CODEWITHSUNDEEP
pythonneural-networktensorflowtraining-data
HakunaMaData
HakunaMaData

Reputation: 1321

TensorFlow FullyConnected Tutorial: How are the trained weights used for Eval and Test?

I've been looking through the TensorFlow FullyConnected tutorial. This also uses the helper code mnist.py

I understand the code but for one nagging piece. After training the Neural Net, the weights obtained from training should be used to evaluate the precision of the model on the Validation (and Test) data. However, I don't see that being done anywhere.

Infact, this is the only thing I see in fully_connected_feed.py

    # Evaluate against the validation set.
    print('Validation Data Eval:')
    do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            data_sets.validation)

    # Evaluate against the test set.
    print('Test Data Eval:')
    do_eval(sess,
            eval_correct,
            images_placeholder,
            labels_placeholder,
            data_sets.test)

the do_eval() function seems to be passed a parameter eval_correct which seems to be recalculating the logits again on this new data. I've been playing around with TF for a while now but I'm baffled by this code. Any thoughts would be great.

Upvotes: 1

Views: 806

Answers (1)

Tobias Scheithauer
Tobias Scheithauer

Reputation: 365

TensorFlow creates a graph with the weights and biases. Roughly speaking while you train this neural net the weights and biases get changed so it produces expected outputs. The line 131 in fully_connected_feed.py (with tf.Graph().as_default():) is used to tell TensorFlow to use the default graph. Therefore every line in the training loop including the calls of the do_eval() function use the default graph. Since the weights obtained from training are not resetted before evaluation they are used for it.

eval_correct is the operation used instead of the training operation to just evaluate the neural net without training it. This is important because otherwise the neural net would be trained to them which would result in distorted (too good) results.

Upvotes: 1

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