layers/layers.py
def forward(self, inputs):
        """ Fully connected layer with non-linear operation.

        Args:
          inputs: 2-D tensor BxN
          num_outputs: int

        Returns:
          Variable tensor of size B x num_outputs.
        """
        inputs = inputs.float()
        if self.activation_fn == None:
            x = self.fc1(inputs)
        else:
            x = F.relu(self.fc1(inputs))
        x = F.dropout(x, self.dropout_prob, training=self.training)

        return x
 Fully connected layer with non-linear operation.

        Args:
          inputs: 2-D tensor BxN
          num_outputs: int

        Returns:
          Variable tensor of size B x num_outputs.
        
Fully connected layer with non-linear operation.
Fully connected layer with non - linear operation .
def forward(self, inputs):
        inputs = inputs.float()
        if self.activation_fn == None:
            x = self.fc1(inputs)
        else:
            x = F.relu(self.fc1(inputs))
        x = F.dropout(x, self.dropout_prob, training=self.training)
        return x
def forward ( self , inputs ) : inputs = inputs . float ( ) if self . activation_fn == None : x = self . fc1 ( inputs ) else : x = F . relu ( self . fc1 ( inputs ) ) x = F . dropout ( x , self . dropout_prob , training = self . training ) return x
Fully connected layer with non-linear operation.
Fully connected layer with non - linear operation .
""" Fully connected layer with non-linear operation.

        Args:
          inputs: 2-D tensor BxN
          num_outputs: int

        Returns:
          Variable tensor of size B x num_outputs.
        """
Variable tensor of size B x num_outputs.
Variable tensor of size B x num_outputs .
self
inputs
2-D tensor BxN
2 - D tensor BxN
num_outputs
None