from utils.load_batch import load_batch
from utils.handle_data import data_split
from utils import train
from utils.lrate import StepLR
from utils.lrate import CyclicLR
from clsr.nn_kl import KLayerNeuralNetwork
from utils.preprocess import StandardScaler
if __name__ == "__main__":
train_data = load_batch("cifar-10-batches-py/data_batch_1")
net = KLayerNeuralNetwork(n_hidden_nodes=[50, 50],
p_dropout=0.0,
batch_norm=False,
batch_norm_momentum=.9)
scaler = StandardScaler()
scaler.fit(train_data['pixel_data'])
train_data['pixel_data'] = scaler.transform(train_data['pixel_data'])
batch_size = 100
train_loader = cifar10_DataLoader(train_data,
batch_size=batch_size,
shuffle=False)
ntrain = train_data['labels'].shape[0]
n_epoch = 200
scheduler = StepLR(0.05, 20, gamma=.5)
# scheduler = CyclicLR(eta_min=1e-5, eta_max=1e-1, step_size=1000)
for epoch in range(n_epoch):
train(train_loader, net, 0.0, scheduler)
import time
from utils import evaluate
from utils.img_jitter import ImageJitter
from scipy.special import softmax
if __name__ == "__main__":
# config:
is_jiter = True
merged_data = get_all_train_data("cifar-10-batches-py")
train_data, valid_data = data_split(merged_data, n_valid=1000)
test_data = load_batch("cifar-10-batches-py/test_batch")
scaler = StandardScaler()
scaler.fit(train_data['pixel_data'])
valid_data['pixel_data'] = scaler.transform(valid_data['pixel_data'])
test_data['pixel_data'] = scaler.transform(test_data['pixel_data'])
print("Done preprocessing!")
# ==================================================================
# make dataloader
batch_size = 100
train_loader = cifar10_DataLoader(train_data, batch_size=batch_size)
valid_loader = cifar10_DataLoader(valid_data, batch_size=batch_size)
test_loader = cifar10_DataLoader(test_data, batch_size=batch_size)
# ==================================================================
net = TwoLayerNeuralNetwork(n_hidden_nodes=[50], p_dropout=0.0)
# net = TwoLayerNeuralNetwork(n_hidden_nodes=[768])
ntrain = train_data['labels'].shape[0]
n_step_per_cycle = 2