossD: -0.05291 LossG: -0.92511\n",
            "[83/1000] LossD: -0.02560 LossG: -0.94996\n",
            "[84/1000] LossD: 0.00596 LossG: -0.97261\n",
            "[85/1000] LossD: 0.03130 LossG: -0.99345\n",
            "[86/1000] LossD: 0.04801 LossG: -1.00793\n",
            "[87/1000] LossD: 0.05623 LossG: -1.01169\n",
            "[88/1000] LossD: 0.05675 LossG: -1.01032\n",
            "[89/1000] LossD: 0.05447 LossG: -1.00637\n",
            "[90/1000] LossD: 0.05054 LossG: -0.99961\n",
            "[91/1000] LossD: 0.04629 LossG: -0.98829\n",
            "[92/1000] LossD: 0.04202 LossG: -0.97296\n",
            "[93/1000] LossD: 0.03764 LossG: -0.95578\n",
            "[94/1000] LossD: 0.03378 LossG: -0.93542\n",
            "[95/1000] LossD: 0.03013 LossG: -0.91296\n",
            "[96/1000] LossD: 0.02683 LossG: -0.89053\n",
            "[97/1000] LossD: 0.02377 LossG: -0.86822\n",
            "[98/1000] LossD: 0.02075 LossG: -0.84537\n",
            "[99/1000] LossD: 0.01831 LossG: -0.82415\n",
            "[100/1000] LossD: 0.01606 LossG: -0.80411\n",
            "[101/1000] LossD: 0.01390 LossG: -0.78482\n",
            "[102/1000] LossD: 0.01203 LossG: -0.76732\n",
            "[103/1000] LossD: 0.01045 LossG: -0.75115\n",
            "[104/1000] LossD: 0.00891 LossG: -0.73500\n",
            "[105/1000] LossD: 0.00754 LossG: -0.71918\n",
            "[106/1000] LossD: 0.00630 LossG: -0.70399\n",
            "[107/1000] LossD: 0.00519 LossG: -0.68985\n",
            "[108/1000] LossD: 0.00422 LossG: -0.67583\n",
            "[109/1000] LossD: 0.00330 LossG: -0.66287\n",
            "[110/1000] LossD: 0.00244 LossG: -0.65011\n",
            "[111/1000] LossD: 0.00170 LossG: -0.63847\n",
            "[112/1000] LossD: 0.00104 LossG: -0.62714\n",
            "[113/1000] LossD: 0.00057 LossG: -0.61592\n",
            "[114/1000] LossD: 0.00016 LossG: -0.60548\n",
            "[115/1000] LossD: -0.00019 LossG: -0.59575\n",
            "[116/1000] LossD: -0.00038 LossG: -0.58665\n",
            "[117/1000] LossD: -0.00058 LossG: -0.57794\n",
            "[118/1000] LossD: -0.00073 LossG: -0.57002\n",
            "[119/1000] LossD: -0.00091 LossG: -0.56150\n",
            "[120/1000] LossD: -0.00106 LossG: -0.55364\n",
            "[121/1000] LossD: -0.00114 LossG: -0.54609\n",
            "[122/1000] LossD: -0.00121 LossG: -0.53868\n",
            "[123/1000] LossD: -0.00128 LossG: -0.53102\n",
            "[124/1000] LossD: -0.00130 LossG: -0.52389\n",
            "[125/1000] LossD: -0.00138 LossG: -0.51714\n",
            "[126/1000] LossD: -0.00139 LossG: -0.51027\n",
            "[127/1000] LossD: -0.00146 LossG: -0.50380\n",
            "[128/1000] LossD: -0.00143 LossG: -0.49666\n",
            "[129/1000] LossD: -0.00147 LossG: -0.48881\n",
            "[130/1000] LossD: -0.00140 LossG: -0.48037\n",
            "[131/1000] LossD: -0.00139 LossG: -0.47259\n",
            "[132/1000] LossD: -0.00135 LossG: -0.46611\n",
            "[133/1000] LossD: -0.00142 LossG: -0.45821\n",
            "[134/1000] LossD: -0.00136 LossG: -0.45029\n",
            "[135/1000] LossD: -0.00135 LossG: -0.44218\n",
            "[136/1000] LossD: -0.00141 LossG: -0.43392\n",
            "[137/1000] LossD: -0.00137 LossG: -0.42755\n",
            "[138/1000] LossD: -0.00143 LossG: -0.42068\n",
            "[139/1000] LossD: -0.00142 LossG: -0.41296\n",
            "[140/1000] LossD: -0.00149 LossG: -0.40416\n",
            "[141/1000] LossD: -0.00145 LossG: -0.39768\n",
            "[142/1000] LossD: -0.00149 LossG: -0.39089\n",
            "[143/1000] LossD: -0.00152 LossG: -0.38370\n",
            "[144/1000] LossD: -0.00161 LossG: -0.37713\n",
            "[145/1000] LossD: -0.00165 LossG: -0.37080\n",
            "[146/1000] LossD: -0.00174 LossG: -0.36466\n",
            "[147/1000] LossD: -0.00187 LossG: -0.35826\n",
            "[148/1000] LossD: -0.00193 LossG: -0.35206\n",
            "[149/1000] LossD: -0.00199 LossG: -0.34562\n",
            "[150/1000] LossD: -0.00206 LossG: -0.34008\n",
            "[151/1000] LossD: -0.00210 LossG: -0.33470\n",
            "[152/1000] LossD: -0.00214 LossG: -0.32902\n",
            "[153/1000] LossD: -0.00216 LossG: -0.32338\n",
            "[154/1000] LossD: -0.00213 LossG: -0.31817\n",
            "[155/1000] LossD: -0.00214 LossG: -0.31284\n",
            "[156/1000] LossD: -0.00208 LossG: -0.30730\n",
            "[157/1000] LossD: -0.00207 LossG: -0.30196\n",
            "[158/1000] LossD: -0.00204 LossG: -0.29702\n",
            "[159/1000] LossD: -0.00199 LossG: -0.29160\n",
            "[160/1000] LossD: -0.00201 LossG: -0.28705\n",
            "[161/1000] LossD: -0.00201 LossG: -0.28234\n",
            "[162/1000] LossD: -0.00204 LossG: -0.27752\n",
            "[163/1000] LossD: -0.00208 LossG: -0.27197\n",
            "[164/1000] LossD: -0.00207 LossG: -0.26660\n",
            "[165/1000] LossD: -0.00207 LossG: -0.26144\n",
            "[166/1000] LossD: -0.00214 LossG: -0.25596\n",
            "[167/1000] LossD: -0.00218 LossG: -0.25202\n",
            "[168/1000] LossD: -0.00220 LossG: -0.24843\n",
            "[169/1000] LossD: -0.00231 LossG: -0.24479\n",
            "[170/1000] LossD: -0.00233 LossG: -0.24098\n",
            "[171/1000] LossD: -0.00242 LossG: -0.23736\n",
            "[172/1000] LossD: -0.00252 LossG: -0.23400\n",
            "[173/1000] LossD: -0.00260 LossG: -0.22989\n",
            "[174/1000] LossD: -0.00270 LossG: -0.22685\n",
            "[175/1000] LossD: -0.00280 LossG: -0.22339\n",
            "[176/1000] LossD: -0.00289 LossG: -0.21976\n",
            "[177/1000] LossD: -0.00294 LossG: -0.21625\n",
            "[178/1000] LossD: -0.00295 LossG: -0.21399\n",
            "[179/1000] LossD: -0.00293 LossG: -0.21141\n",
            "[180/1000] LossD: -0.00290 LossG: -0.20880\n",
            "[181/1000] LossD: -0.00291 LossG: -0.20599\n",
            "[182/1000] LossD: -0.00281 LossG: -0.20360\n",
            "[183/1000] LossD: -0.00279 LossG: -0.20037\n",
            "[184/1000] LossD: -0.00270 LossG: -0.19824\n",
            "[185/1000] LossD: -0.00264 LossG: -0.19527\n",
            "[186/1000] LossD: -0.00254 LossG: -0.19295\n",
            "[187/1000] LossD: -0.00250 LossG: -0.18997\n",
            "[188/1000] LossD: -0.00249 LossG: -0.18727\n",
            "[189/1000] LossD: -0.00248 LossG: -0.18426\n",
            "[190/1000] LossD: -0.00252 LossG: -0.18236\n",
            "[191/1000] LossD: -0.00258 LossG: -0.17928\n",
            "[192/1000] LossD: -0.00267 LossG: -0.17711\n",
            "[193/1000] LossD: -0.00275 LossG: -0.17421\n",
            "[194/1000] LossD: -0.00290 LossG: -0.17202\n",
            "[195/1000] LossD: -0.00305 LossG: -0.16975\n",
            "[196/1000] LossD: -0.00316 LossG: -0.16814\n",
            "[197/1000] LossD: -0.00327 LossG: -0.16611\n",
            "[198/1000] LossD: -0.00337 LossG: -0.16491\n",
            "[199/1000] LossD: -0.00343 LossG: -0.16319\n",
            "[200/1000] LossD: -0.00350 LossG: -0.16256\n",
            "[201/1000] LossD: -0.00349 LossG: -0.16096\n",
            "[202/1000] LossD: -0.00344 LossG: -0.16036\n",
            "[203/1000] LossD: -0.00337 LossG: -0.15852\n",
            "[204/1000] LossD: -0.00324 LossG: -0.15737\n",
            "[205/1000] LossD: -0.00311 LossG: -0.15525\n",
            "[206/1000] LossD: -0.00295 LossG: -0.15374\n",
            "[207/1000] LossD: -0.00279 LossG: -0.15143\n",
            "[208/1000] LossD: -0.00267 LossG: -0.14941\n",
            "[209/1000] LossD: -0.00256 LossG: -0.14648\n",
            "[210/1000] LossD: -0.00251 LossG: -0.14371\n",
            "[211/1000] LossD: -0.00247 LossG: -0.14044\n",
            "[212/1000] LossD: -0.00255 LossG: -0.13806\n",
            "[213/1000] LossD: -0.00265 LossG: -0.13483\n",
            "[214/1000] LossD: -0.00273 LossG: -0.13287\n",
            "[215/1000] LossD: -0.00291 LossG: -0.13028\n",
            "[216/1000] LossD: -0.00311 LossG: -0.12776\n",
            "[217/1000] LossD: -0.00328 LossG: -0.12487\n",
            "[218/1000] LossD: -0.00347 LossG: -0.12304\n",
            "[219/1000] LossD: -0.00363 LossG: -0.12051\n",
            "[220/1000] LossD: -0.00374 LossG: -0.11895\n",
            "[221/1000] LossD: -0.00383 LossG: -0.11704\n",
            "[222/1000] LossD: -0.00388 LossG: -0.11548\n",
            "[223/1000] LossD: -0.00382 LossG: -0.11407\n",
            "[224/1000] LossD: -0.00369 LossG: -0.11248\n",
            "[225/1000] LossD: -0.00349 LossG: -0.11046\n",
            "[226/1000] LossD: -0.00321 LossG: -0.10874\n",
            "[227/1000] LossD: -0.00293 LossG: -0.10622\n",
            "[228/1000] LossD: -0.00262 LossG: -0.10393\n",
            "[229/1000] LossD: -0.00245 LossG: -0.10128\n",
            "[230/1000] LossD: -0.00228 LossG: -0.09940\n",
            "[231/1000] LossD: -0.00229 LossG: -0.09573\n",
            "[232/1000] LossD: -0.00235 LossG: -0.09290\n",
            "[233/1000] LossD: -0.00254 LossG: -0.09000\n",
            "[234/1000] LossD: -0.00276 LossG: -0.08799\n",
            "[235/1000] LossD: -0.00308 LossG: -0.08569\n",
            "[236/1000] LossD: -0.00337 LossG: -0.08473\n",
            "[237/1000] LossD: -0.00367 LossG: -0.08330\n",
            "[238/1000] LossD: -0.00394 LossG: -0.08274\n",
            "[239/1000] LossD: -0.00417 LossG: -0.08139\n",
            "[240/1000] LossD: -0.00431 LossG: -0.08061\n",
            "[241/1000] LossD: -0.00432 LossG: -0.07936\n",
            "[242/1000] LossD: -0.00425 LossG: -0.07839\n",
            "[243/1000] LossD: -0.00403 LossG: -0.07732\n",
            "[244/1000] LossD: -0.00364 LossG: -0.07678\n",
            "[245/1000] LossD: -0.00318 LossG: -0.07502\n",
            "[246/1000] LossD: -0.00262 LossG: -0.07342\n",
            "[247/1000] LossD: -0.00210 LossG: -0.07103\n",
            "[248/1000] LossD: -0.00184 LossG: -0.06817\n",
            "[249/1000] LossD: -0.00177 LossG: -0.06444\n",
            "[250/1000] LossD: -0.00195 LossG: -0.06169\n",
            "[251/1000] LossD: -0.00228 LossG: -0.05933\n",
            "[252/1000] LossD: -0.00276 LossG: -0.05721\n",
            "[253/1000] LossD: -0.00328 LossG: -0.05610\n",
            "[254/1000] LossD: -0.00381 LossG: -0.05563\n",
            "[255/1000] LossD: -0.00436 LossG: -0.05517\n",
            "[256/1000] LossD: -0.00485 LossG: -0.05499\n",
            "[257/1000] LossD: -0.00525 LossG: -0.05465\n",
            "[258/1000] LossD: -0.00555 LossG: -0.05393\n",
            "[259/1000] LossD: -0.00561 LossG: -0.05407\n",
            "[260/1000] LossD: -0.00551 LossG: -0.05399\n",
            "[261/1000] LossD: -0.00502 LossG: -0.05346\n",
            "[262/1000] LossD: -0.00418 LossG: -0.05266\n",
            "[263/1000] LossD: -0.00299 LossG: -0.05108\n",
            "[264/1000] LossD: -0.00178 LossG: -0.04904\n",
            "[265/1000] LossD: -0.00101 LossG: -0.04429\n",
            "[266/1000] LossD: -0.00074 LossG: -0.04006\n",
            "[267/1000] LossD: -0.00103 LossG: -0.03652\n",
            "[268/1000] LossD: -0.00157 LossG: -0.03471\n",
            "[269/1000] LossD: -0.00229 LossG: -0.03248\n",
            "[270/1000] LossD: -0.00309 LossG: -0.03134\n",
            "[271/1000] LossD: -0.00402 LossG: -0.03070\n",
            "[272/1000] LossD: -0.00493 LossG: -0.03008\n",
            "[273/1000] LossD: -0.00589 LossG: -0.02934\n",
            "[274/1000] LossD: -0.00681 LossG: -0.02923\n",
            "[275/1000] LossD: -0.00768 LossG: -0.02871\n",
            "[276/1000] LossD: -0.00849 LossG: -0.02873\n",
            "[277/1000] LossD: -0.00917 LossG: -0.02833\n",
            "[278/1000] LossD: -0.00963 LossG: -0.02835\n",
            "[279/1000] LossD: -0.00966 LossG: -0.02839\n",
            "[280/1000] LossD: -0.00921 LossG: -0.02915\n",
            "[281/1000] LossD: -0.00785 LossG: -0.02976\n",
            "[282/1000] LossD: -0.00548 LossG: -0.03115\n",
            "[283/1000] LossD: -0.00206 LossG: -0.03090\n",
            "[284/1000] LossD: 0.00054 LossG: -0.02806\n",
            "[285/1000] LossD: 0.00124 LossG: -0.02372\n",
            "[286/1000] LossD: 0.00088 LossG: -0.01944\n",
            "[287/1000] LossD: 0.00013 LossG: -0.01698\n",
            "[288/1000] LossD: -0.00062 LossG: -0.01602\n",
            "[289/1000] LossD: -0.00132 LossG: -0.01801\n",
            "[290/1000] LossD: -0.00214 LossG: -0.01653\n",
            "[291/1000] LossD: -0.00301 LossG: -0.01616\n",
            "[292/1000] LossD: -0.00393 LossG: -0.01571\n",
            "[293/1000] LossD: -0.00492 LossG: -0.01505\n",
            "[294/1000] LossD: -0.00594 LossG: -0.01482\n",
            "[295/1000] LossD: -0.00691 LossG: -0.01465\n",
            "[296/1000] LossD: -0.00787 LossG: -0.01424\n",
            "[297/1000] LossD: -0.00877 LossG: -0.01377\n",
            "[298/1000] LossD: -0.00950 LossG: -0.01383\n",
            "[299/1000] LossD: -0.01003 LossG: -0.01351\n",
            "[300/1000] LossD: -0.01020 LossG: -0.01383\n",
            "[301/1000] LossD: -0.00973 LossG: -0.01410\n",
            "[302/1000] LossD: -0.00832 LossG: -0.01524\n",
            "[303/1000] LossD: -0.00559 LossG: -0.01640\n",
            "[304/1000] LossD: -0.00187 LossG: -0.01752\n",
            "[305/1000] LossD: 0.00100 LossG: -0.01689\n",
            "[306/1000] LossD: 0.00159 LossG: -0.01619\n",
            "[307/1000] LossD: 0.00108 LossG: -0.01301\n",
            "[308/1000] LossD: 0.00032 LossG: -0.01040\n",
            "[309/1000] LossD: -0.00036 LossG: -0.00947\n",
            "[310/1000] LossD: -0.00114 LossG: -0.00864\n",
            "[311/1000] LossD: -0.00202 LossG: -0.00667\n",
            "[312/1000] LossD: -0.00302 LossG: -0.00516\n",
            "[313/1000] LossD: -0.00411 LossG: -0.00523\n",
            "[314/1000] LossD: -0.00523 LossG: -0.00469\n",
            "[315/1000] LossD: -0.00641 LossG: -0.00400\n",
            "[316/1000] LossD: -0.00760 LossG: -0.00343\n",
            "[317/1000] LossD: -0.00881 LossG: -0.00271\n",
            "[318/1000] LossD: -0.01007 LossG: -0.00221\n",
            "[319/1000] LossD: -0.01128 LossG: -0.00190\n",
            "[320/1000] LossD: -0.01247 LossG: -0.00161\n",
            "[321/1000] LossD: -0.01354 LossG: -0.00084\n",
            "[322/1000] LossD: -0.01431 LossG: -0.00033\n",
            "[323/1000] LossD: -0.01453 LossG: 0.00093\n",
            "[324/1000] LossD: -0.01381 LossG: 0.00033\n",
            "[325/1000] LossD: -0.01170 LossG: -0.00004\n",
            "[326/1000] LossD: -0.00737 LossG: -0.00284\n",
            "[327/1000] LossD: -0.00123 LossG: -0.00555\n",
            "[328/1000] LossD: 0.00281 LossG: -0.00674\n",
            "[329/1000] LossD: 0.00308 LossG: -0.00217\n",
            "[330/1000] LossD: 0.00202 LossG: 0.00287\n",
            "[331/1000] LossD: 0.00072 LossG: 0.00900\n",
            "[332/1000] LossD: -0.00043 LossG: 0.00830\n",
            "[333/1000] LossD: -0.00090 LossG: 0.00187\n",
            "[334/1000] LossD: -0.00108 LossG: -0.00176\n",
            "[335/1000] LossD: -0.00145 LossG: -0.00106\n",
            "[336/1000] LossD: -0.00205 LossG: 0.00049\n",
            "[337/1000] LossD: -0.00290 LossG: 0.00118\n",
            "[338/1000] LossD: -0.00382 LossG: 0.00093\n",
            "[339/1000] LossD: -0.00481 LossG: 0.00206\n",
            "[340/1000] LossD: -0.00579 LossG: 0.00274\n",
            "[341/1000] LossD: -0.00677 LossG: 0.00332\n",
            "[342/1000] LossD: -0.00766 LossG: 0.00326\n",
            "[343/1000] LossD: -0.00836 LossG: 0.00383\n",
            "[344/1000] LossD: -0.00878 LossG: 0.00402\n",
            "[345/1000] LossD: -0.00874 LossG: 0.00445\n",
            "[346/1000] LossD: -0.00800 LossG: 0.00395\n",
            "[347/1000] LossD: -0.00617 LossG: 0.00365\n",
            "[348/1000] LossD: -0.00314 LossG: 0.00193\n",
            "[349/1000] LossD: -0.00006 LossG: -0.00012\n",
            "[350/1000] LossD: 0.00111 LossG: -0.00158\n",
            "[351/1000] LossD: 0.00073 LossG: 0.00003\n",
            "[352/1000] LossD: -0.00000 LossG: 0.00021\n",
            "[353/1000] LossD: -0.00075 LossG: -0.00025\n",
            "[354/1000] LossD: -0.00156 LossG: 0.00098\n",
            "[355/1000] LossD: -0.00253 LossG: 0.00278\n",
            "[356/1000] LossD: -0.00364 LossG: 0.00364\n",
            "[357/1000] LossD: -0.00482 LossG: 0.00352\n",
            "[358/1000] LossD: -0.00603 LossG: 0.00404\n",
            "[359/1000] LossD: -0.00730 LossG: 0.00462\n",
            "[360/1000] LossD: -0.00860 LossG: 0.00538\n",
            "[361/1000] LossD: -0.00999 LossG: 0.00577\n",
            "[362/1000] LossD: -0.01145 LossG: 0.00512\n",
            "[363/1000] LossD: -0.01303 LossG: 0.00471\n",
            "[364/1000] LossD: -0.01473 LossG: 0.00594\n",
            "[365/1000] LossD: -0.01655 LossG: 0.00562\n",
            "[366/1000] LossD: -0.01846 LossG: 0.00675\n",
            "[367/1000] LossD: -0.02036 LossG: 0.00702\n",
            "[368/1000] LossD: -0.02205 LossG: 0.00847\n",
            "[369/1000] LossD: -0.02315 LossG: 0.00889\n",
            "[370/1000] LossD: -0.02283 LossG: 0.01001\n",
            "[371/1000] LossD: -0.02022 LossG: 0.01032\n",
            "[372/1000] LossD: -0.01426 LossG: 0.00727\n",
            "[373/1000] LossD: -0.00400 LossG: 0.00088\n",
            "[374/1000] LossD: 0.00435 LossG: -0.00380\n",
            "[375/1000] LossD: 0.00577 LossG: -0.00671\n",
            "[376/1000] LossD: 0.00460 LossG: -0.00913\n",
            "[377/1000] LossD: 0.00334 LossG: -0.01021\n",
            "[378/1000] LossD: 0.00226 LossG: -0.00836\n",
            "[379/1000] LossD: 0.00134 LossG: -0.00771\n",
            "[380/1000] LossD: 0.00036 LossG: -0.00916\n",
            "[381/1000] LossD: -0.00031 LossG: -0.00638\n",
            "[382/1000] LossD: -0.00055 LossG: -0.00261\n",
            "[383/1000] LossD: -0.00095 LossG: -0.00056\n",
            "[384/1000] LossD: -0.00150 LossG: 0.00352\n",
            "[385/1000] LossD: -0.00213 LossG: 0.00609\n",
            "[386/1000] LossD: -0.00296 LossG: 0.00660\n",
            "[387/1000] LossD: -0.00391 LossG: 0.00652\n",
            "[388/1000] LossD: -0.00493 LossG: 0.00672\n",
            "[389/1000] LossD: -0.00590 LossG: 0.00630\n",
            "[390/1000] LossD: -0.00678 LossG: 0.00609\n",
            "[391/1000] LossD: -0.00754 LossG: 0.00626\n",
            "[392/1000] LossD: -0.00805 LossG: 0.00664\n",
            "[393/1000] LossD: -0.00820 LossG: 0.00675\n",
            "[394/1000] LossD: -0.00764 LossG: 0.00688\n",
            "[395/1000] LossD: -0.00609 LossG: 0.00559\n",
            "[396/1000] LossD: -0.00335 LossG: 0.00419\n",
            "[397/1000] LossD: -0.00041 LossG: 0.00324\n",
            "[398/1000] LossD: 0.00076 LossG: 0.00253\n",
            "[399/1000] LossD: 0.00040 LossG: 0.00273\n",
            "[400/1000] LossD: -0.00034 LossG: 0.00402\n",
            "[401/1000] LossD: -0.00108 LossG: 0.00301\n",
            "[402/1000] LossD: -0.00198 LossG: 0.00448\n",
            "[403/1000] LossD: -0.00301 LossG: 0.00493\n",
            "[404/1000] LossD: -0.00416 LossG: 0.00481\n",
            "[405/1000] LossD: -0.00536 LossG: 0.00489\n",
            "[406/1000] LossD: -0.00659 LossG: 0.00567\n",
            "[407/1000] LossD: -0.00783 LossG: 0.00582\n",
            "[408/1000] LossD: -0.00908 LossG: 0.00599\n",
            "[409/1000] LossD: -0.01038 LossG: 0.00645\n",
            "[410/1000] LossD: -0.01162 LossG: 0.00593\n",
            "[411/1000] LossD: -0.01272 LossG: 0.00689\n",
            "[412/1000] LossD: -0.01341 LossG: 0.00670\n",
            "[413/1000] LossD: -0.01331 LossG: 0.00838\n",
            "[414/1000] LossD: -0.01188 LossG: 0.00900\n",
            "[415/1000] LossD: -0.00847 LossG: 0.00891\n",
            "[416/1000] LossD: -0.00283 LossG: 0.00546\n",
            "[417/1000] LossD: 0.00181 LossG: 0.00090\n",
            "[418/1000] LossD: 0.00250 LossG: -0.00012\n",
            "[419/1000] LossD: 0.00171 LossG: -0.00250\n",
            "[420/1000] LossD: 0.00080 LossG: -0.00612\n",
            "[421/1000] LossD: -0.00017 LossG: -0.00833\n",
            "[422/1000] LossD: -0.00059 LossG: -0.00486\n",
            "[423/1000] LossD: -0.00105 LossG: -0.00129\n",
            "[424/1000] LossD: -0.00174 LossG: 0.00399\n",
            "[425/1000] LossD: -0.00262 LossG: 0.00664\n",
            "[426/1000] LossD: -0.00366 LossG: 0.00671\n",
            "[427/1000] LossD: -0.00478 LossG: 0.00682\n",
            "[428/1000] LossD: -0.00590 LossG: 0.00710\n",
            "[429/1000] LossD: -0.00698 LossG: 0.00588\n",
            "[430/1000] LossD: -0.00799 LossG: 0.00677\n",
            "[431/1000] LossD: -0.00888 LossG: 0.00659\n",
            "[432/1000] LossD: -0.00955 LossG: 0.00738\n",
            "[433/1000] LossD: -0.00975 LossG: 0.00742\n",
            "[434/1000] LossD: -0.00913 LossG: 0.00808\n",
            "[435/1000] LossD: -0.00723 LossG: 0.00803\n",
            "[436/1000] LossD: -0.00374 LossG: 0.00585\n",
            "[437/1000] LossD: 0.00000 LossG: 0.00330\n",
            "[438/1000] LossD: 0.00135 LossG: 0.00217\n",
            "[439/1000] LossD: 0.00093 LossG: 0.00266\n",
            "[440/1000] LossD: 0.00005 LossG: 0.00162\n",
            "[441/1000] LossD: -0.00077 LossG: 0.00121\n",
            "[442/1000] LossD: -0.00140 LossG: 0.00343\n",
            "[443/1000] LossD: -0.00218 LossG: 0.00459\n",
            "[444/1000] LossD: -0.00316 LossG: 0.00583\n",
            "[445/1000] LossD: -0.00426 LossG: 0.00562\n",
            "[446/1000] LossD: -0.00538 LossG: 0.00586\n",
            "[447/1000] LossD: -0.00647 LossG: 0.00622\n",
            "[448/1000] LossD: -0.00753 LossG: 0.00619\n",
            "[449/1000] LossD: -0.00851 LossG: 0.00607\n",
            "[450/1000] LossD: -0.00927 LossG: 0.00726\n",
            "[451/1000] LossD: -0.00961 LossG: 0.00709\n",
            "[452/1000] LossD: -0.00915 LossG: 0.00793\n",
            "[453/1000] LossD: -0.00755 LossG: 0.00834\n",
            "[454/1000] LossD: -0.00433 LossG: 0.00597\n",
            "[455/1000] LossD: -0.00054 LossG: 0.00210\n",
            "[456/1000] LossD: 0.00123 LossG: 0.00092\n",
            "[457/1000] LossD: 0.00094 LossG: 0.00027\n",
            "[458/1000] LossD: 0.00013 LossG: -0.00023\n",
            "[459/1000] LossD: -0.00067 LossG: -0.00038\n",
            "[460/1000] LossD: -0.00130 LossG: 0.00020\n",
            "[461/1000] LossD: -0.00211 LossG: 0.00253\n",
            "[462/1000] LossD: -0.00313 LossG: 0.00460\n",
            "[463/1000] LossD: -0.00427 LossG: 0.00524\n",
            "[464/1000] LossD: -0.00543 LossG: 0.00534\n",
            "[465/1000] LossD: -0.00660 LossG: 0.00588\n",
            "[466/1000] LossD: -0.00776 LossG: 0.00589\n",
            "[467/1000] LossD: -0.00899 LossG: 0.00618\n",
            "[468/1000] LossD: -0.01015 LossG: 0.00677\n",
            "[469/1000] LossD: -0.01111 LossG: 0.00719\n",
            "[470/1000] LossD: -0.01154 LossG: 0.00733\n",
            "[471/1000] LossD: -0.01091 LossG: 0.00839\n",
            "[472/1000] LossD: -0.00880 LossG: 0.00939\n",
            "[473/1000] LossD: -0.00456 LossG: 0.00758\n",
            "[474/1000] LossD: 0.00007 LossG: 0.00414\n",
            "[475/1000] LossD: 0.00179 LossG: 0.00142\n",
            "[476/1000] LossD: 0.00138 LossG: 0.00091\n",
            "[477/1000] LossD: 0.00056 LossG: -0.00029\n",
            "[478/1000] LossD: -0.00031 LossG: -0.00208\n",
            "[479/1000] LossD: -0.00085 LossG: -0.00051\n",
            "[480/1000] LossD: -0.00139 LossG: 0.00259\n",
            "[481/1000] LossD: -0.00214 LossG: 0.00567\n",
            "[482/1000] LossD: -0.00317 LossG: 0.00683\n",
            "[483/1000] LossD: -0.00429 LossG: 0.00640\n",
            "[484/1000] LossD: -0.00544 LossG: 0.00662\n",
            "[485/1000] LossD: -0.00655 LossG: 0.00640\n",
            "[486/1000] LossD: -0.00761 LossG: 0.00663\n",
            "[487/1000] LossD: -0.00855 LossG: 0.00653\n",
            "[488/1000] LossD: -0.00928 LossG: 0.00742\n",
            "[489/1000] LossD: -0.00954 LossG: 0.00808\n",
            "[490/1000] LossD: -0.00883 LossG: 0.00842\n",
            "[491/1000] LossD: -0.00672 LossG: 0.00829\n",
            "[492/1000] LossD: -0.00307 LossG: 0.00612\n",
            "[493/1000] LossD: 0.00035 LossG: 0.00256\n",
            "[494/1000] LossD: 0.00092 LossG: -0.00217\n",
            "[495/1000] LossD: 0.00025 LossG: -0.00519\n",
            "[496/1000] LossD: -0.00072 LossG: -0.00652\n",
            "[497/1000] LossD: -0.00094 LossG: -0.00282\n",
            "[498/1000] LossD: -0.00143 LossG: -0.00010\n",
            "[499/1000] LossD: -0.00220 LossG: 0.00361\n",
            "[500/1000] LossD: -0.00327 LossG: 0.00616\n",
            "[501/1000] LossD: -0.00440 LossG: 0.00735\n",
            "[502/1000] LossD: -0.00557 LossG: 0.00704\n",
            "[503/1000] LossD: -0.00666 LossG: 0.00600\n",
            "[504/1000] LossD: -0.00777 LossG: 0.00680\n",
            "[505/1000] LossD: -0.00886 LossG: 0.00722\n",
            "[506/1000] LossD: -0.00973 LossG: 0.00773\n",
            "[507/1000] LossD: -0.01007 LossG: 0.00769\n",
            "[508/1000] LossD: -0.00938 LossG: 0.00962\n",
            "[509/1000] LossD: -0.00728 LossG: 0.01040\n",
            "[510/1000] LossD: -0.00341 LossG: 0.00750\n",
            "[511/1000] LossD: 0.00037 LossG: 0.00438\n",
            "[512/1000] LossD: 0.00132 LossG: 0.00256\n",
            "[513/1000] LossD: 0.00076 LossG: 0.00146\n",
            "[514/1000] LossD: -0.00007 LossG: 0.00119\n",
            "[515/1000] LossD: -0.00069 LossG: 0.00130\n",
            "[516/1000] LossD: -0.00125 LossG: 0.00287\n",
            "[517/1000] LossD: -0.00208 LossG: 0.00481\n",
            "[518/1000] LossD: -0.00316 LossG: 0.00606\n",
            "[519/1000] LossD: -0.00439 LossG: 0.00612\n",
            "[520/1000] LossD: -0.00564 LossG: 0.00600\n",
            "[521/1000] LossD: -0.00691 LossG: 0.00648\n",
            "[522/1000] LossD: -0.00826 LossG: 0.00643\n",
            "[523/1000] LossD: -0.00982 LossG: 0.00681\n",
            "[524/1000] LossD: -0.01173 LossG: 0.00687\n",
            "[525/1000] LossD: -0.01417 LossG: 0.00804\n",
            "[526/1000] LossD: -0.01720 LossG: 0.00916\n",
            "[527/1000] LossD: -0.02065 LossG: 0.00916\n",
            "[528/1000] LossD: -0.02415 LossG: 0.00937\n",
            "[529/1000] LossD: -0.02768 LossG: 0.01094\n",
            "[530/1000] LossD: -0.03135 LossG: 0.00980\n",
            "[531/1000] LossD: -0.03522 LossG: 0.01188\n",
            "[532/1000] LossD: -0.03941 LossG: 0.01062\n",
            "[533/1000] LossD: -0.04398 LossG: 0.01104\n",
            "[534/1000] LossD: -0.04896 LossG: 0.01071\n",
            "[535/1000] LossD: -0.05440 LossG: 0.01095\n",
            "[536/1000] LossD: -0.06034 LossG: 0.01160\n",
            "[537/1000] LossD: -0.06683 LossG: 0.01310\n",
            "[538/1000] LossD: -0.07388 LossG: 0.01533\n",
            "[539/1000] LossD: -0.08155 LossG: 0.01840\n",
            "[540/1000] LossD: -0.08993 LossG: 0.02191\n",
            "[541/1000] LossD: -0.09906 LossG: 0.02526\n",
            "[542/1000] LossD: -0.10896 LossG: 0.02845\n",
            "[543/1000] LossD: -0.11967 LossG: 0.03264\n",
            "[544/1000] LossD: -0.13125 LossG: 0.03811\n",
            "[545/1000] LossD: -0.14376 LossG: 0.04432\n",
            "[546/1000] LossD: -0.15723 LossG: 0.05094\n",
            "[547/1000] LossD: -0.17172 LossG: 0.05829\n",
            "[548/1000] LossD: -0.18727 LossG: 0.06631\n",
            "[549/1000] LossD: -0.20389 LossG: 0.07483\n",
            "[550/1000] LossD: -0.22166 LossG: 0.08418\n",
            "[551/1000] LossD: -0.24062 LossG: 0.09463\n",
            "[552/1000] LossD: -0.26083 LossG: 0.10614\n",
            "[553/1000] LossD: -0.28235 LossG: 0.11838\n",
            "[554/1000] LossD: -0.30524 LossG: 0.13132\n",
            "[555/1000] LossD: -0.32956 LossG: 0.14485\n",
            "[556/1000] LossD: -0.35541 LossG: 0.15903\n",
            "[557/1000] LossD: -0.38281 LossG: 0.17394\n",
            "[558/1000] LossD: -0.41183 LossG: 0.18962\n",
            "[559/1000] LossD: -0.44250 LossG: 0.20605\n",
            "[560/1000] LossD: -0.47487 LossG: 0.22347\n",
            "[561/1000] LossD: -0.50897 LossG: 0.24200\n",
            "[562/1000] LossD: -0.54479 LossG: 0.26135\n",
            "[563/1000] LossD: -0.58238 LossG: 0.28148\n",
            "[564/1000] LossD: -0.62174 LossG: 0.30248\n",
            "[565/1000] LossD: -0.66294 LossG: 0.32470\n",
            "[566/1000] LossD: -0.70606 LossG: 0.34830\n",
            "[567/1000] LossD: -0.75114 LossG: 0.37311\n",
            "[568/1000] LossD: -0.79824 LossG: 0.39904\n",
            "[569/1000] LossD: -0.84750 LossG: 0.42561\n",
            "[570/1000] LossD: -0.89935 LossG: 0.44948\n",
            "[571/1000] LossD: -0.95348 LossG: 0.47303\n",
            "[572/1000] LossD: -1.01014 LossG: 0.49677\n",
            "[573/1000] LossD: -1.06935 LossG: 0.52113\n",
            "[574/1000] LossD: -1.13119 LossG: 0.54638\n",
            "[575/1000] LossD: -1.19574 LossG: 0.57256\n",
            "[576/1000] LossD: -1.26299 LossG: 0.59977\n",
            "[577/1000] LossD: -1.33299 LossG: 0.62805\n",
            "[578/1000] LossD: -1.40587 LossG: 0.65764\n",
            "[579/1000] LossD: -1.48175 LossG: 0.68842\n",
            "[580/1000] LossD: -1.56073 LossG: 0.72044\n",
            "[581/1000] LossD: -1.64293 LossG: 0.75361\n",
            "[582/1000] LossD: -1.72842 LossG: 0.78799\n",
            "[583/1000] LossD: -1.81719 LossG: 0.82342\n",
            "[584/1000] LossD: -1.90923 LossG: 0.85982\n",
            "[585/1000] LossD: -2.00453 LossG: 0.89780\n",
            "[586/1000] LossD: -2.10317 LossG: 0.93749\n",
            "[587/1000] LossD: -2.20535 LossG: 0.97886\n",
            "[588/1000] LossD: -2.31117 LossG: 1.02160\n",
            "[589/1000] LossD: -2.42083 LossG: 1.06505\n",
            "[590/1000] LossD: -2.53437 LossG: 1.10932\n",
            "[591/1000] LossD: -2.65189 LossG: 1.15502\n",
            "[592/1000] LossD: -2.77348 LossG: 1.20204\n",
            "[593/1000] LossD: -2.89921 LossG: 1.25088\n",
            "[594/1000] LossD: -3.02923 LossG: 1.30150\n",
            "[595/1000] LossD: -3.16363 LossG: 1.35427\n",
            "[596/1000] LossD: -3.30250 LossG: 1.40872\n",
            "[597/1000] LossD: -3.44593 LossG: 1.46435\n",
            "[598/1000] LossD: -3.59405 LossG: 1.52136\n",
            "[599/1000] LossD: -3.74695 LossG: 1.57947\n",
            "[600/1000] LossD: -3.90479 LossG: 1.63907\n",
            "[601/1000] LossD: -4.06762 LossG: 1.70050\n",
            "[602/1000] LossD: -4.23560 LossG: 1.76384\n",
            "[603/1000] LossD: -4.40889 LossG: 1.82907\n",
            "[604/1000] LossD: -4.58751 LossG: 1.89684\n",
            "[605/1000] LossD: -4.77158 LossG: 1.96604\n",
            "[606/1000] LossD: -4.96152 LossG: 2.03694\n",
            "[607/1000] LossD: -5.15710 LossG: 2.10954\n",
            "[608/1000] LossD: -5.35863 LossG: 2.18384\n",
            "[609/1000] LossD: -5.56605 LossG: 2.26019\n",
            "[610/1000] LossD: -5.77962 LossG: 2.33914\n",
            "[611/1000] LossD: -5.99909 LossG: 2.41957\n",
            "[612/1000] LossD: -6.22511 LossG: 2.50210\n",
            "[613/1000] LossD: -6.45714 LossG: 2.58651\n",
            "[614/1000] LossD: -6.69548 LossG: 2.67209\n",
            "[615/1000] LossD: -6.94049 LossG: 2.75926\n",
            "[616/1000] LossD: -7.19168 LossG: 2.84862\n",
            "[617/1000] LossD: -7.44985 LossG: 2.93987\n",
            "[618/1000] LossD: -7.71425 LossG: 3.03309\n",
            "[619/1000] LossD: -7.98509 LossG: 3.12739\n",
            "[620/1000] LossD: -8.26242 LossG: 3.22342\n",
            "[621/1000] LossD: -8.54582 LossG: 3.32137\n",
            "[622/1000] LossD: -8.83594 LossG: 3.42173\n",
            "[623/1000] LossD: -9.13115 LossG: 3.52505\n",
            "[624/1000] LossD: -9.43183 LossG: 3.63000\n",
            "[625/1000] LossD: -9.73817 LossG: 3.73629\n",
            "[626/1000] LossD: -10.04940 LossG: 3.84178\n",
            "[627/1000] LossD: -10.36456 LossG: 3.94823\n",
            "[628/1000] LossD: -10.68349 LossG: 4.05655\n",
            "[629/1000] LossD: -11.00553 LossG: 4.16972\n",
            "[630/1000] LossD: -11.32875 LossG: 4.28602\n",
            "[631/1000] LossD: -11.65317 LossG: 4.40809\n",
            "[632/1000] LossD: -11.97448 LossG: 4.53059\n",
            "[633/1000] LossD: -12.29312 LossG: 4.65139\n",
            "[634/1000] LossD: -12.60462 LossG: 4.76765\n",
            "[635/1000] LossD: -12.90981 LossG: 4.87616\n",
            "[636/1000] LossD: -13.20480 LossG: 4.97463\n",
            "[637/1000] LossD: -13.48574 LossG: 5.06146\n",
            "[638/1000] LossD: -13.75102 LossG: 5.14469\n",
            "[639/1000] LossD: -13.99224 LossG: 5.21525\n",
            "[640/1000] LossD: -14.19841 LossG: 5.26338\n",
            "[641/1000] LossD: -14.37254 LossG: 5.29266\n",
            "[642/1000] LossD: -14.50259 LossG: 5.29174\n",
            "[643/1000] LossD: -14.59396 LossG: 5.26008\n",
            "[644/1000] LossD: -14.63540 LossG: 5.18938\n",
            "[645/1000] LossD: -14.62846 LossG: 5.08577\n",
            "[646/1000] LossD: -14.58641 LossG: 4.96147\n",
            "[647/1000] LossD: -14.48940 LossG: 4.78575\n",
            "[648/1000] LossD: -14.35578 LossG: 4.56563\n",
            "[649/1000] LossD: -14.19227 LossG: 4.30269\n",
            "[650/1000] LossD: -14.02918 LossG: 4.01460\n",
            "[651/1000] LossD: -13.84371 LossG: 3.67268\n",
            "[652/1000] LossD: -13.66678 LossG: 3.30716\n",
            "[653/1000] LossD: -13.53116 LossG: 2.93715\n",
            "[654/1000] LossD: -13.45975 LossG: 2.58475\n",
            "[655/1000] LossD: -13.45786 LossG: 2.16843\n",
            "[656/1000] LossD: -13.63863 LossG: 1.51950\n",
            "[657/1000] LossD: -14.04764 LossG: 0.88925\n",
            "[658/1000] LossD: -14.58166 LossG: 0.33141\n",
            "[659/1000] LossD: -15.21709 LossG: -0.08959\n",
            "[660/1000] LossD: -15.86195 LossG: -0.43565\n",
            "[661/1000] LossD: -16.51539 LossG: -0.72912\n",
            "[662/1000] LossD: -17.17388 LossG: -0.98432\n",
            "[663/1000] LossD: -17.78310 LossG: -1.25096\n",
            "[664/1000] LossD: -17.84544 LossG: -1.91899\n",
            "[665/1000] LossD: -17.43115 LossG: -2.95479\n",
            "[666/1000] LossD: -16.96310 LossG: -4.07397\n",
            "[667/1000] LossD: -16.13889 LossG: -5.59082\n",
            "[668/1000] LossD: -14.17433 LossG: -8.21705\n",
            "[669/1000] LossD: -9.84756 LossG: -13.07688\n",
            "[670/1000] LossD: -3.45869 LossG: -19.77620\n",
            "[671/1000] LossD: 0.78561 LossG: -24.14533\n",
            "[672/1000] LossD: 3.74442 LossG: -27.10697\n",
            "[673/1000] LossD: 6.30048 LossG: -29.36655\n",
            "[674/1000] LossD: 8.39414 LossG: -30.86414\n",
            "[675/1000] LossD: 9.19172 LossG: -30.89331\n",
            "[676/1000] LossD: 8.96774 LossG: -29.92980\n",
            "[677/1000] LossD: 8.94213 LossG: -29.25905\n",
            "[678/1000] LossD: 9.04432 LossG: -28.77612\n",
            "[679/1000] LossD: 9.33777 LossG: -28.54086\n",
            "[680/1000] LossD: 9.70577 LossG: -28.44343\n",
            "[681/1000] LossD: 10.15058 LossG: -28.48285\n",
            "[682/1000] LossD: 10.72696 LossG: -28.70780\n",
            "[683/1000] LossD: 11.25780 LossG: -28.94036\n",
            "[684/1000] LossD: 11.67568 LossG: -29.09087\n",
            "[685/1000] LossD: 12.20778 LossG: -29.37851\n",
            "[686/1000] LossD: 12.80480 LossG: -29.71734\n",
            "[687/1000] LossD: 13.64754 LossG: -30.28594\n",
            "[688/1000] LossD: 14.71052 LossG: -31.06162\n",
            "[689/1000] LossD: 15.95589 LossG: -32.01707\n",
            "[690/1000] LossD: 17.14810 LossG: -32.94623\n",
            "[691/1000] LossD: 17.97420 LossG: -33.54228\n",
            "[692/1000] LossD: 18.72177 LossG: -34.09361\n",
            "[693/1000] LossD: 19.66677 LossG: -34.88007\n",
            "[694/1000] LossD: 21.05216 LossG: -36.13989\n",
            "[695/1000] LossD: 22.24593 LossG: -37.21851\n",
            "[696/1000] LossD: 22.69700 LossG: -37.54758\n",
            "[697/1000] LossD: 22.82304 LossG: -37.54123\n",
            "[698/1000] LossD: 22.70512 LossG: -37.27945\n",
            "[699/1000] LossD: 22.32379 LossG: -36.74304\n",
            "[700/1000] LossD: 21.89627 LossG: -36.15423\n",
            "[701/1000] LossD: 21.44247 LossG: -35.54401\n",
            "[702/1000] LossD: 20.81913 LossG: -34.78094\n",
            "[703/1000] LossD: 20.28970 LossG: -34.14048\n",
            "[704/1000] LossD: 19.77769 LossG: -33.51352\n",
            "[705/1000] LossD: 21.70938 LossG: -35.36448\n",
            "[706/1000] LossD: 27.19717 LossG: -40.85950\n",
            "[707/1000] LossD: 28.56964 LossG: -42.20329\n",
            "[708/1000] LossD: 27.68138 LossG: -41.24421\n",
            "[709/1000] LossD: 26.29128 LossG: -39.74327\n",
            "[710/1000] LossD: 24.87225 LossG: -38.17436\n",
            "[711/1000] LossD: 23.43931 LossG: -36.55422\n",
            "[712/1000] LossD: 21.98944 LossG: -34.89479\n",
            "[713/1000] LossD: 20.55498 LossG: -33.23925\n",
            "[714/1000] LossD: 19.12734 LossG: -31.58343\n",
            "[715/1000] LossD: 17.69603 LossG: -29.92046\n",
            "[716/1000] LossD: 16.27882 LossG: -28.26943\n",
            "[717/1000] LossD: 14.89015 LossG: -26.64418\n",
            "[718/1000] LossD: 13.52609 LossG: -25.04133\n",
            "[719/1000] LossD: 12.16992 LossG: -23.44754\n",
            "[720/1000] LossD: 10.83380 LossG: -21.87246\n",
            "[721/1000] LossD: 9.48381 LossG: -20.29051\n",
            "[722/1000] LossD: 8.20986 LossG: -18.80097\n",
            "[723/1000] LossD: 6.99329 LossG: -17.37573\n",
            "[724/1000] LossD: 5.85055 LossG: -16.02659\n",
            "[725/1000] LossD: 4.75124 LossG: -14.72437\n",
            "[726/1000] LossD: 3.68682 LossG: -13.46262\n",
            "[727/1000] LossD: 2.63307 LossG: -12.22078\n",
            "[728/1000] LossD: 1.56359 LossG: -10.99183\n",
            "[729/1000] LossD: 0.47212 LossG: -9.81190\n",
            "[730/1000] LossD: -0.66448 LossG: -8.73018\n",
            "[731/1000] LossD: -2.36011 LossG: -7.04409\n",
            "[732/1000] LossD: -7.48436 LossG: -1.91929\n",
            "[733/1000] LossD: -13.06525 LossG: 3.29293\n",
            "[734/1000] LossD: -17.50225 LossG: 7.30004\n",
            "[735/1000] LossD: -20.22884 LossG: 9.61922\n",
            "[736/1000] LossD: -21.64341 LossG: 10.64325\n",
            "[737/1000] LossD: -21.82317 LossG: 10.44929\n",
            "[738/1000] LossD: -21.17374 LossG: 9.45654\n",
            "[739/1000] LossD: -19.66429 LossG: 7.59429\n",
            "[740/1000] LossD: -18.39378 LossG: 5.98322\n",
            "[741/1000] LossD: -17.40093 LossG: 4.66973\n",
            "[742/1000] LossD: -16.57222 LossG: 3.53749\n",
            "[743/1000] LossD: -15.78509 LossG: 2.46341\n",
            "[744/1000] LossD: -15.00846 LossG: 1.42320\n",
            "[745/1000] LossD: -13.89322 LossG: 0.07194\n",
            "[746/1000] LossD: -4.40371 LossG: -9.45395\n",
            "[747/1000] LossD: -0.35525 LossG: -13.52944\n",
            "[748/1000] LossD: 0.28624 LossG: -14.03299\n",
            "[749/1000] LossD: 0.28823 LossG: -13.82157\n",
            "[750/1000] LossD: 0.33198 LossG: -13.63643\n",
            "[751/1000] LossD: 0.47272 LossG: -13.56232\n",
            "[752/1000] LossD: 0.58757 LossG: -13.48409\n",
            "[753/1000] LossD: 0.68679 LossG: -13.41091\n",
            "[754/1000] LossD: 0.75070 LossG: -13.31663\n",
            "[755/1000] LossD: 0.83412 LossG: -13.24977\n",
            "[756/1000] LossD: 0.92391 LossG: -13.19307\n",
            "[757/1000] LossD: 0.92521 LossG: -13.04134\n",
            "[758/1000] LossD: 1.03962 LossG: -13.00206\n",
            "[759/1000] LossD: 1.06978 LossG: -12.87021\n",
            "[760/1000] LossD: 0.94337 LossG: -12.57860\n",
            "[761/1000] LossD: 0.72587 LossG: -12.20521\n",
            "[762/1000] LossD: 0.36917 LossG: -11.70932\n",
            "[763/1000] LossD: -0.01518 LossG: -11.21142\n",
            "[764/1000] LossD: -0.40630 LossG: -10.73974\n",
            "[765/1000] LossD: -0.80707 LossG: -10.29381\n",
            "[766/1000] LossD: -1.13555 LossG: -9.97956\n",
            "[767/1000] LossD: -1.28768 LossG: -9.90221\n",
            "[768/1000] LossD: -1.10961 LossG: -10.17992\n",
            "[769/1000] LossD: -0.87872 LossG: -10.50753\n",
            "[770/1000] LossD: -0.73172 LossG: -10.74052\n",
            "[771/1000] LossD: -0.57644 LossG: -10.97131\n",
            "[772/1000] LossD: -0.50664 LossG: -11.10368\n",
            "[773/1000] LossD: -0.45748 LossG: -11.20415\n",
            "[774/1000] LossD: -0.38651 LossG: -11.31987\n",
            "[775/1000] LossD: -0.32356 LossG: -11.42266\n",
            "[776/1000] LossD: -0.24628 LossG: -11.52468\n",
            "[777/1000] LossD: -0.11764 LossG: -11.64023\n",
            "[778/1000] LossD: -0.06955 LossG: -11.65028\n",
            "[779/1000] LossD: -0.09897 LossG: -11.57645\n",
            "[780/1000] LossD: -0.02615 LossG: -11.56645\n",
            "[781/1000] LossD: -0.02615 LossG: -11.45086\n",
            "[782/1000] LossD: -0.01862 LossG: -11.34066\n",
            "[783/1000] LossD: -0.01394 LossG: -11.23075\n",
            "[784/1000] LossD: -0.01505 LossG: -11.12319\n",
            "[785/1000] LossD: -0.01648 LossG: -11.02392\n",
            "[786/1000] LossD: -0.00766 LossG: -10.90724\n",
            "[787/1000] LossD: -0.00724 LossG: -10.75476\n",
            "[788/1000] LossD: -0.00795 LossG: -10.59865\n",
            "[789/1000] LossD: -0.00338 LossG: -10.46298\n",
            "[790/1000] LossD: -0.00290 LossG: -10.31417\n",
            "[791/1000] LossD: -0.00383 LossG: -10.15218\n",
            "[792/1000] LossD: 0.00122 LossG: -9.99972\n",
            "[793/1000] LossD: 0.00010 LossG: -9.83016\n",
            "[794/1000] LossD: 0.00077 LossG: -9.66787\n",
            "[795/1000] LossD: 0.00019 LossG: -9.49792\n",
            "[796/1000] LossD: -0.00023 LossG: -9.36114\n",
            "[797/1000] LossD: 0.00078 LossG: -9.23328\n",
            "[798/1000] LossD: 0.00199 LossG: -9.08451\n",
            "[799/1000] LossD: 0.00286 LossG: -8.94541\n",
            "[800/1000] LossD: 0.00162 LossG: -8.81565\n",
            "[801/1000] LossD: 0.00367 LossG: -8.67685\n",
            "[802/1000] LossD: 0.00377 LossG: -8.53066\n",
            "[803/1000] LossD: 0.00153 LossG: -8.38287\n",
            "[804/1000] LossD: 0.00353 LossG: -8.25343\n",
            "[805/1000] LossD: 0.00334 LossG: -8.10263\n",
            "[806/1000] LossD: 0.00343 LossG: -7.94719\n",
            "[807/1000] LossD: 0.00331 LossG: -7.79383\n",
            "[808/1000] LossD: 0.00530 LossG: -7.63876\n",
            "[809/1000] LossD: 0.00143 LossG: -7.46929\n",
            "[810/1000] LossD: 0.00314 LossG: -7.31601\n",
            "[811/1000] LossD: 0.00387 LossG: -7.16734\n",
            "[812/1000] LossD: 0.00396 LossG: -7.02187\n",
            "[813/1000] LossD: 0.00495 LossG: -6.88640\n",
            "[814/1000] LossD: 0.00393 LossG: -6.73784\n",
            "[815/1000] LossD: 0.00458 LossG: -6.58441\n",
            "[816/1000] LossD: 0.00092 LossG: -6.41888\n",
            "[817/1000] LossD: 0.00124 LossG: -6.27274\n",
            "[818/1000] LossD: 0.00217 LossG: -6.11190\n",
            "[819/1000] LossD: 0.00183 LossG: -5.94050\n",
            "[820/1000] LossD: 0.00153 LossG: -5.77559\n",
            "[821/1000] LossD: -0.00178 LossG: -5.62122\n",
            "[822/1000] LossD: 0.00066 LossG: -5.45819\n",
            "[823/1000] LossD: 0.00104 LossG: -5.30438\n",
            "[824/1000] LossD: 0.00083 LossG: -5.15092\n",
            "[825/1000] LossD: 0.00033 LossG: -4.99997\n",
            "[826/1000] LossD: 0.00099 LossG: -4.85521\n",
            "[827/1000] LossD: 0.00101 LossG: -4.70360\n",
            "[828/1000] LossD: 0.00146 LossG: -4.55560\n",
            "[829/1000] LossD: 0.00066 LossG: -4.40218\n",
            "[830/1000] LossD: 0.00162 LossG: -4.26112\n",
            "[831/1000] LossD: 0.00045 LossG: -4.11584\n",
            "[832/1000] LossD: 0.00050 LossG: -3.97365\n",
            "[833/1000] LossD: 0.00041 LossG: -3.84115\n",
            "[834/1000] LossD: 0.00083 LossG: -3.70986\n",
            "[835/1000] LossD: 0.00026 LossG: -3.57508\n",
            "[836/1000] LossD: 0.00094 LossG: -3.44934\n",
            "[837/1000] LossD: 0.00002 LossG: -3.31575\n",
            "[838/1000] LossD: 0.00011 LossG: -3.19913\n",
            "[839/1000] LossD: 0.00029 LossG: -3.08172\n",
            "[840/1000] LossD: 0.00023 LossG: -2.96259\n",
            "[841/1000] LossD: 0.00032 LossG: -2.84689\n",
            "[842/1000] LossD: 0.00028 LossG: -2.73057\n",
            "[843/1000] LossD: 0.00016 LossG: -2.61672\n",
            "[844/1000] LossD: 0.00011 LossG: -2.50685\n",
            "[845/1000] LossD: 0.00015 LossG: -2.39926\n",
            "[846/1000] LossD: 0.00015 LossG: -2.29192\n",
            "[847/1000] LossD: 0.00021 LossG: -2.18817\n",
            "[848/1000] LossD: -0.00002 LossG: -2.08553\n",
            "[849/1000] LossD: 0.00008 LossG: -1.98682\n",
            "[850/1000] LossD: 0.00004 LossG: -1.88842\n",
            "[851/1000] LossD: -0.00000 LossG: -1.79464\n",
            "[852/1000] LossD: 0.00003 LossG: -1.70306\n",
            "[853/1000] LossD: -0.00004 LossG: -1.61242\n",
            "[854/1000] LossD: -0.00004 LossG: -1.52713\n",
            "[855/1000] LossD: -0.00005 LossG: -1.44221\n",
            "[856/1000] LossD: -0.00012 LossG: -1.36177\n",
            "[857/1000] LossD: -0.00013 LossG: -1.28303\n",
            "[858/1000] LossD: -0.00019 LossG: -1.20974\n",
            "[859/1000] LossD: -0.00019 LossG: -1.13820\n",
            "[860/1000] LossD: -0.00025 LossG: -1.07221\n",
            "[861/1000] LossD: -0.00030 LossG: -1.00928\n",
            "[862/1000] LossD: -0.00028 LossG: -0.94924\n",
            "[863/1000] LossD: -0.00032 LossG: -0.88845\n",
            "[864/1000] LossD: -0.00024 LossG: -0.81842\n",
            "[865/1000] LossD: -0.00019 LossG: -0.73667\n",
            "[866/1000] LossD: -0.00014 LossG: -0.64245\n",
            "[867/1000] LossD: -0.00020 LossG: -0.53430\n",
            "[868/1000] LossD: -0.00026 LossG: -0.43584\n",
            "[869/1000] LossD: -0.00035 LossG: -0.34105\n",
            "[870/1000] LossD: -0.00033 LossG: -0.24827\n",
            "[871/1000] LossD: -0.00032 LossG: -0.15283\n",
            "[872/1000] LossD: -0.00040 LossG: -0.06362\n",
            "[873/1000] LossD: -0.00032 LossG: 0.01656\n",
            "[874/1000] LossD: -0.00039 LossG: 0.09036\n",
            "[875/1000] LossD: -0.00041 LossG: 0.15722\n",
            "[876/1000] LossD: -0.00043 LossG: 0.21736\n",
            "[877/1000] LossD: -0.00046 LossG: 0.27223\n",
            "[878/1000] LossD: -0.00057 LossG: 0.32196\n",
            "[879/1000] LossD: -0.00060 LossG: 0.36497\n",
            "[880/1000] LossD: -0.00069 LossG: 0.40284\n",
            "[881/1000] LossD: -0.00073 LossG: 0.43524\n",
            "[882/1000] LossD: -0.00070 LossG: 0.46409\n",
            "[883/1000] LossD: -0.00075 LossG: 0.48806\n",
            "[884/1000] LossD: -0.00067 LossG: 0.50827\n",
            "[885/1000] LossD: -0.00072 LossG: 0.52637\n",
            "[886/1000] LossD: -0.00077 LossG: 0.54129\n",
            "[887/1000] LossD: -0.00083 LossG: 0.55310\n",
            "[888/1000] LossD: -0.00093 LossG: 0.56186\n",
            "[889/1000] LossD: -0.00097 LossG: 0.56729\n",
            "[890/1000] LossD: -0.00101 LossG: 0.57008\n",
            "[891/1000] LossD: -0.00108 LossG: 0.57049\n",
            "[892/1000] LossD: -0.00118 LossG: 0.56927\n",
            "[893/1000] LossD: -0.00130 LossG: 0.56617\n",
            "[894/1000] LossD: -0.00128 LossG: 0.56194\n",
            "[895/1000] LossD: -0.00127 LossG: 0.55787\n",
            "[896/1000] LossD: -0.00134 LossG: 0.55299\n",
            "[897/1000] LossD: -0.00139 LossG: 0.54795\n",
            "[898/1000] LossD: -0.00146 LossG: 0.54196\n",
            "[899/1000] LossD: -0.00162 LossG: 0.53553\n",
            "[900/1000] LossD: -0.00180 LossG: 0.52853\n",
            "[901/1000] LossD: -0.00197 LossG: 0.52041\n",
            "[902/1000] LossD: -0.00211 LossG: 0.51271\n",
            "[903/1000] LossD: -0.00230 LossG: 0.50312\n",
            "[904/1000] LossD: -0.00251 LossG: 0.49259\n",
            "[905/1000] LossD: -0.00262 LossG: 0.48262\n",
            "[906/1000] LossD: -0.00264 LossG: 0.47159\n",
            "[907/1000] LossD: -0.00255 LossG: 0.46081\n",
            "[908/1000] LossD: -0.00235 LossG: 0.45004\n",
            "[909/1000] LossD: -0.00220 LossG: 0.43987\n",
            "[910/1000] LossD: -0.00211 LossG: 0.42899\n",
            "[911/1000] LossD: -0.00211 LossG: 0.41884\n",
            "[912/1000] LossD: -0.00221 LossG: 0.40794\n",
            "[913/1000] LossD: -0.00238 LossG: 0.39728\n",
            "[914/1000] LossD: -0.00260 LossG: 0.38625\n",
            "[915/1000] LossD: -0.00273 LossG: 0.37488\n",
            "[916/1000] LossD: -0.00274 LossG: 0.36287\n",
            "[917/1000] LossD: -0.00260 LossG: 0.35230\n",
            "[918/1000] LossD: -0.00237 LossG: 0.34069\n",
            "[919/1000] LossD: -0.00214 LossG: 0.33090\n",
            "[920/1000] LossD: -0.00207 LossG: 0.32116\n",
            "[921/1000] LossD: -0.00216 LossG: 0.31192\n",
            "[922/1000] LossD: -0.00242 LossG: 0.30103\n",
            "[923/1000] LossD: -0.00267 LossG: 0.29018\n",
            "[924/1000] LossD: -0.00293 LossG: 0.27801\n",
            "[925/1000] LossD: -0.00295 LossG: 0.26704\n",
            "[926/1000] LossD: -0.00281 LossG: 0.25610\n",
            "[927/1000] LossD: -0.00248 LossG: 0.24496\n",
            "[928/1000] LossD: -0.00213 LossG: 0.23562\n",
            "[929/1000] LossD: -0.00197 LossG: 0.22686\n",
            "[930/1000] LossD: -0.00201 LossG: 0.21856\n",
            "[931/1000] LossD: -0.00230 LossG: 0.20965\n",
            "[932/1000] LossD: -0.00274 LossG: 0.19946\n",
            "[933/1000] LossD: -0.00306 LossG: 0.18871\n",
            "[934/1000] LossD: -0.00322 LossG: 0.17833\n",
            "[935/1000] LossD: -0.00314 LossG: 0.16721\n",
            "[936/1000] LossD: -0.00276 LossG: 0.15548\n",
            "[937/1000] LossD: -0.00224 LossG: 0.14809\n",
            "[938/1000] LossD: -0.00191 LossG: 0.14114\n",
            "[939/1000] LossD: -0.00185 LossG: 0.13354\n",
            "[940/1000] LossD: -0.00209 LossG: 0.12684\n",
            "[941/1000] LossD: -0.00256 LossG: 0.11765\n",
            "[942/1000] LossD: -0.00311 LossG: 0.10798\n",
            "[943/1000] LossD: -0.00353 LossG: 0.09826\n",
            "[944/1000] LossD: -0.00367 LossG: 0.08806\n",
            "[945/1000] LossD: -0.00342 LossG: 0.07809\n",
            "[946/1000] LossD: -0.00273 LossG: 0.06923\n",
            "[947/1000] LossD: -0.00199 LossG: 0.06146\n",
            "[948/1000] LossD: -0.00152 LossG: 0.05605\n",
            "[949/1000] LossD: -0.00161 LossG: 0.05258\n",
            "[950/1000] LossD: -0.00219 LossG: 0.04823\n",
            "[951/1000] LossD: -0.00298 LossG: 0.04134\n",
            "[952/1000] LossD: -0.00380 LossG: 0.03453\n",
            "[953/1000] LossD: -0.00444 LossG: 0.02836\n",
            "[954/1000] LossD: -0.00472 LossG: 0.02310\n",
            "[955/1000] LossD: -0.00440 LossG: 0.01859\n",
            "[956/1000] LossD: -0.00331 LossG: 0.01443\n",
            "[957/1000] LossD: -0.00179 LossG: 0.01381\n",
            "[958/1000] LossD: -0.00106 LossG: 0.01767\n",
            "[959/1000] LossD: -0.00117 LossG: 0.02150\n",
            "[960/1000] LossD: -0.00171 LossG: 0.02416\n",
            "[961/1000] LossD: -0.00258 LossG: 0.02417\n",
            "[962/1000] LossD: -0.00363 LossG: 0.02149\n",
            "[963/1000] LossD: -0.00464 LossG: 0.01757\n",
            "[964/1000] LossD: -0.00547 LossG: 0.01345\n",
            "[965/1000] LossD: -0.00587 LossG: 0.01174\n",
            "[966/1000] LossD: -0.00554 LossG: 0.01008\n",
            "[967/1000] LossD: -0.00418 LossG: 0.00879\n",
            "[968/1000] LossD: -0.00200 LossG: 0.00912\n",
            "[969/1000] LossD: -0.00084 LossG: 0.01542\n",
            "[970/1000] LossD: -0.00085 LossG: 0.01809\n",
            "[971/1000] LossD: -0.00115 LossG: 0.01999\n",
            "[972/1000] LossD: -0.00180 LossG: 0.02149\n",
            "[973/1000] LossD: -0.00279 LossG: 0.02129\n",
            "[974/1000] LossD: -0.00390 LossG: 0.01805\n",
            "[975/1000] LossD: -0.00493 LossG: 0.01372\n",
            "[976/1000] LossD: -0.00576 LossG: 0.01131\n",
            "[977/1000] LossD: -0.00607 LossG: 0.00862\n",
            "[978/1000] LossD: -0.00557 LossG: 0.00829\n",
            "[979/1000] LossD: -0.00388 LossG: 0.00704\n",
            "[980/1000] LossD: -0.00168 LossG: 0.00901\n",
            "[981/1000] LossD: -0.00057 LossG: 0.01228\n",
            "[982/1000] LossD: -0.00067 LossG: 0.01370\n",
            "[983/1000] LossD: -0.00130 LossG: 0.01621\n",
            "[984/1000] LossD: -0.00210 LossG: 0.01767\n",
            "[985/1000] LossD: -0.00317 LossG: 0.01633\n",
            "[986/1000] LossD: -0.00428 LossG: 0.01271\n",
            "[987/1000] LossD: -0.00528 LossG: 0.00939\n",
            "[988/1000] LossD: -0.00594 LossG: 0.00851\n",
            "[989/1000] LossD: -0.00596 LossG: 0.00829\n",
            "[990/1000] LossD: -0.00495 LossG: 0.00794\n",
            "[991/1000] LossD: -0.00285 LossG: 0.00785\n",
            "[992/1000] LossD: -0.00103 LossG: 0.00968\n",
            "[993/1000] LossD: -0.00064 LossG: 0.01053\n",
            "[994/1000] LossD: -0.00105 LossG: 0.01297\n",
            "[995/1000] LossD: -0.00169 LossG: 0.01468\n",
            "[996/1000] LossD: -0.00264 LossG: 0.01403\n",
            "[997/1000] LossD: -0.00378 LossG: 0.01140\n",
            "[998/1000] LossD: -0.00484 LossG: 0.00766\n",
            "[999/1000] LossD: -0.00566 LossG: 0.00696\n",
            "[1000/1000] LossD: -0.00595 LossG: 0.00619\n",
            "Training MH for 0 epochs\n"
          ]
        }
      ],
      "source": [
        "# Train the MHWGAN model and will fine-tune the model later\n",
        "mhwgan = MHWGAN(67, 10, 1)\n",
        "mhwgan.to(device)\n",
        "init_epochs = 1000\n",
        "learning_rate = 0.0000115\n",
        "mhwgan_batch_size = 128\n",
        "mhwgan.optimizers(lr=learning_rate)\n",
        "\n",
        "# Use the whole dataset except testing data to train the model\n",
        "mhwgan.mh_training(init_epochs, 0, 128, train_x, train_y, seq_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "eGcscyz6sYWA",
        "outputId": "d203c799-ce07-43f1-8037-5de8b3881770"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mhwgan.eval()\n",
        "with torch.no_grad():\n",
        "    pred_y = mhwgan(torch.from_numpy(train_x).float().to(mhwgan.device))\n",
        "\n",
        "pred_y = pred_y.cpu().numpy()\n",
        "pred_y = y_scaler.inverse_transform(pred_y.reshape(-1, 1))\n",
        "inverse_test_y = y_scaler.inverse_transform(train_y[:, seq_length, :])\n",
        "date = pd.to_datetime(data.iloc[10:-330, :].index)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "scatter_pred = ax.scatter(date, pred_y, color='blue', label='Predicted Price')\n",
        "scatter_actual = ax.scatter(date, inverse_test_y, color='orange', label='Actual Price')\n",
        "ax.set_title('Stock prediction in training dataset')\n",
        "ax.set_ylabel('TWD')\n",
        "ax.set_xlabel('Date')\n",
        "ax.legend(loc='upper right')\n",
        "\n",
        "# Set the x-axis tick format to display three months interval\n",
        "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=12))\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
        "\n",
        "fig.autofmt_xdate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyS8WdTeuolr",
        "outputId": "b6270742-9737-44f9-d87a-6dc73187bc6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train_x: (2696, 57) train_y: (2696, 1)\n",
            "test_x: (2696, 57) test_y: (2696, 1)\n",
            "After concate latent variables\n",
            "train_x: (2686, 10, 67), train_y: (2686, 11, 1)\n",
            "test_x: (2686, 10, 67) test_y: (2686, 11, 1)\n",
            "Training Basic for 280 epochs\n",
            "[1/280] LossD: -0.00975 LossG: 0.00816\n",
            "[2/280] LossD: -0.01213 LossG: 0.00952\n",
            "[3/280] LossD: -0.00924 LossG: 0.00734\n",
            "[4/280] LossD: -0.00205 LossG: 0.00043\n",
            "[5/280] LossD: 0.00072 LossG: 0.00093\n",
            "[6/280] LossD: 0.00010 LossG: 0.00739\n",
            "[7/280] LossD: -0.00067 LossG: 0.01403\n",
            "[8/280] LossD: -0.00157 LossG: 0.01855\n",
            "[9/280] LossD: -0.00193 LossG: 0.01840\n",
            "[10/280] LossD: -0.00275 LossG: 0.01682\n",
            "[11/280] LossD: -0.00375 LossG: 0.01350\n",
            "[12/280] LossD: -0.00440 LossG: 0.00953\n",
            "[13/280] LossD: -0.00412 LossG: 0.00797\n",
            "[14/280] LossD: -0.00264 LossG: 0.00832\n",
            "[15/280] LossD: -0.00105 LossG: 0.00763\n",
            "[16/280] LossD: -0.00109 LossG: 0.00895\n",
            "[17/280] LossD: -0.00219 LossG: 0.01080\n",
            "[18/280] LossD: -0.00392 LossG: 0.00954\n",
            "[19/280] LossD: -0.00590 LossG: 0.00685\n",
            "[20/280] LossD: -0.00887 LossG: 0.00761\n",
            "[21/280] LossD: -0.01308 LossG: 0.00941\n",
            "[22/280] LossD: -0.01766 LossG: 0.01182\n",
            "[23/280] LossD: -0.02233 LossG: 0.01239\n",
            "[24/280] LossD: -0.02727 LossG: 0.01183\n",
            "[25/280] LossD: -0.03277 LossG: 0.01523\n",
            "[26/280] LossD: -0.03890 LossG: 0.01625\n",
            "[27/280] LossD: -0.04589 LossG: 0.02037\n",
            "[28/280] LossD: -0.05385 LossG: 0.02428\n",
            "[29/280] LossD: -0.06289 LossG: 0.02868\n",
            "[30/280] LossD: -0.07318 LossG: 0.03316\n",
            "[31/280] LossD: -0.08483 LossG: 0.03913\n",
            "[32/280] LossD: -0.09788 LossG: 0.04640\n",
            "[33/280] LossD: -0.11242 LossG: 0.05402\n",
            "[34/280] LossD: -0.12854 LossG: 0.06304\n",
            "[35/280] LossD: -0.14635 LossG: 0.07378\n",
            "[36/280] LossD: -0.16599 LossG: 0.08601\n",
            "[37/280] LossD: -0.18768 LossG: 0.09992\n",
            "[38/280] LossD: -0.21156 LossG: 0.11511\n",
            "[39/280] LossD: -0.23780 LossG: 0.13111\n",
            "[40/280] LossD: -0.26657 LossG: 0.14803\n",
            "[41/280] LossD: -0.29804 LossG: 0.16681\n",
            "[42/280] LossD: -0.33228 LossG: 0.18750\n",
            "[43/280] LossD: -0.36943 LossG: 0.20975\n",
            "[44/280] LossD: -0.40950 LossG: 0.23431\n",
            "[45/280] LossD: -0.45252 LossG: 0.26095\n",
            "[46/280] LossD: -0.49852 LossG: 0.28926\n",
            "[47/280] LossD: -0.54764 LossG: 0.31941\n",
            "[48/280] LossD: -0.60011 LossG: 0.35217\n",
            "[49/280] LossD: -0.65610 LossG: 0.38759\n",
            "[50/280] LossD: -0.71576 LossG: 0.42664\n",
            "[51/280] LossD: -0.77943 LossG: 0.46733\n",
            "[52/280] LossD: -0.84715 LossG: 0.50809\n",
            "[53/280] LossD: -0.91914 LossG: 0.54943\n",
            "[54/280] LossD: -0.99577 LossG: 0.59097\n",
            "[55/280] LossD: -1.07732 LossG: 0.63424\n",
            "[56/280] LossD: -1.16377 LossG: 0.68022\n",
            "[57/280] LossD: -1.25555 LossG: 0.72971\n",
            "[58/280] LossD: -1.35280 LossG: 0.78135\n",
            "[59/280] LossD: -1.45544 LossG: 0.83554\n",
            "[60/280] LossD: -1.56346 LossG: 0.89281\n",
            "[61/280] LossD: -1.67699 LossG: 0.95373\n",
            "[62/280] LossD: -1.79627 LossG: 1.01816\n",
            "[63/280] LossD: -1.92149 LossG: 1.08676\n",
            "[64/280] LossD: -2.05301 LossG: 1.15887\n",
            "[65/280] LossD: -2.19077 LossG: 1.23411\n",
            "[66/280] LossD: -2.33511 LossG: 1.31216\n",
            "[67/280] LossD: -2.48602 LossG: 1.39368\n",
            "[68/280] LossD: -2.64389 LossG: 1.47828\n",
            "[69/280] LossD: -2.80902 LossG: 1.56631\n",
            "[70/280] LossD: -2.98142 LossG: 1.65684\n",
            "[71/280] LossD: -3.16147 LossG: 1.75112\n",
            "[72/280] LossD: -3.34917 LossG: 1.84995\n",
            "[73/280] LossD: -3.54465 LossG: 1.95395\n",
            "[74/280] LossD: -3.74800 LossG: 2.06199\n",
            "[75/280] LossD: -3.95935 LossG: 2.17330\n",
            "[76/280] LossD: -4.17891 LossG: 2.28765\n",
            "[77/280] LossD: -4.40647 LossG: 2.40611\n",
            "[78/280] LossD: -4.64231 LossG: 2.52877\n",
            "[79/280] LossD: -4.88630 LossG: 2.65482\n",
            "[80/280] LossD: -5.13797 LossG: 2.78412\n",
            "[81/280] LossD: -5.39736 LossG: 2.91770\n",
            "[82/280] LossD: -5.66334 LossG: 3.05416\n",
            "[83/280] LossD: -5.93445 LossG: 3.19215\n",
            "[84/280] LossD: -6.20992 LossG: 3.33161\n",
            "[85/280] LossD: -6.48580 LossG: 3.47042\n",
            "[86/280] LossD: -6.76087 LossG: 3.60423\n",
            "[87/280] LossD: -7.03297 LossG: 3.73273\n",
            "[88/280] LossD: -7.29806 LossG: 3.85514\n",
            "[89/280] LossD: -7.55052 LossG: 3.98200\n",
            "[90/280] LossD: -7.79119 LossG: 4.10926\n",
            "[91/280] LossD: -8.01556 LossG: 4.22310\n",
            "[92/280] LossD: -8.21356 LossG: 4.31946\n",
            "[93/280] LossD: -8.37373 LossG: 4.38804\n",
            "[94/280] LossD: -8.49350 LossG: 4.43126\n",
            "[95/280] LossD: -8.57150 LossG: 4.44526\n",
            "[96/280] LossD: -8.61896 LossG: 4.43575\n",
            "[97/280] LossD: -8.60831 LossG: 4.37877\n",
            "[98/280] LossD: -8.56871 LossG: 4.28476\n",
            "[99/280] LossD: -8.51025 LossG: 4.16293\n",
            "[100/280] LossD: -8.44283 LossG: 4.01611\n",
            "[101/280] LossD: -8.40086 LossG: 3.86599\n",
            "[102/280] LossD: -8.43475 LossG: 3.76479\n",
            "[103/280] LossD: -8.53174 LossG: 3.69172\n",
            "[104/280] LossD: -8.73859 LossG: 3.68271\n",
            "[105/280] LossD: -9.07983 LossG: 3.66471\n",
            "[106/280] LossD: -9.57041 LossG: 3.56675\n",
            "[107/280] LossD: -10.15285 LossG: 3.46518\n",
            "[108/280] LossD: -10.76819 LossG: 3.37059\n",
            "[109/280] LossD: -11.36473 LossG: 3.30702\n",
            "[110/280] LossD: -11.93195 LossG: 3.27218\n",
            "[111/280] LossD: -12.48550 LossG: 3.24127\n",
            "[112/280] LossD: -13.02265 LossG: 3.23506\n",
            "[113/280] LossD: -13.55412 LossG: 3.31743\n",
            "[114/280] LossD: -14.06710 LossG: 3.42757\n",
            "[115/280] LossD: -14.53402 LossG: 3.51284\n",
            "[116/280] LossD: -14.86771 LossG: 3.53749\n",
            "[117/280] LossD: -14.36517 LossG: 2.89867\n",
            "[118/280] LossD: -12.79119 LossG: 1.13760\n",
            "[119/280] LossD: -9.52077 LossG: -2.22208\n",
            "[120/280] LossD: -4.41739 LossG: -7.21826\n",
            "[121/280] LossD: -2.02001 LossG: -9.31445\n",
            "[122/280] LossD: -0.92357 LossG: -9.81298\n",
            "[123/280] LossD: -0.16273 LossG: -9.84922\n",
            "[124/280] LossD: 0.30977 LossG: -9.78807\n",
            "[125/280] LossD: 0.65805 LossG: -9.91563\n",
            "[126/280] LossD: 1.03593 LossG: -10.21280\n",
            "[127/280] LossD: 1.46474 LossG: -10.58340\n",
            "[128/280] LossD: 1.98877 LossG: -10.99973\n",
            "[129/280] LossD: 2.55629 LossG: -11.40602\n",
            "[130/280] LossD: 3.22153 LossG: -11.92384\n",
            "[131/280] LossD: 3.97608 LossG: -12.59589\n",
            "[132/280] LossD: 4.67286 LossG: -13.27496\n",
            "[133/280] LossD: 5.28678 LossG: -13.86798\n",
            "[134/280] LossD: 5.84523 LossG: -14.40106\n",
            "[135/280] LossD: 6.31754 LossG: -14.84650\n",
            "[136/280] LossD: 6.73895 LossG: -15.21199\n",
            "[137/280] LossD: 7.14966 LossG: -15.55177\n",
            "[138/280] LossD: 7.55720 LossG: -15.88514\n",
            "[139/280] LossD: 7.95212 LossG: -16.20152\n",
            "[140/280] LossD: 8.24486 LossG: -16.40041\n",
            "[141/280] LossD: 8.41220 LossG: -16.46190\n",
            "[142/280] LossD: 8.37323 LossG: -16.29158\n",
            "[143/280] LossD: 8.25384 LossG: -16.03091\n",
            "[144/280] LossD: 7.98972 LossG: -15.60959\n",
            "[145/280] LossD: 7.68834 LossG: -15.14625\n",
            "[146/280] LossD: 7.22985 LossG: -14.53250\n",
            "[147/280] LossD: 6.73829 LossG: -13.87479\n",
            "[148/280] LossD: 6.27723 LossG: -13.24229\n",
            "[149/280] LossD: 5.88142 LossG: -12.67429\n",
            "[150/280] LossD: 5.45514 LossG: -12.09705\n",
            "[151/280] LossD: 5.05998 LossG: -11.54641\n",
            "[152/280] LossD: 4.32675 LossG: -10.65582\n",
            "[153/280] LossD: 3.94763 LossG: -10.10739\n",
            "[154/280] LossD: 3.58770 LossG: -9.58505\n",
            "[155/280] LossD: 2.97701 LossG: -8.84127\n",
            "[156/280] LossD: 2.60877 LossG: -8.34993\n",
            "[157/280] LossD: 2.15479 LossG: -7.74922\n",
            "[158/280] LossD: 1.76597 LossG: -7.23582\n",
            "[159/280] LossD: 1.28731 LossG: -6.61560\n",
            "[160/280] LossD: 0.86426 LossG: -6.08745\n",
            "[161/280] LossD: 0.44021 LossG: -5.57493\n",
            "[162/280] LossD: -0.05145 LossG: -4.97637\n",
            "[163/280] LossD: -0.62955 LossG: -4.28844\n",
            "[164/280] LossD: -1.21066 LossG: -3.63968\n",
            "[165/280] LossD: -1.50896 LossG: -3.31481\n",
            "[166/280] LossD: -1.28327 LossG: -3.44399\n",
            "[167/280] LossD: -0.74086 LossG: -3.70923\n",
            "[168/280] LossD: -0.48249 LossG: -3.54417\n",
            "[169/280] LossD: -0.47010 LossG: -3.15202\n",
            "[170/280] LossD: -0.50158 LossG: -2.81557\n",
            "[171/280] LossD: -0.49193 LossG: -2.60461\n",
            "[172/280] LossD: -0.40294 LossG: -2.51876\n",
            "[173/280] LossD: -0.38964 LossG: -2.41158\n",
            "[174/280] LossD: -0.38092 LossG: -2.23828\n",
            "[175/280] LossD: -0.49081 LossG: -1.96975\n",
            "[176/280] LossD: -0.44629 LossG: -1.93828\n",
            "[177/280] LossD: -0.37679 LossG: -1.86764\n",
            "[178/280] LossD: -0.34987 LossG: -1.67813\n",
            "[179/280] LossD: -0.33686 LossG: -1.50722\n",
            "[180/280] LossD: -0.32171 LossG: -1.39233\n",
            "[181/280] LossD: -0.28836 LossG: -1.38386\n",
            "[182/280] LossD: -0.29572 LossG: -1.39833\n",
            "[183/280] LossD: -0.27769 LossG: -1.32564\n",
            "[184/280] LossD: -0.23061 LossG: -1.48535\n",
            "[185/280] LossD: -0.21482 LossG: -1.47163\n",
            "[186/280] LossD: -0.18795 LossG: -1.41418\n",
            "[187/280] LossD: -0.17917 LossG: -1.32255\n",
            "[188/280] LossD: -0.18030 LossG: -1.29339\n",
            "[189/280] LossD: -0.17195 LossG: -1.24349\n",
            "[190/280] LossD: -0.17506 LossG: -1.11907\n",
            "[191/280] LossD: -0.23280 LossG: -0.87733\n",
            "[192/280] LossD: -0.20012 LossG: -0.78418\n",
            "[193/280] LossD: -0.18748 LossG: -0.61764\n",
            "[194/280] LossD: -0.16897 LossG: -0.35013\n",
            "[195/280] LossD: -0.16411 LossG: -0.10778\n",
            "[196/280] LossD: -0.14354 LossG: 0.14099\n",
            "[197/280] LossD: -0.14037 LossG: 0.35474\n",
            "[198/280] LossD: -0.17372 LossG: 0.56316\n",
            "[199/280] LossD: -0.17797 LossG: 0.83988\n",
            "[200/280] LossD: -0.19889 LossG: 1.12669\n",
            "[201/280] LossD: -0.09091 LossG: 1.28121\n",
            "[202/280] LossD: -0.12373 LossG: 1.63192\n",
            "[203/280] LossD: -0.23171 LossG: 2.01249\n",
            "[204/280] LossD: -0.12210 LossG: 2.32961\n",
            "[205/280] LossD: -0.12213 LossG: 2.78617\n",
            "[206/280] LossD: -0.11147 LossG: 3.00453\n",
            "[207/280] LossD: -0.11632 LossG: 3.27669\n",
            "[208/280] LossD: -0.11559 LossG: 3.53769\n",
            "[209/280] LossD: -0.12400 LossG: 3.79057\n",
            "[210/280] LossD: -0.12479 LossG: 4.15111\n",
            "[211/280] LossD: -0.13058 LossG: 4.64368\n",
            "[212/280] LossD: -0.13947 LossG: 5.21710\n",
            "[213/280] LossD: -0.13842 LossG: 5.74540\n",
            "[214/280] LossD: -0.15344 LossG: 6.10187\n",
            "[215/280] LossD: -0.14771 LossG: 6.39510\n",
            "[216/280] LossD: -0.13652 LossG: 6.59253\n",
            "[217/280] LossD: -0.18242 LossG: 6.82715\n",
            "[218/280] LossD: -0.14886 LossG: 6.93096\n",
            "[219/280] LossD: -0.16431 LossG: 6.97916\n",
            "[220/280] LossD: -0.15670 LossG: 6.98615\n",
            "[221/280] LossD: -0.18028 LossG: 7.11273\n",
            "[222/280] LossD: -0.08006 LossG: 7.09879\n",
            "[223/280] LossD: -0.08536 LossG: 7.01656\n",
            "[224/280] LossD: -0.20145 LossG: 7.27262\n",
            "[225/280] LossD: -0.08566 LossG: 7.36310\n",
            "[226/280] LossD: -0.11047 LossG: 7.30515\n",
            "[227/280] LossD: -0.14264 LossG: 7.36857\n",
            "[228/280] LossD: -0.36549 LossG: 7.75311\n",
            "[229/280] LossD: -0.34377 LossG: 8.01812\n",
            "[230/280] LossD: -0.34466 LossG: 8.23020\n",
            "[231/280] LossD: -0.34211 LossG: 8.31267\n",
            "[232/280] LossD: -0.36738 LossG: 8.33358\n",
            "[233/280] LossD: -0.37728 LossG: 8.28845\n",
            "[234/280] LossD: -0.33381 LossG: 8.13569\n",
            "[235/280] LossD: -0.34058 LossG: 8.06869\n",
            "[236/280] LossD: -0.35686 LossG: 7.99983\n",
            "[237/280] LossD: -0.31121 LossG: 7.87417\n",
            "[238/280] LossD: -0.37047 LossG: 7.88707\n",
            "[239/280] LossD: -0.36279 LossG: 7.94319\n",
            "[240/280] LossD: -0.33419 LossG: 7.96694\n",
            "[241/280] LossD: -0.33856 LossG: 7.95939\n",
            "[242/280] LossD: -0.34701 LossG: 8.03060\n",
            "[243/280] LossD: -0.33510 LossG: 7.99801\n",
            "[244/280] LossD: -0.34430 LossG: 7.98548\n",
            "[245/280] LossD: -0.35019 LossG: 8.00071\n",
            "[246/280] LossD: -0.31530 LossG: 8.05550\n",
            "[247/280] LossD: -0.35493 LossG: 8.17733\n",
            "[248/280] LossD: -0.32190 LossG: 8.11315\n",
            "[249/280] LossD: -0.33542 LossG: 8.10073\n",
            "[250/280] LossD: -0.34666 LossG: 8.09118\n",
            "[251/280] LossD: -0.34402 LossG: 8.16503\n",
            "[252/280] LossD: -0.32822 LossG: 8.35416\n",
            "[253/280] LossD: -0.33684 LossG: 8.44501\n",
            "[254/280] LossD: -0.32163 LossG: 8.58520\n",
            "[255/280] LossD: -0.32970 LossG: 8.70769\n",
            "[256/280] LossD: -0.28966 LossG: 8.56705\n",
            "[257/280] LossD: -0.31829 LossG: 8.52276\n",
            "[258/280] LossD: -0.30694 LossG: 8.63805\n",
            "[259/280] LossD: -0.32505 LossG: 8.62524\n",
            "[260/280] LossD: -0.31165 LossG: 8.52590\n",
            "[261/280] LossD: -0.30891 LossG: 8.69748\n",
            "[262/280] LossD: -0.30654 LossG: 8.86003\n",
            "[263/280] LossD: -0.27818 LossG: 8.90599\n",
            "[264/280] LossD: -0.30041 LossG: 9.11470\n",
            "[265/280] LossD: -0.25523 LossG: 9.64574\n",
            "[266/280] LossD: -0.32975 LossG: 9.89041\n",
            "[267/280] LossD: -0.31663 LossG: 9.85738\n",
            "[268/280] LossD: -0.30247 LossG: 10.15091\n",
            "[269/280] LossD: -0.27423 LossG: 10.31450\n",
            "[270/280] LossD: -0.30486 LossG: 10.54483\n",
            "[271/280] LossD: -0.27935 LossG: 10.53854\n",
            "[272/280] LossD: -0.27352 LossG: 10.83404\n",
            "[273/280] LossD: -0.30039 LossG: 10.84001\n",
            "[274/280] LossD: -0.29554 LossG: 10.90315\n",
            "[275/280] LossD: -0.28909 LossG: 10.99756\n",
            "[276/280] LossD: -0.27834 LossG: 11.02251\n",
            "[277/280] LossD: -0.20690 LossG: 11.20796\n",
            "[278/280] LossD: -0.30399 LossG: 11.38312\n",
            "[279/280] LossD: -0.29281 LossG: 11.34410\n",
            "[280/280] LossD: -0.26986 LossG: 11.42980\n",
            "Training MH for 20 epochs\n",
            "[1/20] LossD: -0.05515 LossG: 7.16998\n",
            "[2/20] LossD: -0.47139 LossG: 4.53285\n",
            "[3/20] LossD: -0.62086 LossG: 2.69990\n",
            "[4/20] LossD: -0.52876 LossG: 2.94959\n",
            "[5/20] LossD: -0.33307 LossG: 3.69848\n",
            "[6/20] LossD: -0.31644 LossG: 4.52968\n",
            "[7/20] LossD: -0.21443 LossG: 5.13786\n",
            "[8/20] LossD: -0.25833 LossG: 5.67026\n",
            "[9/20] LossD: -0.20812 LossG: 5.83448\n",
            "[10/20] LossD: -0.23518 LossG: 5.86817\n",
            "[11/20] LossD: -0.25510 LossG: 5.89431\n",
            "[12/20] LossD: -0.22205 LossG: 5.92952\n",
            "[13/20] LossD: -0.31783 LossG: 5.78792\n",
            "[14/20] LossD: -0.33331 LossG: 5.71420\n",
            "[15/20] LossD: -0.33725 LossG: 5.72206\n",
            "[16/20] LossD: -0.35200 LossG: 5.77297\n",
            "[17/20] LossD: -0.35896 LossG: 5.63435\n",
            "[18/20] LossD: -0.40524 LossG: 5.62407\n",
            "[19/20] LossD: -0.35251 LossG: 5.66494\n",
            "[20/20] LossD: -0.36584 LossG: 5.68648\n"
          ]
        }
      ],
      "source": [
        "# Fine-tune the MHWGAN model\n",
        "train_x, test_x = data_x[:-330, :], data_x[-330:, :]\n",
        "train_y, test_y = data_y[:-330], data_y[-330:]\n",
        "\n",
        "train_x = x_scaler.transform(train_x)\n",
        "train_y = y_scaler.transform(train_y.reshape(-1, 1))\n",
        "test_x = x_scaler.transform(test_x)\n",
        "test_y = y_scaler.transform(test_y.reshape(-1, 1))\n",
        "\n",
        "print(f'train_x: {train_x.shape} train_y: {train_y.shape}')\n",
        "print(f'test_x: {train_x.shape} test_y: {train_y.shape}')\n",
        "\n",
        "vae.eval()\n",
        "with torch.no_grad():\n",
        "    _, _, mu_train_x, _ = vae(torch.from_numpy(train_x).float().to(vae.device))\n",
        "    _, _, mu_test_x, _ = vae(torch.from_numpy(test_x).float().to(vae.device))\n",
        "    mu_train_x = mu_train_x.cpu().numpy()\n",
        "    mu_test_x = mu_test_x.cpu().numpy()\n",
        "\n",
        "train_x = np.concatenate((train_x, mu_train_x), axis=1)\n",
        "test_x = np.concatenate((test_x, mu_test_x), axis=1)\n",
        "\n",
        "train_x, train_y = sliding_windows(train_x, train_y, seq_length)\n",
        "test_x, test_y = sliding_windows(test_x, test_y, seq_length)\n",
        "print(f'After concate latent variables')\n",
        "print(f'train_x: {train_x.shape}, train_y: {train_y.shape}')\n",
        "print(f'test_x: {train_x.shape} test_y: {train_y.shape}')\n",
        "\n",
        "basic_epochs = 280\n",
        "mh_epochs = 20\n",
        "learning_rate = 0.000015\n",
        "mhwgan_batch_size = 128\n",
        "mhwgan.optimizers(lr=learning_rate)\n",
        "mhwgan.mh_training(basic_epochs, mh_epochs, 128, train_x, train_y, seq_length)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "id": "1uvhiDibiRLm",
        "outputId": "b7c89b78-acb3-47ea-ff5b-a0cfe898f527"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE : 2.8567034851443642\n",
            "MAE : 2.1985332594604494\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mhwgan.eval()\n",
        "with torch.no_grad():\n",
        "    pred_y = mhwgan(torch.from_numpy(test_x).float().to(mhwgan.device))\n",
        "\n",
        "pred_y = pred_y.cpu().numpy()\n",
        "pred_y = y_scaler.inverse_transform(pred_y.reshape(-1, 1))\n",
        "inverse_test_y = y_scaler.inverse_transform(test_y[:, seq_length, :])\n",
        "date = pd.to_datetime(data.iloc[-320:, :].index)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "scatter_pred = ax.scatter(date, pred_y, color='blue', label='Predicted Price')\n",
        "scatter_actual = ax.scatter(date, inverse_test_y, color='orange', label='Actual Price')\n",
        "ax.set_title('Stock prediction in testing dataset')\n",
        "ax.set_ylabel('TWD')\n",
        "ax.set_xlabel('Date')\n",
        "ax.legend(loc='upper right')\n",
        "\n",
        "# Set the x-axis tick format to display three months interval\n",
        "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n",
        "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
        "\n",
        "fig.autofmt_xdate()\n",
        "\n",
        "MSE = mean_squared_error(inverse_test_y, pred_y)\n",
        "RMSE = math.sqrt(MSE)\n",
        "MAE = np.mean(np.abs(inverse_test_y - pred_y))\n",
        "print(f'RMSE : {RMSE}')\n",
        "print(f'MAE : {MAE}')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}