gan_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate a curve artwork by using GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# !python --version\n",
    "# !python3 --version\n",
    "# !{sys.executable} --version\n",
    "# !{sys.executable} -m pip install torch\n",
    "# !{sys.executable} -m pip install torchsummary\n",
    "# !{sys.executable} -m pip install tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from torchsummary import summary\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import IntSlider\n",
    "from IPython.display import clear_output\n",
    "from ipywidgets.widgets.interaction import show_inline_matplotlib_plots\n",
    "%matplotlib inline\n",
    "# from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyper parameters\n",
    "BATCH_SIZE = 64\n",
    "LR_G = .0001\n",
    "LR_D = .0001\n",
    "N_IDEAS = 5 # input size\n",
    "ART_COMPONENTS = 20 # output size\n",
    "PAINT_POINTS = np.vstack([np.linspace(-1, 1, ART_COMPONENTS) for _ in range(BATCH_SIZE)])\n",
    "TOTAL_STEPS = 10000\n",
    "SHOW_INTERVAL = TOTAL_STEPS / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def artist_works():\n",
    "    # artwork 1\n",
    "#     paintings = np.tan(np.tan(PAINT_POINTS * np.pi))\n",
    "    # artwork 2\n",
    "#     paintings = np.sin(np.exp(PAINT_POINTS*2))**2\n",
    "    # artwork 3\n",
    "    paintings = np.sin(PAINT_POINTS*np.pi)\n",
    "    # artwork 4\n",
    "#     paintings = np.exp(PAINT_POINTS*np.pi)\n",
    "    # artwork 5\n",
    "#     paintings = np.arccos(np.sin(PAINT_POINTS*np.e)) + np.sin(np.cos(PAINT_POINTS*np.pi))\n",
    "    r = .05 * np.random.randn(1, ART_COMPONENTS)\n",
    "    paintings += r\n",
    "    paintings = torch.from_numpy(paintings).float()\n",
    "    return paintings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tDEp5iQ82HKJjdBOGdY21O4py0UOjuhAkwmvf7Lc7ymXRwqCUF8gqPMu6rONMTdVOZ3/QrkUTJg3pyEfpORw+dd7uOA2mhUEpL7BQO539zsOjugLwmg/2NWhhUMpmJeUVfLQplzE9tdPZn3Ro2YQ7UuL5MC2X/CLf2mrQwqCUzb6q6nS+Qjud/c3DI7tQaQyvf+1bfQ1aGJSy2QcbD9GhZROGa6ez34mPieT2wR35IC2HgtMX7I5Tb1oYlLJR9rFzrN1/nKl6eW2/9ciorlRUGl73oSOUtDAoZaMFGw8RHCRMSomvu7HySQmtIrl1YAfe33CIoz6y1aCFQSmb/LvTuTWto7TT2Z89dk1XyisNc77NsjtKvWhhUMomy3brmc6BolOrpkwc0J6/bzhI4ZmSuhewmRYGpWzy/gar0zlZRyQMBD+5JpmyCsMbPtDXoIVBKRs4dzoHa6dzQEiKbcotAzvwt/UHvb6vwS2FQUTGi8heEckUkVk1zH9CRHaLyHYRWSEinZzmVYjIVuu25OJllfJHC9Icnc53aKdzQPnpNcmUVxpe9fLzGlwuDCISDLwCXA/0AqaKSK+Lmm0BUowx/YBFwB+d5p03xgywbhNQys+VlleyKD2Xa3u0po12OgeUhFaR3D6oI+9vPOTVZ0O7Y4shFcg0xmQZY0qBBcBE5wbGmFXGmKrrz64H9IIwKmB9tfsIx/VM54D12DVdqaw0vLrKe7ca3FEYOgA5Ts9zrWm1mQF84fQ8QkTSRWS9iNzshjxKebWqM51HaKdzQIqPiWTSkHgWpnnvlVfdURhq6jkzNTYU+TGQAvzJaXKCNTj1ncCLItKllmVnWgUkvbCw0NXMStniwLFzfJd5nClDtNM5kD062nHlVW8dG9odhSEXcO5B6wjkXdxIRMYATwETjDH/OpDXGJNn3WcBXwMDa3oTY8wcY0yKMSYlLk5/aSnftCAtRzudFR1aNmFKajwfpuV45Shv7igMaUCyiCSJSBgwBah2dJGIDATewFEUjjpNjxaRcOtxLHA1sNsNmZTyOqXllSzalMM1PVrTtoV2Oge6R0Z1JShIeHml9201uFwYjDHlwGPAUmAP8KExZpeIPC0iVUcZ/QloBnx00WGpPYF0EdkGrAKeM8ZoYVB+adnuAo6dLeVOPdNZAW1bRHBnagKLNudy8Pg5u+NUE+KOFzHGfA58ftG0/3J6PKaW5dYCfd2RQSlvtyDtEO1bRDCim+4KVQ6PjOrCBxsPMXtlJn++o7/dcf5Fz3xWqhHknChmTeYx7kjRTmf1b62jIrj7yk58vDmX7GPes9WghUGpRvDRplwAHdNZ/cCDI7sQHhLMSysy7I7yL1oYlPKwikrDovQchnWNpWN0pN1xlJeJax7OPUM7sXjrYTKPnrE7DqCFQSmPW51RSF7RBSYP0UNUVc0eHNGFiNBg/rrCO45Q0sKglId9mJ5DdGQoY3u1sTuK8lIxTcO4d2gi/9yex74C+7catDAo5UHHz5awbHcBtw7qSHhIsN1xlBd7YHhnmoaF8Nfl9vc1aGFQyoM+2XKYsgqju5FUnaKbhjH96kQ+25HPnvzTtmbRwqCUhxhjWJCWw8CElnRr09zuOMoHzBjWmeYRIby4fJ+tObQwKOUhmw+dJPPoWSbrdZFUPbWIDGXGsCSW7ipg5+Ei23JoYVDKQxam5RAZFsyP+re3O4ryIdOHJREVEcKLNvY1aGFQygPOlpTzz+353NSvPc3C3XLlGRUgoiJCmTmiM8v3FLA995QtGbQwKOUB/9yWR3FpBZO001ldhnuvTqJlZCgvLLOnr0ELg1IesCAth+TWzRiU0NLuKMoHNQsP4cERXVi1t5DNh042+vtrYVDKzfYeOcPWnFNMHhKPiF4wT12ee67qREzTMFu2GrQwKOVmC9NyCA0Wbhl4qaHPlbq0puEhPDSyM6szjpF+4ESjvrcWBqXcqKS8gk+25DK2VxtaNQu3O47ycXdfmUhss3BeaOTzGtxSGERkvIjsFZFMEZlVw/xwEVlozd8gIolO835tTd8rIte5I49Sdlm2u4CTxWVMHqKjtCnXNQkL5uFRXfgu8zjrs4432vu6XBhEJBh4Bbge6AVMFZFeFzWbAZw0xnQFXgCet5bthWOM6N7AeOBV6/WU8kkL03Lo0LIJw7rG2h1F+Ym7rkigdfPwRu1rcMcWQyqQaYzJMsaUAguAiRe1mQjMtx4vAq4VR6/cRGCBMabEGJMNZFqvp5TPqRql7fbBHXWUNuU2EaHBPDq6KxuyT7B2/7FGeU93FIYOQI7T81xrWo1tjDHlQBHQqp7LAiAiM0UkXUTSCwsL3RBbKffSUdqUp0weEk/bqAheWLYPY4zH388dhaGmn0YXJ6+tTX2WdUw0Zo4xJsUYkxIXp4OpK++io7QpT4oIDebRa7qSduAk23I9fw0ld5yrnws4n97ZEcirpU2uiIQALYAT9VzWbcoqKik8U0L7lk089RYqQFWN0vbUjRd3rynlHpNSOtK9TXMGxHv+pEl3bDGkAckikiQiYTg6k5dc1GYJMM16fDuw0ji2h5YAU6yjlpKAZGCjGzLVaNrbG3n4vc2NsimmAkvVKG1jerW2O4ryU+EhwaQmxTTKe7lcGKw+g8eApcAe4ENjzC4ReVpEJljN3gJaiUgm8AQwy1p2F/AhsBv4EnjUGFPhaqba3NC3HdtyTrGuEQ/7Uv7vmI7SpvyMWy77aIz5HPj8omn/5fT4AnBHLcs+Azzjjhx1uX1wR15cnsFrX+9naBc9nFC5xyebdZQ25V8C6szniNBgZgxLYnXGMXY0QgeO8n/GGBam6yhtyr8EVGEA+PGVCTSPCOG1bzLtjqL8QNUobVN0a0H5kYArDM0jQrnnqk58sfMIWYVn7Y6jfFzVKG039tNR2pT/CLjCAHDf1UmEBQfxxjdZdkdRPkxHaVP+KiALQ2yzcCYPiefjLbkcKbpgdxzlo6pGaZucqruRlH8JyMIA8MDwzlQamLtatxrU5akapW1gI5xwpFRjCtjCEB8TyYT+7Xl/4yFOFZfaHUf5GB2lTfmzgC0MAA+N7EJxaQXz1x60O4ryMVWjtN06SC+Yp/xPQBeG7m2bM6Zna95Zm01xabndcZSPKCmv4OMtuYzr1ZaYpmF2x1HK7QK6MAA8PKoLJ4vLWLAxp+7GSuEYpe1UcRmT9NwF5acCvjAM7hRDalIMc1dnUVpeaXcc5QN0lDbl7wK+MIBjqyGv6AKLtx62O4ryclWjtN2RoqO0Kf+lhQEY1S2Onu2ieP2b/VRW6iW5Ve3+PUqb7kZS/ksLAyAiPDyqC/sLz/HV7gK74ygvVTVK2/DkODroYE/Kj2lhsNzQpy2dWkXy2jf7dSAfVaNvrVHaJuvWgvJzLhUGEYkRkWUikmHdR9fQZoCIrBORXSKyXUQmO817R0SyRWSrdRvgSh5XhAQHMXNEZ8dAPvt1IB9VXX7ReZ76eAdtosJ1lDbl91zdYpgFrDDGJAMrrOcXKwbuMcb0BsYDL4qI8zUEfmWMGWDdtrqYxyW3DepIXPNwXvtmv50xlJc5ea6Uu9/ayOkL5bw1bYiO0qb8nquFYSIw33o8H7j54gbGmH3GmAzrcR5wFIhz8X09QgfyURcrLi1n+vw0Dh0v5s17UujToYXdkZTyOFcLQxtjTD6AdX/JbWwRSQXCAOef5M9Yu5heEJFwF/O47K4rdCAf5VBaXsnDf9/MtpxTvDR1IFd1aWV3JKUaRZ2FQUSWi8jOGm4TG/JGItIO+BtwnzGm6kyyXwM9gCFADPDkJZafKSLpIpJeWFjYkLduEOeBfPbrQD4Bq7LS8KtF2/hmXyF/uKUv4/u0tTuSUo2mzsJgjBljjOlTw20xUGB94Vd98R+t6TVEJAr4DPiNMWa902vnG4cSYB6Qeokcc4wxKcaYlLg4z+6JqhrIZ44O5BOQjDE8/c/dLN6ax6+u686U1AS7IynVqFzdlbQEmGY9ngYsvriBiIQBnwDvGmM+umheVVERHP0TO13M4xbOA/nkF523O45qZK+syuSdtQeYfnUSj4zqYnccpRqdq4XhOWCsiGQAY63niEiKiMy12kwCRgD31nBY6nsisgPYAcQCv3cxj9tUDeTz1upsu6OoRvT+hkP8+at93DKwA7+5saeOtaACkvjiyVwpKSkmPT3d4+/z84VbWbrrCN89eQ3Renllv/fFjnwefX8zI7vFMeeeFEKD9fxP5V9EZJMxJqWudvrJv4SqgXzeXacD+fi7tZnH+NmCrQxMiObVuwZrUVABTT/9l6AD+QSGHblFPPBuOomxkbw1LYUmYXoCmwpsWhjq8PCorjqQjx/LKjzLvfM20jIyjHenX0HLSN1lqJQWhjoM7hRNalIMb+pAPn6n4PQF7n5rIwb424xU2raIsDuSUl5BC0M9PDKqC/k6kI9fKSou4563NnKquJT596XSOa6Z3ZGU8hpaGOphZLc4eulAPn7jfGkF0+enkX3sHG/ek0Lfjnr9I6WcaWGoBx3Ix3+UVVTy6Pub2XzoJC9OGcBQHbdZqR/QwlBP11sD+byyKlMH8vFRlZWGJxdtZ+X3R/n9zX24oW87uyMp5ZW0MNRTSHAQj47uyo7DRbrV4KPmrM7i4y2H+cXYbtx1RSe74yjltbQwNMCtAzvQObYp//vVPu1r8DEXyip489ssRnaL47FrutodRymvpoWhAUKCg3h8bDf2Fpzh0+15dsdRDfB/Ww5z/FwpD47orNc/UqoOWhga6Ed929GjbXNeXJ5BeYWe1+ALKisNc9dk06tdlA62o1Q9aGFooKAg4Ymx3cg+do6PN+t5Db7gm32FZB49ywMjknRrQal60MJwGcb2akP/ji3464oMSsor7I6j6jB3TRZtosK5sW97u6Mo5RO0MFwGEeEX47pz+NR5FqbpNZS82a68Ir7LPM69Q5MIC9GPu1L1of9TLtPw5FhSk2KYvTKT86W61eCt3lqdTWRYMHfq8JxK1ZtLhUFEYkRkmYhkWPfRtbSrcBq9bYnT9CQR2WAtv9AaBtQniAi/HNedwjMl/G39AbvjqBocKbrAkm15TEqJp0VkqN1xlPIZrm4xzAJWGGOSgRXW85qcN8YMsG4TnKY/D7xgLX8SmOFinkaVmhTDiG5xvPb1fs5cKLM7jrrI/HUHqDSG6Vcn2R1FKZ/iamGYCMy3Hs8Hbq7vguI4POQaYNHlLO8tfjmuGyeLy5j33QG7oygn50rKeW/9Qa7r3ZaEVpF2x1HKp7haGNoYY/IBrPvWtbSLEJF0EVkvIlVf/q2AU8aYqqHRcoEOLuZpdP06tmRcrza8+W0Wp4pL7Y6jLIs25XL6Qjn3D9etBaUaqs7CICLLRWRnDbeJDXifBGsA6juBF0WkC1DTAeW1XmdCRGZaxSW9sLCwAW/teU+M68bZ0nLmfJtldxQFVFQa3lqTzcCElgzuFGN3HKV8Tp2FwRgzxhjTp4bbYqBARNoBWPdHa3mNPOs+C/gaGAgcA1qKSIjVrCNQ63UmjDFzjDEpxpiUuLi4BvwTPa9H2yhu6teeed8doPBMid1xAt6y3QUcOlHM/cM62x1FKZ/k6q6kJcA06/E0YPHFDUQkWkTCrcexwNXAbuO4dvUq4PZLLe8rHh+TTGlFJa9+nWl3lIA3d3UWHaObcF3vNnZHUconuVoYngPGikgGMNZ6joikiMhcq01PIF1EtuEoBM8ZY3Zb854EnhCRTBx9Dm+5mMc2neOacdugDry3/hB5p87bHSdgbTl0kvSDJ5l+dRIhwXqajlKXI6TuJrUzxhwHrq1hejpwv/V4LdC3luWzgFRXMniTn16bzCdbDjN7ZSbP3lrjP1l52Nw12TSPCGHSkHi7oyjls/QnlRt1jI5kamoCH6XncPD4ObvjBJycE8V8sSOfO1MTaBbu0m8epQKaFgY3e2x0V4KDhL8uz7A7SsCZ990BgkS49+pEu6Mo5dO0MLhZ66gIpg1N5JOth8koOGN3nIBx+kIZC9MOcWO/drRr0cTuOEr5NC0MHvDQyC5EhgbzwvJ9dkcJGAs2HuJcaYUeoqqUG2hh8ICYpmHMGJbE5zuOsPNwkd1x/F5ZRSXzvjvAFUkx9O3Ywu44Svk8LQzYqMPZAAAQjklEQVQeMmN4Z1o0CeV/l+lWg6d9viOf/KILPDBctxaUcgctDB7SokkoM0d0ZuX3R9l08KTdcfyWMYa5q7PpHNuUa3rUdqkupVRDaGHwoPuuTiS2WRh/+Wqv3VH81sbsE+w4XMT0YUkEBel4zkq5gxYGD4oMC+HhUV1Zu/84azOP2R3HL725OpvoyFBuG9TR7ihK+Q0tDB521xUJtI2K4M9f7cVxeSjlLlmFZ1nxfQF3X9mJJmHBdsdRym9oYfCwiNBgfnJtVzYfOsWqvTVefFZdpre/yyY0KIgfX9XJ7ihK+RUtDI1gUko8CTGR/OWrfVRW6laDO5w8V8qiTbncPLA9rZtH2B1HKb+ihaERhAYH8bNrk9mVd5ovdx2xO45f+Pv6g1woq2SGntCmlNtpYWgkNw/sQNfWzXjui+85faHM7jg+raS8gvnrDjKiWxzd2za3O45SfkcLQyMJDhKevbUvh0+d5z8+2q4d0S5YvDWPY2dLeEDHc1bKI7QwNKIhiTHMGt+DL3cd4a012XbH8UnGGN5anU2Pts0Z1jXW7jhK+SWXCoOIxIjIMhHJsO6ja2gzWkS2Ot0uiMjN1rx3RCTbad4AV/L4gvuHJ3Fd7zY8+8X3pB04YXccn7M64xh7C84wY1gSInpCm1Ke4OoWwyxghTEmGVhhPa/GGLPKGDPAGDMAuAYoBr5yavKrqvnGmK0u5vF6IsKf7uhPfHQTHnt/M8fOltgdyae8uTqLuObhTBjQ3u4oSvktVwvDRGC+9Xg+cHMd7W8HvjDGFLv4vj4tKiKUV+8azKniMn76wRYq9BDWetl75AyrM44x7apOhIfoCW1KeYqrhaGNMSYfwLqv6ypmU4APLpr2jIhsF5EXRCS8tgVFZKaIpItIemFhoWupvUCv9lH8/uY+rN1/nBf0Cqx1qqw0PPP5HiJCg7jrCj2hTSlPqrMwiMhyEdlZw21iQ95IRNoBfYGlTpN/DfQAhgAxwJO1LW+MmWOMSTHGpMTFxTXkrb3WHSnxTE6J5+VVmaz8vsDuOF5t9spMvt1XyG9u7EV00zC74yjl1+osDMaYMcaYPjXcFgMF1hd+1Rf/pa75MAn4xBjzr4P4jTH5xqEEmAekuvbP8T3/PbE3vdpF8fOF28g5EdB72Gr19d6jvLhiH7cO6sBdVyTYHUcpv+fqrqQlwDTr8TRg8SXaTuWi3UhORUVw9E/sdDGPz4kIDeb1Hw+m0hgeeW8zJeUVdkfyKrkni3l84Va6t2nOMzf31SORlGoErhaG54CxIpIBjLWeIyIpIjK3qpGIJALxwDcXLf+eiOwAdgCxwO9dzOOTElpF8r+TBrDjcBFPf7rb7jheo6S8gkfe20xFheG1Hw/WK6gq1UhCXFnYGHMcuLaG6enA/U7PDwAdamh3jSvv70/G9mrDgyM788Y3WaQkRnPLQB1f4OlPd7M9t4g37h5MUmxTu+MoFTD0zGcv8qtx3bkiKYZff7yDvUfO2B3HVv/YlMt7Gw7x4MjOXNe7rd1xlAooWhi8SEhwELPvHEjziFAe/vsmzpaU2x3JFnvyT/PU/+3gys4x/Gpcd7vjKBVwtDB4mdbNI5g9dSAHTxTz5KLAu9he0fkyHv77JqIiQpk9dRAhwfoRVaqx6f86L3Rl51b86rrufLYjn3nfHbA7TqMxxvDLj7aRe/I8r941iLjmtZ7vqJTyIC0MXurBEZ0Z26sNf/h8D5sOnrQ7TqN449sslu0u4Nc39CQlMcbuOEoFLC0MXkpE+PMd/Wnf0nGxveN+frG9dfuP88cvv+fGvu2YfnWi3XGUCmhaGLxYiyahvHrXII6fK+VnC7b67cX2Ck5f4CcfbCYptinP395PT2JTymZaGLxcnw4t+J+JvVmTeYy/rsiwO47blVVU8uh7mykureD1Hw+mWbhLp9YopdxAC4MPmDwkgTsGd2T2ygxW7b3U5ah8z7Off0/6wZM8d1s/ktvo+M1KeQMtDD7i6Yl96NE2isfe28zqDN+/7DjAP7fn8fZ32dw7NJEJ/XXgHaW8hRYGH9EkLJj59w0hPiaS++al8X9bDtsdySWZR8/w5KLtDEpoyX/e0NPuOEopJ1oYfEjrqAg+fOgqUhKjeXzhVuauzrI70mU5V1LOQ3/fTERoMK/cNYiwEP0YKuVN9H+kj4mKCGX+9FRu7NuO33+2h2c+202lDx2tZIxh1sc7yCo8y+ypA2nXoondkZRSF9FDQHxQeEgwL00dSGyzMN5cnU3hmRL+eHt/n/jlPX/tAT7dlsd/jO/O0K6xdsdRStVAC4OPCg4SfjehN62jIvjT0r0cP1fKax4+3HN33mnSDpyg0hiMAQP/upaTMTimW48NjjZYbYyBkvJKXv9mP2N6tuGhEV08llMp5RqXvkVE5A7gd0BPINUah6GmduOBvwLBwFxjTNWAPknAAhzjPW8G7jbGlLqSKZCICI+O7krr5uHM+ngHU+asY969qW6/xtDB4+f4y1f7WLItz+XX6tkuir9M6k9QkJ7EppS3cvXn5U7gVuCN2hqISDDwCo4R3nKBNBFZYozZDTwPvGCMWSAirwMzgNdczBRw7kiJJ7ZZOI+8t5nbXlvLu9NTSXTDwDaFZ0p4eWUG7204REiw8OjoLtx9ZSLhIUGIgCBIEAiOIuW4t6Zb3/vOzwXHlo6e2ayUd3N1BLc9QF3/0VOBTGNMltV2ATBRRPYA1wB3Wu3m49j60MJwGUb3aM37D1zB9HfSuP31tcy7N5W+HVtc1mudLSlnzrdZzF2dRUl5JZOHxPOza5NpExXh5tRKKW/UGL2VHYAcp+e51rRWwCljTPlF09VlGpgQzaKHhxIeEszkOev4dl/DToQrKa9g3nfZjPzjKl5akcHo7q1Z9vMR/OGWvloUlAogdW4xiMhyoKaxFZ8yxiyux3vUtDlhLjG9thwzgZkACQkJ9XjbwNQlrhkfPzKUe+elMf2dNP58R39uHnjpeltZaVi87TB/+WofuSfPM7RLK54c34P+8S0bKbVSypvUWRiMMWNcfI9cIN7peUcgDzgGtBSREGuroWp6bTnmAHMAUlJSfOfAfRu0iYpg4YNXMvPddB5fuJXCMyU8MKLzD9oZY/h6XyF//HIve/JP07t9FH+4pS/Dk2O1H0CpANYYh6umAcnWEUiHgSnAncYYIyKrgNtxHJk0DajPFoiqh6oT4Z5YuI1nPt9DwekL/OcNPf91NNCWQyd57ovv2ZB9goSYSF6aOpAf9W2nRwsppVw+XPUWYDYQB3wmIluNMdeJSHsch6XeYIwpF5HHgKU4Dld92xizy3qJJ4EFIvJ7YAvwlit5VHXhIcHMnjqQuObhzF2TTeHZEh4e1YUXl2Xw5a4jxDYL4+mJvZkyJMEnTo5TSjUO8cXB5lNSUkx6eo2nTKgaGGN47Zv9/PHLvQA0DQvmwZFdmDEsiaY6/oFSAUNENhljUupqp98KAUBEeGRUV+KjI9mTf5oZw5Jo1cy9J8EppfyHFoYAclP/9tyk4x4opeqgO5aVUkpVo4VBKaVUNVoYlFJKVaOFQSmlVDVaGJRSSlWjhUEppVQ1WhiUUkpVo4VBKaVUNT55SQwRKQQOXubisTiu7OptNFfDaK6G0VwN46+5Ohlj4upq5JOFwRUikl6fa4U0Ns3VMJqrYTRXwwR6Lt2VpJRSqhotDEoppaoJxMIwx+4AtdBcDaO5GkZzNUxA5wq4PgallFKXFohbDEoppS7BLwuDiNwhIrtEpFJEau3BF5HxIrJXRDJFZJbT9CQR2SAiGSKyUETC3JQrRkSWWa+7TESia2gzWkS2Ot0uiMjN1rx3RCTbad6Axspltatweu8lTtPtXF8DRGSd9ffeLiKTnea5dX3V9nlxmh9u/fszrfWR6DTv19b0vSJynSs5LiPXEyKy21o/K0Skk9O8Gv+mjZTrXhEpdHr/+53mTbP+7hkiMq2Rc73glGmfiJxymueR9SUib4vIURHZWct8EZGXrMzbRWSQ0zz3rytjjN/dgJ5Ad+BrIKWWNsHAfqAzEAZsA3pZ8z4EpliPXwcedlOuPwKzrMezgOfraB8DnAAirefvALd7YH3VKxdwtpbptq0voBuQbD1uD+QDLd29vi71eXFq8wjwuvV4CrDQetzLah8OJFmvE9yIuUY7fYYersp1qb9pI+W6F3i5hmVjgCzrPtp6HN1YuS5q/xMc49R7en2NAAYBO2uZfwPwBSDAlcAGT64rv9xiMMbsMcbsraNZKpBpjMkyxpQCC4CJIiLANcAiq9184GY3RZtovV59X/d24AtjTLGb3r82Dc31L3avL2PMPmNMhvU4DzgK1HkCz2Wo8fNyibyLgGut9TMRWGCMKTHGZAOZ1us1Si5jzCqnz9B6oKOb3tulXJdwHbDMGHPCGHMSWAaMtynXVOADN713rYwx3+L4EVibicC7xmE90FJE2uGhdeWXhaGeOgA5Ts9zrWmtgFPGmPKLprtDG2NMPoB137qO9lP44YfyGWtT8gURcdfAzfXNFSEi6SKyvmr3Fl60vkQkFcevwP1Ok921vmr7vNTYxlofRTjWT32W9WQuZzNw/PKsUtPftDFz3Wb9fRaJSHwDl/VkLqxdbknASqfJnlpfdaktt0fWlc+O+Swiy4G2Ncx6yhizuD4vUcM0c4npLueq72tYr9MO6AssdZr8a+AIji+/OcCTwNONmCvBGJMnIp2BlSKyAzhdQzu71tffgGnGmEpr8mWvr5reooZpF/87PfKZqkO9X1tEfgykACOdJv/gb2qM2V/T8h7I9SnwgTGmREQewrG1dU09l/VkripTgEXGmAqnaZ5aX3Vp1M+WzxYGY8wYF18iF4h3et4RyMNxHZKWIhJi/eqrmu5yLhEpEJF2xph864vs6CVeahLwiTGmzOm1862HJSIyD/hlY+aydtVgjMkSka+BgcA/sHl9iUgU8BnwG2szu+q1L3t91aC2z0tNbXJFJARogWP3QH2W9WQuRGQMjmI70hhTUjW9lr+pO77o6sxljDnu9PRN4HmnZUddtOzXbshUr1xOpgCPOk/w4PqqS225PbKuAnlXUhqQLI4jasJwfAiWGEePzioc+/cBpgH12QKpjyXW69XndX+wb9P6cqzar38zUOMRDJ7IJSLRVbtiRCQWuBrYbff6sv52n+DY//rRRfPcub5q/LxcIu/twEpr/SwBpojjqKUkIBnY6EKWBuUSkYHAG8AEY8xRp+k1/k0bMVc7p6cTgD3W46XAOCtfNDCO6lvOHs1lZeuOozN3ndM0T66vuiwB7rGOTroSKLJ++HhmXXmih93uG3ALjkpaAhQAS63p7YHPndrdAOzDUfGfcpreGcd/3EzgIyDcTblaASuADOs+xpqeAsx1apcIHAaCLlp+JbADxxfc34FmjZULGGq99zbrfoY3rC/gx0AZsNXpNsAT66umzwuOXVMTrMcR1r8/01ofnZ2Wfcpabi9wvZs/73XlWm79P6haP0vq+ps2Uq5ngV3W+68CejgtO91aj5nAfY2Zy3r+O+C5i5bz2PrC8SMw3/os5+LoC3oIeMiaL8ArVuYdOB1t6Yl1pWc+K6WUqiaQdyUppZSqgRYGpZRS1WhhUEopVY0WBqWUUtVoYVBKKVWNFgallFLVaGFQSilVjRYGpZRS1fx/kVXjtpClRocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc11f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show one of the paintings\n",
    "plt.plot(PAINT_POINTS[0], artist_works()[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nn.Sequential(\n",
    "    nn.Linear(N_IDEAS, 128),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(128, ART_COMPONENTS),\n",
    ")\n",
    "\n",
    "D = nn.Sequential(\n",
    "    nn.Linear(ART_COMPONENTS, 128),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(128, 1),\n",
    "    nn.Sigmoid(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1               [-1, 1, 128]             768\n",
      "              ReLU-2               [-1, 1, 128]               0\n",
      "            Linear-3                [-1, 1, 20]           2,580\n",
      "================================================================\n",
      "Total params: 3,348\n",
      "Trainable params: 3,348\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.01\n",
      "Estimated Total Size (MB): 0.01\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(G, (1, N_IDEAS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1               [-1, 1, 128]           2,688\n",
      "              ReLU-2               [-1, 1, 128]               0\n",
      "            Linear-3                 [-1, 1, 1]             129\n",
      "           Sigmoid-4                 [-1, 1, 1]               0\n",
      "================================================================\n",
      "Total params: 2,817\n",
      "Trainable params: 2,817\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.01\n",
      "Estimated Total Size (MB): 0.01\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(D, (1, ART_COMPONENTS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1265edef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt_D = torch.optim.Adam(D.parameters(), lr=LR_D)\n",
    "opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)\n",
    "plt.ion()\n",
    "D_loss_history = []\n",
    "G_loss_history = []\n",
    "for step in range(TOTAL_STEPS):\n",
    "    artist_paintings = artist_works()\n",
    "    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS)\n",
    "    G_paintings = G(G_ideas)\n",
    "    prob_artist0 = D(artist_paintings)\n",
    "    prob_artist1 = D(G_paintings)\n",
    "    D_loss = -torch.mean(torch.log(prob_artist0) + torch.log(1. -prob_artist1))\n",
    "    G_loss = torch.mean(torch.log(1. -prob_artist1))\n",
    "    D_loss_history.append(D_loss)\n",
    "    G_loss_history.append(G_loss)\n",
    "    opt_D.zero_grad() \n",
    "    D_loss.backward(retain_graph=True)\n",
    "    opt_D.step() # update the network parameters\n",
    "    opt_G.zero_grad()\n",
    "    G_loss.backward()\n",
    "    opt_G.step()\n",
    "    # plotting\n",
    "    if step % SHOW_INTERVAL == 0:\n",
    "        plt.cla()\n",
    "        plt.plot(PAINT_POINTS[0], G_paintings.data.numpy()[0], label='Generated paiting')\n",
    "        plt.plot(PAINT_POINTS[0], artist_paintings[0], label='Artist\\'s paiting')\n",
    "        plt.text(-1, 1, 'D accuracy=%.2f' % prob_artist0.data.numpy().mean())\n",
    "        plt.text(-1, .83, 'D loss=%.2f' % D_loss.data.numpy())\n",
    "        plt.text(-1, .66, 'G loss=%.2f' % G_loss.data.numpy())\n",
    "        plt.ylim((-1.5, 1.5))\n",
    "        plt.text(.45, -.9, 'cur step: %d' % step)\n",
    "        plt.legend(loc='lower right')\n",
    "        clear_output(wait=True)\n",
    "        plt.show()\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G3/7zC+2b1OH3A5nseW58seMpKDSl/i4f+2/xZrezH1nIxMEdnK9TTuSQeiKXnm0a8o/P3J/nTxL2k3zsFCNfXM4j47vz1IItJDxyHvvSs0k5nsOADk2K5Z++aJvXzwZwVmL2PDeepxds5t8/JLFx6gXUq+D/pj/87c45FxgFNBORZBw9dSIBjDEl2/WdjDHpIjINWGclPWmM8XWTuMpUZRNGdXD/Z7+ReiKX7UdO0K998c5Vmw5m0rx+FC3qlw6YJc3/7SB3/sfRi2X1lD/QplFt57pTpwtYtyfdGTw7NK3LH2eu4XRBIZf2aeO8CZr4yHn8si+DTda3CXcX4b5PLqZBdNn+CS5+bRUAD43rxiV92hRb94pLoIl7YpHzecuugf+aWWvLtD9/pGXlYoAXF21j9qokEh89n7yCQr75/RAHMk7xz89/Y3CnJjz4xe+s3pVW7L2zf0zii1+SS21zXqIj7fp/F59PyjUQbjyQyf70bMb2bOVMe2rBFufy4cwc0rNPc+ucBJ/H8OHaveTkFRS7iPVr14hzOzdjX1o2LRpEER0ZDjiaolKO55DvUtstNI7ms6e/2UL31g04r3uLYtsv+jtYuT2V+KcWc9uwToQJzm8bH63dR9tGtZ3l/93Nzf2i5pMhnZrw0e1DCA8TnvlmS7E8Ty/YTG5+IbN/THKmDXra8Q3w7ZviPR5/8rFTzuUXvnUE9finljjTfvjnaLfH48pTvCkoNPz7B0d5pi/axhOX9vRYjkCR6tjOHR8fbxISfP8xVtRnCfu5f573Hhiu/0g12Zfrk5211nvO68LIrs0dbd/92vLyNX2JnbKA+lERPHpJD7q0qEe/9o259PVV/Jacyad/OYdBHZvw3w0H6NaqAS8u2sYSq9nhzYn9GRfX2rmfuz9ez383VJvWvGppz3Pj3TZZBcKLE3qz8UAmUy/r5dzHzBsGMPnDxIBsf+2DY6gTFU7vJ74D4JNJQ7hm1lrG927NG9f3Z9PBTMa/6rj4fnjbYOdEh+sfPZ/+Ty0m0OGmfZM6XNCzJa0a1mba/M3O9OsGteMfY89mgEtwvntMl2IX/0Dq1Kyus3nHk41TL6DX44u85oHyxxwRSTTGeL56uebVwO898P845Q+0danRevK/Xw/y6H83su7h84gMr9wB0Yczc2jVMJpjJ0/zxfoD3Do0FhHvT43xFmimXdaTR0t8HZ44uL2zicEf4WFCi/pRABzKDK0mtLL69p7hXDjjh0rdx61DOxar1QZSg+gIjueU7T5Di/pRpJzw3rNKOVRF4A/pKRv8ueR9v8V3d0GAqf/bREa2o601UA5knCK/oJBp8zeTYjWFLNuWwpBnl/Lq0h3c9+kGps3fzKqdRzmUeYrL3viR2CkL2JeWzaaDmTw1fzPGGG5/z/tFtGTQB8oU9MHxdfVQZo4GfT9UdtAHKi3oA2UO+oAG/Wqm8u8iVGOvfe/+a1+3VvXZethzn273rBp3Bb5A5eQV8O3Gw1zapw3fbjrMHR/9wp+Hd+SdVUl8vzWFpKMnGRTruIn0kkt3xxvfKd5jYenWI0z9n+Nr79urKi8AKKVqppAO/PvTT7lNj49tXObAX3TjZtAzS1n/6Pk0rlvLbb70k6e5/b11vDGxP60b1ibzVB59pn5XLM/LS7bTwOoBUXTTJ8lqP/x5j+9740VBXyml3LFVU891s9by3uo9Fd7OI+N7OJf97YbrOqjGtTve54nJPGv1LFj4+yE+WruXX/Zl8I4V0Oe4GeizNy3bba8FpZQKBFvV+NfsTmPN7jRuPje2Qtsp6pYG/nX59Db8+v9Z/YLH9mzFX11mdzxtbTdQfaZV9fGHbi34fmuK74xKldCsnvuWgkCzVY2/MrgOQvHEn3k3Ss7w+P6avZXWpS+UFfUs8teLE3qz+5lxhAk8f1Vcufb5+V/PcS4nPTuO2bcMLJWnlZsBZa9d1w+Ah8d1L7UO4JVr+/q1/5L9zzs0rePX+9zp2rKe2+1f1T/G+bp2ZDjzJp9TKl8R1wFYvgzv0szvvG9O7O9c3vPceL6849xSeUZ29T3dy4xr/Ptcy+OTSUOKvU585Dw+LpHmyWvX9WPe5NLHVBlsVeOvDP+3eDt/H9OlwttxNxpUeVYrIozT+aW/bbVqEE2YwMeTzmHG0u18YQ3lrx8VwYncfN6c2J946wb4ydx88gtNsXsoP/xzNMNfWMZN53TgiUt6EhbmuCm/+1lHF7qr+sdwx0e/OKeO8GXT1AuoGxXBL4+eT05egbNb7W9PjOVwZg4ncvKIiginc4t6pJ7I5ZGvNrJieyqdW9Tjkj5tuKhXKyLCw3i6xEAjcIx63nEki9eX7eSe87owY8kO/vu3oVxmjQheePdwjmblMrxLcx69uAfT5m/mm7uGc3ar+pz1kGMqgmb1anE0y3NPs3FxrRjRpTnXDmoPwOaDxxn3qqPX0ZYnL2Rv+km6tWrAeT1a8rk1kOz7f4ykdcPaJD07jp+S0unbrhEncvIZ+LSjz/y5ZzXlvVsHcfPsnxkY25i7x3Tlhnd+4vK+bRh5dnO2Hc4ivkNjRGBM95b8a8Uunl241VmmW86N5a4xXehvNZk+eFE33l+zt9h4EYB+7Ruz7uHziI4MY8mWI9SODOfCXq2Zl5jsHIH76V/OITxMuOqt1c5tNapzZgTxFf3aMrxLM+771JH/rj90JrZZXTo0ret8jzv/d3Ufsk/nc0X/mGJ98wd3alosX9N6UTStF0XSs+PIzS/k618PMvrsFs7Pqk+7Rvy6P4OFdw+ne+sGHvcXaLbqx+86BLos+UsqOcDG1/Zmr0riyfnFb6gWvUdr9d7N+dNAerVtSPxTS7jv/K7Uj47g8r5taVy3Fidy8oh7oviN75Ln4uekdAbGNkZEnOMbSjqcmcOvyRlcYI1gzTyVR72oCMLDPI99uODllWzzMlunp/L4Yowh7eRpmtUr/s3k242H2JV6kr+M6MSx7Dz2H8umf/vG5BcUsvnQcXrHNHLm/dSaPuC+87t63M/cn/chwPk9WjoHMQ3u2ISfktKpUyucbOubrLvye/o/2nQwkw/W7OWZK+KcF0xXJ3PzST95mnZNSn/jyMkrICoizOt4k6zcfGat2MVdY7oQER7GzbN/ZsX21FKjkU8XFNK/vffHeqRl5VI3KsLZbLtk8xH++lEi26ZdhAHnhbHk/6nrvorSLu7dmvm/HWLZP0bxeWIyPyel86nLN563lu/i9e938M3dw+nQtK7zfed1b8nbN5fuVn86v5CujywEHBfXjFOnad3Q91ghX0J2AFcgAn90ZBhbp11UpsD/j89+dQ6hL/meUAz88/8+jK9/PeicIO1/dw7jQEY2kz/8hQkDYnjwom68syqJm86JdRuoXR07eZratcIxBk7lFdDEQ2+pQPt+6xFunZNA20a1OZDh6P3VskEUR447+qP/ZUQnbjo31q/BfcGUX1BI54cdQWbWjQOY9EEiqx4YTYPakdQKDyt2P6vIyBeXkV9g+HHKH6q6uMXk5BVwPCfPr2lEKsoxWSDFLmgrt6dyPCeP83u0ZPvhLOJiGvq1raNZuby0eDtPXtqTCA+DOd9ZlcSos5tzVvPSTWvlVZbAr009JVTFH1lNMqJrc1ZuL/18hKKmBYD/d35X/j6mC/+c9ytHs07Tq21DerVtyFX9Y+jYrC61IsLo2aYBD43rxnWD2lM/OpJ/XtjNr/27doutXat0kKosYo3L6NyiHjNvGMAlr69ixf2jnfPBnHNW02of9AEiwsN4+Zo+9I5pxFnN65H07Difo7xX3D/a6/qqEh0Z7vbCVBlEhJIfywiX+wX+Bn2AZvWieOYK7/eLbhvW0ev6yqaBv5LETllAt1b1g12Mcrvl3Fgev8TRrfWdVUnOybGeuzKOCQNiiAgP47ZhHTmcmeO8oVryubNnuxx/WJgwacRZVVT6iiuqiY2La0VcTMNS3/p6VGF7bEVd0e/MjVlfQV+FBg38lpX3j+aaWWt47OIevjP7qeyjf4Pn87+ey1VvraZeVARZufk8cGE3Z5C4fXgnruofw4mcfNqX6DHiq6mmpmrftA5bp13oscbZwk0vHaVqCg38lvZN67DmwTHlem91rEP1adeIWTcOYPAzS5l6aU8e/3oTk0eexcW9WzunLn7pj33Ym5bNqLOb0699Y/Y8N56TufkkHT1Zqlmlcd1aHkcj25W7oP/qdf3c9jZSqibRwG8D/7l9MKfyCrjtvQTqR0XQuG4t/vu3ocCZm8xX9m9L3VoRxW5eXenSN7tI3agIerX1vz0z1FxaYo5/pWoiDfw1TNO6tbjpnFjW7D7K2t3pTBrRiXM7OwbBeOt95Pr0o7aNahOnwV2pkKWB34P7LzibF63Hp2Vkn6ZRHffNHKkncvkssfQTkgLluSvjmPLF786uhAa4+7wu3E0XZ9/osgp2Nz2lVHDZMvD/uPMoQzv7PxTcnb+N7uwM/IeP53gM/DvcPOC7oko+zvDaQe3JKyik52OLePCiM90gq6qrm1LKXmwZ+FdsT61w4HdVlWPcZt04oFjQLxIZHsb2py+quoIopWzLloG/rOr6GBhUWCLyZ2bnUS/aMeRfXPr0uA6F92XCgBiOn8rju81H+MvITlzapw0922i7u1Kq8tlyds6DGe4fsOKJr1F5P+486lw+kZNHnye/4xk3k2rVj47wq9dHp+Z1mX51H/4y0jGg6YbBHTToK6WqjC0Df1mf+9q5hff5Mn5NPvNQlBPW80YX/HbIbd6IcN+9+ouGcw/o4Og7725SK6WUqiy2DPxeJl0sl4KCM009hzId3yZSTuRQWGiKze8hSLGnd7nz5R3nMqTE1K1KKVWVbBr4fUf+soy+/HbTYefyVW+tARyPZOxkTe1axGA8zh5573ldad0wmn4+ppNVSqnKZsubu97mWS+y+2hWwPfrrffP3ed14e7zKv5AF6WUqihb1vj9CfxVqSqfrKOUUr5o4Cdw/fQ97ff8Hi0DswOllAoAWwb+5dtKPziksriG+rduGADAOyUet3ah9cg/pZSqDmwV+MvyjIn8At/V/E8mDSn2Ois3v1Qe163EWnPVj+levIbfo4029Silqg9bBf46ZZi7Zu7P+3zmKfkA5AI3F4tXl+5wLku1nJlfKaWKs1Xg79/B/66SrtMwXNm/rds8JZ829dr3O0rlWb0rzbksbj7Nq9zMea+UUsHkM/CLyGwRSRGRjR7WXyYiv4nIBhFJEJFhLusKrPQNIvJ1IAvuzh/j2/md1/XZowM6NPHrPfM9jNZ1btNlOaax49vCXWM6+10mpZSqCv7U+OcAF3pZvxToY4zpC9wKvO2y7pQxpq/1c2n5i+mfkpOpeVOenjyREd6bclwvJm9O7M/4uNbENNbpGJRS1YvPwG+MWQmke1mfZYwzjNal+P3OKpWb5/9o3LLcCAZI3JvO/nTvk79FuHTn7B3TiDcm9q92YwqUUiogbfwicoWIbAUW4Kj1F4m2mn/WisjlPrYxycqbkJpazu6YlRhji6Zq8CZCg7xSqgYISOA3xnxpjOkGXA5Mc1nV3hgTD1wPzBCRs7xsY5YxJt4YE9+8efNyFqR8bwuUiHBb3StXStlUQCOV1Sx0log0s14ftH7vBpYD/QK5v5LK0savlFKhqsKBX0Q6i3VXU0T6A7WANBFpLCJRVnozYCiwuaL782Zc79Z+5z16IrcSS6KUUtWXz9k5RWQuMApoJiLJwONAJIAxZiZwFXCTiOQBp4BrjDFGRLoD/xKRQhwXmOeMMZUa+BtER/qd97vNRyqxJEopVX35DPzGmOt8rH8eeN5N+mogrvxFU0opVRlsezfSVHF7/8vX9KnS/SmlVHnZNvAv25ZSpfu7QGfgVErVELYN/EUPRa+oUWf717XUn8c9KqVUdWDbwB8ovWMalUqbNKJTqTQN/EqpmsK2gT/Pj/n2/eFuNG7tyHBuHNKhWJoO2lVK1RS2DfxdWtQLyHbcxfOBsU348/DitX6dk0cpVVP47M5ZU1Vm00t8bGOiSzz0RbSpRylVQ9i2xh8o4eGlA3qkzsmjlKrBbBvBTIBmbGtUu1apNG3WUUrVZLYL/E3rlg7UFfHHeH10olLKXmwX+B+7pAcA3/x+OCDb8zYYheT+AAAOLklEQVTVcucA3UBWSqmqZLvA/8aynQDMXLGr0vfVumF0pe9DKaUCzXaBvypNv1rn51FK1Ty2C/xVOTdbywZa41dK1Ty2C/yV8RSud28Z6Fx2N3fPiK7lfFSkUkoFge0GcDWpW4tdqSe95ikoLNvFYWDHJs7lN67vX2zd2gfH0KiO/w+AUUqpYLNdjf+OUZ195lmxvWxTNteLclwfG0RHUDeq+LWyVcPoUqN4lVKqOrNdjT8qwve1LN9lAre2jWr7td1dz4xzO2+PUkrVNLYL/GFlHFXrb198Ha2rlLIL2zX1lHVyNp1bTSkVamwX+JVSSnlnu8CvLTJKKeWd7QJ/WZtu9DqhlAo1tgv8ZRXTuE6wi6CUUlXKhoG/bHX4h8d3r6RyKKVU9WS7wF/Wph4dfKWUCjX2C/x+5DmadbrSy6GUUtWV7QK/P3LzC4JdBKWUChrbBX7XAVyeJmOryqmblVKqurFd4I9r29C5fPh4jts8GveVUqHMdoHfda4eTxO2ad99pVQo8yvwi8hsEUkRkY0e1l8mIr+JyAYRSRCRYS7rbhaRHdbPzYEquD8OZbiv8SulVCjzt8Y/B7jQy/qlQB9jTF/gVuBtABFpAjwODAYGAY+LSONyl7aM8goL3aZrU49SKpT5FfiNMSuBdC/rs4xx3jKty5nYegGw2BiTbow5BizG+wUkoBpE227WaaWUqrCAtfGLyBUishVYgKPWD9AW2O+SLdlKqxK1a7kP/PkF7r8JKKVUKAhY4DfGfGmM6QZcDkyzkt3dR3Xb0iIik6z7AwmpqakBKVOhh+6czy7cGpDtK6VUTRTwXj1Ws9BZItIMRw2/ncvqGOCgh/fNMsbEG2PimzdvHuhiKaWUsgQk8ItIZxHHyCkR6Q/UAtKARcBYEWls3dQda6VViUIdqaWUUqX4dfdTROYCo4BmIpKMo6dOJIAxZiZwFXCTiOQBp4BrrJu96SIyDVhnbepJY4zHm8SB9nNSOh2a1q2q3SmlVI3gV+A3xlznY/3zwPMe1s0GZpe9aBU3/bttXB3fzndGpZQKIbYbuQswvndrQOfkUUopd2wZ+Iv672vcV0qp0mwZ+HU2HqWU8syWgb9oZmajbT1KKVWKPQO/9VvjvlJKlWbPwG9F/rST+ohFpZQqyZ6BX9v4lVLKI1sG/jA/4/79F5xduQVRSqlqyJaBX8S/yF87MrySS6KUUtWPLQO/Nx/9tDfYRVBKqaCyZeD3VuGf+/M+53KD2pFVUBqllKpebBn4w/xs6lFKqVBky8Af27SOX/l0gJdSKhTZMvDfMKRDsIuglFLVli0Dv7+9es5uVb+SS6KUUtWPLQO/N66Duzq3qBfEkiilVHCEXOB3pSN8lVKhyPaB/8jxHI/rtPOPUioU2T7wH8r0HPiVUioU2T7wK6WUKi7kAv/vBzKDXQSllAoq2wd+HaSllFLF2T/wB7sASilVzdg+8CullCrO9oHfW1NPuL9PbFFKKRuxfeD/v++2e1wXGW77w1dKqVJsH/lW70oLdhGUUqpasX3gV0opVZwGfqWUCjEa+JVSKsRo4FdKqRDjM/CLyGwRSRGRjR7WTxSR36yf1SLSx2XdHhH5XUQ2iEhCIAuulFKqfPyp8c8BLvSyPgkYaYzpDUwDZpVYP9oY09cYE1++IiqllAqkCF8ZjDErRSTWy/rVLi/XAjEVL5ZSSqnKEug2/tuAhS6vDfCdiCSKyKQA70sppVQ5+Kzx+0tERuMI/MNckocaYw6KSAtgsYhsNcas9PD+ScAkgPbt2weqWEoppUoISI1fRHoDbwOXGWOcQ2WNMQet3ynAl8AgT9swxswyxsQbY+KbN28eiGIppZRyo8KBX0TaA18ANxpjtruk1xWR+kXLwFjAbc8gpZRSVcdnU4+IzAVGAc1EJBl4HIgEMMbMBB4DmgJviuPp5flWD56WwJdWWgTwH2PMt5VwDG5d2a8tX6w/UCztUOapqtq9UkpVW/706rnOx/rbgdvdpO8G+pR+R9WoF1360FJP5AahJEopVb3oyF2llAoxtg38jWpHlkrTx+8qpZSNA/+Y7i2DXQSllKqWbBv4w+TMYxXX7UkHYNPB48EqjlJKVRu2DfwucZ+U446bug99+XuQSqOUUtWHbQO/q9z8glJp3Vs3CEJJlFIq+Gwb+F1r/O5cP6hd1RREKaWqGdsG/oYuvXrc9eYJD7PtoSullFe2jX4xjes4lwvdRP6rBrStyuIopVS1YdvA7+qlxdvZn55dLC0qIjxIpVFKqeAKicB/KDOH4S8sC3YxlFKqWgiJwK+UUuoMDfxKKRViNPArpVSI0cCvlFIhRgO/UkqFGFsH/uFdmgW7CEopVe3YOvDfOrRjsIuglFLVjq0Dv1JKqdJsHfgN+sgtpZQqydaBXymlVGkhGfjr1NJ5epRSocvWgd/Tw9XdzdaplFKhwtaB3xON+0qpUBaagT/YBVBKqSCydeD3VLM3WuVXSoUwWwd+T/6kA7uUUiEsJAN/15b1g10EpZQKGlsH/rNbuQ/wQzo1qeKSKKVU9WHrwN+uSR236a4PYldKqVBj68CvlFKqNA38SikVYnwGfhGZLSIpIrLRw/qJIvKb9bNaRPq4rLtQRLaJyE4RmRLIgvurQXREsddL7hsZjGIopVS14U+Nfw5woZf1ScBIY0xvYBowC0BEwoE3gIuAHsB1ItKjQqUthy4levB0blGvqouglFLVis/Ab4xZCaR7Wb/aGHPMerkWiLGWBwE7jTG7jTGngY+ByypY3jK7pHfrqt6lUkpVa4Fu478NWGgttwX2u6xLttLcEpFJIpIgIgmpqakBK9DN58YGbFtKKWUHAQv8IjIaR+B/oCjJTTaPcyUYY2YZY+KNMfHNmzcPVLEQcVcMpZQKXRG+s/gmIr2Bt4GLjDFpVnIy0M4lWwxwMBD7U0opVX4VrvGLSHvgC+BGY8x2l1XrgC4i0lFEagHXAl9XdH9KKaUqxmeNX0TmAqOAZiKSDDwORAIYY2YCjwFNgTetZpV8q8kmX0TuBBYB4cBsY8ymSjkKP82bfE4wd6+UUtWCVMcpiuPj401CQkLAtrd48xGMMYzt2Spg21RKqepERBKNMfH+5A1IG391d36PlsEuglJKVRs6ZYNSSoUYDfxKKRViNPArpVSI0cCvlFIhRgO/UkqFGA38SikVYjTwK6VUiNHAr5RSIaZajtwVkVRgbznf3gw4GsDi1AR6zPYXascLesxl1cEY49fUxtUy8FeEiCT4O2zZLvSY7S/Ujhf0mCuTNvUopVSI0cCvlFIhxo6Bf1awCxAEesz2F2rHC3rMlcZ2bfxKKaW8s2ONXymllBe2CfwicqGIbBORnSIyJdjlqQgRaSciy0Rki4hsEpG7rfQmIrJYRHZYvxtb6SIir1rH/puI9HfZ1s1W/h0icnOwjskfIhIuIutFZL71uqOI/GSV/RPrEZ6ISJT1eqe1PtZlGw9a6dtE5ILgHIn/RKSRiMwTka3W+T7HzudZRO61/qY3ishcEYm243kWkdkikiIiG13SAnZeRWSAiPxuvedVsR5/6DdjTI3/wfFox11AJ6AW8CvQI9jlqsDxtAb6W8v1ge1AD+AFYIqVPgV43loeBywEBBgC/GSlNwF2W78bW8uNg318Xo77PuA/wHzr9afAtdbyTOCv1vIdwExr+VrgE2u5h3Xuo4CO1t9EeLCPy8cxvwfcbi3XAhrZ9TwDbYEkoLbL+b3FjucZGAH0Bza6pAXsvAI/A+dY71kIXFSm8gX7AwrQh3wOsMjl9YPAg8EuVwCP77/A+cA2oLWV1hrYZi3/C7jOJf82a/11wL9c0ovlq04/QAywFPgDMN/6gz4KRJQ8xzie43yOtRxh5ZOS5901X3X8ARpYgVBKpNvyPFuBf78VyCKs83yBXc8zEFsi8AfkvFrrtrqkF8vnz49dmnqK/qCKJFtpNZ719bYf8BPQ0hhzCMD63cLK5un4a9LnMgP4J1BovW4KZBhj8q3XrmV3Hpe1PtPKX5OOFxzfUFOBd60mrrdFpC42Pc/GmAPAdGAfcAjHeUvE/ue5SKDOa1truWS63+wS+N21b9X47koiUg/4HLjHGHPcW1Y3acZLerUiIhcDKcaYRNdkN1mNj3U14nhdROBoDnjLGNMPOImjCcCTGn3cVpv2ZTiaZ9oAdYGL3GS123n2pazHWeHjt0vgTwbaubyOAQ4GqSwBISKROIL+R8aYL6zkIyLS2lrfGkix0j0df035XIYCl4rIHuBjHM09M4BGIhJh5XEtu/O4rPUNgXRqzvEWSQaSjTE/Wa/n4bgQ2PU8nwckGWNSjTF5wBfAudj/PBcJ1HlNtpZLpvvNLoF/HdDF6h1QC8eNoK+DXKZys+7QvwNsMca85LLqa6Dozv7NONr+i9JvsnoHDAEyra+Si4CxItLYqm2NtdKqFWPMg8aYGGNMLI5z970xZiKwDJhgZSt5vEWfwwQrv7HSr7V6g3QEuuC4CVYtGWMOA/tF5GwraQywGZueZxxNPENEpI71N150vLY+zy4Ccl6tdSdEZIj1Od7ksi3/BPsGSABvpIzD0ftlF/BwsMtTwWMZhuOr22/AButnHI72zaXADut3Eyu/AG9Yx/47EO+yrVuBndbPn4J9bH4c+yjO9OrphOMfeifwGRBlpUdbr3da6zu5vP9h63PYRhl7OgTpePsCCda5/gpH7w3bnmdgKrAV2Ah8gKNnju3OMzAXx32MPBw19NsCeV6BeOsz3AW8TokOAr5+dOSuUkqFGLs09SillPKTBn6llAoxGviVUirEaOBXSqkQo4FfKaVCjAZ+pZQKMRr4lVIqxGjgV0qpEPP/AeqBguY2GBVcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc3d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# discriminator loss\n",
    "plt.plot(D_loss_history, label='Discriminator loss')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126334f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generator loss\n",
    "plt.plot(G_loss_history, label='Generator loss')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the slider\n",
    "def slider(value, ideas_, idx, model):\n",
    "    int_range = widgets.FloatSlider(continuous_update=False,\n",
    "    min=-10, max=10, value=value)\n",
    "    output = widgets.Output()\n",
    "    display(int_range, output)\n",
    "    def on_value_change(change):\n",
    "        with output:\n",
    "            clear_output(wait=True)\n",
    "            ideas_[idx] = change['new']\n",
    "            ideas_tensor = torch.from_numpy(ideas_)\n",
    "            res = model(ideas_tensor)\n",
    "            plt.plot(res.data.numpy())\n",
    "            show_inline_matplotlib_plots()\n",
    "    int_range.observe(on_value_change, names='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d9aa15927e142a397ccd2e231baa0c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>FloatSlider</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "FloatSlider(value=-1.3798211812973022, continuous_update=False, max=10.0, min=-10.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "984733bcea5443f9b939ca49e219d2fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Output</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d05d4bb4c074b47a639a6888ff32b1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>FloatSlider</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "<p>\n",
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       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>FloatSlider</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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      },
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       "<p>Failed to display Jupyter Widget of type <code>Output</code>.</p>\n",
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       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "Output()"
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     },
     "metadata": {},
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   ],
   "source": [
    "# test generator\n",
    "ideas = np.random.randn(5).astype(np.float32)\n",
    "for i, x in enumerate(ideas):\n",
    "    slider(x, ideas, i, G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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      "state": {
       "layout": "IPY_MODEL_3c30dd3a4bc14b02b4bb7846813dea36",
       "outputs": [
        {
         "data": {
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\n",
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\n",
          "text/plain": "<matplotlib.figure.Figure at 0x110b59e10>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "a5aabfa8ae4d4f5283569d621fc5c852": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "1.0.0",
       "_view_module_version": "1.0.0"
      }
     },
     "a668848518bb4a2bbebde2ed71b2d82b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.1.0",
      "model_name": "SliderStyleModel",
      "state": {
       "_model_module_version": "1.1.0",
       "_view_module_version": "1.0.0",
       "description_width": ""
      }
     },
     "b41f10baa45d4309ac3e5e73f7ea9f70": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_d343ad8c29c24a5995b772473ddd0471",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<matplotlib.figure.Figure at 0x1262a07b8>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "c9e9945311a146b68d3b5ebbe8a2783f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_43e85443a291479ab8dee4800cc629f0",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<matplotlib.figure.Figure at 0x11ed64b00>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "cc22ecf73c4940de88a6b70a349e9f73": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_89f2b2d8744c407aaad4225dfdce733a",
       "outputs": [
        {
         "data": {
          "image/png": 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\n",
          "text/plain": "<matplotlib.figure.Figure at 0x12666ae80>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "d343ad8c29c24a5995b772473ddd0471": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module_version": "1.0.0",
       "_view_module_version": "1.0.0"
      }
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