Done: AE, VAE, AAE
ToDo: Double AAE, Visualization All Modularized
This commit is contained in:
Si11ium
66
networks/adverserial_auto_encoder.py
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66
networks/adverserial_auto_encoder.py
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from networks.auto_encoder import AutoEncoder
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from torch.nn.functional import mse_loss
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from torch.nn import Sequential, Linear, ReLU, Dropout, Sigmoid
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from torch.distributions import Normal
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from networks.modules import *
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import torch
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class AdversarialAutoEncoder(AutoEncoder):
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def __init__(self, *args, **kwargs):
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super(AdversarialAutoEncoder, self).__init__(*args, **kwargs)
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self.discriminator = Discriminator(self.latent_dim, self.dataParams)
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def forward(self, batch):
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# Encoder
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# outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
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z = self.encoder(batch)
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# Decoder
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# First repeat the data accordingly to the batch size
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z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
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x_hat = self.decoder(z_repeatet)
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return z, x_hat
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class AdversarialAELightningOverrides:
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def forward(self, x):
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return self.network.forward(x)
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def training_step(self, batch, _, optimizer_i):
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if optimizer_i == 0:
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# ---------------------
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# Train Discriminator
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# ---------------------
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# latent_fake, reconstruction
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latent_fake, _ = self.network.forward(batch)
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latent_real = self.normal.sample(latent_fake.shape)
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# Evaluate the input
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d_real_prediction = self.network.discriminator.forward(latent_real)
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d_fake_prediction = self.network.discriminator.forward(latent_fake)
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# Train the discriminator
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d_loss_real = mse_loss(d_real_prediction, torch.zeros(d_real_prediction.shape))
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d_loss_fake = mse_loss(d_fake_prediction, torch.ones(d_fake_prediction.shape))
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# Calculate the mean over both the real and the fake acc
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d_loss = 0.5 * torch.add(d_loss_real, d_loss_fake)
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return {'loss': d_loss}
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elif optimizer_i == 1:
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# ---------------------
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# Train AutoEncoder
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# ---------------------
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# z, x_hat
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_, batch_hat = self.forward(batch)
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loss = mse_loss(batch, batch_hat)
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return {'loss': loss}
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else:
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raise RuntimeError('This should not have happened, catch me if u can.')
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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45
networks/auto_encoder.py
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45
networks/auto_encoder.py
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@ -0,0 +1,45 @@
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from .modules import *
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from torch.nn.functional import mse_loss
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from torch import Tensor
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#######################
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# Basic AE-Implementation
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class AutoEncoder(Module, ABC):
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@property
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def name(self):
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return self.__class__.__name__
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def __init__(self, dataParams, **kwargs):
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super(AutoEncoder, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = kwargs.get('latent_dim', 2)
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self.encoder = Encoder(self.latent_dim)
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self.decoder = Decoder(self.latent_dim, self.dataParams['features'])
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def forward(self, batch: Tensor):
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# Encoder
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# outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
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z = self.encoder(batch)
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# Decoder
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# First repeat the data accordingly to the batch size
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z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
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x_hat = self.decoder(z_repeatet)
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return z, x_hat
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class AutoEncoderLightningOverrides:
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def forward(self, x):
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return self.network.forward(x)
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def training_step(self, x, batch_nb):
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# z, x_hat
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_, x_hat = self.forward(x)
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loss = mse_loss(x, x_hat)
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return {'loss': loss}
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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@ -1,73 +0,0 @@
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from torch.nn import Sequential, Linear, GRU, ReLU, Tanh
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from .modules import *
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from torch.nn.functional import mse_loss
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#######################
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# Basic AE-Implementation
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class BasicAE(Module, ABC):
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@property
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def name(self):
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return self.__class__.__name__
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def __init__(self, dataParams, **kwargs):
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super(BasicAE, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = kwargs.get('latent_dim', 2)
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self.encoder = self._build_encoder()
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self.decoder = self._build_decoder(out_shape=self.dataParams['features'])
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def _build_encoder(self):
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encoder = Sequential(
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Linear(6, 100, bias=True),
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ReLU(),
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Linear(100, 10, bias=True),
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ReLU()
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)
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gru = Sequential(
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TimeDistributed(encoder),
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GRU(10, 10, batch_first=True),
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RNNOutputFilter(only_last=True),
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Linear(10, self.latent_dim)
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)
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return gru
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def _build_decoder(self, out_shape):
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decoder = Sequential(
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Linear(10, 100, bias=True),
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ReLU(),
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Linear(100, out_shape, bias=True),
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Tanh()
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)
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gru = Sequential(
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GRU(self.latent_dim, 10,batch_first=True),
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RNNOutputFilter(),
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TimeDistributed(decoder)
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)
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return gru
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def forward(self, batch: torch.Tensor):
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# Encoder
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# outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
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z = self.encoder(batch)
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# Decoder
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# First repeat the data accordingly to the batch size
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z = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
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x_hat = self.decoder(z)
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return z, x_hat
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class AELightningOverrides:
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def training_step(self, x, batch_nb):
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# z, x_hat
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_, x_hat = self.forward(x)
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loss = mse_loss(x, x_hat)
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return {'loss': loss}
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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@ -1,81 +0,0 @@
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from torch.nn import Sequential, Linear, GRU, ReLU
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from .modules import *
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from torch.nn.functional import mse_loss
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#######################
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# Basic AE-Implementation
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class BasicVAE(Module, ABC):
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@property
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def name(self):
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return self.__class__.__name__
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def __init__(self, dataParams, **kwargs):
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super(BasicVAE, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = kwargs.get('latent_dim', 2)
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self.encoder = self._build_encoder()
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self.decoder = self._build_decoder(out_shape=self.dataParams['features'])
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self.mu, self.logvar = Linear(10, self.latent_dim), Linear(10, self.latent_dim)
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def _build_encoder(self):
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linear_stack = Sequential(
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Linear(6, 100, bias=True),
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ReLU(),
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Linear(100, 10, bias=True),
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ReLU()
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)
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encoder = Sequential(
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TimeDistributed(linear_stack),
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GRU(10, 10, batch_first=True),
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RNNOutputFilter(only_last=True),
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)
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return encoder
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def reparameterize(self, mu, logvar):
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# Lambda Layer, add gaussian noise
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std = torch.exp(0.5*logvar)
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eps = torch.randn_like(std)
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return mu + eps*std
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def _build_decoder(self, out_shape):
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decoder = Sequential(
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Linear(10, 100, bias=True),
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ReLU(),
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Linear(100, out_shape, bias=True),
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ReLU()
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)
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sequential_decoder = Sequential(
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GRU(self.latent_dim, 10, batch_first=True),
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RNNOutputFilter(),
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TimeDistributed(decoder)
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)
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return sequential_decoder
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def forward(self, batch):
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encoding = self.encoder(batch)
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mu_logvar = self.mu(encoding), self.logvar(encoding)
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z = self.reparameterize(*mu_logvar)
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repeat = Repeater((batch.shape[0], self.dataParams['size'], -1))
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x_hat = self.decoder(repeat(z))
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return (x_hat, *mu_logvar)
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class VAELightningOverrides:
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def training_step(self, x, batch_nb):
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x_hat, logvar, mu = self.forward(x)
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BCE = mse_loss(x_hat, x, reduction='mean')
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# see Appendix B from VAE paper:
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# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
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# https://arxiv.org/abs/1312.6114
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# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
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KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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return {'loss': BCE + KLD}
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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@ -1,6 +1,6 @@
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import torch
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import pytorch_lightning as pl
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from torch.nn import Module
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from torch.nn import Module, Linear, ReLU, Tanh, Sigmoid, Dropout, GRU
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from abc import ABC, abstractmethod
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@ -85,9 +85,10 @@ class Repeater(Module):
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self.shape = shape
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def forward(self, x: torch.Tensor):
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x.unsqueeze_(-2)
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x = x.unsqueeze(-2)
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return x.expand(self.shape)
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class RNNOutputFilter(Module):
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def __init__(self, return_output=True, only_last=False):
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@ -101,5 +102,108 @@ class RNNOutputFilter(Module):
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return out if not self.only_last else out[:, -1, :]
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#######################
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# Network Modules
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# Generators, Decoders, Encoders, Discriminators
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class Discriminator(Module):
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def __init__(self, latent_dim, dataParams, dropout=.0, activation=ReLU):
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super(Discriminator, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = latent_dim
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self.l1 = Linear(self.latent_dim, self.dataParams['features'] * 10)
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self.l2 = Linear(self.dataParams['features']*10, self.dataParams['features'] * 20)
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self.lout = Linear(self.dataParams['features']*20, 1)
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self.dropout = Dropout(dropout)
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self.activation = activation()
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self.sigmoid = Sigmoid()
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def forward(self, x, **kwargs):
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tensor = self.l1(x)
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tensor = self.dropout(self.activation(tensor))
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tensor = self.l2(tensor)
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tensor = self.dropout(self.activation(tensor))
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tensor = self.lout(tensor)
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tensor = self.sigmoid(tensor)
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return tensor
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class DecoderLinearStack(Module):
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def __init__(self, out_shape):
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super(DecoderLinearStack, self).__init__()
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self.l1 = Linear(10, 100, bias=True)
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self.l2 = Linear(100, out_shape, bias=True)
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self.activation = ReLU()
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self.activation_out = Tanh()
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def forward(self, x):
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tensor = self.l1(x)
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tensor = self.activation(tensor)
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tensor = self.l2(tensor)
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tensor = self.activation_out(tensor)
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return tensor
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class EncoderLinearStack(Module):
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def __init__(self):
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super(EncoderLinearStack, self).__init__()
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self.l1 = Linear(6, 100, bias=True)
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self.l2 = Linear(100, 10, bias=True)
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self.activation = ReLU()
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def forward(self, x):
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tensor = self.l1(x)
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tensor = self.activation(tensor)
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tensor = self.l2(tensor)
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tensor = self.activation(tensor)
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return tensor
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class Encoder(Module):
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def __init__(self, lat_dim, variational=False):
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self.lat_dim = lat_dim
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self.variational = variational
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super(Encoder, self).__init__()
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self.l_stack = TimeDistributed(EncoderLinearStack())
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self.gru = GRU(10, 10, batch_first=True)
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self.filter = RNNOutputFilter(only_last=True)
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if variational:
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self.mu = Linear(10, self.lat_dim)
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self.logvar = Linear(10, self.lat_dim)
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else:
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self.lat_dim_layer = Linear(10, self.lat_dim)
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def forward(self, x):
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tensor = self.l_stack(x)
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tensor = self.gru(tensor)
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tensor = self.filter(tensor)
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if self.variational:
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tensor = self.mu(tensor), self.logvar(tensor)
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else:
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tensor = self.lat_dim_layer(tensor)
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return tensor
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class Decoder(Module):
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def __init__(self, latent_dim, *args, variational=False):
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self.variational = variational
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super(Decoder, self).__init__()
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self.g = GRU(latent_dim, 10, batch_first=True)
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self.filter = RNNOutputFilter()
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self.l_stack = TimeDistributed(DecoderLinearStack(*args))
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pass
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def forward(self, x):
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tensor = self.g(x)
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tensor = self.filter(tensor)
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tensor = self.l_stack(tensor)
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return tensor
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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53
networks/variational_auto_encoder.py
Normal file
53
networks/variational_auto_encoder.py
Normal file
@ -0,0 +1,53 @@
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from .modules import *
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from torch.nn.functional import mse_loss
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#######################
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# Basic AE-Implementation
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class VariationalAutoEncoder(Module, ABC):
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@property
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def name(self):
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return self.__class__.__name__
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def __init__(self, dataParams, **kwargs):
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super(VariationalAutoEncoder, self).__init__()
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self.dataParams = dataParams
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self.latent_dim = kwargs.get('latent_dim', 2)
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self.encoder = Encoder(self.latent_dim, variational=True)
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self.decoder = Decoder(self.latent_dim, self.dataParams['features'], variational=True)
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@staticmethod
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def reparameterize(mu, logvar):
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# Lambda Layer, add gaussian noise
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std = torch.exp(0.5*logvar)
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eps = torch.randn_like(std)
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return mu + eps*std
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def forward(self, batch):
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mu, logvar = self.encoder(batch)
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z = self.reparameterize(mu, logvar)
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repeat = Repeater((batch.shape[0], self.dataParams['size'], -1))
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x_hat = self.decoder(repeat(z))
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return x_hat, mu, logvar
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class VariationalAutoEncoderLightningOverrides:
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def forward(self, x):
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return self.network.forward(x)
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def training_step(self, x, _):
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x_hat, logvar, mu = self.forward(x)
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BCE = mse_loss(x_hat, x, reduction='mean')
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# see Appendix B from VAE paper:
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# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
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# https://arxiv.org/abs/1312.6114
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# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
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KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
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return {'loss': BCE + KLD}
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if __name__ == '__main__':
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raise PermissionError('Get out of here - never run this module')
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