Tensorflow: How to feed a placeholder variable with a tensor?

Question

I have a placeholder variable that expects a batch of input images:

input_placeholder = tf.placeholder(tf.float32, [None] + image_shape, name='input_images') 

Now I have 2 sources for the input data:
1) a tensor and
2) some numpy data.

For the numpy input data, I know how to feed data to the placeholder variable:

sess = tf.Session() mLoss, = sess.run([loss], feed_dict = {input_placeholder: myNumpyData}) 

How can I feed a tensor to that placeholder variable?

mLoss, = sess.run([loss], feed_dict = {input_placeholder: myInputTensor}) 

gives me an error:

TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays. 

I don't want to convert the tensor into a numpy array using .eval(), since that would slow my program down, is there any other way?

Answer 1

This has been discussed on GitHub in 2016, and please check here. Here is the key point by concretevitamin:

One key thing to note is that Tensor is simply a symbolic object. The values of your feed_dict are the actual values, e.g. a Numpy ndarry.

The tensor as a symbolic object is flowing in the graph while the actual values are outside of it, then we can only pass the actual values into the graph and the symbolic object can not exist outside the graph.

Answer 2

You can use feed_dict to feed data into non-placeholders. So, first, wire up your dataflow graph directly to your myInputTensor tensor data source (i.e. don't use a placeholder). Then when you want to run with your numpy data you can effectively mask myImportTensor with myNumpyData, like this:

mLoss, = sess.run([loss], feed_dict={myImportTensor: myNumpyData}) 

[I'm still trying to figure out how to do this with multiple tensor data sources however.]