np.random.shuffle(scores)
#sample the initial training set
daal_set_indices = random.sample(range(train_data[0].shape[0]),
INITIAL_TRAINING_SIZE)
#train the classifier with the initial set
daal_train_data = (images[daal_set_indices],
targets[daal_set_indices])
classifier_daal.train(daal_train_data,
MAX_ITER / len(daal_train_data))
acc = classifier_daal.evaluate(test_data)
#arr_range = np.arange(train_data[0].shape[0])
daal_accuracies.append(acc)
for step in range(ACTIVE_LEARNING_STEPS):
print "step", step
#get predicted probabilities of the unlabel set and compute the entropies
predictions = classifier_daal.predict(images)
entropies = get_entropy(predictions) #lower the better
entropies = normalize(entropies)
#entropies -> less better [0-1]
entropies = 1 - entropies
weighted_entropies = entropies * scores
# we would like to select the largest
argsorted = np.argsort(
weighted_entropies)[::-1] #increasing sort, then reversed
#argsorted = arr_range[daal_set_indices][argsorted]
candidates = []
i = 0
while len(candidates) < ACTIVE_LEARNING_STEP_SIZE:
if argsorted[i] not in daal_set_indices:
candidates.append(argsorted[i])
i += 1
#train the classifier with the initial set
my_train_data = (images[train_set_indices], targets[train_set_indices])
daal_train_data = (images[daal_set_indices], targets[daal_set_indices])
classifier_daal.train(daal_train_data)
classifier_entropy.train(my_train_data)
acc = classifier_entropy.evaluate(test_data)
entropy_accuracies.append(acc)
acc = classifier_daal.evaluate(test_data)
daal_accuracies.append(acc)
for step in range(ACTIVE_LEARNING_STEPS):
print "step", step
## Entropy:
#get predicted probabilities of the unlabel set and compute the entropies
print "making entropy decisions"
predictions = classifier_entropy.predict(images[train_set_indices])
entropies = get_entropy(predictions)#lower the better
argsorted = np.argsort(entropies)[::-1]#increasing sort
#start from the beginning and find the first 10 samples that are not already labelled
candidates = []
i = 0
while len(candidates)<ACTIVE_LEARNING_STEP_SIZE:
if argsorted[i] not in train_set_indices:
candidates.append(argsorted[i])
i+=1
#merge training set with the labelled
train_set_indices+=candidates
#traind and test
classifier_entropy.train((images[train_set_indices], targets[train_set_indices]))
acc = classifier_entropy.evaluate(test_data)
entropy_accuracies.append(acc)
