TensorFlow: questions regarding tf.argmax() and tf.equal() : <p>I am learning the TensorFlow, building a multilayer_perceptron model. I am looking into some examples like the one at: <a href="https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb" rel="noreferrer">https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb</a></p>

<p>I then have some questions in the code below:</p>

<pre><code>def multilayer_perceptron(x, weights, biases):
    :
    :

pred = multilayer_perceptron(x, weights, biases)
    :
    :

with tf.Session() as sess:
    sess.run(init)
         :
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print ("Accuracy:", accuracy.eval({x: X_test, y: y_test_onehot}))
</code></pre>

<p>I am wondering what do <code>tf.argmax(prod,1)</code> and <code>tf.argmax(y,1)</code> mean and return (type and value) exactly? And is <code>correct_prediction</code> a variable instead of real values?</p>

<p>Finally, how do we get the <code>y_test_prediction</code> array (the prediction result when the input data is <code>X_test</code>) from the tf session? Thanks a lot!</p>
