Stage 2-Image Consistency Estimator.ipynb

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Consistency Estimator:\n","Train with triplets of 28x28 patches from real images\n","- Triplet of patches (same-1, same-2, diff)\n","- Contrastive loss on a siamese network\n","\n","Train with pairs of 40x40 patches from real and fake images\n","- One in top-left, one in bottom-right, both 40x40, sharing one 28x28 square in the center.\n","\n","The network is trained on it along with training on patch-consistency.\n"]},{"cell_type":"code","metadata":{"id":"SPtxXvYljFIk","colab_type":"code","colab":{}},"source":["# Importing useful libraries\n","import math\n","import os\n","import random\n","import torch\n","import cv2\n","from glob import glob\n","from PIL import Image\n","from sklearn.model_selection import train_test_split\n","from torchsummary import summary\n","from torchvision import datasets, models, transforms\n","\n","from datetime import datetime\n","from torch.utils.data import DataLoader, Dataset, IterableDataset\n","from tqdm.auto import tqdm\n","from tqdm.notebook import tqdm as new_tqdm\n","\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","import matplotlib.pyplot as plt\n","import torchvision.transforms.functional as TF\n","from google.colab.patches import cv2_imshow\n","import numpy as np\n","import copy\n","import gc #gc is garbage collector.\n","\n","#used in feature extraction\n","from itertools import accumulate  # to cumulatively sum a list\n","import bisect # bisect needed for finding index of the first element bigger than given input.\n","import random # needed for shuffling"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ePagVuXkuwfv","colab_type":"text"},"source":["## Mount Shared Drive"]},{"cell_type":"code","metadata":{"id":"hHaMQm0jbiYO","colab_type":"code","outputId":"9dc4d54c-c204-4bd2-d983-ed8c9d3c240d","executionInfo":{"status":"ok","timestamp":1587943723504,"user_tz":240,"elapsed":27794,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}},"colab":{"base_uri":"https://localhost:8080/","height":121}},"source":["# Mount Shared Drive for project to /content/drive/ folder\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FqdLRkf1YQfG","colab_type":"text"},"source":["## Global Variables"]},{"cell_type":"code","metadata":{"id":"LvhdELyuzQQ9","colab_type":"code","outputId":"3e48e6fd-78d8-442f-9819-363a5be8004f","executionInfo":{"status":"ok","timestamp":1587944022246,"user_tz":240,"elapsed":1245,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["# Torch variables\n","global_dtype = torch.float32  #Changed on purpose to save memory issues.\n","global_device = \"cuda\"\n","local_device=\"cpu\"\n","print(f\"We will be using global dtype = {global_dtype} and global device = {global_device}\")\n","if torch.cuda.is_available():\n","  print (\"CUDA is available!\")\n","\n","# Image transform variables\n","image_resize = 224 # Resize all images to be 224 x 224\n","image_crop = 224 \n","image_center_crop = 256\n","image_rotate = 10 # Rotate images randomly +/- 10 degrees (not used)\n","image_mean = torch.tensor([0.485, 0.456, 0.406])\n","image_std= torch.tensor([0.229, 0.224, 0.225])"],"execution_count":3,"outputs":[{"output_type":"stream","text":["We will be using global dtype = torch.float32 and global device = cuda\n","CUDA is available!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qCH2254f-y3H","colab_type":"text"},"source":["**List of Number of Available Images:**\n","\n","```\n","Fouhey -> originals -> real -> 1580\n","Refined -> Flicker30K -> real -> 2820\n","Refined -> PS_battles -> real -> 1500\n","Total Real Images: 5900\n","\n","Fouhey -> a_0000 -> fake -> 800\n","Fouhey -> b_0001-> fake -> 800\n","Fouhey -> c_0002-> fake -> 400\n","Refined -> PS_battles -> fake -> a_000 -> 500\n","Refined -> PS_battles -> fake -> z_0011 -> 1800\n","Refined -> PS_battles -> fake -> z_0012 -> 1600\n","Total Fake Images: 5900\n","\n","```\n","**Data Credits:**\n","We thank Prof. David Fouhey for his collection of Real and Fake images. We are grateful to Prof. Andrew Owens for making available these datsets and suggestions for other datasets and augmentation techniques.\n"]},{"cell_type":"markdown","metadata":{"id":"xL1T6E4RsCJo","colab_type":"text"},"source":["# Define Dataset Class"]},{"cell_type":"code","metadata":{"id":"-MyL9Z5QMmvR","colab_type":"code","colab":{}},"source":["# This is needed even if we directly load the loaders from saved files.\n","# We will use this class to create datasets containing image tensors and their labels\n","class ImageTensorDataset(Dataset):\n","  # We do not need transform member since we preprocessed already\n","  def __init__ (self, image_tensors, labels, augment=False):\n","    self.image_tensors = image_tensors\n","    self.labels = labels    \n","    \n","  # Used to index the dataset [#]\n","  def __getitem__(self, index):\n","        \n","    # else: \n","    image_tensor = self.image_tensors[index]#self.transform()#.cuda()\n","    label = self.labels[index]\n","    return image_tensor, label\n","\n","  # Used to count how many tensors are in the dataset\n","  def __len__(self):\n","    return len(self.image_tensors)\n","\n","# This is needed even if we directly load the loaders from saved files.\n","# We will use this class to create datasets containing image tensors and their labels\n","class ImageTensorDataset_noaugment(Dataset):\n","  # We do not need transform member since we preprocessed already\n","  def __init__ (self, image_tensors, labels, augment=False):\n","    self.image_tensors = image_tensors\n","    self.labels = labels    \n","    \n","  # Used to index the dataset [#]\n","  def __getitem__(self, index):\n","        \n","    # else: \n","    image_tensor = self.image_tensors[index]#self.transform()#.cuda()\n","    label = self.labels[index]\n","    return image_tensor, label\n","\n","  # Used to count how many tensors are in the dataset\n","  def __len__(self):\n","    return len(self.image_tensors)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"irsu_w723nbS"},"source":["#Load DataLoaders\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"NV7x3eFV32i_","colab_type":"code","colab":{}},"source":["# # Loading the Saved DataLoaders:\n","train_loader_file=\"/content/drive/Shared drives/EECS504/Saved_Objects/train_loader_P3_A4_5900_28x28-5_1pair_64_2020-04-18_193021.pt\"\n","val_loader_file=\"/content/drive/Shared drives/EECS504/Saved_Objects/val_loader_P3_A4_5900_28x28-5_1pair_64_2020-04-18_193309.pt\"\n","test_loader_file=\"/content/drive/Shared drives/EECS504/Saved_Objects/test_loader_P3_A4_5900_28x28-5_1pair_64_2020-04-18_193604.pt\"\n","\n","train_loader = torch.load(train_loader_file)\n","val_loader = torch.load(val_loader_file)\n","test_loader = torch.load(test_loader_file)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"WFAPEbp2NBxv","colab_type":"code","colab":{}},"source":["#custom loaders: download only full_train loader.\n","custom_full_train_file=\"/content/drive/Shared drives/EECS504/Saved_Objects/MW/Custom_Full_train_loader_P3_A5_3000_3000_64_40x40to28x28.pt\"\n","\n","custom_train_loader = torch.load(custom_full_train_file)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VXc5iAkKpGXl","colab_type":"code","outputId":"3cb13f10-9fd2-4583-b60d-3f129146ef90","executionInfo":{"status":"ok","timestamp":1587945392091,"user_tz":240,"elapsed":30998,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["print(len(train_loader),len(val_loader)) #,len(test_loader))\n","print(len(custom_train_loader))"],"execution_count":12,"outputs":[{"output_type":"stream","text":["111 37\n","94\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"colab_type":"text","id":"nJCmWAFcVObg"},"source":["# Network Setup - The Consistency Estimator:"]},{"cell_type":"markdown","metadata":{"id":"kzcx_5v5MHRf","colab_type":"text"},"source":["###Weight Initialization:\n","\n","In practice, the current recommendation is to use ReLU units and use the \n","```w = np.random.randn(n) * sqrt(2.0/n)```, as discussed by Kaiming He and others at:\n","http://arxiv-web3.library.cornell.edu/abs/1502.01852\n","\n","A short overview on different types of initializations:\n","https://cs231n.github.io/neural-networks-2/#init\n","\n","A full lecture on weight initialization and other setup:\n","https://web.eecs.umich.edu/~justincj/slides/eecs498/498_FA2019_lecture10.pdf\n","\n","$$ W = \\sqrt{2} * {Gaussian(D_{in},D_{out})} / {\\sqrt{D_{in}}} $$\n","\n","The concept of $1/ \\sqrt{D_{in}}$  is taken from the Xavier Initialization (2010). Kaiming Initialization is more recent version (2015).\n","\n","$$\\text{ Since we have only 4-5 convolution layers and 2-3 linear layers,}$$\n","$${\\text{Batch-normalization and proper Weight Initialization will remove the need of Residual Skip Connections.  }}$$"]},{"cell_type":"code","metadata":{"id":"QVGpZRAVhPU5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":571},"outputId":"00998c3c-e232-4bb9-cccc-3d5741b4c260","executionInfo":{"status":"ok","timestamp":1587945420546,"user_tz":240,"elapsed":10925,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}}},"source":["class Estimator(nn.Module):\n","  \"\"\"\n","  Estimates consistency between pairs of patches. \n","\n","  \"\"\"\n","  def __init__(self,verbose=False):\n","    super().__init__()\n","    #input N x 64 x H x W . Typically  H x W = 28 x 28.\n","    self.fc1=nn.Linear(512,8 )\n","    self.lr=nn.LeakyReLU(0.02)\n","    self.fc2=nn.Linear(8,2)    \n","\n","    nn.init.kaiming_normal_(self.fc1.weight, mode='fan_in', nonlinearity='leaky_relu' )\n","    nn.init.kaiming_normal_(self.fc2.weight, mode='fan_in', nonlinearity='leaky_relu')\n","    \n","    self.estm =nn.Sequential( \n","    nn.Conv2d(64, 128, (3,3),stride=2,padding=1),\n","    nn.BatchNorm2d(128),\n","    nn.LeakyReLU(0.02),\n","    nn.MaxPool2d((3,3),stride=2,padding=1), #downsample -> 15x15\n","\n","    nn.Conv2d(128, 256, (3,3),stride=1,padding=1),\n","    \n","    nn.BatchNorm2d(256),\n","    nn.LeakyReLU(0.02),\n","    nn.MaxPool2d((3,3),stride=2,padding=1),  #downsample -> 8x8\n","\n","    nn.Conv2d( 256,512, (3,3),stride=1,padding=1),\n","    \n","    nn.BatchNorm2d(512),\n","    nn.LeakyReLU(0.02),\n","    nn.MaxPool2d((3,3),stride=2,padding=1), #downsample -> 4x4   \n","\n","    nn.AdaptiveAvgPool2d((1,1)),  #downsample -> 1x1\n","    # N x 128 x 1 x 1 \n","    nn.Flatten()) #-> N x 128\n","    \n","    #initialize:\n","    for m in self.estm.modules():\n","      if isinstance(m, nn.Conv2d):\n","        print(\"conv\",m)\n","        nn.init.kaiming_normal_(m.weight, mode='fan_out', a=0.02, nonlinearity='leaky_relu')\n","\n","      elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):\n","        print(\"batch-norm\",m)\n","        nn.init.constant_(m.weight, 1)\n","        nn.init.constant_(m.bias, 0)\n","    \n","      elif isinstance(m,nn.Linear):\n","        print(\"linear\",m)\n","        nn.init.kaiming_normal_(m.weight, mode='fan_out', a=0.02, nonlinearity='leaky_relu')    \n","\n","    if verbose==True:\n","      summary(self.estm.cuda(), (64, 28, 28),device=global_device)\n","\n","  def forward(self, x):  # Siamese Structure  x = Nx(64*2)x28x28, passed 64 channels at once.\n","    self.cuda()\n","    local_dtype=x.dtype\n","    local_device=x.device\n","    \n","    out_p1=self.lr(self.fc1(self.estm(x[:,:64,:,:]).squeeze().squeeze()))  #Nx32\n","    out_p2=self.lr(self.fc1(self.estm(x[:,64:,:,:]).squeeze().squeeze()))  #Nx32\n","    out=torch.abs(out_p2-out_p1)\n","\n","    scores=self.fc2(out) #Nx2\n","    scores=scores.to(device=local_device,dtype=local_dtype) \n","    \n","    return scores\n","\n","net1=Estimator(verbose=True).cuda()"],"execution_count":14,"outputs":[{"output_type":"stream","text":["conv Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n","batch-norm BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","conv Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","batch-norm BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","conv Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","batch-norm BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","----------------------------------------------------------------\n","        Layer (type)               Output Shape         Param #\n","================================================================\n","            Conv2d-1          [-1, 128, 14, 14]          73,856\n","       BatchNorm2d-2          [-1, 128, 14, 14]             256\n","         LeakyReLU-3          [-1, 128, 14, 14]               0\n","         MaxPool2d-4            [-1, 128, 7, 7]               0\n","            Conv2d-5            [-1, 256, 7, 7]         295,168\n","       BatchNorm2d-6            [-1, 256, 7, 7]             512\n","         LeakyReLU-7            [-1, 256, 7, 7]               0\n","         MaxPool2d-8            [-1, 256, 4, 4]               0\n","            Conv2d-9            [-1, 512, 4, 4]       1,180,160\n","      BatchNorm2d-10            [-1, 512, 4, 4]           1,024\n","        LeakyReLU-11            [-1, 512, 4, 4]               0\n","        MaxPool2d-12            [-1, 512, 2, 2]               0\n","AdaptiveAvgPool2d-13            [-1, 512, 1, 1]               0\n","          Flatten-14                  [-1, 512]               0\n","================================================================\n","Total params: 1,550,976\n","Trainable params: 1,550,976\n","Non-trainable params: 0\n","----------------------------------------------------------------\n","Input size (MB): 0.19\n","Forward/backward pass size (MB): 1.15\n","Params size (MB): 5.92\n","Estimated Total Size (MB): 7.26\n","----------------------------------------------------------------\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Qjiid0508LLE","colab_type":"text"},"source":["# Setting-up the Training Process\n","Earlier versions have been moved to Partial_Backup.ipynb"]},{"cell_type":"code","metadata":{"id":"bW2EOcLE1i0D","colab_type":"code","colab":{}},"source":["class Assembly(): \n","  \"\"\"\n","  Processing assembly-line that defines how the model training/ testing commands \n","  will be executed.\n","  \"\"\"\n","  ### Calculate Accuracy: Validation/ Testing Commands\n","  def check_accuracy(self,loader, model):\n","    num_correct = 0\n","    num_samples = 0\n","    model.eval()  # set model to evaluation mode\n","    accurate_labels = 0\n","    all_labels = 0\n","\n","    for  t, (x, y) in enumerate(loader):\n","      x = x.to(device='cuda', dtype=torch.float32)  # move to device, e.g. GPU\n","      y = y.to(device='cuda', dtype=torch.long)\n","\n","      pair0 = x[:,:128]\n","      out0=model(pair0).squeeze().to(dtype=global_dtype)\n","      target0 = y[:,0].type(torch.LongTensor).to(device=global_device)\n","\n","      pair1 = torch.cat((x[:,0:64],x[:,128:192]),dim=1)\n","      out1=model(pair1).squeeze().to(dtype=global_dtype)\n","      target1 = y[:,1].type(torch.LongTensor).to(device=global_device)\n","      \n","      accurate_labels_positive = torch.sum(torch.argmax(out0, dim=1) == target0)\n","      accurate_labels_negative = torch.sum(torch.argmax(out1, dim=1) == target1)\n","          \n","      accurate_labels = accurate_labels + accurate_labels_positive + accurate_labels_negative\n","      all_labels = all_labels + len(target0) + len(target1)\n","      if t==0:\n","        print(torch.max(out0),torch.min(out0),torch.max(out1),torch.min(out1),torch.max(target0),torch.min(target0),torch.max(target1),torch.min(target1))\n","    \n","    acc = 100. * accurate_labels / all_labels\n","    acc=np.round(acc.cpu(),4)\n","       \n","    accurate_labels2 = 0\n","    all_labels2 = 0\n","    for  z, (x2, y2) in enumerate(custom_train_loader):\n","      x2 = x2.to(device='cuda', dtype=torch.float32)  # move to device, e.g. GPU\n","      y2 = y2.to(device='cuda', dtype=torch.long)\n","      pair2 = x2\n","      out2=model(pair2).squeeze().to(dtype=global_dtype)\n","      target2 = y2.type(torch.LongTensor).to(device=global_device)\n","      accurate_labels_positive = torch.sum(torch.argmax(out2, dim=1) == target2)\n","\n","      accurate_labels2 = accurate_labels2 + accurate_labels_positive\n","      all_labels2 = all_labels2 + len(target2)\n","    \n","    acc2 = 100. * accurate_labels2 / all_labels2\n","    print('Consistency Accuracy: {}/{} ({:.3f}%) \\t and Real vs. Fake Accuracy: {}/{} ({:.3f}%) '\\\n","          .format(accurate_labels, all_labels, acc,accurate_labels2,all_labels2,acc2))\n","    \n","    acc2=np.round(acc2.cpu(),4)\n","    \n","    return acc,acc2\n","\n","  def adjust_learning_rate(self,optimizer, lr_decay, epoch, schedule):\n","    \"\"\"\n","    Multiply lr_decay to the learning rate if epoch is in schedule\n","  \n","    Inputs:\n","    - optimizer: An Optimizer object we will use to train the model\n","    - lr_decay: learning rate decay; can be a list or just a single number.\n","      a factor multiplied with present learning rate at scheduled epochs (fpor example, 0.95*learning_rate)\n","    - epochs: the current epoch number\n","    - schedule: the list of epochs that requires learning rate update\n","  \n","    Returns: Nothing, but learning rate might be updated\n","    \"\"\"\n","    schedule=sorted(schedule) #sort the lr_schedule.\n","    if epoch in schedule:\n","      for param_group in optimizer.param_groups:\n","        if type(lr_decay) is list:\n","          if len(lr_decay)>=schedule.index(epoch)+1: # if length of lr_decay list is greater than or equal to length of the lr_schedule\n","            decay_factor=lr_decay[schedule.index(epoch)]\n","          else:\n","            decay_factor=lr_decay[-1] #the last decay_factor available in the lr_decay list.\n","        else:\n","          decay_factor=lr_decay #If not a list.        \n","        \n","        print('                                            \\\n","        lr decay from {} to {}'.format(param_group['lr'], param_group['lr'] * decay_factor)) #printed before updating.\n","        param_group['lr'] *= decay_factor #updated.\n","\n","  def model_run(self,model, loader, optimizer, train_flag=False, v_loader=val_loader, \\\n","                epochs=1, lr_scheduler='manual',lr_schedule=[],lr_decay=.95, num_patience=1, \\\n","                verbose=True):  \n","    \"\"\"\n","    Train a model on dataset using the PyTorch Module API.\n","  \n","    Inputs:\n","    - model: A PyTorch module giving the model to train.\n","    - loader: Train-loader or Test-loader\n","    - v_loader: Loader to check validation accuracy against, during training. \n","                Default = default validation loader. Only changes during toy-training.\n","                Ignored during testing.\n","    - optimizer: An Optimizer object we will use to train the model\n","    - train_flag: should be set to 'True' while training, default='False'.\n","    - epochs: (Optional) A Python integer giving the number of epochs to train for.\n","              Testing only runs for 1 epoch, value of epochs is disregarded. \n","    - lr_scheduler== 'manual': uses self.adjust_learning_rate() function to decay\n","                               the learning rate by multiplying it with 'lr_decay' value for \n","                               epochs scheduled in 'lr_schedule' list.\n","    - lr_decay: value (can be a list in 'manual' mode) multiplied with existing learning rate to decrease it.                           \n","    - lr_schedule: list of epochs when learning rate is reduced. ex. [8,16].\n","\n","    - lr_scheduler=='auto': uses optim.lr_scheduler.ReduceLROnPlateau() to automatically\n","                            reduce the learning rate by multiplying it with 'lr_decay' when \n","                            it starts plateauing, after waiting for 'num_patience' epochs.\n","    - num_patience: number of epochs to wait for before reducing the learning rate.\n","\n","    See that lr_decay is used with both types of lr_scheduler but its value may be drastically\n","    different for both based on when they reduce the learning rate.\n","    Example case: \n","    lr_scheduler= 'manual', lr_decay=0.9 \n","    lr_scheduler= 'auto', lr_decay=0.1.\n","    Further, lr_decay can be a list in 'manual' mode, as we might want to apply different decay at different epochs.\n","\n","    Output(s): \n","    if train_flag==1:\n","      acc_history, iter_history,loss_list\n","    else:\n","      acc_history: just one test-accuracy value.\n","\n","    \"\"\"\n","    loss_class = nn.BCELoss()\n","    if train_flag==True:\n","      model.train()  # put model to training mode\n","    else:      \n","      model.eval()   # put model to testing mode.\n","   \n","    loss_list=[]\n","    print_every = 10  #print accuracy at every x number of iterations.\n","    model = model.to(device=global_device)  # move the model parameters to CPU/GPU\n","    num_iters = epochs * len(loader)\n","  \n","    if verbose:\n","      num_prints = num_iters // print_every + 1\n","    else:\n","      num_prints = epochs\n","  \n","    acc1_history = torch.empty(num_prints, dtype=global_dtype, device=global_device)\n","    acc2_history = torch.empty(num_prints, dtype=global_dtype, device=global_device)\n","    iter_history = torch.empty(num_prints, dtype=torch.long, device=global_device) \n","    # best_acc1=0\n","    # best_acc2=0\n","    best_acc1, best_acc2 = self.check_accuracy(v_loader, model)\n","    best_model_wts = copy.deepcopy(model.state_dict()) \n","\n","    if train_flag==True: #Training: Multiple Epochs. (Testing -> only one epoch.)\n","      model.requires_grad_(requires_grad=True)\n","      print(\"ESTM parameters being trained: \")\n","      for name, child in model.named_children():\n","        for name_2, params in child.named_parameters():\n","          print(name_2, params.requires_grad)\n","\n","      if lr_scheduler == 'auto':\n","        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', lr_decay, num_patience, verbose)\n","\n","      for e in new_tqdm(range(epochs)):\n","        custom_iterator = iter(custom_train_loader) #create one iterator in each epoch. \n","        if lr_scheduler == 'manual':\n","          self.adjust_learning_rate(optimizer, lr_decay, e, lr_schedule)\n","    \n","        for t, (x, y) in enumerate(loader):   \n","         \n","          x=x.cuda()\n","          y=y.type(torch.LongTensor).cuda()\n","          \n","          x.requires_grad = True         \n","\n","          # Zero out all of the gradients before running a fresh iteration so that \n","          # no cached gradients affect this computation.\n","          optimizer.zero_grad()\n","\n","          pair0 = x[:,:128]   #first and second patches from the same image\n","          out0=model(pair0).squeeze().to(dtype=global_dtype)\n","          target0 = y[:,0].to(device=global_device)          \n","          loss0 = F.cross_entropy(out0,target0)\n","\n","\n","          pair1 = torch.cat((x[:,0:64],x[:,128:192]),dim=1)   #first and third patches from the same image\n","          out1=model(pair1).squeeze().to(dtype=global_dtype)\n","          target1 = y[:,1].to(device=global_device)\n","          loss1 = F.cross_entropy(out1,target1)\n","\n","          loss_part1=loss0+loss1 \n","\n","          if t< len(custom_train_loader):\n","            X2,y2 = next(custom_iterator)\n","            X2=X2.cuda()\n","            y2=y2.type(torch.LongTensor).cuda()            \n","            X2.requires_grad = True                          \n","                       \n","            out2=model(X2).squeeze().to(dtype=global_dtype)\n","            \n","            loss_part2 = F.cross_entropy(out2,y2)\n","            loss=loss_part1+2*loss_part2 #real versus fake loss needs to be the main focus while backprop.\n","          \n","          else:\n","            loss=loss_part1\n","\n","          # This is the backwards pass: compute the gradient of the loss with\n","          # respect to each  parameter of the model.\n","          loss.backward()\n","\n","          # Update the parameters of the model using the gradients\n","          # computed by the backwards pass.\n","          optimizer.step()          \n","          \n","          loss_list.append(loss.item())#.item() helps detatching the computational graphs and helps reduce memory leaks.\n","          \n","          tt = t + e * len(loader)\n","          print(\"loss: \", loss.item(), \"Epoch: \", e, \"Iteration: \", tt)              \n","          \n","          if verbose and (tt % print_every == 0 or (e == epochs-1 and t == len(loader)-1)):\n","            acc1,acc2 = self.check_accuracy(v_loader, model)            \n","            acc1_history[tt // print_every] = acc1\n","            acc2_history[tt // print_every] = acc2\n","            iter_history[tt // print_every] = tt\n","\n","            if acc1 >= best_acc1 and acc2>=best_acc2:\n","              best_acc1 = acc1\n","              best_acc2 = acc2\n","              best_model_wts = copy.deepcopy(model.state_dict()) \n","        \n","          elif not verbose and (t == len(loader)-1): #Keep >= to have a little 'or' margin.\n","            acc = self.check_accuracy(v_loader, model)            \n","            acc1_history[e] = acc1\n","            acc2_history[e] = acc2\n","            iter_history[e] = tt\n","\n","            if acc1 >= best_acc1 and acc2>=best_acc2:\n","              best_acc1 = acc1\n","              best_acc2 = acc2\n","              best_model_wts = copy.deepcopy(model.state_dict())                \n","        \n","        if lr_scheduler == 'auto':\n","          scheduler.step(loss)\n","\n","      if acc1_history[-1]==0:\n","        if (len(acc1_history) == len(iter_history)\n","            or len(acc2_history) == len(iter_history)):\n","          iter_history = iter_history[:-1]\n","        \n","        acc1_history=acc1_history[:-1]\n","        acc2_history=acc2_history[:-1]\n","\n","      \n","    else: #Inference (testing)\n","        with torch.no_grad():\n","\n","          acc1_history,acc2_history = torch.tensor(self.check_accuracy(loader, model), dtype=global_dtype, device=global_device) #just one value.\n","\n","    if train_flag==True:\n","      model.load_state_dict(best_model_wts)\n","      best_model=model\n","      best_params=copy.deepcopy(optimizer.state_dict()) \n","      \n","      return acc1_history,acc2_history, iter_history,loss_list,best_model,best_params\n","    else:\n","      return acc1_history,acc2_history"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xpFHiXE2LCHE","colab_type":"text"},"source":["#Training:"]},{"cell_type":"code","metadata":{"id":"SwkBIqoyBkaO","colab_type":"code","outputId":"47fb5dce-359f-4c5f-b0b2-51f300ca13d2","executionInfo":{"status":"ok","timestamp":1587948627691,"user_tz":240,"elapsed":1172677,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}},"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["a8c350ee4c1c4eb4851cab2e499f91f8","5d10dd3e93c743579b937d2e3d6cec89","57a650bfa1a740c19407e2e66dce8aee","981ed364198e4a1e9711e03f73b899b8","e751b1da7e9d49aabe74fa561683f51b","9cb6b2b8386e46f5b50ab711deceae00","f0a0ed18ab954c5c8751fc104707be26","eddb54d6e7b742d3ab2b70af9148fad5"]}},"source":["\"\"\"\n","Training Commands. Hyperparameters can be tuned for performance. \\\n","Do not forget to save the trained model by uncommenting the next cell.\n","\"\"\"\n","\n","assembly=Assembly()\n","# Be patient while training the model, it is a large model. \n","# Training will take some time, stay tuned.\n","\n","# Optimizer hyperparameters \n","learning_rate = 1e-4       \n","reg =0.001      # coefficient in front of ||W||^2 (= lambda of L2 regularization term.)\n","momentum = 0.9    # Model hyperparameters\n","train_epochs=20\n","lr_schedule=[]\n","lr_decay=[]\n","\n","torch.autograd.set_detect_anomaly(True) #enabled anomaly detection to find the operation that failed to compute its gradient\n","\n","model2 = Estimator().to(device=global_device, dtype=global_dtype)\n","model2.load_state_dict(torch.load(\"/content/drive/Shared drives/EECS504/Trained_Models/MW/P3_A4_model-28x28-5_one_pair-batch_size_64_train_99.9718_val_85.4661_test_acc_85.4661.pt\"))\n","\n","optimizer = optim.SGD(model2.parameters(), lr=learning_rate, weight_decay=reg,momentum=momentum,nesterov=False )\n","\n","acc1_history,acc2_history, iter_history,loss_list,best_model2,best_params2 = assembly.model_run(model2, train_loader, optimizer, train_flag=True, \\\n","                                                v_loader=val_loader, epochs=train_epochs,lr_scheduler='manual',lr_schedule=lr_schedule,lr_decay=lr_decay)\n","\n","# plot the Training Loss vs. iterations\n","plt.plot(loss_list, '-o')\n","plt.legend(['Training Loss'], loc='upper right')\n","plt.xlabel('Cumulative Iterations')\n","plt.ylabel('Loss')\n","plt.title('Training Loss')\n","plt.gcf().set_size_inches(9, 4)\n","plt.show()\n","\n","# plot the Validation Accuracy vs. iterations\n","plt.plot(list(iter_history[:-1]),list(acc1_history[:-1]), '-o')\n","plt.legend(['Validation_acc_history'], loc='lower left')\n","plt.xlabel('Cumulative Iterations')\n","plt.ylabel('Validation Accuracy')\n","plt.title('Validation Accuracy')\n","plt.gcf().set_size_inches(9, 4)\n","plt.show()\n","print(acc1_history)\n"],"execution_count":22,"outputs":[{"output_type":"stream","text":["conv Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n","batch-norm BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","conv Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","batch-norm BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","conv Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","batch-norm BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","tensor(7.8833, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7319, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.3197, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8786, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3742/4720 (79.280%) \t and Real vs. Fake Accuracy: 3027/6000 (50.450%) \n","ESTM parameters being trained: \n","weight True\n","bias True\n","weight True\n","bias True\n","0.weight True\n","0.bias True\n","1.weight True\n","1.bias True\n","4.weight True\n","4.bias True\n","5.weight True\n","5.bias True\n","8.weight True\n","8.bias True\n","9.weight True\n","9.bias True\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a8c350ee4c1c4eb4851cab2e499f91f8","version_minor":0,"version_major":2},"text/plain":["HBox(children=(IntProgress(value=0, max=20), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["loss:  2.3103392124176025 Epoch:  0 Iteration:  0\n","tensor(5.0171, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0376, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.1547, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0105, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3738/4720 (79.195%) \t and Real vs. Fake Accuracy: 3034/6000 (50.567%) \n","loss:  2.864546775817871 Epoch:  0 Iteration:  1\n","loss:  2.7030930519104004 Epoch:  0 Iteration:  2\n","loss:  2.541677236557007 Epoch:  0 Iteration:  3\n","loss:  2.3485474586486816 Epoch:  0 Iteration:  4\n","loss:  3.2838242053985596 Epoch:  0 Iteration:  5\n","loss:  2.435338258743286 Epoch:  0 Iteration:  6\n","loss:  2.5962562561035156 Epoch:  0 Iteration:  7\n","loss:  3.1404685974121094 Epoch:  0 Iteration:  8\n","loss:  2.5919089317321777 Epoch:  0 Iteration:  9\n","loss:  2.519399404525757 Epoch:  0 Iteration:  10\n","tensor(7.7001, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0853, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.1670, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5539, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3661/4720 (77.564%) \t and Real vs. Fake Accuracy: 3097/6000 (51.617%) \n","loss:  2.269005060195923 Epoch:  0 Iteration:  11\n","loss:  2.6502645015716553 Epoch:  0 Iteration:  12\n","loss:  2.292968988418579 Epoch:  0 Iteration:  13\n","loss:  2.386155366897583 Epoch:  0 Iteration:  14\n","loss:  2.545410633087158 Epoch:  0 Iteration:  15\n","loss:  2.301607847213745 Epoch:  0 Iteration:  16\n","loss:  2.137634515762329 Epoch:  0 Iteration:  17\n","loss:  2.3054466247558594 Epoch:  0 Iteration:  18\n","loss:  2.238840341567993 Epoch:  0 Iteration:  19\n","loss:  1.8094888925552368 Epoch:  0 Iteration:  20\n","tensor(4.1954, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1885, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.5174, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3547, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3724/4720 (78.898%) \t and Real vs. Fake Accuracy: 3056/6000 (50.933%) \n","loss:  2.3127665519714355 Epoch:  0 Iteration:  21\n","loss:  2.0841422080993652 Epoch:  0 Iteration:  22\n","loss:  2.4649574756622314 Epoch:  0 Iteration:  23\n","loss:  2.494009256362915 Epoch:  0 Iteration:  24\n","loss:  2.3612194061279297 Epoch:  0 Iteration:  25\n","loss:  2.2021450996398926 Epoch:  0 Iteration:  26\n","loss:  2.2286527156829834 Epoch:  0 Iteration:  27\n","loss:  2.2426352500915527 Epoch:  0 Iteration:  28\n","loss:  2.2923107147216797 Epoch:  0 Iteration:  29\n","loss:  2.218193769454956 Epoch:  0 Iteration:  30\n","tensor(3.3630, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0310, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.8071, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0811, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3708/4720 (78.559%) \t and Real vs. Fake Accuracy: 3138/6000 (52.300%) \n","loss:  2.5222582817077637 Epoch:  0 Iteration:  31\n","loss:  2.2433464527130127 Epoch:  0 Iteration:  32\n","loss:  2.2376294136047363 Epoch:  0 Iteration:  33\n","loss:  2.1641221046447754 Epoch:  0 Iteration:  34\n","loss:  2.0345005989074707 Epoch:  0 Iteration:  35\n","loss:  2.079392194747925 Epoch:  0 Iteration:  36\n","loss:  2.3891074657440186 Epoch:  0 Iteration:  37\n","loss:  2.190044403076172 Epoch:  0 Iteration:  38\n","loss:  2.4660754203796387 Epoch:  0 Iteration:  39\n","loss:  1.938364863395691 Epoch:  0 Iteration:  40\n","tensor(3.4072, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0971, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.6514, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.5714, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3781/4720 (80.106%) \t and Real vs. Fake Accuracy: 3197/6000 (53.283%) \n","loss:  2.0077965259552 Epoch:  0 Iteration:  41\n","loss:  2.2419726848602295 Epoch:  0 Iteration:  42\n","loss:  2.1576266288757324 Epoch:  0 Iteration:  43\n","loss:  2.2532758712768555 Epoch:  0 Iteration:  44\n","loss:  2.2379422187805176 Epoch:  0 Iteration:  45\n","loss:  2.0781617164611816 Epoch:  0 Iteration:  46\n","loss:  2.0109317302703857 Epoch:  0 Iteration:  47\n","loss:  2.260007858276367 Epoch:  0 Iteration:  48\n","loss:  2.4533543586730957 Epoch:  0 Iteration:  49\n","loss:  2.1455979347229004 Epoch:  0 Iteration:  50\n","tensor(4.6077, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3753, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.9727, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.6272, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3785/4720 (80.191%) \t and Real vs. Fake Accuracy: 3225/6000 (53.750%) \n","loss:  2.3751604557037354 Epoch:  0 Iteration:  51\n","loss:  2.220440626144409 Epoch:  0 Iteration:  52\n","loss:  2.5145318508148193 Epoch:  0 Iteration:  53\n","loss:  2.038625955581665 Epoch:  0 Iteration:  54\n","loss:  2.1691203117370605 Epoch:  0 Iteration:  55\n","loss:  2.255559206008911 Epoch:  0 Iteration:  56\n","loss:  2.208937406539917 Epoch:  0 Iteration:  57\n","loss:  2.2553563117980957 Epoch:  0 Iteration:  58\n","loss:  1.818436622619629 Epoch:  0 Iteration:  59\n","loss:  2.3737869262695312 Epoch:  0 Iteration:  60\n","tensor(5.4645, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9160, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.0677, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0519, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3799/4720 (80.487%) \t and Real vs. Fake Accuracy: 3241/6000 (54.017%) \n","loss:  1.8386565446853638 Epoch:  0 Iteration:  61\n","loss:  2.0077054500579834 Epoch:  0 Iteration:  62\n","loss:  2.463019371032715 Epoch:  0 Iteration:  63\n","loss:  1.986782431602478 Epoch:  0 Iteration:  64\n","loss:  2.147749900817871 Epoch:  0 Iteration:  65\n","loss:  2.169236183166504 Epoch:  0 Iteration:  66\n","loss:  2.226731300354004 Epoch:  0 Iteration:  67\n","loss:  2.2155189514160156 Epoch:  0 Iteration:  68\n","loss:  2.1058740615844727 Epoch:  0 Iteration:  69\n","loss:  1.756139874458313 Epoch:  0 Iteration:  70\n","tensor(4.8385, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2426, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.0094, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1890, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3752/4720 (79.492%) \t and Real vs. Fake Accuracy: 3288/6000 (54.800%) \n","loss:  2.3228511810302734 Epoch:  0 Iteration:  71\n","loss:  1.8474479913711548 Epoch:  0 Iteration:  72\n","loss:  2.3940398693084717 Epoch:  0 Iteration:  73\n","loss:  1.9641287326812744 Epoch:  0 Iteration:  74\n","loss:  2.27936053276062 Epoch:  0 Iteration:  75\n","loss:  2.0322279930114746 Epoch:  0 Iteration:  76\n","loss:  2.0962560176849365 Epoch:  0 Iteration:  77\n","loss:  2.0943031311035156 Epoch:  0 Iteration:  78\n","loss:  2.04608416557312 Epoch:  0 Iteration:  79\n","loss:  2.23280668258667 Epoch:  0 Iteration:  80\n","tensor(4.8844, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7767, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.6476, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7007, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3823/4720 (80.996%) \t and Real vs. Fake Accuracy: 3306/6000 (55.100%) \n","loss:  2.1674890518188477 Epoch:  0 Iteration:  81\n","loss:  2.016104221343994 Epoch:  0 Iteration:  82\n","loss:  1.8712369203567505 Epoch:  0 Iteration:  83\n","loss:  2.1964926719665527 Epoch:  0 Iteration:  84\n","loss:  2.1880574226379395 Epoch:  0 Iteration:  85\n","loss:  2.2676377296447754 Epoch:  0 Iteration:  86\n","loss:  2.0660252571105957 Epoch:  0 Iteration:  87\n","loss:  2.0710902214050293 Epoch:  0 Iteration:  88\n","loss:  1.9454702138900757 Epoch:  0 Iteration:  89\n","loss:  2.243129014968872 Epoch:  0 Iteration:  90\n","tensor(5.8823, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6018, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.6386, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1259, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3830/4720 (81.144%) \t and Real vs. Fake Accuracy: 3356/6000 (55.933%) \n","loss:  1.9054235219955444 Epoch:  0 Iteration:  91\n","loss:  2.1074283123016357 Epoch:  0 Iteration:  92\n","loss:  2.3536951541900635 Epoch:  0 Iteration:  93\n","loss:  0.40740466117858887 Epoch:  0 Iteration:  94\n","loss:  0.4946444630622864 Epoch:  0 Iteration:  95\n","loss:  0.43531668186187744 Epoch:  0 Iteration:  96\n","loss:  0.37115180492401123 Epoch:  0 Iteration:  97\n","loss:  0.38688191771507263 Epoch:  0 Iteration:  98\n","loss:  0.4979188144207001 Epoch:  0 Iteration:  99\n","loss:  0.38148826360702515 Epoch:  0 Iteration:  100\n","tensor(4.9607, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7809, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.8638, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8985, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3872/4720 (82.034%) \t and Real vs. Fake Accuracy: 3383/6000 (56.383%) \n","loss:  0.4532046914100647 Epoch:  0 Iteration:  101\n","loss:  0.43990087509155273 Epoch:  0 Iteration:  102\n","loss:  0.4242197275161743 Epoch:  0 Iteration:  103\n","loss:  0.40257471799850464 Epoch:  0 Iteration:  104\n","loss:  0.3394978940486908 Epoch:  0 Iteration:  105\n","loss:  0.5086079835891724 Epoch:  0 Iteration:  106\n","loss:  0.3979548215866089 Epoch:  0 Iteration:  107\n","loss:  0.4482821822166443 Epoch:  0 Iteration:  108\n","loss:  0.35588422417640686 Epoch:  0 Iteration:  109\n","loss:  0.38682883977890015 Epoch:  0 Iteration:  110\n","tensor(4.3636, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8349, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.0707, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8943, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3918/4720 (83.008%) \t and Real vs. Fake Accuracy: 3377/6000 (56.283%) \n","loss:  1.7710318565368652 Epoch:  1 Iteration:  111\n","loss:  2.2636499404907227 Epoch:  1 Iteration:  112\n","loss:  1.8989206552505493 Epoch:  1 Iteration:  113\n","loss:  1.8866302967071533 Epoch:  1 Iteration:  114\n","loss:  1.956660270690918 Epoch:  1 Iteration:  115\n","loss:  2.1439881324768066 Epoch:  1 Iteration:  116\n","loss:  2.0258121490478516 Epoch:  1 Iteration:  117\n","loss:  1.8154840469360352 Epoch:  1 Iteration:  118\n","loss:  1.667945146560669 Epoch:  1 Iteration:  119\n","loss:  2.1374998092651367 Epoch:  1 Iteration:  120\n","tensor(4.5002, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3112, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.2775, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4940, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3885/4720 (82.309%) \t and Real vs. Fake Accuracy: 3403/6000 (56.717%) \n","loss:  1.9707376956939697 Epoch:  1 Iteration:  121\n","loss:  1.6405259370803833 Epoch:  1 Iteration:  122\n","loss:  2.0374464988708496 Epoch:  1 Iteration:  123\n","loss:  1.8172883987426758 Epoch:  1 Iteration:  124\n","loss:  1.8467202186584473 Epoch:  1 Iteration:  125\n","loss:  1.9193464517593384 Epoch:  1 Iteration:  126\n","loss:  1.9619200229644775 Epoch:  1 Iteration:  127\n","loss:  1.8299126625061035 Epoch:  1 Iteration:  128\n","loss:  1.9290556907653809 Epoch:  1 Iteration:  129\n","loss:  1.937817096710205 Epoch:  1 Iteration:  130\n","tensor(4.8605, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7981, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.1471, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9415, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3865/4720 (81.886%) \t and Real vs. Fake Accuracy: 3489/6000 (58.150%) \n","loss:  1.7241069078445435 Epoch:  1 Iteration:  131\n","loss:  1.8048288822174072 Epoch:  1 Iteration:  132\n","loss:  2.1305553913116455 Epoch:  1 Iteration:  133\n","loss:  1.9068801403045654 Epoch:  1 Iteration:  134\n","loss:  1.881869912147522 Epoch:  1 Iteration:  135\n","loss:  1.7020180225372314 Epoch:  1 Iteration:  136\n","loss:  1.7559807300567627 Epoch:  1 Iteration:  137\n","loss:  1.76716947555542 Epoch:  1 Iteration:  138\n","loss:  1.7807269096374512 Epoch:  1 Iteration:  139\n","loss:  2.2921395301818848 Epoch:  1 Iteration:  140\n","tensor(4.8115, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0053, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2087, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8686, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3880/4720 (82.203%) \t and Real vs. Fake Accuracy: 3493/6000 (58.217%) \n","loss:  1.5248496532440186 Epoch:  1 Iteration:  141\n","loss:  1.721293568611145 Epoch:  1 Iteration:  142\n","loss:  1.7633118629455566 Epoch:  1 Iteration:  143\n","loss:  1.7973816394805908 Epoch:  1 Iteration:  144\n","loss:  1.8269569873809814 Epoch:  1 Iteration:  145\n","loss:  1.687652826309204 Epoch:  1 Iteration:  146\n","loss:  1.798811674118042 Epoch:  1 Iteration:  147\n","loss:  1.9177806377410889 Epoch:  1 Iteration:  148\n","loss:  1.8477745056152344 Epoch:  1 Iteration:  149\n","loss:  2.1171810626983643 Epoch:  1 Iteration:  150\n","tensor(4.0802, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7032, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.3799, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9931, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3888/4720 (82.373%) \t and Real vs. Fake Accuracy: 3511/6000 (58.517%) \n","loss:  1.9525730609893799 Epoch:  1 Iteration:  151\n","loss:  1.6757798194885254 Epoch:  1 Iteration:  152\n","loss:  1.688992977142334 Epoch:  1 Iteration:  153\n","loss:  1.8986248970031738 Epoch:  1 Iteration:  154\n","loss:  1.7718002796173096 Epoch:  1 Iteration:  155\n","loss:  1.8454638719558716 Epoch:  1 Iteration:  156\n","loss:  2.0911800861358643 Epoch:  1 Iteration:  157\n","loss:  2.07485294342041 Epoch:  1 Iteration:  158\n","loss:  1.8007341623306274 Epoch:  1 Iteration:  159\n","loss:  2.0160136222839355 Epoch:  1 Iteration:  160\n","tensor(6.3803, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5419, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4096, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2249, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3869/4720 (81.970%) \t and Real vs. Fake Accuracy: 3590/6000 (59.833%) \n","loss:  1.9922080039978027 Epoch:  1 Iteration:  161\n","loss:  1.8800370693206787 Epoch:  1 Iteration:  162\n","loss:  1.995797038078308 Epoch:  1 Iteration:  163\n","loss:  1.7108867168426514 Epoch:  1 Iteration:  164\n","loss:  1.9971224069595337 Epoch:  1 Iteration:  165\n","loss:  1.655576229095459 Epoch:  1 Iteration:  166\n","loss:  1.7568427324295044 Epoch:  1 Iteration:  167\n","loss:  2.2265465259552 Epoch:  1 Iteration:  168\n","loss:  2.059192419052124 Epoch:  1 Iteration:  169\n","loss:  1.9170284271240234 Epoch:  1 Iteration:  170\n","tensor(4.3726, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4263, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.7294, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8814, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3912/4720 (82.881%) \t and Real vs. Fake Accuracy: 3590/6000 (59.833%) \n","loss:  1.9770804643630981 Epoch:  1 Iteration:  171\n","loss:  1.9994480609893799 Epoch:  1 Iteration:  172\n","loss:  1.8710260391235352 Epoch:  1 Iteration:  173\n","loss:  1.809140682220459 Epoch:  1 Iteration:  174\n","loss:  1.9997258186340332 Epoch:  1 Iteration:  175\n","loss:  1.9474774599075317 Epoch:  1 Iteration:  176\n","loss:  1.6233923435211182 Epoch:  1 Iteration:  177\n","loss:  1.9969598054885864 Epoch:  1 Iteration:  178\n","loss:  1.7862178087234497 Epoch:  1 Iteration:  179\n","loss:  1.8252519369125366 Epoch:  1 Iteration:  180\n","tensor(6.2965, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2991, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8836, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2295, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3899/4720 (82.606%) \t and Real vs. Fake Accuracy: 3655/6000 (60.917%) \n","loss:  2.246628761291504 Epoch:  1 Iteration:  181\n","loss:  1.8749170303344727 Epoch:  1 Iteration:  182\n","loss:  1.8680047988891602 Epoch:  1 Iteration:  183\n","loss:  2.077889919281006 Epoch:  1 Iteration:  184\n","loss:  1.720386028289795 Epoch:  1 Iteration:  185\n","loss:  1.7777810096740723 Epoch:  1 Iteration:  186\n","loss:  1.9104632139205933 Epoch:  1 Iteration:  187\n","loss:  1.9771182537078857 Epoch:  1 Iteration:  188\n","loss:  1.9034876823425293 Epoch:  1 Iteration:  189\n","loss:  1.8328917026519775 Epoch:  1 Iteration:  190\n","tensor(3.8791, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8924, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.0480, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2317, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3902/4720 (82.670%) \t and Real vs. Fake Accuracy: 3682/6000 (61.367%) \n","loss:  1.8826510906219482 Epoch:  1 Iteration:  191\n","loss:  1.9402101039886475 Epoch:  1 Iteration:  192\n","loss:  1.966622233390808 Epoch:  1 Iteration:  193\n","loss:  1.9235256910324097 Epoch:  1 Iteration:  194\n","loss:  1.9841713905334473 Epoch:  1 Iteration:  195\n","loss:  1.9678757190704346 Epoch:  1 Iteration:  196\n","loss:  1.890127182006836 Epoch:  1 Iteration:  197\n","loss:  1.9297518730163574 Epoch:  1 Iteration:  198\n","loss:  1.999706745147705 Epoch:  1 Iteration:  199\n","loss:  1.855316400527954 Epoch:  1 Iteration:  200\n","tensor(5.5986, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7861, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.6271, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9081, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3875/4720 (82.098%) \t and Real vs. Fake Accuracy: 3686/6000 (61.433%) \n","loss:  1.7850210666656494 Epoch:  1 Iteration:  201\n","loss:  1.8899239301681519 Epoch:  1 Iteration:  202\n","loss:  1.7010653018951416 Epoch:  1 Iteration:  203\n","loss:  2.0031683444976807 Epoch:  1 Iteration:  204\n","loss:  0.37173324823379517 Epoch:  1 Iteration:  205\n","loss:  0.3704954981803894 Epoch:  1 Iteration:  206\n","loss:  0.31003060936927795 Epoch:  1 Iteration:  207\n","loss:  0.3587934374809265 Epoch:  1 Iteration:  208\n","loss:  0.39914214611053467 Epoch:  1 Iteration:  209\n","loss:  0.4795916676521301 Epoch:  1 Iteration:  210\n","tensor(4.2709, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7451, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.7365, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2442, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3900/4720 (82.627%) \t and Real vs. Fake Accuracy: 3644/6000 (60.733%) \n","loss:  0.39854323863983154 Epoch:  1 Iteration:  211\n","loss:  0.3296234607696533 Epoch:  1 Iteration:  212\n","loss:  0.3533819913864136 Epoch:  1 Iteration:  213\n","loss:  0.3860146701335907 Epoch:  1 Iteration:  214\n","loss:  0.38943400979042053 Epoch:  1 Iteration:  215\n","loss:  0.32770031690597534 Epoch:  1 Iteration:  216\n","loss:  0.34191784262657166 Epoch:  1 Iteration:  217\n","loss:  0.3352586030960083 Epoch:  1 Iteration:  218\n","loss:  0.3351333439350128 Epoch:  1 Iteration:  219\n","loss:  0.31543904542922974 Epoch:  1 Iteration:  220\n","tensor(3.9592, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8503, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.7379, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6519, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3926/4720 (83.178%) \t and Real vs. Fake Accuracy: 3675/6000 (61.250%) \n","loss:  0.4071877896785736 Epoch:  1 Iteration:  221\n","loss:  1.8953790664672852 Epoch:  2 Iteration:  222\n","loss:  1.8875569105148315 Epoch:  2 Iteration:  223\n","loss:  1.818528652191162 Epoch:  2 Iteration:  224\n","loss:  1.6468169689178467 Epoch:  2 Iteration:  225\n","loss:  1.94010329246521 Epoch:  2 Iteration:  226\n","loss:  1.6209286451339722 Epoch:  2 Iteration:  227\n","loss:  1.8142906427383423 Epoch:  2 Iteration:  228\n","loss:  1.4818369150161743 Epoch:  2 Iteration:  229\n","loss:  1.5171130895614624 Epoch:  2 Iteration:  230\n","tensor(5.1040, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0790, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4311, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.6312, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3937/4720 (83.411%) \t and Real vs. Fake Accuracy: 3683/6000 (61.383%) \n","loss:  1.7051867246627808 Epoch:  2 Iteration:  231\n","loss:  1.7033751010894775 Epoch:  2 Iteration:  232\n","loss:  1.7259010076522827 Epoch:  2 Iteration:  233\n","loss:  1.7793651819229126 Epoch:  2 Iteration:  234\n","loss:  1.5772274732589722 Epoch:  2 Iteration:  235\n","loss:  1.6954057216644287 Epoch:  2 Iteration:  236\n","loss:  1.7073700428009033 Epoch:  2 Iteration:  237\n","loss:  1.7187410593032837 Epoch:  2 Iteration:  238\n","loss:  1.5934383869171143 Epoch:  2 Iteration:  239\n","loss:  1.6278271675109863 Epoch:  2 Iteration:  240\n","tensor(6.4997, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9496, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.2826, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5137, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3914/4720 (82.924%) \t and Real vs. Fake Accuracy: 3759/6000 (62.650%) \n","loss:  1.7182668447494507 Epoch:  2 Iteration:  241\n","loss:  1.6477903127670288 Epoch:  2 Iteration:  242\n","loss:  1.8622632026672363 Epoch:  2 Iteration:  243\n","loss:  1.7326390743255615 Epoch:  2 Iteration:  244\n","loss:  1.7864439487457275 Epoch:  2 Iteration:  245\n","loss:  1.587436318397522 Epoch:  2 Iteration:  246\n","loss:  1.7821614742279053 Epoch:  2 Iteration:  247\n","loss:  1.670611023902893 Epoch:  2 Iteration:  248\n","loss:  1.6236425638198853 Epoch:  2 Iteration:  249\n","loss:  1.6972153186798096 Epoch:  2 Iteration:  250\n","tensor(4.1009, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8861, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.1561, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0311, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3910/4720 (82.839%) \t and Real vs. Fake Accuracy: 3828/6000 (63.800%) \n","loss:  1.562606692314148 Epoch:  2 Iteration:  251\n","loss:  1.5922362804412842 Epoch:  2 Iteration:  252\n","loss:  1.763494849205017 Epoch:  2 Iteration:  253\n","loss:  1.7668766975402832 Epoch:  2 Iteration:  254\n","loss:  1.524515986442566 Epoch:  2 Iteration:  255\n","loss:  1.7506502866744995 Epoch:  2 Iteration:  256\n","loss:  1.5704917907714844 Epoch:  2 Iteration:  257\n","loss:  1.6765581369400024 Epoch:  2 Iteration:  258\n","loss:  1.8099706172943115 Epoch:  2 Iteration:  259\n","loss:  1.7560515403747559 Epoch:  2 Iteration:  260\n","tensor(4.5714, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2073, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.0943, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7414, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3923/4720 (83.114%) \t and Real vs. Fake Accuracy: 3793/6000 (63.217%) \n","loss:  1.655042052268982 Epoch:  2 Iteration:  261\n","loss:  1.5327880382537842 Epoch:  2 Iteration:  262\n","loss:  1.7841813564300537 Epoch:  2 Iteration:  263\n","loss:  1.8613457679748535 Epoch:  2 Iteration:  264\n","loss:  1.7931694984436035 Epoch:  2 Iteration:  265\n","loss:  1.6664676666259766 Epoch:  2 Iteration:  266\n","loss:  1.5277683734893799 Epoch:  2 Iteration:  267\n","loss:  1.8916184902191162 Epoch:  2 Iteration:  268\n","loss:  1.6796507835388184 Epoch:  2 Iteration:  269\n","loss:  1.786879301071167 Epoch:  2 Iteration:  270\n","tensor(5.7628, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9034, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.3635, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6732, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3923/4720 (83.114%) \t and Real vs. Fake Accuracy: 3877/6000 (64.617%) \n","loss:  2.020860433578491 Epoch:  2 Iteration:  271\n","loss:  1.6261121034622192 Epoch:  2 Iteration:  272\n","loss:  1.7934672832489014 Epoch:  2 Iteration:  273\n","loss:  1.6252387762069702 Epoch:  2 Iteration:  274\n","loss:  1.6528279781341553 Epoch:  2 Iteration:  275\n","loss:  1.524112582206726 Epoch:  2 Iteration:  276\n","loss:  1.7870739698410034 Epoch:  2 Iteration:  277\n","loss:  1.8478813171386719 Epoch:  2 Iteration:  278\n","loss:  1.8104124069213867 Epoch:  2 Iteration:  279\n","loss:  1.7677319049835205 Epoch:  2 Iteration:  280\n","tensor(4.7694, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9694, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.3507, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7229, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3924/4720 (83.136%) \t and Real vs. Fake Accuracy: 3872/6000 (64.533%) \n","loss:  1.707425832748413 Epoch:  2 Iteration:  281\n","loss:  1.7651695013046265 Epoch:  2 Iteration:  282\n","loss:  1.730680227279663 Epoch:  2 Iteration:  283\n","loss:  1.5016281604766846 Epoch:  2 Iteration:  284\n","loss:  1.687788963317871 Epoch:  2 Iteration:  285\n","loss:  1.9137732982635498 Epoch:  2 Iteration:  286\n","loss:  1.6576645374298096 Epoch:  2 Iteration:  287\n","loss:  1.6725544929504395 Epoch:  2 Iteration:  288\n","loss:  1.461076259613037 Epoch:  2 Iteration:  289\n","loss:  1.7213817834854126 Epoch:  2 Iteration:  290\n","tensor(3.9236, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0658, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.6335, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7667, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3942/4720 (83.517%) \t and Real vs. Fake Accuracy: 3912/6000 (65.200%) \n","loss:  1.6536779403686523 Epoch:  2 Iteration:  291\n","loss:  2.046480178833008 Epoch:  2 Iteration:  292\n","loss:  1.5798425674438477 Epoch:  2 Iteration:  293\n","loss:  1.5870141983032227 Epoch:  2 Iteration:  294\n","loss:  1.4919530153274536 Epoch:  2 Iteration:  295\n","loss:  1.5326614379882812 Epoch:  2 Iteration:  296\n","loss:  1.7081778049468994 Epoch:  2 Iteration:  297\n","loss:  1.67892587184906 Epoch:  2 Iteration:  298\n","loss:  1.7825682163238525 Epoch:  2 Iteration:  299\n","loss:  1.839482307434082 Epoch:  2 Iteration:  300\n","tensor(5.1529, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1289, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8958, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1739, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3916/4720 (82.966%) \t and Real vs. Fake Accuracy: 3932/6000 (65.533%) \n","loss:  1.737640380859375 Epoch:  2 Iteration:  301\n","loss:  1.887292742729187 Epoch:  2 Iteration:  302\n","loss:  1.535776972770691 Epoch:  2 Iteration:  303\n","loss:  1.6792373657226562 Epoch:  2 Iteration:  304\n","loss:  1.5149236917495728 Epoch:  2 Iteration:  305\n","loss:  1.831869125366211 Epoch:  2 Iteration:  306\n","loss:  1.979674220085144 Epoch:  2 Iteration:  307\n","loss:  1.6282446384429932 Epoch:  2 Iteration:  308\n","loss:  2.006290912628174 Epoch:  2 Iteration:  309\n","loss:  1.9425525665283203 Epoch:  2 Iteration:  310\n","tensor(4.5341, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8645, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5092, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1781, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3903/4720 (82.691%) \t and Real vs. Fake Accuracy: 3968/6000 (66.133%) \n","loss:  1.8955856561660767 Epoch:  2 Iteration:  311\n","loss:  1.656479835510254 Epoch:  2 Iteration:  312\n","loss:  1.8218960762023926 Epoch:  2 Iteration:  313\n","loss:  1.7658395767211914 Epoch:  2 Iteration:  314\n","loss:  1.8598475456237793 Epoch:  2 Iteration:  315\n","loss:  0.37745559215545654 Epoch:  2 Iteration:  316\n","loss:  0.3414393663406372 Epoch:  2 Iteration:  317\n","loss:  0.34675776958465576 Epoch:  2 Iteration:  318\n","loss:  0.33564072847366333 Epoch:  2 Iteration:  319\n","loss:  0.4150451421737671 Epoch:  2 Iteration:  320\n","tensor(3.9341, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0083, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.9870, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4797, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3946/4720 (83.602%) \t and Real vs. Fake Accuracy: 4023/6000 (67.050%) \n","loss:  0.317168653011322 Epoch:  2 Iteration:  321\n","loss:  0.31010758876800537 Epoch:  2 Iteration:  322\n","loss:  0.3377261757850647 Epoch:  2 Iteration:  323\n","loss:  0.3352386951446533 Epoch:  2 Iteration:  324\n","loss:  0.28780290484428406 Epoch:  2 Iteration:  325\n","loss:  0.35112422704696655 Epoch:  2 Iteration:  326\n","loss:  0.3538319170475006 Epoch:  2 Iteration:  327\n","loss:  0.37531888484954834 Epoch:  2 Iteration:  328\n","loss:  0.35004302859306335 Epoch:  2 Iteration:  329\n","loss:  0.302448570728302 Epoch:  2 Iteration:  330\n","tensor(4.9068, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8027, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.1048, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0170, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3950/4720 (83.686%) \t and Real vs. Fake Accuracy: 3963/6000 (66.050%) \n","loss:  0.28067901730537415 Epoch:  2 Iteration:  331\n","loss:  0.3042370080947876 Epoch:  2 Iteration:  332\n","loss:  1.5702731609344482 Epoch:  3 Iteration:  333\n","loss:  1.3817620277404785 Epoch:  3 Iteration:  334\n","loss:  1.5793275833129883 Epoch:  3 Iteration:  335\n","loss:  1.3063238859176636 Epoch:  3 Iteration:  336\n","loss:  1.4359338283538818 Epoch:  3 Iteration:  337\n","loss:  1.4632794857025146 Epoch:  3 Iteration:  338\n","loss:  1.5667062997817993 Epoch:  3 Iteration:  339\n","loss:  1.7294561862945557 Epoch:  3 Iteration:  340\n","tensor(4.1047, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4574, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.2121, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7167, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3933/4720 (83.326%) \t and Real vs. Fake Accuracy: 3948/6000 (65.800%) \n","loss:  1.3862031698226929 Epoch:  3 Iteration:  341\n","loss:  1.7046229839324951 Epoch:  3 Iteration:  342\n","loss:  1.5142717361450195 Epoch:  3 Iteration:  343\n","loss:  1.575987696647644 Epoch:  3 Iteration:  344\n","loss:  1.6146682500839233 Epoch:  3 Iteration:  345\n","loss:  1.517261266708374 Epoch:  3 Iteration:  346\n","loss:  1.668063759803772 Epoch:  3 Iteration:  347\n","loss:  1.4125339984893799 Epoch:  3 Iteration:  348\n","loss:  1.5050597190856934 Epoch:  3 Iteration:  349\n","loss:  1.6172353029251099 Epoch:  3 Iteration:  350\n","tensor(5.7634, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4134, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.2222, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.6742, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3955/4720 (83.792%) \t and Real vs. Fake Accuracy: 3967/6000 (66.117%) \n","loss:  1.5234785079956055 Epoch:  3 Iteration:  351\n","loss:  1.6287298202514648 Epoch:  3 Iteration:  352\n","loss:  1.4023079872131348 Epoch:  3 Iteration:  353\n","loss:  1.4071831703186035 Epoch:  3 Iteration:  354\n","loss:  1.475713849067688 Epoch:  3 Iteration:  355\n","loss:  1.4410407543182373 Epoch:  3 Iteration:  356\n","loss:  1.4265203475952148 Epoch:  3 Iteration:  357\n","loss:  1.5940650701522827 Epoch:  3 Iteration:  358\n","loss:  1.5834743976593018 Epoch:  3 Iteration:  359\n","loss:  1.6119294166564941 Epoch:  3 Iteration:  360\n","tensor(5.9252, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8503, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.6835, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1469, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3922/4720 (83.093%) \t and Real vs. Fake Accuracy: 4041/6000 (67.350%) \n","loss:  1.4765164852142334 Epoch:  3 Iteration:  361\n","loss:  1.366866111755371 Epoch:  3 Iteration:  362\n","loss:  1.5497312545776367 Epoch:  3 Iteration:  363\n","loss:  1.7218550443649292 Epoch:  3 Iteration:  364\n","loss:  1.7126684188842773 Epoch:  3 Iteration:  365\n","loss:  1.4658970832824707 Epoch:  3 Iteration:  366\n","loss:  1.491407036781311 Epoch:  3 Iteration:  367\n","loss:  1.4501374959945679 Epoch:  3 Iteration:  368\n","loss:  1.666527509689331 Epoch:  3 Iteration:  369\n","loss:  1.5705430507659912 Epoch:  3 Iteration:  370\n","tensor(4.0589, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9887, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.1091, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9403, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3958/4720 (83.856%) \t and Real vs. Fake Accuracy: 4055/6000 (67.583%) \n","loss:  1.746923565864563 Epoch:  3 Iteration:  371\n","loss:  1.4538445472717285 Epoch:  3 Iteration:  372\n","loss:  1.481357455253601 Epoch:  3 Iteration:  373\n","loss:  1.3663386106491089 Epoch:  3 Iteration:  374\n","loss:  1.7034947872161865 Epoch:  3 Iteration:  375\n","loss:  1.4256362915039062 Epoch:  3 Iteration:  376\n","loss:  1.7418434619903564 Epoch:  3 Iteration:  377\n","loss:  1.6265292167663574 Epoch:  3 Iteration:  378\n","loss:  1.455343246459961 Epoch:  3 Iteration:  379\n","loss:  1.6008188724517822 Epoch:  3 Iteration:  380\n","tensor(4.1781, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1350, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2852, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5031, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3921/4720 (83.072%) \t and Real vs. Fake Accuracy: 4114/6000 (68.567%) \n","loss:  1.6079274415969849 Epoch:  3 Iteration:  381\n","loss:  1.7781965732574463 Epoch:  3 Iteration:  382\n","loss:  1.5065810680389404 Epoch:  3 Iteration:  383\n","loss:  1.7037851810455322 Epoch:  3 Iteration:  384\n","loss:  1.756365418434143 Epoch:  3 Iteration:  385\n","loss:  1.58443284034729 Epoch:  3 Iteration:  386\n","loss:  1.7598340511322021 Epoch:  3 Iteration:  387\n","loss:  1.5272176265716553 Epoch:  3 Iteration:  388\n","loss:  1.581390142440796 Epoch:  3 Iteration:  389\n","loss:  1.6538441181182861 Epoch:  3 Iteration:  390\n","tensor(4.0926, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0396, device='cuda:0', grad_fn=<MinBackward1>) tensor(5.8197, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4728, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3926/4720 (83.178%) \t and Real vs. Fake Accuracy: 4168/6000 (69.467%) \n","loss:  1.636527180671692 Epoch:  3 Iteration:  391\n","loss:  1.8082103729248047 Epoch:  3 Iteration:  392\n","loss:  1.5397849082946777 Epoch:  3 Iteration:  393\n","loss:  1.5315027236938477 Epoch:  3 Iteration:  394\n","loss:  1.5251390933990479 Epoch:  3 Iteration:  395\n","loss:  1.7068591117858887 Epoch:  3 Iteration:  396\n","loss:  1.5412665605545044 Epoch:  3 Iteration:  397\n","loss:  1.44516122341156 Epoch:  3 Iteration:  398\n","loss:  1.7049179077148438 Epoch:  3 Iteration:  399\n","loss:  1.462646722793579 Epoch:  3 Iteration:  400\n","tensor(4.6293, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6646, device='cuda:0', grad_fn=<MinBackward1>) tensor(5.8715, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9135, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3938/4720 (83.432%) \t and Real vs. Fake Accuracy: 4214/6000 (70.233%) \n","loss:  1.5090208053588867 Epoch:  3 Iteration:  401\n","loss:  1.62027907371521 Epoch:  3 Iteration:  402\n","loss:  1.5868914127349854 Epoch:  3 Iteration:  403\n","loss:  1.55979585647583 Epoch:  3 Iteration:  404\n","loss:  1.5710594654083252 Epoch:  3 Iteration:  405\n","loss:  1.50156831741333 Epoch:  3 Iteration:  406\n","loss:  1.5054292678833008 Epoch:  3 Iteration:  407\n","loss:  1.417236328125 Epoch:  3 Iteration:  408\n","loss:  1.9189331531524658 Epoch:  3 Iteration:  409\n","loss:  1.7167904376983643 Epoch:  3 Iteration:  410\n","tensor(4.8380, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4007, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.4207, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0583, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3946/4720 (83.602%) \t and Real vs. Fake Accuracy: 4214/6000 (70.233%) \n","loss:  1.6707407236099243 Epoch:  3 Iteration:  411\n","loss:  1.578122854232788 Epoch:  3 Iteration:  412\n","loss:  1.7321745157241821 Epoch:  3 Iteration:  413\n","loss:  1.650910496711731 Epoch:  3 Iteration:  414\n","loss:  1.767524003982544 Epoch:  3 Iteration:  415\n","loss:  1.3976151943206787 Epoch:  3 Iteration:  416\n","loss:  1.6276299953460693 Epoch:  3 Iteration:  417\n","loss:  1.500807523727417 Epoch:  3 Iteration:  418\n","loss:  1.463975429534912 Epoch:  3 Iteration:  419\n","loss:  1.7272588014602661 Epoch:  3 Iteration:  420\n","tensor(5.7285, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5557, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.1992, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2576, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3942/4720 (83.517%) \t and Real vs. Fake Accuracy: 4212/6000 (70.200%) \n","loss:  1.5671271085739136 Epoch:  3 Iteration:  421\n","loss:  1.5506361722946167 Epoch:  3 Iteration:  422\n","loss:  1.5913103818893433 Epoch:  3 Iteration:  423\n","loss:  1.5724177360534668 Epoch:  3 Iteration:  424\n","loss:  1.7863882780075073 Epoch:  3 Iteration:  425\n","loss:  1.586024284362793 Epoch:  3 Iteration:  426\n","loss:  0.27402085065841675 Epoch:  3 Iteration:  427\n","loss:  0.3011017441749573 Epoch:  3 Iteration:  428\n","loss:  0.2982902526855469 Epoch:  3 Iteration:  429\n","loss:  0.347648561000824 Epoch:  3 Iteration:  430\n","tensor(4.6657, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4867, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.2458, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2171, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3944/4720 (83.559%) \t and Real vs. Fake Accuracy: 4251/6000 (70.850%) \n","loss:  0.3219350576400757 Epoch:  3 Iteration:  431\n","loss:  0.3144340217113495 Epoch:  3 Iteration:  432\n","loss:  0.3117128014564514 Epoch:  3 Iteration:  433\n","loss:  0.3342655897140503 Epoch:  3 Iteration:  434\n","loss:  0.33999955654144287 Epoch:  3 Iteration:  435\n","loss:  0.29653650522232056 Epoch:  3 Iteration:  436\n","loss:  0.3263261318206787 Epoch:  3 Iteration:  437\n","loss:  0.30431801080703735 Epoch:  3 Iteration:  438\n","loss:  0.28972136974334717 Epoch:  3 Iteration:  439\n","loss:  0.2640414535999298 Epoch:  3 Iteration:  440\n","tensor(3.6882, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8936, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.9555, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8412, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3964/4720 (83.983%) \t and Real vs. Fake Accuracy: 4201/6000 (70.017%) \n","loss:  0.2966955304145813 Epoch:  3 Iteration:  441\n","loss:  0.35298609733581543 Epoch:  3 Iteration:  442\n","loss:  0.24511289596557617 Epoch:  3 Iteration:  443\n","loss:  1.5072200298309326 Epoch:  4 Iteration:  444\n","loss:  1.3118462562561035 Epoch:  4 Iteration:  445\n","loss:  1.4375053644180298 Epoch:  4 Iteration:  446\n","loss:  1.3930222988128662 Epoch:  4 Iteration:  447\n","loss:  1.2492778301239014 Epoch:  4 Iteration:  448\n","loss:  1.4049499034881592 Epoch:  4 Iteration:  449\n","loss:  1.2513809204101562 Epoch:  4 Iteration:  450\n","tensor(4.9855, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9865, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.2323, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0417, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3945/4720 (83.580%) \t and Real vs. Fake Accuracy: 4228/6000 (70.467%) \n","loss:  1.546897292137146 Epoch:  4 Iteration:  451\n","loss:  1.4537630081176758 Epoch:  4 Iteration:  452\n","loss:  1.4071787595748901 Epoch:  4 Iteration:  453\n","loss:  1.4667929410934448 Epoch:  4 Iteration:  454\n","loss:  1.3813914060592651 Epoch:  4 Iteration:  455\n","loss:  1.119650959968567 Epoch:  4 Iteration:  456\n","loss:  1.3192888498306274 Epoch:  4 Iteration:  457\n","loss:  1.33353590965271 Epoch:  4 Iteration:  458\n","loss:  1.4024569988250732 Epoch:  4 Iteration:  459\n","loss:  1.363162636756897 Epoch:  4 Iteration:  460\n","tensor(4.4158, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5111, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.3259, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2746, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3948/4720 (83.644%) \t and Real vs. Fake Accuracy: 4272/6000 (71.200%) \n","loss:  1.3976038694381714 Epoch:  4 Iteration:  461\n","loss:  1.5177702903747559 Epoch:  4 Iteration:  462\n","loss:  1.4541888236999512 Epoch:  4 Iteration:  463\n","loss:  1.3138468265533447 Epoch:  4 Iteration:  464\n","loss:  1.610074520111084 Epoch:  4 Iteration:  465\n","loss:  1.4827144145965576 Epoch:  4 Iteration:  466\n","loss:  1.5886222124099731 Epoch:  4 Iteration:  467\n","loss:  1.545011281967163 Epoch:  4 Iteration:  468\n","loss:  1.4597969055175781 Epoch:  4 Iteration:  469\n","loss:  1.5779341459274292 Epoch:  4 Iteration:  470\n","tensor(5.7497, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7392, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.5832, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1838, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3901/4720 (82.648%) \t and Real vs. Fake Accuracy: 4284/6000 (71.400%) \n","loss:  1.7002955675125122 Epoch:  4 Iteration:  471\n","loss:  1.4836159944534302 Epoch:  4 Iteration:  472\n","loss:  1.342413067817688 Epoch:  4 Iteration:  473\n","loss:  1.3258004188537598 Epoch:  4 Iteration:  474\n","loss:  1.2729406356811523 Epoch:  4 Iteration:  475\n","loss:  1.6724843978881836 Epoch:  4 Iteration:  476\n","loss:  1.3917902708053589 Epoch:  4 Iteration:  477\n","loss:  1.4282636642456055 Epoch:  4 Iteration:  478\n","loss:  1.3304626941680908 Epoch:  4 Iteration:  479\n","loss:  1.3853780031204224 Epoch:  4 Iteration:  480\n","tensor(4.9910, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2140, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.4866, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9327, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3926/4720 (83.178%) \t and Real vs. Fake Accuracy: 4315/6000 (71.917%) \n","loss:  1.4524614810943604 Epoch:  4 Iteration:  481\n","loss:  1.1712796688079834 Epoch:  4 Iteration:  482\n","loss:  1.3921704292297363 Epoch:  4 Iteration:  483\n","loss:  1.3712775707244873 Epoch:  4 Iteration:  484\n","loss:  1.5162959098815918 Epoch:  4 Iteration:  485\n","loss:  1.6330738067626953 Epoch:  4 Iteration:  486\n","loss:  1.5100809335708618 Epoch:  4 Iteration:  487\n","loss:  1.3651694059371948 Epoch:  4 Iteration:  488\n","loss:  1.4437925815582275 Epoch:  4 Iteration:  489\n","loss:  1.518829584121704 Epoch:  4 Iteration:  490\n","tensor(4.7409, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2333, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.3543, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8899, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3920/4720 (83.051%) \t and Real vs. Fake Accuracy: 4326/6000 (72.100%) \n","loss:  1.4648025035858154 Epoch:  4 Iteration:  491\n","loss:  1.56746506690979 Epoch:  4 Iteration:  492\n","loss:  1.5484963655471802 Epoch:  4 Iteration:  493\n","loss:  1.4417307376861572 Epoch:  4 Iteration:  494\n","loss:  1.6064982414245605 Epoch:  4 Iteration:  495\n","loss:  1.528005838394165 Epoch:  4 Iteration:  496\n","loss:  1.4410922527313232 Epoch:  4 Iteration:  497\n","loss:  1.3771696090698242 Epoch:  4 Iteration:  498\n","loss:  1.4187605381011963 Epoch:  4 Iteration:  499\n","loss:  1.649667501449585 Epoch:  4 Iteration:  500\n","tensor(4.3280, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2777, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4410, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3865, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3957/4720 (83.835%) \t and Real vs. Fake Accuracy: 4394/6000 (73.233%) \n","loss:  1.4359272718429565 Epoch:  4 Iteration:  501\n","loss:  1.5070621967315674 Epoch:  4 Iteration:  502\n","loss:  1.4292166233062744 Epoch:  4 Iteration:  503\n","loss:  1.6622612476348877 Epoch:  4 Iteration:  504\n","loss:  1.4300895929336548 Epoch:  4 Iteration:  505\n","loss:  1.308754324913025 Epoch:  4 Iteration:  506\n","loss:  1.4550154209136963 Epoch:  4 Iteration:  507\n","loss:  1.5142741203308105 Epoch:  4 Iteration:  508\n","loss:  1.5932273864746094 Epoch:  4 Iteration:  509\n","loss:  1.3108521699905396 Epoch:  4 Iteration:  510\n","tensor(3.8837, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3615, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.5069, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6584, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3952/4720 (83.729%) \t and Real vs. Fake Accuracy: 4445/6000 (74.083%) \n","loss:  1.1679083108901978 Epoch:  4 Iteration:  511\n","loss:  1.4403765201568604 Epoch:  4 Iteration:  512\n","loss:  1.4759290218353271 Epoch:  4 Iteration:  513\n","loss:  1.4722307920455933 Epoch:  4 Iteration:  514\n","loss:  1.6596603393554688 Epoch:  4 Iteration:  515\n","loss:  1.4684498310089111 Epoch:  4 Iteration:  516\n","loss:  1.5570062398910522 Epoch:  4 Iteration:  517\n","loss:  1.6265027523040771 Epoch:  4 Iteration:  518\n","loss:  1.4239885807037354 Epoch:  4 Iteration:  519\n","loss:  1.4776326417922974 Epoch:  4 Iteration:  520\n","tensor(4.4202, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5579, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.2295, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8562, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3957/4720 (83.835%) \t and Real vs. Fake Accuracy: 4441/6000 (74.017%) \n","loss:  1.5631378889083862 Epoch:  4 Iteration:  521\n","loss:  1.4557744264602661 Epoch:  4 Iteration:  522\n","loss:  1.4357973337173462 Epoch:  4 Iteration:  523\n","loss:  1.3972121477127075 Epoch:  4 Iteration:  524\n","loss:  1.3053126335144043 Epoch:  4 Iteration:  525\n","loss:  1.5464589595794678 Epoch:  4 Iteration:  526\n","loss:  1.6431783437728882 Epoch:  4 Iteration:  527\n","loss:  1.4140844345092773 Epoch:  4 Iteration:  528\n","loss:  1.5125739574432373 Epoch:  4 Iteration:  529\n","loss:  1.5330398082733154 Epoch:  4 Iteration:  530\n","tensor(5.0155, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4446, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6003, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2349, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3936/4720 (83.390%) \t and Real vs. Fake Accuracy: 4503/6000 (75.050%) \n","loss:  1.151886224746704 Epoch:  4 Iteration:  531\n","loss:  1.4616940021514893 Epoch:  4 Iteration:  532\n","loss:  1.1956149339675903 Epoch:  4 Iteration:  533\n","loss:  1.5120303630828857 Epoch:  4 Iteration:  534\n","loss:  1.3089933395385742 Epoch:  4 Iteration:  535\n","loss:  1.3987950086593628 Epoch:  4 Iteration:  536\n","loss:  1.731257677078247 Epoch:  4 Iteration:  537\n","loss:  0.2807367444038391 Epoch:  4 Iteration:  538\n","loss:  0.34764882922172546 Epoch:  4 Iteration:  539\n","loss:  0.3130223751068115 Epoch:  4 Iteration:  540\n","tensor(5.2292, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1489, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4027, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9226, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3926/4720 (83.178%) \t and Real vs. Fake Accuracy: 4488/6000 (74.800%) \n","loss:  0.294999897480011 Epoch:  4 Iteration:  541\n","loss:  0.2759341597557068 Epoch:  4 Iteration:  542\n","loss:  0.3258183002471924 Epoch:  4 Iteration:  543\n","loss:  0.3136047124862671 Epoch:  4 Iteration:  544\n","loss:  0.29244405031204224 Epoch:  4 Iteration:  545\n","loss:  0.28935301303863525 Epoch:  4 Iteration:  546\n","loss:  0.27761316299438477 Epoch:  4 Iteration:  547\n","loss:  0.26379624009132385 Epoch:  4 Iteration:  548\n","loss:  0.38034987449645996 Epoch:  4 Iteration:  549\n","loss:  0.31212562322616577 Epoch:  4 Iteration:  550\n","tensor(3.8059, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9101, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.8257, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3623, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3946/4720 (83.602%) \t and Real vs. Fake Accuracy: 4454/6000 (74.233%) \n","loss:  0.30848604440689087 Epoch:  4 Iteration:  551\n","loss:  0.28693559765815735 Epoch:  4 Iteration:  552\n","loss:  0.2950059771537781 Epoch:  4 Iteration:  553\n","loss:  0.28454896807670593 Epoch:  4 Iteration:  554\n","loss:  1.3570058345794678 Epoch:  5 Iteration:  555\n","loss:  1.319422721862793 Epoch:  5 Iteration:  556\n","loss:  1.2395975589752197 Epoch:  5 Iteration:  557\n","loss:  1.2819304466247559 Epoch:  5 Iteration:  558\n","loss:  1.2857296466827393 Epoch:  5 Iteration:  559\n","loss:  1.5309860706329346 Epoch:  5 Iteration:  560\n","tensor(4.7860, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4079, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6356, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9940, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3961/4720 (83.920%) \t and Real vs. Fake Accuracy: 4369/6000 (72.817%) \n","loss:  1.3326833248138428 Epoch:  5 Iteration:  561\n","loss:  1.2360007762908936 Epoch:  5 Iteration:  562\n","loss:  1.1351280212402344 Epoch:  5 Iteration:  563\n","loss:  1.2734700441360474 Epoch:  5 Iteration:  564\n","loss:  1.393180251121521 Epoch:  5 Iteration:  565\n","loss:  1.2268075942993164 Epoch:  5 Iteration:  566\n","loss:  1.4819592237472534 Epoch:  5 Iteration:  567\n","loss:  1.2445849180221558 Epoch:  5 Iteration:  568\n","loss:  1.303480863571167 Epoch:  5 Iteration:  569\n","loss:  1.3730891942977905 Epoch:  5 Iteration:  570\n","tensor(4.4978, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8969, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.1602, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2616, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3946/4720 (83.602%) \t and Real vs. Fake Accuracy: 4418/6000 (73.633%) \n","loss:  1.3616732358932495 Epoch:  5 Iteration:  571\n","loss:  1.223268747329712 Epoch:  5 Iteration:  572\n","loss:  1.450513482093811 Epoch:  5 Iteration:  573\n","loss:  1.2405681610107422 Epoch:  5 Iteration:  574\n","loss:  1.3850456476211548 Epoch:  5 Iteration:  575\n","loss:  1.4605612754821777 Epoch:  5 Iteration:  576\n","loss:  1.2280316352844238 Epoch:  5 Iteration:  577\n","loss:  1.322674036026001 Epoch:  5 Iteration:  578\n","loss:  1.37790048122406 Epoch:  5 Iteration:  579\n","loss:  1.2515579462051392 Epoch:  5 Iteration:  580\n","tensor(6.2148, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1977, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.5439, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2428, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3951/4720 (83.708%) \t and Real vs. Fake Accuracy: 4519/6000 (75.317%) \n","loss:  1.2560064792633057 Epoch:  5 Iteration:  581\n","loss:  1.5224195718765259 Epoch:  5 Iteration:  582\n","loss:  1.2709101438522339 Epoch:  5 Iteration:  583\n","loss:  1.3207648992538452 Epoch:  5 Iteration:  584\n","loss:  1.3311622142791748 Epoch:  5 Iteration:  585\n","loss:  1.3549610376358032 Epoch:  5 Iteration:  586\n","loss:  1.5759626626968384 Epoch:  5 Iteration:  587\n","loss:  1.2884762287139893 Epoch:  5 Iteration:  588\n","loss:  1.417772889137268 Epoch:  5 Iteration:  589\n","loss:  1.237569808959961 Epoch:  5 Iteration:  590\n","tensor(4.3709, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5936, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2983, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7438, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3962/4720 (83.941%) \t and Real vs. Fake Accuracy: 4558/6000 (75.967%) \n","loss:  1.4064960479736328 Epoch:  5 Iteration:  591\n","loss:  1.3060362339019775 Epoch:  5 Iteration:  592\n","loss:  1.1588973999023438 Epoch:  5 Iteration:  593\n","loss:  1.3873021602630615 Epoch:  5 Iteration:  594\n","loss:  1.3329768180847168 Epoch:  5 Iteration:  595\n","loss:  1.2983052730560303 Epoch:  5 Iteration:  596\n","loss:  1.2455246448516846 Epoch:  5 Iteration:  597\n","loss:  1.491492509841919 Epoch:  5 Iteration:  598\n","loss:  1.3493502140045166 Epoch:  5 Iteration:  599\n","loss:  1.3770732879638672 Epoch:  5 Iteration:  600\n","tensor(4.2572, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8901, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.4055, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5059, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3966/4720 (84.025%) \t and Real vs. Fake Accuracy: 4585/6000 (76.417%) \n","loss:  1.4215524196624756 Epoch:  5 Iteration:  601\n","loss:  1.2605350017547607 Epoch:  5 Iteration:  602\n","loss:  1.2681933641433716 Epoch:  5 Iteration:  603\n","loss:  1.388897180557251 Epoch:  5 Iteration:  604\n","loss:  1.3038262128829956 Epoch:  5 Iteration:  605\n","loss:  1.4770164489746094 Epoch:  5 Iteration:  606\n","loss:  1.243723750114441 Epoch:  5 Iteration:  607\n","loss:  1.3388640880584717 Epoch:  5 Iteration:  608\n","loss:  1.3073145151138306 Epoch:  5 Iteration:  609\n","loss:  1.2769615650177002 Epoch:  5 Iteration:  610\n","tensor(5.6813, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1385, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.5615, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0919, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3949/4720 (83.665%) \t and Real vs. Fake Accuracy: 4590/6000 (76.500%) \n","loss:  1.4360061883926392 Epoch:  5 Iteration:  611\n","loss:  1.4795172214508057 Epoch:  5 Iteration:  612\n","loss:  1.3273355960845947 Epoch:  5 Iteration:  613\n","loss:  1.217238187789917 Epoch:  5 Iteration:  614\n","loss:  1.3592711687088013 Epoch:  5 Iteration:  615\n","loss:  1.2899781465530396 Epoch:  5 Iteration:  616\n","loss:  1.4078526496887207 Epoch:  5 Iteration:  617\n","loss:  1.5030107498168945 Epoch:  5 Iteration:  618\n","loss:  1.2994096279144287 Epoch:  5 Iteration:  619\n","loss:  1.4454309940338135 Epoch:  5 Iteration:  620\n","tensor(4.3387, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9215, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5403, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1497, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3928/4720 (83.220%) \t and Real vs. Fake Accuracy: 4665/6000 (77.750%) \n","loss:  1.1984423398971558 Epoch:  5 Iteration:  621\n","loss:  1.3856558799743652 Epoch:  5 Iteration:  622\n","loss:  1.3733775615692139 Epoch:  5 Iteration:  623\n","loss:  1.4294626712799072 Epoch:  5 Iteration:  624\n","loss:  1.410937786102295 Epoch:  5 Iteration:  625\n","loss:  1.3818869590759277 Epoch:  5 Iteration:  626\n","loss:  1.3442978858947754 Epoch:  5 Iteration:  627\n","loss:  1.2611198425292969 Epoch:  5 Iteration:  628\n","loss:  1.4190958738327026 Epoch:  5 Iteration:  629\n","loss:  1.2746292352676392 Epoch:  5 Iteration:  630\n","tensor(4.8742, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0248, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.1357, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8432, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3971/4720 (84.131%) \t and Real vs. Fake Accuracy: 4682/6000 (78.033%) \n","loss:  1.26019287109375 Epoch:  5 Iteration:  631\n","loss:  1.299079418182373 Epoch:  5 Iteration:  632\n","loss:  1.4154020547866821 Epoch:  5 Iteration:  633\n","loss:  1.3442671298980713 Epoch:  5 Iteration:  634\n","loss:  1.1814113855361938 Epoch:  5 Iteration:  635\n","loss:  1.3495155572891235 Epoch:  5 Iteration:  636\n","loss:  1.5265662670135498 Epoch:  5 Iteration:  637\n","loss:  1.3679637908935547 Epoch:  5 Iteration:  638\n","loss:  1.4758379459381104 Epoch:  5 Iteration:  639\n","loss:  1.2998557090759277 Epoch:  5 Iteration:  640\n","tensor(4.4738, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4560, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.1545, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7357, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3947/4720 (83.623%) \t and Real vs. Fake Accuracy: 4722/6000 (78.700%) \n","loss:  1.2997151613235474 Epoch:  5 Iteration:  641\n","loss:  1.1714388132095337 Epoch:  5 Iteration:  642\n","loss:  1.12796950340271 Epoch:  5 Iteration:  643\n","loss:  1.564595341682434 Epoch:  5 Iteration:  644\n","loss:  1.4333600997924805 Epoch:  5 Iteration:  645\n","loss:  1.5964062213897705 Epoch:  5 Iteration:  646\n","loss:  1.4146008491516113 Epoch:  5 Iteration:  647\n","loss:  1.3063764572143555 Epoch:  5 Iteration:  648\n","loss:  0.3040461540222168 Epoch:  5 Iteration:  649\n","loss:  0.2968865633010864 Epoch:  5 Iteration:  650\n","tensor(4.3615, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0156, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.9618, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7183, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3960/4720 (83.898%) \t and Real vs. Fake Accuracy: 4705/6000 (78.417%) \n","loss:  0.23330464959144592 Epoch:  5 Iteration:  651\n","loss:  0.2779940366744995 Epoch:  5 Iteration:  652\n","loss:  0.28741884231567383 Epoch:  5 Iteration:  653\n","loss:  0.253914475440979 Epoch:  5 Iteration:  654\n","loss:  0.29243922233581543 Epoch:  5 Iteration:  655\n","loss:  0.2614673376083374 Epoch:  5 Iteration:  656\n","loss:  0.2420578896999359 Epoch:  5 Iteration:  657\n","loss:  0.2759467363357544 Epoch:  5 Iteration:  658\n","loss:  0.2944651246070862 Epoch:  5 Iteration:  659\n","loss:  0.2334369271993637 Epoch:  5 Iteration:  660\n","tensor(4.0902, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6869, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.2419, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9008, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3952/4720 (83.729%) \t and Real vs. Fake Accuracy: 4675/6000 (77.917%) \n","loss:  0.26711976528167725 Epoch:  5 Iteration:  661\n","loss:  0.2426116168498993 Epoch:  5 Iteration:  662\n","loss:  0.2554481029510498 Epoch:  5 Iteration:  663\n","loss:  0.24528872966766357 Epoch:  5 Iteration:  664\n","loss:  0.2653106451034546 Epoch:  5 Iteration:  665\n","loss:  1.2784931659698486 Epoch:  6 Iteration:  666\n","loss:  1.233412504196167 Epoch:  6 Iteration:  667\n","loss:  1.2920525074005127 Epoch:  6 Iteration:  668\n","loss:  1.1608127355575562 Epoch:  6 Iteration:  669\n","loss:  1.1241940259933472 Epoch:  6 Iteration:  670\n","tensor(6.1649, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7714, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.0673, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2853, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3965/4720 (84.004%) \t and Real vs. Fake Accuracy: 4667/6000 (77.783%) \n","loss:  1.2390007972717285 Epoch:  6 Iteration:  671\n","loss:  1.2065205574035645 Epoch:  6 Iteration:  672\n","loss:  1.140770435333252 Epoch:  6 Iteration:  673\n","loss:  1.445218563079834 Epoch:  6 Iteration:  674\n","loss:  1.3140045404434204 Epoch:  6 Iteration:  675\n","loss:  1.1153128147125244 Epoch:  6 Iteration:  676\n","loss:  1.1499316692352295 Epoch:  6 Iteration:  677\n","loss:  1.1958057880401611 Epoch:  6 Iteration:  678\n","loss:  1.2126212120056152 Epoch:  6 Iteration:  679\n","loss:  1.383263349533081 Epoch:  6 Iteration:  680\n","tensor(5.3713, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1202, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.9967, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6362, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3959/4720 (83.877%) \t and Real vs. Fake Accuracy: 4735/6000 (78.917%) \n","loss:  1.2970433235168457 Epoch:  6 Iteration:  681\n","loss:  1.5119037628173828 Epoch:  6 Iteration:  682\n","loss:  1.2132946252822876 Epoch:  6 Iteration:  683\n","loss:  1.1000683307647705 Epoch:  6 Iteration:  684\n","loss:  1.1518725156784058 Epoch:  6 Iteration:  685\n","loss:  1.2778124809265137 Epoch:  6 Iteration:  686\n","loss:  1.4000320434570312 Epoch:  6 Iteration:  687\n","loss:  1.260130524635315 Epoch:  6 Iteration:  688\n","loss:  1.287127137184143 Epoch:  6 Iteration:  689\n","loss:  1.0791682004928589 Epoch:  6 Iteration:  690\n","tensor(4.4856, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7133, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.4700, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1054, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3955/4720 (83.792%) \t and Real vs. Fake Accuracy: 4736/6000 (78.933%) \n","loss:  1.2505505084991455 Epoch:  6 Iteration:  691\n","loss:  1.3835537433624268 Epoch:  6 Iteration:  692\n","loss:  1.427236557006836 Epoch:  6 Iteration:  693\n","loss:  1.1737982034683228 Epoch:  6 Iteration:  694\n","loss:  1.3436377048492432 Epoch:  6 Iteration:  695\n","loss:  1.232269048690796 Epoch:  6 Iteration:  696\n","loss:  1.2176849842071533 Epoch:  6 Iteration:  697\n","loss:  1.0538878440856934 Epoch:  6 Iteration:  698\n","loss:  1.1981974840164185 Epoch:  6 Iteration:  699\n","loss:  1.2427557706832886 Epoch:  6 Iteration:  700\n","tensor(4.0182, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2176, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.1127, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5461, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3935/4720 (83.369%) \t and Real vs. Fake Accuracy: 4799/6000 (79.983%) \n","loss:  1.2185542583465576 Epoch:  6 Iteration:  701\n","loss:  1.2920243740081787 Epoch:  6 Iteration:  702\n","loss:  1.2084407806396484 Epoch:  6 Iteration:  703\n","loss:  1.1926536560058594 Epoch:  6 Iteration:  704\n","loss:  1.3363267183303833 Epoch:  6 Iteration:  705\n","loss:  1.0903959274291992 Epoch:  6 Iteration:  706\n","loss:  1.3421266078948975 Epoch:  6 Iteration:  707\n","loss:  1.301486611366272 Epoch:  6 Iteration:  708\n","loss:  1.0563931465148926 Epoch:  6 Iteration:  709\n","loss:  1.1009101867675781 Epoch:  6 Iteration:  710\n","tensor(4.6613, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4067, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8738, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9002, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3959/4720 (83.877%) \t and Real vs. Fake Accuracy: 4809/6000 (80.150%) \n","loss:  1.1632940769195557 Epoch:  6 Iteration:  711\n","loss:  1.1004619598388672 Epoch:  6 Iteration:  712\n","loss:  1.388214349746704 Epoch:  6 Iteration:  713\n","loss:  1.1964685916900635 Epoch:  6 Iteration:  714\n","loss:  1.2169610261917114 Epoch:  6 Iteration:  715\n","loss:  1.0946664810180664 Epoch:  6 Iteration:  716\n","loss:  1.0174014568328857 Epoch:  6 Iteration:  717\n","loss:  1.4449450969696045 Epoch:  6 Iteration:  718\n","loss:  1.4832768440246582 Epoch:  6 Iteration:  719\n","loss:  1.0971063375473022 Epoch:  6 Iteration:  720\n","tensor(6.5457, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3108, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.7157, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0058, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3930/4720 (83.263%) \t and Real vs. Fake Accuracy: 4805/6000 (80.083%) \n","loss:  1.2796075344085693 Epoch:  6 Iteration:  721\n","loss:  1.2233643531799316 Epoch:  6 Iteration:  722\n","loss:  1.1837749481201172 Epoch:  6 Iteration:  723\n","loss:  1.182367205619812 Epoch:  6 Iteration:  724\n","loss:  1.2043936252593994 Epoch:  6 Iteration:  725\n","loss:  1.1214427947998047 Epoch:  6 Iteration:  726\n","loss:  1.2997455596923828 Epoch:  6 Iteration:  727\n","loss:  1.3313536643981934 Epoch:  6 Iteration:  728\n","loss:  1.1817247867584229 Epoch:  6 Iteration:  729\n","loss:  1.2704548835754395 Epoch:  6 Iteration:  730\n","tensor(7.1683, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0845, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2039, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1281, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3956/4720 (83.814%) \t and Real vs. Fake Accuracy: 4875/6000 (81.250%) \n","loss:  1.2851011753082275 Epoch:  6 Iteration:  731\n","loss:  1.3484872579574585 Epoch:  6 Iteration:  732\n","loss:  1.3424700498580933 Epoch:  6 Iteration:  733\n","loss:  1.4765756130218506 Epoch:  6 Iteration:  734\n","loss:  1.2381114959716797 Epoch:  6 Iteration:  735\n","loss:  1.2174553871154785 Epoch:  6 Iteration:  736\n","loss:  1.2002837657928467 Epoch:  6 Iteration:  737\n","loss:  1.1981468200683594 Epoch:  6 Iteration:  738\n","loss:  1.3720917701721191 Epoch:  6 Iteration:  739\n","loss:  1.3209283351898193 Epoch:  6 Iteration:  740\n","tensor(5.6144, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5035, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8214, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1302, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3953/4720 (83.750%) \t and Real vs. Fake Accuracy: 4880/6000 (81.333%) \n","loss:  1.3109357357025146 Epoch:  6 Iteration:  741\n","loss:  1.3436365127563477 Epoch:  6 Iteration:  742\n","loss:  1.2485324144363403 Epoch:  6 Iteration:  743\n","loss:  1.2240043878555298 Epoch:  6 Iteration:  744\n","loss:  1.4840080738067627 Epoch:  6 Iteration:  745\n","loss:  1.2781561613082886 Epoch:  6 Iteration:  746\n","loss:  1.4493863582611084 Epoch:  6 Iteration:  747\n","loss:  1.4140492677688599 Epoch:  6 Iteration:  748\n","loss:  1.0777264833450317 Epoch:  6 Iteration:  749\n","loss:  1.2194240093231201 Epoch:  6 Iteration:  750\n","tensor(4.4564, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9642, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.5880, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1779, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3957/4720 (83.835%) \t and Real vs. Fake Accuracy: 4929/6000 (82.150%) \n","loss:  1.243719458580017 Epoch:  6 Iteration:  751\n","loss:  1.254930019378662 Epoch:  6 Iteration:  752\n","loss:  1.3011475801467896 Epoch:  6 Iteration:  753\n","loss:  1.2694556713104248 Epoch:  6 Iteration:  754\n","loss:  1.273141860961914 Epoch:  6 Iteration:  755\n","loss:  1.3440556526184082 Epoch:  6 Iteration:  756\n","loss:  1.3601433038711548 Epoch:  6 Iteration:  757\n","loss:  1.2920258045196533 Epoch:  6 Iteration:  758\n","loss:  1.1568832397460938 Epoch:  6 Iteration:  759\n","loss:  0.28701579570770264 Epoch:  6 Iteration:  760\n","tensor(6.1549, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5426, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.7376, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1990, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3956/4720 (83.814%) \t and Real vs. Fake Accuracy: 4945/6000 (82.417%) \n","loss:  0.2746100425720215 Epoch:  6 Iteration:  761\n","loss:  0.20687156915664673 Epoch:  6 Iteration:  762\n","loss:  0.2799150347709656 Epoch:  6 Iteration:  763\n","loss:  0.2784285545349121 Epoch:  6 Iteration:  764\n","loss:  0.23681199550628662 Epoch:  6 Iteration:  765\n","loss:  0.23595136404037476 Epoch:  6 Iteration:  766\n","loss:  0.2036815881729126 Epoch:  6 Iteration:  767\n","loss:  0.2556493282318115 Epoch:  6 Iteration:  768\n","loss:  0.28677940368652344 Epoch:  6 Iteration:  769\n","loss:  0.21191605925559998 Epoch:  6 Iteration:  770\n","tensor(5.5786, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0208, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.3804, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6416, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3965/4720 (84.004%) \t and Real vs. Fake Accuracy: 4850/6000 (80.833%) \n","loss:  0.2679802477359772 Epoch:  6 Iteration:  771\n","loss:  0.2316291481256485 Epoch:  6 Iteration:  772\n","loss:  0.23840424418449402 Epoch:  6 Iteration:  773\n","loss:  0.23263993859291077 Epoch:  6 Iteration:  774\n","loss:  0.24812793731689453 Epoch:  6 Iteration:  775\n","loss:  0.21571633219718933 Epoch:  6 Iteration:  776\n","loss:  1.1038053035736084 Epoch:  7 Iteration:  777\n","loss:  1.2285875082015991 Epoch:  7 Iteration:  778\n","loss:  1.0419328212738037 Epoch:  7 Iteration:  779\n","loss:  1.2485085725784302 Epoch:  7 Iteration:  780\n","tensor(7.8776, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4181, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2125, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5106, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3958/4720 (83.856%) \t and Real vs. Fake Accuracy: 4800/6000 (80.000%) \n","loss:  1.1620726585388184 Epoch:  7 Iteration:  781\n","loss:  1.2324997186660767 Epoch:  7 Iteration:  782\n","loss:  1.0069814920425415 Epoch:  7 Iteration:  783\n","loss:  1.3569639921188354 Epoch:  7 Iteration:  784\n","loss:  1.1565560102462769 Epoch:  7 Iteration:  785\n","loss:  0.9510539770126343 Epoch:  7 Iteration:  786\n","loss:  1.0920928716659546 Epoch:  7 Iteration:  787\n","loss:  1.256176471710205 Epoch:  7 Iteration:  788\n","loss:  1.1224653720855713 Epoch:  7 Iteration:  789\n","loss:  1.2215391397476196 Epoch:  7 Iteration:  790\n","tensor(6.1915, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2111, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.0670, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9898, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3954/4720 (83.771%) \t and Real vs. Fake Accuracy: 4901/6000 (81.683%) \n","loss:  1.0378128290176392 Epoch:  7 Iteration:  791\n","loss:  1.1061798334121704 Epoch:  7 Iteration:  792\n","loss:  1.1469773054122925 Epoch:  7 Iteration:  793\n","loss:  1.2533400058746338 Epoch:  7 Iteration:  794\n","loss:  1.104393482208252 Epoch:  7 Iteration:  795\n","loss:  1.183336615562439 Epoch:  7 Iteration:  796\n","loss:  1.0991425514221191 Epoch:  7 Iteration:  797\n","loss:  1.1342864036560059 Epoch:  7 Iteration:  798\n","loss:  0.88491290807724 Epoch:  7 Iteration:  799\n","loss:  1.1558454036712646 Epoch:  7 Iteration:  800\n","tensor(6.6501, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8333, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.2625, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0796, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3963/4720 (83.962%) \t and Real vs. Fake Accuracy: 4934/6000 (82.233%) \n","loss:  1.1788240671157837 Epoch:  7 Iteration:  801\n","loss:  1.0924959182739258 Epoch:  7 Iteration:  802\n","loss:  1.184851050376892 Epoch:  7 Iteration:  803\n","loss:  1.1395790576934814 Epoch:  7 Iteration:  804\n","loss:  1.0523736476898193 Epoch:  7 Iteration:  805\n","loss:  1.1902477741241455 Epoch:  7 Iteration:  806\n","loss:  1.2008099555969238 Epoch:  7 Iteration:  807\n","loss:  1.2452627420425415 Epoch:  7 Iteration:  808\n","loss:  1.2270023822784424 Epoch:  7 Iteration:  809\n","loss:  1.1919993162155151 Epoch:  7 Iteration:  810\n","tensor(5.6566, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3247, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.7424, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1327, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3959/4720 (83.877%) \t and Real vs. Fake Accuracy: 4943/6000 (82.383%) \n","loss:  1.273088812828064 Epoch:  7 Iteration:  811\n","loss:  1.1868841648101807 Epoch:  7 Iteration:  812\n","loss:  1.226433277130127 Epoch:  7 Iteration:  813\n","loss:  1.054738998413086 Epoch:  7 Iteration:  814\n","loss:  1.136343240737915 Epoch:  7 Iteration:  815\n","loss:  1.0656819343566895 Epoch:  7 Iteration:  816\n","loss:  1.2940672636032104 Epoch:  7 Iteration:  817\n","loss:  1.1503159999847412 Epoch:  7 Iteration:  818\n","loss:  1.1896106004714966 Epoch:  7 Iteration:  819\n","loss:  1.1673426628112793 Epoch:  7 Iteration:  820\n","tensor(7.6108, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4713, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.3126, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8640, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3953/4720 (83.750%) \t and Real vs. Fake Accuracy: 4988/6000 (83.133%) \n","loss:  1.1518239974975586 Epoch:  7 Iteration:  821\n","loss:  1.091957688331604 Epoch:  7 Iteration:  822\n","loss:  1.1537991762161255 Epoch:  7 Iteration:  823\n","loss:  1.1341633796691895 Epoch:  7 Iteration:  824\n","loss:  1.2045930624008179 Epoch:  7 Iteration:  825\n","loss:  1.092208981513977 Epoch:  7 Iteration:  826\n","loss:  0.9037107825279236 Epoch:  7 Iteration:  827\n","loss:  1.0773370265960693 Epoch:  7 Iteration:  828\n","loss:  1.0928785800933838 Epoch:  7 Iteration:  829\n","loss:  1.194335699081421 Epoch:  7 Iteration:  830\n","tensor(6.5559, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8886, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8912, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5025, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3956/4720 (83.814%) \t and Real vs. Fake Accuracy: 5023/6000 (83.717%) \n","loss:  1.2466402053833008 Epoch:  7 Iteration:  831\n","loss:  1.306535243988037 Epoch:  7 Iteration:  832\n","loss:  1.2063610553741455 Epoch:  7 Iteration:  833\n","loss:  1.1053217649459839 Epoch:  7 Iteration:  834\n","loss:  1.1761555671691895 Epoch:  7 Iteration:  835\n","loss:  1.1167525053024292 Epoch:  7 Iteration:  836\n","loss:  1.0854915380477905 Epoch:  7 Iteration:  837\n","loss:  1.1527212858200073 Epoch:  7 Iteration:  838\n","loss:  1.2629388570785522 Epoch:  7 Iteration:  839\n","loss:  1.1789186000823975 Epoch:  7 Iteration:  840\n","tensor(5.0171, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.8820, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6005, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4417, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3959/4720 (83.877%) \t and Real vs. Fake Accuracy: 5060/6000 (84.333%) \n","loss:  1.0741814374923706 Epoch:  7 Iteration:  841\n","loss:  1.2487773895263672 Epoch:  7 Iteration:  842\n","loss:  0.9630768299102783 Epoch:  7 Iteration:  843\n","loss:  1.165187954902649 Epoch:  7 Iteration:  844\n","loss:  1.2172657251358032 Epoch:  7 Iteration:  845\n","loss:  1.0722633600234985 Epoch:  7 Iteration:  846\n","loss:  1.1195294857025146 Epoch:  7 Iteration:  847\n","loss:  1.120585322380066 Epoch:  7 Iteration:  848\n","loss:  1.2452278137207031 Epoch:  7 Iteration:  849\n","loss:  1.191772222518921 Epoch:  7 Iteration:  850\n","tensor(3.9207, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1668, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.4346, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0597, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3952/4720 (83.729%) \t and Real vs. Fake Accuracy: 5057/6000 (84.283%) \n","loss:  1.2311137914657593 Epoch:  7 Iteration:  851\n","loss:  1.1285209655761719 Epoch:  7 Iteration:  852\n","loss:  0.9789268970489502 Epoch:  7 Iteration:  853\n","loss:  1.0519522428512573 Epoch:  7 Iteration:  854\n","loss:  1.3552817106246948 Epoch:  7 Iteration:  855\n","loss:  1.0807465314865112 Epoch:  7 Iteration:  856\n","loss:  1.2291991710662842 Epoch:  7 Iteration:  857\n","loss:  1.1869961023330688 Epoch:  7 Iteration:  858\n","loss:  1.0018218755722046 Epoch:  7 Iteration:  859\n","loss:  1.2045773267745972 Epoch:  7 Iteration:  860\n","tensor(6.8432, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7208, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2422, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9472, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3946/4720 (83.602%) \t and Real vs. Fake Accuracy: 5115/6000 (85.250%) \n","loss:  1.1379649639129639 Epoch:  7 Iteration:  861\n","loss:  1.138500690460205 Epoch:  7 Iteration:  862\n","loss:  1.1283936500549316 Epoch:  7 Iteration:  863\n","loss:  1.157827377319336 Epoch:  7 Iteration:  864\n","loss:  1.026811122894287 Epoch:  7 Iteration:  865\n","loss:  1.219456672668457 Epoch:  7 Iteration:  866\n","loss:  1.3834059238433838 Epoch:  7 Iteration:  867\n","loss:  1.1509666442871094 Epoch:  7 Iteration:  868\n","loss:  1.167624831199646 Epoch:  7 Iteration:  869\n","loss:  1.2929997444152832 Epoch:  7 Iteration:  870\n","tensor(5.4434, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0701, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.8587, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6812, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3952/4720 (83.729%) \t and Real vs. Fake Accuracy: 5110/6000 (85.167%) \n","loss:  0.29249948263168335 Epoch:  7 Iteration:  871\n","loss:  0.2000349760055542 Epoch:  7 Iteration:  872\n","loss:  0.2554326057434082 Epoch:  7 Iteration:  873\n","loss:  0.2567741870880127 Epoch:  7 Iteration:  874\n","loss:  0.2155487835407257 Epoch:  7 Iteration:  875\n","loss:  0.22782841324806213 Epoch:  7 Iteration:  876\n","loss:  0.23055554926395416 Epoch:  7 Iteration:  877\n","loss:  0.23698455095291138 Epoch:  7 Iteration:  878\n","loss:  0.24995937943458557 Epoch:  7 Iteration:  879\n","loss:  0.2208472341299057 Epoch:  7 Iteration:  880\n","tensor(4.3743, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2159, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.9036, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0167, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3947/4720 (83.623%) \t and Real vs. Fake Accuracy: 5077/6000 (84.617%) \n","loss:  0.24055343866348267 Epoch:  7 Iteration:  881\n","loss:  0.3156158924102783 Epoch:  7 Iteration:  882\n","loss:  0.2511712312698364 Epoch:  7 Iteration:  883\n","loss:  0.25256025791168213 Epoch:  7 Iteration:  884\n","loss:  0.28294235467910767 Epoch:  7 Iteration:  885\n","loss:  0.22850364446640015 Epoch:  7 Iteration:  886\n","loss:  0.23977826535701752 Epoch:  7 Iteration:  887\n","loss:  1.3280057907104492 Epoch:  8 Iteration:  888\n","loss:  1.143019437789917 Epoch:  8 Iteration:  889\n","loss:  1.0252594947814941 Epoch:  8 Iteration:  890\n","tensor(5.0869, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3767, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0363, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2698, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3961/4720 (83.920%) \t and Real vs. Fake Accuracy: 5072/6000 (84.533%) \n","loss:  1.0087968111038208 Epoch:  8 Iteration:  891\n","loss:  0.9622244834899902 Epoch:  8 Iteration:  892\n","loss:  1.0481395721435547 Epoch:  8 Iteration:  893\n","loss:  1.1767659187316895 Epoch:  8 Iteration:  894\n","loss:  0.9602066874504089 Epoch:  8 Iteration:  895\n","loss:  0.9857421517372131 Epoch:  8 Iteration:  896\n","loss:  1.0762913227081299 Epoch:  8 Iteration:  897\n","loss:  0.9378941059112549 Epoch:  8 Iteration:  898\n","loss:  1.0815601348876953 Epoch:  8 Iteration:  899\n","loss:  1.0556151866912842 Epoch:  8 Iteration:  900\n","tensor(5.1844, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4049, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8595, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5554, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3966/4720 (84.025%) \t and Real vs. Fake Accuracy: 5098/6000 (84.967%) \n","loss:  1.0629069805145264 Epoch:  8 Iteration:  901\n","loss:  1.0237423181533813 Epoch:  8 Iteration:  902\n","loss:  1.0031272172927856 Epoch:  8 Iteration:  903\n","loss:  1.3138704299926758 Epoch:  8 Iteration:  904\n","loss:  0.9140965938568115 Epoch:  8 Iteration:  905\n","loss:  0.9945051670074463 Epoch:  8 Iteration:  906\n","loss:  1.1176326274871826 Epoch:  8 Iteration:  907\n","loss:  1.102623701095581 Epoch:  8 Iteration:  908\n","loss:  1.0859872102737427 Epoch:  8 Iteration:  909\n","loss:  0.9539459347724915 Epoch:  8 Iteration:  910\n","tensor(4.9019, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4063, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.0062, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3691, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3971/4720 (84.131%) \t and Real vs. Fake Accuracy: 5122/6000 (85.367%) \n","loss:  0.9726545810699463 Epoch:  8 Iteration:  911\n","loss:  1.1012530326843262 Epoch:  8 Iteration:  912\n","loss:  1.201277494430542 Epoch:  8 Iteration:  913\n","loss:  1.0327211618423462 Epoch:  8 Iteration:  914\n","loss:  0.9339753985404968 Epoch:  8 Iteration:  915\n","loss:  0.9920579195022583 Epoch:  8 Iteration:  916\n","loss:  1.0071020126342773 Epoch:  8 Iteration:  917\n","loss:  1.069521427154541 Epoch:  8 Iteration:  918\n","loss:  1.0158963203430176 Epoch:  8 Iteration:  919\n","loss:  0.951461136341095 Epoch:  8 Iteration:  920\n","tensor(8.1170, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2594, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0982, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9002, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3972/4720 (84.152%) \t and Real vs. Fake Accuracy: 5129/6000 (85.483%) \n","loss:  0.9225154519081116 Epoch:  8 Iteration:  921\n","loss:  0.9766992330551147 Epoch:  8 Iteration:  922\n","loss:  1.017997145652771 Epoch:  8 Iteration:  923\n","loss:  1.0565377473831177 Epoch:  8 Iteration:  924\n","loss:  1.0250025987625122 Epoch:  8 Iteration:  925\n","loss:  0.9628974795341492 Epoch:  8 Iteration:  926\n","loss:  1.0522650480270386 Epoch:  8 Iteration:  927\n","loss:  1.1098591089248657 Epoch:  8 Iteration:  928\n","loss:  1.185097098350525 Epoch:  8 Iteration:  929\n","loss:  1.0891014337539673 Epoch:  8 Iteration:  930\n","tensor(5.9402, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6144, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.0863, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1761, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3966/4720 (84.025%) \t and Real vs. Fake Accuracy: 5152/6000 (85.867%) \n","loss:  1.1080161333084106 Epoch:  8 Iteration:  931\n","loss:  1.0495613813400269 Epoch:  8 Iteration:  932\n","loss:  0.9307432770729065 Epoch:  8 Iteration:  933\n","loss:  1.1631004810333252 Epoch:  8 Iteration:  934\n","loss:  1.1250436305999756 Epoch:  8 Iteration:  935\n","loss:  1.0508840084075928 Epoch:  8 Iteration:  936\n","loss:  1.0633686780929565 Epoch:  8 Iteration:  937\n","loss:  1.0888644456863403 Epoch:  8 Iteration:  938\n","loss:  1.304196834564209 Epoch:  8 Iteration:  939\n","loss:  1.3639285564422607 Epoch:  8 Iteration:  940\n","tensor(4.0318, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4530, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4867, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8180, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3951/4720 (83.708%) \t and Real vs. Fake Accuracy: 5199/6000 (86.650%) \n","loss:  1.06794273853302 Epoch:  8 Iteration:  941\n","loss:  1.0362114906311035 Epoch:  8 Iteration:  942\n","loss:  1.0703846216201782 Epoch:  8 Iteration:  943\n","loss:  1.1390187740325928 Epoch:  8 Iteration:  944\n","loss:  1.0012251138687134 Epoch:  8 Iteration:  945\n","loss:  1.0754386186599731 Epoch:  8 Iteration:  946\n","loss:  1.051189661026001 Epoch:  8 Iteration:  947\n","loss:  1.0851480960845947 Epoch:  8 Iteration:  948\n","loss:  1.0337735414505005 Epoch:  8 Iteration:  949\n","loss:  1.1816024780273438 Epoch:  8 Iteration:  950\n","tensor(4.1461, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3494, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8244, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4196, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3955/4720 (83.792%) \t and Real vs. Fake Accuracy: 5226/6000 (87.100%) \n","loss:  1.132477045059204 Epoch:  8 Iteration:  951\n","loss:  1.0763576030731201 Epoch:  8 Iteration:  952\n","loss:  0.9941548705101013 Epoch:  8 Iteration:  953\n","loss:  1.080381155014038 Epoch:  8 Iteration:  954\n","loss:  0.9398696422576904 Epoch:  8 Iteration:  955\n","loss:  0.9141464233398438 Epoch:  8 Iteration:  956\n","loss:  0.9997643828392029 Epoch:  8 Iteration:  957\n","loss:  1.1827259063720703 Epoch:  8 Iteration:  958\n","loss:  0.9969125986099243 Epoch:  8 Iteration:  959\n","loss:  0.9729820489883423 Epoch:  8 Iteration:  960\n","tensor(5.0929, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8343, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.3414, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2400, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3963/4720 (83.962%) \t and Real vs. Fake Accuracy: 5212/6000 (86.867%) \n","loss:  1.1582807302474976 Epoch:  8 Iteration:  961\n","loss:  1.1153593063354492 Epoch:  8 Iteration:  962\n","loss:  1.0444881916046143 Epoch:  8 Iteration:  963\n","loss:  1.069639801979065 Epoch:  8 Iteration:  964\n","loss:  1.1884959936141968 Epoch:  8 Iteration:  965\n","loss:  1.0146617889404297 Epoch:  8 Iteration:  966\n","loss:  1.087929368019104 Epoch:  8 Iteration:  967\n","loss:  1.1529854536056519 Epoch:  8 Iteration:  968\n","loss:  1.1235015392303467 Epoch:  8 Iteration:  969\n","loss:  1.2026755809783936 Epoch:  8 Iteration:  970\n","tensor(5.2410, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0099, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.6341, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2416, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3964/4720 (83.983%) \t and Real vs. Fake Accuracy: 5203/6000 (86.717%) \n","loss:  1.1103533506393433 Epoch:  8 Iteration:  971\n","loss:  1.0334928035736084 Epoch:  8 Iteration:  972\n","loss:  1.0817152261734009 Epoch:  8 Iteration:  973\n","loss:  1.1958298683166504 Epoch:  8 Iteration:  974\n","loss:  1.1146659851074219 Epoch:  8 Iteration:  975\n","loss:  0.9742200374603271 Epoch:  8 Iteration:  976\n","loss:  1.2323179244995117 Epoch:  8 Iteration:  977\n","loss:  1.2083884477615356 Epoch:  8 Iteration:  978\n","loss:  1.0190589427947998 Epoch:  8 Iteration:  979\n","loss:  1.086644172668457 Epoch:  8 Iteration:  980\n","tensor(6.1261, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4484, device='cuda:0', grad_fn=<MinBackward1>) tensor(5.9598, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3342, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3967/4720 (84.047%) \t and Real vs. Fake Accuracy: 5287/6000 (88.117%) \n","loss:  0.9205831289291382 Epoch:  8 Iteration:  981\n","loss:  0.22397026419639587 Epoch:  8 Iteration:  982\n","loss:  0.18182100355625153 Epoch:  8 Iteration:  983\n","loss:  0.23148782551288605 Epoch:  8 Iteration:  984\n","loss:  0.21928399801254272 Epoch:  8 Iteration:  985\n","loss:  0.1959153562784195 Epoch:  8 Iteration:  986\n","loss:  0.23007947206497192 Epoch:  8 Iteration:  987\n","loss:  0.250654399394989 Epoch:  8 Iteration:  988\n","loss:  0.219931960105896 Epoch:  8 Iteration:  989\n","loss:  0.25452154874801636 Epoch:  8 Iteration:  990\n","tensor(4.7151, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4999, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.3371, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0902, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3975/4720 (84.216%) \t and Real vs. Fake Accuracy: 5246/6000 (87.433%) \n","loss:  0.21696531772613525 Epoch:  8 Iteration:  991\n","loss:  0.20632928609848022 Epoch:  8 Iteration:  992\n","loss:  0.2472599297761917 Epoch:  8 Iteration:  993\n","loss:  0.20033803582191467 Epoch:  8 Iteration:  994\n","loss:  0.22085069119930267 Epoch:  8 Iteration:  995\n","loss:  0.18077442049980164 Epoch:  8 Iteration:  996\n","loss:  0.2540009915828705 Epoch:  8 Iteration:  997\n","loss:  0.20475473999977112 Epoch:  8 Iteration:  998\n","loss:  0.9392023682594299 Epoch:  9 Iteration:  999\n","loss:  0.8936737775802612 Epoch:  9 Iteration:  1000\n","tensor(5.0520, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7871, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.7778, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4492, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3976/4720 (84.237%) \t and Real vs. Fake Accuracy: 5216/6000 (86.933%) \n","loss:  1.1411107778549194 Epoch:  9 Iteration:  1001\n","loss:  0.9112902879714966 Epoch:  9 Iteration:  1002\n","loss:  1.0301152467727661 Epoch:  9 Iteration:  1003\n","loss:  0.869141697883606 Epoch:  9 Iteration:  1004\n","loss:  1.0132346153259277 Epoch:  9 Iteration:  1005\n","loss:  0.8746567964553833 Epoch:  9 Iteration:  1006\n","loss:  1.1860450506210327 Epoch:  9 Iteration:  1007\n","loss:  0.9822465181350708 Epoch:  9 Iteration:  1008\n","loss:  0.9161237478256226 Epoch:  9 Iteration:  1009\n","loss:  0.9509307146072388 Epoch:  9 Iteration:  1010\n","tensor(4.4579, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5983, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.2283, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1285, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3971/4720 (84.131%) \t and Real vs. Fake Accuracy: 5270/6000 (87.833%) \n","loss:  0.8329322338104248 Epoch:  9 Iteration:  1011\n","loss:  0.8915200233459473 Epoch:  9 Iteration:  1012\n","loss:  0.9344403147697449 Epoch:  9 Iteration:  1013\n","loss:  0.8424926400184631 Epoch:  9 Iteration:  1014\n","loss:  0.9981492757797241 Epoch:  9 Iteration:  1015\n","loss:  0.9205907583236694 Epoch:  9 Iteration:  1016\n","loss:  0.9710643887519836 Epoch:  9 Iteration:  1017\n","loss:  1.0246269702911377 Epoch:  9 Iteration:  1018\n","loss:  1.0519922971725464 Epoch:  9 Iteration:  1019\n","loss:  1.0008561611175537 Epoch:  9 Iteration:  1020\n","tensor(4.5003, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3357, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8625, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0185, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3957/4720 (83.835%) \t and Real vs. Fake Accuracy: 5286/6000 (88.100%) \n","loss:  0.9644590020179749 Epoch:  9 Iteration:  1021\n","loss:  1.0816209316253662 Epoch:  9 Iteration:  1022\n","loss:  0.8772947192192078 Epoch:  9 Iteration:  1023\n","loss:  0.9470758438110352 Epoch:  9 Iteration:  1024\n","loss:  0.9412025213241577 Epoch:  9 Iteration:  1025\n","loss:  0.9817433953285217 Epoch:  9 Iteration:  1026\n","loss:  0.9739607572555542 Epoch:  9 Iteration:  1027\n","loss:  1.0259281396865845 Epoch:  9 Iteration:  1028\n","loss:  0.8945455551147461 Epoch:  9 Iteration:  1029\n","loss:  0.8688963055610657 Epoch:  9 Iteration:  1030\n","tensor(5.5378, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5989, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.9118, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0659, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3962/4720 (83.941%) \t and Real vs. Fake Accuracy: 5295/6000 (88.250%) \n","loss:  1.3190193176269531 Epoch:  9 Iteration:  1031\n","loss:  1.0505709648132324 Epoch:  9 Iteration:  1032\n","loss:  0.9599421620368958 Epoch:  9 Iteration:  1033\n","loss:  1.0945703983306885 Epoch:  9 Iteration:  1034\n","loss:  0.9469048976898193 Epoch:  9 Iteration:  1035\n","loss:  1.006566047668457 Epoch:  9 Iteration:  1036\n","loss:  1.2306410074234009 Epoch:  9 Iteration:  1037\n","loss:  0.9254100918769836 Epoch:  9 Iteration:  1038\n","loss:  0.9739354848861694 Epoch:  9 Iteration:  1039\n","loss:  0.9455822706222534 Epoch:  9 Iteration:  1040\n","tensor(5.3191, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.7706, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.9951, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2952, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3962/4720 (83.941%) \t and Real vs. Fake Accuracy: 5347/6000 (89.117%) \n","loss:  0.9888718724250793 Epoch:  9 Iteration:  1041\n","loss:  1.0919368267059326 Epoch:  9 Iteration:  1042\n","loss:  1.0107027292251587 Epoch:  9 Iteration:  1043\n","loss:  0.7431530952453613 Epoch:  9 Iteration:  1044\n","loss:  0.9599100947380066 Epoch:  9 Iteration:  1045\n","loss:  1.0198105573654175 Epoch:  9 Iteration:  1046\n","loss:  1.0050309896469116 Epoch:  9 Iteration:  1047\n","loss:  1.144946813583374 Epoch:  9 Iteration:  1048\n","loss:  1.0199674367904663 Epoch:  9 Iteration:  1049\n","loss:  1.020955204963684 Epoch:  9 Iteration:  1050\n","tensor(6.2136, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3056, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.0071, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6022, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3972/4720 (84.152%) \t and Real vs. Fake Accuracy: 5333/6000 (88.883%) \n","loss:  0.9344567656517029 Epoch:  9 Iteration:  1051\n","loss:  0.991462230682373 Epoch:  9 Iteration:  1052\n","loss:  0.9374450445175171 Epoch:  9 Iteration:  1053\n","loss:  1.0421526432037354 Epoch:  9 Iteration:  1054\n","loss:  0.8782894611358643 Epoch:  9 Iteration:  1055\n","loss:  1.1566675901412964 Epoch:  9 Iteration:  1056\n","loss:  1.0057860612869263 Epoch:  9 Iteration:  1057\n","loss:  1.0018424987792969 Epoch:  9 Iteration:  1058\n","loss:  0.8834279179573059 Epoch:  9 Iteration:  1059\n","loss:  0.9893324971199036 Epoch:  9 Iteration:  1060\n","tensor(6.0489, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5728, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.4931, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5067, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3983/4720 (84.386%) \t and Real vs. Fake Accuracy: 5359/6000 (89.317%) \n","loss:  0.8456317782402039 Epoch:  9 Iteration:  1061\n","loss:  1.0262876749038696 Epoch:  9 Iteration:  1062\n","loss:  0.9755092859268188 Epoch:  9 Iteration:  1063\n","loss:  1.0470117330551147 Epoch:  9 Iteration:  1064\n","loss:  1.0258127450942993 Epoch:  9 Iteration:  1065\n","loss:  0.9413933753967285 Epoch:  9 Iteration:  1066\n","loss:  1.0152604579925537 Epoch:  9 Iteration:  1067\n","loss:  0.9614592790603638 Epoch:  9 Iteration:  1068\n","loss:  0.9596136808395386 Epoch:  9 Iteration:  1069\n","loss:  1.0639369487762451 Epoch:  9 Iteration:  1070\n","tensor(4.3671, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7696, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.4481, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8388, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3966/4720 (84.025%) \t and Real vs. Fake Accuracy: 5364/6000 (89.400%) \n","loss:  1.0184664726257324 Epoch:  9 Iteration:  1071\n","loss:  1.0443097352981567 Epoch:  9 Iteration:  1072\n","loss:  0.9550391435623169 Epoch:  9 Iteration:  1073\n","loss:  0.9782701134681702 Epoch:  9 Iteration:  1074\n","loss:  1.0603880882263184 Epoch:  9 Iteration:  1075\n","loss:  1.1156156063079834 Epoch:  9 Iteration:  1076\n","loss:  1.1005504131317139 Epoch:  9 Iteration:  1077\n","loss:  0.8889358043670654 Epoch:  9 Iteration:  1078\n","loss:  1.102934718132019 Epoch:  9 Iteration:  1079\n","loss:  0.9482104778289795 Epoch:  9 Iteration:  1080\n","tensor(6.3464, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2678, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.1532, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5789, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3976/4720 (84.237%) \t and Real vs. Fake Accuracy: 5368/6000 (89.467%) \n","loss:  1.0409159660339355 Epoch:  9 Iteration:  1081\n","loss:  0.9697731733322144 Epoch:  9 Iteration:  1082\n","loss:  0.9086025953292847 Epoch:  9 Iteration:  1083\n","loss:  0.9850854277610779 Epoch:  9 Iteration:  1084\n","loss:  0.9554320573806763 Epoch:  9 Iteration:  1085\n","loss:  1.0899196863174438 Epoch:  9 Iteration:  1086\n","loss:  1.038245677947998 Epoch:  9 Iteration:  1087\n","loss:  0.9773250222206116 Epoch:  9 Iteration:  1088\n","loss:  0.867234468460083 Epoch:  9 Iteration:  1089\n","loss:  0.9555693864822388 Epoch:  9 Iteration:  1090\n","tensor(6.4888, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0824, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.2997, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0391, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3977/4720 (84.258%) \t and Real vs. Fake Accuracy: 5411/6000 (90.183%) \n","loss:  0.9809431433677673 Epoch:  9 Iteration:  1091\n","loss:  1.0298887491226196 Epoch:  9 Iteration:  1092\n","loss:  0.22494372725486755 Epoch:  9 Iteration:  1093\n","loss:  0.20628952980041504 Epoch:  9 Iteration:  1094\n","loss:  0.20977303385734558 Epoch:  9 Iteration:  1095\n","loss:  0.2482771873474121 Epoch:  9 Iteration:  1096\n","loss:  0.18501372635364532 Epoch:  9 Iteration:  1097\n","loss:  0.23695841431617737 Epoch:  9 Iteration:  1098\n","loss:  0.1780512034893036 Epoch:  9 Iteration:  1099\n","loss:  0.2050691843032837 Epoch:  9 Iteration:  1100\n","tensor(4.3186, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4230, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0966, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1100, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3968/4720 (84.068%) \t and Real vs. Fake Accuracy: 5411/6000 (90.183%) \n","loss:  0.22033265233039856 Epoch:  9 Iteration:  1101\n","loss:  0.20120781660079956 Epoch:  9 Iteration:  1102\n","loss:  0.21383392810821533 Epoch:  9 Iteration:  1103\n","loss:  0.23600783944129944 Epoch:  9 Iteration:  1104\n","loss:  0.22755174338817596 Epoch:  9 Iteration:  1105\n","loss:  0.23305150866508484 Epoch:  9 Iteration:  1106\n","loss:  0.22712130844593048 Epoch:  9 Iteration:  1107\n","loss:  0.2246110737323761 Epoch:  9 Iteration:  1108\n","loss:  0.27398523688316345 Epoch:  9 Iteration:  1109\n","loss:  0.9635577201843262 Epoch:  10 Iteration:  1110\n","tensor(4.8147, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9990, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.9951, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6907, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3956/4720 (83.814%) \t and Real vs. Fake Accuracy: 5347/6000 (89.117%) \n","loss:  0.9335291385650635 Epoch:  10 Iteration:  1111\n","loss:  0.8520081043243408 Epoch:  10 Iteration:  1112\n","loss:  0.933026134967804 Epoch:  10 Iteration:  1113\n","loss:  0.9645570516586304 Epoch:  10 Iteration:  1114\n","loss:  0.9307430982589722 Epoch:  10 Iteration:  1115\n","loss:  0.8179347515106201 Epoch:  10 Iteration:  1116\n","loss:  0.9254509806632996 Epoch:  10 Iteration:  1117\n","loss:  0.9644978046417236 Epoch:  10 Iteration:  1118\n","loss:  0.7491210699081421 Epoch:  10 Iteration:  1119\n","loss:  0.8114302754402161 Epoch:  10 Iteration:  1120\n","tensor(5.0344, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8420, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.9372, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4616, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3968/4720 (84.068%) \t and Real vs. Fake Accuracy: 5379/6000 (89.650%) \n","loss:  0.9414627552032471 Epoch:  10 Iteration:  1121\n","loss:  0.8606081604957581 Epoch:  10 Iteration:  1122\n","loss:  0.8669068813323975 Epoch:  10 Iteration:  1123\n","loss:  0.8109713196754456 Epoch:  10 Iteration:  1124\n","loss:  0.9849923253059387 Epoch:  10 Iteration:  1125\n","loss:  0.9598941802978516 Epoch:  10 Iteration:  1126\n","loss:  1.0372872352600098 Epoch:  10 Iteration:  1127\n","loss:  0.8865323662757874 Epoch:  10 Iteration:  1128\n","loss:  0.9030320644378662 Epoch:  10 Iteration:  1129\n","loss:  0.9715176820755005 Epoch:  10 Iteration:  1130\n","tensor(4.6535, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7458, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5864, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2226, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3964/4720 (83.983%) \t and Real vs. Fake Accuracy: 5431/6000 (90.517%) \n","loss:  0.855451226234436 Epoch:  10 Iteration:  1131\n","loss:  0.9548935890197754 Epoch:  10 Iteration:  1132\n","loss:  0.9261850118637085 Epoch:  10 Iteration:  1133\n","loss:  0.7364388704299927 Epoch:  10 Iteration:  1134\n","loss:  0.9480372667312622 Epoch:  10 Iteration:  1135\n","loss:  0.8212372064590454 Epoch:  10 Iteration:  1136\n","loss:  0.8943713307380676 Epoch:  10 Iteration:  1137\n","loss:  0.8707083463668823 Epoch:  10 Iteration:  1138\n","loss:  0.839605450630188 Epoch:  10 Iteration:  1139\n","loss:  1.0555247068405151 Epoch:  10 Iteration:  1140\n","tensor(4.6138, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9860, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.6062, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9525, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3976/4720 (84.237%) \t and Real vs. Fake Accuracy: 5446/6000 (90.767%) \n","loss:  0.9216668605804443 Epoch:  10 Iteration:  1141\n","loss:  0.802339494228363 Epoch:  10 Iteration:  1142\n","loss:  0.9319154024124146 Epoch:  10 Iteration:  1143\n","loss:  0.9075095653533936 Epoch:  10 Iteration:  1144\n","loss:  0.9500385522842407 Epoch:  10 Iteration:  1145\n","loss:  0.973366379737854 Epoch:  10 Iteration:  1146\n","loss:  0.9053295850753784 Epoch:  10 Iteration:  1147\n","loss:  0.8865654468536377 Epoch:  10 Iteration:  1148\n","loss:  0.8986231088638306 Epoch:  10 Iteration:  1149\n","loss:  0.9100425243377686 Epoch:  10 Iteration:  1150\n","tensor(5.9328, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4585, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.2356, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3427, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5434/6000 (90.567%) \n","loss:  0.8562222719192505 Epoch:  10 Iteration:  1151\n","loss:  0.9689155220985413 Epoch:  10 Iteration:  1152\n","loss:  0.9669784307479858 Epoch:  10 Iteration:  1153\n","loss:  1.0022703409194946 Epoch:  10 Iteration:  1154\n","loss:  0.9434020519256592 Epoch:  10 Iteration:  1155\n","loss:  1.036215901374817 Epoch:  10 Iteration:  1156\n","loss:  0.9557666182518005 Epoch:  10 Iteration:  1157\n","loss:  0.941179096698761 Epoch:  10 Iteration:  1158\n","loss:  0.7843301892280579 Epoch:  10 Iteration:  1159\n","loss:  1.0555237531661987 Epoch:  10 Iteration:  1160\n","tensor(8.3970, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1408, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.0990, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2071, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3975/4720 (84.216%) \t and Real vs. Fake Accuracy: 5459/6000 (90.983%) \n","loss:  0.9913055896759033 Epoch:  10 Iteration:  1161\n","loss:  0.7777172327041626 Epoch:  10 Iteration:  1162\n","loss:  0.8785498142242432 Epoch:  10 Iteration:  1163\n","loss:  0.9109605550765991 Epoch:  10 Iteration:  1164\n","loss:  0.9792450666427612 Epoch:  10 Iteration:  1165\n","loss:  0.885665774345398 Epoch:  10 Iteration:  1166\n","loss:  0.9904073476791382 Epoch:  10 Iteration:  1167\n","loss:  0.8731144070625305 Epoch:  10 Iteration:  1168\n","loss:  0.9412566423416138 Epoch:  10 Iteration:  1169\n","loss:  0.9973790645599365 Epoch:  10 Iteration:  1170\n","tensor(5.1962, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1688, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5378, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9348, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3969/4720 (84.089%) \t and Real vs. Fake Accuracy: 5475/6000 (91.250%) \n","loss:  0.8272857666015625 Epoch:  10 Iteration:  1171\n","loss:  0.9715339541435242 Epoch:  10 Iteration:  1172\n","loss:  0.8102083206176758 Epoch:  10 Iteration:  1173\n","loss:  0.8849628567695618 Epoch:  10 Iteration:  1174\n","loss:  0.9821560382843018 Epoch:  10 Iteration:  1175\n","loss:  0.8975161910057068 Epoch:  10 Iteration:  1176\n","loss:  0.8426979780197144 Epoch:  10 Iteration:  1177\n","loss:  1.033727765083313 Epoch:  10 Iteration:  1178\n","loss:  0.8180917501449585 Epoch:  10 Iteration:  1179\n","loss:  0.9709674119949341 Epoch:  10 Iteration:  1180\n","tensor(5.1600, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5934, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.6956, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0982, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3969/4720 (84.089%) \t and Real vs. Fake Accuracy: 5487/6000 (91.450%) \n","loss:  0.9985033273696899 Epoch:  10 Iteration:  1181\n","loss:  0.9236059188842773 Epoch:  10 Iteration:  1182\n","loss:  0.9501487016677856 Epoch:  10 Iteration:  1183\n","loss:  0.9959916472434998 Epoch:  10 Iteration:  1184\n","loss:  0.9652140736579895 Epoch:  10 Iteration:  1185\n","loss:  0.8866118788719177 Epoch:  10 Iteration:  1186\n","loss:  0.976076602935791 Epoch:  10 Iteration:  1187\n","loss:  0.9906070232391357 Epoch:  10 Iteration:  1188\n","loss:  0.9597383141517639 Epoch:  10 Iteration:  1189\n","loss:  0.8379291296005249 Epoch:  10 Iteration:  1190\n","tensor(6.1816, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4698, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.5230, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2257, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3966/4720 (84.025%) \t and Real vs. Fake Accuracy: 5499/6000 (91.650%) \n","loss:  0.8663697242736816 Epoch:  10 Iteration:  1191\n","loss:  0.8948104977607727 Epoch:  10 Iteration:  1192\n","loss:  0.9055039286613464 Epoch:  10 Iteration:  1193\n","loss:  0.8347284197807312 Epoch:  10 Iteration:  1194\n","loss:  0.85027015209198 Epoch:  10 Iteration:  1195\n","loss:  0.8801354169845581 Epoch:  10 Iteration:  1196\n","loss:  0.8848815560340881 Epoch:  10 Iteration:  1197\n","loss:  0.94166100025177 Epoch:  10 Iteration:  1198\n","loss:  0.9115180969238281 Epoch:  10 Iteration:  1199\n","loss:  0.9899312257766724 Epoch:  10 Iteration:  1200\n","tensor(6.5766, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9097, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.9561, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-0.9507, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3972/4720 (84.152%) \t and Real vs. Fake Accuracy: 5466/6000 (91.100%) \n","loss:  0.7973268032073975 Epoch:  10 Iteration:  1201\n","loss:  0.9720208644866943 Epoch:  10 Iteration:  1202\n","loss:  1.0919297933578491 Epoch:  10 Iteration:  1203\n","loss:  0.17648744583129883 Epoch:  10 Iteration:  1204\n","loss:  0.1668572723865509 Epoch:  10 Iteration:  1205\n","loss:  0.22236280143260956 Epoch:  10 Iteration:  1206\n","loss:  0.16826102137565613 Epoch:  10 Iteration:  1207\n","loss:  0.1933772712945938 Epoch:  10 Iteration:  1208\n","loss:  0.2162400484085083 Epoch:  10 Iteration:  1209\n","loss:  0.20780599117279053 Epoch:  10 Iteration:  1210\n","tensor(5.6813, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7220, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.5408, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0969, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3963/4720 (83.962%) \t and Real vs. Fake Accuracy: 5512/6000 (91.867%) \n","loss:  0.1774415373802185 Epoch:  10 Iteration:  1211\n","loss:  0.1663323938846588 Epoch:  10 Iteration:  1212\n","loss:  0.18092170357704163 Epoch:  10 Iteration:  1213\n","loss:  0.1949150562286377 Epoch:  10 Iteration:  1214\n","loss:  0.19706186652183533 Epoch:  10 Iteration:  1215\n","loss:  0.18586134910583496 Epoch:  10 Iteration:  1216\n","loss:  0.22180910408496857 Epoch:  10 Iteration:  1217\n","loss:  0.18005099892616272 Epoch:  10 Iteration:  1218\n","loss:  0.19630882143974304 Epoch:  10 Iteration:  1219\n","loss:  0.1426711529493332 Epoch:  10 Iteration:  1220\n","tensor(7.0527, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7874, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.5398, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1493, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3971/4720 (84.131%) \t and Real vs. Fake Accuracy: 5491/6000 (91.517%) \n","loss:  0.8678781390190125 Epoch:  11 Iteration:  1221\n","loss:  0.6814725995063782 Epoch:  11 Iteration:  1222\n","loss:  0.9093923568725586 Epoch:  11 Iteration:  1223\n","loss:  0.7900990843772888 Epoch:  11 Iteration:  1224\n","loss:  0.8005249500274658 Epoch:  11 Iteration:  1225\n","loss:  0.7644845247268677 Epoch:  11 Iteration:  1226\n","loss:  0.8101641535758972 Epoch:  11 Iteration:  1227\n","loss:  0.8610588312149048 Epoch:  11 Iteration:  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0.8789851665496826 Epoch:  11 Iteration:  1248\n","loss:  0.8635213375091553 Epoch:  11 Iteration:  1249\n","loss:  0.8019275069236755 Epoch:  11 Iteration:  1250\n","tensor(5.8206, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2347, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.6389, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5162, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3975/4720 (84.216%) \t and Real vs. Fake Accuracy: 5549/6000 (92.483%) \n","loss:  0.812510073184967 Epoch:  11 Iteration:  1251\n","loss:  0.7474156022071838 Epoch:  11 Iteration:  1252\n","loss:  0.9336979389190674 Epoch:  11 Iteration:  1253\n","loss:  0.8131386041641235 Epoch:  11 Iteration:  1254\n","loss:  0.7162064909934998 Epoch:  11 Iteration:  1255\n","loss:  0.8513616919517517 Epoch:  11 Iteration:  1256\n","loss:  0.9403289556503296 Epoch:  11 Iteration:  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0.8197093605995178 Epoch:  11 Iteration:  1277\n","loss:  0.8432471752166748 Epoch:  11 Iteration:  1278\n","loss:  0.8862281441688538 Epoch:  11 Iteration:  1279\n","loss:  0.8819249868392944 Epoch:  11 Iteration:  1280\n","tensor(5.4409, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7911, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.5601, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6938, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5581/6000 (93.017%) \n","loss:  0.874012291431427 Epoch:  11 Iteration:  1281\n","loss:  0.7771437168121338 Epoch:  11 Iteration:  1282\n","loss:  0.8677241206169128 Epoch:  11 Iteration:  1283\n","loss:  0.8645421266555786 Epoch:  11 Iteration:  1284\n","loss:  0.8852565288543701 Epoch:  11 Iteration:  1285\n","loss:  0.8308956027030945 Epoch:  11 Iteration:  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0.9483535289764404 Epoch:  11 Iteration:  1306\n","loss:  0.9078595042228699 Epoch:  11 Iteration:  1307\n","loss:  0.8411808013916016 Epoch:  11 Iteration:  1308\n","loss:  0.8072198033332825 Epoch:  11 Iteration:  1309\n","loss:  0.9214968681335449 Epoch:  11 Iteration:  1310\n","tensor(6.7203, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6066, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.0802, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2645, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3977/4720 (84.258%) \t and Real vs. Fake Accuracy: 5618/6000 (93.633%) \n","loss:  0.9349722266197205 Epoch:  11 Iteration:  1311\n","loss:  0.8705993890762329 Epoch:  11 Iteration:  1312\n","loss:  0.8967375159263611 Epoch:  11 Iteration:  1313\n","loss:  0.8657264709472656 Epoch:  11 Iteration:  1314\n","loss:  0.20638620853424072 Epoch:  11 Iteration: 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0.16227197647094727 Epoch:  11 Iteration:  1325\n","loss:  0.1703682541847229 Epoch:  11 Iteration:  1326\n","loss:  0.18677474558353424 Epoch:  11 Iteration:  1327\n","loss:  0.20805983245372772 Epoch:  11 Iteration:  1328\n","loss:  0.1845720261335373 Epoch:  11 Iteration:  1329\n","loss:  0.17711985111236572 Epoch:  11 Iteration:  1330\n","tensor(6.8711, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6260, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5341, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3575, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5550/6000 (92.500%) \n","loss:  0.2884022891521454 Epoch:  11 Iteration:  1331\n","loss:  0.8454179763793945 Epoch:  12 Iteration:  1332\n","loss:  0.7888146042823792 Epoch:  12 Iteration:  1333\n","loss:  0.7172284126281738 Epoch:  12 Iteration:  1334\n","loss:  0.768283486366272 Epoch:  12 Iteration:  1335\n","loss:  0.6865593194961548 Epoch:  12 Iteration:  1336\n","loss:  0.7224570512771606 Epoch:  12 Iteration:  1337\n","loss:  0.790642261505127 Epoch:  12 Iteration:  1338\n","loss:  0.7223877906799316 Epoch:  12 Iteration:  1339\n","loss:  0.8057137727737427 Epoch:  12 Iteration:  1340\n","tensor(5.3539, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6666, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.9153, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4782, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3981/4720 (84.343%) \t and Real vs. Fake Accuracy: 5591/6000 (93.183%) \n","loss:  0.7343062162399292 Epoch:  12 Iteration:  1341\n","loss:  0.7094709873199463 Epoch:  12 Iteration:  1342\n","loss:  0.7451107501983643 Epoch:  12 Iteration:  1343\n","loss:  0.7079830169677734 Epoch:  12 Iteration:  1344\n","loss:  0.7887557744979858 Epoch:  12 Iteration:  1345\n","loss:  0.8120673894882202 Epoch:  12 Iteration:  1346\n","loss:  0.6549634337425232 Epoch:  12 Iteration:  1347\n","loss:  0.8376283049583435 Epoch:  12 Iteration:  1348\n","loss:  0.8407988548278809 Epoch:  12 Iteration:  1349\n","loss:  0.7449788451194763 Epoch:  12 Iteration:  1350\n","tensor(4.0247, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4873, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.1509, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1805, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3975/4720 (84.216%) \t and Real vs. Fake Accuracy: 5609/6000 (93.483%) \n","loss:  0.8339523077011108 Epoch:  12 Iteration:  1351\n","loss:  0.752854585647583 Epoch:  12 Iteration:  1352\n","loss:  0.6864690184593201 Epoch:  12 Iteration:  1353\n","loss:  0.7369904518127441 Epoch:  12 Iteration:  1354\n","loss:  0.7260962724685669 Epoch:  12 Iteration:  1355\n","loss:  0.6972886919975281 Epoch:  12 Iteration:  1356\n","loss:  0.8205771446228027 Epoch:  12 Iteration:  1357\n","loss:  0.7606645822525024 Epoch:  12 Iteration:  1358\n","loss:  0.7128722667694092 Epoch:  12 Iteration:  1359\n","loss:  0.7413337230682373 Epoch:  12 Iteration:  1360\n","tensor(4.8322, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5122, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8385, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0522, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3974/4720 (84.195%) \t and Real vs. Fake Accuracy: 5622/6000 (93.700%) \n","loss:  0.9179582595825195 Epoch:  12 Iteration:  1361\n","loss:  0.8670400381088257 Epoch:  12 Iteration:  1362\n","loss:  0.7306106090545654 Epoch:  12 Iteration:  1363\n","loss:  0.7892332077026367 Epoch:  12 Iteration:  1364\n","loss:  0.8286572098731995 Epoch:  12 Iteration:  1365\n","loss:  0.727070689201355 Epoch:  12 Iteration:  1366\n","loss:  0.8053699731826782 Epoch:  12 Iteration:  1367\n","loss:  0.7769147157669067 Epoch:  12 Iteration:  1368\n","loss:  0.7560099363327026 Epoch:  12 Iteration:  1369\n","loss:  0.7781935334205627 Epoch:  12 Iteration:  1370\n","tensor(5.7102, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6499, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8466, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1196, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3965/4720 (84.004%) \t and Real vs. Fake Accuracy: 5614/6000 (93.567%) \n","loss:  0.7572846412658691 Epoch:  12 Iteration:  1371\n","loss:  0.8311001062393188 Epoch:  12 Iteration:  1372\n","loss:  0.8202817440032959 Epoch:  12 Iteration:  1373\n","loss:  0.8582461476325989 Epoch:  12 Iteration:  1374\n","loss:  0.7408387660980225 Epoch:  12 Iteration:  1375\n","loss:  0.6827069520950317 Epoch:  12 Iteration:  1376\n","loss:  0.7795717716217041 Epoch:  12 Iteration:  1377\n","loss:  0.8383904695510864 Epoch:  12 Iteration:  1378\n","loss:  0.720743715763092 Epoch:  12 Iteration:  1379\n","loss:  0.8225410580635071 Epoch:  12 Iteration:  1380\n","tensor(6.4477, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9859, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.6674, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1962, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3953/4720 (83.750%) \t and Real vs. Fake Accuracy: 5632/6000 (93.867%) \n","loss:  0.7999991178512573 Epoch:  12 Iteration:  1381\n","loss:  0.9071158766746521 Epoch:  12 Iteration:  1382\n","loss:  0.8844813108444214 Epoch:  12 Iteration:  1383\n","loss:  0.7761309146881104 Epoch:  12 Iteration:  1384\n","loss:  0.885319173336029 Epoch:  12 Iteration:  1385\n","loss:  0.7792823314666748 Epoch:  12 Iteration:  1386\n","loss:  0.825321614742279 Epoch:  12 Iteration:  1387\n","loss:  0.7526029944419861 Epoch:  12 Iteration:  1388\n","loss:  0.8201088309288025 Epoch:  12 Iteration:  1389\n","loss:  0.8470478653907776 Epoch:  12 Iteration:  1390\n","tensor(5.2793, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5032, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.3563, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0548, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3957/4720 (83.835%) \t and Real vs. Fake Accuracy: 5663/6000 (94.383%) \n","loss:  0.8580069541931152 Epoch:  12 Iteration:  1391\n","loss:  0.736859142780304 Epoch:  12 Iteration:  1392\n","loss:  0.7685794830322266 Epoch:  12 Iteration:  1393\n","loss:  0.7772164344787598 Epoch:  12 Iteration:  1394\n","loss:  0.7893646955490112 Epoch:  12 Iteration:  1395\n","loss:  0.7902407646179199 Epoch:  12 Iteration:  1396\n","loss:  0.8550794124603271 Epoch:  12 Iteration:  1397\n","loss:  0.7166623473167419 Epoch:  12 Iteration:  1398\n","loss:  0.7508958578109741 Epoch:  12 Iteration:  1399\n","loss:  0.7625575661659241 Epoch:  12 Iteration:  1400\n","tensor(6.9352, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3722, device='cuda:0', grad_fn=<MinBackward1>) tensor(6.8638, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9186, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3984/4720 (84.407%) \t and Real vs. Fake Accuracy: 5682/6000 (94.700%) \n","loss:  0.931304931640625 Epoch:  12 Iteration:  1401\n","loss:  0.6359976530075073 Epoch:  12 Iteration:  1402\n","loss:  0.696973443031311 Epoch:  12 Iteration:  1403\n","loss:  0.8390772342681885 Epoch:  12 Iteration:  1404\n","loss:  0.7037798762321472 Epoch:  12 Iteration:  1405\n","loss:  0.8276636600494385 Epoch:  12 Iteration:  1406\n","loss:  0.7909951210021973 Epoch:  12 Iteration:  1407\n","loss:  0.7809172868728638 Epoch:  12 Iteration:  1408\n","loss:  0.868526816368103 Epoch:  12 Iteration:  1409\n","loss:  0.7671858072280884 Epoch:  12 Iteration:  1410\n","tensor(5.8606, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9429, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.8320, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8017, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3990/4720 (84.534%) \t and Real vs. Fake Accuracy: 5680/6000 (94.667%) \n","loss:  0.86014324426651 Epoch:  12 Iteration:  1411\n","loss:  0.8815895915031433 Epoch:  12 Iteration:  1412\n","loss:  0.7573617696762085 Epoch:  12 Iteration:  1413\n","loss:  0.8487515449523926 Epoch:  12 Iteration:  1414\n","loss:  0.8252513408660889 Epoch:  12 Iteration:  1415\n","loss:  0.7563113570213318 Epoch:  12 Iteration:  1416\n","loss:  0.6808784604072571 Epoch:  12 Iteration:  1417\n","loss:  0.6995969414710999 Epoch:  12 Iteration:  1418\n","loss:  0.7922120094299316 Epoch:  12 Iteration:  1419\n","loss:  0.8363518714904785 Epoch:  12 Iteration:  1420\n","tensor(5.4748, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6426, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.6131, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4530, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5679/6000 (94.650%) \n","loss:  0.7745294570922852 Epoch:  12 Iteration:  1421\n","loss:  0.6784706115722656 Epoch:  12 Iteration:  1422\n","loss:  0.8128526210784912 Epoch:  12 Iteration:  1423\n","loss:  0.6957486271858215 Epoch:  12 Iteration:  1424\n","loss:  0.8604596853256226 Epoch:  12 Iteration:  1425\n","loss:  0.17522725462913513 Epoch:  12 Iteration:  1426\n","loss:  0.18735021352767944 Epoch:  12 Iteration:  1427\n","loss:  0.1652146428823471 Epoch:  12 Iteration:  1428\n","loss:  0.1911066770553589 Epoch:  12 Iteration:  1429\n","loss:  0.16557666659355164 Epoch:  12 Iteration:  1430\n","tensor(5.8420, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2349, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.3109, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4170, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3970/4720 (84.110%) \t and Real vs. Fake Accuracy: 5669/6000 (94.483%) \n","loss:  0.14729100465774536 Epoch:  12 Iteration:  1431\n","loss:  0.24709045886993408 Epoch:  12 Iteration:  1432\n","loss:  0.1744065284729004 Epoch:  12 Iteration:  1433\n","loss:  0.19271548092365265 Epoch:  12 Iteration:  1434\n","loss:  0.1606276035308838 Epoch:  12 Iteration:  1435\n","loss:  0.20233051478862762 Epoch:  12 Iteration:  1436\n","loss:  0.16013211011886597 Epoch:  12 Iteration:  1437\n","loss:  0.15211552381515503 Epoch:  12 Iteration:  1438\n","loss:  0.17585715651512146 Epoch:  12 Iteration:  1439\n","loss:  0.18855738639831543 Epoch:  12 Iteration:  1440\n","tensor(4.8902, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8252, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6642, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6701, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3978/4720 (84.280%) \t and Real vs. Fake Accuracy: 5670/6000 (94.500%) \n","loss:  0.18946045637130737 Epoch:  12 Iteration:  1441\n","loss:  0.17683345079421997 Epoch:  12 Iteration:  1442\n","loss:  0.7570834159851074 Epoch:  13 Iteration:  1443\n","loss:  0.6472946405410767 Epoch:  13 Iteration:  1444\n","loss:  0.6634167432785034 Epoch:  13 Iteration:  1445\n","loss:  0.8310678005218506 Epoch:  13 Iteration:  1446\n","loss:  0.7621793746948242 Epoch:  13 Iteration:  1447\n","loss:  0.789216935634613 Epoch:  13 Iteration:  1448\n","loss:  0.7123263478279114 Epoch:  13 Iteration:  1449\n","loss:  0.6931349039077759 Epoch:  13 Iteration:  1450\n","tensor(6.8580, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4609, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5411, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3760, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3970/4720 (84.110%) \t and Real vs. Fake Accuracy: 5684/6000 (94.733%) \n","loss:  0.7420104742050171 Epoch:  13 Iteration:  1451\n","loss:  0.701951265335083 Epoch:  13 Iteration:  1452\n","loss:  0.667549729347229 Epoch:  13 Iteration:  1453\n","loss:  0.6135232448577881 Epoch:  13 Iteration:  1454\n","loss:  0.6777377128601074 Epoch:  13 Iteration:  1455\n","loss:  0.7256631255149841 Epoch:  13 Iteration:  1456\n","loss:  0.6656122207641602 Epoch:  13 Iteration:  1457\n","loss:  0.6603788733482361 Epoch:  13 Iteration:  1458\n","loss:  0.7054316997528076 Epoch:  13 Iteration:  1459\n","loss:  0.7730388641357422 Epoch:  13 Iteration:  1460\n","tensor(5.4235, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.0991, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.5097, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5226, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3989/4720 (84.513%) \t and Real vs. Fake Accuracy: 5703/6000 (95.050%) \n","loss:  0.6522846221923828 Epoch:  13 Iteration:  1461\n","loss:  0.6383740901947021 Epoch:  13 Iteration:  1462\n","loss:  0.6924178004264832 Epoch:  13 Iteration:  1463\n","loss:  0.7800641059875488 Epoch:  13 Iteration:  1464\n","loss:  0.7720924615859985 Epoch:  13 Iteration:  1465\n","loss:  0.7055507898330688 Epoch:  13 Iteration:  1466\n","loss:  0.6484479904174805 Epoch:  13 Iteration:  1467\n","loss:  0.7650704383850098 Epoch:  13 Iteration:  1468\n","loss:  0.7011971473693848 Epoch:  13 Iteration:  1469\n","loss:  0.6487797498703003 Epoch:  13 Iteration:  1470\n","tensor(5.0808, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4224, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.1321, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7132, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3987/4720 (84.470%) \t and Real vs. Fake Accuracy: 5706/6000 (95.100%) \n","loss:  0.7015673518180847 Epoch:  13 Iteration:  1471\n","loss:  0.8203819394111633 Epoch:  13 Iteration:  1472\n","loss:  0.7307498455047607 Epoch:  13 Iteration:  1473\n","loss:  0.8197588920593262 Epoch:  13 Iteration:  1474\n","loss:  0.7104730606079102 Epoch:  13 Iteration:  1475\n","loss:  0.7981139421463013 Epoch:  13 Iteration:  1476\n","loss:  0.779199481010437 Epoch:  13 Iteration:  1477\n","loss:  0.7520018219947815 Epoch:  13 Iteration:  1478\n","loss:  0.8063226938247681 Epoch:  13 Iteration:  1479\n","loss:  0.574844479560852 Epoch:  13 Iteration:  1480\n","tensor(5.3858, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6753, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.3408, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4645, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3986/4720 (84.449%) \t and Real vs. Fake Accuracy: 5720/6000 (95.333%) \n","loss:  0.6627320051193237 Epoch:  13 Iteration:  1481\n","loss:  0.7053328156471252 Epoch:  13 Iteration:  1482\n","loss:  0.7254387140274048 Epoch:  13 Iteration:  1483\n","loss:  0.7363840937614441 Epoch:  13 Iteration:  1484\n","loss:  0.7544275522232056 Epoch:  13 Iteration:  1485\n","loss:  0.7127991914749146 Epoch:  13 Iteration:  1486\n","loss:  0.7211204767227173 Epoch:  13 Iteration:  1487\n","loss:  0.5947225093841553 Epoch:  13 Iteration:  1488\n","loss:  0.6838642358779907 Epoch:  13 Iteration:  1489\n","loss:  0.7965975403785706 Epoch:  13 Iteration:  1490\n","tensor(4.9675, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3140, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.8651, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4815, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3979/4720 (84.301%) \t and Real vs. Fake Accuracy: 5715/6000 (95.250%) \n","loss:  0.7109069228172302 Epoch:  13 Iteration:  1491\n","loss:  0.7290849089622498 Epoch:  13 Iteration:  1492\n","loss:  0.5909665822982788 Epoch:  13 Iteration:  1493\n","loss:  0.7166157364845276 Epoch:  13 Iteration:  1494\n","loss:  0.7439797520637512 Epoch:  13 Iteration:  1495\n","loss:  0.6837000846862793 Epoch:  13 Iteration:  1496\n","loss:  0.7485852241516113 Epoch:  13 Iteration:  1497\n","loss:  0.7220366597175598 Epoch:  13 Iteration:  1498\n","loss:  0.7244629263877869 Epoch:  13 Iteration:  1499\n","loss:  0.7722405195236206 Epoch:  13 Iteration:  1500\n","tensor(6.1129, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5049, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.6938, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4497, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3993/4720 (84.598%) \t and Real vs. Fake Accuracy: 5696/6000 (94.933%) \n","loss:  0.7920374870300293 Epoch:  13 Iteration:  1501\n","loss:  0.699867844581604 Epoch:  13 Iteration:  1502\n","loss:  0.6628138422966003 Epoch:  13 Iteration:  1503\n","loss:  0.7022470831871033 Epoch:  13 Iteration:  1504\n","loss:  0.6874192357063293 Epoch:  13 Iteration:  1505\n","loss:  0.6954072117805481 Epoch:  13 Iteration:  1506\n","loss:  0.7523196339607239 Epoch:  13 Iteration:  1507\n","loss:  0.7777590751647949 Epoch:  13 Iteration:  1508\n","loss:  0.716039776802063 Epoch:  13 Iteration:  1509\n","loss:  0.7536835074424744 Epoch:  13 Iteration:  1510\n","tensor(5.6862, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1936, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.8459, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2168, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3990/4720 (84.534%) \t and Real vs. Fake Accuracy: 5712/6000 (95.200%) \n","loss:  0.618679940700531 Epoch:  13 Iteration:  1511\n","loss:  0.7806069254875183 Epoch:  13 Iteration:  1512\n","loss:  0.8065089583396912 Epoch:  13 Iteration:  1513\n","loss:  0.7708793878555298 Epoch:  13 Iteration:  1514\n","loss:  0.7403165102005005 Epoch:  13 Iteration:  1515\n","loss:  0.6556590795516968 Epoch:  13 Iteration:  1516\n","loss:  0.6931824684143066 Epoch:  13 Iteration:  1517\n","loss:  0.7975249290466309 Epoch:  13 Iteration:  1518\n","loss:  0.6576000452041626 Epoch:  13 Iteration:  1519\n","loss:  0.7128441333770752 Epoch:  13 Iteration:  1520\n","tensor(5.1801, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3125, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.8129, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3248, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3978/4720 (84.280%) \t and Real vs. Fake Accuracy: 5716/6000 (95.267%) \n","loss:  0.8198739290237427 Epoch:  13 Iteration:  1521\n","loss:  0.8309918642044067 Epoch:  13 Iteration:  1522\n","loss:  0.7681527137756348 Epoch:  13 Iteration:  1523\n","loss:  0.7656548023223877 Epoch:  13 Iteration:  1524\n","loss:  0.746543824672699 Epoch:  13 Iteration:  1525\n","loss:  0.7420948147773743 Epoch:  13 Iteration:  1526\n","loss:  0.6665971875190735 Epoch:  13 Iteration:  1527\n","loss:  0.8795633912086487 Epoch:  13 Iteration:  1528\n","loss:  0.7125499248504639 Epoch:  13 Iteration:  1529\n","loss:  0.6816866397857666 Epoch:  13 Iteration:  1530\n","tensor(7.1831, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7873, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.2136, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2552, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3986/4720 (84.449%) \t and Real vs. Fake Accuracy: 5750/6000 (95.833%) \n","loss:  0.8056843876838684 Epoch:  13 Iteration:  1531\n","loss:  0.6319242715835571 Epoch:  13 Iteration:  1532\n","loss:  0.7606970071792603 Epoch:  13 Iteration:  1533\n","loss:  0.7271257638931274 Epoch:  13 Iteration:  1534\n","loss:  0.8203600645065308 Epoch:  13 Iteration:  1535\n","loss:  0.734040379524231 Epoch:  13 Iteration:  1536\n","loss:  0.17782577872276306 Epoch:  13 Iteration:  1537\n","loss:  0.1405474692583084 Epoch:  13 Iteration:  1538\n","loss:  0.15399624407291412 Epoch:  13 Iteration:  1539\n","loss:  0.16929972171783447 Epoch:  13 Iteration:  1540\n","tensor(7.5705, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1535, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.5430, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7683, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5757/6000 (95.950%) \n","loss:  0.16620071232318878 Epoch:  13 Iteration:  1541\n","loss:  0.1521196961402893 Epoch:  13 Iteration:  1542\n","loss:  0.11188601702451706 Epoch:  13 Iteration:  1543\n","loss:  0.16495338082313538 Epoch:  13 Iteration:  1544\n","loss:  0.1606554388999939 Epoch:  13 Iteration:  1545\n","loss:  0.17051546275615692 Epoch:  13 Iteration:  1546\n","loss:  0.15197080373764038 Epoch:  13 Iteration:  1547\n","loss:  0.18535465002059937 Epoch:  13 Iteration:  1548\n","loss:  0.1492617279291153 Epoch:  13 Iteration:  1549\n","loss:  0.17890581488609314 Epoch:  13 Iteration:  1550\n","tensor(5.2885, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8723, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.2927, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4927, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5734/6000 (95.567%) \n","loss:  0.16085562109947205 Epoch:  13 Iteration:  1551\n","loss:  0.1561424732208252 Epoch:  13 Iteration:  1552\n","loss:  0.15566863119602203 Epoch:  13 Iteration:  1553\n","loss:  0.6511586904525757 Epoch:  14 Iteration:  1554\n","loss:  0.5390751361846924 Epoch:  14 Iteration:  1555\n","loss:  0.5806076526641846 Epoch:  14 Iteration:  1556\n","loss:  0.6828457117080688 Epoch:  14 Iteration:  1557\n","loss:  0.6914867162704468 Epoch:  14 Iteration:  1558\n","loss:  0.6554864645004272 Epoch:  14 Iteration:  1559\n","loss:  0.6124246120452881 Epoch:  14 Iteration:  1560\n","tensor(5.3557, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6063, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.5526, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2240, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5751/6000 (95.850%) \n","loss:  0.7150667309761047 Epoch:  14 Iteration:  1561\n","loss:  0.7259554266929626 Epoch:  14 Iteration:  1562\n","loss:  0.7486279606819153 Epoch:  14 Iteration:  1563\n","loss:  0.6484217643737793 Epoch:  14 Iteration:  1564\n","loss:  0.6758023500442505 Epoch:  14 Iteration:  1565\n","loss:  0.6301247477531433 Epoch:  14 Iteration:  1566\n","loss:  0.6240696907043457 Epoch:  14 Iteration:  1567\n","loss:  0.5972177386283875 Epoch:  14 Iteration:  1568\n","loss:  0.7287641763687134 Epoch:  14 Iteration:  1569\n","loss:  0.5884754657745361 Epoch:  14 Iteration:  1570\n","tensor(6.4018, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0011, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.1059, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9368, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5763/6000 (96.050%) \n","loss:  0.7007047533988953 Epoch:  14 Iteration:  1571\n","loss:  0.621713399887085 Epoch:  14 Iteration:  1572\n","loss:  0.7152032852172852 Epoch:  14 Iteration:  1573\n","loss:  0.7134584784507751 Epoch:  14 Iteration:  1574\n","loss:  0.6117010116577148 Epoch:  14 Iteration:  1575\n","loss:  0.7418853044509888 Epoch:  14 Iteration:  1576\n","loss:  0.6181623935699463 Epoch:  14 Iteration:  1577\n","loss:  0.602587878704071 Epoch:  14 Iteration:  1578\n","loss:  0.6052323579788208 Epoch:  14 Iteration:  1579\n","loss:  0.6272392272949219 Epoch:  14 Iteration:  1580\n","tensor(7.2300, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4541, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.5773, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7297, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3992/4720 (84.576%) \t and Real vs. Fake Accuracy: 5760/6000 (96.000%) \n","loss:  0.6465743184089661 Epoch:  14 Iteration:  1581\n","loss:  0.5679900050163269 Epoch:  14 Iteration:  1582\n","loss:  0.7720316052436829 Epoch:  14 Iteration:  1583\n","loss:  0.6826014518737793 Epoch:  14 Iteration:  1584\n","loss:  0.7054260969161987 Epoch:  14 Iteration:  1585\n","loss:  0.5700193643569946 Epoch:  14 Iteration:  1586\n","loss:  0.7415880560874939 Epoch:  14 Iteration:  1587\n","loss:  0.678631603717804 Epoch:  14 Iteration:  1588\n","loss:  0.7593623995780945 Epoch:  14 Iteration:  1589\n","loss:  0.6779754161834717 Epoch:  14 Iteration:  1590\n","tensor(5.9282, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0696, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.4007, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4662, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3983/4720 (84.386%) \t and Real vs. Fake Accuracy: 5766/6000 (96.100%) \n","loss:  0.5900548696517944 Epoch:  14 Iteration:  1591\n","loss:  0.7247233986854553 Epoch:  14 Iteration:  1592\n","loss:  0.7037904262542725 Epoch:  14 Iteration:  1593\n","loss:  0.6794545650482178 Epoch:  14 Iteration:  1594\n","loss:  0.5690034627914429 Epoch:  14 Iteration:  1595\n","loss:  0.6399674415588379 Epoch:  14 Iteration:  1596\n","loss:  0.6442875862121582 Epoch:  14 Iteration:  1597\n","loss:  0.6551457643508911 Epoch:  14 Iteration:  1598\n","loss:  0.6658122539520264 Epoch:  14 Iteration:  1599\n","loss:  0.6834808588027954 Epoch:  14 Iteration:  1600\n","tensor(5.5874, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5918, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8423, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4895, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3977/4720 (84.258%) \t and Real vs. Fake Accuracy: 5752/6000 (95.867%) \n","loss:  0.5968540906906128 Epoch:  14 Iteration:  1601\n","loss:  0.6490795016288757 Epoch:  14 Iteration:  1602\n","loss:  0.752319872379303 Epoch:  14 Iteration:  1603\n","loss:  0.6688804626464844 Epoch:  14 Iteration:  1604\n","loss:  0.693267822265625 Epoch:  14 Iteration:  1605\n","loss:  0.7303757071495056 Epoch:  14 Iteration:  1606\n","loss:  0.7707297801971436 Epoch:  14 Iteration:  1607\n","loss:  0.6394948363304138 Epoch:  14 Iteration:  1608\n","loss:  0.658458948135376 Epoch:  14 Iteration:  1609\n","loss:  0.5620294213294983 Epoch:  14 Iteration:  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Iteration:  1620\n","tensor(6.1617, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4706, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.7508, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5946, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5792/6000 (96.533%) \n","loss:  0.7277922630310059 Epoch:  14 Iteration:  1621\n","loss:  0.6691391468048096 Epoch:  14 Iteration:  1622\n","loss:  0.7439621090888977 Epoch:  14 Iteration:  1623\n","loss:  0.72066730260849 Epoch:  14 Iteration:  1624\n","loss:  0.571180522441864 Epoch:  14 Iteration:  1625\n","loss:  0.7050651907920837 Epoch:  14 Iteration:  1626\n","loss:  0.6277101039886475 Epoch:  14 Iteration:  1627\n","loss:  0.6470769643783569 Epoch:  14 Iteration:  1628\n","loss:  0.6010760068893433 Epoch:  14 Iteration:  1629\n","loss:  0.6003603935241699 Epoch:  14 Iteration:  1630\n","tensor(4.7741, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1665, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.6437, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2830, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3986/4720 (84.449%) \t and Real vs. Fake Accuracy: 5803/6000 (96.717%) \n","loss:  0.6781355142593384 Epoch:  14 Iteration:  1631\n","loss:  0.8064857721328735 Epoch:  14 Iteration:  1632\n","loss:  0.6737825870513916 Epoch:  14 Iteration:  1633\n","loss:  0.6479870080947876 Epoch:  14 Iteration:  1634\n","loss:  0.5924361944198608 Epoch:  14 Iteration:  1635\n","loss:  0.6986018419265747 Epoch:  14 Iteration:  1636\n","loss:  0.731002926826477 Epoch:  14 Iteration:  1637\n","loss:  0.7907978892326355 Epoch:  14 Iteration:  1638\n","loss:  0.6625658273696899 Epoch:  14 Iteration:  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Epoch:  14 Iteration:  1649\n","loss:  0.1498662233352661 Epoch:  14 Iteration:  1650\n","tensor(5.9411, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5422, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6958, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1650, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3993/4720 (84.598%) \t and Real vs. Fake Accuracy: 5794/6000 (96.567%) \n","loss:  0.12385769188404083 Epoch:  14 Iteration:  1651\n","loss:  0.15356884896755219 Epoch:  14 Iteration:  1652\n","loss:  0.16414377093315125 Epoch:  14 Iteration:  1653\n","loss:  0.15033186972141266 Epoch:  14 Iteration:  1654\n","loss:  0.1739528775215149 Epoch:  14 Iteration:  1655\n","loss:  0.1604585498571396 Epoch:  14 Iteration:  1656\n","loss:  0.13499173521995544 Epoch:  14 Iteration:  1657\n","loss:  0.1409737765789032 Epoch:  14 Iteration:  1658\n","loss: 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0.5885576009750366 Epoch:  15 Iteration:  1688\n","loss:  0.6307783126831055 Epoch:  15 Iteration:  1689\n","loss:  0.5463346838951111 Epoch:  15 Iteration:  1690\n","tensor(7.2900, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.6057, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.3153, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5780, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3991/4720 (84.555%) \t and Real vs. Fake Accuracy: 5794/6000 (96.567%) \n","loss:  0.6280755400657654 Epoch:  15 Iteration:  1691\n","loss:  0.5999511480331421 Epoch:  15 Iteration:  1692\n","loss:  0.6151514649391174 Epoch:  15 Iteration:  1693\n","loss:  0.6479541659355164 Epoch:  15 Iteration:  1694\n","loss:  0.5739989876747131 Epoch:  15 Iteration:  1695\n","loss:  0.6589205265045166 Epoch:  15 Iteration:  1696\n","loss:  0.7032041549682617 Epoch:  15 Iteration:  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Epoch:  15 Iteration:  1765\n","loss:  0.15973015129566193 Epoch:  15 Iteration:  1766\n","loss:  0.1456809788942337 Epoch:  15 Iteration:  1767\n","loss:  0.1701379120349884 Epoch:  15 Iteration:  1768\n","loss:  0.15542763471603394 Epoch:  15 Iteration:  1769\n","loss:  0.18334075808525085 Epoch:  15 Iteration:  1770\n","tensor(4.9694, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7487, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.7721, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.1985, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3989/4720 (84.513%) \t and Real vs. Fake Accuracy: 5853/6000 (97.550%) \n","loss:  0.14287115633487701 Epoch:  15 Iteration:  1771\n","loss:  0.15456236898899078 Epoch:  15 Iteration:  1772\n","loss:  0.13420097529888153 Epoch:  15 Iteration:  1773\n","loss:  0.1470842957496643 Epoch:  15 Iteration:  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16 Iteration:  1784\n","loss:  0.6170320510864258 Epoch:  16 Iteration:  1785\n","loss:  0.5465347766876221 Epoch:  16 Iteration:  1786\n","loss:  0.5895513296127319 Epoch:  16 Iteration:  1787\n","loss:  0.5309126973152161 Epoch:  16 Iteration:  1788\n","loss:  0.5693476796150208 Epoch:  16 Iteration:  1789\n","loss:  0.533635139465332 Epoch:  16 Iteration:  1790\n","tensor(5.6488, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6142, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.3081, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-3.1260, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4001/4720 (84.767%) \t and Real vs. Fake Accuracy: 5863/6000 (97.717%) \n","loss:  0.5123631358146667 Epoch:  16 Iteration:  1791\n","loss:  0.5615884065628052 Epoch:  16 Iteration:  1792\n","loss:  0.6713824272155762 Epoch:  16 Iteration:  1793\n","loss:  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Epoch:  16 Iteration:  1813\n","loss:  0.5018777251243591 Epoch:  16 Iteration:  1814\n","loss:  0.6192534565925598 Epoch:  16 Iteration:  1815\n","loss:  0.6020599007606506 Epoch:  16 Iteration:  1816\n","loss:  0.5728520154953003 Epoch:  16 Iteration:  1817\n","loss:  0.5998550057411194 Epoch:  16 Iteration:  1818\n","loss:  0.592492401599884 Epoch:  16 Iteration:  1819\n","loss:  0.5835103392601013 Epoch:  16 Iteration:  1820\n","tensor(8.0855, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1701, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6504, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5595, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5866/6000 (97.767%) \n","loss:  0.5211506485939026 Epoch:  16 Iteration:  1821\n","loss:  0.5407043695449829 Epoch:  16 Iteration:  1822\n","loss:  0.5940296053886414 Epoch:  16 Iteration:  1823\n","loss:  0.5691767930984497 Epoch:  16 Iteration:  1824\n","loss:  0.5616495609283447 Epoch:  16 Iteration:  1825\n","loss:  0.541837215423584 Epoch:  16 Iteration:  1826\n","loss:  0.6679721474647522 Epoch:  16 Iteration:  1827\n","loss:  0.586894154548645 Epoch:  16 Iteration:  1828\n","loss:  0.6149300932884216 Epoch:  16 Iteration:  1829\n","loss:  0.4891849160194397 Epoch:  16 Iteration:  1830\n","tensor(6.1504, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9813, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.8759, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9897, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3993/4720 (84.598%) \t and Real vs. Fake Accuracy: 5860/6000 (97.667%) \n","loss:  0.6013634204864502 Epoch:  16 Iteration:  1831\n","loss:  0.532569408416748 Epoch:  16 Iteration:  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0.6904220581054688 Epoch:  16 Iteration:  1852\n","loss:  0.6516543030738831 Epoch:  16 Iteration:  1853\n","loss:  0.6153864860534668 Epoch:  16 Iteration:  1854\n","loss:  0.5032603740692139 Epoch:  16 Iteration:  1855\n","loss:  0.5877454876899719 Epoch:  16 Iteration:  1856\n","loss:  0.610257625579834 Epoch:  16 Iteration:  1857\n","loss:  0.6048439741134644 Epoch:  16 Iteration:  1858\n","loss:  0.56667160987854 Epoch:  16 Iteration:  1859\n","loss:  0.5946993827819824 Epoch:  16 Iteration:  1860\n","tensor(7.2766, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.6679, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.4069, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3873, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3984/4720 (84.407%) \t and Real vs. Fake Accuracy: 5886/6000 (98.100%) \n","loss:  0.5070434212684631 Epoch:  16 Iteration:  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Epoch:  16 Iteration:  1871\n","loss:  0.12396347522735596 Epoch:  16 Iteration:  1872\n","loss:  0.14236535131931305 Epoch:  16 Iteration:  1873\n","loss:  0.17008614540100098 Epoch:  16 Iteration:  1874\n","loss:  0.12094530463218689 Epoch:  16 Iteration:  1875\n","loss:  0.12270210683345795 Epoch:  16 Iteration:  1876\n","loss:  0.16207337379455566 Epoch:  16 Iteration:  1877\n","loss:  0.14215132594108582 Epoch:  16 Iteration:  1878\n","loss:  0.12942498922348022 Epoch:  16 Iteration:  1879\n","loss:  0.14162135124206543 Epoch:  16 Iteration:  1880\n","tensor(5.2840, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2203, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.3574, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0165, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3993/4720 (84.598%) \t and Real vs. Fake Accuracy: 5867/6000 (97.783%) \n","loss:  0.1384730339050293 Epoch:  16 Iteration:  1881\n","loss:  0.12079890817403793 Epoch:  16 Iteration:  1882\n","loss:  0.13609199225902557 Epoch:  16 Iteration:  1883\n","loss:  0.12161190807819366 Epoch:  16 Iteration:  1884\n","loss:  0.13258089125156403 Epoch:  16 Iteration:  1885\n","loss:  0.10752477496862411 Epoch:  16 Iteration:  1886\n","loss:  0.5513719320297241 Epoch:  17 Iteration:  1887\n","loss:  0.4884534180164337 Epoch:  17 Iteration:  1888\n","loss:  0.5216923356056213 Epoch:  17 Iteration:  1889\n","loss:  0.5790673494338989 Epoch:  17 Iteration:  1890\n","tensor(6.1463, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2545, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.6699, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9374, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3988/4720 (84.492%) \t and Real vs. Fake Accuracy: 5863/6000 (97.717%) \n","loss:  0.6154831647872925 Epoch:  17 Iteration:  1891\n","loss:  0.4806433916091919 Epoch:  17 Iteration:  1892\n","loss:  0.5085077285766602 Epoch:  17 Iteration:  1893\n","loss:  0.4895426034927368 Epoch:  17 Iteration:  1894\n","loss:  0.546607255935669 Epoch:  17 Iteration:  1895\n","loss:  0.5080175399780273 Epoch:  17 Iteration:  1896\n","loss:  0.4851427972316742 Epoch:  17 Iteration:  1897\n","loss:  0.5536110997200012 Epoch:  17 Iteration:  1898\n","loss:  0.5156162977218628 Epoch:  17 Iteration:  1899\n","loss:  0.44107094407081604 Epoch:  17 Iteration:  1900\n","tensor(7.6221, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5429, device='cuda:0', grad_fn=<MinBackward1>) tensor(12.7561, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4639, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3980/4720 (84.322%) \t and Real vs. Fake Accuracy: 5885/6000 (98.083%) \n","loss:  0.5154150724411011 Epoch:  17 Iteration:  1901\n","loss:  0.5271022915840149 Epoch:  17 Iteration:  1902\n","loss:  0.48513662815093994 Epoch:  17 Iteration:  1903\n","loss:  0.6033604145050049 Epoch:  17 Iteration:  1904\n","loss:  0.5505827069282532 Epoch:  17 Iteration:  1905\n","loss:  0.4750114679336548 Epoch:  17 Iteration:  1906\n","loss:  0.6165817975997925 Epoch:  17 Iteration:  1907\n","loss:  0.42940932512283325 Epoch:  17 Iteration:  1908\n","loss:  0.5465564727783203 Epoch:  17 Iteration:  1909\n","loss:  0.6153728365898132 Epoch:  17 Iteration:  1910\n","tensor(5.8484, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1995, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.9440, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.8614, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3994/4720 (84.619%) \t and Real vs. Fake Accuracy: 5883/6000 (98.050%) \n","loss:  0.52103590965271 Epoch:  17 Iteration:  1911\n","loss:  0.49661731719970703 Epoch:  17 Iteration:  1912\n","loss:  0.5183526873588562 Epoch:  17 Iteration:  1913\n","loss:  0.533991813659668 Epoch:  17 Iteration:  1914\n","loss:  0.4401499629020691 Epoch:  17 Iteration:  1915\n","loss:  0.4938100576400757 Epoch:  17 Iteration:  1916\n","loss:  0.5629761815071106 Epoch:  17 Iteration:  1917\n","loss:  0.432697594165802 Epoch:  17 Iteration:  1918\n","loss:  0.4893399477005005 Epoch:  17 Iteration:  1919\n","loss:  0.46961572766304016 Epoch:  17 Iteration:  1920\n","tensor(5.3916, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7666, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.7015, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.6570, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5888/6000 (98.133%) \n","loss:  0.5771257877349854 Epoch:  17 Iteration:  1921\n","loss:  0.5037631392478943 Epoch:  17 Iteration:  1922\n","loss:  0.5491533279418945 Epoch:  17 Iteration:  1923\n","loss:  0.5382381081581116 Epoch:  17 Iteration:  1924\n","loss:  0.5590425729751587 Epoch:  17 Iteration:  1925\n","loss:  0.5939003229141235 Epoch:  17 Iteration:  1926\n","loss:  0.5177000761032104 Epoch:  17 Iteration:  1927\n","loss:  0.5405661463737488 Epoch:  17 Iteration:  1928\n","loss:  0.5795655250549316 Epoch:  17 Iteration:  1929\n","loss:  0.6104235053062439 Epoch:  17 Iteration:  1930\n","tensor(6.6711, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5408, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.7096, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5357, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3993/4720 (84.598%) \t and Real vs. Fake Accuracy: 5894/6000 (98.233%) \n","loss:  0.46533089876174927 Epoch:  17 Iteration:  1931\n","loss:  0.509437620639801 Epoch:  17 Iteration:  1932\n","loss:  0.5342709422111511 Epoch:  17 Iteration:  1933\n","loss:  0.5390337705612183 Epoch:  17 Iteration:  1934\n","loss:  0.6007201075553894 Epoch:  17 Iteration:  1935\n","loss:  0.4620361328125 Epoch:  17 Iteration:  1936\n","loss:  0.49411728978157043 Epoch:  17 Iteration:  1937\n","loss:  0.4598008394241333 Epoch:  17 Iteration:  1938\n","loss:  0.58287113904953 Epoch:  17 Iteration:  1939\n","loss:  0.45641541481018066 Epoch:  17 Iteration:  1940\n","tensor(7.4621, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7655, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.3785, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5251, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4001/4720 (84.767%) \t and Real vs. Fake Accuracy: 5900/6000 (98.333%) \n","loss:  0.4776926636695862 Epoch:  17 Iteration:  1941\n","loss:  0.48903918266296387 Epoch:  17 Iteration:  1942\n","loss:  0.5890185832977295 Epoch:  17 Iteration:  1943\n","loss:  0.4952011704444885 Epoch:  17 Iteration:  1944\n","loss:  0.5471100211143494 Epoch:  17 Iteration:  1945\n","loss:  0.4639914929866791 Epoch:  17 Iteration:  1946\n","loss:  0.47967231273651123 Epoch:  17 Iteration:  1947\n","loss:  0.5438231229782104 Epoch:  17 Iteration:  1948\n","loss:  0.5187027454376221 Epoch:  17 Iteration:  1949\n","loss:  0.5068069100379944 Epoch:  17 Iteration:  1950\n","tensor(6.2915, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1979, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.6477, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9477, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3979/4720 (84.301%) \t and Real vs. Fake Accuracy: 5902/6000 (98.367%) \n","loss:  0.4275013208389282 Epoch:  17 Iteration:  1951\n","loss:  0.5656650066375732 Epoch:  17 Iteration:  1952\n","loss:  0.5115609169006348 Epoch:  17 Iteration:  1953\n","loss:  0.5509641170501709 Epoch:  17 Iteration:  1954\n","loss:  0.6258777976036072 Epoch:  17 Iteration:  1955\n","loss:  0.5282307267189026 Epoch:  17 Iteration:  1956\n","loss:  0.5959526300430298 Epoch:  17 Iteration:  1957\n","loss:  0.526187002658844 Epoch:  17 Iteration:  1958\n","loss:  0.5802034139633179 Epoch:  17 Iteration:  1959\n","loss:  0.4550939202308655 Epoch:  17 Iteration:  1960\n","tensor(7.8673, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1778, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.0598, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7017, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3987/4720 (84.470%) \t and Real vs. Fake Accuracy: 5904/6000 (98.400%) \n","loss:  0.44554346799850464 Epoch:  17 Iteration:  1961\n","loss:  0.533440113067627 Epoch:  17 Iteration:  1962\n","loss:  0.5477347373962402 Epoch:  17 Iteration:  1963\n","loss:  0.5370005369186401 Epoch:  17 Iteration:  1964\n","loss:  0.552325963973999 Epoch:  17 Iteration:  1965\n","loss:  0.5591996908187866 Epoch:  17 Iteration:  1966\n","loss:  0.5632153153419495 Epoch:  17 Iteration:  1967\n","loss:  0.44719821214675903 Epoch:  17 Iteration:  1968\n","loss:  0.49946779012680054 Epoch:  17 Iteration:  1969\n","loss:  0.4820129871368408 Epoch:  17 Iteration:  1970\n","tensor(7.4453, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2899, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.4702, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7160, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3992/4720 (84.576%) \t and Real vs. Fake Accuracy: 5903/6000 (98.383%) \n","loss:  0.49008476734161377 Epoch:  17 Iteration:  1971\n","loss:  0.5574133396148682 Epoch:  17 Iteration:  1972\n","loss:  0.5613771677017212 Epoch:  17 Iteration:  1973\n","loss:  0.6135286688804626 Epoch:  17 Iteration:  1974\n","loss:  0.5300032496452332 Epoch:  17 Iteration:  1975\n","loss:  0.6909717917442322 Epoch:  17 Iteration:  1976\n","loss:  0.615200400352478 Epoch:  17 Iteration:  1977\n","loss:  0.4829750955104828 Epoch:  17 Iteration:  1978\n","loss:  0.5157474279403687 Epoch:  17 Iteration:  1979\n","loss:  0.5795785188674927 Epoch:  17 Iteration:  1980\n","tensor(5.4689, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1472, device='cuda:0', grad_fn=<MinBackward1>) tensor(7.9067, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5418, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3982/4720 (84.364%) \t and Real vs. Fake Accuracy: 5910/6000 (98.500%) \n","loss:  0.12066465616226196 Epoch:  17 Iteration:  1981\n","loss:  0.13555939495563507 Epoch:  17 Iteration:  1982\n","loss:  0.13428398966789246 Epoch:  17 Iteration:  1983\n","loss:  0.14138203859329224 Epoch:  17 Iteration:  1984\n","loss:  0.13676150143146515 Epoch:  17 Iteration:  1985\n","loss:  0.13099521398544312 Epoch:  17 Iteration:  1986\n","loss:  0.131678506731987 Epoch:  17 Iteration:  1987\n","loss:  0.0977141261100769 Epoch:  17 Iteration:  1988\n","loss:  0.10530659556388855 Epoch:  17 Iteration:  1989\n","loss:  0.1566624939441681 Epoch:  17 Iteration:  1990\n","tensor(6.5818, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5317, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.6642, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.2469, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3988/4720 (84.492%) \t and Real vs. Fake Accuracy: 5903/6000 (98.383%) \n","loss:  0.127961203455925 Epoch:  17 Iteration:  1991\n","loss:  0.12023527175188065 Epoch:  17 Iteration:  1992\n","loss:  0.11368364840745926 Epoch:  17 Iteration:  1993\n","loss:  0.13083034753799438 Epoch:  17 Iteration:  1994\n","loss:  0.14074431359767914 Epoch:  17 Iteration:  1995\n","loss:  0.12844346463680267 Epoch:  17 Iteration:  1996\n","loss:  0.1073388010263443 Epoch:  17 Iteration:  1997\n","loss:  0.443940669298172 Epoch:  18 Iteration:  1998\n","loss:  0.5007097125053406 Epoch:  18 Iteration:  1999\n","loss:  0.4410490393638611 Epoch:  18 Iteration:  2000\n","tensor(5.9362, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8930, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0042, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1210, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3994/4720 (84.619%) \t and Real vs. Fake Accuracy: 5894/6000 (98.233%) \n","loss:  0.4667516052722931 Epoch:  18 Iteration:  2001\n","loss:  0.487537682056427 Epoch:  18 Iteration:  2002\n","loss:  0.44785037636756897 Epoch:  18 Iteration:  2003\n","loss:  0.4025380611419678 Epoch:  18 Iteration:  2004\n","loss:  0.48701608180999756 Epoch:  18 Iteration:  2005\n","loss:  0.48834913969039917 Epoch:  18 Iteration:  2006\n","loss:  0.46805626153945923 Epoch:  18 Iteration:  2007\n","loss:  0.5045337080955505 Epoch:  18 Iteration:  2008\n","loss:  0.41183406114578247 Epoch:  18 Iteration:  2009\n","loss:  0.5518687963485718 Epoch:  18 Iteration:  2010\n","tensor(8.8776, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-3.2439, device='cuda:0', grad_fn=<MinBackward1>) tensor(13.9078, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-3.0759, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3998/4720 (84.703%) \t and Real vs. Fake Accuracy: 5921/6000 (98.683%) \n","loss:  0.4787386655807495 Epoch:  18 Iteration:  2011\n","loss:  0.44320738315582275 Epoch:  18 Iteration:  2012\n","loss:  0.47928524017333984 Epoch:  18 Iteration:  2013\n","loss:  0.5303502678871155 Epoch:  18 Iteration:  2014\n","loss:  0.5210286378860474 Epoch:  18 Iteration:  2015\n","loss:  0.4498337507247925 Epoch:  18 Iteration:  2016\n","loss:  0.4411792755126953 Epoch:  18 Iteration:  2017\n","loss:  0.49948713183403015 Epoch:  18 Iteration:  2018\n","loss:  0.5097591876983643 Epoch:  18 Iteration:  2019\n","loss:  0.47245678305625916 Epoch:  18 Iteration:  2020\n","tensor(7.9338, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.5669, device='cuda:0', grad_fn=<MinBackward1>) tensor(12.3620, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1180, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3999/4720 (84.725%) \t and Real vs. Fake Accuracy: 5908/6000 (98.467%) \n","loss:  0.4738890826702118 Epoch:  18 Iteration:  2021\n","loss:  0.4586094617843628 Epoch:  18 Iteration:  2022\n","loss:  0.5218886733055115 Epoch:  18 Iteration:  2023\n","loss:  0.45435917377471924 Epoch:  18 Iteration:  2024\n","loss:  0.4510995149612427 Epoch:  18 Iteration:  2025\n","loss:  0.49116477370262146 Epoch:  18 Iteration:  2026\n","loss:  0.47501397132873535 Epoch:  18 Iteration:  2027\n","loss:  0.4870617985725403 Epoch:  18 Iteration:  2028\n","loss:  0.47027087211608887 Epoch:  18 Iteration:  2029\n","loss:  0.4063512682914734 Epoch:  18 Iteration:  2030\n","tensor(6.1226, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9649, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.0943, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.9146, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3990/4720 (84.534%) \t and Real vs. Fake Accuracy: 5916/6000 (98.600%) \n","loss:  0.5103426575660706 Epoch:  18 Iteration:  2031\n","loss:  0.5556889772415161 Epoch:  18 Iteration:  2032\n","loss:  0.5001581907272339 Epoch:  18 Iteration:  2033\n","loss:  0.4358401596546173 Epoch:  18 Iteration:  2034\n","loss:  0.49477171897888184 Epoch:  18 Iteration:  2035\n","loss:  0.5477887392044067 Epoch:  18 Iteration:  2036\n","loss:  0.541537880897522 Epoch:  18 Iteration:  2037\n","loss:  0.4822254478931427 Epoch:  18 Iteration:  2038\n","loss:  0.4633162319660187 Epoch:  18 Iteration:  2039\n","loss:  0.5675619840621948 Epoch:  18 Iteration:  2040\n","tensor(5.9788, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1770, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.2587, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7857, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4012/4720 (85.000%) \t and Real vs. Fake Accuracy: 5924/6000 (98.733%) \n","loss:  0.5276042222976685 Epoch:  18 Iteration:  2041\n","loss:  0.4157327115535736 Epoch:  18 Iteration:  2042\n","loss:  0.45827752351760864 Epoch:  18 Iteration:  2043\n","loss:  0.5442503094673157 Epoch:  18 Iteration:  2044\n","loss:  0.40682488679885864 Epoch:  18 Iteration:  2045\n","loss:  0.5808108448982239 Epoch:  18 Iteration:  2046\n","loss:  0.3860061764717102 Epoch:  18 Iteration:  2047\n","loss:  0.4852831959724426 Epoch:  18 Iteration:  2048\n","loss:  0.5082839131355286 Epoch:  18 Iteration:  2049\n","loss:  0.5016591548919678 Epoch:  18 Iteration:  2050\n","tensor(7.0525, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2637, device='cuda:0', grad_fn=<MinBackward1>) tensor(14.1375, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.7292, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3996/4720 (84.661%) \t and Real vs. Fake Accuracy: 5922/6000 (98.700%) \n","loss:  0.5946606993675232 Epoch:  18 Iteration:  2051\n","loss:  0.538749098777771 Epoch:  18 Iteration:  2052\n","loss:  0.4079785943031311 Epoch:  18 Iteration:  2053\n","loss:  0.4515530467033386 Epoch:  18 Iteration:  2054\n","loss:  0.50484299659729 Epoch:  18 Iteration:  2055\n","loss:  0.4103471636772156 Epoch:  18 Iteration:  2056\n","loss:  0.4993593692779541 Epoch:  18 Iteration:  2057\n","loss:  0.5350024700164795 Epoch:  18 Iteration:  2058\n","loss:  0.5215478539466858 Epoch:  18 Iteration:  2059\n","loss:  0.4639626741409302 Epoch:  18 Iteration:  2060\n","tensor(5.7828, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3040, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0121, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-3.2661, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3994/4720 (84.619%) \t and Real vs. Fake Accuracy: 5920/6000 (98.667%) \n","loss:  0.5066210627555847 Epoch:  18 Iteration:  2061\n","loss:  0.4978092312812805 Epoch:  18 Iteration:  2062\n","loss:  0.48184797167778015 Epoch:  18 Iteration:  2063\n","loss:  0.48756104707717896 Epoch:  18 Iteration:  2064\n","loss:  0.526516854763031 Epoch:  18 Iteration:  2065\n","loss:  0.4240555763244629 Epoch:  18 Iteration:  2066\n","loss:  0.4229503571987152 Epoch:  18 Iteration:  2067\n","loss:  0.6174927949905396 Epoch:  18 Iteration:  2068\n","loss:  0.5060878992080688 Epoch:  18 Iteration:  2069\n","loss:  0.4785783290863037 Epoch:  18 Iteration:  2070\n","tensor(6.3191, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.6948, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.0068, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.8957, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3988/4720 (84.492%) \t and Real vs. Fake Accuracy: 5927/6000 (98.783%) \n","loss:  0.541442334651947 Epoch:  18 Iteration:  2071\n","loss:  0.5352883338928223 Epoch:  18 Iteration:  2072\n","loss:  0.47943544387817383 Epoch:  18 Iteration:  2073\n","loss:  0.5041611790657043 Epoch:  18 Iteration:  2074\n","loss:  0.5042494535446167 Epoch:  18 Iteration:  2075\n","loss:  0.45396772027015686 Epoch:  18 Iteration:  2076\n","loss:  0.48326146602630615 Epoch:  18 Iteration:  2077\n","loss:  0.49595382809638977 Epoch:  18 Iteration:  2078\n","loss:  0.4768563508987427 Epoch:  18 Iteration:  2079\n","loss:  0.5159937143325806 Epoch:  18 Iteration:  2080\n","tensor(5.9405, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.7384, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.7036, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0207, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3986/4720 (84.449%) \t and Real vs. Fake Accuracy: 5933/6000 (98.883%) \n","loss:  0.479809045791626 Epoch:  18 Iteration:  2081\n","loss:  0.509433388710022 Epoch:  18 Iteration:  2082\n","loss:  0.4790143072605133 Epoch:  18 Iteration:  2083\n","loss:  0.501763105392456 Epoch:  18 Iteration:  2084\n","loss:  0.469256192445755 Epoch:  18 Iteration:  2085\n","loss:  0.4582703411579132 Epoch:  18 Iteration:  2086\n","loss:  0.4441564679145813 Epoch:  18 Iteration:  2087\n","loss:  0.5572952628135681 Epoch:  18 Iteration:  2088\n","loss:  0.46778419613838196 Epoch:  18 Iteration:  2089\n","loss:  0.5121756792068481 Epoch:  18 Iteration:  2090\n","tensor(6.6045, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.0398, device='cuda:0', grad_fn=<MinBackward1>) tensor(8.9064, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.3356, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3987/4720 (84.470%) \t and Real vs. Fake Accuracy: 5927/6000 (98.783%) \n","loss:  0.39956724643707275 Epoch:  18 Iteration:  2091\n","loss:  0.10332497209310532 Epoch:  18 Iteration:  2092\n","loss:  0.13943631947040558 Epoch:  18 Iteration:  2093\n","loss:  0.13950717449188232 Epoch:  18 Iteration:  2094\n","loss:  0.1086956113576889 Epoch:  18 Iteration:  2095\n","loss:  0.13559773564338684 Epoch:  18 Iteration:  2096\n","loss:  0.10328571498394012 Epoch:  18 Iteration:  2097\n","loss:  0.1439400464296341 Epoch:  18 Iteration:  2098\n","loss:  0.10129669308662415 Epoch:  18 Iteration:  2099\n","loss:  0.09432907402515411 Epoch:  18 Iteration:  2100\n","tensor(6.4669, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9237, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.4352, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.1390, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3990/4720 (84.534%) \t and Real vs. Fake Accuracy: 5925/6000 (98.750%) \n","loss:  0.10026408731937408 Epoch:  18 Iteration:  2101\n","loss:  0.09848999232053757 Epoch:  18 Iteration:  2102\n","loss:  0.11561302840709686 Epoch:  18 Iteration:  2103\n","loss:  0.10978072881698608 Epoch:  18 Iteration:  2104\n","loss:  0.11897718906402588 Epoch:  18 Iteration:  2105\n","loss:  0.12474191188812256 Epoch:  18 Iteration:  2106\n","loss:  0.13550269603729248 Epoch:  18 Iteration:  2107\n","loss:  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Iteration:  2117\n","loss:  0.42704135179519653 Epoch:  19 Iteration:  2118\n","loss:  0.3987874984741211 Epoch:  19 Iteration:  2119\n","loss:  0.35908710956573486 Epoch:  19 Iteration:  2120\n","tensor(7.1572, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.4111, device='cuda:0', grad_fn=<MinBackward1>) tensor(10.2662, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.9161, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3991/4720 (84.555%) \t and Real vs. Fake Accuracy: 5933/6000 (98.883%) \n","loss:  0.4117346704006195 Epoch:  19 Iteration:  2121\n","loss:  0.4266704022884369 Epoch:  19 Iteration:  2122\n","loss:  0.3693816065788269 Epoch:  19 Iteration:  2123\n","loss:  0.37965720891952515 Epoch:  19 Iteration:  2124\n","loss:  0.4818546772003174 Epoch:  19 Iteration:  2125\n","loss:  0.43968257308006287 Epoch:  19 Iteration:  2126\n","loss:  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Iteration:  2136\n","loss:  0.43192893266677856 Epoch:  19 Iteration:  2137\n","loss:  0.3914748430252075 Epoch:  19 Iteration:  2138\n","loss:  0.40391427278518677 Epoch:  19 Iteration:  2139\n","loss:  0.522620677947998 Epoch:  19 Iteration:  2140\n","tensor(6.3806, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.9338, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.9726, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.4254, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3997/4720 (84.682%) \t and Real vs. Fake Accuracy: 5919/6000 (98.650%) \n","loss:  0.4580702781677246 Epoch:  19 Iteration:  2141\n","loss:  0.43564194440841675 Epoch:  19 Iteration:  2142\n","loss:  0.4872615933418274 Epoch:  19 Iteration:  2143\n","loss:  0.41869863867759705 Epoch:  19 Iteration:  2144\n","loss:  0.4884473979473114 Epoch:  19 Iteration:  2145\n","loss:  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0.10219155251979828 Epoch:  19 Iteration:  2203\n","loss:  0.1189979836344719 Epoch:  19 Iteration:  2204\n","loss:  0.08611810207366943 Epoch:  19 Iteration:  2205\n","loss:  0.140451580286026 Epoch:  19 Iteration:  2206\n","loss:  0.1025984138250351 Epoch:  19 Iteration:  2207\n","loss:  0.08189037442207336 Epoch:  19 Iteration:  2208\n","loss:  0.11588073521852493 Epoch:  19 Iteration:  2209\n","loss:  0.11918670684099197 Epoch:  19 Iteration:  2210\n","tensor(8.2089, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.6539, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.4934, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.9804, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3985/4720 (84.428%) \t and Real vs. Fake Accuracy: 5933/6000 (98.883%) \n","loss:  0.11802705377340317 Epoch:  19 Iteration:  2211\n","loss:  0.10597950965166092 Epoch:  19 Iteration:  2212\n","loss:  0.15908226370811462 Epoch:  19 Iteration:  2213\n","loss:  0.11991292238235474 Epoch:  19 Iteration:  2214\n","loss:  0.12158291786909103 Epoch:  19 Iteration:  2215\n","loss:  0.10263068974018097 Epoch:  19 Iteration:  2216\n","loss:  0.0988067090511322 Epoch:  19 Iteration:  2217\n","loss:  0.12592114508152008 Epoch:  19 Iteration:  2218\n","loss:  0.11160466074943542 Epoch:  19 Iteration:  2219\n","tensor(11.1885, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.8818, device='cuda:0', grad_fn=<MinBackward1>) tensor(11.6441, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-3.5349, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 3994/4720 (84.619%) \t and Real vs. Fake Accuracy: 5938/6000 (98.967%) \n","\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 648x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["tensor([79.1949, 77.5636, 78.8983, 78.5593, 80.1059, 80.1907, 80.4873, 79.4915,\n","        80.9958, 81.1441, 82.0339, 83.0085, 82.3093, 81.8856, 82.2034, 82.3729,\n","        81.9703, 82.8814, 82.6059, 82.6695, 82.0975, 82.6271, 83.1780, 83.4110,\n","        82.9237, 82.8390, 83.1144, 83.1144, 83.1356, 83.5169, 82.9661, 82.6907,\n","        83.6017, 83.6864, 83.3263, 83.7924, 83.0932, 83.8559, 83.0720, 83.1780,\n","        83.4322, 83.6017, 83.5169, 83.5593, 83.9830, 83.5805, 83.6441, 82.6483,\n","        83.1780, 83.0508, 83.8348, 83.7288, 83.8348, 83.3898, 83.1780, 83.6017,\n","        83.9195, 83.6017, 83.7076, 83.9407, 84.0254, 83.6652, 83.2203, 84.1314,\n","        83.6229, 83.8983, 83.7288, 84.0042, 83.8771, 83.7924, 83.3686, 83.8771,\n","        83.2627, 83.8136, 83.7500, 83.8348, 83.8136, 84.0042, 83.8559, 83.7712,\n","        83.9619, 83.8771, 83.7500, 83.8136, 83.8771, 83.7288, 83.6017, 83.7288,\n","        83.6229, 83.9195, 84.0254, 84.1314, 84.1525, 84.0254, 83.7076, 83.7924,\n","        83.9619, 83.9830, 84.0466, 84.2161, 84.2373, 84.1314, 83.8348, 83.9407,\n","        83.9407, 84.1525, 84.3856, 84.0254, 84.2373, 84.2585, 84.0678, 83.8136,\n","        84.0678, 83.9830, 84.2373, 84.3220, 84.2161, 84.0890, 84.0890, 84.0254,\n","        84.1525, 83.9619, 84.1314, 84.2585, 84.2161, 84.2161, 84.1525, 84.4492,\n","        84.3644, 84.1525, 84.2585, 84.2585, 84.1949, 84.3220, 84.3432, 84.2161,\n","        84.1949, 84.0042, 83.7500, 83.8348, 84.4068, 84.5339, 84.3220, 84.1102,\n","        84.2797, 84.1102, 84.5127, 84.4703, 84.4492, 84.3008, 84.5975, 84.5339,\n","        84.2797, 84.4492, 84.3644, 84.3220, 84.3644, 84.3644, 84.5763, 84.3856,\n","        84.2585, 84.3432, 84.3220, 84.4492, 84.4280, 84.5975, 84.5763, 84.7246,\n","        84.5127, 84.5551, 84.2373, 84.2585, 84.4492, 84.5339, 84.6186, 84.5127,\n","        84.4703, 84.5127, 84.6186, 84.7669, 84.4280, 84.6398, 84.3644, 84.5975,\n","        84.7034, 84.5339, 84.4068, 84.3856, 84.5975, 84.4915, 84.3220, 84.6186,\n","        84.3644, 84.5975, 84.7669, 84.3008, 84.4703, 84.5763, 84.3644, 84.4915,\n","        84.6186, 84.7034, 84.7246, 84.5339, 85.0000, 84.6610, 84.6186, 84.4915,\n","        84.4492, 84.4703, 84.5339, 84.5551, 84.5551, 84.4915, 84.6822, 84.7458,\n","        84.6822, 84.6822, 84.7034, 84.6822, 84.5551, 84.6186,  0.9654],\n","       device='cuda:0')\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rcMbpqlAv0Hu","colab_type":"code","outputId":"6c35aabe-445e-408c-efe2-42ff4095b9c3","executionInfo":{"status":"ok","timestamp":1587947334649,"user_tz":240,"elapsed":16421,"user":{"displayName":"Matt Wilmes","photoUrl":"","userId":"10496298162693013399"}},"colab":{"base_uri":"https://localhost:8080/","height":171}},"source":["train_acc1,overfit_acc2=assembly.model_run(best_model2, train_loader, optimizer, train_flag=False,epochs=1)\n","val_acc,overfit_acc2 = assembly.model_run(best_model2, val_loader, optimizer, train_flag=False,epochs=1)\n","test_acc,overfit_acc2 = assembly.model_run(best_model2, test_loader, optimizer, train_flag=False,epochs=1)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["tensor(7.5602, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.3636, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.8805, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.9611, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4016/4720 (85.085%) \t and Real vs. Fake Accuracy: 5835/6000 (97.250%) \n","tensor(9.6817, device='cuda:0') tensor(-2.2099, device='cuda:0') tensor(8.3869, device='cuda:0') tensor(-2.1373, device='cuda:0') tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 14131/14160 (99.795%) \t and Real vs. Fake Accuracy: 5835/6000 (97.250%) \n","tensor(6.4461, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-2.2463, device='cuda:0', grad_fn=<MinBackward1>) tensor(9.2415, device='cuda:0', grad_fn=<MaxBackward1>) tensor(-1.5288, device='cuda:0', grad_fn=<MinBackward1>) tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4016/4720 (85.085%) \t and Real vs. Fake Accuracy: 5835/6000 (97.250%) \n","tensor(5.6476, device='cuda:0') tensor(-2.5682, device='cuda:0') tensor(9.2561, device='cuda:0') tensor(-1.8941, device='cuda:0') tensor(0, device='cuda:0') tensor(0, device='cuda:0') tensor(1, device='cuda:0') tensor(1, device='cuda:0')\n","Consistency Accuracy: 4016/4720 (85.085%) \t and Real vs. Fake Accuracy: 5835/6000 (97.250%) \n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"DxhtW0myWDtK","colab_type":"text"},"source":["#**Saving the trained model:**\n","###Instructions:\n","The following command, when uncommented, will save the above trained model to Google Drive in a secured place such that this trained model can be accessed by this and any other notebooks.\n","\n","**1. Uncomment the following cell and run it.**   \n","**2. Comment it back to remove the possiblity of accidental overwriting.**\n"]},{"cell_type":"code","metadata":{"id":"42LnplOSHs6K","colab_type":"code","colab":{}},"source":["# model_save_name = f\"P3_A5_standard_model_40x40to28x28_2xloss_train_{train_acc1:.7}_val_{val_acc:.7}_test_acc_{test_acc:.7}_part2_{overfit_acc2:.7}.pt\"\n","# path = F\"/content/drive/Shared drives/EECS504/Trained_Models/MW/{model_save_name}\" \n","# torch.save(best_model2.state_dict(), path)\n","\n","# params_save_name = f\"P3_A5_best_params_standard_model_40x40to28x28_2xloss_train_{train_acc1:.7}_val_{val_acc:.7}_test_acc_{test_acc:.7}_part2_{overfit_acc2:.7}.pt\"\n","# path2 = F\"/content/drive/Shared drives/EECS504/Trained_Models/MW/{params_save_name}\" \n","# torch.save(best_params2['param_groups'],path2)  #re-load what you save and see that it is well-saved!"],"execution_count":0,"outputs":[]}]}