def output(self, input=None, dropout_active=True, *args, **kwargs):
        # use the 'dropout_active' keyword argument to disable it at test time.
        # It is on by default.
        if input is None:
            input = self.input_layer.output(dropout_active=dropout_active,
                                            *args, **kwargs)
        if len(self.input_layer.get_output_shape()) > 2:
            input = input.reshape(self.flatinput_shape)

        if dropout_active and (self.dropout > 0.):
            retain_prob = 1 - self.dropout
            input = (input / retain_prob *
                     srng.binomial(input.shape,
                                   p=retain_prob,
                                   dtype='int32').astype('float32'))
            # apply the input mask and rescale the input accordingly.
            # By doing this it's no longer necessary to rescale the weights
            # at test time.

        return self.nonlinearity(T.dot(input, self.W))
dataset/ETHPy150Open ifp-uiuc/anna/anna/layers/layers.py/DenseNoBiasLayer.output/original