tensorflow: efficient feeding of eval/train data using queue runners

Question

I\'m trying to run a tensorflow graph to train a model and periodically evaluate using a separate evaluation dataset. Both training and evaluation data is implemented using

Answer 1

Have you read the last section of this link about multi inputs? I think you can add a is_training argument to your input function to distinguish training data from eval data. Then you can reuse sharing variables to get the logits for eval data and build a op for eval. Then in your graph, run valudation_accuracy=sess.run(eval_op) to get eval accuracy.

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Update:

Hi, from my understanding,if you want to train for n batches, evaluate, train, evaluate, you can keep there two ops in the same graph, no need to build a new one. Assume you have already build all the needed function, then the code should like this:

#the following two steps will add train and eval input queue to the graph
train_inputs,train_labels = inputs(is_train=True)
eval_inputs,eval_labels = inputs(is_train=False)

with tf.variable_scope("inference") as scope:
    train_logits = inference(train_inputs)
    scope.reuse_variables()
    eval_logits = inference(eval_inputs)

loss = loss(train_logits,train_labels)
eval_accuracy = accuracy(eval_logits,eval_labels)

#...add train op here,start queue runner and train it ...