构建TensorFlow双向RNN触发TypeError错误，寻求修复方案

尝试通过TensorFlow手绘图形分类教程训练循环神经网络，运行以下代码时：

def _add_regular_rnn_layers(convolved, lengths):
    """Adds RNN layers."""
    if params["cell_type"] == "lstm":
        cell = tf.compat.v1.nn.rnn_cell.BasicLSTMCell
    elif params["cell_type"] == "block_lstm":
        cell = tf.compat.v1.keras.layers.LSTMCell
    cells_fw = [cell(params["num_nodes"]) for _ in range(params["num_layers"])]
    cells_bw = [cell(params["num_nodes"]) for _ in range(params["num_layers"])]
    if params["dropout"] > 0.0:
        cells_fw = [tf.compat.v1.nn.rnn_cell.DropoutWrapper(cell) for cell in cells_fw]
        cells_bw = [tf.compat.v1.nn.rnn_cell.DropoutWrapper(cell) for cell in cells_bw]
    bidirectional_rnn = tf.keras.layers.Bidirectional(
        tf.keras.layers.RNN(cells_fw, return_sequences=True),
        merge_mode='concat',
        backward_layer=tf.keras.layers.RNN(cells_bw, return_sequences=True)
    )
    outputs = bidirectional_rnn(convolved)
    return outputs

在执行bidirectional_rnn = tf.keras.layers.Bidirectional(这一行时，出现错误：

TypeError: The argument 'cell' ({'class_name': 'BasicLSTMCell', 'config': {'name': 'basic_lstm_cell_1', 'trainable': True, 'dtype': None, 'num_units': 128, 'forget_bias': 1.0, 'state_is_tuple': True, 'activation': 'tanh', 'reuse': None}}) is not an RNNCell: 'output_size' property is missing, 'state_size' property is missing, either 'zero_state' or 'get_initial_state' method is required, is not callable.

• 统一API风格：代码混用了TensorFlow 1.x低阶RNN Cell和TensorFlow 2.x Keras RNN层，两者不兼容，需统一使用Keras原生组件：
  1. 改用Keras原生LSTM Cell适配tf.keras.layers.RNN：

     if params["cell_type"] == "lstm":
         cell = tf.keras.layers.LSTMCell
     elif params["cell_type"] == "block_lstm":
         cell = tf.keras.layers.LSTMCell  # Keras中无需区分普通LSTM和BlockLSTM，直接用LSTMCell即可
     

  2. 更简洁的方案是直接使用Keras完整LSTM层，无需手动构建Cell列表：

     def _add_regular_rnn_layers(convolved, lengths):
         """Adds RNN layers."""
         # 构建堆叠的LSTM层
         stacked_layers = []
         for _ in range(params["num_layers"]):
             lstm = tf.keras.layers.LSTM(
                 params["num_nodes"],
                 return_sequences=True,
                 dropout=params["dropout"] if params["dropout"] > 0 else 0
             )
             stacked_layers.append(lstm)
         
         # 双向RNN包装
         if params["num_layers"] > 1:
             rnn_cell = tf.keras.layers.StackedRNNCells(stacked_layers)
             bidirectional_rnn = tf.keras.layers.Bidirectional(
                 tf.keras.layers.RNN(rnn_cell, return_sequences=True),
                 merge_mode='concat'
             )
         else:
             bidirectional_rnn = tf.keras.layers.Bidirectional(
                 stacked_layers[0],
                 merge_mode='concat'
             )
         
         outputs = bidirectional_rnn(convolved)
         return outputs
     

• 替换DropoutWrapper：TensorFlow 1.x的DropoutWrapper不适用于Keras Cell，直接使用Keras层自带的dropout参数即可实现正则化。
• 多层RNN处理：若需堆叠多层RNN，用tf.keras.layers.StackedRNNCells包裹Cell列表后传入tf.keras.layers.RNN，或直接堆叠多个LSTM层再接入双向层。

内容的提问来源于stack exchange，提问作者Bobby Martins