why define a new graph when running test ?

[00001101-xt]

To my knowledge, the test process may be simplified as follows:
restore the model and get tensors we need, including the input utterances and the output embedding matrix, something like:

# code below may not work, just for illustrating the idea
saver.restore(sess, model_path)
x = tf.get_default_graph().get_tensor_by_name("x:0")
embedding_matrix = tf.get_default_graph().get_tensor_by_name("embedding_matrix:0")

enroll = sess.run(embedding_matrix, feed_dict={x:next_batch()})
verif = sess.run(embedding_matrix, feed_dict={x:next_batch(start=M)})

enroll_center = cal_center(enroll)

S = calculate_similarity_matrix(verif, enroll)

If the above process is right, why define a new graph in test(), any differences except larger batch size?

Thanks.

[BingqingWei]

I guess the reason for defining a new graph is because the input dimension of the "batch" variable has changed. Your code would work but can be slower. In fact, that's why the 'batch' exists in Tensorflow in the first place: to train or test neural networks faster.