3.8.1 从零开始实现
3.8.1.1 获取和读取数据
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
3.8.1.2 定义模型参数
Mxnet
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens))
b1 = nd.zeros(num_hiddens)
W2 = nd.random.normal(scale=0.01, shape=(num_hiddens, num_outputs))
b2 = nd.zeros(num_outputs)
params = [W1, b1, W2, b2]
for param in params:
param.attach_grad()
Pytorch
num_inputs, num_outputs, num_hiddens = 784, 10, 256
W1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float)
b1 = torch.zeros(num_hiddens, dtype=torch.float)
W2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float)
b2 = torch.zeros(num_outputs, dtype=torch.float)
params = [W1, b1, W2, b2]
for param in params:
param.requires_grad_(requires_grad=True)
3.8.1.3 定义激活函数
Mxnet
def relu(X):
return nd.maximum(X, 0)
Pytorch
def relu(X):
return torch.max(input=X, other=torch.tensor(0.0))
3.8.1.4 定义模型
Mxnet
def net(X):
X = X.reshape((-1, num_inputs))
H = relu(nd.dot(X, W1) + b1)
return nd.dot(H, W2) + b2
Pytorch
def net(X):
X = X.view((-1, num_inputs))
H = relu(torch.matmul(X, W1) + b1)
return torch.matmul(H, W2) + b2
3.8.1.5 定义损失函数
Mxnet
loss = gloss.SoftmaxCrossEntropyLoss()
Pytorch
loss = torch.nn.CrossEntropyLoss()
3.8.1.6 训练模型
num_epochs, lr = 5, 0.5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,
params, lr)
3.8.2 简洁实现
3.8.2.1 定义模型
Mxnet
net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'),
nn.Dense(10))
net.initialize(init.Normal(sigma=0.01))
Pytorch
num_inputs, num_outputs, num_hiddens = 784, 10, 256
net = nn.Sequential(
d2l.FlattenLayer(),
nn.Linear(num_inputs, num_hiddens),
nn.ReLU(),
nn.Linear(num_hiddens, num_outputs),
)
for params in net.parameters():
init.normal_(params, mean=0, std=0.01)
3.8.2.2 读取数据并训练模型
Mxnet
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batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None,
None, trainer)
Pytorch
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)
