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pytorch初体验

发表于 更新于 分类于 本文字数： 5k 阅读时长 ≈ 5 分钟

上一次写神经网络应该还是大三的时候学TensorFlow，当时学了也没怎么用，最近作业需要pytorch，刚好整理一下pytorch的入门第一讲。
pytorch和numpy在很多地方相似，使用简单，最近使用量有超过TF的势头。
这一节通过对于一个简单的numpy网络，一步一步改进使用pytorch的高级接口。

  • Step 0: numpy 定义每一步的操作，包括Forward, loss, Backward, updata weight
    （如果这里看不懂需要去看神经网络的基本知识）
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N, D_in, H, D_out = 64, 1000, 100, 10
# 随机创建一些训练数据
x = np.random.randn(N, D_in)
y = np.random.randn(N, D_out) 
w1 = np.random.randn(D_in, H)
w2 = np.random.randn(H, D_out)

learning_rate = 1e-6
for it in range(500):
    # Forward pass
    h = x.dot(w1) # N * H
    h_relu = np.maximum(h, 0) # N * H
    y_pred = h_relu.dot(w2) # N * D_out
    
    # compute loss
    loss = np.square(y_pred - y).mean()
    if it%100==0: print(it, loss)
    
    # Backward pass
    # compute the gradient
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.T.dot(grad_y_pred)
    grad_h_relu = grad_y_pred.dot(w2.T)
    grad_h = grad_h_relu.copy()
    grad_h[h<0] = 0
    grad_w1 = x.T.dot(grad_h)
    
    # update weights of w1 and w2
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2
  • Step 1: 现在把数据类型换成torch的数据接口
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N, D_in, H, D_out = 64, 1000, 100, 10
# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
w1 = torch.randn(D_in, H)
w2 = torch.randn(H, D_out)

learning_rate = 1e-6
  for it in range(500):
    # Forward pass
    h = x.mm(w1) # N * H
    h_relu = h.clamp(min=0) # N * H #上下限
    y_pred = h_relu.mm(w2) # N * D_out
    
    # compute loss
    loss = (y_pred - y).pow(2).sum().item()
    if it%100==0 :print(it, loss)
    
    # Backward pass
    # compute the gradient
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.t().mm(grad_y_pred)
    grad_h_relu = grad_y_pred.mm(w2.t())
    grad_h = grad_h_relu.clone()
    grad_h[h<0] = 0
    grad_w1 = x.t().mm(grad_h)
    
    # update weights of w1 and w2
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2


通过对比，可以发现基本没有差异，就是矩阵计算的符号有所改变。

  • Step 2: PyTorch的一个重要功能就是autograd，也就是说只要定义了forward pass(前向神经网络)，计算了loss之后，PyTorch可以自动求导计算模型所有参数的梯度。一个PyTorch的Tensor表示计算图中的一个节点。如果x是一个Tensor并且x.requires_grad=True那么x.grad是另一个储存着x当前梯度(相对于一个scalar，常常是loss)的向量。
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N, D_in, H, D_out = 64, 1000, 100, 10

# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

w1 = torch.randn(D_in, H, requires_grad=True)
w2 = torch.randn(H, D_out, requires_grad=True)

learning_rate = 1e-6
for it in range(500):
    # Forward pass
    y_pred = x.mm(w1).clamp(min=0).mm(w2)
    
    # compute loss
    loss = (y_pred - y).pow(2).sum() # computation graph
    if it%100==0: print(it, loss.item())
    
    # Backward pass
    loss.backward()
    
    # update weights of w1 and w2
    with torch.no_grad():
        w1 -= learning_rate * w1.grad
        w2 -= learning_rate * w2.grad
        w1.grad.zero_()
        w2.grad.zero_()

  • Step 3: 调用torch.nn.Sequential来定义网络
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import torch.nn as nn
N, D_in, H, D_out = 64, 1000, 100, 10
# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
model = torch.nn.Sequential(
    torch.nn.Linear(D_in, H, bias=False), # w_1 * x + b_1
    torch.nn.ReLU(),
    torch.nn.Linear(H, D_out, bias=False),
)

torch.nn.init.normal_(model[0].weight)
torch.nn.init.normal_(model[2].weight)

# model = model.cuda()

loss_fn = nn.MSELoss(reduction='sum')

learning_rate = 1e-6
for it in range(500):
    # Forward pass
    y_pred = model(x) # model.forward() 
    
    # compute loss
    loss = loss_fn(y_pred, y) # computation graph
    if it%100==0 :print(it, loss.item())
    
    # Backward pass
    loss.backward()
    
    # update weights of w1 and w2
    with torch.no_grad():
        for param in model.parameters(): # param (tensor, grad)
            param -= learning_rate * param.grad
            
    model.zero_grad()

  • Step 4: 这一次我们不再手动更新模型的weights,而是使用optim这个包来帮助我们更新参数。 optim这个package提供了各种不同的模型优化方法，包括SGD+momentum, RMSProp, Adam等等。
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import torch.nn as nn

N, D_in, H, D_out = 64, 1000, 100, 10

# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

model = torch.nn.Sequential(
    torch.nn.Linear(D_in, H, bias=False), # w_1 * x + b_1
    torch.nn.ReLU(),
    torch.nn.Linear(H, D_out, bias=False),
)

torch.nn.init.normal_(model[0].weight)
torch.nn.init.normal_(model[2].weight)

# model = model.cuda()

loss_fn = nn.MSELoss(reduction='sum')
# learning_rate = 1e-4
# optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

learning_rate = 1e-6
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

for it in range(500):
    # Forward pass
    y_pred = model(x) # model.forward() 
    
    # compute loss
    loss = loss_fn(y_pred, y) # computation graph
    if it%100==0 :print(it, loss.item())

    optimizer.zero_grad() #梯度初始化为零
    # Backward pass
    loss.backward()
    
    # update model parameters
    optimizer.step()

  • Step 5: 这个模型继承自nn.Module类。如果需要定义一个比Sequential模型更加复杂的模型，就需要定义nn.Module模型。
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import torch.nn as nn

N, D_in, H, D_out = 64, 1000, 100, 10

# 随机创建一些训练数据
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)

class TwoLayerNet(torch.nn.Module):
    def __init__(self, D_in, H, D_out):
        super(TwoLayerNet, self).__init__()
        # define the model architecture
        self.linear1 = torch.nn.Linear(D_in, H, bias=False)
        self.linear2 = torch.nn.Linear(H, D_out, bias=False)
    
    def forward(self, x):
        y_pred = self.linear2(self.linear1(x).clamp(min=0))
        return y_pred

model = TwoLayerNet(D_in, H, D_out)
loss_fn = nn.MSELoss(reduction='sum')
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

for it in range(500):
    # Forward pass
    y_pred = model(x) # model.forward() 
    
    # compute loss
    loss = loss_fn(y_pred, y) # computation graph
    if it%100==0 :print(it, loss.item())

    optimizer.zero_grad()
    # Backward pass
    loss.backward()
    
    # update model parameters
    optimizer.step()

通过一步一步的改进，从numpy使用上了torch的接口，还真是收获满满的一天呢。🤪

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