edc5cf0c0e22b02d52de1b2e3ab89daceaa70c97,tensorlayer/layers.py,BiDynamicRNNLayer,__init__,#BiDynamicRNNLayer#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,4125

Before Change

                // cell_instance_fn2=cell_instance_fn            // HanSheng
                // cell_instance_fn=lambda: MultiRNNCell_fn([cell_instance_fn2() for _ in range(n_layer)])
                self.fw_cell = MultiRNNCell_fn([self.fw_cell] * n_layer)
                self.bw_cell = MultiRNNCell_fn([self.bw_cell] * n_layer)
            // self.fw_cell=cell_instance_fn()
            // self.bw_cell=cell_instance_fn()
            // Initial state of RNN
            if fw_initial_state is None:

After Change

            else:
                cell_creator = rnn_creator
            self.fw_cell = cell_creator()
            self.bw_cell = cell_creator()
            // Apply multiple layers
            if n_layer > 1:
                try:
                    MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell

Instances