def experiment(state, channel):
DS = Dataset(is_binary=True)
DS.setup_dataset(data_path=state.dataset)
kfoldCrossValidation = KfoldCrossvalidation(no_of_folds=state.no_of_folds)
cs_args = {
"train_args": {
"L1_reg": state.l1_reg,
"learning_rate": state.learning_rate,
"L2_reg": state.l2_reg,
"nepochs": state.n_epochs,
"cost_type": state.cost_type,
"save_exp_data": state.save_exp_data,
"batch_size": state.batch_size
},
"test_args": {
"save_exp_data": state.save_exp_data,
"batch_size": state.batch_size
}
}
post_input = T.matrix('post_input')
mlp = PostMLP(post_input,
n_in=state.n_in,
n_hiddens=state.n_hiddens,
n_out=state.n_out,
n_hidden_layers=state.n_hidden_layers,
is_binary=True,
exp_id=state.exid)
valid_errs, test_errs = kfoldCrossValidation.crossvalidate(DS.Xtrain, \
DS.Ytrain, DS.Xtest, DS.Ytest, mlp, **cs_args)
errors = \
kfoldCrossValidation.get_best_valid_scores(valid_errs, test_errs)
state.best_valid_error = errors["valid_scores"]["error"]
state.best_test_error = errors["test_scores"]["error"]
return channel.COMPLETE
def experiment(state, channel):
DS = Dataset(is_binary=True)
DS.setup_dataset(data_path=state.dataset)
kfoldCrossValidation = KfoldCrossvalidation(no_of_folds=state.no_of_folds)
cs_args = {
"train_args":{
"L1_reg": state.l1_reg,
"learning_rate": state.learning_rate,
"L2_reg": state.l2_reg,
"nepochs":state.n_epochs,
"cost_type": state.cost_type,
"save_exp_data": state.save_exp_data,
"batch_size": state.batch_size
},
"test_args":{
"save_exp_data": state.save_exp_data,
"batch_size": state.batch_size
}
}
post_input = T.matrix('post_input')
mlp = PostMLP(post_input, n_in=state.n_in, n_hiddens=state.n_hiddens,
n_out=state.n_out, n_hidden_layers=state.n_hidden_layers,
is_binary=True, exp_id=state.exid)
valid_errs, test_errs = kfoldCrossValidation.crossvalidate(DS.Xtrain, \
DS.Ytrain, DS.Xtest, DS.Ytest, mlp, **cs_args)
errors = \
kfoldCrossValidation.get_best_valid_scores(valid_errs, test_errs)
state.best_valid_error = errors["valid_scores"]["error"]
state.best_test_error = errors["test_scores"]["error"]
return channel.COMPLETE
def get_logger(self):
logger = logging.getLogger("Crossvalidation")
logger.setLevel(logging.INFO)
# create file handler which logs even debug messages
fh = logging.FileHandler("svm_tetromino_crossvalidation.log")
fh.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
if __name__ == "__main__":
from kcv import KfoldCrossvalidation
DS = Dataset(is_binary=True)
DS.setup_dataset(data_path="/home/gulcehre/dataset/pentomino/pieces/pento64x64_4k_task4_seed_98981222.npy")
kfoldCrossValidation = KfoldCrossvalidation(no_of_folds=2)
cs_args = {"train_args": {"kern": "rbf", "gamma": 0.01, "C": 10}, "test_args": {"binary_data": True}}
csvm = CSVM()
valid_errs, test_errs = kfoldCrossValidation.crossvalidate(
DS.Xtrain, DS.Ytrain, DS.Xtest, DS.Ytest, csvm, **cs_args
)
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
fh.setFormatter(formatter)
logger.addHandler(fh)
return logger
if __name__ == "__main__":
from kcv import KfoldCrossvalidation
DS = Dataset(is_binary=True)
DS.setup_dataset(
data_path=
"/home/gulcehre/dataset/pentomino/pieces/pento64x64_4k_task4_seed_98981222.npy"
)
kfoldCrossValidation = KfoldCrossvalidation(no_of_folds=2)
cs_args = {
"train_args": {
"kern": "rbf",
"gamma": 0.01,
"C": 10
},
"test_args": {
"binary_data": True
}
}
csvm = CSVM()
valid_errs, test_errs = kfoldCrossValidation.crossvalidate(
DS.Xtrain, DS.Ytrain, DS.Xtest, DS.Ytest, csvm, **cs_args)
