Issue 1:
System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04

TensorFlow installed from (source or binary): source

TensorFlow version: 1.13.1

Python version: 2.7

Bazel version (if compiling from source): 0.21.0

GCC/Compiler version (if compiling from source): 7.3.0

CUDA/cuDNN version: 10.0

GPU model and memory: GeForce GTX 970

Describe the problem

The set of CUDA compute capabilities (CCC from now on) is set as {3.0, 5.2} during configuration. File .tf_configure.bazelrc contains build --action_env TF_CUDA_COMPUTE_CAPABILITIES="3.0,5.2" (full file: tf_configure.bazelrc.txt). After transfering the resulting pip package to a PC with CCC 3.0, tensorflow will reject a GPU with CCC lower than 3.5.

Provide the exact sequence of commands / steps that you executed before running into the problem

./configure (resulting in tf_configure.bazelrc.txt)

bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

move /tmp/tensorflow_pkg/tensorflow-1.13.1-cp27-cp27mu-linux_x86_64.whl to other PC with CCC 3.0

Issue 2:
Error message occurs when applying L2 loss weight decay with 1080ti gpu activated

No Error message occurs when applying weight decay with only cpu running

The error message always come out if training with all the Optimizer in anaconda, win10, gpu 1080ti, tensorflow 1.6.0. CUDA 9 CUDNN 7.0

Traceback (most recent call last):
File "train_ADAM_add1CNN.py", line 89, in <module>
_ = sess.run([train_step_adam],feed_dict={img_in:X_mb, label_gt:y_gt})
File "D:\ProgramData\Anaconda4\envs\tff\lib\site-packages\tensorflow\python\client\session.py", line 905, in run
run_metadata_ptr)
File "D:\ProgramData\Anaconda4\envs\tff\lib\site-packages\tensorflow\python\client\session.py", line 1137, in _run
feed_dict_tensor, options, run_metadata)
File "D:\ProgramData\Anaconda4\envs\tff\lib\site-packages\tensorflow\python\client\session.py", line 1355, in _do_run
options, run_metadata)
File "D:\ProgramData\Anaconda4\envs\tff\lib\site-packages\tensorflow\python\client\session.py", line 1374, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: AttrValue must not have reference type value of float_ref
for attr 'tensor_type'

Here is the example code that can duplicate the issue:

...
def optimize_adam(loss, learning_rate, LRdecaysteps, LRdecayrate):
decay_learning_rate = tf.train.exponential_decay(learning_rate, global_step, LRdecaysteps, LRdecayrate, staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
return tf.train.AdamOptimizer(decay_learning_rate).minimize(loss, global_step=global_step), decay_learning_rate

regularization_loss = tf.add_n(tf.losses.get_regularization_losses())
total_loss = total_loss + regularization_loss
train_step_adam,decay_learning_rate = optimize_adam(total_loss,learning_rate,LRdecaysteps,LRdecayrate)
...

it can be run with :

session_conf = tf.ConfigProto(

device_count={'CPU' : 1, 'GPU' : 0},

allow_soft_placement=True,

log_device_placement=False

)

But it will come out this error message with:

session_conf = tf.ConfigProto(

device_count={'CPU' : 1, 'GPU' : 1},

allow_soft_placement=True,

log_device_placement=False

)