def main():
# hyperparams
epochs = 1000
lr = 0.02
ls = 128
size = 1000
# collect data and split with sklearn
# data types: "moons", "multi", "diabetes", "digit"
data_type = "digit"
features, labels = get_data(size, data_type)
one_hot_target = pd.get_dummies(labels)
train_x, x_val, train_y, y_val = train_test_split(features, one_hot_target, test_size=0.1, random_state=20)
train_y = np.array(train_y)
y_val = np.array(y_val)
# training
model = Net(train_x, train_y, epochs, ls, lr)
model.train()
# testing
if data_type != "digit":
plt.subplot(2,1,1)
plt.title('Training Batch')
print("Training accuracy: ", test(model, train_x, train_y, data_type))
if data_type != "digit":
plt.tight_layout(pad=3.0)
plt.subplot(2,1,2)
print("Test accuracy: ", test(model, x_val, np.array(y_val), data_type))
if data_type != "digit":
plt.title('Testing Batch')
plt.show()
def train(flags=FLAGS, hps=HPS):
from Network import Net
net = Net(flags, hps)
for g in range(flags.global_epoch):
with timer(f'Global epoch #{g}'):
logger.debug(f'Start global epoch {g}')
net.train(porportion=0.01)
l, acc = net.test(porportion=0.1)
logger.debug(f'Finish global epoch {g}')
net.save_model(name=f'{l:.4f}-{acc:.4f}')
logger.info('All done')