将非符号张量传递给 Keras Lambda 层

Question

I am trying to pass a RNNCell object to a Keras lambda layer so that I can use the Tensorflow layer within a Keras model, as follows.

conv_cell = ConvGRUCell(shape = [14, 14],
                       filters = 32,
                       kernel = [3,3],
                       padding = 'SAME')

def convGRU(inputs, cell, length):
    output, final = tf.nn.bidirectional_dynamic_rnn(
            cell, cell, x, length, dtype=tf.float32)
    output = tf.concat(output, -1)
    final = tf.concat(final, -1)
    return [output, final]

lm = Lambda(lambda x: convGRU(x[0], x[1], x[2])([input, conv_cell, length])

但是，我收到一个错误，即conv_cell不是符号张量（它是基于 Tensorflow 的 GRUCell 的自定义层）。

有没有办法将单元格传递到 lambda 层？ 我让它与 functools.partial 一起工作，但它无法保存/加载 model，因为它无法访问 model 内的 function。

Answer 1

def convGRU(cell, length): # if length is produced by the model, use it with the inputs    
    def inner_func(inputs):
        code...
    return inner_func

lm = Lambda(convGRU(cell, length))(input)

对于保存/加载，您需要使用custom_objects = {'convGRU': convGRU, 'cell':cell, 'length': length}等。无论 Keras 不知道自动需要在custom_objects中加载保存的 model。