Feeding tensors for training vs validation data

Question

In the tensorflow examples, feed_dict is used to send either training or validation input into the same model graph. Unfortunately, you cannot feed tensors:

    Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.

I've been using an input pipeline and TFRecordReader, so my data never really enters python. Having to call run to get the data into python just to feed it back to tensorflow seems silly and is definitely slow.

Does anyone have a good solution for this?

Currently, I just create two identical copies of the model subgraph that use the same parameters. This works, but forces me to organize my code in an odd way.

EDIT

For example, I'm currently doing something like:

model_params = BuildModelParams()
train_model = BuildModel(model_params, train_input)
test_model = BuildModel(model_params, test_input)

so that the test model uses the parameters learned by training. The nice thing about feed_dict is that I only need to define the model once and I do not have to separate the model's parameters from its structure.

Answer 1

Warning:

This solution can cause significant problems when input queues are involved. See: https://groups.google.com/a/tensorflow.org/forum/#!msg/discuss/mLrt5qc9_uU/sGNbC7GpAwAJ

Thanks to @fwalch for pointing this out in the comments

---

There's no way to do exactly what you're asking, see the answer to my question here.

But the newly public "cond" from version 0.7 can fill your use case:

# Here are the two data streams.
train_data = tf.Variable(999)
test_data = tf.Variable(1000)

# This selects which stream to use.
select_test = tf.placeholder(dtype=bool,shape=[],name='select_test')
data = tf.cond(
    select_test,
    lambda:test_data,
    lambda:train_data
)

# Here is the model.
model = data-500;

init = tf.initialize_all_variables()
with tf.Session():
    init.run()

    # You just have to feed `select_test` when you evaluate the model.
    print(model.eval({select_test:False})) #499
    print(model.eval({select_test:True})) #500

You can use the same sort of trick for switching Batch Normalization to use a moving average during test.