CODEWITHSUNDEEP
tensorflow
mskel
mskel

Reputation: 872

Running the same RNN over two tensors in tensorflow

I'd like to run the same RNN over two tensors in tensorflow. My current solution looks like this:

cell = tf.nn.rnn_cell.GRUCell(cell_size)

with tf.variable_scope("encoder", reuse=None):
    out1 = tf.nn.dynamic_rnn(cell, tensor1, dtype=tf.float32)

with tf.variable_scope("encoder", reuse=True):
    out2 = tf.nn.dynamic_rnn(cell, tensor2, dtype=tf.float32)

Is this is the best way to ensure that the weights between the two RNN ops are shared?

Upvotes: 1

Views: 143

Answers (1)

chasep255
chasep255

Reputation: 12175

Yeah that is basically how I would do it. For a really simple model like this it does not matter much but for a more complicated model I would define a function to build the graph. 

def makeEncoder(input_tensor):
    cell = tf.nn.rnn_cell.GRUCell(cell_size)
    return tf.nn.dynamic_rnn(cell, tensor1, dtype=tf.float32)

with tf.variable_scope('encoder') as scope:
    out1 = makeEncoder(tensor1)
    scope.reuse_variables()
    out2 = makeEncoder(tensor2)

The other way to do it would be to use tf.cond(...) as a switch to change between the inputs based on a boolean placeholder. They would then go to just one output. I have found that this can get a bit messy. Also you would need to provide both inputs even if you really only need one. I think my first solution is the best.

Upvotes: 1

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