Explain accuracy op binary accuracy op top k op r2 op weighted r2 op in TF learn

This recipe explains what accuracy op binary accuracy op top k op r2 op weighted r2 op in TF learn

Recipe Objective

This recipe explains accuracy_op, binary_accuracy_op and top_k_op.

accuracy_op

accuracy_op assumes prediction and targets are both one-hot encoded to calculate mean accuracy.
Its syntax is: tflearn.metrics.accuracy_op (predictions, targets) where its arguments are predictions and targets. accuracy_op returns mean accuracy which is of float data type.

# accuracy_op
data_ip = att1(shape=[None, 392])
pred_Y = net(data_ip)
true_Y = att1(shape=[None, 5])
op_acc = accuracy_op(pred_Y, true_Y)

# Calculate accuracy by feeding data X and labels Y
accuracy = sess.run(op_acc, feed_dict={data_ip: X, true_Y: Y})

binary_accuracy_op

binary_accuracy_op assumes targets are binary encoded and assumes predictions are logits to calculate mean accuracy.
Its syntax is: tflearn.metrics.binary_accuracy_op (predictions, targets) where its arguments are predictions and targets. binary_accuracy_op returns mean accuracy which is of float data type.

# binary_accuracy_op
data_ip = att1(shape=[None, 392])
pred_Y = net(data_ip)
true_Y = att1(shape=[None, 5])
bin_op_acc = binary_accuracy_op(pred_Y, true_Y)

# Calculate accuracy by feeding data X and labels Y
accuracy = sess.run(bin_op_acc, feed_dict={data_ip: X, true_Y: Y})

top_k_op

top_k_op calculates top-k mean accuracy.
Its syntax is: tflearn.metrics.top_k_op (predictions, targets, k=1) where its arguments are predictions, targets, and k where k is the number of top elements to look at for computing precision. top_k_op returns top k mean accuracy which is of float data type.

# top_k_op
data_ip = att1(shape=[None, 392])
pred_Y = net(data_ip)
true_Y = att1(shape=[None, 5])
top_op = top_k_op(pred_Y, true_Y, 5)

# Calculate Top-5 accuracy by feeding data X and labels Y
accuracy = sess.run(top_op, feed_dict={input_data: X, true_Y: Y})

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