我无法解决我要到达这里的这个问题。我在Tensorflow 2上运行，我真的不明白为什么会出现此错误。有什么我想念的吗？
这是代码中出现错误的相关部分：
from tensorflow.lite.experimental.examples.lstm.rnn import bidirectional_dynamic_rnn
from tensorflow.lite.experimental.examples.lstm.rnn_cell import TFLiteLSTMCell
...
lstm_cells = []
lstm_0 = TFLiteLSTMCell(num_units=256, forget_bias=0, name='rnn_0')
lstm_1 = TFLiteLSTMCell(num_units=256, forget_bias=0, name='rnn_1')
lstm_2 = TFLiteLSTMCell(num_units=128, forget_bias=0, name='rnn_2')
lstm_3 = TFLiteLSTMCell(num_units=128, forget_bias=0, name='rnn_3')
lstm_cells.append(lstm_0)
lstm_cells.append(lstm_1)
lstm_cells.append(lstm_2)
lstm_cells.append(lstm_3)
bi_LSTM_2 = layers.Lambda(buildLstmLayer, arguments={'layers' : lstm_cells})(fc_1)
...
这是相应的Lambda层。我正在创建双向RNN，但我认为错误更多是与TFLiteLSTMCell本身有关，但我认为我正确使用了它。
def buildLstmLayer(inputs, layers):
inputs = tf.transpose(inputs, [1,0,2])
# inputs = tf.unstack(inputs, axis=1)
inter_output, _ = bidirectional_dynamic_rnn (
layers[0],
layers[1],
inputs,
dtype='float32',
time_major=True)
inter_output = tf.concat(inter_output, 2)
output, _ = bidirectional_dynamic_rnn (
layers[2],
layers[3],
inter_output,
dtype='float32',
time_major=True)
output = tf.concat(output, 2)
# output = tf.stack(output, axis=1)
output = tf.transpose(output, [1,0,2])
return output
这是我得到的回溯：
Traceback (most recent call last):
File "crnn_architecture.py", line 279, in <module>
model, base_model = CRNN_model(is_training=True)
File "crnn_architecture.py", line 108, in CRNN_model
bi_LSTM_2 = layers.Lambda(buildLstmLayer, arguments={'layers' : lstm_cells})(fc_1)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py", line 847, in __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/core.py", line 795, in call
return self.function(inputs, **arguments)
File "crnn_architecture.py", line 146, in buildLstmLayer
time_major=True)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/lite/experimental/examples/lstm/rnn.py", line 379, in bidirectional_dynamic_rnn
scope=fw_scope)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/lite/experimental/examples/lstm/rnn.py", line 266, in dynamic_rnn
dtype=dtype)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn.py", line 916, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/control_flow_ops.py", line 2675, in while_loop
back_prop=back_prop)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/while_v2.py", line 198, in while_loop
add_control_dependencies=add_control_dependencies)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/while_v2.py", line 176, in wrapped_body
outputs = body(*_pack_sequence_as(orig_loop_vars, args))
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn.py", line 884, in _time_step
(output, new_state) = call_cell()
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn.py", line 870, in <lambda>
call_cell = lambda: cell(input_t, state)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py", line 847, in __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/recurrent.py", line 137, in call
inputs, states = cell.call(inputs, states, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/lite/experimental/examples/lstm/rnn_cell.py", line 440, in call
if input_size.value is None:
AttributeError: 'int' object has no attribute 'value'