learn_pytorch_43_pytorch实战项目之全连接生成对抗网络WGAN基于MNIST数据集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# 超参数设置\n",
    "random_seed = 0\n",
    "generator_learning_rate = 0.0005\n",
    "discriminator_learning_rate = 0.0005\n",
    "NUM_EPOCHS = 100\n",
    "BATCH_SIZE = 128\n",
    "LATENT_DIM = 50\n",
    "IMG_SHAPE = (1, 28, 28)\n",
    "IMG_SIZE = 1\n",
    "for x in IMG_SHAPE:\n",
    "    IMG_SIZE *= x\n",
    "\n",
    "## WGAN 参数设置\n",
    "num_iter_critic = 5\n",
    "weight_clip_value = 0.01\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = datasets.MNIST(root='data', \n",
    "                               train=True, \n",
    "                               transform=transforms.ToTensor(),\n",
    "                               download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='data', \n",
    "                              train=False, \n",
    "                              transform=transforms.ToTensor())\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, \n",
    "                          batch_size=BATCH_SIZE, \n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=BATCH_SIZE, \n",
    "                         shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wasserstein_loss(y_true, y_pred):\n",
    "    return torch.mean(y_true * y_pred)\n",
    "\n",
    "\n",
    "class GAN(torch.nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(GAN, self).__init__()\n",
    "        \n",
    "        \n",
    "        self.generator = nn.Sequential(\n",
    "            nn.Linear(LATENT_DIM, 128),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            #nn.Dropout(p=0.5),\n",
    "            nn.Linear(128, IMG_SIZE),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        \n",
    "        self.discriminator = nn.Sequential(\n",
    "            nn.Linear(IMG_SIZE, 128),\n",
    "            nn.LeakyReLU(inplace=True),\n",
    "            #nn.Dropout(p=0.5),\n",
    "            nn.Linear(128, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "            \n",
    "    def generator_forward(self, z):\n",
    "        img = self.generator(z)\n",
    "        return img\n",
    "    \n",
    "    def discriminator_forward(self, img):\n",
    "        pred = model.discriminator(img)\n",
    "        return pred.view(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(random_seed)\n",
    "\n",
    "model = GAN()\n",
    "model = model.to(device)\n",
    "\n",
    "optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
    "optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/100 | Batch 000/469 | Gen/Dis Loss: -0.5135/-0.0640\n",
      "Epoch: 001/100 | Batch 100/469 | Gen/Dis Loss: -0.6194/-0.1880\n",
      "Epoch: 001/100 | Batch 200/469 | Gen/Dis Loss: -0.5825/-0.1102\n",
      "Epoch: 001/100 | Batch 300/469 | Gen/Dis Loss: -0.5470/-0.0933\n",
      "Epoch: 001/100 | Batch 400/469 | Gen/Dis Loss: -0.5313/-0.0952\n",
      "Time elapsed: 0.33 min\n",
      "Epoch: 002/100 | Batch 000/469 | Gen/Dis Loss: -0.5421/-0.0747\n",
      "Epoch: 002/100 | Batch 100/469 | Gen/Dis Loss: -0.5374/-0.0782\n",
      "Epoch: 002/100 | Batch 200/469 | Gen/Dis Loss: -0.5263/-0.0668\n",
      "Epoch: 002/100 | Batch 300/469 | Gen/Dis Loss: -0.5443/-0.0667\n",
      "Epoch: 002/100 | Batch 400/469 | Gen/Dis Loss: -0.5530/-0.0531\n",
      "Time elapsed: 0.73 min\n",
      "Epoch: 003/100 | Batch 000/469 | Gen/Dis Loss: -0.5435/-0.0531\n",
      "Epoch: 003/100 | Batch 100/469 | Gen/Dis Loss: -0.5624/-0.0402\n",
      "Epoch: 003/100 | Batch 200/469 | Gen/Dis Loss: -0.5371/-0.0523\n",
      "Epoch: 003/100 | Batch 300/469 | Gen/Dis Loss: -0.5591/-0.0480\n",
      "Epoch: 003/100 | Batch 400/469 | Gen/Dis Loss: -0.5138/-0.0624\n",
      "Time elapsed: 1.12 min\n",
      "Epoch: 004/100 | Batch 000/469 | Gen/Dis Loss: -0.5486/-0.0484\n",
      "Epoch: 004/100 | Batch 100/469 | Gen/Dis Loss: -0.5703/-0.0494\n",
      "Epoch: 004/100 | Batch 200/469 | Gen/Dis Loss: -0.5805/-0.0507\n",
      "Epoch: 004/100 | Batch 300/469 | Gen/Dis Loss: -0.5558/-0.0390\n",
      "Epoch: 004/100 | Batch 400/469 | Gen/Dis Loss: -0.4682/-0.0494\n",
      "Time elapsed: 1.49 min\n",
      "Epoch: 005/100 | Batch 000/469 | Gen/Dis Loss: -0.4787/-0.0606\n",
      "Epoch: 005/100 | Batch 100/469 | Gen/Dis Loss: -0.6510/-0.0385\n",
      "Epoch: 005/100 | Batch 200/469 | Gen/Dis Loss: -0.5007/-0.0463\n",
      "Epoch: 005/100 | Batch 300/469 | Gen/Dis Loss: -0.5192/-0.0532\n",
      "Epoch: 005/100 | Batch 400/469 | Gen/Dis Loss: -0.5633/-0.0325\n",
      "Time elapsed: 1.87 min\n",
      "Epoch: 006/100 | Batch 000/469 | Gen/Dis Loss: -0.4892/-0.0434\n",
      "Epoch: 006/100 | Batch 100/469 | Gen/Dis Loss: -0.4827/-0.0540\n",
      "Epoch: 006/100 | Batch 200/469 | Gen/Dis Loss: -0.5733/-0.0377\n",
      "Epoch: 006/100 | Batch 300/469 | Gen/Dis Loss: -0.5759/-0.0420\n",
      "Epoch: 006/100 | Batch 400/469 | Gen/Dis Loss: -0.5080/-0.0614\n",
      "Time elapsed: 2.24 min\n",
      "Epoch: 007/100 | Batch 000/469 | Gen/Dis Loss: -0.5552/-0.0575\n",
      "Epoch: 007/100 | Batch 100/469 | Gen/Dis Loss: -0.5042/-0.0252\n",
      "Epoch: 007/100 | Batch 200/469 | Gen/Dis Loss: -0.4792/-0.0540\n",
      "Epoch: 007/100 | Batch 300/469 | Gen/Dis Loss: -0.5981/-0.0323\n",
      "Epoch: 007/100 | Batch 400/469 | Gen/Dis Loss: -0.5564/-0.0312\n",
      "Time elapsed: 2.63 min\n",
      "Epoch: 008/100 | Batch 000/469 | Gen/Dis Loss: -0.5818/-0.0410\n",
      "Epoch: 008/100 | Batch 100/469 | Gen/Dis Loss: -0.4996/-0.0274\n",
      "Epoch: 008/100 | Batch 200/469 | Gen/Dis Loss: -0.4403/-0.0454\n",
      "Epoch: 008/100 | Batch 300/469 | Gen/Dis Loss: -0.5530/-0.0489\n",
      "Epoch: 008/100 | Batch 400/469 | Gen/Dis Loss: -0.5633/-0.0321\n",
      "Time elapsed: 3.02 min\n",
      "Epoch: 009/100 | Batch 000/469 | Gen/Dis Loss: -0.4738/-0.0430\n",
      "Epoch: 009/100 | Batch 100/469 | Gen/Dis Loss: -0.4522/-0.0586\n",
      "Epoch: 009/100 | Batch 200/469 | Gen/Dis Loss: -0.5725/-0.0392\n",
      "Epoch: 009/100 | Batch 300/469 | Gen/Dis Loss: -0.5667/-0.0187\n",
      "Epoch: 009/100 | Batch 400/469 | Gen/Dis Loss: -0.5099/-0.0430\n",
      "Time elapsed: 3.41 min\n",
      "Epoch: 010/100 | Batch 000/469 | Gen/Dis Loss: -0.4816/-0.0318\n",
      "Epoch: 010/100 | Batch 100/469 | Gen/Dis Loss: -0.4879/-0.0407\n",
      "Epoch: 010/100 | Batch 200/469 | Gen/Dis Loss: -0.6010/-0.0260\n",
      "Epoch: 010/100 | Batch 300/469 | Gen/Dis Loss: -0.5596/-0.0424\n",
      "Epoch: 010/100 | Batch 400/469 | Gen/Dis Loss: -0.5075/-0.0253\n",
      "Time elapsed: 3.80 min\n",
      "Epoch: 011/100 | Batch 000/469 | Gen/Dis Loss: -0.5386/-0.0419\n",
      "Epoch: 011/100 | Batch 100/469 | Gen/Dis Loss: -0.4309/-0.0361\n",
      "Epoch: 011/100 | Batch 200/469 | Gen/Dis Loss: -0.6012/-0.0358\n",
      "Epoch: 011/100 | Batch 300/469 | Gen/Dis Loss: -0.5427/-0.0498\n",
      "Epoch: 011/100 | Batch 400/469 | Gen/Dis Loss: -0.5742/-0.0244\n",
      "Time elapsed: 4.19 min\n",
      "Epoch: 012/100 | Batch 000/469 | Gen/Dis Loss: -0.5340/-0.0344\n",
      "Epoch: 012/100 | Batch 100/469 | Gen/Dis Loss: -0.4438/-0.0286\n",
      "Epoch: 012/100 | Batch 200/469 | Gen/Dis Loss: -0.5724/-0.0318\n",
      "Epoch: 012/100 | Batch 300/469 | Gen/Dis Loss: -0.5712/-0.0511\n",
      "Epoch: 012/100 | Batch 400/469 | Gen/Dis Loss: -0.5007/-0.0386\n",
      "Time elapsed: 4.59 min\n",
      "Epoch: 013/100 | Batch 000/469 | Gen/Dis Loss: -0.5719/-0.0457\n",
      "Epoch: 013/100 | Batch 100/469 | Gen/Dis Loss: -0.4341/-0.0396\n",
      "Epoch: 013/100 | Batch 200/469 | Gen/Dis Loss: -0.5545/-0.0349\n",
      "Epoch: 013/100 | Batch 300/469 | Gen/Dis Loss: -0.5344/-0.0254\n",
      "Epoch: 013/100 | Batch 400/469 | Gen/Dis Loss: -0.5601/-0.0366\n",
      "Time elapsed: 4.98 min\n",
      "Epoch: 014/100 | Batch 000/469 | Gen/Dis Loss: -0.6570/-0.0202\n",
      "Epoch: 014/100 | Batch 100/469 | Gen/Dis Loss: -0.5397/-0.0316\n",
      "Epoch: 014/100 | Batch 200/469 | Gen/Dis Loss: -0.5054/-0.0294\n",
      "Epoch: 014/100 | Batch 300/469 | Gen/Dis Loss: -0.4727/-0.0288\n",
      "Epoch: 014/100 | Batch 400/469 | Gen/Dis Loss: -0.4580/-0.0316\n",
      "Time elapsed: 5.39 min\n",
      "Epoch: 015/100 | Batch 000/469 | Gen/Dis Loss: -0.4776/-0.0302\n",
      "Epoch: 015/100 | Batch 100/469 | Gen/Dis Loss: -0.4695/-0.0261\n",
      "Epoch: 015/100 | Batch 200/469 | Gen/Dis Loss: -0.4693/-0.0256\n",
      "Epoch: 015/100 | Batch 300/469 | Gen/Dis Loss: -0.4767/-0.0255\n",
      "Epoch: 015/100 | Batch 400/469 | Gen/Dis Loss: -0.4678/-0.0332\n",
      "Time elapsed: 5.83 min\n",
      "Epoch: 016/100 | Batch 000/469 | Gen/Dis Loss: -0.4956/-0.0297\n",
      "Epoch: 016/100 | Batch 100/469 | Gen/Dis Loss: -0.4908/-0.0268\n",
      "Epoch: 016/100 | Batch 200/469 | Gen/Dis Loss: -0.4912/-0.0340\n",
      "Epoch: 016/100 | Batch 300/469 | Gen/Dis Loss: -0.4956/-0.0251\n",
      "Epoch: 016/100 | Batch 400/469 | Gen/Dis Loss: -0.4845/-0.0261\n",
      "Time elapsed: 6.25 min\n",
      "Epoch: 017/100 | Batch 000/469 | Gen/Dis Loss: -0.4893/-0.0249\n",
      "Epoch: 017/100 | Batch 100/469 | Gen/Dis Loss: -0.5822/-0.0217\n",
      "Epoch: 017/100 | Batch 200/469 | Gen/Dis Loss: -0.5718/-0.0259\n",
      "Epoch: 017/100 | Batch 300/469 | Gen/Dis Loss: -0.5276/-0.0286\n",
      "Epoch: 017/100 | Batch 400/469 | Gen/Dis Loss: -0.5086/-0.0275\n",
      "Time elapsed: 6.66 min\n",
      "Epoch: 018/100 | Batch 000/469 | Gen/Dis Loss: -0.4989/-0.0311\n",
      "Epoch: 018/100 | Batch 100/469 | Gen/Dis Loss: -0.5234/-0.0319\n",
      "Epoch: 018/100 | Batch 200/469 | Gen/Dis Loss: -0.5394/-0.0270\n",
      "Epoch: 018/100 | Batch 300/469 | Gen/Dis Loss: -0.5005/-0.0349\n",
      "Epoch: 018/100 | Batch 400/469 | Gen/Dis Loss: -0.5188/-0.0220\n",
      "Time elapsed: 7.08 min\n",
      "Epoch: 019/100 | Batch 000/469 | Gen/Dis Loss: -0.4946/-0.0285\n",
      "Epoch: 019/100 | Batch 100/469 | Gen/Dis Loss: -0.4910/-0.0350\n",
      "Epoch: 019/100 | Batch 200/469 | Gen/Dis Loss: -0.4720/-0.0182\n",
      "Epoch: 019/100 | Batch 300/469 | Gen/Dis Loss: -0.5314/-0.0187\n",
      "Epoch: 019/100 | Batch 400/469 | Gen/Dis Loss: -0.5618/-0.0209\n",
      "Time elapsed: 7.53 min\n",
      "Epoch: 020/100 | Batch 000/469 | Gen/Dis Loss: -0.5783/-0.0256\n",
      "Epoch: 020/100 | Batch 100/469 | Gen/Dis Loss: -0.5525/-0.0228\n",
      "Epoch: 020/100 | Batch 200/469 | Gen/Dis Loss: -0.5736/-0.0266\n",
      "Epoch: 020/100 | Batch 300/469 | Gen/Dis Loss: -0.4980/-0.0308\n",
      "Epoch: 020/100 | Batch 400/469 | Gen/Dis Loss: -0.4961/-0.0350\n",
      "Time elapsed: 7.96 min\n",
      "Epoch: 021/100 | Batch 000/469 | Gen/Dis Loss: -0.5429/-0.0191\n",
      "Epoch: 021/100 | Batch 100/469 | Gen/Dis Loss: -0.5275/-0.0213\n",
      "Epoch: 021/100 | Batch 200/469 | Gen/Dis Loss: -0.5303/-0.0252\n",
      "Epoch: 021/100 | Batch 300/469 | Gen/Dis Loss: -0.4592/-0.0182\n",
      "Epoch: 021/100 | Batch 400/469 | Gen/Dis Loss: -0.5128/-0.0190\n",
      "Time elapsed: 8.43 min\n",
      "Epoch: 022/100 | Batch 000/469 | Gen/Dis Loss: -0.4715/-0.0122\n",
      "Epoch: 022/100 | Batch 100/469 | Gen/Dis Loss: -0.5320/-0.0266\n",
      "Epoch: 022/100 | Batch 200/469 | Gen/Dis Loss: -0.4862/-0.0167\n",
      "Epoch: 022/100 | Batch 300/469 | Gen/Dis Loss: -0.5002/-0.0237\n",
      "Epoch: 022/100 | Batch 400/469 | Gen/Dis Loss: -0.5403/-0.0212\n",
      "Time elapsed: 8.86 min\n",
      "Epoch: 023/100 | Batch 000/469 | Gen/Dis Loss: -0.5455/-0.0119\n",
      "Epoch: 023/100 | Batch 100/469 | Gen/Dis Loss: -0.5200/-0.0151\n",
      "Epoch: 023/100 | Batch 200/469 | Gen/Dis Loss: -0.5236/-0.0127\n",
      "Epoch: 023/100 | Batch 300/469 | Gen/Dis Loss: -0.5481/-0.0168\n",
      "Epoch: 023/100 | Batch 400/469 | Gen/Dis Loss: -0.5066/-0.0170\n",
      "Time elapsed: 9.30 min\n",
      "Epoch: 024/100 | Batch 000/469 | Gen/Dis Loss: -0.5268/-0.0196\n",
      "Epoch: 024/100 | Batch 100/469 | Gen/Dis Loss: -0.4793/-0.0167\n",
      "Epoch: 024/100 | Batch 200/469 | Gen/Dis Loss: -0.5123/-0.0136\n",
      "Epoch: 024/100 | Batch 300/469 | Gen/Dis Loss: -0.5148/-0.0137\n",
      "Epoch: 024/100 | Batch 400/469 | Gen/Dis Loss: -0.5363/-0.0178\n",
      "Time elapsed: 9.76 min\n",
      "Epoch: 025/100 | Batch 000/469 | Gen/Dis Loss: -0.4937/-0.0214\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 025/100 | Batch 100/469 | Gen/Dis Loss: -0.4897/-0.0172\n",
      "Epoch: 025/100 | Batch 200/469 | Gen/Dis Loss: -0.4789/-0.0209\n",
      "Epoch: 025/100 | Batch 300/469 | Gen/Dis Loss: -0.5076/-0.0200\n",
      "Epoch: 025/100 | Batch 400/469 | Gen/Dis Loss: -0.5344/-0.0172\n",
      "Time elapsed: 10.18 min\n",
      "Epoch: 026/100 | Batch 000/469 | Gen/Dis Loss: -0.4900/-0.0183\n",
      "Epoch: 026/100 | Batch 100/469 | Gen/Dis Loss: -0.5107/-0.0270\n",
      "Epoch: 026/100 | Batch 200/469 | Gen/Dis Loss: -0.5249/-0.0120\n",
      "Epoch: 026/100 | Batch 300/469 | Gen/Dis Loss: -0.4780/-0.0163\n",
      "Epoch: 026/100 | Batch 400/469 | Gen/Dis Loss: -0.4944/-0.0107\n",
      "Time elapsed: 10.58 min\n",
      "Epoch: 027/100 | Batch 000/469 | Gen/Dis Loss: -0.4933/-0.0299\n",
      "Epoch: 027/100 | Batch 100/469 | Gen/Dis Loss: -0.5313/-0.0224\n",
      "Epoch: 027/100 | Batch 200/469 | Gen/Dis Loss: -0.5083/-0.0265\n",
      "Epoch: 027/100 | Batch 300/469 | Gen/Dis Loss: -0.4586/-0.0282\n",
      "Epoch: 027/100 | Batch 400/469 | Gen/Dis Loss: -0.4534/-0.0440\n",
      "Time elapsed: 10.95 min\n",
      "Epoch: 028/100 | Batch 000/469 | Gen/Dis Loss: -0.6432/-0.0272\n",
      "Epoch: 028/100 | Batch 100/469 | Gen/Dis Loss: -0.4675/-0.0319\n",
      "Epoch: 028/100 | Batch 200/469 | Gen/Dis Loss: -0.8684/-0.0006\n",
      "Epoch: 028/100 | Batch 300/469 | Gen/Dis Loss: -0.6391/-0.0158\n",
      "Epoch: 028/100 | Batch 400/469 | Gen/Dis Loss: -0.5787/-0.0021\n",
      "Time elapsed: 11.32 min\n",
      "Epoch: 029/100 | Batch 000/469 | Gen/Dis Loss: -0.5992/-0.0009\n",
      "Epoch: 029/100 | Batch 100/469 | Gen/Dis Loss: -0.5669/-0.0040\n",
      "Epoch: 029/100 | Batch 200/469 | Gen/Dis Loss: -0.5275/-0.0032\n",
      "Epoch: 029/100 | Batch 300/469 | Gen/Dis Loss: -0.5525/-0.0002\n",
      "Epoch: 029/100 | Batch 400/469 | Gen/Dis Loss: -0.5858/-0.0004\n",
      "Time elapsed: 11.70 min\n",
      "Epoch: 030/100 | Batch 000/469 | Gen/Dis Loss: -0.5432/-0.0005\n",
      "Epoch: 030/100 | Batch 100/469 | Gen/Dis Loss: -0.5263/-0.0055\n",
      "Epoch: 030/100 | Batch 200/469 | Gen/Dis Loss: -0.5216/-0.0057\n",
      "Epoch: 030/100 | Batch 300/469 | Gen/Dis Loss: -0.5259/-0.0043\n",
      "Epoch: 030/100 | Batch 400/469 | Gen/Dis Loss: -0.5350/-0.0051\n",
      "Time elapsed: 12.07 min\n",
      "Epoch: 031/100 | Batch 000/469 | Gen/Dis Loss: -0.5195/-0.0049\n",
      "Epoch: 031/100 | Batch 100/469 | Gen/Dis Loss: -0.5047/-0.0116\n",
      "Epoch: 031/100 | Batch 200/469 | Gen/Dis Loss: -0.5167/-0.0169\n",
      "Epoch: 031/100 | Batch 300/469 | Gen/Dis Loss: -0.4523/-0.0201\n",
      "Epoch: 031/100 | Batch 400/469 | Gen/Dis Loss: -0.4771/-0.0177\n",
      "Time elapsed: 12.45 min\n",
      "Epoch: 032/100 | Batch 000/469 | Gen/Dis Loss: -0.4769/-0.0203\n",
      "Epoch: 032/100 | Batch 100/469 | Gen/Dis Loss: -0.4810/-0.0183\n",
      "Epoch: 032/100 | Batch 200/469 | Gen/Dis Loss: -0.4722/-0.0146\n",
      "Epoch: 032/100 | Batch 300/469 | Gen/Dis Loss: -0.5042/-0.0114\n",
      "Epoch: 032/100 | Batch 400/469 | Gen/Dis Loss: -0.4934/-0.0220\n",
      "Time elapsed: 12.83 min\n",
      "Epoch: 033/100 | Batch 000/469 | Gen/Dis Loss: -0.4802/-0.0254\n",
      "Epoch: 033/100 | Batch 100/469 | Gen/Dis Loss: -0.4937/-0.0178\n",
      "Epoch: 033/100 | Batch 200/469 | Gen/Dis Loss: -0.5788/-0.0135\n",
      "Epoch: 033/100 | Batch 300/469 | Gen/Dis Loss: -0.5094/-0.0538\n",
      "Epoch: 033/100 | Batch 400/469 | Gen/Dis Loss: -0.5574/-0.0301\n",
      "Time elapsed: 13.21 min\n",
      "Epoch: 034/100 | Batch 000/469 | Gen/Dis Loss: -0.5417/-0.0224\n",
      "Epoch: 034/100 | Batch 100/469 | Gen/Dis Loss: -0.4858/-0.0114\n",
      "Epoch: 034/100 | Batch 200/469 | Gen/Dis Loss: -0.4905/-0.0135\n",
      "Epoch: 034/100 | Batch 300/469 | Gen/Dis Loss: -0.5052/-0.0081\n",
      "Epoch: 034/100 | Batch 400/469 | Gen/Dis Loss: -0.5074/-0.0054\n",
      "Time elapsed: 13.59 min\n",
      "Epoch: 035/100 | Batch 000/469 | Gen/Dis Loss: -0.5122/-0.0050\n",
      "Epoch: 035/100 | Batch 100/469 | Gen/Dis Loss: -0.5215/-0.0047\n",
      "Epoch: 035/100 | Batch 200/469 | Gen/Dis Loss: -0.5277/-0.0050\n",
      "Epoch: 035/100 | Batch 300/469 | Gen/Dis Loss: -0.4891/-0.0057\n",
      "Epoch: 035/100 | Batch 400/469 | Gen/Dis Loss: -0.5101/-0.0092\n",
      "Time elapsed: 13.97 min\n",
      "Epoch: 036/100 | Batch 000/469 | Gen/Dis Loss: -0.5315/-0.0071\n",
      "Epoch: 036/100 | Batch 100/469 | Gen/Dis Loss: -0.5202/-0.0041\n",
      "Epoch: 036/100 | Batch 200/469 | Gen/Dis Loss: -0.5115/-0.0062\n",
      "Epoch: 036/100 | Batch 300/469 | Gen/Dis Loss: -0.5302/-0.0053\n",
      "Epoch: 036/100 | Batch 400/469 | Gen/Dis Loss: -0.5126/-0.0061\n",
      "Time elapsed: 14.35 min\n",
      "Epoch: 037/100 | Batch 000/469 | Gen/Dis Loss: -0.5123/-0.0052\n",
      "Epoch: 037/100 | Batch 100/469 | Gen/Dis Loss: -0.5146/-0.0045\n",
      "Epoch: 037/100 | Batch 200/469 | Gen/Dis Loss: -0.5258/-0.0056\n",
      "Epoch: 037/100 | Batch 300/469 | Gen/Dis Loss: -0.5168/-0.0050\n",
      "Epoch: 037/100 | Batch 400/469 | Gen/Dis Loss: -0.5178/-0.0046\n",
      "Time elapsed: 14.74 min\n",
      "Epoch: 038/100 | Batch 000/469 | Gen/Dis Loss: -0.5181/-0.0070\n",
      "Epoch: 038/100 | Batch 100/469 | Gen/Dis Loss: -0.5053/-0.0049\n",
      "Epoch: 038/100 | Batch 200/469 | Gen/Dis Loss: -0.5120/-0.0066\n",
      "Epoch: 038/100 | Batch 300/469 | Gen/Dis Loss: -0.5087/-0.0050\n",
      "Epoch: 038/100 | Batch 400/469 | Gen/Dis Loss: -0.5093/-0.0068\n",
      "Time elapsed: 15.14 min\n",
      "Epoch: 039/100 | Batch 000/469 | Gen/Dis Loss: -0.4967/-0.0066\n",
      "Epoch: 039/100 | Batch 100/469 | Gen/Dis Loss: -0.4943/-0.0073\n",
      "Epoch: 039/100 | Batch 200/469 | Gen/Dis Loss: -0.5031/-0.0105\n",
      "Epoch: 039/100 | Batch 300/469 | Gen/Dis Loss: -0.5175/-0.0198\n",
      "Epoch: 039/100 | Batch 400/469 | Gen/Dis Loss: -0.5162/-0.0088\n",
      "Time elapsed: 15.53 min\n",
      "Epoch: 040/100 | Batch 000/469 | Gen/Dis Loss: -0.5385/-0.0271\n",
      "Epoch: 040/100 | Batch 100/469 | Gen/Dis Loss: -0.4619/-0.0078\n",
      "Epoch: 040/100 | Batch 200/469 | Gen/Dis Loss: -0.4940/-0.0248\n",
      "Epoch: 040/100 | Batch 300/469 | Gen/Dis Loss: -0.3990/-0.0124\n",
      "Epoch: 040/100 | Batch 400/469 | Gen/Dis Loss: -0.5118/-0.0165\n",
      "Time elapsed: 15.93 min\n",
      "Epoch: 041/100 | Batch 000/469 | Gen/Dis Loss: -0.5165/-0.0456\n",
      "Epoch: 041/100 | Batch 100/469 | Gen/Dis Loss: -0.5266/-0.0516\n",
      "Epoch: 041/100 | Batch 200/469 | Gen/Dis Loss: -0.5221/-0.0169\n",
      "Epoch: 041/100 | Batch 300/469 | Gen/Dis Loss: -0.6721/-0.0259\n",
      "Epoch: 041/100 | Batch 400/469 | Gen/Dis Loss: -0.8406/-0.0057\n",
      "Time elapsed: 16.34 min\n",
      "Epoch: 042/100 | Batch 000/469 | Gen/Dis Loss: -0.7280/-0.0113\n",
      "Epoch: 042/100 | Batch 100/469 | Gen/Dis Loss: -0.5707/-0.0086\n",
      "Epoch: 042/100 | Batch 200/469 | Gen/Dis Loss: -0.5126/-0.0089\n",
      "Epoch: 042/100 | Batch 300/469 | Gen/Dis Loss: -0.5039/-0.0116\n",
      "Epoch: 042/100 | Batch 400/469 | Gen/Dis Loss: -0.4984/-0.0094\n",
      "Time elapsed: 16.73 min\n",
      "Epoch: 043/100 | Batch 000/469 | Gen/Dis Loss: -0.4867/-0.0083\n",
      "Epoch: 043/100 | Batch 100/469 | Gen/Dis Loss: -0.5094/-0.0081\n",
      "Epoch: 043/100 | Batch 200/469 | Gen/Dis Loss: -0.4987/-0.0107\n",
      "Epoch: 043/100 | Batch 300/469 | Gen/Dis Loss: -0.5052/-0.0058\n",
      "Epoch: 043/100 | Batch 400/469 | Gen/Dis Loss: -0.4962/-0.0103\n",
      "Time elapsed: 17.11 min\n",
      "Epoch: 044/100 | Batch 000/469 | Gen/Dis Loss: -0.5152/-0.0095\n",
      "Epoch: 044/100 | Batch 100/469 | Gen/Dis Loss: -0.4925/-0.0097\n",
      "Epoch: 044/100 | Batch 200/469 | Gen/Dis Loss: -0.4887/-0.0099\n",
      "Epoch: 044/100 | Batch 300/469 | Gen/Dis Loss: -0.4933/-0.0090\n",
      "Epoch: 044/100 | Batch 400/469 | Gen/Dis Loss: -0.5112/-0.0167\n",
      "Time elapsed: 17.53 min\n",
      "Epoch: 045/100 | Batch 000/469 | Gen/Dis Loss: -0.5010/-0.0126\n",
      "Epoch: 045/100 | Batch 100/469 | Gen/Dis Loss: -0.4410/-0.0137\n",
      "Epoch: 045/100 | Batch 200/469 | Gen/Dis Loss: -0.4763/-0.0187\n",
      "Epoch: 045/100 | Batch 300/469 | Gen/Dis Loss: -0.5032/-0.0123\n",
      "Epoch: 045/100 | Batch 400/469 | Gen/Dis Loss: -0.4835/-0.0116\n",
      "Time elapsed: 17.93 min\n",
      "Epoch: 046/100 | Batch 000/469 | Gen/Dis Loss: -0.5065/-0.0116\n",
      "Epoch: 046/100 | Batch 100/469 | Gen/Dis Loss: -0.4904/-0.0103\n",
      "Epoch: 046/100 | Batch 200/469 | Gen/Dis Loss: -0.4950/-0.0076\n",
      "Epoch: 046/100 | Batch 300/469 | Gen/Dis Loss: -0.5062/-0.0142\n",
      "Epoch: 046/100 | Batch 400/469 | Gen/Dis Loss: -0.5048/-0.0104\n",
      "Time elapsed: 18.32 min\n",
      "Epoch: 047/100 | Batch 000/469 | Gen/Dis Loss: -0.4762/-0.0076\n",
      "Epoch: 047/100 | Batch 100/469 | Gen/Dis Loss: -0.2966/-0.0166\n",
      "Epoch: 047/100 | Batch 200/469 | Gen/Dis Loss: -0.4663/-0.0335\n",
      "Epoch: 047/100 | Batch 300/469 | Gen/Dis Loss: -0.5350/-0.0204\n",
      "Epoch: 047/100 | Batch 400/469 | Gen/Dis Loss: -0.6554/-0.0246\n",
      "Time elapsed: 18.74 min\n",
      "Epoch: 048/100 | Batch 000/469 | Gen/Dis Loss: -0.3957/-0.0023\n",
      "Epoch: 048/100 | Batch 100/469 | Gen/Dis Loss: -0.6550/-0.0203\n",
      "Epoch: 048/100 | Batch 200/469 | Gen/Dis Loss: -0.6106/-0.0068\n",
      "Epoch: 048/100 | Batch 300/469 | Gen/Dis Loss: -0.7745/-0.0048\n",
      "Epoch: 048/100 | Batch 400/469 | Gen/Dis Loss: -0.6690/-0.0215\n",
      "Time elapsed: 19.15 min\n",
      "Epoch: 049/100 | Batch 000/469 | Gen/Dis Loss: -0.5566/-0.0245\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 049/100 | Batch 100/469 | Gen/Dis Loss: -0.5805/-0.0048\n",
      "Epoch: 049/100 | Batch 200/469 | Gen/Dis Loss: -0.4969/-0.0011\n",
      "Epoch: 049/100 | Batch 300/469 | Gen/Dis Loss: -0.4646/-0.0029\n",
      "Epoch: 049/100 | Batch 400/469 | Gen/Dis Loss: -0.4853/-0.0017\n",
      "Time elapsed: 19.57 min\n",
      "Epoch: 050/100 | Batch 000/469 | Gen/Dis Loss: -0.4902/-0.0007\n",
      "Epoch: 050/100 | Batch 100/469 | Gen/Dis Loss: -0.3506/0.0001\n",
      "Epoch: 050/100 | Batch 200/469 | Gen/Dis Loss: -0.5160/-0.0001\n",
      "Epoch: 050/100 | Batch 300/469 | Gen/Dis Loss: -0.5138/-0.0000\n",
      "Epoch: 050/100 | Batch 400/469 | Gen/Dis Loss: -0.5120/-0.0003\n",
      "Time elapsed: 19.96 min\n",
      "Epoch: 051/100 | Batch 000/469 | Gen/Dis Loss: -0.5095/0.0000\n",
      "Epoch: 051/100 | Batch 100/469 | Gen/Dis Loss: -0.5079/-0.0001\n",
      "Epoch: 051/100 | Batch 200/469 | Gen/Dis Loss: -0.5080/-0.0006\n",
      "Epoch: 051/100 | Batch 300/469 | Gen/Dis Loss: -0.5081/-0.0003\n",
      "Epoch: 051/100 | Batch 400/469 | Gen/Dis Loss: -0.5141/-0.0001\n",
      "Time elapsed: 20.36 min\n",
      "Epoch: 052/100 | Batch 000/469 | Gen/Dis Loss: -0.4970/0.0001\n",
      "Epoch: 052/100 | Batch 100/469 | Gen/Dis Loss: -0.4973/-0.0012\n",
      "Epoch: 052/100 | Batch 200/469 | Gen/Dis Loss: -0.5084/-0.0039\n",
      "Epoch: 052/100 | Batch 300/469 | Gen/Dis Loss: -0.5052/-0.0045\n",
      "Epoch: 052/100 | Batch 400/469 | Gen/Dis Loss: -0.4743/-0.0076\n",
      "Time elapsed: 20.76 min\n",
      "Epoch: 053/100 | Batch 000/469 | Gen/Dis Loss: -0.4936/-0.0023\n",
      "Epoch: 053/100 | Batch 100/469 | Gen/Dis Loss: -0.4921/-0.0057\n",
      "Epoch: 053/100 | Batch 200/469 | Gen/Dis Loss: -0.4752/-0.0061\n",
      "Epoch: 053/100 | Batch 300/469 | Gen/Dis Loss: -0.5151/-0.0063\n",
      "Epoch: 053/100 | Batch 400/469 | Gen/Dis Loss: -0.5257/-0.0071\n",
      "Time elapsed: 21.17 min\n",
      "Epoch: 054/100 | Batch 000/469 | Gen/Dis Loss: -0.4904/-0.0084\n",
      "Epoch: 054/100 | Batch 100/469 | Gen/Dis Loss: -0.5100/-0.0076\n",
      "Epoch: 054/100 | Batch 200/469 | Gen/Dis Loss: -0.4986/-0.0093\n",
      "Epoch: 054/100 | Batch 300/469 | Gen/Dis Loss: -0.4773/-0.0089\n",
      "Epoch: 054/100 | Batch 400/469 | Gen/Dis Loss: -0.4860/-0.0100\n",
      "Time elapsed: 21.58 min\n",
      "Epoch: 055/100 | Batch 000/469 | Gen/Dis Loss: -0.4009/-0.0256\n",
      "Epoch: 055/100 | Batch 100/469 | Gen/Dis Loss: -0.4816/-0.0210\n",
      "Epoch: 055/100 | Batch 200/469 | Gen/Dis Loss: -0.5696/-0.0139\n",
      "Epoch: 055/100 | Batch 300/469 | Gen/Dis Loss: -0.5447/-0.0138\n",
      "Epoch: 055/100 | Batch 400/469 | Gen/Dis Loss: -0.6307/-0.0453\n",
      "Time elapsed: 22.03 min\n",
      "Epoch: 056/100 | Batch 000/469 | Gen/Dis Loss: -0.4774/-0.0276\n",
      "Epoch: 056/100 | Batch 100/469 | Gen/Dis Loss: -0.4782/-0.0216\n",
      "Epoch: 056/100 | Batch 200/469 | Gen/Dis Loss: -0.6612/-0.0379\n",
      "Epoch: 056/100 | Batch 300/469 | Gen/Dis Loss: -0.8642/-0.0049\n",
      "Epoch: 056/100 | Batch 400/469 | Gen/Dis Loss: -0.9878/0.0007\n",
      "Time elapsed: 22.49 min\n",
      "Epoch: 057/100 | Batch 000/469 | Gen/Dis Loss: -0.9885/-0.0004\n",
      "Epoch: 057/100 | Batch 100/469 | Gen/Dis Loss: -0.8765/-0.0140\n",
      "Epoch: 057/100 | Batch 200/469 | Gen/Dis Loss: -0.5418/-0.0107\n",
      "Epoch: 057/100 | Batch 300/469 | Gen/Dis Loss: -0.5412/-0.0061\n",
      "Epoch: 057/100 | Batch 400/469 | Gen/Dis Loss: -0.5723/-0.0046\n",
      "Time elapsed: 22.88 min\n",
      "Epoch: 058/100 | Batch 000/469 | Gen/Dis Loss: -0.5562/0.0014\n",
      "Epoch: 058/100 | Batch 100/469 | Gen/Dis Loss: -0.5420/0.0014\n",
      "Epoch: 058/100 | Batch 200/469 | Gen/Dis Loss: -0.5106/-0.0002\n",
      "Epoch: 058/100 | Batch 300/469 | Gen/Dis Loss: -0.4839/-0.0005\n",
      "Epoch: 058/100 | Batch 400/469 | Gen/Dis Loss: -0.5376/0.0003\n",
      "Time elapsed: 23.30 min\n",
      "Epoch: 059/100 | Batch 000/469 | Gen/Dis Loss: -0.5046/-0.0013\n",
      "Epoch: 059/100 | Batch 100/469 | Gen/Dis Loss: -0.5063/-0.0019\n",
      "Epoch: 059/100 | Batch 200/469 | Gen/Dis Loss: -0.4879/-0.0017\n",
      "Epoch: 059/100 | Batch 300/469 | Gen/Dis Loss: -0.4393/-0.0038\n",
      "Epoch: 059/100 | Batch 400/469 | Gen/Dis Loss: -0.4969/-0.0004\n",
      "Time elapsed: 23.71 min\n",
      "Epoch: 060/100 | Batch 000/469 | Gen/Dis Loss: -0.4885/-0.0010\n",
      "Epoch: 060/100 | Batch 100/469 | Gen/Dis Loss: -0.5011/-0.0054\n",
      "Epoch: 060/100 | Batch 200/469 | Gen/Dis Loss: -0.5023/-0.0018\n",
      "Epoch: 060/100 | Batch 300/469 | Gen/Dis Loss: -0.5137/-0.0019\n",
      "Epoch: 060/100 | Batch 400/469 | Gen/Dis Loss: -0.5134/-0.0014\n",
      "Time elapsed: 24.12 min\n",
      "Epoch: 061/100 | Batch 000/469 | Gen/Dis Loss: -0.5262/-0.0014\n",
      "Epoch: 061/100 | Batch 100/469 | Gen/Dis Loss: -0.4805/-0.0017\n",
      "Epoch: 061/100 | Batch 200/469 | Gen/Dis Loss: -0.4843/0.0011\n",
      "Epoch: 061/100 | Batch 300/469 | Gen/Dis Loss: -0.4817/-0.0110\n",
      "Epoch: 061/100 | Batch 400/469 | Gen/Dis Loss: -0.4398/-0.0130\n",
      "Time elapsed: 24.52 min\n",
      "Epoch: 062/100 | Batch 000/469 | Gen/Dis Loss: -0.5468/0.0016\n",
      "Epoch: 062/100 | Batch 100/469 | Gen/Dis Loss: -0.5247/-0.0174\n",
      "Epoch: 062/100 | Batch 200/469 | Gen/Dis Loss: -0.4320/-0.0105\n",
      "Epoch: 062/100 | Batch 300/469 | Gen/Dis Loss: -0.4811/-0.0315\n",
      "Epoch: 062/100 | Batch 400/469 | Gen/Dis Loss: -0.5573/-0.0039\n",
      "Time elapsed: 24.92 min\n",
      "Epoch: 063/100 | Batch 000/469 | Gen/Dis Loss: -0.5888/-0.0104\n",
      "Epoch: 063/100 | Batch 100/469 | Gen/Dis Loss: -0.5825/-0.0203\n",
      "Epoch: 063/100 | Batch 200/469 | Gen/Dis Loss: -0.5411/-0.0028\n",
      "Epoch: 063/100 | Batch 300/469 | Gen/Dis Loss: -0.4813/-0.0154\n",
      "Epoch: 063/100 | Batch 400/469 | Gen/Dis Loss: -0.5333/0.0063\n",
      "Time elapsed: 25.32 min\n",
      "Epoch: 064/100 | Batch 000/469 | Gen/Dis Loss: -0.5743/-0.0026\n",
      "Epoch: 064/100 | Batch 100/469 | Gen/Dis Loss: -0.5741/0.0088\n",
      "Epoch: 064/100 | Batch 200/469 | Gen/Dis Loss: -0.5844/0.0068\n",
      "Epoch: 064/100 | Batch 300/469 | Gen/Dis Loss: -0.5924/-0.0192\n",
      "Epoch: 064/100 | Batch 400/469 | Gen/Dis Loss: -0.4625/-0.0014\n",
      "Time elapsed: 25.72 min\n",
      "Epoch: 065/100 | Batch 000/469 | Gen/Dis Loss: -0.5343/-0.0061\n",
      "Epoch: 065/100 | Batch 100/469 | Gen/Dis Loss: -0.5933/-0.0121\n",
      "Epoch: 065/100 | Batch 200/469 | Gen/Dis Loss: -0.5876/0.0089\n",
      "Epoch: 065/100 | Batch 300/469 | Gen/Dis Loss: -0.5985/-0.0024\n",
      "Epoch: 065/100 | Batch 400/469 | Gen/Dis Loss: -0.5278/-0.0024\n",
      "Time elapsed: 26.11 min\n",
      "Epoch: 066/100 | Batch 000/469 | Gen/Dis Loss: -0.5353/-0.0088\n",
      "Epoch: 066/100 | Batch 100/469 | Gen/Dis Loss: -0.5630/-0.0024\n",
      "Epoch: 066/100 | Batch 200/469 | Gen/Dis Loss: -0.4838/-0.0036\n",
      "Epoch: 066/100 | Batch 300/469 | Gen/Dis Loss: -0.4772/-0.0032\n",
      "Epoch: 066/100 | Batch 400/469 | Gen/Dis Loss: -0.4882/-0.0020\n",
      "Time elapsed: 26.51 min\n",
      "Epoch: 067/100 | Batch 000/469 | Gen/Dis Loss: -0.5508/-0.0027\n",
      "Epoch: 067/100 | Batch 100/469 | Gen/Dis Loss: -0.4932/-0.0009\n",
      "Epoch: 067/100 | Batch 200/469 | Gen/Dis Loss: -0.4569/-0.0031\n",
      "Epoch: 067/100 | Batch 300/469 | Gen/Dis Loss: -0.4333/-0.0050\n",
      "Epoch: 067/100 | Batch 400/469 | Gen/Dis Loss: -0.4867/-0.0015\n",
      "Time elapsed: 26.90 min\n",
      "Epoch: 068/100 | Batch 000/469 | Gen/Dis Loss: -0.4350/-0.0058\n",
      "Epoch: 068/100 | Batch 100/469 | Gen/Dis Loss: -0.4626/0.0011\n",
      "Epoch: 068/100 | Batch 200/469 | Gen/Dis Loss: -0.5437/-0.0060\n",
      "Epoch: 068/100 | Batch 300/469 | Gen/Dis Loss: -0.4088/0.0011\n",
      "Epoch: 068/100 | Batch 400/469 | Gen/Dis Loss: -0.3896/-0.0053\n",
      "Time elapsed: 27.30 min\n",
      "Epoch: 069/100 | Batch 000/469 | Gen/Dis Loss: -0.5121/0.0001\n",
      "Epoch: 069/100 | Batch 100/469 | Gen/Dis Loss: -0.5092/-0.0000\n",
      "Epoch: 069/100 | Batch 200/469 | Gen/Dis Loss: -0.5091/-0.0007\n",
      "Epoch: 069/100 | Batch 300/469 | Gen/Dis Loss: -0.5233/-0.0002\n",
      "Epoch: 069/100 | Batch 400/469 | Gen/Dis Loss: -0.5241/-0.0001\n",
      "Time elapsed: 27.70 min\n",
      "Epoch: 070/100 | Batch 000/469 | Gen/Dis Loss: -0.4980/0.0001\n",
      "Epoch: 070/100 | Batch 100/469 | Gen/Dis Loss: -0.4991/-0.0004\n",
      "Epoch: 070/100 | Batch 200/469 | Gen/Dis Loss: -0.4609/-0.0042\n",
      "Epoch: 070/100 | Batch 300/469 | Gen/Dis Loss: -0.4892/-0.0096\n",
      "Epoch: 070/100 | Batch 400/469 | Gen/Dis Loss: -0.5494/0.0003\n",
      "Time elapsed: 28.09 min\n",
      "Epoch: 071/100 | Batch 000/469 | Gen/Dis Loss: -0.5508/-0.0002\n",
      "Epoch: 071/100 | Batch 100/469 | Gen/Dis Loss: -0.5017/-0.0001\n",
      "Epoch: 071/100 | Batch 200/469 | Gen/Dis Loss: -0.5336/-0.0018\n",
      "Epoch: 071/100 | Batch 300/469 | Gen/Dis Loss: -0.5303/-0.0012\n",
      "Epoch: 071/100 | Batch 400/469 | Gen/Dis Loss: -0.5266/-0.0013\n",
      "Time elapsed: 28.49 min\n",
      "Epoch: 072/100 | Batch 000/469 | Gen/Dis Loss: -0.5330/0.0002\n",
      "Epoch: 072/100 | Batch 100/469 | Gen/Dis Loss: -0.4966/-0.0065\n",
      "Epoch: 072/100 | Batch 200/469 | Gen/Dis Loss: -0.4746/-0.0147\n",
      "Epoch: 072/100 | Batch 300/469 | Gen/Dis Loss: -0.5104/-0.0019\n",
      "Epoch: 072/100 | Batch 400/469 | Gen/Dis Loss: -0.5005/0.0018\n",
      "Time elapsed: 28.90 min\n",
      "Epoch: 073/100 | Batch 000/469 | Gen/Dis Loss: -0.6016/-0.0027\n",
      "Epoch: 073/100 | Batch 100/469 | Gen/Dis Loss: -0.5247/-0.0057\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 073/100 | Batch 200/469 | Gen/Dis Loss: -0.5092/-0.0071\n",
      "Epoch: 073/100 | Batch 300/469 | Gen/Dis Loss: -0.4956/-0.0072\n",
      "Epoch: 073/100 | Batch 400/469 | Gen/Dis Loss: -0.5074/-0.0128\n",
      "Time elapsed: 29.28 min\n",
      "Epoch: 074/100 | Batch 000/469 | Gen/Dis Loss: -0.4899/-0.0188\n",
      "Epoch: 074/100 | Batch 100/469 | Gen/Dis Loss: -0.4817/-0.0118\n",
      "Epoch: 074/100 | Batch 200/469 | Gen/Dis Loss: -0.4840/-0.0129\n",
      "Epoch: 074/100 | Batch 300/469 | Gen/Dis Loss: -0.4945/-0.0125\n",
      "Epoch: 074/100 | Batch 400/469 | Gen/Dis Loss: -0.4892/-0.0158\n",
      "Time elapsed: 29.66 min\n",
      "Epoch: 075/100 | Batch 000/469 | Gen/Dis Loss: -0.4843/-0.0078\n",
      "Epoch: 075/100 | Batch 100/469 | Gen/Dis Loss: -0.4824/-0.0110\n",
      "Epoch: 075/100 | Batch 200/469 | Gen/Dis Loss: -0.5119/-0.0134\n",
      "Epoch: 075/100 | Batch 300/469 | Gen/Dis Loss: -0.4799/-0.0075\n",
      "Epoch: 075/100 | Batch 400/469 | Gen/Dis Loss: -0.5003/-0.0145\n",
      "Time elapsed: 30.04 min\n",
      "Epoch: 076/100 | Batch 000/469 | Gen/Dis Loss: -0.4660/-0.0185\n",
      "Epoch: 076/100 | Batch 100/469 | Gen/Dis Loss: -0.4735/-0.0176\n",
      "Epoch: 076/100 | Batch 200/469 | Gen/Dis Loss: -0.4717/-0.0202\n",
      "Epoch: 076/100 | Batch 300/469 | Gen/Dis Loss: -0.4812/-0.0121\n",
      "Epoch: 076/100 | Batch 400/469 | Gen/Dis Loss: -0.4659/-0.0186\n",
      "Time elapsed: 30.42 min\n",
      "Epoch: 077/100 | Batch 000/469 | Gen/Dis Loss: -0.4789/-0.0125\n",
      "Epoch: 077/100 | Batch 100/469 | Gen/Dis Loss: -0.5083/-0.0227\n",
      "Epoch: 077/100 | Batch 200/469 | Gen/Dis Loss: -0.4473/-0.0195\n",
      "Epoch: 077/100 | Batch 300/469 | Gen/Dis Loss: -0.5125/-0.0216\n",
      "Epoch: 077/100 | Batch 400/469 | Gen/Dis Loss: -0.4990/-0.0147\n",
      "Time elapsed: 30.81 min\n",
      "Epoch: 078/100 | Batch 000/469 | Gen/Dis Loss: -0.5243/-0.0158\n",
      "Epoch: 078/100 | Batch 100/469 | Gen/Dis Loss: -0.4937/-0.0152\n",
      "Epoch: 078/100 | Batch 200/469 | Gen/Dis Loss: -0.4729/-0.0077\n",
      "Epoch: 078/100 | Batch 300/469 | Gen/Dis Loss: -0.4753/-0.0105\n",
      "Epoch: 078/100 | Batch 400/469 | Gen/Dis Loss: -0.4978/-0.0087\n",
      "Time elapsed: 31.19 min\n",
      "Epoch: 079/100 | Batch 000/469 | Gen/Dis Loss: -0.5272/-0.0150\n",
      "Epoch: 079/100 | Batch 100/469 | Gen/Dis Loss: -0.5091/-0.0126\n",
      "Epoch: 079/100 | Batch 200/469 | Gen/Dis Loss: -0.5343/-0.0117\n",
      "Epoch: 079/100 | Batch 300/469 | Gen/Dis Loss: -0.6154/-0.0213\n",
      "Epoch: 079/100 | Batch 400/469 | Gen/Dis Loss: -0.6042/-0.0058\n",
      "Time elapsed: 31.57 min\n",
      "Epoch: 080/100 | Batch 000/469 | Gen/Dis Loss: -0.6652/0.0076\n",
      "Epoch: 080/100 | Batch 100/469 | Gen/Dis Loss: -0.5293/0.0055\n",
      "Epoch: 080/100 | Batch 200/469 | Gen/Dis Loss: -0.8105/-0.0212\n",
      "Epoch: 080/100 | Batch 300/469 | Gen/Dis Loss: -0.7896/0.0009\n",
      "Epoch: 080/100 | Batch 400/469 | Gen/Dis Loss: -0.6597/-0.0314\n",
      "Time elapsed: 31.95 min\n",
      "Epoch: 081/100 | Batch 000/469 | Gen/Dis Loss: -0.5702/-0.0304\n",
      "Epoch: 081/100 | Batch 100/469 | Gen/Dis Loss: -0.7924/0.0043\n",
      "Epoch: 081/100 | Batch 200/469 | Gen/Dis Loss: -0.6162/-0.0323\n",
      "Epoch: 081/100 | Batch 300/469 | Gen/Dis Loss: -0.6788/-0.0143\n",
      "Epoch: 081/100 | Batch 400/469 | Gen/Dis Loss: -0.6261/-0.0208\n",
      "Time elapsed: 32.34 min\n",
      "Epoch: 082/100 | Batch 000/469 | Gen/Dis Loss: -0.5936/-0.0018\n",
      "Epoch: 082/100 | Batch 100/469 | Gen/Dis Loss: -0.5520/-0.0088\n",
      "Epoch: 082/100 | Batch 200/469 | Gen/Dis Loss: -0.5282/-0.0082\n",
      "Epoch: 082/100 | Batch 300/469 | Gen/Dis Loss: -0.5658/-0.0100\n",
      "Epoch: 082/100 | Batch 400/469 | Gen/Dis Loss: -0.5301/-0.0092\n",
      "Time elapsed: 32.73 min\n",
      "Epoch: 083/100 | Batch 000/469 | Gen/Dis Loss: -0.5323/-0.0118\n",
      "Epoch: 083/100 | Batch 100/469 | Gen/Dis Loss: -0.4923/-0.0105\n",
      "Epoch: 083/100 | Batch 200/469 | Gen/Dis Loss: -0.6261/-0.0001\n",
      "Epoch: 083/100 | Batch 300/469 | Gen/Dis Loss: -0.4404/-0.0026\n",
      "Epoch: 083/100 | Batch 400/469 | Gen/Dis Loss: -0.5073/-0.0037\n",
      "Time elapsed: 33.12 min\n",
      "Epoch: 084/100 | Batch 000/469 | Gen/Dis Loss: -0.4934/-0.0033\n",
      "Epoch: 084/100 | Batch 100/469 | Gen/Dis Loss: -0.5144/-0.0022\n",
      "Epoch: 084/100 | Batch 200/469 | Gen/Dis Loss: -0.5199/-0.0033\n",
      "Epoch: 084/100 | Batch 300/469 | Gen/Dis Loss: -0.5271/-0.0016\n",
      "Epoch: 084/100 | Batch 400/469 | Gen/Dis Loss: -0.5176/-0.0015\n",
      "Time elapsed: 33.51 min\n",
      "Epoch: 085/100 | Batch 000/469 | Gen/Dis Loss: -0.5246/-0.0026\n",
      "Epoch: 085/100 | Batch 100/469 | Gen/Dis Loss: -0.5164/-0.0008\n",
      "Epoch: 085/100 | Batch 200/469 | Gen/Dis Loss: -0.5173/0.0002\n",
      "Epoch: 085/100 | Batch 300/469 | Gen/Dis Loss: -0.5149/-0.0001\n",
      "Epoch: 085/100 | Batch 400/469 | Gen/Dis Loss: -0.5213/-0.0013\n",
      "Time elapsed: 33.90 min\n",
      "Epoch: 086/100 | Batch 000/469 | Gen/Dis Loss: -0.5010/-0.0017\n",
      "Epoch: 086/100 | Batch 100/469 | Gen/Dis Loss: -0.4825/0.0001\n",
      "Epoch: 086/100 | Batch 200/469 | Gen/Dis Loss: -0.5327/0.0011\n",
      "Epoch: 086/100 | Batch 300/469 | Gen/Dis Loss: -0.5231/0.0001\n",
      "Epoch: 086/100 | Batch 400/469 | Gen/Dis Loss: -0.5127/-0.0011\n",
      "Time elapsed: 34.29 min\n",
      "Epoch: 087/100 | Batch 000/469 | Gen/Dis Loss: -0.5145/0.0005\n",
      "Epoch: 087/100 | Batch 100/469 | Gen/Dis Loss: -0.4806/0.0001\n",
      "Epoch: 087/100 | Batch 200/469 | Gen/Dis Loss: -0.5241/-0.0003\n",
      "Epoch: 087/100 | Batch 300/469 | Gen/Dis Loss: -0.4117/-0.0013\n",
      "Epoch: 087/100 | Batch 400/469 | Gen/Dis Loss: -0.3575/-0.0095\n",
      "Time elapsed: 34.68 min\n",
      "Epoch: 088/100 | Batch 000/469 | Gen/Dis Loss: -0.3438/-0.0093\n",
      "Epoch: 088/100 | Batch 100/469 | Gen/Dis Loss: -0.4464/-0.0222\n",
      "Epoch: 088/100 | Batch 200/469 | Gen/Dis Loss: -0.4887/-0.0234\n",
      "Epoch: 088/100 | Batch 300/469 | Gen/Dis Loss: -0.5331/-0.0119\n",
      "Epoch: 088/100 | Batch 400/469 | Gen/Dis Loss: -0.4893/-0.0092\n",
      "Time elapsed: 35.07 min\n",
      "Epoch: 089/100 | Batch 000/469 | Gen/Dis Loss: -0.5525/-0.0183\n",
      "Epoch: 089/100 | Batch 100/469 | Gen/Dis Loss: -0.5284/-0.0121\n",
      "Epoch: 089/100 | Batch 200/469 | Gen/Dis Loss: -0.4954/-0.0137\n",
      "Epoch: 089/100 | Batch 300/469 | Gen/Dis Loss: -0.5276/0.0010\n",
      "Epoch: 089/100 | Batch 400/469 | Gen/Dis Loss: -0.5322/-0.0005\n",
      "Time elapsed: 35.46 min\n",
      "Epoch: 090/100 | Batch 000/469 | Gen/Dis Loss: -0.5235/0.0000\n",
      "Epoch: 090/100 | Batch 100/469 | Gen/Dis Loss: -0.5204/-0.0008\n",
      "Epoch: 090/100 | Batch 200/469 | Gen/Dis Loss: -0.5261/-0.0007\n",
      "Epoch: 090/100 | Batch 300/469 | Gen/Dis Loss: -0.5675/0.0019\n",
      "Epoch: 090/100 | Batch 400/469 | Gen/Dis Loss: -0.4958/0.0003\n",
      "Time elapsed: 35.88 min\n",
      "Epoch: 091/100 | Batch 000/469 | Gen/Dis Loss: -0.4955/-0.0005\n",
      "Epoch: 091/100 | Batch 100/469 | Gen/Dis Loss: -0.4996/-0.0002\n",
      "Epoch: 091/100 | Batch 200/469 | Gen/Dis Loss: -0.5044/-0.0001\n",
      "Epoch: 091/100 | Batch 300/469 | Gen/Dis Loss: -0.5035/0.0000\n",
      "Epoch: 091/100 | Batch 400/469 | Gen/Dis Loss: -0.4984/-0.0001\n",
      "Time elapsed: 36.30 min\n",
      "Epoch: 092/100 | Batch 000/469 | Gen/Dis Loss: -0.4988/-0.0000\n",
      "Epoch: 092/100 | Batch 100/469 | Gen/Dis Loss: -0.4985/0.0000\n",
      "Epoch: 092/100 | Batch 200/469 | Gen/Dis Loss: -0.5307/-0.0006\n",
      "Epoch: 092/100 | Batch 300/469 | Gen/Dis Loss: -0.4864/0.0022\n",
      "Epoch: 092/100 | Batch 400/469 | Gen/Dis Loss: -0.5250/-0.0007\n",
      "Time elapsed: 36.73 min\n",
      "Epoch: 093/100 | Batch 000/469 | Gen/Dis Loss: -0.4800/-0.0002\n",
      "Epoch: 093/100 | Batch 100/469 | Gen/Dis Loss: -0.5519/0.0021\n",
      "Epoch: 093/100 | Batch 200/469 | Gen/Dis Loss: -0.5038/-0.0054\n",
      "Epoch: 093/100 | Batch 300/469 | Gen/Dis Loss: -0.5147/-0.0020\n",
      "Epoch: 093/100 | Batch 400/469 | Gen/Dis Loss: -0.4985/0.0009\n",
      "Time elapsed: 37.17 min\n",
      "Epoch: 094/100 | Batch 000/469 | Gen/Dis Loss: -0.5034/-0.0020\n",
      "Epoch: 094/100 | Batch 100/469 | Gen/Dis Loss: -0.5194/-0.0005\n",
      "Epoch: 094/100 | Batch 200/469 | Gen/Dis Loss: -0.5034/-0.0010\n",
      "Epoch: 094/100 | Batch 300/469 | Gen/Dis Loss: -0.5182/-0.0004\n",
      "Epoch: 094/100 | Batch 400/469 | Gen/Dis Loss: -0.5205/-0.0008\n",
      "Time elapsed: 37.58 min\n",
      "Epoch: 095/100 | Batch 000/469 | Gen/Dis Loss: -0.5015/-0.0001\n",
      "Epoch: 095/100 | Batch 100/469 | Gen/Dis Loss: -0.5009/-0.0002\n",
      "Epoch: 095/100 | Batch 200/469 | Gen/Dis Loss: -0.5061/-0.0005\n",
      "Epoch: 095/100 | Batch 300/469 | Gen/Dis Loss: -0.4932/-0.0001\n",
      "Epoch: 095/100 | Batch 400/469 | Gen/Dis Loss: -0.4940/-0.0006\n",
      "Time elapsed: 37.99 min\n",
      "Epoch: 096/100 | Batch 000/469 | Gen/Dis Loss: -0.5101/-0.0002\n",
      "Epoch: 096/100 | Batch 100/469 | Gen/Dis Loss: -0.4951/0.0000\n",
      "Epoch: 096/100 | Batch 200/469 | Gen/Dis Loss: -0.4882/0.0002\n",
      "Epoch: 096/100 | Batch 300/469 | Gen/Dis Loss: -0.4996/0.0001\n",
      "Epoch: 096/100 | Batch 400/469 | Gen/Dis Loss: -0.4922/-0.0011\n",
      "Time elapsed: 38.38 min\n",
      "Epoch: 097/100 | Batch 000/469 | Gen/Dis Loss: -0.5052/-0.0007\n",
      "Epoch: 097/100 | Batch 100/469 | Gen/Dis Loss: -0.4850/-0.0001\n",
      "Epoch: 097/100 | Batch 200/469 | Gen/Dis Loss: -0.5573/-0.0014\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 097/100 | Batch 300/469 | Gen/Dis Loss: -0.5280/-0.0192\n",
      "Epoch: 097/100 | Batch 400/469 | Gen/Dis Loss: -0.4999/-0.0162\n",
      "Time elapsed: 38.79 min\n",
      "Epoch: 098/100 | Batch 000/469 | Gen/Dis Loss: -0.4930/-0.0137\n",
      "Epoch: 098/100 | Batch 100/469 | Gen/Dis Loss: -0.6459/-0.0083\n",
      "Epoch: 098/100 | Batch 200/469 | Gen/Dis Loss: -0.5762/-0.0231\n",
      "Epoch: 098/100 | Batch 300/469 | Gen/Dis Loss: -0.4556/-0.0008\n",
      "Epoch: 098/100 | Batch 400/469 | Gen/Dis Loss: -0.4892/0.0092\n",
      "Time elapsed: 39.19 min\n",
      "Epoch: 099/100 | Batch 000/469 | Gen/Dis Loss: -0.4902/-0.0144\n",
      "Epoch: 099/100 | Batch 100/469 | Gen/Dis Loss: -0.6229/-0.0089\n",
      "Epoch: 099/100 | Batch 200/469 | Gen/Dis Loss: -0.4423/-0.0004\n",
      "Epoch: 099/100 | Batch 300/469 | Gen/Dis Loss: -0.5366/-0.0031\n",
      "Epoch: 099/100 | Batch 400/469 | Gen/Dis Loss: -0.5097/-0.0051\n",
      "Time elapsed: 39.58 min\n",
      "Epoch: 100/100 | Batch 000/469 | Gen/Dis Loss: -0.4751/-0.0008\n",
      "Epoch: 100/100 | Batch 100/469 | Gen/Dis Loss: -0.5304/-0.0053\n",
      "Epoch: 100/100 | Batch 200/469 | Gen/Dis Loss: -0.5224/-0.0027\n",
      "Epoch: 100/100 | Batch 300/469 | Gen/Dis Loss: -0.5590/-0.0048\n",
      "Epoch: 100/100 | Batch 400/469 | Gen/Dis Loss: -0.5527/-0.0030\n",
      "Time elapsed: 39.98 min\n",
      "Total Training Time: 39.98 min\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()    \n",
    "\n",
    "discr_costs = []\n",
    "gener_costs = []\n",
    "for epoch in range(NUM_EPOCHS):\n",
    "    model = model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "\n",
    "        \n",
    "        \n",
    "        features = (features - 0.5)*2.\n",
    "        features = features.view(-1, IMG_SIZE).to(device) \n",
    "        targets = targets.to(device)\n",
    "\n",
    "       \n",
    "        # WGAN\n",
    "        valid = -(torch.ones(targets.size(0)).float()).to(device)\n",
    "        fake = torch.ones(targets.size(0)).float().to(device)\n",
    "        \n",
    "\n",
    "        ### FORWARD AND BACK PROP\n",
    "        \n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Generator\n",
    "        # --------------------------\n",
    "        \n",
    "        # Make new images\n",
    "        z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "        generated_features = model.generator_forward(z)\n",
    "        \n",
    "        # Loss for fooling the discriminator\n",
    "        discr_pred = model.discriminator_forward(generated_features)\n",
    "        \n",
    "        \n",
    "        # Regular GAN:\n",
    "        # gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid)\n",
    "        \n",
    "        # WGAN:\n",
    "        gener_loss = wasserstein_loss(valid, discr_pred)\n",
    "        \n",
    "        optim_gener.zero_grad()\n",
    "        gener_loss.backward()\n",
    "        optim_gener.step()\n",
    "        \n",
    "        # --------------------------\n",
    "        # Train Discriminator\n",
    "        # --------------------------        \n",
    "\n",
    "        \n",
    "        # WGAN: 5 loops for discriminator\n",
    "        for _ in range(num_iter_critic):\n",
    "        \n",
    "            discr_pred_real = model.discriminator_forward(features.view(-1, IMG_SIZE))\n",
    "            # Regular GAN:\n",
    "            # real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid)\n",
    "            # WGAN:\n",
    "            real_loss = wasserstein_loss(valid, discr_pred_real)\n",
    "\n",
    "            discr_pred_fake = model.discriminator_forward(generated_features.detach())\n",
    "\n",
    "            # Regular GAN:\n",
    "            # fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)\n",
    "            # WGAN:\n",
    "            fake_loss = wasserstein_loss(fake, discr_pred_fake)\n",
    "\n",
    "            # Regular GAN:\n",
    "            discr_loss = (real_loss + fake_loss)\n",
    "            # WGAN:\n",
    "            #discr_loss = -(real_loss - fake_loss)\n",
    "\n",
    "            optim_discr.zero_grad()\n",
    "            discr_loss.backward()\n",
    "            optim_discr.step()        \n",
    "\n",
    "            # WGAN:\n",
    "            for p in model.discriminator.parameters():\n",
    "                p.data.clamp_(-weight_clip_value, weight_clip_value)\n",
    "\n",
    "        \n",
    "        discr_costs.append(discr_loss.item())\n",
    "        gener_costs.append(gener_loss.item())\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 100:\n",
    "            print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
    "                   %(epoch+1, NUM_EPOCHS, batch_idx, \n",
    "                     len(train_loader), gener_loss, discr_loss))\n",
    "\n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.plot(range(len(gener_costs)), gener_costs, label='Generator loss')\n",
    "ax1.plot(range(len(discr_costs)), discr_costs, label='Discriminator loss')\n",
    "ax1.set_xlabel('Iterations')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.legend()\n",
    "\n",
    "###################\n",
    "# Set scond x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(NUM_EPOCHS+1))\n",
    "iter_per_epoch = len(train_loader)\n",
    "newpos = [e*iter_per_epoch for e in newlabel]\n",
    "\n",
    "ax2.set_xticklabels(newlabel[::10])\n",
    "ax2.set_xticks(newpos[::10])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "###################\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABH4AAACACAYAAAB9Yq5jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO2de7BeVXn/v1sLrQKVkAAJSSCBBEgChIQQw7WAprHWCp0OIrWAU3ub0dE62sH+GKdjb6O2OC3TTme8oO3UQvFSoTQKFghgBU1AEwghF0gIgRhCg0Aoirb790cOy+96ct4377m9Z+91Pp+ZDM971jr7XWc/+1l77c36Pk9V17UAAAAAAAAAAKA8XjPeAwAAAAAAAAAAgLGBFz8AAAAAAAAAAIXCix8AAAAAAAAAgELhxQ8AAAAAAAAAQKHw4gcAAAAAAAAAoFB48QMAAAAAAAAAUCgjevFTVdVbqqraWFXVlqqqPjJag4L+gh/bDz4sA/zYfvBhGeDH9oMPywA/th98WAb4sf1UdV0P7xer6rWSNklaLmmHpNWSLq/r+pHRGx6MNfix/eDDMsCP7QcflgF+bD/4sAzwY/vBh2WAH8vg50bwu0slbanr+nFJqqrqRkkXS+p4AUyZMqWeNWvWCL4ShsO2bdv07LPPVh2ah+RHfDh+PPDAA8/WdX3kIE3EYksgFsuAWGw/xGIZEIvth1gsA2Kx/RCLZdAlFkf04me6pCft8w5Jb+z2C7NmzdKaNWtG8JUwHJYsWdKteUh+xIfjR1VVT3RoIhZbArFYBsRi+yEWy4BYbD/EYhkQi+2HWCyDLrE4ohw/g70R3E83VlXV71VVtaaqqjW7d+8ewdfBGHFAP+LDxkMslgGx2H6IxTIgFtsPsVgGxGL7IRbLgFgsgJG8+NkhaaZ9niHp6diprutP13W9pK7rJUceOeiuIxhfDuhHfNh4iMUyIBbbD7FYBsRi+yEWy4BYbD/EYhkQiwUwkhc/qyXNrapqdlVVB0t6p6RbRmdY0EfwY/vBh2WAH9sPPiwD/Nh+8GEZ4Mf2gw/LAD8WwLBz/NR1/dOqqt4n6TZJr5V0fV3X60dtZNAX8GP7wYdlgB/bDz4sA/zYfvBhGeDH9oMPywA/lsFIkjurruuVklaO0lhgnMCP7QcflgF+bD/4sAzwY/vBh2WAH9sPPiwD/Nh+RvTip63U9c9yUf3f//1fx37eFvv5Zz+eJFXVz/JfveY1rxn055Gf+7ncFf57MDS6+aZTv2508yF+golIpzn0f/7nf7J+P/zhDwf9/Tjf/e///m+yY0y97nWvS/brX//6ZL/2ta/N+vnnbnMtAAAAwFDo9jwxGr/n65a4hvFjsL4ZX6I//XMbngmbP0IAAAAAAAAAABgWvPgBAAAAAAAAACiUoqRe3SRc/vmnP/1psn/0ox9l/X7yk58c8NiS9OMf/zjZcWtXlCC8Stye5xKGKH046KCDBrXZ4rcPl4a4P6Xch+63V155Jevnbb/wC7+QtXWSd7kvpPy6in7HV9Bm/Np++eWXs7bHH3882Tt27Ej2iy++mPXz2HzmmWcG/bkkHXLIIck+/PDDs7YTTzwx2dOnT0/2oYcemvU7+OCDk410FgAAAMaKTukiuj1/Dve5wNcwcT3D+mb08Wd8Sdq7d2+y4zrX18ALFixI9hve8IasX1P81IxRAAAAAAAAAADAqMOLHwAAAAAAAACAQuHFDwAAAAAAAABAobQux4/nZYl5IjzvSywr7J+fe+65ZMdyw66/9ON5/og4jngM/z3PNzNp0qSsn+eo8Hw/kvSLv/iLyT7ssMMG/R1p/5wzJRH9+9JLLyX76aefTvb27duzfq6/dF1mzOfkRO3ltGnTkn3yyScn+4gjjsj6eW6g6Au/Rjz/T8w/MtHw8+KxEnMwecz6OYvnuZtu1uPZfRBzdvUaRyXnbYrx9tRTTyX7wQcfzNpWr16d7K1btyY7zoX+2f0bNfAzZsxI9rx587K2KVOmJNvjL8ZRt5Kpna6DpmiuJzI+R0vSww8/nOyFCxdmbfE+CaOPz8mRTvkLASDH4yje7zZu3JhsX0MeffTRWT9f75e89mganfLFxjWS+6RbufVen039GSc+c/o6KK51Xv/61yfb10Wsb4aGn9d4jt033/3ud7O2//7v/062n3Nf10r5c+V4PrtzVQAAAAAAAAAAFAovfgAAAAAAAAAACqUVmpNOJbljSbVnn3022bt27cradu/eneydO3cm27dvSfm2Pt+KFcu8e1uUEPnxfdvd7NmzOx5j6tSpWZvLwnz7Z5QaTZ48Odk///M/r7YRt5V7Cb1HH300a1u5cmWyXd61ZcuWrJ9LVF544YVkxy2Wfi3FbZXz589P9gUXXJDsSy65JOvnvonHcImet8VtoCVsn49+9HjZtm1b1rZq1apku4QoyoR8C7THeow3/+5YCvyyyy5L9qmnnprs448/fr+/YSLifvJy65J0zz33JPuOO+7I2jrJu5544omsn8dzp+3TUn6NxHH4HOpb36M00OfGuMW5k7wrbudlO33/ifLlZcuWjdNIJg4ef3Ft43GFtG5iEa8Fnyvj3IiMpDu+TvnqV7+atfma1Z9Nvve972X9XBri/STp4osvTvbv/M7vJDuWkOaeto94r/c5MK4l3Hcvv/xysp9//vmsn69Ru82Vvg6Kz63+LOPPo/HZ1NevngokjsvbjjzyyKyfr4+5Lvbhzw++lr3++uuzfi7vcmmXJE2fPn3Q40Xpps+v3daoYw0zNwAAAAAAAABAofDiBwAAAAAAAACgUFoh9fJM6r7tLkpDfPuky76kXH7lW+u6VYLxrVguGZK6y0u84pRv/4vf5ds4PWO7lMuS9uzZk2zP3i7lW8ei1KipW/ncn1F+5b65/fbbs7ZHHnkk2U8++eSgvyPlvnI7yks6jUnK5Sbu6yhlcald9KHTVF+MBD9nfo1K0rp165L95S9/OWtzeZfHczx/vmV106ZNyfZYlvKts3FrpY/xgx/8YLI99qSJVT3D48Dn0LVr12b91qxZk2yXdkl5zPkx4jZm/65u1Sa8LW67/sEPfpBsr/jkUkpJOvHEE5N93HHHZW0ug+0mXyjd9wAHwuULJciQ24ivOb75zW9mbS7BPeGEE5K9ZMmSrJ/fP6Mfv/GNbwxqxyo073rXu5K9ePHirA2pV3d87XHRRRdlbW9/+9uT7RKu97///Vm/W2+9ddDjSfn92Z85rr322mGOuGzi+t+fDXwdKuXSc4+/uG70dYWnD4jrCJd6xWfTzZs3J9tTlMR49hQW8TnQ10z+7BLj3te5JVeE7kaMow0bNiT7uuuuS/Ztt92W9fPzddppp2VtCxYsSPa5556b7Ph86+vj8ZRRM3MDAAAAAAAAABQKL34AAAAAAAAAAAqFFz8AAAAAAAAAAIXSyBw/MReO651dnxdzQbhO87nnnsvaXFfp/aK200shHnXUUcmO+XNcL+r9pFzXd8ghhyTbtfNSrvGLba7h9LZYNjt+biruU89L1E136/lzpPw8u9+iZtaP4Xlj4vXi59VLJUrS6aefnuy5c+cmO+YOmTp1arJd7yvlOt9Odtvw+PPz7nlYJGn16tXJdn2zlF/3J598crLjuXVN8+WXX57sHTt2ZP289KaX9Jby6yvGutNmnwwVjz+PlRhHnrcplq/0c+7Hi6VkPf5OOumkZHtMSdKkSZOS7Tp3Kfebl7udM2dO1s/163EcPpd4PqF4r/HPE+ma6Acei6XMh00mXtu+LvFzPtxcA8TKyIj+8bn4wgsvTLbnoJDyudLvmQ888EDWz+fb6OOPfvSjyfZS0H5sKc9b4msiGBz3abf7keP3N7+XSvlzR2zze/C///u/J/uv/uqvsn7k6Rocf3bynD6SdPfddyd748aNyf72t7+d9Yu5Xl7Fc29JeT5Cvy6kPMeP+yqux3yOdd9L+fOiP3O6LXXPaVsa/rf6c8tDDz2U9fNnFb8OYsxeeeWVyT7vvPOytpkzZybbnwM991Zsi77o5z2UHT8AAAAAAAAAAIXCix8AAAAAAAAAgEJppNQryn98S57LRnyL6oF46aWXku1brHwrqyTNnz9/0N+PMiHfuhfLyvvxvaTmsccem/Xztigl8y163hYlYb49bDy3jg0FH1c8r72eLz/G4YcfnvVz+ZBLPOK2O98mHcuAdxpH3P7nW6hjedMSt1V6bHosxjL3Rx55ZLK9XKmUb0F3qZyXOJXyWPRtzvF4Tz75ZLJjSXLfWjlr1qxkx3KYEwmPHS9R2U1G6vIoqXOp4NmzZ2f9VqxYkWwvTxrlf+4nL1Mr5TJCl589+OCDWT+XssS5w7dXd9tuCyPD4zT6x++Zfh+L92DoTrxmXSri83OMZ1+nPP7448k+++yzs34ezzHunW5rD/9M2e/BiXOgy5nXr1+f7Fh+2O9/Xl560aJFWb/f+I3fSPaWLVuyNi8r3Gl9LeWSsyhlh/3xmOhVYuVrJfeZJK1atWrQY0t5jLnEPUrCJrLUy89Rt+dKX1dI0p133pnsvXv3Jjs+H5555pnJPuaYY5Lt0h8pv/fF51Y/hvsurlFdhhlTGjgep1FWNpHmYo8Xf/7/j//4j6zfP/zDPyTbY2Xp0qVZv1//9V9Pdlzn+nXWSVLdbXz9ZuJcBQAAAAAAAAAAEwxe/AAAAAAAAAAAFAovfgAAAAAAAAAACqWROX4irp/rVr7c9Ysx745rLF2L6Xk/pFxv63q/WBbPy75FXXSnHDaxVKaXJ496P9ftex6OqLP2tqbm9JHysXnOIs8VIuXnOZZz9zLefs6j3tWP6fr4qLt1XW/UwrpvfEyxLOpE00/79ebn3fO3SHn575iXyvMSuE9iXhbPL+H6bI9lSbr55puT/cwzz2Rty5YtS7ZfTxPNb06nec3nPikvlx7zBnhfj52ofV6wYEGyp0+fnuyYO+S5555Ldox7L2vqWu04Jo/nqKOPObxehRw/IyPej//u7/4u2f/2b/+Wtfn973d/93eTTY6fA+Pzn+e8kqRrrrkm2evWrUt2vN+dddZZyZ4yZUqy582bl/VzP3XL8dNpfJJ0++23J/vUU0/N2mbMmNHTMUvE55utW7dmbd/61reS7evGmP/x0ksvTfZnP/vZZMf7rNMtJ4XHcMwrMnfu3I7HhNHB18b33Xdf1ub36pjrya+Lv//7v092t+tgIhPP365du5K9cuXKrM3XHL5GijlyPOen5xaNzxMebzGvqR/f5/a4Hvb1dnwe9bwyTpy/J1KOH8ffB/g8K+Xnzu+ZH/vYx7J+vk6Jz9q+FvXrLK5DfR5udI6fqqqur6rqmaqqHrafHVFV1Terqto88N9J3Y4B4w9+LIJZ+LD9EItFQCwWALFYBMRiARCLRUAsFgCxWDa9vP77gqS3hJ99RNIddV3PlXTHwGdoNl8Qfmw7zwoflsAXhB/bDrFYBl8Qfmw7xGIZfEH4se0Qi2XwBeHHYjngPt66ru+pqmpW+PHFki4YsP9R0ipJV4/iuDJ8S5RvVYvb2FwG5ZKU2NelIlE65Vv8fJtlLPfnkgMvJx2/y2VHcRutb9WM43V822D8m/1zt61jTfDjq7gP47ZU3xIZtzg7Xo42yjW8zbe+u8+kXK7y0EMPZW1XXXXVAcc+FEZpW99eSXvCz8YtFv26jFtP/dxGyYHj1328tl0+4PH3F3/xF1m/e+65p+PxvURut+upn4x3LLoP/ZxHOZTLtKIk07c4u/wxbk+eOnVqsn2ujfHg5YWfeOKJjmP3rdAxFn27rR9P2n97dadxDCFOxz0Wm0CUet1yyy3J/q//+q+szeeEP/iDPxjbgfXIeMdir/h17+VnJemf/umfku1zcpRMLl++PNmnnHJKssdCDnDvvfd2PP4YSL1aE4u+VrnuuuuyNl+f+Fx55ZVXZv1c1tNt3egcfvjh2WefD31MMR1BP0u4tyUWRxufQ+M6NKaRcNz3UU45jjQqFv3ajuv/LVu2JNufE6R8DeJrGk9TIOXSHZ974xzq44jzoaf16CbT6/bs68d3qVG8P/e6vikhFv354a677kq2+13K50afW126J+XnLqYZ8PO8efPmZG/cuDHr5/fd8WS4d/ij67reKUkD/z3qAP2hmeDH9oMPywA/th98WAb4sf3gwzLAj+0HH5YBfiyEMc/0VFXV71VVtaaqqjW7d+8e66+DMQAflgF+bD/4sAzwY/vBh2WAH9sPPiwD/Nh+8GHzGW5Vr11VVU2r63pnVVXTJD3TqWNd15+W9GlJWrJkSU8lVOI2Od/S6FvhXG4gSU899VSyvaqQlMsRfvSjHyV77969WT/f9uVbA+P2MD/e9u3bszbfztVJViHtv13M8b/ZJWdxa+8IqxP15Mfh+HC4+PmK2xLdpy6vW79+fcd+vu0unn/fnnfeeedlbb510rc7NzAr/pjGYje6+cqv01iFq1MG/Lit2eP0fe97X7Lvv//+rJ9vlY1b1X2bbpRaNoy+xaL7yq/teO58G3Pc8u++8TkzVvfx+dXnqlh1ce3atcmOVYu8UptLcb0ykZTHfZz//Trz669XqUSPjFssjhdPP/109tn9GOfKs88+O9m//Mu/PLYDGxnjfl+McbRp06ZkR4mQ9/X5zmV30thW+4kx6xXdvJpYH2lkLPqaw+VwUh4vLq19//vfn/XrtdKa41IHaf/r61Uuuuii7HMDql+OeyyONV5V6JFHHun597ziWj8lecNg3GLR7/txjrrzzjuT/cADD2Rtft37s8Bpp52W9XMpe7dnAz9efBni/vc0FTFG/Zkzpi3w9ZiPN1YXG2HKiVbFosfS17/+9WS/8MILWb8VK1Yk26sTR3/63B0ldF4p7Etf+lKy4zPN0qVLkx3lt/1kuE+xt0h6NQnKVZJu7tIXmgt+bD/4sAzwY/vBh2WAH9sPPiwD/Nh+8GEZ4MdC6KWc+w2S7pN0UlVVO6qqeo+kj0taXlXVZknLBz5Dg8GPRTBb+LD1EItFQCwWALFYBMRiARCLRUAsFgCxWDa9VPW6vEPTm0Z5LDCG4Mci2FrX9ZJBfo4PWwSxWATEYgEQi0VALBYAsVgExGIBEItlM9wcP32lUxm1WJ7PifpIz9HjusxY9tdLznpuiXg8L7t41FF5cnM/ppdZjMdwnWAsSe75SLwt6g5HqUx4I4k6Si8PvHLlymTH/Euuq3Q9Z9TGu2b2ueeey9pcd+vnPPrQz3+30okN0Mr3Ff/bu5W2dF/FfFteCnHVqlXJjjHr37Vs2bKszUuU95qfKcZiaTHWKTdTzIHUrXS6x4HbMefLo48+mmzPMeJ5meJ37dq1K2vz0qr+ez4/S3lejBjP3rdTzjgYOl/72teyz55rYM6cOVnbhz/84WSXFlOjTcwNcP311yc73oPe8pa3JPumm25K9lhf2z7GP/qjP8raPGdezDMxkfFzFudKx9cS3e6f3eLI58rLL+/0HJfz3ve+t6d+MDI8hj1vTLfy7ZGPfvSjozqmUvD48PtRzJ/kzxNxzee5gTzG4hrVn0c9z1Jc7z///PPJjs8Jnr/N/R+fb/27XnnllazN/05fHx9yyCFZv/HMKzPWuM8k6T//8z+TvXr16mTH50Av294pJ6WUn//vfOc7WZvnYduzZ0+y/V2DJG3YsCHZsbR7P9dEjctUCwAAAAAAAAAAowMvfgAAAAAAAAAACqWRUq9uW568LW559hK+cau/t3Ur/+ySAz+Gb9WLxG2Cvr3Lt33F0sEuTXDZkZRv7/XSfXGbYMnErXteMs/LuUfZiG/X82PEEoi+lS/KEvw8+zZK3+4n5X6KZQJ9W2X070QixrN/9nN24403Zv3c3+7TeC5dSvBbv/VbWZvHXIzTTpQu9XJ8Do1/t8eASx8jLrWM58rnXfdbLMXuxO3UPif7dur4Xb41Oso/XY7r44jzv18vJft9JPg18/nPfz5r81h829velrX5HNur7HIi4fEX1xv333//oP0k6ROf+ESyDzvssDEa3f64dCKWKHaJLVKvn9FN8uF4jH3mM5/J2q666qpkz5o1K9nRBxdffHGyfR0aecMb3pDsY489tmM/GD2+//3vJ3v79u09/U68H/3ar/3aqI6pFHx+dEmUy2ykXEIeJXa+zvD1fzzG/Pnzk+1rjDhHu489TYW0vwTtVWbMmJF99nXRs88+m7W5lN3n27iWKhm/R0q51OuHP/xhsmfOnJn1W7duXbI9NUG8b3nMfuhDH8ra/DmmW2qQrVu3JjvO/1GCNpaw+gIAAAAAAAAAKBRe/AAAAAAAAAAAFEojpV4R3y7l26OiFMi31/nWLimXAfiW2FhBxo/x2GOPJTtmR/dM4L4VMBK35DkuG4oZxL26mLfFyju9VnhoI1FWdeKJJybbt0fGv9u3afr1EjOse8W1X/mVX8naXNriW/yin/y7Yxb+hQsXJtsrrJTmp6Hichq/nhcvXpz18/Pu0oHox0suuSTZF110UdbmW3h9Toh+dPlP6RXYOlUJjPIbn9diFQmfe/33XDYgSdOmTRv0eLG6hEuzYrUu3wLr8Rwlni6djZXffFv37NmzOx7Dr4OJHqed+M3f/M1kx8pE7qs77rgja6NiUHd8neNbzqX8/uHyHim/3401Hn9+v5s7d27WzyvmfOADHxj7gbUEl15Mnjw5a/M51uelG264Iev3z//8z8lesWJFsr/yla9k/bqtSx2vCtfPa2kiE587euGKK67IPsfUEbAPX9+4BCfKtOLzheNzsT8venUoKZeh+/ojSqxcgnvnnXdmbS579/VHlIv5s6RL6KU89YWvrUqXbvr96F//9V+ztnvvvTfZ7usorfRn+b/5m79JdpTHPvXUU8mOaT2ipKvTz30d2mvqibGAHT8AAAAAAAAAAIXCix8AAAAAAAAAgELhxQ8AAAAAAAAAQKG0IseP43kXosbVNXkx/4+3uf4y6uw8J4iXc496UNf4nXHGGVmb54fZuXPnoMeW8jwUsbyx6+dd9xnHMZ46wbHA/57o30WLFiXbtZOeT0DK88F4zonzzjsv67dgwYJkT58+PWvrVH4xarN9HOecc07W5vlNOuVsmgjEXCmdcsJ4DidJeutb35ps1/K+853vzPr90i/9UrJdZy3lMec66OhHz8cUj+HjLS3vS7eS7f63xnwRfg37/BTzVnj8eVvMwePxEfMEeR4onxe7lYmOf4v726+DqIEvbT4dLfw68ZKkMdfAwQcfnOyYN+2YY44Zo9GVgV97O3bsyNrmzJmT7BiLnrvi+OOPT3avOUBiWVmf72Kbz6cnnXRSsi+77LKsn+eNifm8JjJeSvhP//RPs7Y///M/T7avV6O/3Sdf+tKXkh1ztPXKO97xjmSXdn9rKr6W9XtkfG5xPv/5z4/pmErE56tJkyZlbb6GiaW0fS72Y/i9T5K2bduWbM8hGdcwnpct5mn1vFqeyy3Gs+dB9LxDkrR58+Zkn3766cmOuRlLywnrc+OqVauyNn/O9+e0mHfn1ltvTbZfE/H8+LN3PIbfa/33fD0k5WsgcvwAAAAAAAAAAMCow4sfAAAAAAAAAIBCaYXUy7dEuR3lAl4C06UDUr6Vz7fFPf/881k/33bn8p9YQnXJkiXJnjFjRtbmsgLfJhjlJb4NzEvFSflWQd+yNpGkXpFTTjkl2e4n31Yu5eWgjzvuuGRHOZ2f47it73vf+16yu5Wa9q2BsRSqS5fcb3ELZwlbLoeC//1+zo466qis3wUXXJDsN7/5zcmO0oG4ddbxOcLLw8ft815u08s7SrmPS5DpeYz5dRnnU58bXfYaP3t59LgF1uc493vc0u7SrDe+8Y1Zm8tq3W9eGjN+d5wnveyqy5O8RKqUzxEl+Hq0+Pa3v53s7373ux37uQ+WLl2atcVtz5Dj587vOVIuI/Z1jiSddtppyfZrNt5LXSLkUgG/10nSmWeemWyXHsTj+9zt92ZJuu2225J9yCGHCPbhsliXWEnSueeem+zrr78+2V/84hezfi7n83MbrwuXwLtEVsrXHPF+B2PP3Xffnexu8q5u0h8YnE5Sm7g28VQMcT3ofX2ei+sK94+v92O6AE8zEZ8Dva/72KVjUr5uiRJrH5dL4LtJ2Ep47vDn6xhHnUqsRx/6Pc3vkVEq7c8d8XqJ5/lV4jPn8uXLB/3efsNMAgAAAAAAAABQKLz4AQAAAAAAAAAoFF78AAAAAAAAAAAUSiNz/ERtumsRXUsXy6O7Ji/mW/FSfq7FjDpKz/XhOUfmz5+f9fO8FlGz6RpL1+pHrbvrBKNG0POWuO4z6hNdk9hJZ9hkoq9dlxlLyXpOHs/hFLWcrlmfOnVqsmOOCT9fUbPpflu7dm2yY3lEv85cvy/l15b7LWpHJ3IuEY/teF7cx94Wr3OPj3gt+GfP7RV95Tm2jj766KzNr5sSfOXnpFu+G8/xE3OQec4I9008r/5dnscnzpmexyzmFfE52cupxpwWnTTd8Zg+jjifdjvGRCKel5hnpBMeK29729tGdUyl43F09dVXZ22+1olzkK8rfD6NORz8c6c5QJKefPLJZMfcPZ3ys8UcMp7LoI3rkrGiW6lfz0f4J3/yJ8n+4Ac/mPXzOdbXI3EtddFFFyX7wQcfzNp8PVxCro+m4/ccSfqzP/uznn7vk5/85FgMp2j8eva50cucS3nuwLge8ZyunoPHf0fKY87zuXhOHyl/Xox5Zf05wddg8dnU8yrG+7N/tz9/lp5Xz+9dPqdJ+XXgfuq2hvd72rx587I2fx/gz4RSfs79OTXmT/M2cvwAAAAAAAAAAMCow4sfAAAAAAAAAIBCad0eXN/aGreo+rY+L8Uu5dvmfItV7Odbtnyb1pw5c7J+LjOIJb7XrVuXbN8aHaUse/bsSbbLGeJ4fZto3LrXxm26vu0uluBzeVcsX+nlprtJ3Fwi1K0cpm+XXL16ddZ2ww03JNu3X/r1IeXXQZS5ePk//5u7layfyMStj+4v93G85v18RqnO+vXrk+3bdP0akXIfP/bYY1mbbyH1raBtjD0pn0N9Dorl0V3ysWPHjo7H8O2r27dvz/r53OixGM9xJxmKlMf9tm3bBv2dSJSBuRSl2++11aejTZTZfu1rXxu0X11VVWkAABLHSURBVJx7XZoQ763QHb/2otxxtOWmHr9xi7z7NEpU/P7n443yBaREQ8fPk/s4+sfx6yTK1X09Etcmv//7v59slzDA2PDlL385++yycyemqLjwwgvHbEyl4nHk5/PUU0/N+vnc5msMKZdOucw23hd9XbRw4cJkL1q0KOvna9SYLsKvBV/7PP744x2/y9dcUi5N87F7yfo4jjbSLTVIjB3/7D6Mx/A5dMaMGcl+97vfnfW7/fbbkx1leP7uwWWDnlZGko499lg1AXb8AAAAAAAAAAAUCi9+AAAAAAAAAAAKpRVSr06Sj7gd2rfhxe3QLik65phjBrWlXALi1X2OOOKIrJ9XuYlyJf9uH2/MHO/bw6LUy7fm+t/ZTQ7TFnyrXawG4j6Mco2HHnoo2ZMnT0523M4Yt/wNdmwp98d1112XtbnMpdsxXM4Qt5L6dky/Dtros9Gkk+wtSgKGc57idnePTb8uYpUTr9wQt4L6ds1OUocmE/8elzh6pYidO3dm/TwG4lzrW6MffvjhZEd5j1ddcKlrr1urpXyedElJ/Lu8EmKUNngsugTTbfgZ//Iv/5J99q3N7seZM2dm/X77t3872W2JjyYSz91w5F0xPjzm7rrrrmRH2YlXwoyyBK++5/NpvOdGWTv0H79mVqxYkbUtXrw42e67KLOd6GuVkfCpT30q2ddcc03WFuPqVXwdIuUSHhg6vnaYO3du1uZzWUzX4df9D37wg2RHiZXLJN1X3SrPRummpxlwuf2mTZvUiSjPPOGEE5LtMrPSKgjH+6KvS6O8ztf4Pq/Fc+LyrmXLlnX8rkceeSTZ3c6jy7ne8573dBzveMKsDgAAAAAAAABQKAd88VNV1cyqqu6qqmpDVVXrq6r6wMDPj6iq6ptVVW0e+G/nDHQwrrzyyivCh0VwEH5sN8RiMRCLLYdYLAZiseUQi8VALLYcYrF8etnx81NJH6rrep6kZZLeW1XVfEkfkXRHXddzJd0x8BkayMCWNXxYBvixxRCLRYEfWwyxWBT4scUQi0WBH1sMsVg+B8zxU9f1Tkk7B+wXq6raIGm6pIslXTDQ7R8lrZJ09WgMqlvJOdcjxzwOrueMZUhd1+favahh9hwVnncn5qTw0tBbt27N2jxHhetDo77PP3crRed5ReIxesmjcNBBB6mu6wel/vmwG663jDlZ/Dy79lXKS0W7xtJzMUn5+ffcFJ5vRJI++9nPJvvee+/N2jy/kOcOiRrs2bNnJ3vevHlZm+ce8nK8I8h98ZMm+bFXYl4k97+3xfMSddKvEucHP8aGDRuytm984xvJvu+++5Idrzv31cknn5y1+dwx0pwH4xGL8Xz5te2xEuc4/z2fx6RcH+85g+IxPMeS6+hjTjP3dcxrsH79+mR7/MXrxX/P402STjvttGR7Xpo4nw7Bv62MxW64vz1vk5Sfdz/Pt956a9avKRr2XmjafXE0cB/G+6eXlF67dm2yFyxYkPXz9UbMRdIp/mK8xc9jTHGxOBw876SUz3lxjep9N27cmGzP/dNPSolFz5n3mc98Jtkxd1InYo68Ns2nAzQqFn3tFtccnrsx5szx50e34/rA14q+roj9/Hnl6aefztp8PeZ5afxakqT58+cne8mSJVmbx7rnLRxOrrU2xaLn3j3nnHOyNj+Xnh825uT1Z4Ft27YlO5Zs37NnT7KXLl2atbl/L7300mR7ziCpOTnThjSKqqpmSVok6TuSjh54KfTqy6GjOvzO71VVtaaqqjW7d+8e2WhhxODDMsCP7QcflgF+bD/4sAzwY/vBh2WAH9sPPiyTnl/8VFV1qKSvSPrDuq5fOFD/V6nr+tN1XS+p63qJV8yC/oMPywA/th98WAb4sf3gwzLAj+0HH5YBfmw/+LBceirnXlXVQdp3AXyxruuvDvx4V1VV0+q63llV1TRJz3Q+wtCIW/j9s0sCYineOXPmJDtKDnyLsm+n9NKlUr7tzreExRLDjz32WLKjbMQlSS5Did/lEqL4t3hJOJehRElYr1vH+u3DiG9Bd2lIlKF4Cemvf/3rWZvLS/ycR9+437xs7c0335z1c+mXj0nKz7NvEY0lIc8666xkx3Lu/nu+FXAEUq9x92OvuF89pqS8bLjLEbysopRLN93fsfTmTTfdlOzbbrsta9u1a1eyPRZjWU7/fO6552Ztvp10NBhvH/q16L6J8gxvi9JZl4P4luS4PdmvA/fh8ccf33F8UVbmUhQ/vs+LUr4tPkoyFy5cmGyXZ8broJO8cDDG24+jjc9L7373u7O2K6+8MtmnnHJKsodyvppIyT6M8Xzccccl2+MhykkmT56c7Li+6CR7jd81knvccCjNj73iEqK4XlqzZk2y4z342muvTbbP5WecccZoD7Fn2ujDKOH62Mc+luwtW7YkO8pLOnHhhReOzsDGkSb50eeh+Ox0zDHHJHvKlClZm69L/dksPmv4+sHvhVFi5alBXDIk5XHqbXFO9fvu6aefnrW5/Nrn8+GWb2+SD7vhz9Bnn312xzZP8xBlr36N+Lk7//zzs36+Jopx7+8U/DlwOFK7ftBLVa9K0uckbajr+lPWdIukqwbsqyTdHH8XmsHAwxc+LAP82GKIxaLAjy2GWCwK/NhiiMWiwI8thlgsn17+d905kq6Q9FBVVd8f+Nn/k/RxSTdVVfUeSdslXdrh92GceemllyR8WAKHCj+2GmKxGIjFlkMsFgOx2HKIxWIgFlsOsVg+vVT1+pakTvt23zS6w4Gx4NBDD1Vd1/iw/ezFj+2GWCwGYrHlEIvFQCy2HGKxGIjFlkMslk8jBfpRH+5actdRxtwbrrsbeGuZ8LwRnjsk6ij9GJ4D5tFHH836rV69OtleAk7Ky8B5cqs4Xi8nGEuqeo4fH2/UbPZbSz8aeN6PmOOnU04QKS+D6BpcL00rSZ5J3ttibhjP+fK6170ua3NfnXTSScmO+V9cVxpLcbq+s41+Gi2iHtbzYz3xxBPJvv/++7N+fq17m+t1pTzvSzzP7mP3zwknnJD1e8c73pHsmGtouDrpphDPiWvdfX6Kifi6Xb8eS5MmTRrUlnLfeEx5Xgkp19t7WVQp113HsquO6/Rjjh+fTz2HSYx72IeXh4V2EmNg+fLlyfY5Ld6DnbbPfSUQ/ePros997nPJvvHGG7N+mzZtSnbMTeK5CS+77LJRGedEJD5n3H333cnuNa+P32evuOKKrK3tOdSaRJzLOj1XSvlayPN/+noyfvZ1VXx28Rw/69aty9o8r6l/r6+JJOnMM89MdlwjeT6biTRn+9969NFHZ22eLyuWX3c65a2L+Xm63Se75btrIs0oKg8AAAAAAAAAAKMOL34AAAAAAAAAAAqlFfsIO5UojfIDlw/ErXC+DcylWFGG4qWmXc7gW/WkfGtg3FrnZYbnzZuX7Pnz52f9Tj755GR7KXopl0z439zWrZ/uK5dXxO1zXi79V3/1V7O2jRs3JtvLdLs/pdxX7ptY/tm3aUaZy3nnnZfsFStWJHvJkiVZP99OH7cGxlK4E5V4XtwPvlX64Ycfzvq5vNJLMHp5zXj8vXv3Zm0u/1m0aFGy3/Wud2X9Fi9enOwoQ2q7H+M86dJRLz0ZZVQeY35+pFxO6fHm0j0pLwPvW6FjOXef71yWJeX+9rkjbqk97LDDBu0X+/ocOpElmFA28dpuamlZ2B+XCbl8SJI+/OEPJ9vn4ShD8bVJXN9cffXVyfb7AQyN+Pzg91C/B0Xpj8fm3/7t3yY7lumG/hDXeJ0k4DHGXMru+FpEkl555ZVkx/WlPyN6efgo9eqWNqStz4WjSfShrzf93hdj1mPRj9Ftbdj2dWO7n2gAAAAAAAAAAKAjvPgBAAAAAAAAACiUVuwP821VLt2J261cLuBZzqU8M7vLi6I0xCu++FY7r5Ag5RKGuHXPt/l5Rago9fKqXnHrnm9NKy1Lu/stbjN2v8UqWV6VxLfrxW20Tz31VLK94tqaNWuyfn5NLFy4MGs75ZRTkj116tSO4+11a+BEw8+FV2WScnmRn2eX1Em5vMuruMWqCN724osvZm0efxdffHGyPc6lzpn9S8TnE58zu1XCitVL/LNvY46SWG/zcxzjyP0R21ym1S3eOm3ZjW3EKQA0GZdunH/++Vnbxz/+8WSvXLky2VEa8vLLLyf7kksuydo8zQDz4fCJkp6//Mu/TLavQ//6r/8663fttdcm26s1QfPw9WuUl7ucyJ9JYkU3v058zSvl8jHvF+Xwng6khOrOY02n9walPU8Ph7KfcAAAAAAAAAAAJjC8+AEAAAAAAAAAKBRe/AAAAAAAAAAAFEorcvw43fI4uP4y6vhcc+ltUaPr5Rg9/49roiVpz549g/aT8pwzrrv20tLxu6N2tPQ8I53opMuMeFssU+u+8jxKb37zmzseL+YJ6pRXpFvuEBicbrlYnJgry3MBORdccEH22bXVntcgfpfrsSl/uY9u17afIz93Ul529Mc//nGyPWeQ1Lmce8zB5r7vdr2UXGITACAS1zee6/BNb3pTsuP8R/7BsSfeM88444xB7be//e19GxP0hseE532Nn7utPzqVfY/9Dj/88GR77kQpX+d6vh9fY0l5riHy1MBImJhvFwAAAAAAAAAAJgC8+AEAAAAAAAAAKJRW6x2GsrXVt8b5Nr5u8gbfYutb8KT9JQ2dxuXHiFt2J6qcq590k7I4UWYEzaVbbOPH0aPTPCbl59zjKkrofHuyHyNKW/14yBIAAAan1zUNAPRGrykcukmsel23dFsjDed4AEOFuwYAAAAAAAAAQKHw4gcAAAAAAAAAoFB48QMAAAAAAAAAUCitzvET6bXs73CI5f46fS8AQOm4Jt7z9cTcPQAAAABtoZ/PdDw/Qr9hxw8AAAAAAAAAQKHw4gcAAAAAAAAAoFCqbhKmUf+yqtot6QlJUyQ927cvHpwmjEHqzziOq+v6yNE4UMN8KE2scYy2H1/SxDl3vdBGHxKL+9NGPxKLOW30IbG4P230I7GY00YfEov700Y/Eos5bfQhsTg+Y+jox76++ElfWlVr6rpe0vcvbtgYmjSOodKUcTOO4dOUMTOOkdGUcTOO4dOUMTOOkdGUcTOO4dOUMTOOkdGUcTOO4dOUMTOOkdGUcTdhHE0YA1IvAAAAAAAAAIBC4cUPAAAAAAAAAEChjNeLn0+P0/c6TRiD1JxxDJWmjJtxDJ+mjJlxjIymjJtxDJ+mjJlxjIymjJtxDJ+mjJlxjIymjJtxDJ+mjJlxjIymjLsJ4xj3MYxLjh8AAAAAAAAAABh7kHoBAAAAAAAAABRKX1/8VFX1lqqqNlZVtaWqqo/08Xuvr6rqmaqqHrafHVFV1Terqto88N9JfRjHzKqq7qqqakNVVeurqvrAeI1lJExkP+LDEX8vPhwlxsuHA9+NH0cJYhEfjvC78eMoQSziwxF+N34cJYhFfDjC78aPnajrui//JL1W0mOSjpd0sKS1kub36bvPl7RY0sP2s09K+siA/RFJn+jDOKZJWjxgHyZpk6T54zEW/IgP8SE+xI8T14/4sP0+xI9l+BEftt+H+LEMP+LD9vsQPx5gXH10wlmSbrPPfyzpj/v4/bPCBbBR0jRzzsZ+nviB771Z0vImjAU/4kN8iA/x48TyIz5svw/xYxl+xIft9yF+LMOP+LD9PsSPnf/1U+o1XdKT9nnHwM/Gi6Prut4pSQP/PaqfX15V1SxJiyR9Z7zHMkTw4wD4cNTAh0OnaT6U8ONwaJof8eHQaZoPJfw4HJrmR3w4dJrmQwk/Doem+REfDp2m+VDCj5L6m+OnGuRndR+/vzFUVXWopK9I+sO6rl8Y7/EMEfwofFgC+LAM8GP7wYdlgB/bDz4sA/zYfvBhGTTNj/188bND0kz7PEPS0338/siuqqqmSdLAf5/px5dWVXWQ9l0AX6zr+qvjOZZhMuH9iA9HHXw4dJrmQwk/Doem+REfDp2m+VDCj8OhaX7Eh0OnaT6U8ONwaJof8eHQaZoPJfwoqb8vflZLmltV1eyqqg6W9E5Jt/Tx+yO3SLpqwL5K+7R3Y0pVVZWkz0naUNf1p8ZzLCNgQvsRH44J+HDoNM2HEn4cDk3zIz4cOk3zoYQfh0PT/IgPh07TfCjhx+HQND/iw6HTNB9K+HEffU5s9Fbty2r9mKRr+vi9N0jaKekn2vcW8j2SJku6Q9Lmgf8e0YdxnKt9W93WSfr+wL+3jsdY8CM+xIf4ED+O/z9iER/ix2b8IxbxIX5sxj9iER/ix7H5Vw0MDgAAAAAAAAAACqOfUi8AAAAAAAAAAOgjvPgBAAAAAAAAACgUXvwAAAAAAAAAABQKL34AAAAAAAAAAAqFFz8AAAAAAAAAAIXCix8AAAAAAAAAgELhxQ8AAAAAAAAAQKHw4gcAAAAAAAAAoFD+P1pF6y0OVe90AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "# Make new images\n",
    "z = torch.zeros((10, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
    "generated_features = model.generator_forward(z)\n",
    "imgs = generated_features.view(-1, 28, 28)\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=10, figsize=(20, 2.5))\n",
    "\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}