{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 64, 64)\n",
      "Generator(\n",
      "  (pipe): Sequential(\n",
      "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1))\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU()\n",
      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU()\n",
      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): ReLU()\n",
      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): ReLU()\n",
      "    (12): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
      "    (13): Tanh()\n",
      "  )\n",
      ")\n",
      "INFO: Iter 50: gen_loss=3.107e+00, dis_loss=3.545e-01\n",
      "INFO: Iter 100: gen_loss=5.438e+00, dis_loss=3.737e-02\n",
      "INFO: Iter 150: gen_loss=6.108e+00, dis_loss=1.413e-02\n",
      "INFO: Iter 200: gen_loss=6.497e+00, dis_loss=7.928e-03\n",
      "INFO: Iter 250: gen_loss=6.661e+00, dis_loss=4.426e-03\n",
      "INFO: Iter 300: gen_loss=6.974e+00, dis_loss=3.058e-03\n",
      "INFO: Iter 350: gen_loss=7.264e+00, dis_loss=3.352e-03\n",
      "INFO: Iter 400: gen_loss=8.008e+00, dis_loss=1.973e-03\n",
      "INFO: Iter 450: gen_loss=7.612e+00, dis_loss=7.998e-02\n",
      "INFO: Iter 500: gen_loss=7.241e+00, dis_loss=2.489e-02\n",
      "INFO: Iter 550: gen_loss=6.099e+00, dis_loss=3.014e-01\n",
      "INFO: Iter 600: gen_loss=5.541e+00, dis_loss=3.168e-01\n",
      "INFO: Iter 650: gen_loss=4.301e+00, dis_loss=3.208e-01\n",
      "INFO: Iter 700: gen_loss=4.265e+00, dis_loss=3.301e-01\n",
      "INFO: Iter 750: gen_loss=4.739e+00, dis_loss=1.881e-01\n",
      "INFO: Iter 800: gen_loss=4.413e+00, dis_loss=2.648e-01\n",
      "INFO: Iter 850: gen_loss=5.190e+00, dis_loss=1.595e-01\n",
      "INFO: Iter 900: gen_loss=5.641e+00, dis_loss=1.241e-01\n",
      "INFO: Iter 950: gen_loss=4.454e+00, dis_loss=4.375e-01\n",
      "INFO: Iter 1000: gen_loss=4.169e+00, dis_loss=2.384e-01\n",
      "INFO: Iter 1050: gen_loss=4.946e+00, dis_loss=1.716e-01\n",
      "INFO: Iter 1100: gen_loss=4.234e+00, dis_loss=2.619e-01\n",
      "INFO: Iter 1150: gen_loss=4.644e+00, dis_loss=1.127e-01\n",
      "INFO: Iter 1200: gen_loss=4.943e+00, dis_loss=2.967e-01\n"
     ]
    }
   ],
   "source": [
    "!python ganSynthesisImage.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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