Pass non-symbolic tensor to Keras Lambda layer

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])

However, I get an error that conv_cell is not a symbolic tensor (it is a custom layer based on Tensorflow's GRUCell).

Is there any way to pass the cell to the lambda layer? I got it to work with functools.partial but it fails to save/load the model because it cannot access the function inside the model.

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)

For save/load you need to use custom_objects = {'convGRU': convGRU, 'cell':cell, 'length': length} , etc. Whatever Keras doesn't know automatically needs to be in custom_objects for loading a saved model.