GAN-MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import DataLoader\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "transform = torchvision.transforms.Compose([\n",
    "    torchvision.transforms.ToTensor()\n",
    "])\n",
    "\n",
    "real_data = torchvision.datasets.MNIST(\n",
    "    root='data',\n",
    "    train=True,\n",
    "    transform=torchvision.transforms.ToTensor(),\n",
    "    download=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "bs = 128\n",
    "real_dataloader = DataLoader(\n",
    "    dataset=real_data,\n",
    "    batch_size=bs\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(in_features=100, out_features=256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=256, out_features=256),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=256, out_features=28*28),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.network(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.network = nn.Sequential(\n",
    "            nn.Linear(in_features=28*28, out_features=64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=64, out_features=64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=64, out_features=1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.network(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discriminator(\n",
       "  (network): Sequential(\n",
       "    (0): Linear(in_features=784, out_features=64, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=64, out_features=64, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=64, out_features=1, bias=True)\n",
       "    (5): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "G = Generator()\n",
    "D = Discriminator()\n",
    "\n",
    "G.to(device)\n",
    "D.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "optimizer_G = torch.optim.Adam(G.parameters(), lr=2e-4)\n",
    "optimizer_D = torch.optim.Adam(D.parameters(), lr=2e-4)\n",
    "\n",
    "bce_loss = torch.nn.BCELoss()\n",
    "epochs = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "loss_D_list = []\n",
    "loss_G_list = []\n",
    "Dx_list = []\n",
    "DGz_list = []\n",
    "iter = 0\n",
    "\n",
    "def train():\n",
    "    for epoch in range(epochs):\n",
    "        print(f\"epoch {epoch+1}\\n-----------------\")\n",
    "        for i, (X_real, _) in enumerate(real_dataloader):\n",
    "            \n",
    "            length = X_real.shape[0]\n",
    "            \n",
    "            y_real = torch.ones(length, 1)\n",
    "            y_fake = torch.zeros(length, 1)\n",
    "\n",
    "            X_real, y_real, y_fake = X_real.to(device), y_real.to(device), y_fake.to(device)\n",
    "\n",
    "            # 1 ------------更新Discriminator--------------\n",
    "\n",
    "            z_batch = torch.randn(length, 100).to(device)\n",
    "            X_fake = G(z_batch)\n",
    "\n",
    "            output_real = D(X_real.reshape(length, 28*28))\n",
    "            loss_real = bce_loss(output_real, y_real)\n",
    "            output_fake = D(X_fake)\n",
    "            loss_fake = bce_loss(output_fake, y_fake)\n",
    "\n",
    "            loss_D = loss_real + loss_fake\n",
    "\n",
    "            D.zero_grad()\n",
    "            loss_D.backward()\n",
    "            optimizer_D.step()\n",
    "\n",
    "            # 2 -------------更新Generator---------------\n",
    "\n",
    "            z_batch = torch.randn(length, 100).to(device)\n",
    "            X_fake = G(z_batch)\n",
    "            fake_output = D(X_fake)\n",
    "            loss_G = bce_loss(fake_output, y_real)\n",
    "\n",
    "            G.zero_grad()\n",
    "            loss_G.backward()\n",
    "            optimizer_G.step()\n",
    "\n",
    "            if i % 100 == 0:\n",
    "                print(f\"loss_G: {loss_G.item()}, loss_D: {loss_D.item()}, D(x): {loss_real.item()}, D(G(z)): {loss_fake.item()}\")\n",
    "                loss_G_list.append(loss_G.item())\n",
    "                loss_D_list.append(loss_D.item())\n",
    "\n",
    "                Dx_list.append(loss_real.item())\n",
    "                DGz_list.append(loss_fake.item())\n",
    "                global iter\n",
    "                iter += 1\n",
    "        # 保存最后的权重文件\n",
    "        torch.save(G.state_dict(), 'model_G.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1\n",
      "-----------------\n",
      "loss_G: 0.7495243549346924, loss_D: 1.363412618637085, D(x): 0.7014787197113037, D(G(z)): 0.6619338989257812\n",
      "loss_G: 1.6420536041259766, loss_D: 0.585554838180542, D(x): 0.33158689737319946, D(G(z)): 0.25396794080734253\n",
      "loss_G: 1.7377078533172607, loss_D: 0.9533674716949463, D(x): 0.7124093770980835, D(G(z)): 0.2409580945968628\n",
      "loss_G: 1.9198389053344727, loss_D: 0.5758934020996094, D(x): 0.3448091149330139, D(G(z)): 0.23108431696891785\n",
      "loss_G: 1.1511222124099731, loss_D: 1.403984785079956, D(x): 0.9345172047615051, D(G(z)): 0.4694676101207733\n",
      "epoch 2\n",
      "-----------------\n",
      "loss_G: 2.531463384628296, loss_D: 0.2856084704399109, D(x): 0.18910859525203705, D(G(z)): 0.09649986028671265\n",
      "loss_G: 3.4109373092651367, loss_D: 0.1684371531009674, D(x): 0.1272028535604477, D(G(z)): 0.04123429208993912\n",
      "loss_G: 3.6132254600524902, loss_D: 0.2388562560081482, D(x): 0.20206165313720703, D(G(z)): 0.03679460659623146\n",
      "loss_G: 2.2859573364257812, loss_D: 0.4029045104980469, D(x): 0.24823899567127228, D(G(z)): 0.1546654999256134\n",
      "loss_G: 3.089569091796875, loss_D: 0.17867310345172882, D(x): 0.11619982123374939, D(G(z)): 0.06247328221797943\n",
      "epoch 3\n",
      "-----------------\n",
      "loss_G: 3.6920406818389893, loss_D: 0.17041617631912231, D(x): 0.12851782143115997, D(G(z)): 0.04189835116267204\n",
      "loss_G: 2.5340681076049805, loss_D: 0.3823227882385254, D(x): 0.24412357807159424, D(G(z)): 0.13819921016693115\n",
      "loss_G: 3.67641544342041, loss_D: 0.15289892256259918, D(x): 0.10803656280040741, D(G(z)): 0.04486236348748207\n",
      "loss_G: 3.0296380519866943, loss_D: 0.2030087411403656, D(x): 0.12886545062065125, D(G(z)): 0.07414329051971436\n",
      "loss_G: 3.008024215698242, loss_D: 0.17229202389717102, D(x): 0.09449472278356552, D(G(z)): 0.0777972936630249\n",
      "epoch 4\n",
      "-----------------\n",
      "loss_G: 2.6706788539886475, loss_D: 0.36986857652664185, D(x): 0.24180063605308533, D(G(z)): 0.12806792557239532\n",
      "loss_G: 2.5836262702941895, loss_D: 0.4119475483894348, D(x): 0.3041097819805145, D(G(z)): 0.1078377515077591\n",
      "loss_G: 1.1768903732299805, loss_D: 1.0625526905059814, D(x): 0.6549692153930664, D(G(z)): 0.40758341550827026\n",
      "loss_G: 3.2297801971435547, loss_D: 0.16065452992916107, D(x): 0.1072804182767868, D(G(z)): 0.053374115377664566\n",
      "loss_G: 1.49955415725708, loss_D: 0.9447931051254272, D(x): 0.6268497109413147, D(G(z)): 0.31794339418411255\n",
      "epoch 5\n",
      "-----------------\n",
      "loss_G: 1.3667855262756348, loss_D: 0.7536188364028931, D(x): 0.2841096520423889, D(G(z)): 0.46950918436050415\n",
      "loss_G: 1.1484835147857666, loss_D: 0.6553311347961426, D(x): 0.15784427523612976, D(G(z)): 0.49748682975769043\n",
      "loss_G: 1.4912385940551758, loss_D: 0.5422563552856445, D(x): 0.24189093708992004, D(G(z)): 0.3003654479980469\n",
      "loss_G: 1.7137681245803833, loss_D: 0.4533718228340149, D(x): 0.21296711266040802, D(G(z)): 0.24040469527244568\n",
      "loss_G: 0.6547737717628479, loss_D: 1.2105437517166138, D(x): 0.411199688911438, D(G(z)): 0.7993440628051758\n",
      "epoch 6\n",
      "-----------------\n",
      "loss_G: 0.47272855043411255, loss_D: 1.3596134185791016, D(x): 0.3033716380596161, D(G(z)): 1.056241750717163\n",
      "loss_G: 0.8884028196334839, loss_D: 0.9576284885406494, D(x): 0.3423101305961609, D(G(z)): 0.6153183579444885\n",
      "loss_G: 1.3265165090560913, loss_D: 0.5646629929542542, D(x): 0.2159501165151596, D(G(z)): 0.34871286153793335\n",
      "loss_G: 1.7103877067565918, loss_D: 0.449391633272171, D(x): 0.2247922271490097, D(G(z)): 0.22459940612316132\n",
      "loss_G: 1.9452861547470093, loss_D: 0.44104844331741333, D(x): 0.24543660879135132, D(G(z)): 0.19561181962490082\n",
      "epoch 7\n",
      "-----------------\n",
      "loss_G: 2.6803998947143555, loss_D: 0.2863233685493469, D(x): 0.17022283375263214, D(G(z)): 0.11610051989555359\n",
      "loss_G: 2.327653408050537, loss_D: 0.3996471166610718, D(x): 0.22738142311573029, D(G(z)): 0.17226570844650269\n",
      "loss_G: 2.4498469829559326, loss_D: 0.49318426847457886, D(x): 0.32483091950416565, D(G(z)): 0.16835333406925201\n",
      "loss_G: 2.823028564453125, loss_D: 0.32579565048217773, D(x): 0.20515114068984985, D(G(z)): 0.12064449489116669\n",
      "loss_G: 2.66306209564209, loss_D: 0.3917572498321533, D(x): 0.22479386627674103, D(G(z)): 0.1669633984565735\n",
      "epoch 8\n",
      "-----------------\n",
      "loss_G: 3.04663348197937, loss_D: 0.34187060594558716, D(x): 0.186983123421669, D(G(z)): 0.15488749742507935\n",
      "loss_G: 2.545743942260742, loss_D: 0.3147295415401459, D(x): 0.16645361483097076, D(G(z)): 0.1482759267091751\n",
      "loss_G: 2.7320492267608643, loss_D: 0.21420088410377502, D(x): 0.11272230744361877, D(G(z)): 0.10147857666015625\n",
      "loss_G: 3.0801074504852295, loss_D: 0.2646006643772125, D(x): 0.18789535760879517, D(G(z)): 0.07670530676841736\n",
      "loss_G: 2.3380727767944336, loss_D: 0.2668115198612213, D(x): 0.10944069176912308, D(G(z)): 0.15737083554267883\n",
      "epoch 9\n",
      "-----------------\n",
      "loss_G: 2.0975804328918457, loss_D: 0.4764956831932068, D(x): 0.25177425146102905, D(G(z)): 0.22472143173217773\n",
      "loss_G: 2.079991579055786, loss_D: 0.3993617594242096, D(x): 0.19154423475265503, D(G(z)): 0.20781752467155457\n",
      "loss_G: 2.555037498474121, loss_D: 0.30781519412994385, D(x): 0.1396033614873886, D(G(z)): 0.16821184754371643\n",
      "loss_G: 2.2742652893066406, loss_D: 0.46945619583129883, D(x): 0.3218124210834503, D(G(z)): 0.14764375984668732\n",
      "loss_G: 1.9155657291412354, loss_D: 0.7769207954406738, D(x): 0.5491431951522827, D(G(z)): 0.2277776002883911\n",
      "epoch 10\n",
      "-----------------\n",
      "loss_G: 2.369617462158203, loss_D: 0.5341055393218994, D(x): 0.33481115102767944, D(G(z)): 0.19929435849189758\n",
      "loss_G: 2.441375255584717, loss_D: 0.5330783128738403, D(x): 0.3587496876716614, D(G(z)): 0.17432865500450134\n",
      "loss_G: 3.511584758758545, loss_D: 0.3829970955848694, D(x): 0.27852118015289307, D(G(z)): 0.10447593033313751\n",
      "loss_G: 3.105835437774658, loss_D: 0.3176293969154358, D(x): 0.17281439900398254, D(G(z)): 0.14481499791145325\n",
      "loss_G: 3.4391937255859375, loss_D: 0.28061094880104065, D(x): 0.11355504393577576, D(G(z)): 0.1670559048652649\n",
      "epoch 11\n",
      "-----------------\n",
      "loss_G: 2.3577256202697754, loss_D: 0.4473326504230499, D(x): 0.1910005807876587, D(G(z)): 0.25633206963539124\n",
      "loss_G: 2.2483177185058594, loss_D: 0.37537604570388794, D(x): 0.15453463792800903, D(G(z)): 0.2208414077758789\n",
      "loss_G: 2.5959601402282715, loss_D: 0.4296019673347473, D(x): 0.23495520651340485, D(G(z)): 0.19464674592018127\n",
      "loss_G: 2.3161163330078125, loss_D: 0.4235587418079376, D(x): 0.23280870914459229, D(G(z)): 0.19075003266334534\n",
      "loss_G: 2.5106139183044434, loss_D: 0.3887207806110382, D(x): 0.22509616613388062, D(G(z)): 0.1636246144771576\n",
      "epoch 12\n",
      "-----------------\n",
      "loss_G: 3.2613577842712402, loss_D: 0.406107097864151, D(x): 0.29233700037002563, D(G(z)): 0.11377010494470596\n",
      "loss_G: 2.9617624282836914, loss_D: 0.42198872566223145, D(x): 0.32718443870544434, D(G(z)): 0.09480427205562592\n",
      "loss_G: 3.3832688331604004, loss_D: 0.494322270154953, D(x): 0.34771981835365295, D(G(z)): 0.14660245180130005\n",
      "loss_G: 3.9129035472869873, loss_D: 0.33186617493629456, D(x): 0.2584397494792938, D(G(z)): 0.07342641800642014\n",
      "loss_G: 3.4923746585845947, loss_D: 0.18522998690605164, D(x): 0.08725260943174362, D(G(z)): 0.09797737002372742\n",
      "epoch 13\n",
      "-----------------\n",
      "loss_G: 4.016331672668457, loss_D: 0.23810119926929474, D(x): 0.16914093494415283, D(G(z)): 0.0689602643251419\n",
      "loss_G: 4.590882301330566, loss_D: 0.20300942659378052, D(x): 0.15063628554344177, D(G(z)): 0.05237313359975815\n",
      "loss_G: 2.7833333015441895, loss_D: 0.4369089603424072, D(x): 0.2541343867778778, D(G(z)): 0.18277457356452942\n",
      "loss_G: 3.135002374649048, loss_D: 0.35757946968078613, D(x): 0.24130216240882874, D(G(z)): 0.11627732217311859\n",
      "loss_G: 3.3387582302093506, loss_D: 0.2963019609451294, D(x): 0.13626188039779663, D(G(z)): 0.16004008054733276\n",
      "epoch 14\n",
      "-----------------\n",
      "loss_G: 3.5768144130706787, loss_D: 0.34947913885116577, D(x): 0.2079298049211502, D(G(z)): 0.14154931902885437\n",
      "loss_G: 3.4681220054626465, loss_D: 0.23928698897361755, D(x): 0.14450302720069885, D(G(z)): 0.0947839692234993\n",
      "loss_G: 3.37699031829834, loss_D: 0.3535647988319397, D(x): 0.2280290424823761, D(G(z)): 0.1255357563495636\n",
      "loss_G: 3.249203681945801, loss_D: 0.32087671756744385, D(x): 0.24940377473831177, D(G(z)): 0.07147294282913208\n",
      "loss_G: 2.575526237487793, loss_D: 0.32163605093955994, D(x): 0.16934582591056824, D(G(z)): 0.1522902250289917\n",
      "epoch 15\n",
      "-----------------\n",
      "loss_G: 3.09867525100708, loss_D: 0.334416925907135, D(x): 0.20952960848808289, D(G(z)): 0.12488730996847153\n",
      "loss_G: 3.134566307067871, loss_D: 0.4542056918144226, D(x): 0.3323517441749573, D(G(z)): 0.12185393273830414\n",
      "loss_G: 2.8177499771118164, loss_D: 0.4829605221748352, D(x): 0.3333836793899536, D(G(z)): 0.1495768427848816\n",
      "loss_G: 2.4524638652801514, loss_D: 0.5786080360412598, D(x): 0.38571667671203613, D(G(z)): 0.19289138913154602\n",
      "loss_G: 2.6369032859802246, loss_D: 0.26252415776252747, D(x): 0.14049282670021057, D(G(z)): 0.1220313310623169\n",
      "epoch 16\n",
      "-----------------\n",
      "loss_G: 2.7154407501220703, loss_D: 0.35452786087989807, D(x): 0.20903578400611877, D(G(z)): 0.1454920768737793\n",
      "loss_G: 2.824354410171509, loss_D: 0.36299949884414673, D(x): 0.2570742666721344, D(G(z)): 0.10592521727085114\n",
      "loss_G: 3.1945228576660156, loss_D: 0.39745450019836426, D(x): 0.2786709666252136, D(G(z)): 0.11878351867198944\n",
      "loss_G: 3.3053817749023438, loss_D: 0.47361934185028076, D(x): 0.3635178208351135, D(G(z)): 0.11010150611400604\n",
      "loss_G: 2.893453598022461, loss_D: 0.5042550563812256, D(x): 0.2920227646827698, D(G(z)): 0.2122322916984558\n",
      "epoch 17\n",
      "-----------------\n",
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      "loss_G: 2.8328895568847656, loss_D: 0.2896902561187744, D(x): 0.11438407748937607, D(G(z)): 0.17530617117881775\n",
      "loss_G: 2.92820143699646, loss_D: 0.3615798056125641, D(x): 0.20336954295635223, D(G(z)): 0.15821026265621185\n",
      "loss_G: 2.8098111152648926, loss_D: 0.5080653429031372, D(x): 0.3188316822052002, D(G(z)): 0.1892336755990982\n",
      "loss_G: 3.3311877250671387, loss_D: 0.4151560068130493, D(x): 0.29096195101737976, D(G(z)): 0.12419407069683075\n",
      "epoch 18\n",
      "-----------------\n",
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      "loss_G: 2.3125452995300293, loss_D: 0.6237407326698303, D(x): 0.33601200580596924, D(G(z)): 0.2877287268638611\n",
      "loss_G: 3.3472297191619873, loss_D: 0.31756800413131714, D(x): 0.203200101852417, D(G(z)): 0.11436791718006134\n",
      "loss_G: 2.3241569995880127, loss_D: 0.6160145998001099, D(x): 0.33982062339782715, D(G(z)): 0.2761939465999603\n",
      "loss_G: 2.257817268371582, loss_D: 0.5748062133789062, D(x): 0.31710100173950195, D(G(z)): 0.2577051818370819\n",
      "epoch 19\n",
      "-----------------\n",
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      "loss_G: 2.719770669937134, loss_D: 0.8058347702026367, D(x): 0.47513842582702637, D(G(z)): 0.33069634437561035\n",
      "loss_G: 2.598270893096924, loss_D: 0.4499499797821045, D(x): 0.25682333111763, D(G(z)): 0.1931266486644745\n",
      "loss_G: 2.501701831817627, loss_D: 0.6775856018066406, D(x): 0.4689319133758545, D(G(z)): 0.20865371823310852\n",
      "loss_G: 2.481051445007324, loss_D: 0.6711231470108032, D(x): 0.4774014353752136, D(G(z)): 0.1937217116355896\n",
      "epoch 20\n",
      "-----------------\n",
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      "loss_G: 2.2665445804595947, loss_D: 0.8323549628257751, D(x): 0.5492331981658936, D(G(z)): 0.2831217646598816\n",
      "loss_G: 2.340148687362671, loss_D: 0.682084858417511, D(x): 0.4193888306617737, D(G(z)): 0.2626960277557373\n",
      "loss_G: 2.2446274757385254, loss_D: 0.6497719287872314, D(x): 0.4112372398376465, D(G(z)): 0.23853471875190735\n",
      "loss_G: 2.389359951019287, loss_D: 0.6647147536277771, D(x): 0.42098289728164673, D(G(z)): 0.24373184144496918\n",
      "epoch 21\n",
      "-----------------\n",
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      "loss_G: 2.1356096267700195, loss_D: 0.7264143228530884, D(x): 0.48054829239845276, D(G(z)): 0.245866060256958\n",
      "loss_G: 2.4969632625579834, loss_D: 0.523208737373352, D(x): 0.3115714490413666, D(G(z)): 0.21163725852966309\n",
      "loss_G: 2.3094794750213623, loss_D: 0.5032933950424194, D(x): 0.3448171019554138, D(G(z)): 0.15847627818584442\n",
      "loss_G: 1.9108386039733887, loss_D: 0.8639222383499146, D(x): 0.5069279074668884, D(G(z)): 0.3569943308830261\n",
      "epoch 22\n",
      "-----------------\n",
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      "loss_G: 2.30771803855896, loss_D: 0.6298867464065552, D(x): 0.3649110198020935, D(G(z)): 0.26497572660446167\n",
      "loss_G: 1.9468388557434082, loss_D: 0.7443199753761292, D(x): 0.3738974332809448, D(G(z)): 0.3704225420951843\n",
      "loss_G: 1.881101369857788, loss_D: 0.8240644931793213, D(x): 0.5595965385437012, D(G(z)): 0.2644679546356201\n",
      "loss_G: 1.8914238214492798, loss_D: 0.707690954208374, D(x): 0.423954039812088, D(G(z)): 0.2837369441986084\n",
      "epoch 23\n",
      "-----------------\n",
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      "loss_G: 1.758962869644165, loss_D: 0.7181148529052734, D(x): 0.37747132778167725, D(G(z)): 0.3406435251235962\n",
      "loss_G: 1.8920196294784546, loss_D: 0.5264339447021484, D(x): 0.28502416610717773, D(G(z)): 0.24140974879264832\n",
      "loss_G: 1.9012856483459473, loss_D: 0.7660217881202698, D(x): 0.42431557178497314, D(G(z)): 0.34170621633529663\n",
      "loss_G: 1.601493239402771, loss_D: 0.7612844705581665, D(x): 0.39313969016075134, D(G(z)): 0.36814478039741516\n",
      "epoch 24\n",
      "-----------------\n",
      "loss_G: 2.127755641937256, loss_D: 0.6004992723464966, D(x): 0.30220794677734375, D(G(z)): 0.29829132556915283\n",
      "loss_G: 2.0336179733276367, loss_D: 0.7618175745010376, D(x): 0.4012320637702942, D(G(z)): 0.360585480928421\n",
      "loss_G: 1.4868464469909668, loss_D: 0.9078601002693176, D(x): 0.4210321307182312, D(G(z)): 0.4868279695510864\n",
      "loss_G: 1.8905359506607056, loss_D: 0.9940086603164673, D(x): 0.6053353548049927, D(G(z)): 0.3886732757091522\n",
      "loss_G: 1.6915078163146973, loss_D: 0.7703524231910706, D(x): 0.3604092597961426, D(G(z)): 0.409943163394928\n",
      "epoch 25\n",
      "-----------------\n",
      "loss_G: 1.8188786506652832, loss_D: 0.8478202819824219, D(x): 0.41409462690353394, D(G(z)): 0.43372562527656555\n",
      "loss_G: 1.6085906028747559, loss_D: 0.9233306050300598, D(x): 0.4715479612350464, D(G(z)): 0.4517826437950134\n",
      "loss_G: 1.3329010009765625, loss_D: 1.0546367168426514, D(x): 0.6297147870063782, D(G(z)): 0.4249218702316284\n",
      "loss_G: 1.7575244903564453, loss_D: 0.8837416172027588, D(x): 0.612111508846283, D(G(z)): 0.27163010835647583\n",
      "loss_G: 1.344904899597168, loss_D: 0.9342654943466187, D(x): 0.44480422139167786, D(G(z)): 0.4894612431526184\n",
      "epoch 26\n",
      "-----------------\n",
      "loss_G: 1.4003511667251587, loss_D: 0.8627036809921265, D(x): 0.4008285403251648, D(G(z)): 0.46187514066696167\n",
      "loss_G: 1.2284314632415771, loss_D: 1.0389997959136963, D(x): 0.46423131227493286, D(G(z)): 0.5747684240341187\n",
      "loss_G: 1.6035678386688232, loss_D: 0.8054376840591431, D(x): 0.395948588848114, D(G(z)): 0.40948906540870667\n",
      "loss_G: 1.292062759399414, loss_D: 1.2011395692825317, D(x): 0.674429178237915, D(G(z)): 0.5267103910446167\n",
      "loss_G: 1.985839605331421, loss_D: 0.8038620948791504, D(x): 0.41048121452331543, D(G(z)): 0.39338091015815735\n",
      "epoch 27\n",
      "-----------------\n",
      "loss_G: 1.692551851272583, loss_D: 0.8425788283348083, D(x): 0.3530051112174988, D(G(z)): 0.48957371711730957\n",
      "loss_G: 2.3362326622009277, loss_D: 0.6856614351272583, D(x): 0.4224861264228821, D(G(z)): 0.2631753385066986\n",
      "loss_G: 1.7622507810592651, loss_D: 0.8972082138061523, D(x): 0.4974634647369385, D(G(z)): 0.39974474906921387\n",
      "loss_G: 1.7872059345245361, loss_D: 0.9869849681854248, D(x): 0.5998257994651794, D(G(z)): 0.38715916872024536\n",
      "loss_G: 1.726911187171936, loss_D: 0.7399387359619141, D(x): 0.3332524299621582, D(G(z)): 0.40668630599975586\n",
      "epoch 28\n",
      "-----------------\n",
      "loss_G: 1.2081153392791748, loss_D: 1.0748636722564697, D(x): 0.3796451687812805, D(G(z)): 0.6952184438705444\n",
      "loss_G: 1.3514537811279297, loss_D: 0.9243005514144897, D(x): 0.37292078137397766, D(G(z)): 0.5513797998428345\n",
      "loss_G: 1.526741623878479, loss_D: 0.929429829120636, D(x): 0.5490937829017639, D(G(z)): 0.38033604621887207\n",
      "loss_G: 1.5576415061950684, loss_D: 1.1948277950286865, D(x): 0.8253146409988403, D(G(z)): 0.3695130944252014\n",
      "loss_G: 1.6675456762313843, loss_D: 0.8214842081069946, D(x): 0.45946598052978516, D(G(z)): 0.3620182275772095\n",
      "epoch 29\n",
      "-----------------\n",
      "loss_G: 1.732893466949463, loss_D: 0.8494628071784973, D(x): 0.4147696793079376, D(G(z)): 0.4346931278705597\n",
      "loss_G: 1.5862059593200684, loss_D: 0.8523378372192383, D(x): 0.4375137388706207, D(G(z)): 0.41482406854629517\n",
      "loss_G: 1.6089335680007935, loss_D: 0.8090900182723999, D(x): 0.3694223165512085, D(G(z)): 0.4396677315235138\n",
      "loss_G: 1.8459755182266235, loss_D: 0.8681327700614929, D(x): 0.5624505281448364, D(G(z)): 0.3056822419166565\n",
      "loss_G: 1.9546337127685547, loss_D: 0.741510272026062, D(x): 0.4102385640144348, D(G(z)): 0.3312717080116272\n",
      "epoch 30\n",
      "-----------------\n",
      "loss_G: 1.4658441543579102, loss_D: 0.7505557537078857, D(x): 0.35591161251068115, D(G(z)): 0.3946441411972046\n",
      "loss_G: 1.717301368713379, loss_D: 0.8316650390625, D(x): 0.5127497911453247, D(G(z)): 0.3189152479171753\n",
      "loss_G: 1.391258716583252, loss_D: 0.8175204992294312, D(x): 0.4606361985206604, D(G(z)): 0.35688430070877075\n",
      "loss_G: 1.516696810722351, loss_D: 0.8638836145401001, D(x): 0.4332171678543091, D(G(z)): 0.430666446685791\n",
      "loss_G: 1.4395942687988281, loss_D: 0.8264756202697754, D(x): 0.3612275719642639, D(G(z)): 0.4652480185031891\n",
      "epoch 31\n",
      "-----------------\n",
      "loss_G: 1.3284261226654053, loss_D: 0.9599267244338989, D(x): 0.4014749825000763, D(G(z)): 0.5584517121315002\n",
      "loss_G: 1.4828362464904785, loss_D: 1.1095161437988281, D(x): 0.6526952981948853, D(G(z)): 0.45682084560394287\n",
      "loss_G: 1.391395926475525, loss_D: 0.9591860771179199, D(x): 0.5418921113014221, D(G(z)): 0.4172939658164978\n",
      "loss_G: 1.521362543106079, loss_D: 0.9581553936004639, D(x): 0.5588666200637817, D(G(z)): 0.39928877353668213\n",
      "loss_G: 1.1632163524627686, loss_D: 1.3446619510650635, D(x): 0.6557081341743469, D(G(z)): 0.6889537572860718\n",
      "epoch 32\n",
      "-----------------\n",
      "loss_G: 1.474027156829834, loss_D: 0.8037709593772888, D(x): 0.3634416460990906, D(G(z)): 0.44032931327819824\n",
      "loss_G: 1.6352139711380005, loss_D: 0.8258533477783203, D(x): 0.4536983072757721, D(G(z)): 0.3721550405025482\n",
      "loss_G: 1.7350400686264038, loss_D: 0.9964056611061096, D(x): 0.6062833070755005, D(G(z)): 0.39012235403060913\n",
      "loss_G: 2.0651209354400635, loss_D: 0.7985750436782837, D(x): 0.5062003135681152, D(G(z)): 0.29237473011016846\n",
      "loss_G: 2.438885450363159, loss_D: 0.7499824166297913, D(x): 0.47626233100891113, D(G(z)): 0.2737200856208801\n",
      "epoch 33\n",
      "-----------------\n",
      "loss_G: 1.6838679313659668, loss_D: 0.9856181144714355, D(x): 0.46574509143829346, D(G(z)): 0.5198730230331421\n",
      "loss_G: 1.7967205047607422, loss_D: 0.8268909454345703, D(x): 0.44644680619239807, D(G(z)): 0.38044410943984985\n",
      "loss_G: 1.642106294631958, loss_D: 0.9158430099487305, D(x): 0.46293026208877563, D(G(z)): 0.4529127776622772\n",
      "loss_G: 1.8789764642715454, loss_D: 0.9947823286056519, D(x): 0.5924271941184998, D(G(z)): 0.4023551344871521\n",
      "loss_G: 1.4086267948150635, loss_D: 1.2030236721038818, D(x): 0.6278584599494934, D(G(z)): 0.5751652717590332\n",
      "epoch 34\n",
      "-----------------\n",
      "loss_G: 1.6038463115692139, loss_D: 0.8702791929244995, D(x): 0.4169943928718567, D(G(z)): 0.4532848000526428\n",
      "loss_G: 1.476076364517212, loss_D: 0.9697844386100769, D(x): 0.4660298228263855, D(G(z)): 0.5037546157836914\n",
      "loss_G: 2.243868350982666, loss_D: 0.6179057359695435, D(x): 0.415591835975647, D(G(z)): 0.20231392979621887\n",
      "loss_G: 1.9616379737854004, loss_D: 0.8422343730926514, D(x): 0.5486067533493042, D(G(z)): 0.29362761974334717\n",
      "loss_G: 1.5465631484985352, loss_D: 0.9708245992660522, D(x): 0.5845470428466797, D(G(z)): 0.38627755641937256\n",
      "epoch 35\n",
      "-----------------\n",
      "loss_G: 1.2877781391143799, loss_D: 0.8158493041992188, D(x): 0.3809410333633423, D(G(z)): 0.43490830063819885\n",
      "loss_G: 1.5027484893798828, loss_D: 1.0786080360412598, D(x): 0.59078049659729, D(G(z)): 0.48782750964164734\n",
      "loss_G: 1.5704255104064941, loss_D: 1.03105628490448, D(x): 0.5817851424217224, D(G(z)): 0.4492711126804352\n",
      "loss_G: 1.6365249156951904, loss_D: 0.961674690246582, D(x): 0.6429197788238525, D(G(z)): 0.3187549412250519\n",
      "loss_G: 1.4111829996109009, loss_D: 1.0891014337539673, D(x): 0.6491264700889587, D(G(z)): 0.43997496366500854\n",
      "epoch 36\n",
      "-----------------\n",
      "loss_G: 1.4696640968322754, loss_D: 0.9129313230514526, D(x): 0.46920010447502136, D(G(z)): 0.4437312185764313\n",
      "loss_G: 1.3248918056488037, loss_D: 1.3065892457962036, D(x): 0.7890511751174927, D(G(z)): 0.5175380706787109\n",
      "loss_G: 1.318102478981018, loss_D: 1.0159194469451904, D(x): 0.5635086894035339, D(G(z)): 0.4524107575416565\n",
      "loss_G: 1.919496774673462, loss_D: 0.9489780068397522, D(x): 0.5706025958061218, D(G(z)): 0.37837541103363037\n",
      "loss_G: 1.2024208307266235, loss_D: 1.2145695686340332, D(x): 0.6236830949783325, D(G(z)): 0.5908864736557007\n",
      "epoch 37\n",
      "-----------------\n",
      "loss_G: 1.1622462272644043, loss_D: 1.0873297452926636, D(x): 0.548628568649292, D(G(z)): 0.5387011766433716\n",
      "loss_G: 1.3425090312957764, loss_D: 0.9074039459228516, D(x): 0.48191311955451965, D(G(z)): 0.4254908561706543\n",
      "loss_G: 1.4068679809570312, loss_D: 0.9525653719902039, D(x): 0.5491101741790771, D(G(z)): 0.4034551978111267\n",
      "loss_G: 1.443432092666626, loss_D: 1.1759694814682007, D(x): 0.7117940187454224, D(G(z)): 0.4641754627227783\n",
      "loss_G: 1.3602688312530518, loss_D: 1.1209666728973389, D(x): 0.6858315467834473, D(G(z)): 0.4351351857185364\n",
      "epoch 38\n",
      "-----------------\n",
      "loss_G: 1.2395524978637695, loss_D: 0.9661554098129272, D(x): 0.4866350293159485, D(G(z)): 0.47952038049697876\n",
      "loss_G: 1.3148621320724487, loss_D: 0.9336507320404053, D(x): 0.46846669912338257, D(G(z)): 0.4651840329170227\n",
      "loss_G: 1.2308828830718994, loss_D: 0.9950850605964661, D(x): 0.572507917881012, D(G(z)): 0.4225771427154541\n",
      "loss_G: 1.5071994066238403, loss_D: 0.9587301015853882, D(x): 0.5613689422607422, D(G(z)): 0.397361159324646\n",
      "loss_G: 1.2512013912200928, loss_D: 1.1841886043548584, D(x): 0.6607802510261536, D(G(z)): 0.5234084129333496\n",
      "epoch 39\n",
      "-----------------\n",
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      "loss_G: 1.229503870010376, loss_D: 1.064486026763916, D(x): 0.509689450263977, D(G(z)): 0.5547965168952942\n",
      "loss_G: 1.0818183422088623, loss_D: 1.1010029315948486, D(x): 0.6150041222572327, D(G(z)): 0.4859987497329712\n",
      "loss_G: 1.2478199005126953, loss_D: 0.9710168242454529, D(x): 0.5061618089675903, D(G(z)): 0.46485501527786255\n",
      "loss_G: 1.2825374603271484, loss_D: 0.9647262692451477, D(x): 0.48920759558677673, D(G(z)): 0.47551867365837097\n",
      "epoch 40\n",
      "-----------------\n",
      "loss_G: 1.3755218982696533, loss_D: 0.9204967021942139, D(x): 0.4875776767730713, D(G(z)): 0.43291905522346497\n",
      "loss_G: 1.2520829439163208, loss_D: 1.0591144561767578, D(x): 0.5965125560760498, D(G(z)): 0.462601900100708\n",
      "loss_G: 1.1591559648513794, loss_D: 1.0747861862182617, D(x): 0.6037921905517578, D(G(z)): 0.4709940552711487\n",
      "loss_G: 1.2263482809066772, loss_D: 1.179094672203064, D(x): 0.6811094284057617, D(G(z)): 0.49798524379730225\n",
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      "epoch 41\n",
      "-----------------\n",
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      "loss_G: 1.10294508934021, loss_D: 1.0410795211791992, D(x): 0.565100371837616, D(G(z)): 0.47597914934158325\n",
      "loss_G: 1.130196452140808, loss_D: 1.1007978916168213, D(x): 0.5939230918884277, D(G(z)): 0.5068748593330383\n",
      "loss_G: 1.1276682615280151, loss_D: 1.2126383781433105, D(x): 0.6885777115821838, D(G(z)): 0.5240606069564819\n",
      "loss_G: 1.1238754987716675, loss_D: 1.2107605934143066, D(x): 0.6691992282867432, D(G(z)): 0.5415613651275635\n",
      "epoch 42\n",
      "-----------------\n",
      "loss_G: 0.9889174699783325, loss_D: 1.1706821918487549, D(x): 0.5703511238098145, D(G(z)): 0.6003310084342957\n",
      "loss_G: 1.2490227222442627, loss_D: 1.1066749095916748, D(x): 0.5662586688995361, D(G(z)): 0.5404162406921387\n",
      "loss_G: 1.0855727195739746, loss_D: 1.1446384191513062, D(x): 0.6601672768592834, D(G(z)): 0.4844711422920227\n",
      "loss_G: 1.3859503269195557, loss_D: 1.064910650253296, D(x): 0.5798894166946411, D(G(z)): 0.4850212335586548\n",
      "loss_G: 1.2515231370925903, loss_D: 1.1785188913345337, D(x): 0.656186044216156, D(G(z)): 0.5223328471183777\n",
      "epoch 43\n",
      "-----------------\n",
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      "loss_G: 1.214228868484497, loss_D: 0.9967503547668457, D(x): 0.4564920961856842, D(G(z)): 0.5402582883834839\n",
      "loss_G: 1.1398382186889648, loss_D: 1.099661111831665, D(x): 0.5733577013015747, D(G(z)): 0.5263033509254456\n",
      "loss_G: 1.2943625450134277, loss_D: 1.0970009565353394, D(x): 0.6324759721755981, D(G(z)): 0.4645249843597412\n",
      "loss_G: 1.01710045337677, loss_D: 1.3809802532196045, D(x): 0.7444477081298828, D(G(z)): 0.6365325450897217\n",
      "epoch 44\n",
      "-----------------\n",
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      "loss_G: 1.1507935523986816, loss_D: 1.1466395854949951, D(x): 0.6497445106506348, D(G(z)): 0.49689507484436035\n",
      "loss_G: 1.0432701110839844, loss_D: 1.3889169692993164, D(x): 0.7416231036186218, D(G(z)): 0.6472938060760498\n",
      "loss_G: 1.8758997917175293, loss_D: 0.8685986399650574, D(x): 0.5584971308708191, D(G(z)): 0.3101015090942383\n",
      "loss_G: 1.337414264678955, loss_D: 0.901980996131897, D(x): 0.4776398241519928, D(G(z)): 0.42434120178222656\n",
      "epoch 45\n",
      "-----------------\n",
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      "loss_G: 1.4603173732757568, loss_D: 0.941362738609314, D(x): 0.4774605333805084, D(G(z)): 0.46390220522880554\n",
      "loss_G: 1.298412799835205, loss_D: 0.9155310988426208, D(x): 0.4709753394126892, D(G(z)): 0.44455575942993164\n",
      "loss_G: 1.280714511871338, loss_D: 0.8843425512313843, D(x): 0.44107282161712646, D(G(z)): 0.4432697594165802\n",
      "loss_G: 1.3984017372131348, loss_D: 1.1255943775177002, D(x): 0.6517353057861328, D(G(z)): 0.473859041929245\n",
      "epoch 46\n",
      "-----------------\n",
      "loss_G: 1.408956527709961, loss_D: 0.9999271631240845, D(x): 0.5690659284591675, D(G(z)): 0.4308612048625946\n",
      "loss_G: 1.1836390495300293, loss_D: 1.1067209243774414, D(x): 0.6237126588821411, D(G(z)): 0.4830082058906555\n",
      "loss_G: 1.3832180500030518, loss_D: 0.8569269180297852, D(x): 0.4711264967918396, D(G(z)): 0.38580042123794556\n",
      "loss_G: 1.3234331607818604, loss_D: 0.9553373456001282, D(x): 0.5124682188034058, D(G(z)): 0.4428691267967224\n",
      "loss_G: 1.228135108947754, loss_D: 0.9808034300804138, D(x): 0.5181276798248291, D(G(z)): 0.4626757502555847\n",
      "epoch 47\n",
      "-----------------\n",
      "loss_G: 1.147578477859497, loss_D: 1.0506573915481567, D(x): 0.4892791509628296, D(G(z)): 0.5613782405853271\n",
      "loss_G: 0.9638894200325012, loss_D: 1.3550834655761719, D(x): 0.6867159605026245, D(G(z)): 0.6683674454689026\n",
      "loss_G: 1.230767846107483, loss_D: 0.9877908229827881, D(x): 0.5029746294021606, D(G(z)): 0.48481619358062744\n",
      "loss_G: 1.156889796257019, loss_D: 1.1768497228622437, D(x): 0.6413435339927673, D(G(z)): 0.5355061888694763\n",
      "loss_G: 1.0688592195510864, loss_D: 1.0241649150848389, D(x): 0.5428227782249451, D(G(z)): 0.481342077255249\n",
      "epoch 48\n",
      "-----------------\n",
      "loss_G: 0.9654440879821777, loss_D: 1.035557508468628, D(x): 0.37383902072906494, D(G(z)): 0.6617185473442078\n",
      "loss_G: 0.997608482837677, loss_D: 1.3797756433486938, D(x): 0.6749552488327026, D(G(z)): 0.7048203945159912\n",
      "loss_G: 1.0157794952392578, loss_D: 1.1046086549758911, D(x): 0.516510009765625, D(G(z)): 0.5880986452102661\n",
      "loss_G: 1.0088176727294922, loss_D: 1.1578123569488525, D(x): 0.5931308269500732, D(G(z)): 0.5646815299987793\n",
      "loss_G: 1.1243958473205566, loss_D: 1.1457245349884033, D(x): 0.6310147047042847, D(G(z)): 0.5147098302841187\n",
      "epoch 49\n",
      "-----------------\n",
      "loss_G: 1.0252127647399902, loss_D: 1.0962660312652588, D(x): 0.516355037689209, D(G(z)): 0.5799109935760498\n",
      "loss_G: 1.079089641571045, loss_D: 1.1173471212387085, D(x): 0.6234180331230164, D(G(z)): 0.49392908811569214\n",
      "loss_G: 0.9370924234390259, loss_D: 1.1892948150634766, D(x): 0.5929797887802124, D(G(z)): 0.5963150858879089\n",
      "loss_G: 1.0332140922546387, loss_D: 1.135108470916748, D(x): 0.5843023657798767, D(G(z)): 0.5508060455322266\n",
      "loss_G: 0.9254260063171387, loss_D: 1.250104546546936, D(x): 0.6494905948638916, D(G(z)): 0.6006139516830444\n",
      "epoch 50\n",
      "-----------------\n",
      "loss_G: 0.9865349531173706, loss_D: 1.1726852655410767, D(x): 0.6399269104003906, D(G(z)): 0.532758355140686\n",
      "loss_G: 1.0159804821014404, loss_D: 1.2440460920333862, D(x): 0.6674198508262634, D(G(z)): 0.5766262412071228\n",
      "loss_G: 0.9723869562149048, loss_D: 1.1568830013275146, D(x): 0.588862955570221, D(G(z)): 0.5680199861526489\n",
      "loss_G: 1.0054090023040771, loss_D: 1.3364441394805908, D(x): 0.7832393646240234, D(G(z)): 0.5532047748565674\n",
      "loss_G: 0.9647051692008972, loss_D: 1.137113094329834, D(x): 0.5980211496353149, D(G(z)): 0.539091944694519\n",
      "epoch 51\n",
      "-----------------\n",
      "loss_G: 0.9475312829017639, loss_D: 1.202919602394104, D(x): 0.6149119734764099, D(G(z)): 0.5880076289176941\n",
      "loss_G: 0.9773627519607544, loss_D: 1.3171255588531494, D(x): 0.7144739031791687, D(G(z)): 0.6026516556739807\n",
      "loss_G: 0.8628824353218079, loss_D: 1.235403060913086, D(x): 0.6215137243270874, D(G(z)): 0.6138893365859985\n",
      "loss_G: 1.0081288814544678, loss_D: 1.2442580461502075, D(x): 0.6626871824264526, D(G(z)): 0.5815708637237549\n",
      "loss_G: 0.9116780161857605, loss_D: 1.3474092483520508, D(x): 0.7179008722305298, D(G(z)): 0.629508376121521\n",
      "epoch 52\n",
      "-----------------\n",
      "loss_G: 0.8752636313438416, loss_D: 1.134507417678833, D(x): 0.515642523765564, D(G(z)): 0.618864893913269\n",
      "loss_G: 1.0184520483016968, loss_D: 1.1852223873138428, D(x): 0.6235567331314087, D(G(z)): 0.5616656541824341\n",
      "loss_G: 1.0130581855773926, loss_D: 1.0923945903778076, D(x): 0.5499748587608337, D(G(z)): 0.5424197912216187\n",
      "loss_G: 1.0236971378326416, loss_D: 1.2854316234588623, D(x): 0.766682505607605, D(G(z)): 0.5187491774559021\n",
      "loss_G: 1.083319902420044, loss_D: 1.2072523832321167, D(x): 0.6216192245483398, D(G(z)): 0.5856331586837769\n",
      "epoch 53\n",
      "-----------------\n",
      "loss_G: 0.9002518057823181, loss_D: 1.129364013671875, D(x): 0.4763422906398773, D(G(z)): 0.6530216932296753\n",
      "loss_G: 1.0252364873886108, loss_D: 1.2549326419830322, D(x): 0.7041885852813721, D(G(z)): 0.5507440567016602\n",
      "loss_G: 0.9660306572914124, loss_D: 1.200645923614502, D(x): 0.5964716672897339, D(G(z)): 0.6041741967201233\n",
      "loss_G: 1.0388613939285278, loss_D: 1.0848283767700195, D(x): 0.5804196000099182, D(G(z)): 0.5044087171554565\n",
      "loss_G: 1.0460331439971924, loss_D: 1.1083815097808838, D(x): 0.6339412927627563, D(G(z)): 0.47444021701812744\n",
      "epoch 54\n",
      "-----------------\n",
      "loss_G: 0.8645655512809753, loss_D: 1.1231536865234375, D(x): 0.47245579957962036, D(G(z)): 0.6506979465484619\n",
      "loss_G: 0.9874157309532166, loss_D: 1.1366102695465088, D(x): 0.6025171875953674, D(G(z)): 0.5340931415557861\n",
      "loss_G: 1.0173922777175903, loss_D: 1.030152678489685, D(x): 0.45354124903678894, D(G(z)): 0.5766113996505737\n",
      "loss_G: 1.0198612213134766, loss_D: 1.101452112197876, D(x): 0.5917387008666992, D(G(z)): 0.5097134113311768\n",
      "loss_G: 0.9961053729057312, loss_D: 1.2683653831481934, D(x): 0.6668233871459961, D(G(z)): 0.6015419363975525\n",
      "epoch 55\n",
      "-----------------\n",
      "loss_G: 1.0029683113098145, loss_D: 1.0937708616256714, D(x): 0.5109487771987915, D(G(z)): 0.5828220844268799\n",
      "loss_G: 1.0807926654815674, loss_D: 1.0309157371520996, D(x): 0.5460056662559509, D(G(z)): 0.4849100708961487\n",
      "loss_G: 1.0005220174789429, loss_D: 1.1980259418487549, D(x): 0.6500356197357178, D(G(z)): 0.5479903221130371\n",
      "loss_G: 1.0795494318008423, loss_D: 1.2361259460449219, D(x): 0.7017530202865601, D(G(z)): 0.5343729853630066\n",
      "loss_G: 1.0537779331207275, loss_D: 1.2583720684051514, D(x): 0.6835762858390808, D(G(z)): 0.5747957229614258\n",
      "epoch 56\n",
      "-----------------\n",
      "loss_G: 0.8209922909736633, loss_D: 1.2330304384231567, D(x): 0.5514792203903198, D(G(z)): 0.6815512180328369\n",
      "loss_G: 1.015941858291626, loss_D: 1.2940199375152588, D(x): 0.7405698895454407, D(G(z)): 0.5534501075744629\n",
      "loss_G: 1.1370251178741455, loss_D: 1.0832078456878662, D(x): 0.5527790188789368, D(G(z)): 0.5304288268089294\n",
      "loss_G: 1.0447014570236206, loss_D: 1.3108794689178467, D(x): 0.7790682911872864, D(G(z)): 0.5318112373352051\n",
      "loss_G: 0.9871009588241577, loss_D: 1.325701117515564, D(x): 0.8243016004562378, D(G(z)): 0.5013995170593262\n",
      "epoch 57\n",
      "-----------------\n",
      "loss_G: 1.0167009830474854, loss_D: 1.1692869663238525, D(x): 0.5579802393913269, D(G(z)): 0.6113066673278809\n",
      "loss_G: 0.9451076984405518, loss_D: 1.2991533279418945, D(x): 0.6860834956169128, D(G(z)): 0.6130697727203369\n",
      "loss_G: 0.892215371131897, loss_D: 1.215352177619934, D(x): 0.5791757106781006, D(G(z)): 0.6361764669418335\n",
      "loss_G: 1.0602837800979614, loss_D: 1.258178949356079, D(x): 0.6891336441040039, D(G(z)): 0.5690453052520752\n",
      "loss_G: 0.9392567873001099, loss_D: 1.2140812873840332, D(x): 0.6547726392745972, D(G(z)): 0.559308648109436\n",
      "epoch 58\n",
      "-----------------\n",
      "loss_G: 0.9842360019683838, loss_D: 1.1138100624084473, D(x): 0.4622301757335663, D(G(z)): 0.6515798568725586\n",
      "loss_G: 0.9665563106536865, loss_D: 1.2605761289596558, D(x): 0.6971019506454468, D(G(z)): 0.563474178314209\n",
      "loss_G: 0.8781534433364868, loss_D: 1.1725332736968994, D(x): 0.4730069935321808, D(G(z)): 0.6995262503623962\n",
      "loss_G: 1.1170070171356201, loss_D: 1.1175553798675537, D(x): 0.5988355278968811, D(G(z)): 0.5187199115753174\n",
      "loss_G: 0.9272915720939636, loss_D: 1.1614407300949097, D(x): 0.539941668510437, D(G(z)): 0.6214990615844727\n",
      "epoch 59\n",
      "-----------------\n",
      "loss_G: 0.9326457381248474, loss_D: 1.0704162120819092, D(x): 0.44951367378234863, D(G(z)): 0.6209025382995605\n",
      "loss_G: 1.1341837644577026, loss_D: 1.1745483875274658, D(x): 0.7045859098434448, D(G(z)): 0.4699624478816986\n",
      "loss_G: 0.9651288986206055, loss_D: 1.1257483959197998, D(x): 0.5047224760055542, D(G(z)): 0.6210259199142456\n",
      "loss_G: 1.12668776512146, loss_D: 1.164301872253418, D(x): 0.6705384254455566, D(G(z)): 0.49376338720321655\n",
      "loss_G: 1.0233798027038574, loss_D: 1.259817361831665, D(x): 0.7208727598190308, D(G(z)): 0.538944661617279\n",
      "epoch 60\n",
      "-----------------\n",
      "loss_G: 0.8795547485351562, loss_D: 1.0941516160964966, D(x): 0.42430561780929565, D(G(z)): 0.6698459982872009\n",
      "loss_G: 1.0182956457138062, loss_D: 1.2359232902526855, D(x): 0.655695378780365, D(G(z)): 0.5802278518676758\n",
      "loss_G: 0.9777050018310547, loss_D: 1.3088515996932983, D(x): 0.6243156790733337, D(G(z)): 0.6845359206199646\n",
      "loss_G: 1.122591257095337, loss_D: 1.2929027080535889, D(x): 0.8009270429611206, D(G(z)): 0.49197572469711304\n",
      "loss_G: 0.8802992105484009, loss_D: 1.4436039924621582, D(x): 0.8009451031684875, D(G(z)): 0.6426589488983154\n",
      "epoch 61\n",
      "-----------------\n",
      "loss_G: 0.8734554052352905, loss_D: 1.0550283193588257, D(x): 0.41247275471687317, D(G(z)): 0.6425555944442749\n",
      "loss_G: 1.0076115131378174, loss_D: 1.3619863986968994, D(x): 0.7144534587860107, D(G(z)): 0.6475329995155334\n",
      "loss_G: 0.8788910508155823, loss_D: 1.1873492002487183, D(x): 0.5554920434951782, D(G(z)): 0.63185715675354\n",
      "loss_G: 0.9879278540611267, loss_D: 1.139230728149414, D(x): 0.6703860759735107, D(G(z)): 0.4688447117805481\n",
      "loss_G: 0.9525556564331055, loss_D: 1.254697561264038, D(x): 0.6689803600311279, D(G(z)): 0.5857172012329102\n",
      "epoch 62\n",
      "-----------------\n",
      "loss_G: 0.996601939201355, loss_D: 1.13018000125885, D(x): 0.5201629400253296, D(G(z)): 0.6100170612335205\n",
      "loss_G: 1.0014934539794922, loss_D: 1.2185393571853638, D(x): 0.689577043056488, D(G(z)): 0.5289623141288757\n",
      "loss_G: 0.9554860591888428, loss_D: 1.1509462594985962, D(x): 0.601274847984314, D(G(z)): 0.5496714115142822\n",
      "loss_G: 1.0596983432769775, loss_D: 1.2047290802001953, D(x): 0.6841156482696533, D(G(z)): 0.520613431930542\n",
      "loss_G: 0.9453372955322266, loss_D: 1.2314982414245605, D(x): 0.6666915416717529, D(G(z)): 0.5648066997528076\n",
      "epoch 63\n",
      "-----------------\n",
      "loss_G: 0.8828088045120239, loss_D: 1.169856071472168, D(x): 0.517248272895813, D(G(z)): 0.652607798576355\n",
      "loss_G: 0.8907910585403442, loss_D: 1.3095403909683228, D(x): 0.6862248182296753, D(G(z)): 0.6233155727386475\n",
      "loss_G: 0.9159820079803467, loss_D: 1.151077389717102, D(x): 0.5655453205108643, D(G(z)): 0.5855320692062378\n",
      "loss_G: 1.1358048915863037, loss_D: 1.2416682243347168, D(x): 0.7227861285209656, D(G(z)): 0.518882155418396\n",
      "loss_G: 0.8058475255966187, loss_D: 1.3453142642974854, D(x): 0.6928979754447937, D(G(z)): 0.6524162292480469\n",
      "epoch 64\n",
      "-----------------\n",
      "loss_G: 0.996222972869873, loss_D: 1.1877082586288452, D(x): 0.5855264663696289, D(G(z)): 0.6021817922592163\n",
      "loss_G: 0.9570867419242859, loss_D: 1.3049752712249756, D(x): 0.6864030361175537, D(G(z)): 0.6185722351074219\n",
      "loss_G: 0.8973666429519653, loss_D: 1.2119078636169434, D(x): 0.626598596572876, D(G(z)): 0.5853092670440674\n",
      "loss_G: 0.9964740872383118, loss_D: 1.3475658893585205, D(x): 0.7611578702926636, D(G(z)): 0.5864080786705017\n",
      "loss_G: 0.9505082368850708, loss_D: 1.2921795845031738, D(x): 0.6753199696540833, D(G(z)): 0.6168595552444458\n",
      "epoch 65\n",
      "-----------------\n",
      "loss_G: 0.9256677627563477, loss_D: 1.1353728771209717, D(x): 0.5038896203041077, D(G(z)): 0.6314833164215088\n",
      "loss_G: 0.8806756734848022, loss_D: 1.3121904134750366, D(x): 0.5869842767715454, D(G(z)): 0.7252061367034912\n",
      "loss_G: 0.952244758605957, loss_D: 1.0471863746643066, D(x): 0.45601001381874084, D(G(z)): 0.5911763310432434\n",
      "loss_G: 1.1167519092559814, loss_D: 1.167284607887268, D(x): 0.6406486630439758, D(G(z)): 0.5266359448432922\n",
      "loss_G: 1.0437707901000977, loss_D: 1.05489182472229, D(x): 0.5411595106124878, D(G(z)): 0.513732373714447\n",
      "epoch 66\n",
      "-----------------\n",
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      "loss_G: 0.8852757215499878, loss_D: 1.4037069082260132, D(x): 0.7495132088661194, D(G(z)): 0.6541936993598938\n",
      "loss_G: 1.0518746376037598, loss_D: 1.1388672590255737, D(x): 0.5471080541610718, D(G(z)): 0.591759204864502\n",
      "loss_G: 1.0480666160583496, loss_D: 1.241553544998169, D(x): 0.7097045183181763, D(G(z)): 0.5318489670753479\n",
      "loss_G: 0.9681810140609741, loss_D: 1.21294105052948, D(x): 0.6086549758911133, D(G(z)): 0.6042860746383667\n",
      "epoch 67\n",
      "-----------------\n",
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      "loss_G: 1.093552827835083, loss_D: 1.3318147659301758, D(x): 0.7655813097953796, D(G(z)): 0.5662335157394409\n",
      "loss_G: 0.9371902942657471, loss_D: 1.1780574321746826, D(x): 0.544797956943512, D(G(z)): 0.6332594752311707\n",
      "loss_G: 0.9797685742378235, loss_D: 1.3305644989013672, D(x): 0.7262519598007202, D(G(z)): 0.604312539100647\n",
      "loss_G: 0.8495907783508301, loss_D: 1.2999423742294312, D(x): 0.6430574655532837, D(G(z)): 0.6568849086761475\n",
      "epoch 68\n",
      "-----------------\n",
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      "loss_G: 0.9444941878318787, loss_D: 1.23176908493042, D(x): 0.6250768303871155, D(G(z)): 0.6066922545433044\n",
      "loss_G: 0.9052944183349609, loss_D: 1.162368893623352, D(x): 0.5404268503189087, D(G(z)): 0.6219420433044434\n",
      "loss_G: 1.0100440979003906, loss_D: 1.168825387954712, D(x): 0.5905423164367676, D(G(z)): 0.5782830715179443\n",
      "loss_G: 0.9234580993652344, loss_D: 1.2802183628082275, D(x): 0.6999584436416626, D(G(z)): 0.5802599787712097\n",
      "epoch 69\n",
      "-----------------\n",
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      "loss_G: 0.9481494426727295, loss_D: 1.210257887840271, D(x): 0.6058012843132019, D(G(z)): 0.6044566035270691\n",
      "loss_G: 0.9846208095550537, loss_D: 1.2491110563278198, D(x): 0.5946841835975647, D(G(z)): 0.6544268727302551\n",
      "loss_G: 0.9448179006576538, loss_D: 1.281280517578125, D(x): 0.7280467748641968, D(G(z)): 0.5532337427139282\n",
      "loss_G: 0.8476294279098511, loss_D: 1.2986502647399902, D(x): 0.6791629195213318, D(G(z)): 0.6194873452186584\n",
      "epoch 70\n",
      "-----------------\n",
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      "loss_G: 0.9446893334388733, loss_D: 1.2514398097991943, D(x): 0.6811460256576538, D(G(z)): 0.5702937245368958\n",
      "loss_G: 0.882008969783783, loss_D: 1.2528753280639648, D(x): 0.638942301273346, D(G(z)): 0.6139329671859741\n",
      "loss_G: 1.0122798681259155, loss_D: 1.2692036628723145, D(x): 0.7199314832687378, D(G(z)): 0.5492721796035767\n",
      "loss_G: 0.7989406585693359, loss_D: 1.2977056503295898, D(x): 0.6046989560127258, D(G(z)): 0.6930066347122192\n",
      "epoch 71\n",
      "-----------------\n",
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      "loss_G: 1.0456767082214355, loss_D: 1.237528681755066, D(x): 0.6921533346176147, D(G(z)): 0.5453753471374512\n",
      "loss_G: 0.8769394755363464, loss_D: 1.2266926765441895, D(x): 0.5802085399627686, D(G(z)): 0.6464841365814209\n",
      "loss_G: 1.110605001449585, loss_D: 1.2661569118499756, D(x): 0.7168417572975159, D(G(z)): 0.5493152141571045\n",
      "loss_G: 0.9326599836349487, loss_D: 1.3196532726287842, D(x): 0.7188678979873657, D(G(z)): 0.6007853746414185\n",
      "epoch 72\n",
      "-----------------\n",
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      "loss_G: 0.9079702496528625, loss_D: 1.262373924255371, D(x): 0.7045442461967468, D(G(z)): 0.5578296184539795\n",
      "loss_G: 0.8098738789558411, loss_D: 1.1835367679595947, D(x): 0.5934360027313232, D(G(z)): 0.5901007652282715\n",
      "loss_G: 0.9640231132507324, loss_D: 1.2599875926971436, D(x): 0.6609109044075012, D(G(z)): 0.5990767478942871\n",
      "loss_G: 0.9083535671234131, loss_D: 1.2867344617843628, D(x): 0.7515406608581543, D(G(z)): 0.5351938009262085\n",
      "epoch 73\n",
      "-----------------\n",
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      "loss_G: 0.8780137896537781, loss_D: 1.2840325832366943, D(x): 0.6263810992240906, D(G(z)): 0.6576514840126038\n",
      "loss_G: 0.8434865474700928, loss_D: 1.2409404516220093, D(x): 0.6041073799133301, D(G(z)): 0.6368330717086792\n",
      "loss_G: 1.0004136562347412, loss_D: 1.3072128295898438, D(x): 0.7081412076950073, D(G(z)): 0.5990716814994812\n",
      "loss_G: 0.8153937458992004, loss_D: 1.2834980487823486, D(x): 0.667421817779541, D(G(z)): 0.6160761713981628\n",
      "epoch 74\n",
      "-----------------\n",
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      "loss_G: 0.8624143600463867, loss_D: 1.226480484008789, D(x): 0.5930556654930115, D(G(z)): 0.6334248781204224\n",
      "loss_G: 0.8633308410644531, loss_D: 1.191493272781372, D(x): 0.5839115977287292, D(G(z)): 0.607581615447998\n",
      "loss_G: 0.9499595165252686, loss_D: 1.3006285429000854, D(x): 0.7091995477676392, D(G(z)): 0.5914289951324463\n",
      "loss_G: 0.8450076580047607, loss_D: 1.414438009262085, D(x): 0.7366090416908264, D(G(z)): 0.6778290271759033\n",
      "epoch 75\n",
      "-----------------\n",
      "loss_G: 0.9082548022270203, loss_D: 1.2511647939682007, D(x): 0.5900520086288452, D(G(z)): 0.6611127853393555\n",
      "loss_G: 0.8877291083335876, loss_D: 1.2011792659759521, D(x): 0.5715322494506836, D(G(z)): 0.6296470165252686\n",
      "loss_G: 0.8759466409683228, loss_D: 1.1393595933914185, D(x): 0.4971790313720703, D(G(z)): 0.6421805620193481\n",
      "loss_G: 0.9338310956954956, loss_D: 1.2202929258346558, D(x): 0.6332410573959351, D(G(z)): 0.5870518684387207\n",
      "loss_G: 1.0225660800933838, loss_D: 1.159447193145752, D(x): 0.6238328218460083, D(G(z)): 0.5356143712997437\n",
      "epoch 76\n",
      "-----------------\n",
      "loss_G: 1.0201529264450073, loss_D: 1.216965675354004, D(x): 0.5813971757888794, D(G(z)): 0.6355684995651245\n",
      "loss_G: 0.9046738147735596, loss_D: 1.147265911102295, D(x): 0.6003214716911316, D(G(z)): 0.5469444394111633\n",
      "loss_G: 0.8601177930831909, loss_D: 1.1862282752990723, D(x): 0.5400106906890869, D(G(z)): 0.6462175846099854\n",
      "loss_G: 0.8883559107780457, loss_D: 1.2591304779052734, D(x): 0.6402508616447449, D(G(z)): 0.6188795566558838\n",
      "loss_G: 0.8261542320251465, loss_D: 1.3106099367141724, D(x): 0.6779317259788513, D(G(z)): 0.632678210735321\n",
      "epoch 77\n",
      "-----------------\n",
      "loss_G: 0.9088842868804932, loss_D: 1.220862865447998, D(x): 0.5511348843574524, D(G(z)): 0.6697280406951904\n",
      "loss_G: 0.8811032176017761, loss_D: 1.2222414016723633, D(x): 0.6155627965927124, D(G(z)): 0.6066786050796509\n",
      "loss_G: 0.8152366876602173, loss_D: 1.2408204078674316, D(x): 0.6371462345123291, D(G(z)): 0.6036741137504578\n",
      "loss_G: 0.9944952130317688, loss_D: 1.3176766633987427, D(x): 0.7102795839309692, D(G(z)): 0.6073970794677734\n",
      "loss_G: 0.8356853723526001, loss_D: 1.2360323667526245, D(x): 0.6376395225524902, D(G(z)): 0.5983928442001343\n",
      "epoch 78\n",
      "-----------------\n",
      "loss_G: 0.8406673669815063, loss_D: 1.1019946336746216, D(x): 0.4860188364982605, D(G(z)): 0.6159757971763611\n",
      "loss_G: 0.923072099685669, loss_D: 1.21537446975708, D(x): 0.6054877638816833, D(G(z)): 0.6098867654800415\n",
      "loss_G: 0.8454775810241699, loss_D: 1.3178606033325195, D(x): 0.6487307548522949, D(G(z)): 0.6691298484802246\n",
      "loss_G: 0.8829742670059204, loss_D: 1.257962942123413, D(x): 0.6292009949684143, D(G(z)): 0.6287619471549988\n",
      "loss_G: 0.9548622369766235, loss_D: 1.313044786453247, D(x): 0.7425020933151245, D(G(z)): 0.5705426335334778\n",
      "epoch 79\n",
      "-----------------\n",
      "loss_G: 0.8137818574905396, loss_D: 1.2430208921432495, D(x): 0.5133904218673706, D(G(z)): 0.7296304702758789\n",
      "loss_G: 0.8049519062042236, loss_D: 1.3312814235687256, D(x): 0.6257013082504272, D(G(z)): 0.7055800557136536\n",
      "loss_G: 0.8176071643829346, loss_D: 1.2596373558044434, D(x): 0.6234371662139893, D(G(z)): 0.6362002491950989\n",
      "loss_G: 0.9617125988006592, loss_D: 1.2064588069915771, D(x): 0.6216305494308472, D(G(z)): 0.58482825756073\n",
      "loss_G: 0.7926356196403503, loss_D: 1.4970624446868896, D(x): 0.828432023525238, D(G(z)): 0.6686304807662964\n",
      "epoch 80\n",
      "-----------------\n",
      "loss_G: 0.8840708136558533, loss_D: 1.1710166931152344, D(x): 0.4797152876853943, D(G(z)): 0.6913013458251953\n",
      "loss_G: 0.9018564820289612, loss_D: 1.2803683280944824, D(x): 0.6596807837486267, D(G(z)): 0.6206875443458557\n",
      "loss_G: 0.8342057466506958, loss_D: 1.2299754619598389, D(x): 0.6113617420196533, D(G(z)): 0.6186137199401855\n",
      "loss_G: 0.820427417755127, loss_D: 1.3181796073913574, D(x): 0.7011380195617676, D(G(z)): 0.6170415878295898\n",
      "loss_G: 0.8402383327484131, loss_D: 1.377324104309082, D(x): 0.7002061009407043, D(G(z)): 0.6771180629730225\n",
      "epoch 81\n",
      "-----------------\n",
      "loss_G: 0.7869032621383667, loss_D: 1.1682610511779785, D(x): 0.5506815910339355, D(G(z)): 0.6175794005393982\n",
      "loss_G: 0.8225463628768921, loss_D: 1.2761939764022827, D(x): 0.6116577386856079, D(G(z)): 0.6645362377166748\n",
      "loss_G: 0.8453654646873474, loss_D: 1.2620084285736084, D(x): 0.6360707879066467, D(G(z)): 0.6259376406669617\n",
      "loss_G: 0.8537030220031738, loss_D: 1.244856595993042, D(x): 0.6385883092880249, D(G(z)): 0.6062683463096619\n",
      "loss_G: 0.7938642501831055, loss_D: 1.3458914756774902, D(x): 0.696639895439148, D(G(z)): 0.6492515802383423\n",
      "epoch 82\n",
      "-----------------\n",
      "loss_G: 0.8557302355766296, loss_D: 1.1888468265533447, D(x): 0.552652895450592, D(G(z)): 0.6361939311027527\n",
      "loss_G: 0.8594375252723694, loss_D: 1.2934789657592773, D(x): 0.6370222568511963, D(G(z)): 0.656456708908081\n",
      "loss_G: 0.9491217732429504, loss_D: 1.223825216293335, D(x): 0.680031418800354, D(G(z)): 0.5437938570976257\n",
      "loss_G: 0.9004766345024109, loss_D: 1.281962513923645, D(x): 0.6761296987533569, D(G(z)): 0.6058328151702881\n",
      "loss_G: 0.8703066110610962, loss_D: 1.2496156692504883, D(x): 0.6553512811660767, D(G(z)): 0.5942643880844116\n",
      "epoch 83\n",
      "-----------------\n",
      "loss_G: 0.716407060623169, loss_D: 1.225988507270813, D(x): 0.5189094543457031, D(G(z)): 0.7070790529251099\n",
      "loss_G: 0.9177848100662231, loss_D: 1.2292532920837402, D(x): 0.6139380931854248, D(G(z)): 0.6153152585029602\n",
      "loss_G: 0.8760519623756409, loss_D: 1.19591224193573, D(x): 0.5986077785491943, D(G(z)): 0.5973044633865356\n",
      "loss_G: 0.9169276356697083, loss_D: 1.2865326404571533, D(x): 0.7225359678268433, D(G(z)): 0.5639966130256653\n",
      "loss_G: 0.9564399719238281, loss_D: 1.3707327842712402, D(x): 0.7676328420639038, D(G(z)): 0.6031000018119812\n",
      "epoch 84\n",
      "-----------------\n",
      "loss_G: 0.7968622446060181, loss_D: 1.3441319465637207, D(x): 0.6193782091140747, D(G(z)): 0.724753737449646\n",
      "loss_G: 0.7869485020637512, loss_D: 1.338364601135254, D(x): 0.6650923490524292, D(G(z)): 0.6732721924781799\n",
      "loss_G: 0.8877778053283691, loss_D: 1.2279609441757202, D(x): 0.6176033020019531, D(G(z)): 0.6103576421737671\n",
      "loss_G: 0.9154136180877686, loss_D: 1.2490829229354858, D(x): 0.6807844638824463, D(G(z)): 0.5682984590530396\n",
      "loss_G: 0.9093926548957825, loss_D: 1.3183034658432007, D(x): 0.7332208156585693, D(G(z)): 0.5850826501846313\n",
      "epoch 85\n",
      "-----------------\n",
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      "loss_G: 0.8053985834121704, loss_D: 1.2521804571151733, D(x): 0.6149998307228088, D(G(z)): 0.6371806263923645\n",
      "loss_G: 0.8990119099617004, loss_D: 1.2901020050048828, D(x): 0.6560156941413879, D(G(z)): 0.6340862512588501\n",
      "loss_G: 0.888343095779419, loss_D: 1.247668743133545, D(x): 0.6092408895492554, D(G(z)): 0.6384278535842896\n",
      "loss_G: 0.8307328224182129, loss_D: 1.380383849143982, D(x): 0.7998600006103516, D(G(z)): 0.5805238485336304\n",
      "epoch 86\n",
      "-----------------\n",
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      "loss_G: 0.7974992990493774, loss_D: 1.3124558925628662, D(x): 0.6248127222061157, D(G(z)): 0.6876431703567505\n",
      "loss_G: 0.8344149589538574, loss_D: 1.2639586925506592, D(x): 0.615583598613739, D(G(z)): 0.6483750343322754\n",
      "loss_G: 0.897124171257019, loss_D: 1.2770906686782837, D(x): 0.6958472728729248, D(G(z)): 0.5812433958053589\n",
      "loss_G: 0.8989772796630859, loss_D: 1.3089337348937988, D(x): 0.683403491973877, D(G(z)): 0.6255301833152771\n",
      "epoch 87\n",
      "-----------------\n",
      "loss_G: 0.743435800075531, loss_D: 1.2660092115402222, D(x): 0.5656853914260864, D(G(z)): 0.7003238201141357\n",
      "loss_G: 0.8390477895736694, loss_D: 1.3389270305633545, D(x): 0.6333487033843994, D(G(z)): 0.7055782675743103\n",
      "loss_G: 0.8598002195358276, loss_D: 1.3194098472595215, D(x): 0.642160177230835, D(G(z)): 0.6772496104240417\n",
      "loss_G: 0.8493849635124207, loss_D: 1.3061630725860596, D(x): 0.669092059135437, D(G(z)): 0.6370710134506226\n",
      "loss_G: 0.8069660067558289, loss_D: 1.3645615577697754, D(x): 0.7495458722114563, D(G(z)): 0.6150156259536743\n",
      "epoch 88\n",
      "-----------------\n",
      "loss_G: 0.8672705888748169, loss_D: 1.1908257007598877, D(x): 0.5413187742233276, D(G(z)): 0.6495069265365601\n",
      "loss_G: 0.8090934753417969, loss_D: 1.3164564371109009, D(x): 0.6708661317825317, D(G(z)): 0.6455903053283691\n",
      "loss_G: 0.8170307874679565, loss_D: 1.3309943675994873, D(x): 0.6059247255325317, D(G(z)): 0.7250696420669556\n",
      "loss_G: 0.9506769180297852, loss_D: 1.2232166528701782, D(x): 0.6380115747451782, D(G(z)): 0.585205078125\n",
      "loss_G: 0.8713110685348511, loss_D: 1.3724966049194336, D(x): 0.7564501762390137, D(G(z)): 0.6160464882850647\n",
      "epoch 89\n",
      "-----------------\n",
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      "loss_G: 0.833812415599823, loss_D: 1.4094507694244385, D(x): 0.687860369682312, D(G(z)): 0.7215903997421265\n",
      "loss_G: 0.8631693720817566, loss_D: 1.1944952011108398, D(x): 0.5885604023933411, D(G(z)): 0.6059348583221436\n",
      "loss_G: 0.8651350736618042, loss_D: 1.3567255735397339, D(x): 0.7741403579711914, D(G(z)): 0.5825852155685425\n",
      "loss_G: 0.9010612964630127, loss_D: 1.2281486988067627, D(x): 0.6434130668640137, D(G(z)): 0.5847355723381042\n",
      "epoch 90\n",
      "-----------------\n",
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      "loss_G: 0.7944443821907043, loss_D: 1.3068006038665771, D(x): 0.6670992970466614, D(G(z)): 0.6397013068199158\n",
      "loss_G: 0.8734607696533203, loss_D: 1.327911615371704, D(x): 0.6654226779937744, D(G(z)): 0.6624889969825745\n",
      "loss_G: 0.8429639339447021, loss_D: 1.3230773210525513, D(x): 0.6872631311416626, D(G(z)): 0.6358141899108887\n",
      "loss_G: 0.8898677229881287, loss_D: 1.298492193222046, D(x): 0.6654574871063232, D(G(z)): 0.6330347657203674\n",
      "epoch 91\n",
      "-----------------\n",
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      "loss_G: 0.8002118468284607, loss_D: 1.3213250637054443, D(x): 0.6341060996055603, D(G(z)): 0.6872189044952393\n",
      "loss_G: 0.9074661731719971, loss_D: 1.281057596206665, D(x): 0.689423143863678, D(G(z)): 0.5916343927383423\n",
      "loss_G: 0.7854578495025635, loss_D: 1.310367226600647, D(x): 0.6442751288414001, D(G(z)): 0.6660920977592468\n",
      "loss_G: 0.8599997758865356, loss_D: 1.3614208698272705, D(x): 0.6853561997413635, D(G(z)): 0.676064670085907\n",
      "epoch 92\n",
      "-----------------\n",
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      "loss_G: 0.7999189496040344, loss_D: 1.3011772632598877, D(x): 0.650783121585846, D(G(z)): 0.6503942012786865\n",
      "loss_G: 0.8336862325668335, loss_D: 1.3146429061889648, D(x): 0.6693941354751587, D(G(z)): 0.6452487111091614\n",
      "loss_G: 0.8623060584068298, loss_D: 1.3512848615646362, D(x): 0.7459679841995239, D(G(z)): 0.6053168773651123\n",
      "loss_G: 0.9082654118537903, loss_D: 1.2295470237731934, D(x): 0.6653004884719849, D(G(z)): 0.5642464756965637\n",
      "epoch 93\n",
      "-----------------\n",
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      "loss_G: 0.8353965282440186, loss_D: 1.374558448791504, D(x): 0.7291222810745239, D(G(z)): 0.64543616771698\n",
      "loss_G: 0.8584334850311279, loss_D: 1.2875680923461914, D(x): 0.6591737270355225, D(G(z)): 0.6283944249153137\n",
      "loss_G: 0.928518533706665, loss_D: 1.2952210903167725, D(x): 0.7403014302253723, D(G(z)): 0.5549196004867554\n",
      "epoch 94\n",
      "-----------------\n",
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      "loss_G: 0.78034907579422, loss_D: 1.304689645767212, D(x): 0.6174757480621338, D(G(z)): 0.6872139573097229\n",
      "loss_G: 0.9725649952888489, loss_D: 1.2314484119415283, D(x): 0.6435545682907104, D(G(z)): 0.5878938436508179\n",
      "loss_G: 0.8130525350570679, loss_D: 1.2185642719268799, D(x): 0.629625678062439, D(G(z)): 0.5889385938644409\n",
      "loss_G: 0.9301198720932007, loss_D: 1.1879017353057861, D(x): 0.5861245393753052, D(G(z)): 0.6017772555351257\n",
      "epoch 95\n",
      "-----------------\n",
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      "loss_G: 0.8506048917770386, loss_D: 1.2376086711883545, D(x): 0.5702270269393921, D(G(z)): 0.6673815846443176\n",
      "loss_G: 0.9105240702629089, loss_D: 1.2049951553344727, D(x): 0.6016941070556641, D(G(z)): 0.6033009886741638\n",
      "loss_G: 0.8839534521102905, loss_D: 1.3266613483428955, D(x): 0.6631743907928467, D(G(z)): 0.6634869575500488\n",
      "loss_G: 0.7788141369819641, loss_D: 1.29069185256958, D(x): 0.6495952606201172, D(G(z)): 0.6410966515541077\n",
      "epoch 96\n",
      "-----------------\n",
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      "loss_G: 0.8530304431915283, loss_D: 1.2920341491699219, D(x): 0.6437477469444275, D(G(z)): 0.6482863426208496\n",
      "loss_G: 0.8873516321182251, loss_D: 1.2500193119049072, D(x): 0.6462897062301636, D(G(z)): 0.6037296056747437\n",
      "loss_G: 0.8208113312721252, loss_D: 1.335750937461853, D(x): 0.6917580366134644, D(G(z)): 0.6439929008483887\n",
      "loss_G: 0.8410450220108032, loss_D: 1.3600993156433105, D(x): 0.7156181335449219, D(G(z)): 0.6444812417030334\n",
      "epoch 97\n",
      "-----------------\n",
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      "loss_G: 0.8001183271408081, loss_D: 1.2981975078582764, D(x): 0.6352968215942383, D(G(z)): 0.6629006862640381\n",
      "loss_G: 0.8253251910209656, loss_D: 1.2255133390426636, D(x): 0.615766704082489, D(G(z)): 0.6097466349601746\n",
      "loss_G: 0.8057652115821838, loss_D: 1.2876558303833008, D(x): 0.6226447224617004, D(G(z)): 0.6650111675262451\n",
      "loss_G: 0.8491986989974976, loss_D: 1.274464726448059, D(x): 0.6788699626922607, D(G(z)): 0.5955947637557983\n",
      "epoch 98\n",
      "-----------------\n",
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      "loss_G: 0.8024114370346069, loss_D: 1.3693065643310547, D(x): 0.6663451790809631, D(G(z)): 0.7029614448547363\n",
      "loss_G: 0.8608477115631104, loss_D: 1.22359299659729, D(x): 0.617965817451477, D(G(z)): 0.6056272387504578\n",
      "loss_G: 0.8562033772468567, loss_D: 1.308483600616455, D(x): 0.6551879644393921, D(G(z)): 0.6532955765724182\n",
      "loss_G: 0.8659651279449463, loss_D: 1.3599311113357544, D(x): 0.7129656076431274, D(G(z)): 0.646965503692627\n",
      "epoch 99\n",
      "-----------------\n",
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      "loss_G: 0.7780047655105591, loss_D: 1.3449320793151855, D(x): 0.635922372341156, D(G(z)): 0.7090097665786743\n",
      "loss_G: 0.847434937953949, loss_D: 1.236042857170105, D(x): 0.617011308670044, D(G(z)): 0.619031548500061\n",
      "loss_G: 0.789028525352478, loss_D: 1.315011978149414, D(x): 0.6604617834091187, D(G(z)): 0.6545502543449402\n",
      "loss_G: 0.8150065541267395, loss_D: 1.331982135772705, D(x): 0.6873331665992737, D(G(z)): 0.6446490287780762\n",
      "epoch 100\n",
      "-----------------\n",
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      "loss_G: 0.8212178349494934, loss_D: 1.3001928329467773, D(x): 0.630136251449585, D(G(z)): 0.6700565218925476\n",
      "loss_G: 0.8405592441558838, loss_D: 1.1699481010437012, D(x): 0.5382776856422424, D(G(z)): 0.631670355796814\n",
      "loss_G: 0.8536087274551392, loss_D: 1.2116056680679321, D(x): 0.6346951723098755, D(G(z)): 0.5769104957580566\n",
      "loss_G: 0.8582510948181152, loss_D: 1.320763111114502, D(x): 0.660887598991394, D(G(z)): 0.6598755121231079\n",
      "epoch 101\n",
      "-----------------\n",
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      "loss_G: 0.8326291441917419, loss_D: 1.286349892616272, D(x): 0.6766918897628784, D(G(z)): 0.6096580028533936\n",
      "loss_G: 0.7711712718009949, loss_D: 1.291137933731079, D(x): 0.5692209005355835, D(G(z)): 0.7219170331954956\n",
      "loss_G: 0.9141585826873779, loss_D: 1.3428640365600586, D(x): 0.7265287637710571, D(G(z)): 0.6163352727890015\n",
      "loss_G: 0.8617298007011414, loss_D: 1.3119230270385742, D(x): 0.6918735504150391, D(G(z)): 0.6200494766235352\n",
      "epoch 102\n",
      "-----------------\n",
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      "loss_G: 0.8395892381668091, loss_D: 1.349241018295288, D(x): 0.7051877975463867, D(G(z)): 0.6440532207489014\n",
      "loss_G: 0.8476536273956299, loss_D: 1.1828184127807617, D(x): 0.49111461639404297, D(G(z)): 0.6917037963867188\n",
      "loss_G: 0.8788909316062927, loss_D: 1.2533631324768066, D(x): 0.6606847047805786, D(G(z)): 0.592678427696228\n",
      "loss_G: 0.8577104806900024, loss_D: 1.3075461387634277, D(x): 0.7218853235244751, D(G(z)): 0.5856608152389526\n",
      "epoch 103\n",
      "-----------------\n",
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      "loss_G: 0.7547780871391296, loss_D: 1.4054272174835205, D(x): 0.6872168779373169, D(G(z)): 0.7182103395462036\n",
      "loss_G: 0.8334471583366394, loss_D: 1.2128102779388428, D(x): 0.5343656539916992, D(G(z)): 0.6784445643424988\n",
      "loss_G: 0.8722848296165466, loss_D: 1.2414734363555908, D(x): 0.6316097974777222, D(G(z)): 0.6098635792732239\n",
      "loss_G: 0.8736755847930908, loss_D: 1.1981358528137207, D(x): 0.6246902346611023, D(G(z)): 0.5734456777572632\n",
      "epoch 104\n",
      "-----------------\n",
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      "loss_G: 0.8631853461265564, loss_D: 1.307302474975586, D(x): 0.7005431652069092, D(G(z)): 0.606759250164032\n",
      "loss_G: 0.7940200567245483, loss_D: 1.2290509939193726, D(x): 0.5428564548492432, D(G(z)): 0.6861945390701294\n",
      "loss_G: 0.9012254476547241, loss_D: 1.2296053171157837, D(x): 0.6461744904518127, D(G(z)): 0.583430826663971\n",
      "loss_G: 0.8411339521408081, loss_D: 1.2770713567733765, D(x): 0.6158933639526367, D(G(z)): 0.6611779928207397\n",
      "epoch 105\n",
      "-----------------\n",
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      "loss_G: 0.840999186038971, loss_D: 1.279075026512146, D(x): 0.6402229070663452, D(G(z)): 0.6388521194458008\n",
      "loss_G: 0.7939000725746155, loss_D: 1.2382957935333252, D(x): 0.6006762981414795, D(G(z)): 0.6376194357872009\n",
      "loss_G: 0.8052387833595276, loss_D: 1.30301833152771, D(x): 0.6616638898849487, D(G(z)): 0.641354501247406\n",
      "loss_G: 0.8012115359306335, loss_D: 1.3501299619674683, D(x): 0.6727458238601685, D(G(z)): 0.6773841381072998\n",
      "epoch 106\n",
      "-----------------\n",
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      "loss_G: 0.8542953133583069, loss_D: 1.2472655773162842, D(x): 0.6688200235366821, D(G(z)): 0.5784456133842468\n",
      "loss_G: 0.8546483516693115, loss_D: 1.2000280618667603, D(x): 0.5633000731468201, D(G(z)): 0.6367279887199402\n",
      "loss_G: 0.8486669063568115, loss_D: 1.2500641345977783, D(x): 0.6347766518592834, D(G(z)): 0.6152874827384949\n",
      "loss_G: 0.8186225891113281, loss_D: 1.2778716087341309, D(x): 0.6688183546066284, D(G(z)): 0.6090533137321472\n",
      "epoch 107\n",
      "-----------------\n",
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      "loss_G: 0.8606657981872559, loss_D: 1.217942476272583, D(x): 0.633131742477417, D(G(z)): 0.5848106741905212\n",
      "loss_G: 0.7995603084564209, loss_D: 1.2827873229980469, D(x): 0.6447345614433289, D(G(z)): 0.6380527019500732\n",
      "epoch 108\n",
      "-----------------\n",
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      "loss_G: 0.899502158164978, loss_D: 1.220628261566162, D(x): 0.6377795934677124, D(G(z)): 0.5828487277030945\n",
      "loss_G: 0.856584906578064, loss_D: 1.2778244018554688, D(x): 0.6719376444816589, D(G(z)): 0.6058868169784546\n",
      "epoch 109\n",
      "-----------------\n",
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      "epoch 110\n",
      "-----------------\n",
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      "loss_G: 0.8858145475387573, loss_D: 1.271983027458191, D(x): 0.6717897653579712, D(G(z)): 0.6001932621002197\n",
      "loss_G: 0.8508748412132263, loss_D: 1.3222103118896484, D(x): 0.6859787702560425, D(G(z)): 0.636231541633606\n",
      "epoch 111\n",
      "-----------------\n",
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      "loss_G: 0.8286871910095215, loss_D: 1.3583561182022095, D(x): 0.7196410894393921, D(G(z)): 0.6387150287628174\n",
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      "loss_G: 0.8707210421562195, loss_D: 1.286161184310913, D(x): 0.6478596925735474, D(G(z)): 0.6383014917373657\n",
      "loss_G: 0.8491712808609009, loss_D: 1.370718240737915, D(x): 0.7455349564552307, D(G(z)): 0.6251833438873291\n",
      "epoch 112\n",
      "-----------------\n",
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      "loss_G: 0.9341745376586914, loss_D: 1.2732584476470947, D(x): 0.6757001876831055, D(G(z)): 0.5975582003593445\n",
      "loss_G: 0.8760035634040833, loss_D: 1.3871791362762451, D(x): 0.7601775527000427, D(G(z)): 0.6270015239715576\n",
      "epoch 113\n",
      "-----------------\n",
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      "loss_G: 0.8539993762969971, loss_D: 1.289363980293274, D(x): 0.6451610326766968, D(G(z)): 0.6442029476165771\n",
      "loss_G: 0.8727074861526489, loss_D: 1.3156570196151733, D(x): 0.6720643043518066, D(G(z)): 0.6435927152633667\n",
      "loss_G: 0.8879194259643555, loss_D: 1.3282263278961182, D(x): 0.7202438712120056, D(G(z)): 0.6079824566841125\n",
      "epoch 114\n",
      "-----------------\n",
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      "loss_G: 0.8038455247879028, loss_D: 1.3274016380310059, D(x): 0.6560646295547485, D(G(z)): 0.6713370084762573\n",
      "loss_G: 0.8597933650016785, loss_D: 1.2682536840438843, D(x): 0.6076692342758179, D(G(z)): 0.6605844497680664\n",
      "loss_G: 0.8273581266403198, loss_D: 1.2909948825836182, D(x): 0.6640378832817078, D(G(z)): 0.6269569396972656\n",
      "loss_G: 0.8007962107658386, loss_D: 1.3944790363311768, D(x): 0.7641809582710266, D(G(z)): 0.6302981376647949\n",
      "epoch 115\n",
      "-----------------\n",
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      "loss_G: 0.8650150299072266, loss_D: 1.2517430782318115, D(x): 0.6340343952178955, D(G(z)): 0.617708683013916\n",
      "loss_G: 0.8386013507843018, loss_D: 1.2000035047531128, D(x): 0.6124697923660278, D(G(z)): 0.587533712387085\n",
      "loss_G: 0.8733054399490356, loss_D: 1.2154401540756226, D(x): 0.6018848419189453, D(G(z)): 0.6135553121566772\n",
      "loss_G: 0.8899151086807251, loss_D: 1.365316390991211, D(x): 0.7327274084091187, D(G(z)): 0.6325889229774475\n",
      "epoch 116\n",
      "-----------------\n",
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      "loss_G: 0.8403983116149902, loss_D: 1.3110699653625488, D(x): 0.6772217750549316, D(G(z)): 0.6338481903076172\n",
      "loss_G: 0.844845175743103, loss_D: 1.2374227046966553, D(x): 0.6292233467102051, D(G(z)): 0.6081992983818054\n",
      "loss_G: 0.8989377617835999, loss_D: 1.214313268661499, D(x): 0.6113022565841675, D(G(z)): 0.6030110120773315\n",
      "loss_G: 0.8055830597877502, loss_D: 1.3183649778366089, D(x): 0.6839215159416199, D(G(z)): 0.634443461894989\n",
      "epoch 117\n",
      "-----------------\n",
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      "loss_G: 0.8186020255088806, loss_D: 1.311913013458252, D(x): 0.6527844071388245, D(G(z)): 0.6591286659240723\n",
      "loss_G: 0.7612488269805908, loss_D: 1.2915931940078735, D(x): 0.5974546670913696, D(G(z)): 0.6941385269165039\n",
      "loss_G: 0.8130348920822144, loss_D: 1.2964098453521729, D(x): 0.6544577479362488, D(G(z)): 0.6419521570205688\n",
      "loss_G: 0.8592836260795593, loss_D: 1.3377060890197754, D(x): 0.7352597713470459, D(G(z)): 0.6024463176727295\n",
      "epoch 118\n",
      "-----------------\n",
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      "loss_G: 0.8643681406974792, loss_D: 1.3052327632904053, D(x): 0.6811251640319824, D(G(z)): 0.6241075992584229\n",
      "loss_G: 0.8281344175338745, loss_D: 1.2865684032440186, D(x): 0.6324453353881836, D(G(z)): 0.654123067855835\n",
      "loss_G: 0.8906984329223633, loss_D: 1.2202837467193604, D(x): 0.6259127259254456, D(G(z)): 0.59437096118927\n",
      "loss_G: 0.8246637582778931, loss_D: 1.319019079208374, D(x): 0.6808893084526062, D(G(z)): 0.6381297707557678\n",
      "epoch 119\n",
      "-----------------\n",
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      "loss_G: 0.8217646479606628, loss_D: 1.222947359085083, D(x): 0.6472140550613403, D(G(z)): 0.5757333040237427\n",
      "loss_G: 0.8551769256591797, loss_D: 1.2238396406173706, D(x): 0.6318256855010986, D(G(z)): 0.592013955116272\n",
      "loss_G: 0.8518733978271484, loss_D: 1.284254789352417, D(x): 0.659504771232605, D(G(z)): 0.624750018119812\n",
      "loss_G: 0.9103752374649048, loss_D: 1.3868855237960815, D(x): 0.761581540107727, D(G(z)): 0.6253039836883545\n",
      "epoch 120\n",
      "-----------------\n",
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      "loss_G: 0.8192800283432007, loss_D: 1.2483278512954712, D(x): 0.6275249719619751, D(G(z)): 0.6208028793334961\n",
      "loss_G: 0.7806116342544556, loss_D: 1.280853509902954, D(x): 0.5921663641929626, D(G(z)): 0.6886871457099915\n",
      "loss_G: 0.8461093306541443, loss_D: 1.2430113554000854, D(x): 0.6250549554824829, D(G(z)): 0.6179563999176025\n",
      "loss_G: 0.7938140630722046, loss_D: 1.3732259273529053, D(x): 0.6909627914428711, D(G(z)): 0.6822630763053894\n",
      "epoch 121\n",
      "-----------------\n",
      "loss_G: 0.8386662006378174, loss_D: 1.1469420194625854, D(x): 0.5144967436790466, D(G(z)): 0.6324452757835388\n",
      "loss_G: 0.8298300504684448, loss_D: 1.2572722434997559, D(x): 0.6545892357826233, D(G(z)): 0.6026830673217773\n",
      "loss_G: 0.8065483570098877, loss_D: 1.2601220607757568, D(x): 0.6237807273864746, D(G(z)): 0.6363413333892822\n",
      "loss_G: 0.8493261337280273, loss_D: 1.271116018295288, D(x): 0.6599705815315247, D(G(z)): 0.6111454963684082\n",
      "loss_G: 0.834155797958374, loss_D: 1.333594799041748, D(x): 0.6995704174041748, D(G(z)): 0.6340243816375732\n",
      "epoch 122\n",
      "-----------------\n",
      "loss_G: 0.8306263089179993, loss_D: 1.1819982528686523, D(x): 0.5052270889282227, D(G(z)): 0.6767711639404297\n",
      "loss_G: 0.8751199841499329, loss_D: 1.2101686000823975, D(x): 0.6385506391525269, D(G(z)): 0.5716179609298706\n",
      "loss_G: 0.8232759833335876, loss_D: 1.258070468902588, D(x): 0.6467677354812622, D(G(z)): 0.6113027334213257\n",
      "loss_G: 0.884394109249115, loss_D: 1.2394144535064697, D(x): 0.6363067626953125, D(G(z)): 0.6031076908111572\n",
      "loss_G: 0.8574801087379456, loss_D: 1.2609171867370605, D(x): 0.6818190813064575, D(G(z)): 0.579098105430603\n",
      "epoch 123\n",
      "-----------------\n",
      "loss_G: 0.794562041759491, loss_D: 1.28459894657135, D(x): 0.5798208117485046, D(G(z)): 0.7047781348228455\n",
      "loss_G: 0.8963733911514282, loss_D: 1.2524420022964478, D(x): 0.6424127817153931, D(G(z)): 0.6100292205810547\n",
      "loss_G: 0.8097347021102905, loss_D: 1.217881202697754, D(x): 0.5789950489997864, D(G(z)): 0.6388861536979675\n",
      "loss_G: 0.8351269960403442, loss_D: 1.2326550483703613, D(x): 0.636785089969635, D(G(z)): 0.5958700180053711\n",
      "loss_G: 0.8385204672813416, loss_D: 1.3013077974319458, D(x): 0.7013154029846191, D(G(z)): 0.5999923944473267\n",
      "epoch 124\n",
      "-----------------\n",
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      "loss_G: 0.8077738285064697, loss_D: 1.3035131692886353, D(x): 0.6509476900100708, D(G(z)): 0.6525654792785645\n",
      "loss_G: 0.8343544006347656, loss_D: 1.2253047227859497, D(x): 0.5418251156806946, D(G(z)): 0.6834796071052551\n",
      "loss_G: 0.8211714029312134, loss_D: 1.2736351490020752, D(x): 0.6097452640533447, D(G(z)): 0.6638899445533752\n",
      "loss_G: 0.8584816455841064, loss_D: 1.319248914718628, D(x): 0.7285696268081665, D(G(z)): 0.5906792879104614\n",
      "epoch 125\n",
      "-----------------\n",
      "loss_G: 0.7575788497924805, loss_D: 1.257049560546875, D(x): 0.5540615916252136, D(G(z)): 0.7029879093170166\n",
      "loss_G: 0.831618070602417, loss_D: 1.2829664945602417, D(x): 0.6200032234191895, D(G(z)): 0.6629632711410522\n",
      "loss_G: 0.8153122663497925, loss_D: 1.245301365852356, D(x): 0.5476570129394531, D(G(z)): 0.6976443529129028\n",
      "loss_G: 0.8777438998222351, loss_D: 1.2563512325286865, D(x): 0.6197941899299622, D(G(z)): 0.6365570425987244\n",
      "loss_G: 0.8208414316177368, loss_D: 1.2688308954238892, D(x): 0.6647024154663086, D(G(z)): 0.6041284799575806\n",
      "epoch 126\n",
      "-----------------\n",
      "loss_G: 0.7599213123321533, loss_D: 1.224588394165039, D(x): 0.513481855392456, D(G(z)): 0.711106538772583\n",
      "loss_G: 0.858859658241272, loss_D: 1.3080787658691406, D(x): 0.6578571796417236, D(G(z)): 0.650221586227417\n",
      "loss_G: 0.7963476777076721, loss_D: 1.2697733640670776, D(x): 0.5864065885543823, D(G(z)): 0.6833667755126953\n",
      "loss_G: 0.841618537902832, loss_D: 1.321729302406311, D(x): 0.6584100723266602, D(G(z)): 0.6633192300796509\n",
      "loss_G: 0.87822425365448, loss_D: 1.3544936180114746, D(x): 0.7072427272796631, D(G(z)): 0.6472508311271667\n",
      "epoch 127\n",
      "-----------------\n",
      "loss_G: 0.7677531838417053, loss_D: 1.2371957302093506, D(x): 0.5602189302444458, D(G(z)): 0.6769767999649048\n",
      "loss_G: 0.8698569536209106, loss_D: 1.286879301071167, D(x): 0.6821308135986328, D(G(z)): 0.604748547077179\n",
      "loss_G: 0.819209098815918, loss_D: 1.2419259548187256, D(x): 0.6062840223312378, D(G(z)): 0.635641872882843\n",
      "loss_G: 0.861635148525238, loss_D: 1.303628921508789, D(x): 0.6509244441986084, D(G(z)): 0.6527045369148254\n",
      "loss_G: 0.8005301356315613, loss_D: 1.3061355352401733, D(x): 0.6588563919067383, D(G(z)): 0.6472791433334351\n",
      "epoch 128\n",
      "-----------------\n",
      "loss_G: 0.750758171081543, loss_D: 1.2288140058517456, D(x): 0.5315989255905151, D(G(z)): 0.6972150802612305\n",
      "loss_G: 0.889565110206604, loss_D: 1.3022794723510742, D(x): 0.6723628044128418, D(G(z)): 0.6299166083335876\n",
      "loss_G: 0.7964255809783936, loss_D: 1.2702968120574951, D(x): 0.6179682612419128, D(G(z)): 0.652328610420227\n",
      "loss_G: 0.863282322883606, loss_D: 1.2813397645950317, D(x): 0.6662970781326294, D(G(z)): 0.6150426864624023\n",
      "loss_G: 0.8347676992416382, loss_D: 1.2896157503128052, D(x): 0.684479832649231, D(G(z)): 0.6051359176635742\n",
      "epoch 129\n",
      "-----------------\n",
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      "loss_G: 0.9106079936027527, loss_D: 1.2622867822647095, D(x): 0.6593528985977173, D(G(z)): 0.6029338836669922\n",
      "loss_G: 0.8609710335731506, loss_D: 1.2764923572540283, D(x): 0.6521176099777222, D(G(z)): 0.6243748068809509\n",
      "loss_G: 0.8952165246009827, loss_D: 1.3009576797485352, D(x): 0.6696289777755737, D(G(z)): 0.6313286423683167\n",
      "loss_G: 0.7984027862548828, loss_D: 1.2385506629943848, D(x): 0.6506019830703735, D(G(z)): 0.5879486203193665\n",
      "epoch 130\n",
      "-----------------\n",
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      "loss_G: 0.9031342267990112, loss_D: 1.3195035457611084, D(x): 0.7266544103622437, D(G(z)): 0.5928491353988647\n",
      "loss_G: 0.7993811964988708, loss_D: 1.2669395208358765, D(x): 0.6074827909469604, D(G(z)): 0.659456729888916\n",
      "loss_G: 0.9122986197471619, loss_D: 1.2941834926605225, D(x): 0.7012021541595459, D(G(z)): 0.5929812788963318\n",
      "loss_G: 0.8676831722259521, loss_D: 1.287102222442627, D(x): 0.6725978851318359, D(G(z)): 0.6145043969154358\n",
      "epoch 131\n",
      "-----------------\n",
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      "loss_G: 0.8534067869186401, loss_D: 1.2942018508911133, D(x): 0.6843644976615906, D(G(z)): 0.6098372936248779\n",
      "loss_G: 0.8078430891036987, loss_D: 1.1826667785644531, D(x): 0.5700944066047668, D(G(z)): 0.612572431564331\n",
      "loss_G: 0.8419595956802368, loss_D: 1.2759852409362793, D(x): 0.6778340935707092, D(G(z)): 0.5981512069702148\n",
      "loss_G: 0.8484699726104736, loss_D: 1.3080945014953613, D(x): 0.6927139163017273, D(G(z)): 0.6153806447982788\n",
      "epoch 132\n",
      "-----------------\n",
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      "loss_G: 0.8581746816635132, loss_D: 1.2741246223449707, D(x): 0.65424644947052, D(G(z)): 0.6198782324790955\n",
      "loss_G: 0.779227077960968, loss_D: 1.2516212463378906, D(x): 0.6327531337738037, D(G(z)): 0.6188681721687317\n",
      "loss_G: 0.9288207292556763, loss_D: 1.313621163368225, D(x): 0.661241888999939, D(G(z)): 0.6523792743682861\n",
      "loss_G: 0.7899554967880249, loss_D: 1.325384259223938, D(x): 0.6775064468383789, D(G(z)): 0.6478778123855591\n",
      "epoch 133\n",
      "-----------------\n",
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      "loss_G: 0.8327939510345459, loss_D: 1.3394818305969238, D(x): 0.6911847591400146, D(G(z)): 0.6482970714569092\n",
      "loss_G: 0.8919011354446411, loss_D: 1.267504096031189, D(x): 0.6078513860702515, D(G(z)): 0.6596527099609375\n",
      "loss_G: 0.8817093372344971, loss_D: 1.2601417303085327, D(x): 0.6715371608734131, D(G(z)): 0.5886045694351196\n",
      "loss_G: 0.7785161733627319, loss_D: 1.3469687700271606, D(x): 0.6870591640472412, D(G(z)): 0.6599096059799194\n",
      "epoch 134\n",
      "-----------------\n",
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      "loss_G: 0.8603861331939697, loss_D: 1.290696620941162, D(x): 0.6426780223846436, D(G(z)): 0.6480185389518738\n",
      "loss_G: 0.8073612451553345, loss_D: 1.293881893157959, D(x): 0.6349179148674011, D(G(z)): 0.6589639186859131\n",
      "loss_G: 0.8736112117767334, loss_D: 1.3760862350463867, D(x): 0.769048273563385, D(G(z)): 0.6070379018783569\n",
      "loss_G: 0.8723043203353882, loss_D: 1.3539719581604004, D(x): 0.7348597645759583, D(G(z)): 0.6191121339797974\n",
      "epoch 135\n",
      "-----------------\n",
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      "loss_G: 0.860457181930542, loss_D: 1.253401279449463, D(x): 0.6613776683807373, D(G(z)): 0.5920236110687256\n",
      "loss_G: 0.8807331323623657, loss_D: 1.2761881351470947, D(x): 0.6191112399101257, D(G(z)): 0.657076895236969\n",
      "loss_G: 0.8677923083305359, loss_D: 1.301686406135559, D(x): 0.7129852771759033, D(G(z)): 0.5887011289596558\n",
      "loss_G: 0.8858410716056824, loss_D: 1.2561194896697998, D(x): 0.6503328084945679, D(G(z)): 0.6057866811752319\n",
      "epoch 136\n",
      "-----------------\n",
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      "loss_G: 0.8870297074317932, loss_D: 1.2697573900222778, D(x): 0.6419992446899414, D(G(z)): 0.6277581453323364\n",
      "loss_G: 0.8218956589698792, loss_D: 1.184651494026184, D(x): 0.5795766115188599, D(G(z)): 0.6050748825073242\n",
      "loss_G: 0.9131520390510559, loss_D: 1.2871935367584229, D(x): 0.7146762013435364, D(G(z)): 0.5725173950195312\n",
      "loss_G: 0.8487081527709961, loss_D: 1.3456871509552002, D(x): 0.7119994759559631, D(G(z)): 0.6336876153945923\n",
      "epoch 137\n",
      "-----------------\n",
      "loss_G: 0.8037232756614685, loss_D: 1.2651571035385132, D(x): 0.5234817266464233, D(G(z)): 0.7416753768920898\n",
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      "loss_G: 0.9226274490356445, loss_D: 1.236417531967163, D(x): 0.6400681138038635, D(G(z)): 0.5963493585586548\n",
      "loss_G: 0.8430111408233643, loss_D: 1.2330836057662964, D(x): 0.6535136699676514, D(G(z)): 0.579569935798645\n",
      "epoch 138\n",
      "-----------------\n",
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      "loss_G: 0.9269211292266846, loss_D: 1.2527132034301758, D(x): 0.640304684638977, D(G(z)): 0.6124085187911987\n",
      "loss_G: 0.9114848375320435, loss_D: 1.0919160842895508, D(x): 0.5587059855461121, D(G(z)): 0.533210039138794\n",
      "loss_G: 0.9484455585479736, loss_D: 1.25624680519104, D(x): 0.6823588609695435, D(G(z)): 0.5738879442214966\n",
      "loss_G: 0.849703848361969, loss_D: 1.3747889995574951, D(x): 0.7345819473266602, D(G(z)): 0.640207052230835\n",
      "epoch 139\n",
      "-----------------\n",
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      "loss_G: 0.8549902439117432, loss_D: 1.2408053874969482, D(x): 0.6655815839767456, D(G(z)): 0.5752238035202026\n",
      "loss_G: 0.7994623780250549, loss_D: 1.2239245176315308, D(x): 0.5797569751739502, D(G(z)): 0.6441675424575806\n",
      "loss_G: 0.8804571032524109, loss_D: 1.3050029277801514, D(x): 0.6818358898162842, D(G(z)): 0.6231669783592224\n",
      "loss_G: 0.8789055347442627, loss_D: 1.2505881786346436, D(x): 0.6894776821136475, D(G(z)): 0.5611104369163513\n",
      "epoch 140\n",
      "-----------------\n",
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      "loss_G: 0.8419024348258972, loss_D: 1.3244428634643555, D(x): 0.6759898066520691, D(G(z)): 0.6484531164169312\n",
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      "loss_G: 0.8324494957923889, loss_D: 1.2331676483154297, D(x): 0.6153424978256226, D(G(z)): 0.6178251504898071\n",
      "loss_G: 0.8869601488113403, loss_D: 1.3237687349319458, D(x): 0.6910825967788696, D(G(z)): 0.6326861381530762\n",
      "epoch 141\n",
      "-----------------\n",
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      "loss_G: 0.8552029132843018, loss_D: 1.310683250427246, D(x): 0.6567913293838501, D(G(z)): 0.653891921043396\n",
      "loss_G: 0.7852311134338379, loss_D: 1.2324988842010498, D(x): 0.6042380928993225, D(G(z)): 0.6282607316970825\n",
      "loss_G: 0.8784223198890686, loss_D: 1.2271063327789307, D(x): 0.6054115891456604, D(G(z)): 0.621694803237915\n",
      "loss_G: 0.8560115098953247, loss_D: 1.3189653158187866, D(x): 0.7341831922531128, D(G(z)): 0.5847821235656738\n",
      "epoch 142\n",
      "-----------------\n",
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      "loss_G: 0.8746616840362549, loss_D: 1.275446891784668, D(x): 0.6711956858634949, D(G(z)): 0.6042511463165283\n",
      "loss_G: 0.8318624496459961, loss_D: 1.1947858333587646, D(x): 0.5406078100204468, D(G(z)): 0.6541779637336731\n",
      "loss_G: 0.8905870914459229, loss_D: 1.2896451950073242, D(x): 0.6780444383621216, D(G(z)): 0.6116006970405579\n",
      "loss_G: 0.8451341986656189, loss_D: 1.2791482210159302, D(x): 0.7068772315979004, D(G(z)): 0.5722709894180298\n",
      "epoch 143\n",
      "-----------------\n",
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      "loss_G: 0.8720637559890747, loss_D: 1.2798073291778564, D(x): 0.6632727980613708, D(G(z)): 0.6165345907211304\n",
      "loss_G: 0.9513254165649414, loss_D: 1.180345058441162, D(x): 0.6033234596252441, D(G(z)): 0.577021598815918\n",
      "loss_G: 0.8243461847305298, loss_D: 1.349326729774475, D(x): 0.682738184928894, D(G(z)): 0.666588544845581\n",
      "loss_G: 0.8418731689453125, loss_D: 1.323164463043213, D(x): 0.7263489961624146, D(G(z)): 0.5968154072761536\n",
      "epoch 144\n",
      "-----------------\n",
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      "loss_G: 0.8830468058586121, loss_D: 1.3234587907791138, D(x): 0.6906791925430298, D(G(z)): 0.632779598236084\n",
      "loss_G: 0.8663389682769775, loss_D: 1.2431631088256836, D(x): 0.6277701258659363, D(G(z)): 0.6153930425643921\n",
      "loss_G: 0.8266543745994568, loss_D: 1.3713696002960205, D(x): 0.6497815251350403, D(G(z)): 0.7215880155563354\n",
      "loss_G: 0.8702593445777893, loss_D: 1.3129667043685913, D(x): 0.7574418783187866, D(G(z)): 0.5555248260498047\n",
      "epoch 145\n",
      "-----------------\n",
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      "loss_G: 0.8799794912338257, loss_D: 1.4067296981811523, D(x): 0.7148187160491943, D(G(z)): 0.691910982131958\n",
      "loss_G: 0.8972468376159668, loss_D: 1.2871649265289307, D(x): 0.673956036567688, D(G(z)): 0.6132089495658875\n",
      "loss_G: 0.8962486982345581, loss_D: 1.2777214050292969, D(x): 0.7300868034362793, D(G(z)): 0.5476346015930176\n",
      "loss_G: 0.9542261362075806, loss_D: 1.3262298107147217, D(x): 0.7589836120605469, D(G(z)): 0.5672462582588196\n",
      "epoch 146\n",
      "-----------------\n",
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      "loss_G: 0.9074488878250122, loss_D: 1.3341447114944458, D(x): 0.7013423442840576, D(G(z)): 0.6328023672103882\n",
      "loss_G: 0.8398193120956421, loss_D: 1.2957916259765625, D(x): 0.6595166325569153, D(G(z)): 0.6362749338150024\n",
      "loss_G: 0.9876160621643066, loss_D: 1.3283921480178833, D(x): 0.7361599802970886, D(G(z)): 0.5922321677207947\n",
      "loss_G: 0.9308986663818359, loss_D: 1.3245214223861694, D(x): 0.7561415433883667, D(G(z)): 0.5683798789978027\n",
      "epoch 147\n",
      "-----------------\n",
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      "loss_G: 0.8810901641845703, loss_D: 1.2793521881103516, D(x): 0.6712331771850586, D(G(z)): 0.608119010925293\n",
      "loss_G: 0.8680199980735779, loss_D: 1.2280436754226685, D(x): 0.6433570384979248, D(G(z)): 0.5846866369247437\n",
      "loss_G: 0.92206871509552, loss_D: 1.2744333744049072, D(x): 0.7436870336532593, D(G(z)): 0.530746340751648\n",
      "loss_G: 0.9206793308258057, loss_D: 1.3245842456817627, D(x): 0.7396193742752075, D(G(z)): 0.5849648118019104\n",
      "epoch 148\n",
      "-----------------\n",
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      "loss_G: 0.854719340801239, loss_D: 1.3371564149856567, D(x): 0.6992374658584595, D(G(z)): 0.6379189491271973\n",
      "loss_G: 0.8799317479133606, loss_D: 1.209709644317627, D(x): 0.598246693611145, D(G(z)): 0.6114630103111267\n",
      "loss_G: 0.9499491453170776, loss_D: 1.2405436038970947, D(x): 0.6009122729301453, D(G(z)): 0.6396313905715942\n",
      "loss_G: 0.9161114692687988, loss_D: 1.3189178705215454, D(x): 0.7347413301467896, D(G(z)): 0.5841765403747559\n",
      "epoch 149\n",
      "-----------------\n",
      "loss_G: 0.7873461246490479, loss_D: 1.3407944440841675, D(x): 0.616708517074585, D(G(z)): 0.7240859270095825\n",
      "loss_G: 0.8954702019691467, loss_D: 1.269802451133728, D(x): 0.6556288003921509, D(G(z)): 0.6141736507415771\n",
      "loss_G: 0.8938550353050232, loss_D: 1.1514365673065186, D(x): 0.5337851643562317, D(G(z)): 0.6176514029502869\n",
      "loss_G: 0.9209216237068176, loss_D: 1.2757790088653564, D(x): 0.6880330443382263, D(G(z)): 0.5877459049224854\n",
      "loss_G: 0.8266874551773071, loss_D: 1.335165023803711, D(x): 0.6961156129837036, D(G(z)): 0.6390494704246521\n",
      "epoch 150\n",
      "-----------------\n",
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      "loss_G: 0.843756377696991, loss_D: 1.2395544052124023, D(x): 0.5895737409591675, D(G(z)): 0.6499807238578796\n",
      "loss_G: 0.933157205581665, loss_D: 1.2163633108139038, D(x): 0.6106053590774536, D(G(z)): 0.6057579517364502\n",
      "loss_G: 0.8648027181625366, loss_D: 1.3754630088806152, D(x): 0.6792673468589783, D(G(z)): 0.6961957216262817\n",
      "loss_G: 0.9030183553695679, loss_D: 1.3347723484039307, D(x): 0.7252023816108704, D(G(z)): 0.6095700263977051\n",
      "epoch 151\n",
      "-----------------\n",
      "loss_G: 0.8154688477516174, loss_D: 1.2628322839736938, D(x): 0.5802909731864929, D(G(z)): 0.6825413107872009\n",
      "loss_G: 0.847946047782898, loss_D: 1.307833194732666, D(x): 0.6468160152435303, D(G(z)): 0.661017119884491\n",
      "loss_G: 0.822333574295044, loss_D: 1.2969467639923096, D(x): 0.6499590873718262, D(G(z)): 0.6469877362251282\n",
      "loss_G: 0.9443347454071045, loss_D: 1.3574693202972412, D(x): 0.7549222707748413, D(G(z)): 0.6025469899177551\n",
      "loss_G: 0.9228661060333252, loss_D: 1.3754239082336426, D(x): 0.7508642673492432, D(G(z)): 0.6245596408843994\n",
      "epoch 152\n",
      "-----------------\n",
      "loss_G: 0.8283616900444031, loss_D: 1.2821156978607178, D(x): 0.5770443677902222, D(G(z)): 0.7050713896751404\n",
      "loss_G: 0.8359193801879883, loss_D: 1.3030486106872559, D(x): 0.6437731981277466, D(G(z)): 0.6592754125595093\n",
      "loss_G: 0.8353816270828247, loss_D: 1.2032744884490967, D(x): 0.5892665982246399, D(G(z)): 0.614007830619812\n",
      "loss_G: 0.9188637733459473, loss_D: 1.3248851299285889, D(x): 0.7071852684020996, D(G(z)): 0.6176998615264893\n",
      "loss_G: 0.9276125431060791, loss_D: 1.3036394119262695, D(x): 0.7203395366668701, D(G(z)): 0.5832998752593994\n",
      "epoch 153\n",
      "-----------------\n",
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      "loss_G: 0.7895034551620483, loss_D: 1.3011101484298706, D(x): 0.6607373952865601, D(G(z)): 0.6403727531433105\n",
      "loss_G: 0.8587231636047363, loss_D: 1.2654131650924683, D(x): 0.6305911540985107, D(G(z)): 0.6348220109939575\n",
      "loss_G: 0.8651847839355469, loss_D: 1.3310765027999878, D(x): 0.6918895244598389, D(G(z)): 0.6391869783401489\n",
      "loss_G: 0.8906456232070923, loss_D: 1.3080610036849976, D(x): 0.6885751485824585, D(G(z)): 0.6194858551025391\n",
      "epoch 154\n",
      "-----------------\n",
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      "loss_G: 0.8088098168373108, loss_D: 1.2518001794815063, D(x): 0.6269481182098389, D(G(z)): 0.6248520612716675\n",
      "loss_G: 0.8139450550079346, loss_D: 1.2165031433105469, D(x): 0.6156332492828369, D(G(z)): 0.60086989402771\n",
      "loss_G: 0.9092050194740295, loss_D: 1.2804834842681885, D(x): 0.6688841581344604, D(G(z)): 0.611599326133728\n",
      "loss_G: 0.8674925565719604, loss_D: 1.292298436164856, D(x): 0.6657167673110962, D(G(z)): 0.6265816688537598\n",
      "epoch 155\n",
      "-----------------\n",
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      "loss_G: 0.8957936763763428, loss_D: 1.2681735754013062, D(x): 0.6313462257385254, D(G(z)): 0.6368273496627808\n",
      "loss_G: 0.893180251121521, loss_D: 1.195044994354248, D(x): 0.6301081776618958, D(G(z)): 0.5649367570877075\n",
      "loss_G: 0.8700233697891235, loss_D: 1.278198480606079, D(x): 0.6931409239768982, D(G(z)): 0.5850576162338257\n",
      "loss_G: 0.9673244953155518, loss_D: 1.2750277519226074, D(x): 0.6852114200592041, D(G(z)): 0.5898163318634033\n",
      "epoch 156\n",
      "-----------------\n",
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      "loss_G: 0.7898155450820923, loss_D: 1.2767953872680664, D(x): 0.6018407344818115, D(G(z)): 0.6749547123908997\n",
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      "loss_G: 0.8568100333213806, loss_D: 1.2445874214172363, D(x): 0.6718606352806091, D(G(z)): 0.572726845741272\n",
      "loss_G: 0.8714659214019775, loss_D: 1.2824186086654663, D(x): 0.6739451885223389, D(G(z)): 0.6084734201431274\n",
      "epoch 157\n",
      "-----------------\n",
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      "loss_G: 0.8067094683647156, loss_D: 1.2256392240524292, D(x): 0.6044186353683472, D(G(z)): 0.621220588684082\n",
      "loss_G: 0.8684411644935608, loss_D: 1.2243399620056152, D(x): 0.6041789054870605, D(G(z)): 0.6201610565185547\n",
      "loss_G: 0.8860735893249512, loss_D: 1.283801794052124, D(x): 0.6542915105819702, D(G(z)): 0.629510223865509\n",
      "loss_G: 0.911270797252655, loss_D: 1.3199445009231567, D(x): 0.6967636346817017, D(G(z)): 0.6231808662414551\n",
      "epoch 158\n",
      "-----------------\n",
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      "loss_G: 0.8639698028564453, loss_D: 1.2639143466949463, D(x): 0.596994161605835, D(G(z)): 0.6669201254844666\n",
      "loss_G: 0.8824723958969116, loss_D: 1.2562682628631592, D(x): 0.6184579730033875, D(G(z)): 0.637810230255127\n",
      "loss_G: 0.8366551399230957, loss_D: 1.3184988498687744, D(x): 0.6823999881744385, D(G(z)): 0.6360988020896912\n",
      "loss_G: 0.9105194211006165, loss_D: 1.2338436841964722, D(x): 0.6463190317153931, D(G(z)): 0.5875246524810791\n",
      "epoch 159\n",
      "-----------------\n",
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      "loss_G: 0.8714478015899658, loss_D: 1.2288117408752441, D(x): 0.6242705583572388, D(G(z)): 0.6045411825180054\n",
      "loss_G: 0.8391399383544922, loss_D: 1.337601661682129, D(x): 0.6388184428215027, D(G(z)): 0.6987832188606262\n",
      "loss_G: 0.9211322069168091, loss_D: 1.2611713409423828, D(x): 0.6571941375732422, D(G(z)): 0.6039772033691406\n",
      "loss_G: 0.8816033005714417, loss_D: 1.3066340684890747, D(x): 0.7029025554656982, D(G(z)): 0.6037315130233765\n",
      "epoch 160\n",
      "-----------------\n",
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      "loss_G: 0.805835485458374, loss_D: 1.2495156526565552, D(x): 0.5887484550476074, D(G(z)): 0.6607671976089478\n",
      "loss_G: 0.8792514801025391, loss_D: 1.2389614582061768, D(x): 0.6150612831115723, D(G(z)): 0.6239002346992493\n",
      "loss_G: 0.820508599281311, loss_D: 1.3110108375549316, D(x): 0.702122688293457, D(G(z)): 0.6088881492614746\n",
      "loss_G: 0.8408066034317017, loss_D: 1.3032512664794922, D(x): 0.6802390217781067, D(G(z)): 0.6230123043060303\n",
      "epoch 161\n",
      "-----------------\n",
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      "loss_G: 0.8626567721366882, loss_D: 1.3407847881317139, D(x): 0.7276698350906372, D(G(z)): 0.6131150126457214\n",
      "loss_G: 0.9737111330032349, loss_D: 1.2780777215957642, D(x): 0.6919885873794556, D(G(z)): 0.5860891342163086\n",
      "epoch 162\n",
      "-----------------\n",
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      "loss_G: 0.8403626680374146, loss_D: 1.2411634922027588, D(x): 0.5835574865341187, D(G(z)): 0.6576059460639954\n",
      "loss_G: 0.8498342037200928, loss_D: 1.2046184539794922, D(x): 0.585098147392273, D(G(z)): 0.6195203065872192\n",
      "loss_G: 0.9240442514419556, loss_D: 1.2597806453704834, D(x): 0.7021092772483826, D(G(z)): 0.557671308517456\n",
      "loss_G: 0.9608292579650879, loss_D: 1.3423596620559692, D(x): 0.7175908088684082, D(G(z)): 0.624768853187561\n",
      "epoch 163\n",
      "-----------------\n",
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      "loss_G: 0.8407000303268433, loss_D: 1.2390453815460205, D(x): 0.5975023508071899, D(G(z)): 0.6415430307388306\n",
      "loss_G: 0.9216558337211609, loss_D: 1.191586971282959, D(x): 0.5675185918807983, D(G(z)): 0.6240684390068054\n",
      "loss_G: 0.8607107400894165, loss_D: 1.3126652240753174, D(x): 0.7001304626464844, D(G(z)): 0.612534761428833\n",
      "loss_G: 0.9567279815673828, loss_D: 1.3202871084213257, D(x): 0.7132744789123535, D(G(z)): 0.6070126295089722\n",
      "epoch 164\n",
      "-----------------\n",
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      "loss_G: 0.8887264132499695, loss_D: 1.250333309173584, D(x): 0.6058727502822876, D(G(z)): 0.6444604992866516\n",
      "loss_G: 0.8074004650115967, loss_D: 1.205500602722168, D(x): 0.5639684796333313, D(G(z)): 0.6415321230888367\n",
      "loss_G: 0.8935261964797974, loss_D: 1.2665472030639648, D(x): 0.6749608516693115, D(G(z)): 0.5915862917900085\n",
      "loss_G: 0.8169689178466797, loss_D: 1.3534572124481201, D(x): 0.6921854615211487, D(G(z)): 0.6612718105316162\n",
      "epoch 165\n",
      "-----------------\n",
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      "loss_G: 0.8848210573196411, loss_D: 1.2131953239440918, D(x): 0.6035270094871521, D(G(z)): 0.6096682548522949\n",
      "loss_G: 0.8487440347671509, loss_D: 1.2456550598144531, D(x): 0.5883891582489014, D(G(z)): 0.657265841960907\n",
      "loss_G: 0.9221138954162598, loss_D: 1.2742118835449219, D(x): 0.7007815837860107, D(G(z)): 0.5734302997589111\n",
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      "epoch 166\n",
      "-----------------\n",
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      "loss_G: 0.8367335200309753, loss_D: 1.296891212463379, D(x): 0.5739107728004456, D(G(z)): 0.7229804992675781\n",
      "loss_G: 0.8927047848701477, loss_D: 1.251204013824463, D(x): 0.6223375797271729, D(G(z)): 0.6288663744926453\n",
      "loss_G: 0.9534093141555786, loss_D: 1.2888158559799194, D(x): 0.722503662109375, D(G(z)): 0.5663121938705444\n",
      "loss_G: 0.9660265445709229, loss_D: 1.2446820735931396, D(x): 0.6725074648857117, D(G(z)): 0.5721745491027832\n",
      "epoch 167\n",
      "-----------------\n",
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      "loss_G: 0.9431361556053162, loss_D: 1.2595131397247314, D(x): 0.616083025932312, D(G(z)): 0.6434301137924194\n",
      "loss_G: 0.8259305953979492, loss_D: 1.1754014492034912, D(x): 0.5439282059669495, D(G(z)): 0.631473183631897\n",
      "loss_G: 0.9379016757011414, loss_D: 1.3147236108779907, D(x): 0.7182174921035767, D(G(z)): 0.5965061187744141\n",
      "loss_G: 1.000157356262207, loss_D: 1.2217280864715576, D(x): 0.6587198376655579, D(G(z)): 0.5630083084106445\n",
      "epoch 168\n",
      "-----------------\n",
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      "loss_G: 0.89887535572052, loss_D: 1.2202754020690918, D(x): 0.6043674349784851, D(G(z)): 0.6159079074859619\n",
      "loss_G: 0.9139199256896973, loss_D: 1.219264030456543, D(x): 0.5883504748344421, D(G(z)): 0.6309136152267456\n",
      "loss_G: 0.9247220754623413, loss_D: 1.2736554145812988, D(x): 0.6902176141738892, D(G(z)): 0.5834377408027649\n",
      "loss_G: 0.8552870750427246, loss_D: 1.2144081592559814, D(x): 0.6105929017066956, D(G(z)): 0.6038151979446411\n",
      "epoch 169\n",
      "-----------------\n",
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      "loss_G: 0.8671683669090271, loss_D: 1.2541100978851318, D(x): 0.5889101028442383, D(G(z)): 0.6651999354362488\n",
      "loss_G: 0.9194540977478027, loss_D: 1.2112921476364136, D(x): 0.6125200986862183, D(G(z)): 0.5987720489501953\n",
      "loss_G: 0.9860778450965881, loss_D: 1.2483545541763306, D(x): 0.689049482345581, D(G(z)): 0.5593050718307495\n",
      "loss_G: 0.8755936622619629, loss_D: 1.2320044040679932, D(x): 0.6087919473648071, D(G(z)): 0.623212456703186\n",
      "epoch 170\n",
      "-----------------\n",
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      "loss_G: 0.8789106607437134, loss_D: 1.2084546089172363, D(x): 0.5838664770126343, D(G(z)): 0.6245881915092468\n",
      "loss_G: 0.855290949344635, loss_D: 1.2313321828842163, D(x): 0.6210335493087769, D(G(z)): 0.6102986335754395\n",
      "loss_G: 0.9289290904998779, loss_D: 1.3013836145401, D(x): 0.688672661781311, D(G(z)): 0.6127109527587891\n",
      "loss_G: 0.8713189363479614, loss_D: 1.2333842515945435, D(x): 0.6204297542572021, D(G(z)): 0.6129544973373413\n",
      "epoch 171\n",
      "-----------------\n",
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      "loss_G: 0.8461728096008301, loss_D: 1.171833872795105, D(x): 0.5946991443634033, D(G(z)): 0.5771347284317017\n",
      "loss_G: 0.8372742533683777, loss_D: 1.1974414587020874, D(x): 0.5860689878463745, D(G(z)): 0.6113724708557129\n",
      "loss_G: 0.9377503395080566, loss_D: 1.2369545698165894, D(x): 0.6829634308815002, D(G(z)): 0.5539911389350891\n",
      "loss_G: 0.9102882742881775, loss_D: 1.228240966796875, D(x): 0.5845500230789185, D(G(z)): 0.6436910033226013\n",
      "epoch 172\n",
      "-----------------\n",
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      "loss_G: 1.0122811794281006, loss_D: 1.2373294830322266, D(x): 0.5773114562034607, D(G(z)): 0.6600179672241211\n",
      "loss_G: 0.8656049966812134, loss_D: 1.2183644771575928, D(x): 0.6341954469680786, D(G(z)): 0.5841689705848694\n",
      "loss_G: 0.9893507957458496, loss_D: 1.2856675386428833, D(x): 0.7106437683105469, D(G(z)): 0.5750237703323364\n",
      "loss_G: 0.8960456252098083, loss_D: 1.1648244857788086, D(x): 0.6225425601005554, D(G(z)): 0.5422818660736084\n",
      "epoch 173\n",
      "-----------------\n",
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      "loss_G: 0.8025263547897339, loss_D: 1.2467243671417236, D(x): 0.5483058094978333, D(G(z)): 0.6984186172485352\n",
      "loss_G: 0.8587660789489746, loss_D: 1.2089132070541382, D(x): 0.5841029286384583, D(G(z)): 0.6248102784156799\n",
      "loss_G: 0.9441066384315491, loss_D: 1.2382500171661377, D(x): 0.6719949841499329, D(G(z)): 0.5662550926208496\n",
      "loss_G: 0.8302155137062073, loss_D: 1.2257851362228394, D(x): 0.6183791160583496, D(G(z)): 0.6074060201644897\n",
      "epoch 174\n",
      "-----------------\n",
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      "loss_G: 0.8810989856719971, loss_D: 1.1889033317565918, D(x): 0.5906302332878113, D(G(z)): 0.5982731580734253\n",
      "loss_G: 0.8076362609863281, loss_D: 1.1513627767562866, D(x): 0.5421333312988281, D(G(z)): 0.6092294454574585\n",
      "loss_G: 0.9508758187294006, loss_D: 1.242172122001648, D(x): 0.6997030973434448, D(G(z)): 0.5424690246582031\n",
      "loss_G: 0.9052011966705322, loss_D: 1.2377400398254395, D(x): 0.6148563623428345, D(G(z)): 0.622883677482605\n",
      "epoch 175\n",
      "-----------------\n",
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      "loss_G: 0.8381289839744568, loss_D: 1.2539188861846924, D(x): 0.6223317384719849, D(G(z)): 0.6315872073173523\n",
      "loss_G: 0.8641318678855896, loss_D: 1.2516506910324097, D(x): 0.6421800851821899, D(G(z)): 0.6094706058502197\n",
      "loss_G: 0.9439880847930908, loss_D: 1.2458934783935547, D(x): 0.699339747428894, D(G(z)): 0.5465537905693054\n",
      "loss_G: 0.8967115879058838, loss_D: 1.250401496887207, D(x): 0.6442474126815796, D(G(z)): 0.6061541438102722\n",
      "epoch 176\n",
      "-----------------\n",
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      "loss_G: 0.9183995723724365, loss_D: 1.2302166223526, D(x): 0.6232020258903503, D(G(z)): 0.6070145964622498\n",
      "loss_G: 0.8439337015151978, loss_D: 1.280477523803711, D(x): 0.6477819681167603, D(G(z)): 0.6326954960823059\n",
      "loss_G: 1.024137020111084, loss_D: 1.3374698162078857, D(x): 0.7219485640525818, D(G(z)): 0.6155213117599487\n",
      "loss_G: 0.8414938449859619, loss_D: 1.2735755443572998, D(x): 0.6750056743621826, D(G(z)): 0.5985698699951172\n",
      "epoch 177\n",
      "-----------------\n",
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      "loss_G: 0.8941954374313354, loss_D: 1.2392288446426392, D(x): 0.5979295969009399, D(G(z)): 0.6412992477416992\n",
      "loss_G: 0.8338633179664612, loss_D: 1.2363417148590088, D(x): 0.6042587161064148, D(G(z)): 0.6320829391479492\n",
      "loss_G: 0.8603423833847046, loss_D: 1.289748191833496, D(x): 0.6816294193267822, D(G(z)): 0.6081187725067139\n",
      "loss_G: 0.8934551477432251, loss_D: 1.2760920524597168, D(x): 0.6600897312164307, D(G(z)): 0.6160023212432861\n",
      "epoch 178\n",
      "-----------------\n",
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      "loss_G: 0.8913033604621887, loss_D: 1.2156180143356323, D(x): 0.6225430965423584, D(G(z)): 0.5930749177932739\n",
      "loss_G: 0.9307541847229004, loss_D: 1.212559461593628, D(x): 0.6253878474235535, D(G(z)): 0.5871715545654297\n",
      "loss_G: 0.9302237033843994, loss_D: 1.2327184677124023, D(x): 0.6591834425926208, D(G(z)): 0.5735350847244263\n",
      "loss_G: 0.8947854042053223, loss_D: 1.1825947761535645, D(x): 0.6148931980133057, D(G(z)): 0.5677015781402588\n",
      "epoch 179\n",
      "-----------------\n",
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      "loss_G: 0.8349810838699341, loss_D: 1.2262639999389648, D(x): 0.5651518702507019, D(G(z)): 0.6611121892929077\n",
      "loss_G: 0.89409339427948, loss_D: 1.2187421321868896, D(x): 0.608601450920105, D(G(z)): 0.6101407408714294\n",
      "loss_G: 0.922256350517273, loss_D: 1.2473442554473877, D(x): 0.6462340354919434, D(G(z)): 0.6011102199554443\n",
      "loss_G: 0.9188936948776245, loss_D: 1.147326946258545, D(x): 0.5561785101890564, D(G(z)): 0.5911483764648438\n",
      "epoch 180\n",
      "-----------------\n",
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      "loss_G: 0.9421780109405518, loss_D: 1.2517225742340088, D(x): 0.6034120321273804, D(G(z)): 0.6483106017112732\n",
      "loss_G: 0.8974224328994751, loss_D: 1.1568528413772583, D(x): 0.632540225982666, D(G(z)): 0.5243126153945923\n",
      "loss_G: 0.8625584244728088, loss_D: 1.317427396774292, D(x): 0.7005826830863953, D(G(z)): 0.616844654083252\n",
      "loss_G: 0.928304135799408, loss_D: 1.2555091381072998, D(x): 0.6657097935676575, D(G(z)): 0.5897992849349976\n",
      "epoch 181\n",
      "-----------------\n",
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      "loss_G: 0.8414182662963867, loss_D: 1.2344954013824463, D(x): 0.6467127799987793, D(G(z)): 0.587782621383667\n",
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      "loss_G: 0.9483351111412048, loss_D: 1.3054670095443726, D(x): 0.7233096361160278, D(G(z)): 0.5821573734283447\n",
      "loss_G: 0.9086668491363525, loss_D: 1.2121962308883667, D(x): 0.634036660194397, D(G(z)): 0.5781595706939697\n",
      "epoch 182\n",
      "-----------------\n",
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      "loss_G: 0.9023022651672363, loss_D: 1.316696286201477, D(x): 0.7131940126419067, D(G(z)): 0.6035022735595703\n",
      "loss_G: 0.9652919173240662, loss_D: 1.2573513984680176, D(x): 0.6434124708175659, D(G(z)): 0.6139389872550964\n",
      "epoch 183\n",
      "-----------------\n",
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      "loss_G: 0.8925369381904602, loss_D: 1.213956594467163, D(x): 0.6246904730796814, D(G(z)): 0.5892661809921265\n",
      "loss_G: 0.856045126914978, loss_D: 1.249687910079956, D(x): 0.645630955696106, D(G(z)): 0.6040569543838501\n",
      "loss_G: 0.9727929830551147, loss_D: 1.3066805601119995, D(x): 0.7054195404052734, D(G(z)): 0.6012610197067261\n",
      "loss_G: 0.8547172546386719, loss_D: 1.2271995544433594, D(x): 0.6161206364631653, D(G(z)): 0.6110788583755493\n",
      "epoch 184\n",
      "-----------------\n",
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      "loss_G: 0.9026671648025513, loss_D: 1.1984107494354248, D(x): 0.6741456389427185, D(G(z)): 0.5242650508880615\n",
      "loss_G: 0.9459171295166016, loss_D: 1.31789231300354, D(x): 0.7225440740585327, D(G(z)): 0.5953482389450073\n",
      "loss_G: 0.8931219577789307, loss_D: 1.2684071063995361, D(x): 0.612126350402832, D(G(z)): 0.6562807559967041\n",
      "epoch 185\n",
      "-----------------\n",
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      "loss_G: 0.905750036239624, loss_D: 1.3271347284317017, D(x): 0.6987980604171753, D(G(z)): 0.6283366680145264\n",
      "loss_G: 0.882037341594696, loss_D: 1.2419254779815674, D(x): 0.6673414707183838, D(G(z)): 0.5745840072631836\n",
      "epoch 186\n",
      "-----------------\n",
      "loss_G: 0.876579761505127, loss_D: 1.204770803451538, D(x): 0.5820573568344116, D(G(z)): 0.6227135062217712\n",
      "loss_G: 0.8599292039871216, loss_D: 1.2724465131759644, D(x): 0.6376011967658997, D(G(z)): 0.6348453164100647\n",
      "loss_G: 0.9145767092704773, loss_D: 1.2309739589691162, D(x): 0.6308754682540894, D(G(z)): 0.6000985503196716\n",
      "loss_G: 0.9396970272064209, loss_D: 1.2928552627563477, D(x): 0.6999812126159668, D(G(z)): 0.5928739905357361\n",
      "loss_G: 0.9795513153076172, loss_D: 1.2124079465866089, D(x): 0.6349678039550781, D(G(z)): 0.5774401426315308\n",
      "epoch 187\n",
      "-----------------\n",
      "loss_G: 0.9192038774490356, loss_D: 1.1693854331970215, D(x): 0.5437434315681458, D(G(z)): 0.625641942024231\n",
      "loss_G: 0.9693487286567688, loss_D: 1.2492403984069824, D(x): 0.6378997564315796, D(G(z)): 0.6113406419754028\n",
      "loss_G: 0.8651058077812195, loss_D: 1.2615265846252441, D(x): 0.6136490106582642, D(G(z)): 0.6478775143623352\n",
      "loss_G: 0.987781286239624, loss_D: 1.283072829246521, D(x): 0.6865247488021851, D(G(z)): 0.5965480804443359\n",
      "loss_G: 0.9385654330253601, loss_D: 1.192492127418518, D(x): 0.6003049612045288, D(G(z)): 0.5921871662139893\n",
      "epoch 188\n",
      "-----------------\n",
      "loss_G: 0.9444539546966553, loss_D: 1.2188082933425903, D(x): 0.5407000780105591, D(G(z)): 0.6781082153320312\n",
      "loss_G: 0.924741804599762, loss_D: 1.343623399734497, D(x): 0.6582530736923218, D(G(z)): 0.6853702664375305\n",
      "loss_G: 0.8863369226455688, loss_D: 1.2580891847610474, D(x): 0.6219229698181152, D(G(z)): 0.6361662149429321\n",
      "loss_G: 0.8783186078071594, loss_D: 1.2287780046463013, D(x): 0.6424943804740906, D(G(z)): 0.5862836241722107\n",
      "loss_G: 0.8696883916854858, loss_D: 1.180063247680664, D(x): 0.5637713074684143, D(G(z)): 0.616291880607605\n",
      "epoch 189\n",
      "-----------------\n",
      "loss_G: 0.9309129118919373, loss_D: 1.2106339931488037, D(x): 0.5827182531356812, D(G(z)): 0.6279157996177673\n",
      "loss_G: 0.9142167568206787, loss_D: 1.2758312225341797, D(x): 0.652961790561676, D(G(z)): 0.6228694915771484\n",
      "loss_G: 0.7978991270065308, loss_D: 1.2369978427886963, D(x): 0.6174894571304321, D(G(z)): 0.6195083856582642\n",
      "loss_G: 0.967041015625, loss_D: 1.235060453414917, D(x): 0.6510761976242065, D(G(z)): 0.5839841961860657\n",
      "loss_G: 0.8577755093574524, loss_D: 1.2335669994354248, D(x): 0.5807386040687561, D(G(z)): 0.6528283357620239\n",
      "epoch 190\n",
      "-----------------\n",
      "loss_G: 0.8649009466171265, loss_D: 1.1893806457519531, D(x): 0.5861020088195801, D(G(z)): 0.603278636932373\n",
      "loss_G: 0.9124401807785034, loss_D: 1.2831767797470093, D(x): 0.6778590679168701, D(G(z)): 0.6053177118301392\n",
      "loss_G: 0.9163806438446045, loss_D: 1.303292989730835, D(x): 0.665228545665741, D(G(z)): 0.6380643844604492\n",
      "loss_G: 0.9558066129684448, loss_D: 1.230703592300415, D(x): 0.6537079215049744, D(G(z)): 0.5769956707954407\n",
      "loss_G: 0.9862842559814453, loss_D: 1.1934562921524048, D(x): 0.6087954044342041, D(G(z)): 0.5846608877182007\n",
      "epoch 191\n",
      "-----------------\n",
      "loss_G: 0.8937140107154846, loss_D: 1.2377002239227295, D(x): 0.5987014770507812, D(G(z)): 0.6389987468719482\n",
      "loss_G: 0.9483587145805359, loss_D: 1.2718113660812378, D(x): 0.6801799535751343, D(G(z)): 0.5916314125061035\n",
      "loss_G: 0.9388365745544434, loss_D: 1.2313041687011719, D(x): 0.640579879283905, D(G(z)): 0.5907242298126221\n",
      "loss_G: 0.9568865299224854, loss_D: 1.2503445148468018, D(x): 0.6941902041435242, D(G(z)): 0.5561543703079224\n",
      "loss_G: 0.9257844686508179, loss_D: 1.1920486688613892, D(x): 0.5677089691162109, D(G(z)): 0.6243396997451782\n",
      "epoch 192\n",
      "-----------------\n",
      "loss_G: 0.9135364890098572, loss_D: 1.159806489944458, D(x): 0.55586838722229, D(G(z)): 0.603938102722168\n",
      "loss_G: 0.9398354887962341, loss_D: 1.2659246921539307, D(x): 0.6902679800987244, D(G(z)): 0.5756567120552063\n",
      "loss_G: 0.9330649375915527, loss_D: 1.2585928440093994, D(x): 0.6740080118179321, D(G(z)): 0.5845848917961121\n",
      "loss_G: 0.9242681860923767, loss_D: 1.244749903678894, D(x): 0.671416163444519, D(G(z)): 0.573333740234375\n",
      "loss_G: 0.9040826559066772, loss_D: 1.1889889240264893, D(x): 0.5746572017669678, D(G(z)): 0.6143316626548767\n",
      "epoch 193\n",
      "-----------------\n",
      "loss_G: 0.8946551084518433, loss_D: 1.2190951108932495, D(x): 0.5453498363494873, D(G(z)): 0.6737452745437622\n",
      "loss_G: 0.9075220823287964, loss_D: 1.356937050819397, D(x): 0.7151428461074829, D(G(z)): 0.6417942047119141\n",
      "loss_G: 0.9159978628158569, loss_D: 1.2926268577575684, D(x): 0.6395970582962036, D(G(z)): 0.65302973985672\n",
      "loss_G: 1.0493905544281006, loss_D: 1.2093480825424194, D(x): 0.7084677219390869, D(G(z)): 0.5008803606033325\n",
      "loss_G: 0.9533247947692871, loss_D: 1.2611262798309326, D(x): 0.6333832144737244, D(G(z)): 0.6277430057525635\n",
      "epoch 194\n",
      "-----------------\n",
      "loss_G: 0.9376626014709473, loss_D: 1.298703670501709, D(x): 0.5823251008987427, D(G(z)): 0.7163786292076111\n",
      "loss_G: 0.9026329517364502, loss_D: 1.3238551616668701, D(x): 0.6495780944824219, D(G(z)): 0.674277126789093\n",
      "loss_G: 0.9308594465255737, loss_D: 1.2369282245635986, D(x): 0.6213342547416687, D(G(z)): 0.6155939102172852\n",
      "loss_G: 0.9581255316734314, loss_D: 1.2732633352279663, D(x): 0.675675630569458, D(G(z)): 0.5975877046585083\n",
      "loss_G: 0.9256680011749268, loss_D: 1.2026803493499756, D(x): 0.5953366756439209, D(G(z)): 0.6073436141014099\n",
      "epoch 195\n",
      "-----------------\n",
      "loss_G: 0.9196773767471313, loss_D: 1.109451174736023, D(x): 0.5521343946456909, D(G(z)): 0.557316780090332\n",
      "loss_G: 0.9342841506004333, loss_D: 1.2163032293319702, D(x): 0.6217256784439087, D(G(z)): 0.5945775508880615\n",
      "loss_G: 0.8641546964645386, loss_D: 1.2682580947875977, D(x): 0.6883996725082397, D(G(z)): 0.5798583626747131\n",
      "loss_G: 0.9089230298995972, loss_D: 1.2815271615982056, D(x): 0.7020262479782104, D(G(z)): 0.5795009136199951\n",
      "loss_G: 0.94266277551651, loss_D: 1.183910608291626, D(x): 0.6236754655838013, D(G(z)): 0.5602351427078247\n",
      "epoch 196\n",
      "-----------------\n",
      "loss_G: 0.9865208864212036, loss_D: 1.1403203010559082, D(x): 0.5874226093292236, D(G(z)): 0.5528977513313293\n",
      "loss_G: 0.9086095690727234, loss_D: 1.1975164413452148, D(x): 0.6618400812149048, D(G(z)): 0.5356763601303101\n",
      "loss_G: 0.8667628765106201, loss_D: 1.219313621520996, D(x): 0.6368777751922607, D(G(z)): 0.5824357867240906\n",
      "loss_G: 0.9613769054412842, loss_D: 1.1765005588531494, D(x): 0.6767997145652771, D(G(z)): 0.4997008144855499\n",
      "loss_G: 0.91869056224823, loss_D: 1.1813322305679321, D(x): 0.5791075229644775, D(G(z)): 0.6022247076034546\n",
      "epoch 197\n",
      "-----------------\n",
      "loss_G: 0.8894765973091125, loss_D: 1.275977611541748, D(x): 0.567818284034729, D(G(z)): 0.708159327507019\n",
      "loss_G: 0.9894808530807495, loss_D: 1.255354404449463, D(x): 0.6437128782272339, D(G(z)): 0.6116414666175842\n",
      "loss_G: 0.8932375907897949, loss_D: 1.302109718322754, D(x): 0.6299175024032593, D(G(z)): 0.6721922755241394\n",
      "loss_G: 0.9248371124267578, loss_D: 1.3032962083816528, D(x): 0.7018445134162903, D(G(z)): 0.6014516949653625\n",
      "loss_G: 0.9314684867858887, loss_D: 1.1987847089767456, D(x): 0.6057202816009521, D(G(z)): 0.5930644273757935\n",
      "epoch 198\n",
      "-----------------\n",
      "loss_G: 1.0090161561965942, loss_D: 1.2152435779571533, D(x): 0.6117921471595764, D(G(z)): 0.6034514307975769\n",
      "loss_G: 0.9799791574478149, loss_D: 1.2322181463241577, D(x): 0.6418617963790894, D(G(z)): 0.5903563499450684\n",
      "loss_G: 0.9040610790252686, loss_D: 1.2751530408859253, D(x): 0.6451194286346436, D(G(z)): 0.6300336122512817\n",
      "loss_G: 0.9246748685836792, loss_D: 1.3032176494598389, D(x): 0.6760742664337158, D(G(z)): 0.6271433234214783\n",
      "loss_G: 0.8487818241119385, loss_D: 1.2095263004302979, D(x): 0.6023812890052795, D(G(z)): 0.6071449518203735\n",
      "epoch 199\n",
      "-----------------\n",
      "loss_G: 0.9018123149871826, loss_D: 1.2916277647018433, D(x): 0.6207082271575928, D(G(z)): 0.6709195375442505\n",
      "loss_G: 0.8572068810462952, loss_D: 1.25162935256958, D(x): 0.6066083908081055, D(G(z)): 0.6450210213661194\n",
      "loss_G: 0.9103661775588989, loss_D: 1.2013623714447021, D(x): 0.610772967338562, D(G(z)): 0.5905894637107849\n",
      "loss_G: 0.9547359943389893, loss_D: 1.2741286754608154, D(x): 0.726416289806366, D(G(z)): 0.5477123260498047\n",
      "loss_G: 0.942449688911438, loss_D: 1.1723074913024902, D(x): 0.5738702416419983, D(G(z)): 0.5984373092651367\n",
      "epoch 200\n",
      "-----------------\n",
      "loss_G: 0.8609087467193604, loss_D: 1.269378662109375, D(x): 0.6376166343688965, D(G(z)): 0.6317620277404785\n",
      "loss_G: 0.7920733690261841, loss_D: 1.2244271039962769, D(x): 0.630106508731842, D(G(z)): 0.5943205952644348\n",
      "loss_G: 0.9630614519119263, loss_D: 1.2228671312332153, D(x): 0.6319364309310913, D(G(z)): 0.590930700302124\n",
      "loss_G: 0.9155237078666687, loss_D: 1.284010648727417, D(x): 0.6412093043327332, D(G(z)): 0.6428014039993286\n",
      "loss_G: 0.9621032476425171, loss_D: 1.1774473190307617, D(x): 0.5459054708480835, D(G(z)): 0.6315418481826782\n"
     ]
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.exists('model_G.pth'):\n",
    "    G.load_state_dict(torch.load('model_G.pth'))\n",
    "else:\n",
    "    train()\n",
    "\n",
    "    # 绘制损失图像\n",
    "    plt.figure()\n",
    "    loss_D, = plt.plot(range(1, iter+1), loss_D_list)\n",
    "    loss_G, = plt.plot(range(1, iter+1), loss_G_list)\n",
    "    plt.legend([loss_D, loss_G], ['loss D', 'loss G'], loc='best')\n",
    "    plt.xlabel('Iterations(x100)')\n",
    "    plt.ylabel('Loss Value')\n",
    "    plt.savefig('loss value')\n",
    "\n",
    "    # 绘制D(x)和D(G(z))损失图像\n",
    "    plt.figure()\n",
    "    Dx, = plt.plot(range(1, iter+1), Dx_list)\n",
    "    DGz, = plt.plot(range(1, iter+1), DGz_list)\n",
    "    plt.legend([Dx, DGz], ['D(x)', 'D(G(z))'], loc='best')\n",
    "    plt.xlabel('Iterations(x100)')\n",
    "    plt.ylabel('Loss Value')\n",
    "    plt.savefig('loss D')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe564d69250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z = torch.randn(bs, 100).to(device)\n",
    "fake_images = G(z).reshape(bs, 1, 28, 28).cpu().detach()\n",
    "image_grid = torchvision.utils.make_grid(fake_images)\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.imshow(np.transpose(image_grid, (1, 2, 0)), cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}