Source_pytorch/model.py
def forward(self, x):
        """
        Perform a forward pass of our model on input features, x.
        :param x: A batch of input features of size (batch_size, input_features)
        :return: A single, sigmoid-activated value as output
        """
        
        # define the feedforward behavior
        for layer in self.module_list[:-1]:
            x = F.relu(layer(x))
            x = self.dropout(x)
        x = self.module_list[-1](x)
        
        return torch.sigmoid(x)

        Perform a forward pass of our model on input features, x.
        :param x: A batch of input features of size (batch_size, input_features)
        :return: A single, sigmoid-activated value as output
        
Perform a forward pass of our model on input features, x.
Perform a forward pass of our model on input features x .
def forward(self, x):
        for layer in self.module_list[:-1]:
            x = F.relu(layer(x))
            x = self.dropout(x)
        x = self.module_list[-1](x)
        return torch.sigmoid(x)
def forward ( self , x ) : for layer in self . module_list [ : - 1 ] : x = F . relu ( layer ( x ) ) x = self . dropout ( x ) x = self . module_list [ - 1 ] ( x ) return torch . sigmoid ( x )
Perform a forward pass of our model on input features, x.
Perform a forward pass of our model on input features x .
"""
        Perform a forward pass of our model on input features, x.
        :param x: A batch of input features of size (batch_size, input_features)
        :return: A single, sigmoid-activated value as output
        """ # define the feedforward behavior
A single, sigmoid-activated value as output
A single sigmoid - activated value as output
self
x
A batch of input features of size (batch_size, input_features)
A batch of input features of size ( batch_size input_features )