將非符號張量傳遞給 Keras Lambda 層

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，我收到一個錯誤，即conv_cell不是符號張量（它是基於 Tensorflow 的 GRUCell 的自定義層）。

有沒有辦法將單元格傳遞到 lambda 層？ 我讓它與 functools.partial 一起工作，但它無法保存/加載 model，因為它無法訪問 model 內的 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)

對於保存/加載，您需要使用custom_objects = {'convGRU': convGRU, 'cell':cell, 'length': length}等。無論 Keras 不知道自動需要在custom_objects中加載保存的 model。