import sys
sys.path.append('../')
sys.path.append('../src/')
from data_gen import *
from coreset import *
from variational_trainer import VariationalTrainer
# experiment setup
dictParams = {
'numEpochs': 1,
'batchSize': 100,
'numSamples': 10000,
'dataGen': PermutedMnistGen(),
'numTasks': 5,
'numHeads': 5,
'coresetMethod': coreset_rand,
'coresetSize': 0,
'numLayers': (2, 1),
'hiddenSize': 256,
'taskOrder': [],
'headOrder': [],
}
# run experiment
trainer = VariationalTrainer(dictParams)
trainer.train()
print(trainer.accuracy)
from time import time
from data_gen import *
from coreset import *
from utils import *
from constants import DEFAULT_PARAMETERS
from result_averager import ResultAverager
from variational_trainer import VariationalTrainer
directory = "../exp/final_experiments/adversarial_ordering"
dictUpdate = {
'dataGen': SplitMnistGen(),
'coresetMethod': coreset_rand,
'numLayers': (2, 1),
'coresetSize': 0,
'numEpochs': 120
}
for taskOrder in getAdversarialPermutationList():
startTime = time()
resultAverager = ResultAverager()
dictUpdate['taskOrder'] = taskOrder
dictParams = getAllExpParameters(dictUpdate)
for iter in range(0, 5):
trainer = VariationalTrainer(dictParams)
accuracy = trainer.train()
resultAverager.add(accuracy)
filename = getName(dictParams, 'taskOrder')
writePerformanceRecordAccuracyAvg(directory, filename, resultAverager)
print('Time for single iteration: {}'.format((time() - startTime) / 60))
x_train = torch.ones(10, 5)
y_train = torch.ones(10, 2)
for i in range(y_train.shape[0]):
x_train[i, :] *= i
y_train[i, :] *= i
# ----------------- batchSize: None ----------------- #
x_train1 = deepcopy(x_train)
y_train1 = deepcopy(y_train)
dictParams1 = deepcopy(dictParams)
dictParams1['batchSize'] = None
variationalTrainer1 = VariationalTrainer(dictParams1)
batches1 = variationalTrainer1.getBatch(x_train1, y_train1)
x_train_batch, y_train_batch = batches1[0]
assert len(batches1) == 1
assert torch.all(torch.eq(x_train1, x_train_batch))
assert torch.all(torch.eq(y_train1, y_train_batch))
# ----------------- batchSize: 3 ----------------- #
x_train2 = deepcopy(x_train)
y_train2 = deepcopy(y_train)
dictParams2 = deepcopy(dictParams)
dictParams2['batchSize'] = 3