def get_data_iter(mnist_train, mnist_test, batch_size=32):
""" 获取数据集迭代器.
@params:
mnist_train - 训练数据.
mnist_test - 测试数据.
batch_size - 批次大小.
@return:
On success - train与test数据迭代器.
On failure - 错误信息.
"""
if sys.platform.startswith('win'):
num_workers = 0 # 0表示不用额外的进程来加速读取数据
else:
num_workers = 4
train_iter = torch.utils.data.DataLoader(mnist_train,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(mnist_test,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers)
logger.info('train_iter len:{}'.format(len(train_iter)))
logger.info('test_iter len:{}'.format(len(test_iter)))
return train_iter, test_iter
def train_net(net, train_iter, dev_iter, max_epoch, optimizer, loss_func):
""" 训练神经网络.
@params:
net - 神经网络.
train_iter - 训练数据迭代器.
dev_iter - 开发数据迭代器.
max_epoch - 最大epoch.
optimizer- 优化器.
loss_func - 损失函数.
"""
for epoch in range(max_epoch):
logger.info('epoch {} begin to train'.format(epoch + 1))
train_l_sum, train_acc_sum, n = 0.0, 0.0, 0
for X, y in tqdm(train_iter):
y_hat = net(X)
loss = loss_func(y_hat, y).sum()
# 梯度清零
optimizer.zero_grad()
loss.backward()
optimizer.step() # “softmax回归的简洁实现”一节将用到
train_l_sum += loss.item()
train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()
n += y.shape[0]
test_acc = evaluate_accuracy(dev_iter, net)
logger.info('epoch %d, loss %.4f, train acc %.3f, test acc %.3f' %
(epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))
def get_parameter_number(net):
""" 统计神经网络参数个数.
@params:
net - 神经网络.
@return:
On success - 字典，例如 {'total': total_num, 'trainable': trainable_num}.
On failure - 错误信息.
"""
total_num = sum(p.numel() for p in net.parameters())
trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
logger.info(' total:{}, trainable:{}'.format(_convert_num_to_English(total_num), _convert_num_to_English(trainable_num)))
return {'total': total_num, 'trainable': trainable_num}
def get_dataset(data_path, augmentation_funcs=list()):
""" 获取数据集.
@params:
data_path - 数据保存路径.
augmentation_funcs - 训练数据增强方法.
@return:
On success - train与test数据.
On failure - 错误信息.
"""
train_augmentation_funcs = [torchvision.transforms.ToTensor()]
train_augmentation_funcs.extend(augmentation_funcs)
augmentation_func = torchvision.transforms.Compose(
train_augmentation_funcs)
mnist_train = torchvision.datasets.CIFAR10(root=data_path,
train=True,
download=True,
transform=augmentation_func)
mnist_test = torchvision.datasets.CIFAR10(
root=data_path,
train=False,
download=True,
transform=torchvision.transforms.ToTensor())
logger.info('dataset is :{}'.format(type(mnist_train)))
logger.info('train data len :{}'.format(len(mnist_train)))
logger.info('test data len :{}'.format(len(mnist_test)))
return mnist_train, mnist_test
def train_machine_translation_net(encoder,
decoder,
dataset,
out_vocab,
lr=0.01,
batch_size=8,
max_epoch=5):
""" 训练machine_translation神经网络.
@params:
encoder - 编码器神经网络.
decoder - 解码器神经网络.
dataset - 数据集.
out_vocab - 解码器vocab.
lr - 学习率.
batch_size - 批次大小.
max_epoch - 最大epoch.
"""
enc_optimizer = torch.optim.Adam(encoder.parameters(), lr=lr)
dec_optimizer = torch.optim.Adam(decoder.parameters(), lr=lr)
loss = nn.CrossEntropyLoss(reduction='none')
data_iter = Data.DataLoader(dataset, batch_size, shuffle=True)
for epoch in range(max_epoch):
l_sum = 0.0
for X, Y in data_iter:
enc_optimizer.zero_grad()
dec_optimizer.zero_grad()
loss_func = get_batch_loss(encoder,
decoder,
X,
Y,
loss,
out_vocab=out_vocab)
loss_func.backward()
enc_optimizer.step()
dec_optimizer.step()
l_sum += loss_func.item()
logger.info("epoch %d, loss %.3f" %
(epoch + 1, l_sum / len(data_iter)))
def get_dataset(data_path):
""" 获取数据集.
@params:
data_path - 数据保存路径.
@return:
On success - train与test数据.
On failure - 错误信息.
"""
mnist_train = torchvision.datasets.FashionMNIST(
root=data_path,
train=True,
download=True,
transform=torchvision.transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(
root=data_path,
train=False,
download=True,
transform=torchvision.transforms.ToTensor())
logger.info('dataset is :{}'.format(type(mnist_train)))
logger.info('train data len :{}'.format(len(mnist_train)))
logger.info('test data len :{}'.format(len(mnist_test)))
return mnist_train, mnist_test
def test_train_net(self):
""" 训练神经网络.
"""
print('{} test_train_net {}'.format('-' * 15, '-' * 15))
data_path = './data/FashionMNIST'
batch_size = 64
num_inputs = 1 * 28 * 28
num_outputs = 10
max_epoch = 5
logger.info('加载数据')
mnist_train, mnist_test = get_dataset(data_path=data_path)
train_iter, test_iter = get_data_iter(mnist_train,
mnist_test,
batch_size=batch_size)
logger.info('定义网络')
net = nn.Sequential()
net.add_module('flatten', FlattenLayer())
net.add_module('linear', nn.Linear(num_inputs, num_outputs))
logger.info(net)
logger.info('参数初始化')
torch.nn.init.normal_(net.linear.weight, mean=0, std=0.01)
torch.nn.init.constant_(net.linear.bias, val=0)
logger.info('定义损失函数')
loss_func = nn.CrossEntropyLoss()
logger.info('定义优化器')
# optimizer = torch.optim.SGD(net.parameters(), lr=0.03)
optimizer = torch.optim.Adam(net.parameters())
logger.info('模型训练')
train_net(net, train_iter, test_iter, max_epoch, optimizer, loss_func)
"""
