34 lines
1.4 KiB
Python
34 lines
1.4 KiB
Python
|
import tensorflow as tf
|
||
|
|
|
||
|
|
def ensem_classify(models, test_data, test_labels, round_predictions=True):
|
||
|
|
predictions = [model(test_data.to_numpy()) for model in models]
|
||
|
|
|
||
|
|
rounded_sum = sum(tf.math.round(pred) for pred in predictions)
|
||
|
|
urounded_sum = sum(predictions)
|
||
|
|
# round predictions to onehot vectors and sum over all ensemble models
|
||
|
|
# take argmax for ensemble predicted class
|
||
|
|
|
||
|
|
correct = 0 # number of correct ensemble predictions
|
||
|
|
correct_num_models = 0 # when correctly predicted ensembley, number of models correctly classifying
|
||
|
|
individual_accuracy = 0 # proportion of models correctly classifying
|
||
|
|
|
||
|
|
classes = list()
|
||
|
|
# pc = predicted class, pcr = rounded predicted class, gt = ground truth
|
||
|
|
for pc, pcr, gt in zip(urounded_sum, rounded_sum, test_labels):
|
||
|
|
gt_argmax = tf.math.argmax(gt)
|
||
|
|
|
||
|
|
if round_predictions:
|
||
|
|
pred_val = pcr
|
||
|
|
else:
|
||
|
|
pred_val = pc
|
||
|
|
classes.append(tf.math.argmax(pred_val))
|
||
|
|
|
||
|
|
correct_models = pcr[gt_argmax] / len(models) # use rounded value so will divide nicely
|
||
|
|
individual_accuracy += correct_models
|
||
|
|
|
||
|
|
if tf.math.argmax(pred_val) == gt_argmax: # ENSEMBLE EVALUATE HERE
|
||
|
|
correct += 1
|
||
|
|
correct_num_models += correct_models
|
||
|
|
|
||
|
|
return classes, predictions, correct / len(test_data), correct_num_models / correct, individual_accuracy / len(test_data)
|
||
|
|
|