correct_predictions = tf.equal(tf.argmax(actual_y,1),tf.argmax(predicted_y,1))
tf.argmax(actual_y,1) returns the index with the largest value across axis of a tensor.
How can this handle cases where there are more than one correct answers?
e.g. test case:
i_aunt(gina,Y) +i_aunt(gina,alfonso) +i_aunt(gina,sophia)
correct_predictions = tf.equal(tf.argmax(actual_y,1),tf.argmax(predicted_y,1))tf.argmax(actual_y,1) returns the index with the largest value across axis of a tensor.
How can this handle cases where there are more than one correct answers?
e.g. test case:
i_aunt(gina,Y) +i_aunt(gina,alfonso) +i_aunt(gina,sophia)