# Top5 vs Top1 accuracy

In this article, we have explored the differences between Top5 and Top1 accuracy measurements. Both are frequently used with Machine Learning models.

## Top5 vs Top1 accuracy

Following table summarizes the differences between Top5 and Top1 accuracy:

Top5 vs Top1 accuracy
PointTop5Top1
Higher valueHighLow
# of predictions
considered
51
Strict checkLess strictMore strict

Following is a code snippet in TensorFlow Python which calculates both Top1 and Top5 accuracy which will show that calculating both is similar with minor change:

``````predictions = sess_graph.run(output_tensor,
{input_tensor: np_images})
elapsed_time = time.time() - start_time
accuracy1 = tf.reduce_sum(
input_tensor=tf.cast(tf.nn.in_top_k(predictions=tf.constant(predictions),
targets=tf.constant(np_labels), k=1), tf.float32))

accuracy5 = tf.reduce_sum(
input_tensor=tf.cast(tf.nn.in_top_k(predictions=tf.constant(predictions),
targets=tf.constant(np_labels), k=5), tf.float32))
np_accuracy1, np_accuracy5 =  sess.run([accuracy1, accuracy5])
total_accuracy1 += np_accuracy1
total_accuracy5 += np_accuracy5
print("Iteration time: %0.4f ms" % elapsed_time)
print("Processed %d images. (Top1 accuracy, Top5 accuracy) = (%0.4f, %0.4f)" \
% (num_processed_images, total_accuracy1/num_processed_images,
total_accuracy5/num_processed_images))
``````

With this article at OpenGenus, you must have the complete idea of the differences between Top1 and Top5 accuracy.