def _call(self, inputs):
    x = inputs
    if self.sparse_inputs:
        x = sparse_dropout(x, (1 - self.dropout), self.num_features_nonzero)
    else:
        x = tf.nn.dropout(x, (1 - self.dropout))
    output = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
    if self.bias:
        output += self.vars['bias']
    return self.act(output)
def _call(self, inputs):
    x = inputs
    if self.sparse_inputs:
        x = sparse_dropout(x, (1 - self.dropout), self.num_features_nonzero)
    else:
        x = tf.nn.dropout(x, (1 - self.dropout))
    output = dot(x, self.vars['weights'], sparse=self.sparse_inputs)
    if self.bias:
        output += self.vars['bias']
    return self.act(output)