TensorFlow: questions regarding tf.argmax() and tf.equal()

Question

I am learning the TensorFlow, building a multilayer_perceptron model. I am looking into some examples like the one at: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb

I then have some questions in the code below:

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}))

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

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

Answer 1

tf.argmax(input, axis=None, name=None, dimension=None)

Returns the index with the largest value across axis of a tensor.

input is a Tensor and axis describes which axis of the input Tensor to reduce across. For vectors, use axis = 0.

For your specific case let's use two arrays and demonstrate this

pred = np.array([[31, 23,  4, 24, 27, 34],
                [18,  3, 25,  0,  6, 35],
                [28, 14, 33, 22, 20,  8],
                [13, 30, 21, 19,  7,  9],
                [16,  1, 26, 32,  2, 29],
                [17, 12,  5, 11, 10, 15]])

y = np.array([[31, 23,  4, 24, 27, 34],
                [18,  3, 25,  0,  6, 35],
                [28, 14, 33, 22, 20,  8],
                [13, 30, 21, 19,  7,  9],
                [16,  1, 26, 32,  2, 29],
                [17, 12,  5, 11, 10, 15]])

Evaluating tf.argmax(pred, 1) gives a tensor whose evaluation will give array([5, 5, 2, 1, 3, 0])

Evaluating tf.argmax(y, 1) gives a tensor whose evaluation will give array([5, 5, 2, 1, 3, 0])

tf.equal(x, y, name=None) takes two tensors(x and y) as inputs and returns the truth value of (x == y) element-wise. 

Following our example, tf.equal(tf.argmax(pred, 1),tf.argmax(y, 1)) returns a tensor whose evaluation will givearray(1,1,1,1,1,1).

correct_prediction is a tensor whose evaluation will give a 1-D array of 0's and 1's

y_test_prediction can be obtained by executing pred = tf.argmax(logits, 1)

The documentation for tf.argmax and tf.equal can be accessed by following the links below.

tf.argmax() https://www.tensorflow.org/api_docs/python/math_ops/sequence_comparison_and_indexing#argmax

tf.equal() https://www.tensorflow.org/versions/master/api_docs/python/control_flow_ops/comparison_operators#equal