TensorFlow: How can I reuse Adam optimizer variables?

conv-neural-network, python, tensorflow

Solution

Turns out I didn't need to instantiate two different Adam optimizers. I just created a single instance and there was no name conflict or issue of trying to share variables. I use the same optimizer regardless of which network branches are being updated:

    e_grads = opt.compute_gradients(e_loss)
with tf.control_dependencies([opt.apply_gradients(e_grads), e_increment_step]):
    e_train = tf.no_op(name='english_train')

and...

    s_grads = opt.compute_gradients(s_loss)
with tf.control_dependencies([opt.apply_gradients(s_grads), s_increment_step]):
    s_train = tf.no_op(name='spanish_train')

Interestingly with the older version of Tensorflow there was no issue with using two Adam instances even though the M branch names conflicted...

Problem

After recently upgrading my TensorFlow version, I am encountering this error which I am not able to solve: ``` Traceback (most recent call last): File "cross_train.py", line 177, in <module> train_network(use_gpu=True) File "cross_train.py", line 46, in train_network with tf.control_dependencies([s_opt.apply_gradients(s_grads), s_increment_step]): ... ValueError: Variable image-conv1-layer/weights/Adam/ already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at: File "cross_train.py", line 34, in train_network with tf.control_dependencies([e_opt.apply_gradients(e_grads), e_increment_step]): File "cross_train.py", line 177, in <module> train_network(use_gpu=True) ``` My model architecture is 3 different convolutional neural network branches: M, E, S. In training, I am trying to alternate steps where I propagate samples through M & E (dot product distance of their embeddings) and update with Adam; then propagate samples through M & S and update with Adam; and repeat. So basically M is fixed (getting updated every step), but E and S branches alternate getting updated. As such I created two instances of AdamOptimizer (`e_opt` and `s_opt`) but I get the error because the weight variable `M-conv1/weights/Adam/` already exists when I try to update the S branch. This was not happening to me before I updated my TensorFlow version. I know how to set reuse of variables generally in TensorFlow, for example: ``` with tf.variable_scope(name, values=[input_to_layer]) as scope: try: weights = tf.get_variable("weights", [height, width, input_to_layer.get_shape()[3], channels], initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) bias = tf.get_variable("bias", [channels], initializer=tf.constant_initializer(0.0, dtype=tf.float32)) except ValueError: scope.reuse_variables() weights = tf.get_variable("weights", [height, width, input_to_layer.get_shape()[3], channels], initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) bias = tf.get_variable("bias", [channels], initializer=tf.constant_initializer(0.0, dtype=tf.float32)) ``` But I'm not sure if I can do the same for Adam. Any ideas? Help would be much appreciated.

Original source