model copy 2.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Setup and Load Data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1 Install Dependencies and Define Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import torchvision.models.segmentation\n",
    "import torch\n",
    "import torchvision.transforms as T\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "from PIL import Image\n",
    "import scipy.io\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "from torchviz import make_dot\n",
    "import torch.optim as optim\n",
    "from torchsummary import summary\n",
    "import tqdm\n",
    "import math\n",
    "\n",
    "# Set the device to use\n",
    "# CUDA refers to the GPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "## Hyperparameters\n",
    "num_epochs = 400\n",
    "batch_size = 256\n",
    "\n",
    "## Fixing Random Seed for Reproducibility\n",
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "random.seed(0)\n",
    "\n",
    "\n",
    "# If you are successfully using a GPU, this print should contain \"cuda\" in it\n",
    "print(str(device))\n",
    "assert('cuda' in str(device))  # comment out this assert if you are not using a GPU\n",
    "\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2 Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:28<00:00, 113.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.e+00 5.e-01 5.e+03]\n"
     ]
    }
   ],
   "source": [
    "# change directory to the data directory\n",
    "os.chdir('../HMEI2023-bxliu-data/')\n",
    "\n",
    "# initialize the lists that store the images and thetas\n",
    "image_list = []\n",
    "theta_list = []\n",
    "\n",
    "# name of directories of the images and values\n",
    "image_directory = 'image_5by400'\n",
    "value_directory = 'value_5by400'\n",
    "\n",
    "# iterate over files in image directory\n",
    "for filename in tqdm.tqdm(os.listdir(image_directory)):\n",
    "    f = os.path.join(image_directory, filename)\n",
    "    # checking if it is a file\n",
    "    if os.path.isfile(f):\n",
    "        # load the image data\n",
    "        # use this for color images (3 channels)\n",
    "        # image = Image.open(f)\n",
    "        # data = np.asarray(image)\n",
    "\n",
    "        # use this for gray scale images (1 channel)\n",
    "        data = cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2GRAY)\n",
    "        data = cv2.resize(data, (128,128), interpolation=cv2.INTER_CUBIC)\n",
    "        data = np.expand_dims(data, axis=2)\n",
    "\n",
    "        # load the theta data\n",
    "        matfile = value_directory + '/' + filename[:len(filename) - 4] + '.mat'\n",
    "        mat = scipy.io.loadmat(matfile)\n",
    "\n",
    "        # append to the image and theta lists\n",
    "        image_list.append(data)\n",
    "        x = mat['th0'][0]\n",
    "        # x[0] = math.log(x[0])\n",
    "        theta_list.append(x)\n",
    "\n",
    "# reformat the lists to make the compatable with what we want to do in Pytorch\n",
    "theta_list = np.asarray(theta_list)\n",
    "image_list = np.asarray(image_list)\n",
    "\n",
    "print(theta_list[0])\n",
    "\n",
    "image_list = np.moveaxis(image_list, -1, 1)\n",
    "\n",
    "image_list = torch.tensor(image_list).float()\n",
    "\n",
    "theta_list = torch.tensor(theta_list)\n",
    "\n",
    "os.chdir('../HMEI2023-bxliu/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.3 Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# images\n",
    "imean = torch.std(image_list, dim=0)\n",
    "istd = torch.mean(image_list, dim=0)\n",
    "image_tensor = torch.div(image_list, 255).float()\n",
    "# print(image_tensor)\n",
    "\n",
    "# theta\n",
    "tmean = torch.mean(theta_list, dim=0)\n",
    "tstd = torch.std(theta_list, dim=0)\n",
    "tstd[0] = 1\n",
    "theta_tensor = torch.div(torch.sub(theta_list, tmean), tstd).float()\n",
    "# print(theta_tensor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.4 Make Dataset and DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_dataset = TensorDataset(image_tensor, theta_tensor)\n",
    "\n",
    "# split data into training, validation, and testing data\n",
    "train, val, test = torch.utils.data.random_split(full_dataset, [0.75, 0.125, 0.125])\n",
    "train_loader = DataLoader(train, batch_size=batch_size, shuffle=False)\n",
    "val_loader = DataLoader(val, batch_size=42, shuffle=False)\n",
    "test_loader = DataLoader(test, batch_size=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<torch.utils.data.dataloader.DataLoader object at 0x000001E5947EB640>\n",
      "<torch.utils.data.dataloader.DataLoader object at 0x000001E5947EB670>\n",
      "<torch.utils.data.dataloader.DataLoader object at 0x000001E5947EAB30>\n"
     ]
    }
   ],
   "source": [
    "print(train_loader)\n",
    "print(val_loader)\n",
    "print(test_loader)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Set Up the Convolution Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1 Make Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(ConvNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 5, padding=2)\n",
    "        self.pool1 = nn.MaxPool2d(2, 2) \n",
    "        self.conv2 = nn.Conv2d(32, 64, 5, padding=2) \n",
    "        self.pool2 = nn.MaxPool2d(2, 2)\n",
    "        self.conv3 = nn.Conv2d(64, 128, 5, padding=2) \n",
    "        self.pool3 = nn.MaxPool2d(2, 2)\n",
    "\n",
    "        self.size_linear = 128*16*16\n",
    "        self.fc1 = nn.Linear(self.size_linear, 512)\n",
    "        self.fc2 = nn.Linear(512, 128)\n",
    "        self.fc3 = nn.Linear(128, 3)\n",
    "\n",
    "\n",
    "    def forward(self, x):\n",
    "            x = self.pool1(F.relu(self.conv1(x))) \n",
    "            x = self.pool2(F.relu(self.conv2(x))) \n",
    "            x = self.pool3(F.relu(self.conv3(x)))\n",
    "            x = x.view(-1, self.size_linear) # this flattens x into a 1D vector\n",
    "            x = F.relu(self.fc1(x))\n",
    "            x = F.relu(self.fc2(x)) \n",
    "            x = self.fc3(x)\n",
    "            return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 3])\n"
     ]
    }
   ],
   "source": [
    "# Check if CNN has the correct output shape\n",
    "with torch.no_grad():  # tells PyTorch not to track gradients here\n",
    "    # test_data is 100 random images, 1 channel, 128-by-128\n",
    "    test_data = torch.rand(100,1,128,128)\n",
    "    test_net = ConvNet()\n",
    "    out = test_net.forward(test_data)\n",
    "    # the output should have size (100,3)\n",
    "    print(out.size())\n",
    "    assert(out.size()==(100,3))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2 Make Trainer for Training Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trainer():\n",
    "    def __init__(self,net=None,optim=None,loss_function=None, train_loader=None, val_loader=None):\n",
    "        self.net = net\n",
    "        self.optim = optim\n",
    "        self.loss_function = loss_function\n",
    "        self.train_loader = train_loader\n",
    "        self.val_loader = val_loader\n",
    "\n",
    "    def train(self,epochs):\n",
    "        train_losses = []\n",
    "        val_losses = []\n",
    "        for epoch in range(epochs):\n",
    "            epoch_train_loss = 0.0\n",
    "            epoch_val_loss = 0.0\n",
    "            epoch_train_steps = 0\n",
    "            epoch_val_steps = 0\n",
    "            for data in self.train_loader:\n",
    "                \n",
    "                # Moving this batch to GPU\n",
    "                # Note that X has shape (batch_size, number of channels, height, width)\n",
    "                # which is equal to (256,1,128,128) since our default batch_size = 256 and \n",
    "                # the image has only 1 channel\n",
    "                X = data[0].to(device)\n",
    "                y = data[1].to(device)\n",
    "   \n",
    "                # Zero the gradient in the optimizer i.e. self.optim\n",
    "                self.optim.zero_grad()\n",
    "\n",
    "                # Getting the output of the Network\n",
    "                out = self.net(X)\n",
    "\n",
    "                # Computing loss using loss function i.e. self.loss_function\n",
    "                loss = self.loss_function(out, y)\n",
    "\n",
    "                # Backpropagate to compute gradients of parameteres\n",
    "                loss.backward()\n",
    "\n",
    "                # Call the optimizer i.e. self.optim\n",
    "                self.optim.step()\n",
    "\n",
    "                epoch_train_loss += loss.item()\n",
    "                epoch_train_steps += 1\n",
    "            \n",
    "            # validation\n",
    "            self.net.eval()\n",
    "            for data in val_loader:\n",
    "                X = data[0].to(device)\n",
    "                y = data[1].to(device)\n",
    "        \n",
    "                out = self.net(X)\n",
    "                loss = self.loss_function(out, y)\n",
    "                epoch_val_loss += loss.item()\n",
    "                epoch_val_steps += 1\n",
    "\n",
    "            # average loss of epoch\n",
    "            train_losses.append(epoch_train_loss / epoch_train_steps)\n",
    "            print(\"epoch [%d]: train loss %.3f\" % (epoch+1, train_losses[-1]))\n",
    "            val_losses.append(epoch_val_loss / epoch_val_steps)\n",
    "            print(\"epoch [%d]: val loss %.3f\" % (epoch+1, val_losses[-1]))\n",
    "        # return train_losses\n",
    "        return train_losses, val_losses"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Train and Evaluate Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1 Train Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch [1]: train loss 0.659\n",
      "epoch [1]: val loss 0.655\n",
      "epoch [2]: train loss 0.640\n",
      "epoch [2]: val loss 0.623\n",
      "epoch [3]: train loss 0.592\n",
      "epoch [3]: val loss 0.561\n",
      "epoch [4]: train loss 0.513\n",
      "epoch [4]: val loss 0.475\n",
      "epoch [5]: train loss 0.428\n",
      "epoch [5]: val loss 0.396\n",
      "epoch [6]: train loss 0.381\n",
      "epoch [6]: val loss 0.375\n",
      "epoch [7]: train loss 0.371\n",
      "epoch [7]: val loss 0.371\n",
      "epoch [8]: train loss 0.368\n",
      "epoch [8]: val loss 0.369\n",
      "epoch [9]: train loss 0.365\n",
      "epoch [9]: val loss 0.366\n",
      "epoch [10]: train loss 0.363\n",
      "epoch [10]: val loss 0.364\n",
      "epoch [11]: train loss 0.360\n",
      "epoch [11]: val loss 0.361\n",
      "epoch [12]: train loss 0.358\n",
      "epoch [12]: val loss 0.359\n",
      "epoch [13]: train loss 0.355\n",
      "epoch [13]: val loss 0.357\n",
      "epoch [14]: train loss 0.353\n",
      "epoch [14]: val loss 0.355\n",
      "epoch [15]: train loss 0.351\n",
      "epoch [15]: val loss 0.353\n",
      "epoch [16]: train loss 0.349\n",
      "epoch [16]: val loss 0.351\n",
      "epoch [17]: train loss 0.347\n",
      "epoch [17]: val loss 0.349\n",
      "epoch [18]: train loss 0.344\n",
      "epoch [18]: val loss 0.346\n",
      "epoch [19]: train loss 0.342\n",
      "epoch [19]: val loss 0.344\n",
      "epoch [20]: train loss 0.339\n",
      "epoch [20]: val loss 0.341\n",
      "epoch [21]: train loss 0.336\n",
      "epoch [21]: val loss 0.338\n",
      "epoch [22]: train loss 0.333\n",
      "epoch [22]: val loss 0.335\n",
      "epoch [23]: train loss 0.330\n",
      "epoch [23]: val loss 0.332\n",
      "epoch [24]: train loss 0.325\n",
      "epoch [24]: val loss 0.329\n",
      "epoch [25]: train loss 0.321\n",
      "epoch [25]: val loss 0.326\n",
      "epoch [26]: train loss 0.316\n",
      "epoch [26]: val loss 0.322\n",
      "epoch [27]: train loss 0.310\n",
      "epoch [27]: val loss 0.315\n",
      "epoch [28]: train loss 0.304\n",
      "epoch [28]: val loss 0.309\n",
      "epoch [29]: train loss 0.297\n",
      "epoch [29]: val loss 0.301\n",
      "epoch [30]: train loss 0.290\n",
      "epoch [30]: val loss 0.291\n",
      "epoch [31]: train loss 0.282\n",
      "epoch [31]: val loss 0.281\n",
      "epoch [32]: train loss 0.274\n",
      "epoch [32]: val loss 0.271\n",
      "epoch [33]: train loss 0.264\n",
      "epoch [33]: val loss 0.261\n",
      "epoch [34]: train loss 0.253\n",
      "epoch [34]: val loss 0.252\n",
      "epoch [35]: train loss 0.240\n",
      "epoch [35]: val loss 0.239\n",
      "epoch [36]: train loss 0.226\n",
      "epoch [36]: val loss 0.227\n",
      "epoch [37]: train loss 0.212\n",
      "epoch [37]: val loss 0.214\n",
      "epoch [38]: train loss 0.200\n",
      "epoch [38]: val loss 0.201\n",
      "epoch [39]: train loss 0.188\n",
      "epoch [39]: val loss 0.188\n",
      "epoch [40]: train loss 0.176\n",
      "epoch [40]: val loss 0.176\n",
      "epoch [41]: train loss 0.165\n",
      "epoch [41]: val loss 0.167\n",
      "epoch [42]: train loss 0.156\n",
      "epoch [42]: val loss 0.158\n",
      "epoch [43]: train loss 0.148\n",
      "epoch [43]: val loss 0.150\n",
      "epoch [44]: train loss 0.140\n",
      "epoch [44]: val loss 0.143\n",
      "epoch [45]: train loss 0.134\n",
      "epoch [45]: val loss 0.137\n",
      "epoch [46]: train loss 0.128\n",
      "epoch [46]: val loss 0.132\n",
      "epoch [47]: train loss 0.123\n",
      "epoch [47]: val loss 0.127\n",
      "epoch [48]: train loss 0.119\n",
      "epoch [48]: val loss 0.123\n",
      "epoch [49]: train loss 0.115\n",
      "epoch [49]: val loss 0.119\n",
      "epoch [50]: train loss 0.111\n",
      "epoch [50]: val loss 0.116\n",
      "epoch [51]: train loss 0.108\n",
      "epoch [51]: val loss 0.114\n",
      "epoch [52]: train loss 0.106\n",
      "epoch [52]: val loss 0.111\n",
      "epoch [53]: train loss 0.103\n",
      "epoch [53]: val loss 0.107\n",
      "epoch [54]: train loss 0.100\n",
      "epoch [54]: val loss 0.105\n",
      "epoch [55]: train loss 0.098\n",
      "epoch [55]: val loss 0.103\n",
      "epoch [56]: train loss 0.096\n",
      "epoch [56]: val loss 0.101\n",
      "epoch [57]: train loss 0.094\n",
      "epoch [57]: val loss 0.099\n",
      "epoch [58]: train loss 0.092\n",
      "epoch [58]: val loss 0.097\n",
      "epoch [59]: train loss 0.090\n",
      "epoch [59]: val loss 0.096\n",
      "epoch [60]: train loss 0.089\n",
      "epoch [60]: val loss 0.094\n",
      "epoch [61]: train loss 0.088\n",
      "epoch [61]: val loss 0.093\n",
      "epoch [62]: train loss 0.086\n",
      "epoch [62]: val loss 0.092\n",
      "epoch [63]: train loss 0.085\n",
      "epoch [63]: val loss 0.090\n",
      "epoch [64]: train loss 0.083\n",
      "epoch [64]: val loss 0.089\n",
      "epoch [65]: train loss 0.082\n",
      "epoch [65]: val loss 0.089\n",
      "epoch [66]: train loss 0.081\n",
      "epoch [66]: val loss 0.088\n",
      "epoch [67]: train loss 0.080\n",
      "epoch [67]: val loss 0.087\n",
      "epoch [68]: train loss 0.079\n",
      "epoch [68]: val loss 0.086\n",
      "epoch [69]: train loss 0.078\n",
      "epoch [69]: val loss 0.086\n",
      "epoch [70]: train loss 0.077\n",
      "epoch [70]: val loss 0.085\n",
      "epoch [71]: train loss 0.076\n",
      "epoch [71]: val loss 0.084\n",
      "epoch [72]: train loss 0.075\n",
      "epoch [72]: val loss 0.084\n",
      "epoch [73]: train loss 0.074\n",
      "epoch [73]: val loss 0.083\n",
      "epoch [74]: train loss 0.073\n",
      "epoch [74]: val loss 0.081\n",
      "epoch [75]: train loss 0.072\n",
      "epoch [75]: val loss 0.081\n",
      "epoch [76]: train loss 0.072\n",
      "epoch [76]: val loss 0.080\n",
      "epoch [77]: train loss 0.071\n",
      "epoch [77]: val loss 0.080\n",
      "epoch [78]: train loss 0.070\n",
      "epoch [78]: val loss 0.078\n",
      "epoch [79]: train loss 0.069\n",
      "epoch [79]: val loss 0.077\n",
      "epoch [80]: train loss 0.068\n",
      "epoch [80]: val loss 0.076\n",
      "epoch [81]: train loss 0.067\n",
      "epoch [81]: val loss 0.075\n",
      "epoch [82]: train loss 0.066\n",
      "epoch [82]: val loss 0.074\n",
      "epoch [83]: train loss 0.066\n",
      "epoch [83]: val loss 0.073\n",
      "epoch [84]: train loss 0.065\n",
      "epoch [84]: val loss 0.072\n",
      "epoch [85]: train loss 0.064\n",
      "epoch [85]: val loss 0.072\n",
      "epoch [86]: train loss 0.064\n",
      "epoch [86]: val loss 0.071\n",
      "epoch [87]: train loss 0.063\n",
      "epoch [87]: val loss 0.070\n",
      "epoch [88]: train loss 0.062\n",
      "epoch [88]: val loss 0.070\n",
      "epoch [89]: train loss 0.062\n",
      "epoch [89]: val loss 0.069\n",
      "epoch [90]: train loss 0.061\n",
      "epoch [90]: val loss 0.069\n",
      "epoch [91]: train loss 0.061\n",
      "epoch [91]: val loss 0.068\n",
      "epoch [92]: train loss 0.060\n",
      "epoch [92]: val loss 0.068\n",
      "epoch [93]: train loss 0.060\n",
      "epoch [93]: val loss 0.067\n",
      "epoch [94]: train loss 0.059\n",
      "epoch [94]: val loss 0.067\n",
      "epoch [95]: train loss 0.059\n",
      "epoch [95]: val loss 0.066\n",
      "epoch [96]: train loss 0.058\n",
      "epoch [96]: val loss 0.065\n",
      "epoch [97]: train loss 0.058\n",
      "epoch [97]: val loss 0.065\n",
      "epoch [98]: train loss 0.057\n",
      "epoch [98]: val loss 0.065\n",
      "epoch [99]: train loss 0.057\n",
      "epoch [99]: val loss 0.064\n",
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      "epoch [339]: val loss 0.029\n",
      "epoch [340]: train loss 0.021\n",
      "epoch [340]: val loss 0.029\n",
      "epoch [341]: train loss 0.021\n",
      "epoch [341]: val loss 0.029\n",
      "epoch [342]: train loss 0.021\n",
      "epoch [342]: val loss 0.029\n",
      "epoch [343]: train loss 0.021\n",
      "epoch [343]: val loss 0.029\n",
      "epoch [344]: train loss 0.021\n",
      "epoch [344]: val loss 0.029\n",
      "epoch [345]: train loss 0.021\n",
      "epoch [345]: val loss 0.029\n",
      "epoch [346]: train loss 0.020\n",
      "epoch [346]: val loss 0.029\n",
      "epoch [347]: train loss 0.020\n",
      "epoch [347]: val loss 0.029\n",
      "epoch [348]: train loss 0.020\n",
      "epoch [348]: val loss 0.029\n",
      "epoch [349]: train loss 0.020\n",
      "epoch [349]: val loss 0.029\n",
      "epoch [350]: train loss 0.020\n",
      "epoch [350]: val loss 0.029\n",
      "epoch [351]: train loss 0.020\n",
      "epoch [351]: val loss 0.029\n",
      "epoch [352]: train loss 0.020\n",
      "epoch [352]: val loss 0.029\n",
      "epoch [353]: train loss 0.020\n",
      "epoch [353]: val loss 0.029\n",
      "epoch [354]: train loss 0.020\n",
      "epoch [354]: val loss 0.029\n",
      "epoch [355]: train loss 0.020\n",
      "epoch [355]: val loss 0.029\n",
      "epoch [356]: train loss 0.020\n",
      "epoch [356]: val loss 0.030\n",
      "epoch [357]: train loss 0.020\n",
      "epoch [357]: val loss 0.029\n",
      "epoch [358]: train loss 0.020\n",
      "epoch [358]: val loss 0.029\n",
      "epoch [359]: train loss 0.020\n",
      "epoch [359]: val loss 0.029\n",
      "epoch [360]: train loss 0.020\n",
      "epoch [360]: val loss 0.029\n",
      "epoch [361]: train loss 0.020\n",
      "epoch [361]: val loss 0.029\n",
      "epoch [362]: train loss 0.020\n",
      "epoch [362]: val loss 0.030\n",
      "epoch [363]: train loss 0.021\n",
      "epoch [363]: val loss 0.029\n",
      "epoch [364]: train loss 0.020\n",
      "epoch [364]: val loss 0.029\n",
      "epoch [365]: train loss 0.020\n",
      "epoch [365]: val loss 0.029\n",
      "epoch [366]: train loss 0.020\n",
      "epoch [366]: val loss 0.031\n",
      "epoch [367]: train loss 0.020\n",
      "epoch [367]: val loss 0.030\n",
      "epoch [368]: train loss 0.020\n",
      "epoch [368]: val loss 0.029\n",
      "epoch [369]: train loss 0.020\n",
      "epoch [369]: val loss 0.029\n",
      "epoch [370]: train loss 0.020\n",
      "epoch [370]: val loss 0.029\n",
      "epoch [371]: train loss 0.019\n",
      "epoch [371]: val loss 0.029\n",
      "epoch [372]: train loss 0.019\n",
      "epoch [372]: val loss 0.029\n",
      "epoch [373]: train loss 0.019\n",
      "epoch [373]: val loss 0.029\n",
      "epoch [374]: train loss 0.019\n",
      "epoch [374]: val loss 0.029\n",
      "epoch [375]: train loss 0.019\n",
      "epoch [375]: val loss 0.029\n",
      "epoch [376]: train loss 0.019\n",
      "epoch [376]: val loss 0.028\n",
      "epoch [377]: train loss 0.019\n",
      "epoch [377]: val loss 0.029\n",
      "epoch [378]: train loss 0.019\n",
      "epoch [378]: val loss 0.028\n",
      "epoch [379]: train loss 0.019\n",
      "epoch [379]: val loss 0.029\n",
      "epoch [380]: train loss 0.019\n",
      "epoch [380]: val loss 0.029\n",
      "epoch [381]: train loss 0.019\n",
      "epoch [381]: val loss 0.029\n",
      "epoch [382]: train loss 0.019\n",
      "epoch [382]: val loss 0.029\n",
      "epoch [383]: train loss 0.019\n",
      "epoch [383]: val loss 0.029\n",
      "epoch [384]: train loss 0.019\n",
      "epoch [384]: val loss 0.028\n",
      "epoch [385]: train loss 0.019\n",
      "epoch [385]: val loss 0.029\n",
      "epoch [386]: train loss 0.019\n",
      "epoch [386]: val loss 0.028\n",
      "epoch [387]: train loss 0.019\n",
      "epoch [387]: val loss 0.028\n",
      "epoch [388]: train loss 0.019\n",
      "epoch [388]: val loss 0.028\n",
      "epoch [389]: train loss 0.019\n",
      "epoch [389]: val loss 0.029\n",
      "epoch [390]: train loss 0.019\n",
      "epoch [390]: val loss 0.028\n",
      "epoch [391]: train loss 0.019\n",
      "epoch [391]: val loss 0.028\n",
      "epoch [392]: train loss 0.019\n",
      "epoch [392]: val loss 0.028\n",
      "epoch [393]: train loss 0.019\n",
      "epoch [393]: val loss 0.028\n",
      "epoch [394]: train loss 0.018\n",
      "epoch [394]: val loss 0.028\n",
      "epoch [395]: train loss 0.018\n",
      "epoch [395]: val loss 0.029\n",
      "epoch [396]: train loss 0.019\n",
      "epoch [396]: val loss 0.030\n",
      "epoch [397]: train loss 0.019\n",
      "epoch [397]: val loss 0.030\n",
      "epoch [398]: train loss 0.019\n",
      "epoch [398]: val loss 0.028\n",
      "epoch [399]: train loss 0.019\n",
      "epoch [399]: val loss 0.029\n",
      "epoch [400]: train loss 0.019\n",
      "epoch [400]: val loss 0.028\n"
     ]
    }
   ],
   "source": [
    "#try different learning rates to see which one works \n",
    "learning_rate = 0.00001 # only nu, only sigma, color, gray, constant nu?, constant rho?, constant sigma?, rand 10 by 200\n",
    "# learning_rate = 0.0001 # only rho\n",
    "# learning_rate = 0.0000001 # 5 by 400\n",
    "# learning_rate = 0.000001 # 10 by 200, rand 5 by 400\n",
    "\n",
    "net = ConvNet()\n",
    "net = net.to(device)\n",
    "opt = optim.Adam(net.parameters(), lr=learning_rate)\n",
    "loss_function = nn.MSELoss()\n",
    "\n",
    "trainer = Trainer(net=net, optim=opt, loss_function=loss_function, train_loader=train_loader, val_loader=val_loader)\n",
    "\n",
    "train_losses, val_losses = trainer.train(num_epochs)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2 Plot losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'training loss')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plot the training loss (y-axis) vs epoch number (x-axis) using the losses computed\n",
    "plt.plot(range(1,num_epochs+1), train_losses, color = 'red')\n",
    "plt.plot(range(1,num_epochs+1), val_losses, color = 'blue')\n",
    "plt.xlabel(\"epoch number\")\n",
    "plt.ylabel(\"training loss\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.3 Test Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###################################################\n",
      "tensor([9.7397e-01, 8.6249e-01, 1.2246e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9716e-01, 1.3843e+00, 2.2062e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9686e-01, 5.1101e-01, 1.9424e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8891e-01, 9.2116e-01, 2.5130e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0049e+00, 1.5070e+00, 2.0337e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 4.7880e-01, 1.7435e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9505e-01, 2.0856e+00, 4.5870e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9474e-01, 4.6750e-01, 2.1622e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0136e+00, 2.2049e+00, 9.0498e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0030e+00, 1.0169e+00, 1.0384e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9522e-01, 2.1673e+00, 1.6692e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0032e+00, 2.3979e+00, 2.1922e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0115e+00, 2.0266e+00, 9.4526e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9947e-01, 9.3784e-01, 2.4588e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8734e-01, 2.2825e+00, 1.5752e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9760e-01, 5.8058e-01, 9.6601e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0128e+00, 1.0443e+00, 4.0832e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9563e-01, 2.3872e+00, 2.0986e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0105e+00, 1.0913e+00, 8.8236e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0105e+00, 2.6207e+00, 3.9783e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0081e+00, 1.7715e+00, 2.1185e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9328e-01, 4.8773e-01, 2.1167e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9634e-01, 2.2062e+00, 2.3386e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0061e+00, 2.0802e+00, 1.9824e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0055e+00, 2.6702e+00, 1.9280e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9443e-01, 2.3511e+00, 4.1567e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0047e+00, 2.5607e+00, 2.3789e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0071e+00, 2.2033e+00, 1.8361e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0080e+00, 1.0538e+00, 2.0833e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9063e-01, 2.2310e+00, 2.4697e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0088e+00, 2.1378e+00, 2.0531e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9473e-01, 8.6246e-01, 5.5374e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0058e+00, 4.5079e-01, 1.7780e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8970e-01, 5.2197e-01, 2.2770e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0081e+00, 1.8113e+00, 9.0451e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9609e-01, 1.5775e+00, 2.3183e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9609e-01, 2.0912e+00, 2.2365e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9548e-01, 2.5466e+00, 4.5736e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0127e+00, 5.4607e-01, 1.0921e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0003e+00, 2.4042e+00, 4.5483e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0121e+00, 1.1955e+00, 5.0082e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0002e+00, 5.6838e-01, 2.2693e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0119e+00, 1.6498e+00, 1.9726e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9927e-01, 9.8189e-01, 2.1361e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 2.3560e+00, 9.8602e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8953e-01, 4.5966e-01, 4.9921e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0046e+00, 5.5608e-01, 1.3473e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0026e+00, 1.1087e+00, 1.6117e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0079e+00, 1.1937e+00, 5.0369e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9907e-01, 9.8654e-01, 2.2458e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9974e-01, 1.8580e+00, 1.7232e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0050e+00, 1.0746e+00, 2.0555e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0013e+00, 2.1354e+00, 1.5138e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0089e+00, 2.7909e+00, 8.7696e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0127e+00, 2.0334e+00, 4.4812e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0010e+00, 1.5000e+00, 1.4970e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0033e+00, 2.5830e+00, 1.9527e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0010e+00, 9.5328e-01, 4.7396e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0171e+00, 2.0025e+00, 1.4175e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8742e-01, 9.6446e-01, 2.5178e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0032e+00, 4.5208e-01, 1.2969e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.4128e+00, 6.0545e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 2.4376e+00, 3.8521e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9931e-01, 3.9069e-01, 2.1371e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9619e-01, 5.0779e-01, 1.8910e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0027e+00, 9.9803e-01, 5.0744e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0018e+00, 2.0609e+00, 4.7515e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7762e-01, 8.6312e-01, 1.2967e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0035e+00, 2.2760e+00, 1.8481e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0130e+00, 2.1662e+00, 2.4662e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8522e-01, 1.0368e+00, 2.4766e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8993e-01, 5.1238e-01, 2.2394e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0087e+00, 2.0985e+00, 2.0217e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9408e-01, 4.6537e-01, 5.1136e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0058e+00, 1.7087e+00, 2.1346e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0066e+00, 1.7646e+00, 1.3769e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0041e+00, 5.8904e-01, 3.9669e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0012e+00, 2.1576e+00, 9.3541e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0111e+00, 1.6747e+00, 1.8020e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9867e-01, 4.9557e-01, 1.7073e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9923e-01, 9.8036e-01, 2.1917e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0004e+00, 1.8425e+00, 4.9627e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0056e+00, 2.1377e+00, 1.0030e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 2.2269e+00, 3.9354e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0065e+00, 1.0520e+00, 1.3879e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0162e+00, 4.1475e-01, 2.3459e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0033e+00, 1.5831e+00, 1.5168e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0064e+00, 1.4742e+00, 1.0115e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9422e-01, 2.1513e+00, 2.4148e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 4.7146e-01, 2.3215e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0021e+00, 4.0539e-01, 1.8563e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0036e+00, 2.2923e+00, 4.6759e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0011e+00, 2.4829e+00, 9.4005e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9929e-01, 1.0260e+00, 2.0256e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0100e+00, 1.9397e+00, 4.1178e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9639e-01, 2.3771e+00, 4.0188e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0051e+00, 2.6005e+00, 8.9263e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0124e+00, 2.4865e+00, 2.6172e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0036e+00, 1.8698e+00, 1.1319e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0008e+00, 8.8130e-01, 2.1171e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0103e+00, 5.5050e-01, 2.3739e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9492e-01, 2.0618e+00, 9.6741e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0012e+00, 5.8658e-01, 1.2608e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9266e-01, 5.3977e-01, 1.9670e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9959e-01, 2.2715e+00, 1.5098e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9927e-01, 2.2351e+00, 2.2340e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0019e+00, 1.9354e+00, 1.0851e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8538e-01, 5.8757e-01, 1.9188e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9838e-01, 2.0624e+00, 2.0626e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0002e+00, 2.0474e+00, 1.5923e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9191e-01, 1.6316e+00, 2.4730e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0094e+00, 1.5494e+00, 2.0236e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0115e+00, 2.1545e+00, 4.2856e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9917e-01, 9.5386e-01, 4.8318e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0003e+00, 2.2047e+00, 2.2518e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0124e+00, 1.0928e+00, 1.5507e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0003e+00, 2.0646e+00, 1.0088e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9793e-01, 2.0799e+00, 1.0439e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7483e-01, 8.7789e-01, 1.3407e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0172e+00, 1.8276e+00, 1.3289e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0009e+00, 2.4830e+00, 4.8304e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9567e-01, 2.5257e+00, 4.4766e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9825e-01, 5.0396e-01, 5.0035e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 9.4797e-01, 4.5560e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0066e+00, 2.1032e+00, 1.5014e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0171e+00, 5.2163e-01, 4.3789e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0061e+00, 4.1475e-01, 1.0894e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0078e+00, 1.7407e+00, 9.2051e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0082e+00, 1.0768e+00, 4.7425e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0115e+00, 6.5513e-01, 1.3370e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9274e-01, 5.3564e-01, 9.4830e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0094e+00, 1.6073e+00, 1.4797e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9949e-01, 1.5590e+00, 2.1473e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0160e+00, 2.0985e+00, 8.4355e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0028e+00, 1.9185e+00, 1.5519e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8788e-01, 5.3223e-01, 2.0604e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9842e-01, 5.2118e-01, 4.8778e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0053e+00, 4.7174e-01, 3.5393e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9953e-01, 1.0832e+00, 2.1342e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7725e-01, 5.7126e-01, 1.9002e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0060e+00, 1.4926e+00, 9.4357e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0026e+00, 1.1541e+00, 9.1658e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0224e+00, 1.0677e+00, 1.6182e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9735e-01, 6.6418e-01, 4.3764e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0048e+00, 4.4771e-01, 2.3521e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9986e-01, 7.9676e-01, 5.6467e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9150e-01, 5.0773e-01, 2.2480e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7365e-01, 5.1363e-01, 2.0781e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0004e+00, 9.8533e-01, 2.2793e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7690e-01, 9.3742e-01, 1.1985e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0136e+00, 1.2420e+00, 5.3373e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0022e+00, 2.5071e+00, 5.1456e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0006e+00, 4.5449e-01, 2.3641e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0132e+00, 2.0247e+00, 4.4439e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0073e+00, 6.0820e-01, 4.6851e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9932e-01, 1.7914e+00, 1.6519e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0019e+00, 1.5569e+00, 9.9781e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0130e+00, 1.4902e+00, 1.9502e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0003e+00, 5.2876e-01, 1.7721e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9921e-01, 2.0360e+00, 2.4100e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0187e+00, 1.4627e+00, 5.4217e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0044e+00, 2.4323e+00, 2.0968e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9862e-01, 1.5243e+00, 2.4703e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0150e+00, 1.7585e+00, 1.3352e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0067e+00, 1.8151e+00, 1.1216e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0078e+00, 1.9088e+00, 3.8452e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9094e-01, 1.6951e+00, 9.3273e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9741e-01, 2.2807e+00, 2.2883e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0098e+00, 2.0692e+00, 9.4363e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8386e-01, 4.7262e-01, 2.1581e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9143e-01, 9.8738e-01, 1.2623e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0108e+00, 1.5731e+00, 2.1036e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9980e-01, 2.4313e+00, 2.0717e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0100e+00, 2.7728e+00, 8.8338e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0075e+00, 2.3121e+00, 9.6306e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0076e+00, 5.3537e-01, 5.2917e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9708e-01, 2.4858e+00, 9.5941e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9708e-01, 2.4413e+00, 2.1788e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9984e-01, 1.5928e+00, 9.9444e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8895e-01, 5.4345e-01, 2.1235e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9718e-01, 2.6942e+00, 2.4262e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0044e+00, 2.1310e+00, 1.0408e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9695e-01, 2.1866e+00, 2.5184e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0035e+00, 2.1060e+00, 2.0443e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.2223e+00, 1.7394e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0098e+00, 1.0303e+00, 1.9435e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0117e+00, 1.6768e+00, 2.1078e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0003e+00, 1.5702e+00, 2.5105e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9826e-01, 2.4611e+00, 1.9764e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0211e+00, 2.3819e+00, 8.3018e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9942e-01, 5.6215e-01, 1.0934e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0141e+00, 9.8460e-01, 9.2652e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0102e+00, 9.7036e-01, 1.9556e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0057e+00, 1.0303e+00, 2.0873e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0008e+00, 1.0573e+00, 1.0242e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0069e+00, 1.5204e+00, 2.0563e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9114e-01, 1.6542e+00, 2.4067e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0095e+00, 1.5686e+00, 2.3769e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0118e+00, 2.5975e+00, 8.7339e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0017e+00, 2.2562e+00, 2.4802e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7814e-01, 9.1902e-01, 2.5235e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0032e+00, 1.2790e+00, 1.7972e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9644e-01, 1.8259e+00, 2.3182e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0129e+00, 1.1337e+00, 8.9918e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9583e-01, 4.7040e-01, 2.0181e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0097e+00, 2.0658e+00, 1.4630e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9469e-01, 9.5863e-01, 2.4732e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0077e+00, 2.1364e+00, 1.5480e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0158e+00, 4.2650e-01, 1.6562e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0060e+00, 2.4910e+00, 2.1622e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0028e+00, 2.1381e+00, 2.2654e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0068e+00, 2.1029e+00, 2.4719e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0140e+00, 1.6207e+00, 4.4580e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0055e+00, 1.9725e+00, 9.7931e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8884e-01, 1.6115e+00, 9.6906e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9197e-01, 4.8382e-01, 1.9744e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0021e+00, 9.3668e-01, 4.6277e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0032e+00, 1.1176e+00, 1.9873e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0027e+00, 2.5949e+00, 2.0241e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9917e-01, 2.5848e+00, 1.9563e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 4.2345e-01, 2.0882e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0021e+00, 1.9210e+00, 4.1995e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9136e-01, 5.1827e-01, 2.0898e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 1.4411e+00, 2.0939e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0164e+00, 1.7943e+00, 5.8586e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0032e+00, 1.5597e+00, 2.5355e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0078e+00, 1.5151e+00, 4.9065e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0131e+00, 3.9865e-01, 1.8144e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9967e-01, 2.0163e+00, 2.4164e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0161e+00, 1.6806e+00, 4.5756e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0000e+00, 2.6787e+00, 9.3182e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0114e+00, 2.3245e+00, 8.7702e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0073e+00, 1.0287e+00, 1.0447e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0043e+00, 4.7291e-01, 1.3827e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.0297e+00, 1.8931e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9666e-01, 9.4645e-01, 2.1495e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0080e+00, 4.8855e-01, 5.3865e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0127e+00, 1.4655e+00, 1.4837e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9782e-01, 2.2480e+00, 2.3315e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9717e-01, 2.1541e+00, 1.4759e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9808e-01, 2.1550e+00, 2.4352e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9459e-01, 2.1156e+00, 1.5526e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9733e-01, 4.5340e-01, 4.4783e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9916e-01, 2.3404e+00, 2.2702e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0057e+00, 1.4809e+00, 1.8161e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0148e+00, 1.5731e+00, 4.8587e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9868e-01, 1.0069e+00, 1.3052e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9429e-01, 1.5211e+00, 2.4279e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.8720e-01, 5.2070e-01, 1.9519e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0107e+00, 2.2007e+00, 4.8239e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9611e-01, 1.0697e+00, 2.1688e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0012e+00, 4.1848e-01, 1.5500e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0049e+00, 1.3686e+00, 1.0453e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0042e+00, 4.8221e-01, 4.8273e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0126e+00, 9.2294e-01, 5.2588e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0025e+00, 2.1655e+00, 2.4941e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0108e+00, 1.1992e+00, 4.5364e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0060e+00, 4.6527e-01, 2.5942e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9108e-01, 9.6632e-01, 1.3181e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0023e+00, 1.1317e+00, 4.6030e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0026e+00, 4.5522e-01, 4.9387e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0064e+00, 2.5808e+00, 9.1858e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0160e+00, 4.9963e-01, 4.8310e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0012e+00, 1.7981e+00, 8.8997e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0147e+00, 4.2316e-01, 1.4831e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0008e+00, 9.2090e-01, 5.0204e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0139e+00, 1.6184e+00, 1.9376e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9788e-01, 5.1065e-01, 2.3474e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0017e+00, 1.5129e+00, 4.9293e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0044e+00, 9.7446e-01, 2.2517e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9839e-01, 4.3664e-01, 4.8728e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0042e+00, 5.8021e-01, 1.2255e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0113e+00, 1.9111e+00, 4.7409e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0038e+00, 2.1631e+00, 1.8455e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0011e+00, 2.4382e+00, 2.5738e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0074e+00, 1.7147e+00, 4.8970e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0059e+00, 1.6956e+00, 2.0767e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0041e+00, 1.6388e+00, 2.1820e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0127e+00, 1.9926e+00, 4.7191e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9554e-01, 1.0350e+00, 1.3305e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0084e+00, 1.5448e+00, 2.0126e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0069e+00, 2.4649e+00, 9.6049e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9542e-01, 2.5429e+00, 2.4272e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0129e+00, 2.0716e+00, 2.0886e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9646e-01, 1.6392e+00, 9.7434e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0072e+00, 1.5638e+00, 4.5001e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9995e-01, 4.5083e-01, 1.8172e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8603e-01, 4.6066e-01, 5.8465e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9849e-01, 5.9920e-01, 2.2262e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0097e+00, 1.7870e+00, 8.9182e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8773e-01, 4.9389e-01, 4.9009e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0006e+00, 5.0222e-01, 2.2264e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0155e+00, 1.8307e+00, 2.0598e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9653e-01, 1.0505e+00, 2.2474e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9628e-01, 4.6091e-01, 4.8435e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0053e+00, 8.7349e-01, 1.8090e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9087e-01, 4.8611e-01, 2.3047e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9797e-01, 9.0960e-01, 4.5111e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0151e+00, 6.6137e-01, 8.6652e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8809e-01, 1.3178e+00, 1.0748e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9212e-01, 1.4394e+00, 1.0316e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9866e-01, 9.1921e-01, 4.7854e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0155e+00, 2.1650e+00, 1.9668e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0119e+00, 6.1143e-01, 4.4820e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9878e-01, 1.3640e+00, 1.0431e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9423e-01, 5.4766e-01, 1.8523e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8879e-01, 4.5920e-01, 4.9827e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0149e+00, 1.1147e+00, 1.8152e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0112e+00, 1.0221e+00, 1.5213e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0162e+00, 1.6717e+00, 4.3399e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0117e+00, 2.4776e+00, 2.0490e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0052e+00, 1.7136e+00, 2.2258e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9368e-01, 4.4746e-01, 2.3849e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0039e+00, 1.3395e+00, 2.2145e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8899e-01, 9.6272e-01, 1.0952e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7988e-01, 9.4337e-01, 1.1054e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0191e+00, 1.7418e+00, 4.8360e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9806e-01, 2.5620e+00, 1.8621e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9571e-01, 5.8723e-01, 9.2930e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0170e+00, 1.6104e+00, 5.0286e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8370e-01, 9.8299e-01, 2.4974e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0074e+00, 1.9891e+00, 1.5596e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0103e+00, 2.5380e+00, 2.0239e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9839e-01, 2.2042e+00, 9.5743e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0165e+00, 2.1372e+00, 4.1650e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0156e+00, 1.0303e+00, 1.7081e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0100e+00, 2.4154e+00, 2.1927e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0087e+00, 1.3906e+00, 5.1117e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0089e+00, 9.1510e-01, 1.7276e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.7589e-01, 9.1052e-01, 1.1525e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9384e-01, 2.4390e+00, 4.2867e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0118e+00, 1.1409e+00, 4.0127e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 1.0645e+00, 1.0162e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9821e-01, 5.6421e-01, 1.1909e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9277e-01, 9.8969e-01, 2.5113e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0062e+00, 2.4614e+00, 1.9261e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0005e+00, 2.0348e+00, 2.1031e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8526e-01, 2.4793e+00, 1.5956e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9075e-01, 4.8242e-01, 9.7044e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9486e-01, 5.0154e-01, 4.2627e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0004e+00, 9.9578e-01, 1.9442e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0135e+00, 1.7798e+00, 2.0539e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9279e-01, 1.0183e+00, 1.3873e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7442e-01, 8.8123e-01, 1.2495e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9193e-01, 5.1426e-01, 2.1206e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0215e+00, 1.6952e+00, 4.7445e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0184e+00, 1.8149e+00, 1.7622e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0080e+00, 1.0986e+00, 5.0434e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0088e+00, 4.1441e-01, 1.4818e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8875e-01, 1.6723e+00, 2.2832e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0155e+00, 4.4771e-01, 1.2964e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9711e-01, 2.1312e+00, 1.9459e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9458e-01, 4.3113e-01, 4.8471e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.5041e+00, 2.1041e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8199e-01, 2.4612e+00, 1.5242e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0115e+00, 2.4269e+00, 8.9972e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0076e+00, 1.8385e+00, 1.1200e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8777e-01, 4.6183e-01, 2.1885e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0017e+00, 2.3498e+00, 5.1412e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0062e+00, 1.2985e+00, 1.8466e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9346e-01, 1.0331e+00, 2.2825e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8416e-01, 2.5976e+00, 1.4924e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9247e-01, 1.8091e+00, 2.3179e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0105e+00, 1.0151e+00, 1.8009e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0186e+00, 5.3780e-01, 2.3238e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0082e+00, 2.1464e+00, 9.7507e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0087e+00, 1.8077e+00, 1.3012e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9585e-01, 1.0262e+00, 1.0385e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0068e+00, 2.6338e+00, 8.7810e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0125e+00, 4.4287e-01, 1.9058e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9664e-01, 2.6730e+00, 1.3767e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8878e-01, 1.0680e+00, 1.8482e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0057e+00, 9.7883e-01, 4.7768e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0162e+00, 1.6131e+00, 1.8104e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0064e+00, 1.0695e+00, 2.0153e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0002e+00, 1.9930e+00, 4.6720e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0145e+00, 2.3299e+00, 1.2927e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9378e-01, 9.2646e-01, 1.7500e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9902e-01, 1.5442e+00, 1.0147e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0005e+00, 4.1521e-01, 2.4642e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0213e+00, 1.4083e+00, 4.5242e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9622e-01, 2.3222e+00, 1.5551e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8974e-01, 4.6188e-01, 5.2759e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9956e-01, 2.1480e+00, 1.5079e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9708e-01, 2.3524e+00, 2.6532e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0016e+00, 2.3929e+00, 2.4466e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0009e+00, 2.6217e+00, 1.8630e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0096e+00, 5.8285e-01, 1.5658e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9566e-01, 4.5863e-01, 4.6985e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0088e+00, 1.2281e+00, 1.6605e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0017e+00, 3.8419e-01, 9.3634e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0105e+00, 1.9846e+00, 5.1582e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0049e+00, 2.3163e+00, 9.7039e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9764e-01, 2.0934e+00, 1.9027e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9299e-01, 2.5962e+00, 4.0448e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0004e+00, 2.1725e+00, 1.4971e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0097e+00, 9.6711e-01, 1.7368e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0014e+00, 9.2326e-01, 5.5335e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0027e+00, 2.0723e+00, 2.1423e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0005e+00, 1.0609e+00, 1.2973e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9673e-01, 2.4430e+00, 4.2694e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0050e+00, 2.1095e+00, 2.0281e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0134e+00, 1.3332e+00, 8.7571e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0039e+00, 2.3098e+00, 1.9876e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9018e-01, 1.0830e+00, 2.2281e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0065e+00, 1.6337e+00, 1.9241e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0064e+00, 5.7496e-01, 4.2802e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0046e+00, 2.2263e+00, 1.3700e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9855e-01, 2.2088e+00, 1.4309e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0016e+00, 4.3816e-01, 2.4777e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9615e-01, 1.7942e+00, 2.1904e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9094e-01, 2.5565e+00, 2.5313e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0164e+00, 1.6318e+00, 5.0704e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9336e-01, 5.4491e-01, 1.8342e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 2.0725e+00, 1.5738e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0011e+00, 1.5897e+00, 2.1596e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9326e-01, 2.2402e+00, 5.0655e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7777e-01, 1.4946e+00, 2.1618e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0063e+00, 1.0603e+00, 1.6958e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0016e+00, 2.1860e+00, 5.1053e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0030e+00, 2.2163e+00, 1.9166e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9471e-01, 9.3577e-01, 1.0417e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9997e-01, 2.5615e+00, 2.3845e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0004e+00, 1.0368e+00, 2.2437e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0058e+00, 1.1923e+00, 9.6116e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0024e+00, 2.5834e+00, 2.4245e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0142e+00, 1.0987e+00, 1.7179e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9551e-01, 2.7277e+00, 1.3542e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9248e-01, 5.3207e-01, 9.1900e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0087e+00, 1.5770e+00, 2.1882e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0174e+00, 1.8742e+00, 4.7990e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0150e+00, 4.6233e-01, 1.5752e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9622e-01, 5.2507e-01, 4.6105e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0039e+00, 2.1658e+00, 5.7206e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0023e+00, 2.4752e+00, 2.3661e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0113e+00, 2.4247e+00, 2.0035e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0005e+00, 2.5064e+00, 4.8588e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0164e+00, 1.0817e+00, 1.6479e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0213e+00, 1.6321e+00, 4.6362e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0069e+00, 9.1906e-01, 1.5447e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0054e+00, 1.0723e+00, 4.8305e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0121e+00, 2.2186e+00, 2.2431e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0179e+00, 9.6506e-01, 2.5634e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0045e+00, 1.9596e+00, 2.0441e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9819e-01, 1.9426e+00, 1.6525e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0057e+00, 5.3765e-01, 4.7337e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0084e+00, 2.1372e+00, 9.8043e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8571e-01, 5.7392e-01, 7.8318e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0059e+00, 1.0740e+00, 2.1533e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0068e+00, 4.3748e-01, 1.1257e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0065e+00, 1.4052e+00, 1.5443e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0100e+00, 2.6035e+00, 2.3255e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9844e-01, 1.4633e+00, 2.1789e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 1.6160e+00, 9.4321e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 2.1356e+00, 4.1986e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0031e+00, 1.7460e+00, 2.1094e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0089e+00, 2.0220e+00, 1.4701e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9934e-01, 2.3338e+00, 1.0392e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9472e-01, 1.0299e+00, 2.2090e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0197e+00, 1.9834e+00, 4.3176e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0555e+00, 3.8913e-01, 2.7097e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0061e+00, 2.1959e+00, 4.9888e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0056e+00, 2.2699e+00, 4.8799e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.0820e+00, 1.8858e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0150e+00, 5.0512e-01, 2.3817e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0011e+00, 2.1617e+00, 2.4143e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0018e+00, 2.5200e+00, 9.8631e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0049e+00, 2.0802e+00, 2.0927e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9892e-01, 9.6198e-01, 2.0678e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9673e-01, 2.2638e+00, 1.4566e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9590e-01, 1.6800e+00, 2.2829e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8606e-01, 5.8480e-01, 8.6577e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 9.5928e-01, 2.4667e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0031e+00, 1.6365e+00, 1.4870e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0095e+00, 9.9084e-01, 8.9185e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9536e-01, 2.4956e+00, 4.8751e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0026e+00, 9.6534e-01, 2.1293e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9121e-01, 1.4605e+00, 9.9463e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0066e+00, 2.4913e+00, 1.9535e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0097e+00, 1.9379e+00, 1.4587e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0013e+00, 1.0334e+00, 2.1359e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0146e+00, 1.1171e+00, 1.8113e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0097e+00, 1.0619e+00, 1.8796e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9916e-01, 2.3031e+00, 1.0380e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0041e+00, 2.3676e+00, 9.5029e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 1.5400e+00, 1.5592e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0071e+00, 4.1310e-01, 2.4511e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0174e+00, 1.0221e+00, 1.6875e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0083e+00, 1.4859e+00, 1.5839e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9401e-01, 2.4953e+00, 1.4267e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0103e+00, 1.2101e+00, 4.6935e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0057e+00, 5.2692e-01, 1.6803e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.7548e-01, 9.1880e-01, 1.0822e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9547e-01, 2.1661e+00, 2.3482e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0103e+00, 4.7205e-01, 1.5311e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0091e+00, 4.9657e-01, 1.3881e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0034e+00, 9.8521e-01, 4.4957e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9853e-01, 2.1864e+00, 2.3711e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0065e+00, 2.3503e+00, 2.0411e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0118e+00, 2.7516e+00, 2.1776e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0010e+00, 1.0979e+00, 2.1297e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0022e+00, 1.6451e+00, 1.4347e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0039e+00, 2.2988e+00, 1.0084e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9815e-01, 1.7112e+00, 1.0159e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0049e+00, 1.0454e+00, 5.1236e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9884e-01, 2.5932e+00, 2.5030e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 2.5963e+00, 1.8891e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9603e-01, 9.2018e-01, 1.0133e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0030e+00, 2.5380e+00, 9.2400e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9871e-01, 1.0331e+00, 4.7463e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9675e-01, 2.4769e+00, 1.0394e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8881e-01, 2.6005e+00, 1.7771e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9309e-01, 4.5343e-01, 2.1811e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0072e+00, 2.4492e+00, 2.3771e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 2.6128e+00, 1.8843e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9934e-01, 4.9228e-01, 2.2857e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0061e+00, 2.2365e+00, 2.1436e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0101e+00, 4.3923e-01, 1.5475e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0129e+00, 2.1653e+00, 4.4236e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9652e-01, 9.6571e-01, 2.0154e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9991e-01, 6.0170e-01, 1.5273e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9898e-01, 2.3463e+00, 2.0804e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0113e+00, 1.0320e+00, 1.0081e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0022e+00, 2.5135e+00, 2.5252e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0062e+00, 1.9902e+00, 9.4306e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0025e+00, 1.0356e+00, 1.9801e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9966e-01, 2.5203e+00, 4.5012e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0079e+00, 2.0775e+00, 4.9321e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9296e-01, 1.9503e+00, 1.5988e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9369e-01, 2.2954e+00, 2.4602e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0058e+00, 1.0339e+00, 2.0481e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9552e-01, 5.0379e-01, 2.0697e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0113e+00, 2.0581e+00, 2.1303e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0120e+00, 1.5094e+00, 5.3749e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9424e-01, 6.0268e-01, 1.9001e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0043e+00, 5.2574e-01, 1.7146e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8127e-01, 8.7524e-01, 1.2496e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9576e-01, 1.8554e+00, 1.1226e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0158e+00, 4.8639e-01, 3.9334e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0010e+00, 2.1440e+00, 1.7497e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9424e-01, 9.6876e-01, 1.1092e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0011e+00, 4.5207e-01, 2.0300e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9006e-01, 4.7459e-01, 6.3928e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9643e-01, 2.1775e+00, 2.5002e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0199e+00, 1.5189e+00, 4.7297e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9668e-01, 1.5285e+00, 2.4294e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0056e+00, 5.5403e-01, 5.3240e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0012e+00, 1.7433e+00, 1.7133e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0072e+00, 1.8503e+00, 1.3372e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0047e+00, 5.1908e-01, 1.8259e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0010e+00, 4.4603e-01, 2.2827e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0019e+00, 4.4945e-01, 1.7769e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9588e-01, 9.4374e-01, 5.1209e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0085e+00, 2.3420e+00, 9.2795e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0044e+00, 1.0143e+00, 2.0170e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 2.3605e+00, 1.4366e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9243e-01, 2.5210e+00, 2.3368e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.9209e-01, 1.7865e+00, 2.2277e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0038e+00, 2.2305e+00, 1.0020e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0042e+00, 1.6072e+00, 2.1187e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0084e+00, 2.1522e+00, 9.4227e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9768e-01, 2.3097e+00, 1.8907e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9946e-01, 2.5306e+00, 2.6291e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0035e+00, 4.8071e-01, 1.3830e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8699e-01, 9.6235e-01, 2.4078e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0042e+00, 5.4471e-01, 1.3405e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0004e+00, 2.1134e+00, 1.5302e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0171e+00, 2.0872e+00, 8.8288e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0045e+00, 2.4559e+00, 2.1183e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9635e-01, 5.8886e-01, 1.7193e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([9.8839e-01, 2.3894e+00, 1.5069e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8487e-01, 1.5255e+00, 1.0298e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0126e+00, 1.2700e+00, 5.0404e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0146e+00, 1.7522e+00, 1.8491e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9707e-01, 9.0855e-01, 1.0662e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0105e+00, 5.5892e-01, 4.6327e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0172e+00, 1.9044e+00, 1.0461e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9635e-01, 8.9550e-01, 4.9198e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.8724e-01, 1.0497e+00, 2.2947e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0062e+00, 2.4470e+00, 8.9904e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0036e+00, 1.1582e+00, 4.6064e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0083e+00, 2.1608e+00, 4.6033e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 2.2825e+00, 9.9877e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9441e-01, 5.1528e-01, 2.2307e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0040e+00, 2.1597e+00, 1.4765e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0024e+00, 2.3229e+00, 1.2920e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0117e+00, 1.0156e+00, 1.5121e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
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      "tensor([1.0059e+00, 2.0967e+00, 9.9839e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0237e+00, 3.4975e-01, 1.5638e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0000e+00, 1.4948e+00, 2.4150e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0117e+00, 2.1165e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0002e+00, 2.2761e+00, 5.0457e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0158e+00, 1.8659e+00, 9.7233e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9999e-01, 1.4956e+00, 1.6122e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0151e+00, 5.2227e-01, 2.4530e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0068e+00, 1.5298e+00, 1.9733e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0106e+00, 1.0070e+00, 9.8870e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0015e+00, 1.7681e+00, 2.1528e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9842e-01, 5.5572e-01, 1.9276e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0182e+00, 4.2721e-01, 1.0972e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0099e+00, 1.4982e+00, 2.1785e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9697e-01, 2.4771e+00, 1.5526e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9248e-01, 2.5161e+00, 1.4900e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0038e+00, 2.3644e+00, 9.6764e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0137e+00, 1.1999e+00, 5.0284e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0029e+00, 1.4745e+00, 2.1446e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9083e-01, 4.8736e-01, 2.1406e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9289e-01, 2.3122e+00, 5.4387e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9902e-01, 9.6766e-01, 2.5335e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9166e-01, 5.0026e-01, 2.0158e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9705e-01, 4.7118e-01, 2.2067e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 2.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0068e+00, 5.1960e-01, 4.8390e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 1.0840e+00, 4.7094e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0070e+00, 1.5091e+00, 1.9994e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0059e+00, 2.4306e+00, 2.1000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0090e+00, 1.6539e+00, 5.1954e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.5000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([9.9206e-01, 8.5627e-01, 5.1745e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0029e+00, 1.8601e+00, 1.5867e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0217e+00, 1.1171e+00, 9.0764e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0041e+00, 1.0538e+00, 2.0626e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 1.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0042e+00, 5.9131e-01, 1.2739e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 1.5000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0027e+00, 2.2423e+00, 9.7419e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.5000e+00, 1.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0050e+00, 4.7748e-01, 5.2111e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0064e+00, 2.1848e+00, 2.1064e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 2.0000e+00, 2.0000e+04], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "###################################################\n",
      "tensor([1.0007e+00, 4.6226e-01, 3.9460e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "tensor([1.0000e+00, 5.0000e-01, 5.0000e+03], device='cuda:0',\n",
      "       dtype=torch.float64)\n",
      "###################################################\n",
      "1250\n",
      "tensor(8.7620e-05, device='cuda:0')\n",
      "tensor(0.0385, device='cuda:0')\n",
      "tensor(0.0476, device='cuda:0')\n",
      "Accuracy of prediction on test (1): 100.00%\n",
      "Accuracy of prediction on test (2): 98.08%\n",
      "Accuracy of prediction on test (3): 97.62%\n"
     ]
    }
   ],
   "source": [
    "# keep track of all losses and outputs\n",
    "hist_array = []\n",
    "out_array = []\n",
    "\n",
    "# errors for th0(1), th0(2), th0(3) respectively\n",
    "err1 = 0\n",
    "err2 = 0\n",
    "err3 = 0\n",
    "tot = 0\n",
    "count = 0\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        # retrieve X and y for this batch, from data, and \n",
    "        # move it to the device you are using (probably the GPU)\n",
    "        X = data[0].to(device)\n",
    "        y = data[1].to(device)\n",
    "\n",
    "        # raw output of network for X\n",
    "        output = net(X)\n",
    "\n",
    "        a = torch.add(torch.mul(output[0], tstd.to(device)), tmean.to(device))\n",
    "        b = torch.add(torch.mul(y[0], tstd.to(device)), tmean.to(device))\n",
    "\n",
    "        print(\"###################################################\")\n",
    "        print(a)\n",
    "        print(b)\n",
    "        print(\"###################################################\")\n",
    "\n",
    "        hist_array.append(a.cpu().numpy() - b.cpu().numpy())\n",
    "        out_array.append(a.cpu().numpy())\n",
    "        \n",
    "        tot += y.size(0)\n",
    "\n",
    "        errors = output.sub(y).multiply(output.sub(y)).div(2).sum(axis=0)\n",
    "        err1 += errors[0]\n",
    "        err2 += errors[1]\n",
    "        err3 += errors[2]\n",
    "        count += 1\n",
    "\n",
    "print(tot)\n",
    "print(err1/count)\n",
    "print(err2/count)\n",
    "print(err3/count)\n",
    "\n",
    "print('Accuracy of prediction on test (1): %5.2f%%' % (100-100 * err1 / tot))\n",
    "\n",
    "print('Accuracy of prediction on test (2): %5.2f%%' % (100-100 * err2 / tot))\n",
    "\n",
    "print('Accuracy of prediction on test (3): %5.2f%%' % (100-100 * err3 / tot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.4 Plot Histograms of Losses and Outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_array = np.array(hist_array)\n",
    "a = hist_array[:, 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_array = np.array(out_array)\n",
    "a = out_array[:, 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = hist_array[:, 1]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = out_array[:, 1]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WVsa0adNi69at+3UgAAAAH6TT8bNt27Y4+eST4+abb97r/htvvDF+8pOfxG233RaPP/54fOITn4iGhoZ45513CmOmTp0aq1atisWLF8fChQtj2bJlcckll+z7UQAAAHyI/p19wcSJE2PixIl73ZdSih//+Mdx7bXXxrnnnhsREXfeeWfU1NTE/fffHxdccEG8+OKLsWjRonjyySdj3LhxERExd+7cmDRpUtx0000xYsSI/TgcAACAvevSa37Wrl0bzc3NUV9fX9hWUVER48ePj6ampoiIaGpqisrKykL4RETU19dHaWlpPP7443t93/b29mhrayt6AAAAdEaXxk9zc3NERNTU1BRtr6mpKexrbm6O6urqov39+/ePqqqqwpj3mj17dlRUVBQeI0eO7MppAwAAGTgg7vY2a9asaG1tLTzWr1/f01MCAAAOMF0aP7W1tRER0dLSUrS9paWlsK+2tjY2btxYtH/nzp2xadOmwpj3Kisri/Ly8qIHAABAZ3Rp/IwePTpqa2tjyZIlhW1tbW3x+OOPR11dXURE1NXVxebNm2P58uWFMY888kh0dHTE+PHju3I6AAAABZ2+29vWrVvjlVdeKTxfu3ZtrFixIqqqqmLUqFFxxRVXxD/8wz/EUUcdFaNHj47vfe97MWLEiDjvvPMiIuLYY4+NCRMmxPTp0+O2226LHTt2xIwZM+KCCy5wpzcAAKDbdDp+nnrqqfjSl75UeD5z5syIiLjoooti/vz5cfXVV8e2bdvikksuic2bN8cZZ5wRixYtioEDBxZec9ddd8WMGTPirLPOitLS0pgyZUr85Cc/6YLDAQAA2LuSlFLq6Ul0VltbW1RUVERra6vrfwAyd/g1D/b0FDjAvDZnck9PAehCnWmDA+JubwAAAPtL/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBb69/QEAPhwh1/zYE9PAQAOeM78AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZKF/V7/h97///bjhhhuKth1zzDGxevXqiIh455134sorr4wFCxZEe3t7NDQ0xC233BI1NTVdPRUAgD0cfs2DPT2FXum1OZN7egrQ7brlzM/xxx8fb7zxRuHxX//1X4V93/72t+OXv/xl3HvvvbF06dLYsGFDnH/++d0xDQAAgIIuP/MTEdG/f/+ora3dY3tra2v867/+a9x9993x53/+5xERMW/evDj22GPjsccei9NPP707pgMAANA9Z35efvnlGDFiRBxxxBExderUWLduXURELF++PHbs2BH19fWFsWPGjIlRo0ZFU1PT+75fe3t7tLW1FT0AAAA6o8vjZ/z48TF//vxYtGhR3HrrrbF27dr4whe+EFu2bInm5uYYMGBAVFZWFr2mpqYmmpub3/c9Z8+eHRUVFYXHyJEju3raAABAH9flv/Y2ceLEwj+fdNJJMX78+PjUpz4Vv/jFL2LQoEH79J6zZs2KmTNnFp63tbUJIAAAoFO6/VbXlZWVcfTRR8crr7wStbW1sX379ti8eXPRmJaWlr1eI7RbWVlZlJeXFz0AAAA6o9vjZ+vWrfHqq6/G8OHDY+zYsXHQQQfFkiVLCvvXrFkT69ati7q6uu6eCgAAkLEu/7W373znO3HOOefEpz71qdiwYUNcf/310a9fv7jwwgujoqIipk2bFjNnzoyqqqooLy+Pb33rW1FXV+dObwAAQLfq8vh5/fXX48ILL4y33norDjnkkDjjjDPisccei0MOOSQiIn70ox9FaWlpTJkypeiPnAIAAHSnkpRS6ulJdFZbW1tUVFREa2ur63+ALPiL9EB3e23O5J6eAuyTzrRBt1/zAwAA0BuIHwAAIAviBwAAyIL4AQAAsiB+AACALIgfAAAgC+IHAADIgvgBAACyIH4AAIAsiB8AACAL4gcAAMiC+AEAALIgfgAAgCyIHwAAIAviBwAAyIL4AQAAsiB+AACALIgfAAAgC+IHAADIgvgBAACyIH4AAIAsiB8AACAL4gcAAMiC+AEAALLQv6cnAPBuh1/zYE9PAQDoo5z5AQAAsiB+AACALIgfAAAgC+IHAADIgvgBAACyIH4AAIAsiB8AACAL4gcAAMiC+AEAALIgfgAAgCyIHwAAIAviBwAAyIL4AQAAsiB+AACALIgfAAAgC+IHAADIgvgBAACyIH4AAIAsiB8AACAL4gcAAMiC+AEAALIgfgAAgCz07+kJAADQ8w6/5sGenkKv9NqcyT09BbqQMz8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkoX9PTwBydPg1D/b0FAAAsuPMDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZEH8AAAAWRA/AABAFsQPAACQBfEDAABkQfwAAABZED8AAEAWxA8AAJAF8QMAAGRB/AAAAFkQPwAAQBbEDwAAkAXxAwAAZKF/T08AAAB6q8OvebCnp9BrvTZnck9PodOc+QEAALLQo2d+br755vjBD34Qzc3NcfLJJ8fcuXPjtNNO68kp7RP/jwAAAPR+PXbm5+c//3nMnDkzrr/++nj66afj5JNPjoaGhti4cWNPTQkAAOjDeix+fvjDH8b06dPj61//ehx33HFx2223xeDBg+NnP/tZT00JAADow3rk1962b98ey5cvj1mzZhW2lZaWRn19fTQ1Ne0xvr29Pdrb2wvPW1tbIyKira2t+yf7EXS0/7GnpwAAAB+r3vK/xXfPI6X0oWN7JH5+//vfx65du6KmpqZoe01NTaxevXqP8bNnz44bbrhhj+0jR47stjkCAADvr+LHPT2DYlu2bImKiooPHHNA3Op61qxZMXPmzMLzjo6O2LRpUxx88MFRUlLyga9ta2uLkSNHxvr166O8vLy7p0o3sIZ9g3XsG6xj32Ad+wbr2DdYx/2XUootW7bEiBEjPnRsj8TPsGHDol+/ftHS0lK0vaWlJWpra/cYX1ZWFmVlZUXbKisrO/U1y8vL/Qt1gLOGfYN17BusY99gHfsG69g3WMf982FnfHbrkRseDBgwIMaOHRtLliwpbOvo6IglS5ZEXV1dT0wJAADo43rs195mzpwZF110UYwbNy5OO+20+PGPfxzbtm2Lr3/96z01JQAAoA/rsfj56le/Gm+++WZcd9110dzcHKecckosWrRoj5sg7K+ysrK4/vrr9/i1OQ4c1rBvsI59g3XsG6xj32Ad+wbr+PEqSR/lnnAAAAAHuB77I6cAAAAfJ/EDAABkQfwAAABZED8AAEAWxA8AAJCFAzZ+XnrppTj33HNj2LBhUV5eHmeccUb8+te/Lhqzbt26mDx5cgwePDiqq6vjqquuip07dxaNefTRR+Mzn/lMlJWVxZFHHhnz58/f42vdfPPNcfjhh8fAgQNj/Pjx8cQTT3TnoWXnwQcfjPHjx8egQYNi6NChcd555xXtt44Hjvb29jjllFOipKQkVqxYUbTvueeeiy984QsxcODAGDlyZNx44417vP7ee++NMWPGxMCBA+PEE0+Mhx56qGh/Simuu+66GD58eAwaNCjq6+vj5Zdf7s5DysZrr70W06ZNi9GjR8egQYPi05/+dFx//fWxffv2onHWsW/w87D3mD17dnz2s5+NIUOGRHV1dZx33nmxZs2aojHvvPNONDY2xsEHHxyf/OQnY8qUKdHS0lI0pqs+K9l/c+bMiZKSkrjiiisK26xhL5IOUEcddVSaNGlSevbZZ9NLL72UvvnNb6bBgwenN954I6WU0s6dO9MJJ5yQ6uvr0zPPPJMeeuihNGzYsDRr1qzCe/z2t79NgwcPTjNnzkwvvPBCmjt3burXr19atGhRYcyCBQvSgAED0s9+9rO0atWqNH369FRZWZlaWlo+9mPui+677740dOjQdOutt6Y1a9akVatWpZ///OeF/dbxwPK3f/u3aeLEiSki0jPPPFPY3trammpqatLUqVPTypUr0z333JMGDRqUbr/99sKY//7v/079+vVLN954Y3rhhRfStddemw466KD0/PPPF8bMmTMnVVRUpPvvvz89++yz6Stf+UoaPXp0evvttz/Ow+yTfvWrX6WLL744Pfzww+nVV19NDzzwQKqurk5XXnllYYx17Bv8POxdGhoa0rx589LKlSvTihUr0qRJk9KoUaPS1q1bC2MuvfTSNHLkyLRkyZL01FNPpdNPPz197nOfK+zvqs9K9t8TTzyRDj/88HTSSSelyy+/vLDdGvYeB2T8vPnmmyki0rJlywrb2traUkSkxYsXp5RSeuihh1JpaWlqbm4ujLn11ltTeXl5am9vTymldPXVV6fjjz++6L2/+tWvpoaGhsLz0047LTU2Nhae79q1K40YMSLNnj27W44tJzt27EiHHnpo+pd/+Zf3HWMdDxwPPfRQGjNmTFq1atUe8XPLLbekoUOHFtYspZS++93vpmOOOabw/K//+q/T5MmTi95z/Pjx6Rvf+EZKKaWOjo5UW1ubfvCDHxT2b968OZWVlaV77rmnm44qbzfeeGMaPXp04bl17Bv8POzdNm7cmCIiLV26NKX0p++Pgw46KN17772FMS+++GKKiNTU1JRS6rrPSvbPli1b0lFHHZUWL16c/uzP/qwQP9awdzkgf+3t4IMPjmOOOSbuvPPO2LZtW+zcuTNuv/32qK6ujrFjx0ZERFNTU5x44olRU1NTeF1DQ0O0tbXFqlWrCmPq6+uL3ruhoSGampoiImL79u2xfPnyojGlpaVRX19fGMO+e/rpp+N3v/tdlJaWxqmnnhrDhw+PiRMnxsqVKwtjrOOBoaWlJaZPnx7/9m//FoMHD95jf1NTU3zxi1+MAQMGFLY1NDTEmjVr4g9/+ENhzAet49q1a6O5ubloTEVFRYwfP946dpPW1taoqqoqPLeOBz4/D3u/1tbWiIjC997y5ctjx44dRWs2ZsyYGDVqVGHNuuKzkv3X2NgYkydP3uO/Z2vYuxyQ8VNSUhL/+Z//Gc8880wMGTIkBg4cGD/84Q9j0aJFMXTo0IiIaG5uLvoXKCIKz5ubmz9wTFtbW7z99tvx+9//Pnbt2rXXMbvfg33329/+NiIivv/978e1114bCxcujKFDh8aZZ54ZmzZtigjreCBIKcXFF18cl156aYwbN26vY/ZnHd+9/92v29sYus4rr7wSc+fOjW984xuFbdbxwOfnYe/W0dERV1xxRXz+85+PE044ISL+9D0zYMCAqKysLBr73u+r/f2sZP8sWLAgnn766Zg9e/Ye+6xh79Kr4ueaa66JkpKSD3ysXr06UkrR2NgY1dXV8Zvf/CaeeOKJOO+88+Kcc86JN954o6cPI3sfdR07OjoiIuLv/u7vYsqUKTF27NiYN29elJSUxL333tvDR8FHXce5c+fGli1bYtasWT09Zfbio67ju/3ud7+LCRMmxF/91V/F9OnTe2jmkJ/GxsZYuXJlLFiwoKenQiesX78+Lr/88rjrrrti4MCBPT0dPkT/np7Au1155ZVx8cUXf+CYI444Ih555JFYuHBh/OEPf4jy8vKIiLjlllti8eLFcccdd8Q111wTtbW1e9y9ZvddNWprawv/+d47bbS0tER5eXkMGjQo+vXrF/369dvrmN3vwZ4+6jruDtXjjjuusL2srCyOOOKIWLduXUSEdexBnfl+bGpqirKysqJ948aNi6lTp8Ydd9zxvmsU8eHr+O79u7cNHz68aMwpp5zS6ePLxUddx902bNgQX/rSl+Jzn/tc/PSnPy0aZx0PfMOGDfPzsJeaMWNGLFy4MJYtWxaHHXZYYXttbW1s3749Nm/eXHTm4L3fV/v7Wcm+W758eWzcuDE+85nPFLbt2rUrli1bFv/8z/8cDz/8sDXsTXr6oqN98R//8R+ptLQ0bdmypWj70Ucfnf7xH/8xpfT/F469++41t99+eyovL0/vvPNOSulPF46dcMIJRe9x4YUX7nGh/IwZMwrPd+3alQ499FAXhnaB1tbWVFZWVnTDg+3bt6fq6urC3aOsY+/3v//7v+n5558vPB5++OEUEem+++5L69evTyn9/4Xy27dvL7xu1qxZe1wo/+Uvf7novevq6va4UP6mm24q7N/975AL5bvG66+/no466qh0wQUXpJ07d+6x3zr2DX4e9i4dHR2psbExjRgxIr300kt77N99sfx9991X2LZ69eq9Xiy/v5+V7Ju2traiz8Hnn38+jRs3Lv3N3/xNev75561hL3NAxs+bb76ZDj744HT++eenFStWpDVr1qTvfOc76aCDDkorVqxIKf3/LQPPPvvstGLFirRo0aJ0yCGH7PWWgVdddVV68cUX080337zXWySXlZWl+fPnpxdeeCFdcsklqbKysuhuHOy7yy+/PB166KHp4YcfTqtXr07Tpk1L1dXVadOmTSkl63ggWrt27R53e9u8eXOqqalJX/va19LKlSvTggUL0uDBg/e4RXL//v3TTTfdlF588cV0/fXX7/UWyZWVlemBBx5Izz33XDr33HPdIrmLvP766+nII49MZ511Vnr99dfTG2+8UXjsZh37Bj8Pe5fLLrssVVRUpEcffbTo++6Pf/xjYcyll16aRo0alR555JH01FNPpbq6ulRXV1fY31WflXSdd9/tLSVr2JsckPGTUkpPPvlkOvvss1NVVVUaMmRIOv3009NDDz1UNOa1115LEydOTIMGDUrDhg1LV155ZdqxY0fRmF//+tfplFNOSQMGDEhHHHFEmjdv3h5fa+7cuWnUqFFpwIAB6bTTTkuPPfZYdx5aVrZv356uvPLKVF1dnYYMGZLq6+vTypUri8ZYxwPL3uInpZSeffbZdMYZZ6SysrJ06KGHpjlz5uzx2l/84hfp6KOPTgMGDEjHH398evDBB4v2d3R0pO9973uppqYmlZWVpbPOOiutWbOmOw8nG/PmzUsRsdfHu1nHvsHPw97j/b7v3v059vbbb6dvfvObaejQoWnw4MHpL/7iL4r+j4mUuu6zkq7x3vixhr1HSUopfey/awcAAPAx61V3ewMAAOgu4gcAAMiC+AEAALIgfgAAgCyIHwAAIAviBwAAyIL4AQAAsiB+AACALIgfAAAgC+IHAADIgvgBAACy8H8Z0/F/YB6qtAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = hist_array[:, 2]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = out_array[:, 2]\n",
    "\n",
    "fig, ax = plt.subplots(figsize =(10, 7))\n",
    "ax.hist(a)\n",
    " \n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the Model as a .pt file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.chdir('../HMEI2023-bxliu-models/')\n",
    "# save_file = 'cats.pt'\n",
    "# torch.save(net.state_dict(), save_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually Representation of the Structure of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "test_data = torch.rand(1,1,128,128)\n",
    "test_net = ConvNet()\n",
    "out = test_net.forward(test_data)\n",
    "make_dot(out)"
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   "source": [
    "Next Steps:\n",
    "custom loss function (later)\n",
    "documentation (done)\n",
    "validation (done-ish, needs going thru again with all the datasets probably)\n",
    "more data (done)\n",
    "graph outputs not just loss (done)\n",
    "outside training set for 5by400 and 10by200 (done-ish)\n",
    "\n"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Make a function that takes in image data and outputs the model's prediction"
   ]
  },
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   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 128, 1)\n"
     ]
    }
   ],
   "source": [
    "f = '../HMEI2023-bxliu-data/image_test/gentrain_131.png'\n",
    "data = cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2GRAY)\n",
    "data = cv2.resize(data, (128,128), interpolation=cv2.INTER_CUBIC)\n",
    "data = np.expand_dims(data, axis=2)\n",
    "print(data.shape)\n",
    "# out = net.forward()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def use_net(im_data):\n",
    "    l = []\n",
    "    l.append(im_data)\n",
    "    l = np.asarray(l)\n",
    "    l = np.moveaxis(l, -1, 1)\n",
    "    print(l.shape)\n",
    "    l = torch.tensor(l).float()\n",
    "    l = torch.div(l, 255).float()\n",
    "    l = l.to(device)\n",
    "    print(l.shape)\n",
    "    print(l)\n",
    "    print(l.stride())\n",
    "    \n",
    "\n",
    "    with torch.no_grad():  \n",
    "        test_data = torch.rand(1,1,128,128).to(device)\n",
    "        test_data[0] = l[0]\n",
    "        print(test_data.shape)\n",
    "        print(test_data)\n",
    "        print(test_data.stride())\n",
    "        out = net.forward(test_data)\n",
    "        # out = net.forward(l)\n",
    "        out = torch.add(torch.mul(out[0], tstd.to(device)), tmean.to(device))\n",
    "        x = out.detach().cpu().numpy()\n",
    "        x[0] = math.exp(x[0])\n",
    "    return x"
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     "text": [
      "(1, 1, 128, 128)\n",
      "torch.Size([1, 1, 128, 128])\n",
      "tensor([[[[0.5961, 0.5961, 0.4902,  ..., 0.5490, 0.6000, 0.6000],\n",
      "          [0.5843, 0.5843, 0.4784,  ..., 0.5569, 0.6196, 0.6196],\n",
      "          [0.7020, 0.7020, 0.5922,  ..., 0.4667, 0.4118, 0.4118],\n",
      "          ...,\n",
      "          [0.3490, 0.3490, 0.4627,  ..., 0.4039, 0.3176, 0.3176],\n",
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      "          [0.4745, 0.4745, 0.4471,  ..., 0.4314, 0.3294, 0.3294]]]],\n",
      "       device='cuda:0')\n",
      "(16384, 1, 128, 1)\n",
      "torch.Size([1, 1, 128, 128])\n",
      "tensor([[[[0.5961, 0.5961, 0.4902,  ..., 0.5490, 0.6000, 0.6000],\n",
      "          [0.5843, 0.5843, 0.4784,  ..., 0.5569, 0.6196, 0.6196],\n",
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      "          ...,\n",
      "          [0.3490, 0.3490, 0.4627,  ..., 0.4039, 0.3176, 0.3176],\n",
      "          [0.4784, 0.4784, 0.4510,  ..., 0.4353, 0.3373, 0.3373],\n",
      "          [0.4745, 0.4745, 0.4471,  ..., 0.4314, 0.3294, 0.3294]]]],\n",
      "       device='cuda:0')\n",
      "(16384, 16384, 128, 1)\n"
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    },
    {
     "data": {
      "text/plain": [
       "array([2.69058440e+00, 8.10617998e-01, 1.22281953e+04])"
      ]
     },
     "execution_count": 51,
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    "use_net(data)"
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   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00e+00 7.50e-01 1.25e+04]\n"
     ]
    }
   ],
   "source": [
    "matfile = '../HMEI2023-bxliu-data/value_test/gentrain_131.mat'\n",
    "mat = scipy.io.loadmat(matfile)\n",
    "print(mat['th0'][0])"
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