dcef47168e1d630fe02634f6a542df0c3571228d,tensorlayer/layers.py,BiDynamicRNNLayer,__init__,#BiDynamicRNNLayer#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,4132

Before Change

        with tf.variable_scope(name, initializer=initializer) as vs:
            // Creats the cell function
            // cell_instance_fn=lambda: cell_fn(num_units=n_hidden, **cell_init_args) // HanSheng
            self.fw_cell = cell_fn(num_units=n_hidden, **cell_init_args)
            self.bw_cell = cell_fn(num_units=n_hidden, **cell_init_args)

            // Apply dropout
            if dropout:
                if type(dropout) in [tuple, list]:
                    in_keep_prob = dropout[0]
                    out_keep_prob = dropout[1]
                elif isinstance(dropout, float):
                    in_keep_prob, out_keep_prob = dropout, dropout
                else:
                    raise Exception("Invalid dropout type (must be a 2-D tuple of "
                                    "float)")
                try:
                    DropoutWrapper_fn = tf.contrib.rnn.DropoutWrapper
                except:
                    DropoutWrapper_fn = tf.nn.rnn_cell.DropoutWrapper

                    // cell_instance_fn1=cell_instance_fn            // HanSheng
                    // cell_instance_fn=lambda: DropoutWrapper_fn(
                    //                     cell_instance_fn1(),
                    //                     input_keep_prob=in_keep_prob,
                    //                     output_keep_prob=out_keep_prob)

                self.fw_cell = DropoutWrapper_fn(
                    self.fw_cell,
                    input_keep_prob=in_keep_prob,
                    output_keep_prob=out_keep_prob)
                self.bw_cell = DropoutWrapper_fn(
                    self.bw_cell,
                    input_keep_prob=in_keep_prob,
                    output_keep_prob=out_keep_prob)
            // Apply multiple layers
            if n_layer > 1:
                try:
                    MultiRNNCell_fn = tf.contrib.rnn.MultiRNNCell
                except:
                    MultiRNNCell_fn = tf.nn.rnn_cell.MultiRNNCell

                // 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

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

Instances