pyro bayesian lstm

Caio Henrique answered on February 27, 2024 Popularity 1/10 Helpfulness 1/10

answer pyro bayesian lstm

pyro bayesian lstm

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self.activation = nn.ReLU()  # or nn.ReLU()
    # Correctly initialize the LSTM layer with bidirectional=False
    self.lstm = PyroModule[nn.LSTM](input_size, hidden_size, num_layers, batch_first=True, bidirectional=False)
    self.linear = PyroModule[nn.Linear](hidden_size,  128)  # Adjusted for unidirectional LSTM
    self.fc = PyroModule[nn.Linear](128, num_classes)

    # Initialize weights and biases for each layer
    # Input to hidden layer
    self.lstm.weight_ih_l0 = PyroSample(dist.Normal(0., prior_scale).expand([4*hidden_size, input_size]).to_event(2))
    self.lstm.bias_ih_l0 = PyroSample(dist.Normal(0., prior_scale).expand([4*hidden_size]).to_event(1))
    
    # Hidden to hidden layer
    self.lstm.weight_hh_l0 = PyroSample(dist.Normal(0., prior_scale).expand([4*hidden_size, hidden_size]).to_event(2))
    self.lstm.bias_hh_l0= PyroSample(dist.Normal(0., prior_scale).expand([4*hidden_size]).to_event(1))

 
    self.linear.weight = PyroSample(dist.Normal(0., prior_scale).expand([128,hidden_size]).to_event(2))
    self.linear.bias = PyroSample(dist.Normal(0., prior_scale).expand([128]).to_event(1))
    self.fc.weight = PyroSample(dist.Normal(0., prior_scale).expand([num_classes, 128]).to_event(2))
    self.fc.bias = PyroSample(dist.Normal(0., prior_scale).expand([num_classes]).to_event(1))
    
def forward(self, x, y=None,noise_shape = 0.5):
    
    h_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #hidden state
    c_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) #internal state
    
    output, (hn, cn) = self.lstm(x, (h_0, c_0)) #lstm with input, hidden, and internal state
         
    hn = hn.view(-1, self.hidden_size) #reshaping the data for Dense layer next
    
    out = self.activation(hn)
    out = self.linear(out)
    out = self.activation(out)
    mu = self.fc(out)
    sigma = pyro.sample("sigma", dist.Gamma(noise_shape, 1))  # infer the response noise

    with pyro.plate("data", x.shape[0]):
        obs = pyro.sample("obs", dist.Normal(mu, sigma * sigma), obs=y)
    return mu

Popularity 1/10 Helpfulness 1/10 Language python

Source: forum.pyro.ai

Tags: bayesian lstm pyro python

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Contributed on Feb 27 2024

Caio Henrique

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pyro bayesian lstm

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