a

ipynb/Animation.ipynb → ipynb/AnimationGenerator.ipynb

@@ -180,7 +180,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Build the model"
+    "#### Build the model\n",
+    "We use the following layers\n",
+    "\n",
+    "|Layer| Function|\n",
+    "|---- | --------|\n",
+    "|nn.Conv2d|Applies a 2D convolution over an input signal composed of several input planes.|\n",
+    "|nn.ConvTranspose2d|Applies a 2D transposed convolution operator over an input image composed of several input planes.|\n",
+    "|nn.BatchNorm2d|Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs with additional channel dimension). It can accelerating deep network training by reducing internal covariate shift.|\n",
+    "|LeakyReLU|$LeakyReLU(x)=\\max(0,x)+\\text{negative slope}\\times \\min(0,x)$|\n",
+    "|Tanh|$\\tanh(x)= \\frac{e^x+e^{-x}}{e^{x}−e^{−x}}$|\n",
+    "|Sigmoid|$Sigmoid(x)=\\sigma(x)= \\frac{1}{1+e^{-x}}$|"
    ]
   },
   {

ipynb/SimpleGANs.ipynb

 %% Cell type:markdown id: tags:
 
 ##### ref https://realpython.com/generative-adversarial-networks/#your-first-gan
 
 %% Cell type:markdown id: tags:
 
 ## Simple GANs Example
 This notebook give an example of GANs where we train neural networks to generating a sine function $f(x) = sin(x)$ given a series of random number
 
 %% Cell type:markdown id: tags:
 
-#### Import the essentail pacakge
+### Import the essentail pacakge
 First we import the essentail package, including:
 1. pytorch which is the mainly package to build neural structure
 2. matplotlib is used to visualize the data
 
 %% Cell type:code id: tags:
 
 ``` python
 import torch
 from torch import nn
 
 import math
 import matplotlib.pyplot as plt
 ```
 
 %% Cell type:markdown id: tags:
 
 Set a random seed to reproduce the result
 
 %% Cell type:code id: tags:
 
 ``` python
 torch.manual_seed(111)
 ```
 
 %%%% Output: execute_result
 
     <torch._C.Generator at 0x7f912002f210>
 
 %% Cell type:markdown id: tags:
 
 #### Create dataset
 
 %% Cell type:markdown id: tags:
 
 Create the training set. The training set a series of point on sine function $f(x) = sin(x)$ where $x \in [0, 2\pi]$.
 
 %% Cell type:code id: tags:
 
 ``` python
 train_data_length = 1024
 train_data = torch.zeros((train_data_length, 2))
 train_data[:, 0] = 2 * math.pi * torch.rand(train_data_length)
 train_data[:, 1] = torch.sin(train_data[:, 0])
 train_labels = torch.zeros(train_data_length)
 train_set = [(train_data[i], train_labels[i]) for i in range(train_data_length)]
 ```
 
 %% Cell type:markdown id: tags:
 
 Visualize the plot
 
 %% Cell type:code id: tags:
 
 ``` python
 plt.plot(train_data[:, 0], train_data[:, 1], ".")
 ```
 
 %%%% Output: execute_result
 
     [<matplotlib.lines.Line2D at 0x7f9093ee76d8>]
 
 %%%% Output: display_data
 
     ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAArQ0lEQVR4nO3df3RU9Z038PcnCRECCEEiv2KCQOoGZRtIZBTZqKtYoNsS1lYxrXhau4grz1Oqz9Ys9odV6onu0WX3kQVp0ZWuKVIVZGsU0KcWKSWSkWiA6AYxiZFfAcNvJCT5PH/MxM6deyfJZH7cuXPfr3Nykvu592Y+OZD55H5/iqqCiIjcK8XuBIiIyF4sBERELsdCQETkciwEREQux0JARORyaXYn0BfDhw/XsWPH2p0GEZGjeL3eo6qaFRx3ZCEYO3Ysqqur7U6DiMhRRKTRKs6mISIil2MhICJyORYCIiKXYyEgInI5FgIiIpeLSiEQkWdF5IiI7A5xXkTk30Vkn4h8ICJTAs7NFJGP/OfKopEPERH1XrSGj/4ngKcBrAlxfhaAPP+HB8AKAB4RSQWwHMAMAM0AdorIRlXdG6W8KEwVVU1Y/od6HDvdhg5VCABJEXR0dKJTgb8eMwQbFk23O00iiqKoFAJV3SoiY7u5ZA6ANepb83qHiAwVkVEAxgLYp6r7AUBE1vqvZSGIk/LKOjy3vQHn2ztDX9Txl6XKa5pPYGzZa0hPS0FGvxRMvfwS3HP9eBTmZsYhWyKKhXhNKBsD4NOA42Z/zCrusfoGIrIAwAIAyMnJiU2WLlHy9DZ88NkJCAzv8WFpa+9EW3snNu89jM17DyNVgOsmDMeauy3/+YgogcWrEIhFTLuJm4OqqwCsAoCioiLuptMHi9fuwoaaAzH53h0KbK0/ivH//Bpuyh/BpwQiB4lXIWgGcFnAcTaAAwDSQ8QpivpaAFJTgFQRSIrg/IVumo4CdCi+fEoozuMTApETxKsQbASwyN8H4AFwQlUPikgLgDwRuRzAZwDmASiNU05Jr6KqCT97tRbdNf8HEgBDBqSFbPefv7oKO/YfQ1qKoK1T0d5Du9LW+qOYsOQ1PDJnEko9bM4jSlQSjT2LReS3AG4AMBzAYQA/B9APAFR1pYgIfKOKZgI4C+B7qlrtv3c2gGUAUgE8q6q/7On1ioqKlIvOda+8sg4rt+7v8bqpYzPx4Kz8PjXjeBtb8cwfP8ZbdYd77GvIyxqILQ/cEPZrEFH0iIhXVYtMcSduXs9C0L0ZT76N+pYz3V4zcvBFWP7dwqi14y9euwuv1R7EhW4qQnqq4OFvXsWnAyKbsBC4gLexFd9euR2d3fyTZvRLwW9+cE3MOnIrqprw8427uy0Ij81lUxGRHUIVAi4xkSTKK+tw64rQRWBQeioemzsJex+dFdPRPKWeHNT/cjbysgaGvOYX/70H3sbWmOVAROHhE0ES6G5U0LCMfvjVXVfbMpSzoqoJD2/cjbYQTwclBaOxbN7kOGdF5F5sGkpS81dXYWv9UctzaSnAvse+HueMzLrrs7i4fxqe+95UzjkgigM2DSWhq5duCVkEBqWnJkQRAIAtD9yA4rzhludOftGOW1dsR0VVU5yzIqIuLAQOddXP3kDL6TZTPCM9FQuLx2H3IzNtyCq0NXd78PK905A/crDl+SXra9lvQGQTFgIHmvHk2zjd1mGKD8lIw95HZqJsdr4NWfWsMDcTry8uRvbQ/pbn7/svb5wzIiKAhcBx5q+uCtne/uDXErMABNtWdpNlMTh06jyuXrrFhoyI3I2FwEEWr90Vsk+gpGC0o8bmbyu7ybLfoOV0G6aXv2VDRkTuxULgEBVVTZZDRPunpeCxuZMcOQxzzd0eFGQPMcWbj3+Bkqe32ZARkTuxEDjA4rW7sGR9rSlenDccHy6d5agngWAbFk1H1qB0U7ym+QQWr91lQ0ZE7sNCkODKK+ssnwTyRw5OmiWed/5khmWfwas1BziSiCgOWAgS3HN/+sQUS0sBls6dZEM2sbOt7CZTM5ECeGBdDecYEMUYC0ECu3rpFpwPWp4ha1A6XrxnWlLOxN2waDpKCkYbtq1rOHYWS9bXshgQxRALQYK6eukWywljK+8sSsoi0GXZvMl46d5pGHtJhiH++KY6mzIiSn4sBAlo/uoqyyJQnDc8qYtAl8LcTCwoHm+InTjbzjkGRDESlUIgIjNF5CMR2SciZRbn/0lEavwfu0WkQ0SG+c81iEit/5zrV5LzNrZazhVIS0HSdA73RqknB2kpYoi1nG7D/NVVNmVElLwiLgQikgpgOYBZACYCuENEJgZeo6r/oqoFqloA4J8B/FFVPw+45Eb/edOqeG6zY/8xy/gjc5Krc7g3po2/xBTbWn+UI4mIoiwaTwRTAexT1f2q2gZgLYA53Vx/B4DfRuF1k05FVRM27zmE1KB/lYXF4xw9V6Cv1tztsZxj8BOLORVE1HfRKARjAHwacNzsj5mISAZ8G9i/HBBWAJtFxCsiC0K9iIgsEJFqEaluaWmJQtqJpbyyDkvW1+L95hPo6PRtKv8dTw5evndawi4iFw87fzIDucOMHcd1h05hxpNv25MQURKKRiEQi1io3W6+AeBPQc1C16nqFPialu4TkWKrG1V1laoWqWpRVlZWZBknGG9jK1Zu3W+InW/vxC/nTnJF53BPnrq9wBSrbznDmcdEURKNQtAM4LKA42wA1vsmAvMQ1Cykqgf8n48AWA9fU5OrWDV1XHqx9VLNblSYm4mJo8z7GGzac8iGbIiSTzQKwU4AeSJyuYikw/dmvzH4IhEZAuB6AK8GxAaKyOCurwHcAmB3FHJyjIqqJtQdOmWKL7x+vMXV7vVoibmz/EJHJyeaEUVBxIVAVdsBLAKwCUAdgHWqukdEForIwoBL5wLYrKqBi+mPALBNRN4H8C6A11T1jUhzcoqKqibLxeRKCkazSShIYW4mFhaPM8TaO307m7GJiCgy3LzeJt7GVty6YrspPnHUYFT+0LKbhOArng9v3I22oKU3Hps7yZUjq4jCwc3rE8z9L9aYYgLrJhD6i1JPDgb172eKL/9DvQ3ZECUHFgKbHDxxzhS7p3gcm4R64bbCbFPss+NfoLyS6xER9QULgU2uGWecNZuXNdDV8wXCUTY7HyUFo03xVe/s56xjoj5gIYgzb2Mrlv9hH35481dQnDcc/fuloDhvOLY8cIPdqTnKsnmTTZ3HqqGX6CCi0NLsTsBNvI2t+M6vd6CtvRPpaSl44QfXsCkoAl1PUKve2Q9VIEWA3/y5AafOXeDTFVEY+EQQRzv2H0Nbeyc6FbjQ3sm/XqOgbHY+frdwGorGZqJDgUMnz2Pl1v3sLyAKAwtBnHgbW3Hg+DmkpQhSBeiXlmLqJ6C+KczNxIcHTxpiz21vsCcZIgdi01AceBtb8e2V29GpgAhwx9Qc3Dolm81CUSRiXPLqfHsnyivr2ERE1At8IoiDB9bVoNM//0kV2L7vKItAlJVONU8mW7mVo4iIeoOFIA6ajp01HDe3ng1xJfVV2ex8DMkwP+A+/jr7Coh6wkIQY+WVdegMio0ZmmF5LUXmwa+Zm4HebWjlUwFRD1gIYqiiqsm0zwBgvb4+Ra7Uk4P8keblqsv5VEDULRaCGPE2tuKhDeaVRRdyGYmYWjp3kmmnpJ0NrRxOStQNFoIYeWBdDYIXdp06NpOjWGKsMDcTv5xrXriPHcdEobEQxEB5ZR0ajpk7hB+cxSIQD6WeHIwZat7hjU1ERNZYCGKg4l3zrllsEoqv+27MM8V2suOYyFJUCoGIzBSRj0Rkn4iUWZy/QUROiEiN/+Nnvb3XabyNrTj5RbshNnRAGpuE4qzUk4OsQemmOJf1IDKLuBCISCqA5QBmAZgI4A4RmWhx6TuqWuD/eCTMex3DaiP6H89kEbDDj2ZcYYr9aut+7nNMFCQaTwRTAexT1f2q2gZgLYA5cbg34Sxeu8u0Ef3UsZncQtEmpZ4czJg4whA7fu4ClqyvZTEgChCNQjAGwKcBx83+WLBrReR9EXldRK4M816IyAIRqRaR6paWliikHV3exlZsqDlgirOD2F4Lrx9vGk4KAC/uZCEg6hKNQmD1exY0cBLvAchV1a8C+L8ANoRxry+oukpVi1S1KCsrq6+5xozVUgbDB6Wzg9hmhbmZmGOxm1lbe/B8byL3ikYhaAZwWcBxNgDDn8aqelJVT/u/rgTQT0SG9+ZeJ6ioasK7DebRKPdbtFFT/C2bNxkTRxlnHH90+BRHEBH5RaMQ7ASQJyKXi0g6gHkANgZeICIjxb9OsIhM9b/usd7c6wRPvGF+GlhYPI59Awnk0ZJJSAl4/uxULkhH1CXiQqCq7QAWAdgEoA7AOlXdIyILRWSh/7JvAdgtIu8D+HcA89TH8t5Ic4qn8so6HD9nHC6aPbQ/h4smmMLcTNycb+w4frehlZ3GRIjSxjT+5p7KoNjKgK+fBvB0b+91kheqGk2xf7SYzET2u+f68diy97ChE+rZP33CJzdyPc4sjkBFVRNOne8wxIYOSOMbS4IqzM1E0Vhj5/3HR07zqYBcj4Wgj7yNrXiIk8ccp2xWvuE/vQL46au72XFMrsZC0Ecr//ixaZwrJ48lvsLcTNNS1R2dimf++LFtORHZjYWgj46c/MIU4+QxZ7CacfzWh0f4VECuxULQBxVVTTjTZuwb4OqiznLP9eORGvBYoKpckI5ci4UgTBVVTViyvhb7jpwGAEzIGojH5k7icFGHKczNxKMlk5CWIl82E506d8HWnIjswkIQpuA1akYNHcB+AYcq9eTgB9Mvh8I3wWwlVyYll2IhCIO3sRW7D5w0xK4cdbFN2VA07Dlo/Pd8YtOHLAbkOiwEYdix/xg6O41jhQYP6GdTNhQNs64aZTg+fpbLVJP7sBCEITMjHSkBC9akpwquGXeJjRlRpEo9OXhs7iSkpxoXwl3+9j6bMiKKPxaCXvI2tuKR3++BqiI1RTBj4gj8dsG1HCmUBEo9OUhLNf4qtJ5psykbovhjIeilV95rxvkLnehUAKoouGwoi0ASyRyY3u0xUTJjIegFb2Mrflf96ZcziVNTU9gklGTuu2GC4fjKURdzghm5BgtBLzz+eh3aOnxlQAB8qzCbTwNJpquv4KvZQ9AvVfBm3WHc/syf2WlMrsBC0IPyyjrD7mMpKcCtU7JtzIhipdSTg1uuHImOTkWnAu2dygXpyBWiUghEZKaIfCQi+0SkzOL8d0TkA//HdhH5asC5BhGpFZEaEamORj7RtM77qeF4yIB+fBpIYteMuwT+zfQA+Bake+W9ZhszIoq9iDemEZFUAMsBzIBvD+KdIrJRVfcGXPYJgOtVtVVEZgFYBcATcP5GVT0aaS7R5m1sResZ47IDE7IG2ZQNxUNhbia+cukg1B069WXsPT4RUJKLxhPBVAD7VHW/qrYBWAtgTuAFqrpdVbt+m3bAt0l9wnvlvWbDUtMCrjDqBulpxl+LDw9xo3tKbtEoBGMABLafNPtjodwN4PWAYwWwWUS8IrIg1E0iskBEqkWkuqWlJaKEe6Pk6W14IaCjMDVF8Mu5k9gs5AK3X21cO0oB7ldASS0ahUAsYsF7tvguFLkRvkLwYED4OlWdAmAWgPtEpNjqXlVdpapFqlqUlZUVac7dmr+6CjXNJwyx26++jIvLuUSpJwdXB21p+ebew3wqoKQVjULQDOCygONsAAeCLxKRvwbwawBzVPXLhd9V9YD/8xEA6+FrarKV1br0HCnkLsFbWnbCtysdUTKKRiHYCSBPRC4XkXQA8wBsDLxARHIAvALgTlX9n4D4QBEZ3PU1gFsA7I5CThEZNWSA4Xjk4IvYJOQyhbmZuDloF7M36w5zXgElpYgLgaq2A1gEYBOAOgDrVHWPiCwUkYX+y34G4BIA/xE0THQEgG0i8j6AdwG8pqpvRJpTJLyNrZieN/zLYwGw/LuF9iVEtrnn+vFIDVhkUBV4aH0tm4go6YiqZXN+QisqKtLq6uhPOfA2tmLeqj/jQodvYbnbr74Mt07hLGI369qRLlD+yMF4fbFlVxZRQhMRr6oWBcc5szjA/S/W4IJ/KYmOTsXRU+dZBFyu1JODi/sbp9vUcTgpJRkWAr/yyjo0fn7WEDt88gubsqFE8lcjB5ti979YE/9EiGKEhcBvnde8jEDweHJyJ6tJhMF/NBA5GQsBfH0DnwdtRJI7LIPzBgiAbwTRoPRUU7zk6W02ZEMUfSwEAMpfrzPFnrq9IP6JUMJa8vWJpljtZycsriRyHtcXAm9jK6objB1/Ey4dxE5iMij15CB7aH9DrEPBeQWUFFxfCB5/vc60Hsb3r7vcllwosW0ruwnDMvoZYj/dwHkF5HyuLgTexlbDpjMAMGZof/YNUEizJo0yHHeq9ZIkRE7i6kJgNQTwvhvz4p8IOcbfT8lGv9SA2cYA6g+fCn0DkQO4thBYzRvg0wD1pDA3E2sXXIuJo/4yt2BDzQHMX11lY1ZEkXFtIfjNjkZTjE8D1BuFuZk429ZhiG2tP8qOY3Is1xaCM0G/yKkCPg1Qr828cqQp9h9/qLchE6LIubIQTC9/yxT7xldH25AJOVXZ7Hz0D9rSsvn4FxxBRI7kukJQXlmH5uPGNYRSBFg2b7JNGZFTZQ/LMMW4pSU5kesKwXPbG0yx6ROGmy8k6oHVfJMt3NKSHMhVhaCiqgnn2zsNsVQB1tztsSkjcrJSTw6mBu1trABefs+8gCFRIotKIRCRmSLykYjsE5Eyi/MiIv/uP/+BiEzp7b3RtPztfabYP/zNuFi+JCW5B2flQ4Ji+zivgBwm4kIgIqkAlgOYBWAigDtEJHiFrlkA8vwfCwCsCOPeqKioasJnrecMsbGXZKBstnmJYaLestrb2NvYyuYhcpRoPBFMBbBPVferahuAtQDmBF0zB8Aa9dkBYKiIjOrlvVHx+u6DhmMB8ORtBbF4KXKZhdePR8BkYyiXnSCHiUYhGAPg04DjZn+sN9f05l4AgIgsEJFqEaluaWkJO8lZVxnXiLmneBxXGKWoKMzNxKMlk5Dm3+heBXj7oyN8KqCo8ja24qH1tViyPvoLHab1fEmPgptIAZgW9Ax1TW/u9QVVVwFYBfg2rw8nQeAvk8Ve330Qs64axcljFFWlnhw0HTuDlVv3QxXY2dCKb63YjpfuncY/OChi3sZW3PGrHWjzD3Z5qfpT/HbBtVH7vxWNJ4JmAJcFHGcDONDLa3pzb9SUenLwm7s9LAIUE3sOnjQcK6w3PSIK1zN//PjLIgAAFzo0qs2P0SgEOwHkicjlIpIOYB6AjUHXbAQw3z966BoAJ1T1YC/vJXKE4OZHAPgwqDgQhauiqgmb9x42xPqlCq4Zd0nUXiPiQqCq7QAWAdgEoA7AOlXdIyILRWSh/7JKAPsB7APwKwD/2N29keZEZIdSTw7GXmKcbXzqfAcXo6OIPPunTwzHIy++KKrNQgAgqmE3t9uuqKhIq6ur7U6DyMTb2IpbV2w3xV9mXwH1QUVVE5asrzXEHps7qc/N2yLiVdWi4LirZhYTxVphbiYmXDrIFLfaBImoJy/uND5NxmrPFBYCoiizWoOo8fOzbCKisI24uL/h+MrRQ2LyOiwERFFW6snB0AH9TPHgv+6IenLP9eOR5p+tmJYquOf68TF5nWjMIyCiID+e+Vemtt0T5y7YlA05UUVVE17ffRA/uO5yDB7QD9eMuyRm/Ux8IiCKgVJPDhYWGxc0bDh2FuWVnFdAPZu/ugpL1tfinfqjWLl1PzIz0mM62ICFgChGymbnIyM91RBbY7FXNlGg8so6bK0/aogFr5UWbSwERDGUGvQbdratg2sQUbf+888NppjVZMVoYiEgiqHvTM01xbidJYVSUdWELy4YN8+6KE1iviwOCwFRDJXNzsfwwemG2P6W0zZlQ4nOavOs700zD0eONhYCohi7/+YrDMcNn59l8xCZeBtbTZtnDUpPjcvmWSwERDFW6snBLQG7mLV3KJuHyMRqr+tpE4bH5bVZCIjiIHBiEABs3nuYQ0npS97GVqyr/tQQS02J3QSyYCwERHFQmJuJK0ddbIg9s3U/m4gIAPD463Vo7/jLAqATsgZi3T3RXWG0OywERHFy+9XGkR8K3xsAuVtFVRPebTD+QeCJ4SxiKywERHFS6snBmKHGRcR2NrTyqcDlHt642xT7+ynZcc2BhYAoju67Mc9wrAB+ErQmEbnH4rW70NZh3BMmNUXivndFRIVARIaJyBYRqfd/NmUvIpeJyB9EpE5E9ojIDwPOPSwin4lIjf9jdiT5ECU6qzWI6g6dwvzVVTZlRHaqrDUvHTEsw7xybaxF+kRQBuAtVc0D8Jb/OFg7gAdUNR/ANQDuE5GJAef/VVUL/B+VEeZDlPDKZudjQD/jGkRb64+yichlvI2tpqcBAPjRjCssro6tSAvBHADP+79+HkBJ8AWqelBV3/N/fQq+vYnHRPi6RI4WvLcxAOzYf8yGTMguKy3mkpQUjI75chJWIi0EI1T1IOB7wwdwaXcXi8hYAJMBBD4HLxKRD0TkWaumpYB7F4hItYhUt7S0RJg2kb2Wzp0ECThOFSAzIz3k9ZR8jpz8wnCcPbQ/ls2bbEsuPRYCEXlTRHZbfMwJ54VEZBCAlwEsVtWT/vAKAOMBFAA4CODJUPer6ipVLVLVoqysrHBemijhFOZm4qV7p+GWiSOQIkCHAkvW16Lk6W12p0ZxUFHVhDNtHYbYPwYNJIinHncoU9WbQ50TkcMiMkpVD4rIKABHQlzXD74i8IKqvhLwvQ8HXPMrAL8PJ3kiJyvMzUTW4IvQGdBMXNN8AvNXV2HN3R77EqOYqqhqMuxeNyFrIL4/fZwtTUJdIm0a2gjgLv/XdwF4NfgCEREAqwHUqepTQecCF9meC8A8oJYoiZm7CoFtQZuSUHJ5dtt+w/GooQNsLQJA5IWgHMAMEakHMMN/DBEZLSJdI4CuA3AngL+1GCb6hIjUisgHAG4E8KMI8yFylFstJg51AhxBlKQqqpqwr+WMIRbrTWd6I6LN61X1GICbLOIHAMz2f70NMPSLBV53ZySvT+R0hbmZKCkYjQ01Bwzxx1+vw7qF02zKimLl2T99YjiecOkg258GAM4sJrLdsnmTMTBob+N3ufRE0imvrMO+I8ZNib5/Xew3nekNFgKiBDBq6ABT7KcbuPREsvA2tmLlVmPfwJCMtIR4GgBYCIgSgtVfhp8cPWNxJTmR1UZEqZI4b7+JkwmRi5V6clCQPcQQO3ehk5vXJIk9B06YYrcVxneF0e6wEBAliA2LpiN3mHHpiZVb96OiqsmmjCgavI2tOHDcOIt47CUZcdmLuLdYCIgSyHV55j1qg8edk7O8/F6zYb6IAHjytgKbsrHGQkCUQG6dkm0aa/3x0TMcQeRQ3sZWbP3IuODCjIkj4r7fQE9YCIgSSGFuJn4ZtCCdKvDAuhq7UqI+8ja24lsrtqM5oFkonhvSh4OFgCjBlHpycPPEEYZYw7GzWLx2l00ZUV88sK7GtITIVaMvTrinAYCFgCghLbT4q3HTnkM2ZEJ9UVHVhIZjZ03x269OjHkDwVgIiBJQYW4mhg4wrgBzURp/XZ3iiTfMw35zh2UkzASyYPyfRZSgfjzTOLzw+Ll2zitwgPLKOhw/126KP3V7QfyT6SUWAqIEVerJwS1BfQWcV5D4NtR8ZopNHZuZkH0DXVgIiBLYPdePR0rQeNLgFSwpcXgbW3H45HlDTAA8OCtxJo9ZYSEgSmCFuZlY8DfjDLF9R07zqSBB/WR9rWGk0JABaXjp3mkJ/TQARFgIRGSYiGwRkXr/Z8ufVkQa/BvQ1IhIdbj3E7lZ2ex8TMgaaIg9btEZSfaqqGpC3aFThtjYSwYmfBEAIn8iKAPwlqrmAXjLfxzKjapaoKpFfbyfyLW+P934VHDiXDuml79lUzZkxarJLlGHiwaLtBDMAfC8/+vnAZTE+X4iVyj15CAtqLOg+fgXHEWUIKw2nSkpGJ2ww0WDRVoIRqjqQQDwf740xHUKYLOIeEVkQR/uh4gsEJFqEaluaWmJMG0i55k2/hJTbDU7jm1ntenMhKyBWDZvsk0Zha/HQiAib4rIbouPOWG8znWqOgXALAD3iUhxuImq6ipVLVLVoqysrHBvJ3K8NXd7kBo0guhCh3LpCZvd/2KNKRbclJfoeiwEqnqzql5l8fEqgMMiMgoA/J+PhPgeB/yfjwBYD2Cq/1Sv7icin3/4G/MbzIaaA1yd1CYVVU1o/Ny4lMTA9FTHNAl1ibRpaCOAu/xf3wXg1eALRGSgiAzu+hrALQB29/Z+IvqLstn5GHXxRaa41VaIFHtWHcRXjr7YhkwiE2khKAcwQ0TqAczwH0NERotIpf+aEQC2icj7AN4F8JqqvtHd/UQU2tPfKTTF3v3kcxsycTerDmInTB6zIqrBC6UmvqKiIq2uru75QqIk9e2V27GzwdgcVJA9BBsWTbcpI3fxNrbi1hXbDbHBF6XiP7/vSeh5AyLiDRrCD4Azi4kcqczir86a5hPsOI4Tq6a4a8cPT+gi0B0WAiIHKszNREnBaFOcHcfxsffACVMsEXce6y0WAiKHWjZvMgalp5riP1lfa0M27lFeWWfYfhJI/NVFe8JCQORgS74+0RTb13La4kqKhvLKOtPksRRxZgdxIBYCIgcr9eSgIHuIIXahQ7n0RAx4G1vxzDvGIiAAlpZMcvTTAMBCQOR4GxZNR0ZQE9Hzf25gX0GUPfPHjxE8yPKe4nGOmzxmhYWAKAnMvybXcHzuQie+tWI7i0GUeBtbsXnvYUNs6thMlM12dpNQFxYCoiRQNjsfY4b2N8QU7DiOln94fqcp5vR+gUAsBERJ4r4b80yxukOn+FQQocVrd+HzsxcMsWED0x3fLxCIhYAoSZR6cpA7LMMUX8l1iCKyac8hU+y2wmwbMokdFgKiJPLU7QWmX+otew+j5OlttuTjdOWVdTh3odMQy8samDR9A11YCIiSSGFuJn537zSMDFqhtKb5BItBmLyNrVgVNFx0+OB0bHngBnsSiiEWAqIkU5ibif9901dM8ZrmE6ioarIhI2f6yfpadAYNF73/5ivsSSbGWAiIklCpJ8dy+Yl/2cSJZr0xf3UV6g6dMsQWJsmcASssBERJymr5idaz7Xwq6MH81VXYWn/UEBt0UWrS9QsEYiEgSlKlnhzkZQ00xZesr2UxCGHx2l2mIgAAN+ePsCGb+ImoEIjIMBHZIiL1/s+mgbUicoWI1AR8nBSRxf5zD4vIZwHnZkeSDxEZbXngBmRmpJniT7zBJiIrr9UeNMWK84Zj2bzJNmQTP5E+EZQBeEtV8wC85T82UNWPVLVAVQsAFAI4C98G9l3+teu8qlYG309Ekfmnr5mbNI6fYxNRsPLKOlzoMPYOD8lIw5q7PTZlFD+RFoI5AJ73f/08gJIerr8JwMeq2hjh6xJRL5V6clCcN9wU/8V/72Ex8LNaXhoAHrQoosko0kIwQlUPAoD/86U9XD8PwG+DYotE5AMRedaqaamLiCwQkWoRqW5paYksayKXWXO3B0MGGJuIzrd3sr8AoYtAScHopB0lFKzHQiAib4rIbouPOeG8kIikA/gmgN8FhFcAGA+gAMBBAE+Gul9VV6lqkaoWZWVlhfPSRATgwZnWf90u/f2eOGeSOLyNrZZFYGHxuKTvFwjUYyFQ1ZtV9SqLj1cBHBaRUQDg/3ykm281C8B7qvrlWq6qelhVO1S1E8CvAEyN7McholBKPTlYWDzOFD97oRPzV1fZkJH9bn9muyl2dRItL91bkTYNbQRwl//ruwC82s21dyCoWairiPjNBbA7wnyIqBtls/NNO5oBwNb6o1i8dpcNGdln8dpdaO80x8uSaHnp3oq0EJQDmCEi9QBm+I8hIqNF5MsRQCKS4T//StD9T4hIrYh8AOBGAD+KMB8i6sGGRdMt5xdsqDngmiWrK6qasKHmgCk+dEBaUi0v3VuiwXuvOUBRUZFWV1fbnQaRoxU8shnHg9bZHz4oHc/cWZTUb4aL1+6yLAID0lJQt3SWDRnFj4h4VbUoOM6ZxUQu9eOv/ZUpdvR0G257Jnm3uAxVBPJHDk76ItAdFgIilyr15KCkYLQp3tEJ3PeC14aMYitUcxAALJ07Kc7ZJBYWAiIXWzZvsmUxOHTyfFLtX+BtbMVDIfZvXlg8LqmbwnqDhYDI5ZbNm2w5rDSZ9i+47wUvrHpDk3G3sb5gISAilM3Ox6igXc0A30qlTh9WOuPJt3Ho5HlTvCB7SFLuNtYXLAREBAB4+juFlvENNQdQ8ItNcc4mOqaXv4X6ljOmeHHecGxYNN2GjBITCwERAfBtcfnY3ElIEfO54+facfXSLfFPKgIlT29D8/EvTPGRgy9yxYqi4WAhIKIvlXpy8LuF0yzfGFpOtzmiA7miqglX/ewN1DSfsDy//LvWTz5uxkJARAaFuZn4psVIIsDXgZzIzUTllXVYsr4Wp9s6TOcGX5SKl++d5voRQlZYCIjIZNm8yZZ7GAC+ZqKxZa8l3IiiUMtJA76O4dpfzGQRCIGFgIgsrbnbg5fvnYaRg82jiYDEGlE0f3VVt0WAHcPdYyEgopAKczOx46GbMXSAed9jwDeiaMaTb8c3qQDexlZMeWSz5YbzgG+eAItAz1gIiKhHNT//GtJCvFvUt5xBwSOb495UVPL0Nty6Yjs+D1o4r0tx3nDOE+glFgIi6pV9j3095JPB8bMXsGR9LaY8ugXllXUxz2V6+VshRwUBvmUjOES097gMNRGFpaKqCY/+9x6cs9rVJUBJweiobvdYXlmH3+xoxLkLHegM8bY1IC0FP/3Gla7ZazhcoZahZiEgoj6Zv7oK79QftVzDp0vusAw8dXtBRKN1Fq/dhY3vHwj55t+FncI9i0khEJFvA3gYQD6Aqapq+e4sIjMB/BuAVAC/VtWuncyGAXgRwFgADQBuU9UeF0JnISBKDBVVTVgSYlXPYKkpwFcuHYylcyeFLAzexlYseH4njp29gBRBj2/+AHBRmuDn37iKTwG9EKtCkA+gE8AzAP6PVSEQkVQA/wPfVpXNAHYCuENV94rIEwA+V9VyESkDkKmqD/b0uiwERImjoqoJL+5swvvdtNlbSUsRqCo6+vgWlJGeivnX5HL10DCEKgTWPT+9pKp1/m/e3WVTAexT1f3+a9cCmANgr//zDf7rngfwNoAeCwERJY5STw5KPTnwNrbiztVVOGsxq9dKe2/+3LcgAOZEuf/B7SIqBL00BsCnAcfNALq680eo6kEAUNWDInJpqG8iIgsALACAnBw+AhIlmsLcTOx9ZOaXTwgfHTqFL3roUA5HigDf/CoLQCz0WAhE5E0AIy1OPaSqr/biNaweF8L+U0BVVwFYBfiahsK9n4jio+sJAfDtBdC1DLQgvF98ESBVBJkZ/fCjGVewDyCGeiwEqnpzhK/RDOCygONsAF0bhx4WkVH+p4FRAI5E+FpElECCJ3SVV9bhue2f4Hy7WvYRCIARgy/C8u8Wcl2gOIpH09BOAHkicjmAzwDMA1DqP7cRwF0Ayv2fe/OEQUQOVTY7n527CSiimcUiMldEmgFcC+A1Ednkj48WkUoAUNV2AIsAbAJQB2Cdqu7xf4tyADNEpB6+UUXlkeRDRETh44QyIiKXCDV8lGsNERG5HAsBEZHLsRAQEbkcCwERkcs5srNYRFoANPbx9uEArLczcgan5w/wZ0gETs8fcP7PYEf+uaqaFRx0ZCGIhIhUW/WaO4XT8wf4MyQCp+cPOP9nSKT82TRERORyLARERC7nxkKwyu4EIuT0/AH+DInA6fkDzv8ZEiZ/1/UREBGRkRufCIiIKAALARGRy7mmEIjITBH5SET2+fdHdhQReVZEjojIbrtz6SsRuUxE/iAidSKyR0R+aHdO4RCR/iLyroi878//F3bn1Fcikioiu0Tk93bnEi4RaRCRWhGpERFHrj4pIkNF5CUR+dD/+3Ctrfm4oY9ARFIB/A98S103w7dHwh2qutfWxMIgIsUATgNYo6pX2Z1PX/g3Hxqlqu+JyGAAXgAlTvl3EN/m3ANV9bSI9AOwDcAPVXWHzamFTUTuB1AE4GJV/Tu78wmHiDQAKFJVx04mE5HnAbyjqr8WkXQAGap63K583PJEMBXAPlXdr6ptANYCmGNzTmFR1a0APrc7j0io6kFVfc//9Sn49qcYY29Wvac+p/2H/fwfjvtLSkSyAXwdwK/tzsWNRORiAMUAVgOAqrbZWQQA9xSCMQA+DThuhoPegJKRiIwFMBlAlc2phMXfpFID37aqW1TVUfn7LQPwYwDR21k+vhTAZhHxisgCu5Ppg3EAWgA852+e+7WIDLQzIbcUArGIOe4vuWQhIoMAvAxgsaqetDufcKhqh6oWwLf39lQRcVQznYj8HYAjquq1O5cIXKeqUwDMAnCfv9nUSdIATAGwQlUnAzgDwNZ+S7cUgmYAlwUcZwM4YFMuruZvW38ZwAuq+ord+fSV/1H+bQAz7c0kbNcB+Ka/nX0tgL8Vkf+yN6XwqOoB/+cjANbD1/TrJM0AmgOeJl+CrzDYxi2FYCeAPBG53N8xMw/ARptzch1/Z+tqAHWq+pTd+YRLRLJEZKj/6wEAbgbwoa1JhUlV/1lVs1V1LHy/B/9PVb9rc1q9JiID/QMN4G9OuQWAo0bSqeohAJ+KyBX+0E0AbB0wkWbni8eLqraLyCIAmwCkAnhWVffYnFZYROS3AG4AMFxEmgH8XFVX25tV2K4DcCeAWn87OwAsUdVK+1IKyygAz/tHoaUAWKeqjht+6XAjAKz3/U2BNAAVqvqGvSn1yf8C8IL/D9P9AL5nZzKuGD5KREShuaVpiIiIQmAhICJyORYCIiKXYyEgInI5FgIiIpdjISAicjkWAiIil/v/nw0YtTUqHuYAAAAASUVORK5CYII=)
 
 %% Cell type:markdown id: tags:
 
 #### Build the model
 
 %% Cell type:markdown id: tags:
 
 The GANs consists of two neural networks, one called Discriminator and the other called Generator. The role of the generator is to estimate the probability distribution of the real samples in order to provide generated samples resembling real data. The discriminator, in turn, is trained to estimate the probability that a given sample came from the real data rather than being provided by the generator.
+![Struct](img/GANsStruct.jpg)
 
 %% Cell type:code id: tags:
 
 ``` python
 class Discriminator(nn.Module):
     def __init__(self):
         super().__init__()
         self.model = nn.Sequential(
             nn.Linear(2, 256),
             nn.ReLU(),
             nn.Dropout(0.3),
             nn.Linear(256, 128),
             nn.ReLU(),
             nn.Dropout(0.3),
             nn.Linear(128, 64),
             nn.ReLU(),
             nn.Dropout(0.3),
             nn.Linear(64, 1),
             nn.Sigmoid(),
         )
 
     def forward(self, x):
         output = self.model(x)
         return output
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 class Generator(nn.Module):
     def __init__(self):
         super().__init__()
         self.model = nn.Sequential(
             nn.Linear(2, 16),
             nn.ReLU(),
             nn.Linear(16, 32),
             nn.ReLU(),
             nn.Linear(32, 2),
         )
 
     def forward(self, x):
         output = self.model(x)
         return output
 ```
 
 %% Cell type:markdown id: tags:
 
 Instantiation
 
 %% Cell type:code id: tags:
 
 ``` python
 discriminator = Discriminator()
 generator = Generator()
 ```
 
 %% Cell type:markdown id: tags:
 
 #### Set hyperparameters
 
 %% Cell type:code id: tags:
 
 ``` python
 lr = 0.001
 num_epochs = 300
 loss_function = nn.BCELoss()
 batch_size = 32
 train_loader = torch.utils.data.DataLoader(
     train_set, batch_size=batch_size, shuffle=True
 )
 ```
 
 %% Cell type:markdown id: tags:
 
-Set the optimization object
+#### Set the optimization object
+What do optimizer do in neural network?
+
+![op](img/optim.png)
+
+`Adam` combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems.
 
 %% Cell type:code id: tags:
 
 ``` python
 optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=lr)
 optimizer_generator = torch.optim.Adam(generator.parameters(), lr=lr)
 ```
 
 %% Cell type:markdown id: tags:
 
 Training process:
 1. Train the discriminator using real(1) data and fake(0) data. The discriminator will need to distinguish between the fake and real data points
 2. Train the generator using random numbers as input and let discriminator to do the classify output from generator. The loss is the difference of the classification result and true labels, all of which is of value 1
 
 %% Cell type:code id: tags:
 
 ``` python
 def train(generator, discriminator, num_epochs, batch_size, train_loader):
     for epoch in range(num_epochs):
         for n, (real_samples, _) in enumerate(train_loader):
             # Data for training the discriminator
             real_samples_labels = torch.ones((batch_size, 1))
             latent_space_samples = torch.randn((batch_size, 2))
             generated_samples = generator(latent_space_samples)
             generated_samples_labels = torch.zeros((batch_size, 1))
             all_samples = torch.cat((real_samples, generated_samples))
             all_samples_labels = torch.cat(
                 (real_samples_labels, generated_samples_labels)
             )
 
             # Training the discriminator
             discriminator.zero_grad()
             output_discriminator = discriminator(all_samples)
             loss_discriminator = loss_function(
                 output_discriminator, all_samples_labels)
             loss_discriminator.backward()
             optimizer_discriminator.step()
 
             # Data for training the generator
             latent_space_samples = torch.randn((batch_size, 2))
 
             # Training the generator
             generator.zero_grad()
             generated_samples = generator(latent_space_samples)
             output_discriminator_generated = discriminator(generated_samples)
             loss_generator = loss_function(
                 output_discriminator_generated, real_samples_labels
             )
             loss_generator.backward()
             optimizer_generator.step()
 
             # Show loss
             if epoch % 10 == 0 and n == batch_size - 1:
                 print(f"Epoch: {epoch} Loss D.: {loss_discriminator}")
                 print(f"Epoch: {epoch} Loss G.: {loss_generator}")
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 train(generator, discriminator, num_epochs, batch_size, train_loader)
 ```
 
 %%%% Output: stream
 
     Epoch: 0 Loss D.: 0.19172890484333038
     Epoch: 0 Loss G.: 2.205277919769287
     Epoch: 10 Loss D.: 0.6455698013305664
     Epoch: 10 Loss G.: 0.8941397666931152
     Epoch: 20 Loss D.: 0.643601655960083
     Epoch: 20 Loss G.: 0.8925288915634155
     Epoch: 30 Loss D.: 0.6730794906616211
     Epoch: 30 Loss G.: 0.9668323993682861
     Epoch: 40 Loss D.: 0.6405140161514282
     Epoch: 40 Loss G.: 0.7298990488052368
     Epoch: 50 Loss D.: 0.6809470057487488
     Epoch: 50 Loss G.: 0.7750263214111328
     Epoch: 60 Loss D.: 0.6326340436935425
     Epoch: 60 Loss G.: 0.8059306144714355
     Epoch: 70 Loss D.: 0.5179269313812256
     Epoch: 70 Loss G.: 1.0993049144744873
     Epoch: 80 Loss D.: 0.5927025675773621
     Epoch: 80 Loss G.: 0.8768778443336487
     Epoch: 90 Loss D.: 0.6255234479904175
     Epoch: 90 Loss G.: 0.7707381248474121
     Epoch: 100 Loss D.: 0.5345417857170105
     Epoch: 100 Loss G.: 0.8872535228729248
     Epoch: 110 Loss D.: 0.6842091083526611
     Epoch: 110 Loss G.: 0.6737083196640015
     Epoch: 120 Loss D.: 0.618989109992981
     Epoch: 120 Loss G.: 1.0244462490081787
     Epoch: 130 Loss D.: 0.651526689529419
     Epoch: 130 Loss G.: 0.7835967540740967
     Epoch: 140 Loss D.: 0.6564826965332031
     Epoch: 140 Loss G.: 0.736600935459137
     Epoch: 150 Loss D.: 0.7063291072845459
     Epoch: 150 Loss G.: 0.8015179634094238
     Epoch: 160 Loss D.: 0.7304964661598206
     Epoch: 160 Loss G.: 0.6758403182029724
     Epoch: 170 Loss D.: 0.6666960716247559
     Epoch: 170 Loss G.: 0.7535879611968994
     Epoch: 180 Loss D.: 0.6781765818595886
     Epoch: 180 Loss G.: 0.7857789993286133
     Epoch: 190 Loss D.: 0.6380088925361633
     Epoch: 190 Loss G.: 0.7979979515075684
     Epoch: 200 Loss D.: 0.6476839780807495
     Epoch: 200 Loss G.: 0.7968341112136841
     Epoch: 210 Loss D.: 0.6286032795906067
     Epoch: 210 Loss G.: 1.0192309617996216
     Epoch: 220 Loss D.: 0.6657217741012573
     Epoch: 220 Loss G.: 0.7269636392593384
     Epoch: 230 Loss D.: 0.7014310359954834
     Epoch: 230 Loss G.: 0.7147063612937927
     Epoch: 240 Loss D.: 0.7104332447052002
     Epoch: 240 Loss G.: 0.7164992690086365
     Epoch: 250 Loss D.: 0.7514128088951111
     Epoch: 250 Loss G.: 1.0906519889831543
     Epoch: 260 Loss D.: 0.5960716009140015
     Epoch: 260 Loss G.: 1.1555901765823364
     Epoch: 270 Loss D.: 0.6859428882598877
     Epoch: 270 Loss G.: 0.682672917842865
     Epoch: 280 Loss D.: 0.6769641041755676
     Epoch: 280 Loss G.: 0.7675719857215881
     Epoch: 290 Loss D.: 0.6696629524230957
     Epoch: 290 Loss G.: 0.764472246170044
 
 %% Cell type:markdown id: tags:
 
 #### Prediction
 
 %% Cell type:markdown id: tags:
 
 Generate a series of random point and let the generator performs on these data poins and show the generated results
 
 %% Cell type:code id: tags:
 
 ``` python
 latent_space_samples = torch.randn(500, 2)
 plt.plot(latent_space_samples[:, 0], latent_space_samples[:, 1], ".")
 generated_samples = generator(latent_space_samples)
 ```
 
 %%%% Output: display_data
 
     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)
 
 %% Cell type:code id: tags:
 
 ``` python
 generated_samples = generated_samples.detach()
 plt.plot(generated_samples[:, 0], generated_samples[:, 1], ".")
 ```
 
 %%%% Output: execute_result
 
     [<matplotlib.lines.Line2D at 0x7f9069e33b70>]
 
 %%%% Output: display_data
 
     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)