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| import argparse | ||
| import os | ||
| import time | ||
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| import tinynn as tn | ||
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| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import torch.optim as optim | ||
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| class Dense(nn.Module): | ||
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| def __init__(self): | ||
| super(Dense, self).__init__() | ||
| self.fc1 = nn.Linear(784, 200) | ||
| self.fc2 = nn.Linear(200, 100) | ||
| self.fc3 = nn.Linear(100, 70) | ||
| self.fc4 = nn.Linear(70, 30) | ||
| self.fc5 = nn.Linear(30, 10) | ||
| torch.nn.init.xavier_uniform_(self.fc1.weight) | ||
| torch.nn.init.xavier_uniform_(self.fc2.weight) | ||
| torch.nn.init.xavier_uniform_(self.fc3.weight) | ||
| torch.nn.init.xavier_uniform_(self.fc4.weight) | ||
| torch.nn.init.xavier_uniform_(self.fc5.weight) | ||
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| def forward(self, x): | ||
| x = F.relu(self.fc1(x)) | ||
| x = F.relu(self.fc2(x)) | ||
| x = F.relu(self.fc3(x)) | ||
| x = F.relu(self.fc4(x)) | ||
| x = self.fc5(x) | ||
| x = F.log_softmax(x, dim=1) | ||
| return x | ||
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| class Conv(nn.Module): | ||
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| def __init__(self): | ||
| super(Conv, self).__init__() | ||
| self.conv1 = nn.Conv2d(1, 6, 5, 1, padding="same") | ||
| self.conv2 = nn.Conv2d(6, 16, 5, 1, padding="same") | ||
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| self.fc1 = nn.Linear(784, 120) | ||
| self.fc2 = nn.Linear(120, 84) | ||
| self.fc3 = nn.Linear(84, 10) | ||
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| def forward(self, x): | ||
| x = self.conv1(x) | ||
| x = F.relu(x) | ||
| x = F.max_pool2d(x, kernel_size=2, stride=2) | ||
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| x = self.conv2(x) | ||
| x = F.relu(x) | ||
| x = F.max_pool2d(x, kernel_size=2, stride=2) | ||
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| x = torch.flatten(x, 1) | ||
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| x = self.fc1(x) | ||
| x = F.relu(x) | ||
| x = self.fc2(x) | ||
| x = F.relu(x) | ||
| x = self.fc3(x) | ||
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| x = F.log_softmax(x, dim=1) | ||
| return x | ||
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| class RNN(nn.Module): | ||
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| def __init__(self): | ||
| super(RNN, self).__init__() | ||
| self.recurrent = nn.RNN(28, 30, batch_first=True) | ||
| self.fc1 = nn.Linear(30, 10) | ||
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| def forward(self, x): | ||
| output, hidden = self.recurrent(x) | ||
| x = output[:, -1] | ||
| x = self.fc1(x) | ||
| x = F.log_softmax(x, dim=1) | ||
| return x | ||
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| class LSTM(RNN): | ||
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| def __init__(self): | ||
| super(LSTM, self).__init__() | ||
| self.recurrent = nn.LSTM(28, 30, batch_first=True) | ||
| self.fc1 = nn.Linear(30, 10) | ||
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| def main(): | ||
| if args.seed >= 0: | ||
| tn.seeder.random_seed(args.seed) | ||
| torch.manual_seed(args.seed) | ||
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| mnist = tn.dataset.MNIST(args.data_dir, one_hot=False) | ||
| train_x, train_y = mnist.train_set | ||
| test_x, test_y = mnist.test_set | ||
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| if args.model_type == "mlp": | ||
| model = Dense() | ||
| elif args.model_type == "cnn": | ||
| train_x = train_x.reshape((-1, 1, 28, 28)) | ||
| test_x = test_x.reshape((-1, 1, 28, 28)) | ||
| model = Conv() | ||
| elif args.model_type == "rnn": | ||
| train_x = train_x.reshape((-1, 28, 28)) | ||
| test_x = test_x.reshape((-1, 28, 28)) | ||
| model = RNN() | ||
| elif args.model_type == "lstm": | ||
| train_x = train_x.reshape((-1, 28, 28)) | ||
| test_x = test_x.reshape((-1, 28, 28)) | ||
| model = LSTM() | ||
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| model.to(device) | ||
| optimizer = optim.Adam(model.parameters(), lr=args.lr) | ||
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| model.train() | ||
| iterator = tn.data_iterator.BatchIterator(batch_size=args.batch_size) | ||
| for epoch in range(args.num_ep): | ||
| t_start = time.time() | ||
| f_cost, b_cost = 0, 0 | ||
| for batch in iterator(train_x, train_y): | ||
| x = torch.from_numpy(batch.inputs).to(device) | ||
| y = torch.from_numpy(batch.targets).to(device) | ||
| optimizer.zero_grad() | ||
| pred = model(x) | ||
| loss = F.nll_loss(pred, y) | ||
| loss.backward() | ||
| optimizer.step() | ||
| print(f"Epoch {epoch} time cost: {time.time() - t_start}") | ||
| # evaluate | ||
| evaluate(model, test_x, test_y) | ||
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| def evaluate(model, test_x, test_y): | ||
| model.eval() | ||
| x, y = torch.from_numpy(test_x).to(device), torch.from_numpy(test_y).to(device) | ||
| with torch.no_grad(): | ||
| pred = model(x) | ||
| test_pred_idx = pred.argmax(dim=1).numpy() | ||
| accuracy, info = tn.metric.accuracy(test_pred_idx, test_y) | ||
| print(f"accuracy: {accuracy:.4f} info: {info}") | ||
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| if __name__ == "__main__": | ||
| curr_dir = os.path.dirname(os.path.abspath(__file__)) | ||
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--data_dir", type=str, | ||
| default=os.path.join(curr_dir, "data")) | ||
| parser.add_argument("--model_type", default="mlp", type=str, | ||
| help="[*mlp|cnn|rnn|lstm]") | ||
| parser.add_argument("--num_ep", default=10, type=int) | ||
| parser.add_argument("--lr", default=1e-3, type=float) | ||
| parser.add_argument("--batch_size", default=128, type=int) | ||
| parser.add_argument("--seed", default=31, type=int) | ||
| args = parser.parse_args() | ||
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| device = torch.device("cpu") | ||
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| main() |
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