[ERR] TypeError: Expected int32, got list containing Tensors of type '_Message' instead.

Tensorflow Error 

Error Message:

TypeError: Expected int32, got list containing Tensors of type '_Message' instead.

Error Code:

def average_gradients(tower_grads):
    """Calculate the average gradient for each shared variable across all towers.
    Note that this function provides a synchronization point across all towers.
    Args:
    tower_grads: List of lists of (gradient, variable) tuples. The outer list
      is over individual gradients. The inner list is over the gradient
      calculation for each tower.
    Returns:
     List of pairs of (gradient, variable) where the gradient has been averaged
     across all towers.
     
     
    """
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        # Note that each grad_and_vars looks like the following:
        #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
        grads = []
        for g, _ in grad_and_vars:
            # Add 0 dimension to the gradients to represent the tower.
            expanded_g = tf.expand_dims(g, 0)
 
            # Append on a 'tower' dimension which we will average over below.
            grads.append(expanded_g)
 
        # Average over the 'tower' dimension.
        grad = tf.concat(0, grads)
        grad = tf.reduce_mean(grad, 0)
 
        # Keep in mind that the Variables are redundant because they are shared
        # across towers. So .. we will just return the first tower's pointer to
        # the Variable.
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads
 
--------------------
call function
 
with graph.as_default():
    # Average the gradients
    grads_e = average_gradients(tower_grads_e)
    grads_g = average_gradients(tower_grads_g)
    grads_d = average_gradients(tower_grads_d)
 
    # apply the gradients with our optimizers
    train_E = opt_E.apply_gradients(grads_e, global_step=global_step)
    train_G = opt_G.apply_gradients(grads_g, global_step=global_step)
    train_D = opt_D.apply_gradients(grads_d, global_step=global_step)
 
-------------------------------

Problem:

The order of tf.concat's arguments changed with 1.0.

Solution:

grad = tf.concat(0, grads) 

to

grad = tf.concat(grads, 0) 

Reference:

https://github.com/tensorflow/tensorflow/issues/7031