def get_vhrd_embeddings(self, data_fname, mode, save_name, use_saved_embeddings): if (use_saved_embeddings) and (os.path.exists(save_name)): with open(save_name, 'rb') as handle: data = cPickle.load(handle) else: data_loader = DataLoader(data_fname, mode) data = data_loader.load_data() assert not self.pretrainer is None data = self.pretrainer.get_embeddings(data) with open(save_name, 'wb') as handle: cPickle.dump(data, handle) x,y = self._build_data(data) return x, y
def train():
"""Start the training procedure
"""
num_epochs = 1
learning_rate = 0.05
batch_size = 8
data_loader = DataLoader(os.path.join("data", "fetal_health.csv"))
data_loader.standardize_column("baseline value")
x_train, y_train = data_loader.load_data(subset="train")
x_valid, y_valid = data_loader.load_data(subset="valid")
num_classes = len(np.unique(y_train))
num_samples, num_features = x_train.shape
assert x_train.shape[1] == x_valid.shape[
1], "Number of features should be equal!"
assert x_train.shape[0] == y_train.shape[
0], "Number of training samples should be equal!"
assert x_valid.shape[0] == y_valid.shape[
0], "Number of validation samples should be equal!"
dev = get_default_device()
tx = tensor.Tensor((num_samples, num_features), dev, tensor.float32)
ty = tensor.Tensor((num_samples, ), dev, tensor.int32)
sgd = opt.SGD(learning_rate)
model = create_MLP_model(perceptron_size=10, num_classes=num_classes)
model.set_optimizer(sgd)
model.compile([tx], is_train=True, use_graph=True, sequential=False)
model.train()
for i in range(num_epochs):
tx.copy_from_numpy(x_train.astype(np.float32))
ty.copy_from_numpy(y_train.astype(np.int32))
out, loss = model(tx, ty, 'fp32', spars=None)
# TODO: Add metric evaluation on validation data
if i % 10 == 0:
print("training loss = {:.3f}".format(tensor.to_numpy(loss)[0]))