def offline_online():
start_time = time.time()
# x, y, x_s, y_s, x_test, y_test = load_datasets(path='datasets/svhn/', static_data_ratio=0.1)
x, y, x_s, y_s, x_test, y_test = loadmnist(static_data_ratio=0.1)
model = NeuralNetworkClassifier()
sgd = StochasticGradientDescent(model, x, y)
sgd.train(num_iterations=50, learning_rate=0.001)
error = []
test_increment = math.floor(len(y_s) / len(y_test))
i = 0
idx = 2
while i <= len(y_s):
end_of_batch = i + test_increment
if i + test_increment >= len(y_s):
end_of_batch = len(y_s)
sgd.incremental(x_s[i:end_of_batch, ], y_s[i:end_of_batch], 0.001)
if idx < len(y_test):
error_rate = model.calculate_loss(x_test[0:idx], y_test[0:idx])
else:
error_rate = model.calculate_loss(x_test, y_test)
error += [error_rate]
print("{} items process".format(i))
i += test_increment
idx += 1
error_rate = model.calculate_loss(x_test, y_test)
error += [error_rate]
running_time = time.time() - start_time
return error, running_time
def velox():
start_time = time.time()
# x, y, x_s, y_s, x_test, y_test = load_datasets(path='datasets/svhn/', static_data_ratio=0.1)
x, y, x_s, y_s, x_test, y_test = loadmnist(static_data_ratio=0.1)
model = NeuralNetworkClassifier()
sgd = StochasticGradientDescent(model, x, y)
sgd.train(num_iterations=50, learning_rate=0.001)
buffer_size = 10000
error = []
test_increment = math.floor(len(y_s) / len(y_test))
i = 0
idx = 2
while i < len(y_s):
sgd.incremental(np.array([x_s[i]]), np.array([y_s[i]]), 0.001)
sgd.update_buffer(x_s[i], y_s[i])
# print("{} items process".format(i))
i += 1
if i % test_increment == 0:
if idx < len(y_test):
error_rate = model.calculate_loss(x_test[0:idx], y_test[0:idx])
else:
error_rate = model.calculate_loss(x_test, y_test)
error += [error_rate]
if i % buffer_size == 0:
print("{} items process ... scheduling new iteration".format(i))
sgd.retrain()
idx += 1
error_rate = model.calculate_loss(x_test, y_test)
error += [error_rate]
running_time = time.time() - start_time
return error, running_time
def simple():
x, y, x_s, y_s, x_test, y_test = loadsvhn(path='../datasets/svhn/',
static_data_ratio=1.0)
model = NeuralNetworkClassifier()
sgd = StochasticGradientDescent(model, x, y)
sgd.train(num_iterations=50, learning_rate=0.001)
print(model.calculate_loss(x_test, y_test))
def full():
start_time = time.time()
# x, y, x_s, y_s, x_test, y_test = load_datasets(path='datasets/svhn/', static_data_ratio=0.1)
x, y, x_s, y_s, x_test, y_test = loadmnist(static_data_ratio=1.0)
model = NeuralNetworkClassifier()
sgd = StochasticGradientDescent(model, x, y)
sgd.train(num_iterations=50, learning_rate=0.001)
error = []
error_rate = model.calculate_loss(x_test, y_test)
error += [error_rate]
running_time = time.time() - start_time
return error, running_time