(optimizer, objective, var_list, clip_val=10)
return optimizer.apply_gradients(gradients)
Minimized `objective` using `optimizer` w.r.t. variables in
    `var_list` while ensure the norm of the gradients for each
    variable is clipped to `clip_val`
Minimized `objective` using `optimizer` w.r.t. variables in
    `var_list` while ensure the norm of the gradients for each
    variable is clipped to `clip_val`
Minimized objective using optimizer w . r . t . variables in var_list while ensure the norm of the gradients for each variable is clipped to clip_val
def minimize_and_clip(optimizer, objective, var_list, clip_val=10):
    """Minimized `objective` using `optimizer` w.r.t. variables in
    `var_list` while ensure the norm of the gradients for each
    variable is clipped to `clip_val`
    """
    gradients = optimizer.compute_gradients(objective, var_list=var_list)
    for i, (grad, var) in enumerate(gradients):
        if grad is not None:
            gradients[i] = (tf.clip_by_norm(grad, clip_val), var)
    return optimizer.apply_gradients(gradients)
def minimize_and_clip ( optimizer , objective , var_list , clip_val = 10 ) : gradients = optimizer . compute_gradients ( objective , var_list = var_list ) for i , ( grad , var ) in enumerate ( gradients ) : if grad is not None : gradients [ i ] = ( tf . clip_by_norm ( grad , clip_val ) , var ) return optimizer . apply_gradients ( gradients )