class HyperparameterTuner(object):
def __init__(self, sess, hidden_layers, hidden_units, num_perms, trials,
epochs):
self.hidden_layers = hidden_layers
self.hidden_units = hidden_units
self.num_perms = num_perms
self.epochs = epochs
self.task_list = self.create_permuted_mnist_task(num_perms)
self.trial_learning_rates = [
PRNG.uniform(1e-4, 1e-3) for _ in range(0, trials)
]
self.best_parameters = []
self.sess = sess
self.classifier = Classifier(
num_class=10,
num_features=784,
fc_hidden_units=[hidden_units for _ in range(hidden_layers)],
apply_dropout=True)
def search(self):
for t in range(0, self.num_perms):
queue = PriorityQueue()
for learning_rate in self.trial_learning_rates:
self.train_on_task(t, learning_rate, queue)
self.best_parameters.append(queue.get())
self.evaluate()
def evaluate(self):
accuracies = []
for parameters in self.best_parameters:
accuracy = self.classifier.test(
sess=self.sess,
model_name=parameters[1],
batch_xs=self.task_list[0].test.images,
batch_ys=self.task_list[0].test.labels)
accuracies.append(accuracy)
print(accuracies)
def train_on_task(self, t, lr, queue):
model_name = self.file_name(lr, t)
dataset_train = self.task_list[t].train
dataset_lagged = self.task_list[t - 1] if t > 0 else None
model_init_name = self.best_parameters[t - 1][1] if t > 0 else None
self.classifier.train(sess=self.sess,
model_name=model_name,
model_init_name=model_init_name,
dataset=dataset_train,
dataset_lagged=dataset_lagged,
num_updates=(55000 // MINI_BATCH_SIZE) *
self.epochs,
mini_batch_size=MINI_BATCH_SIZE,
log_frequency=LOG_FREQUENCY,
fisher_multiplier=1.0 / lr,
learning_rate=lr)
accuracy = self.classifier.test(
sess=self.sess,
model_name=model_name,
batch_xs=self.task_list[0].validation.images,
batch_ys=self.task_list[0].validation.labels)
queue.put((-accuracy, model_name))
def create_permuted_mnist_task(self, num_datasets):
mnist = read_data_sets("MNIST_data/", one_hot=True)
task_list = [mnist]
for seed in range(1, num_datasets):
task_list.append(self.permute(mnist, seed))
return task_list
@staticmethod
def permute(task, seed):
np.random.seed(seed)
perm = np.random.permutation(task.train._images.shape[1])
permuted = deepcopy(task)
permuted.train._images = permuted.train._images[:, perm]
permuted.test._images = permuted.test._images[:, perm]
permuted.validation._images = permuted.validation._images[:, perm]
return permuted
def file_name(self, lr, t):
return 'layers=%d,hidden=%d,lr=%.5f,multiplier=%.2f,mbsize=%d,epochs=%d,perm=%d' \
% (self.hidden_layers, self.hidden_units, lr, 1 / lr, MINI_BATCH_SIZE, self.epochs, t)
k = 1
for train, test in sets:
if '$' in classifier.selected_split_name:
classifier.selected_split_name = str(
k) + classifier.selected_split_name[1:]
k += 1
classifier.create_dictionary(train)
classifier.save_word_dictionary()
# print(classifier.hash_dictionary)
for lr in [0.6]: # [0.1, 0.2, 0.25, 0.4, 0.6, 0.75, 0.8, 1]
classifier.create_weights_array(1)
print('{} \tLearning Rate= {}\tSplit Set: {}'.format(
'perceptron', lr, classifier.selected_split_name))
classifier.train(
train,
test,
classifier='perceptron',
list_iteration=[10, 100, 1000], # [10, 100, 1000, 5000]
learning_rate=lr)
print('history: {}'.format(classifier.history))
# # winnow
# k=1
# for train, test in sets:
# if '$' in classifier.selected_split_name:
# classifier.selected_split_name = str(k)+classifier.selected_split_name[1:]
# k+=1
# classifier.create_dictionary(train)
# for lr in [0.1, 0.2, 0.25, 0.4, 0.6, 0.75, 0.8, 1]:
# classifier.create_weights_array(1)
# print('{} \tLearning Rate= {}\tSplit Set: {}'.format('winnow', lr, classifier.selected_split_name))
# classifier.train(train, test, classifier='winnow', list_iteration=[10, 100, 1000, 5000], learning_rate=lr)
