tf.argmax

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)