5e2b9358d6741840f3b86adbaf4f5ac8a99ea9ee,keras/layers/recurrent.py,GRU,get_output,#GRU#Any#,226

Before Change

        outputs, updates = theano.scan(
            self._step, 
            sequences=[x_z, x_r, x_h], 
            outputs_info=alloc_zeros_matrix(X.shape[1], self.output_dim),
            non_sequences=[self.U_z, self.U_r, self.U_h],
            truncate_gradient=self.truncate_gradient
        )

After Change

        outputs, updates = theano.scan(
            self._step, 
            sequences=[x_z, x_r, x_h], 
            outputs_info=T.unbroadcast(alloc_zeros_matrix(X.shape[1], self.output_dim), 1),
            non_sequences=[self.U_z, self.U_r, self.U_h],
            truncate_gradient=self.truncate_gradient
        )

Instances