GAN_step1_ch6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In /opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using cuda: Tesla V100-PCIE-32GB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    torch.set_default_tensor_type(torch.cuda.FloatTensor)\n",
    "    print(\"using cuda:\", torch.cuda.get_device_name(0))\n",
    "    pass\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.cuda.FloatTensor'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([3.0])\n",
    "x.type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_real():\n",
    "    real_data = torch.tensor([np.random.uniform(0.8, 1.0), \n",
    "                              np.random.uniform(0.0, 0.2),\n",
    "                              np.random.uniform(0.8, 1.0), \n",
    "                              np.random.uniform(0.0, 0.2)])\n",
    "    return real_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(nn.Linear(4, 3), \n",
    "                                   nn.Sigmoid(),\n",
    "                                   nn.Linear(3, 1), \n",
    "                                   nn.Sigmoid(),\n",
    "                                  )\n",
    "        self.model = self.model.cuda()\n",
    "        \n",
    "        self.loss_function = nn.MSELoss()\n",
    "        self.optimizer = torch.optim.SGD(self.parameters(), lr=0.01)\n",
    "        \n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "        \n",
    "        self.outputs = 0\n",
    "        \n",
    "    def forward(self, inputs):\n",
    "        return self.model(inputs)\n",
    "    \n",
    "    def train(self, inputs, targets):\n",
    "        self.outputs = self.forward(inputs)\n",
    "        \n",
    "        loss = self.loss_function(self.outputs, targets)\n",
    "        \n",
    "        self.counter += 1\n",
    "        if (self.counter %10 == 0):\n",
    "            self.progress.append(loss.item())\n",
    "            pass\n",
    "\n",
    "        if (self.counter % 1000000 == 0):\n",
    "            print(f\"counter = {self.counter}\")\n",
    "            pass        \n",
    "        \n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        \n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=[\"loss\"])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(16, 8), alpha=0.1, marker=\".\", grid=True, yticks=(0, 0.25, 0.5))\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random(size):\n",
    "    return torch.rand(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = Discriminator()\n",
    "\n",
    "for i in range(10000):\n",
    "    # 실제 데이터 학습\n",
    "    D.train(generate_real(), torch.Tensor([1.0]))\n",
    "    # 생성 데이터 학습\n",
    "    D.train(generate_random(4), torch.Tensor([0.0]))\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7944003343582153\n",
      "0.13235482573509216\n"
     ]
    }
   ],
   "source": [
    "print(D.forward(generate_real()).item())\n",
    "print(D.forward(generate_random(4)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        # 부모 클래스 초기화\n",
    "        super().__init__()\n",
    "        \n",
    "        # 신경망 레이어 정의\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(1, 3),\n",
    "            nn.Sigmoid(),\n",
    "            nn.Linear(3, 4),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "        # SGD 옵티마이저 설정\n",
    "        self.optimiser = torch.optim.SGD(self.parameters(), lr=0.01)\n",
    "        \n",
    "        # 진행 측정을 위한 변수 초기화\n",
    "        self.counter = 0\n",
    "        self.progress = []\n",
    "        \n",
    "        pass\n",
    "    \n",
    "    def forward(self, inputs):\n",
    "        # 모델 실행\n",
    "        return self.model(inputs)\n",
    "    \n",
    "    def train(self, D, inputs, targets):\n",
    "        # 신경망 출력 계산\n",
    "        g_output = self.forward(inputs)\n",
    "        \n",
    "        # 판별기로 전달\n",
    "        d_output = D.forward(g_output)\n",
    "        \n",
    "        # 오차 계산\n",
    "        loss = D.loss_function(d_output, targets)\n",
    "        \n",
    "        # 카운터를 증가시키고 10회마다 오차 저장\n",
    "        self.counter += 1\n",
    "        if (self.counter % 10 == 0):\n",
    "            self.progress.append(loss.item())\n",
    "            pass\n",
    "        \n",
    "        # 기울기를 초기화하고 역전파 후 가중치 갱신\n",
    "        self.optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimiser.step()\n",
    "        \n",
    "        pass\n",
    "    \n",
    "    def plot_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=[\"loss\"])\n",
    "        df.plot(ylim=(0, 1.0), figsize=(16, 8), alpha=0.1, marker=\".\", grid=True, yticks=(0, 0.25, 0.5))\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.5336, 0.4720, 0.4191, 0.3419], grad_fn=<SigmoidBackward>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G = Generator()\n",
    "G.forward(torch.Tensor([0.5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = Discriminator()\n",
    "G = Generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%timeit\n",
    "image_list = []\n",
    "\n",
    "# 판별기와 생성기 훈련\n",
    "for i in range(10000):\n",
    "    # 1단계: 참에 대해 판별기 훈련\n",
    "    D.train(generate_real(), torch.Tensor([1.0]))\n",
    "    \n",
    "    # 2단계: 거짓에 대해 판별기 훈련\n",
    "    D.train(G.forward(torch.Tensor([0.5])).detach(), torch.Tensor([0.0]))\n",
    "    \n",
    "    # 3단계: 생성기 훈련\n",
    "    G.train(D, torch.Tensor([0.5]), torch.Tensor([1.0]))\n",
    "    \n",
    "    if (i%300 == 0):\n",
    "        image_list.append(G.forward(torch.Tensor([0.5])).detach().cpu().numpy())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "D.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G.plot_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8976, 0.0591, 0.9385, 0.0420], grad_fn=<SigmoidBackward>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.forward(torch.Tensor([0.9]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0cc8469490>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.array(image_list).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counter = 1000000\n",
      "counter = 2000000\n",
      "counter = 3000000\n",
      "counter = 4000000\n",
      "counter = 5000000\n",
      "counter = 6000000\n",
      "counter = 7000000\n",
      "counter = 8000000\n",
      "counter = 9000000\n",
      "counter = 10000000\n",
      "counter = 11000000\n",
      "counter = 12000000\n",
      "counter = 13000000\n",
      "counter = 14000000\n",
      "counter = 15000000\n",
      "counter = 16000000\n",
      "counter = 17000000\n",
      "counter = 18000000\n",
      "counter = 19000000\n",
      "counter = 20000000\n",
      "counter = 21000000\n",
      "counter = 22000000\n",
      "counter = 23000000\n",
      "counter = 24000000\n",
      "counter = 25000000\n",
      "counter = 26000000\n",
      "counter = 27000000\n",
      "counter = 28000000\n",
      "counter = 29000000\n",
      "counter = 30000000\n",
      "counter = 31000000\n",
      "counter = 32000000\n",
      "counter = 33000000\n",
      "counter = 34000000\n",
      "counter = 35000000\n",
      "counter = 36000000\n",
      "counter = 37000000\n",
      "counter = 38000000\n",
      "counter = 39000000\n",
      "counter = 40000000\n",
      "counter = 41000000\n",
      "counter = 42000000\n",
      "counter = 43000000\n",
      "counter = 44000000\n",
      "counter = 45000000\n",
      "counter = 46000000\n",
      "counter = 47000000\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "image_list = []\n",
    "\n",
    "# 판별기와 생성기 훈련\n",
    "for k in range(10000):\n",
    "    for i in range(10000):\n",
    "        # 1단계: 참에 대해 판별기 훈련\n",
    "        D.train(generate_real(), torch.Tensor([1.0]))\n",
    "\n",
    "        # 2단계: 거짓에 대해 판별기 훈련\n",
    "        D.train(G.forward(torch.Tensor([0.5])).detach(), torch.Tensor([0.0]))\n",
    "\n",
    "        # 3단계: 생성기 훈련\n",
    "        G.train(D, torch.Tensor([0.5]), torch.Tensor([1.0]))\n",
    "\n",
    "        if (i%300 == 0):\n",
    "            image_list.append(G.forward(torch.Tensor([0.5])).detach().cpu().numpy())"
   ]
  },
  {
   "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": "3.7.4"
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