Commit 6d5cdf72 by Jacky Lin

### a

parent 17d0a7a1
 ... ... @@ -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|$Sigmoid(x)=\\sigma(x)= \\frac{1}{1+e^{-x}}$|" ] }, {
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 %% 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 %% 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 [] %%%% Output: display_data 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) %% 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 ![](data:image/png;base64,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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 [] %%%% Output: display_data ![](data:image/png;base64,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) ... ...