torch
1.從數據直接構建tensor
x = torch.tensor([5.5,3])
2.從已有的tensor構建一個tensor。這些方法會重用原來tensor的特征。
x = x.new_ones(5,3,dtype=torch.double)
torch.randn_like(x,dtype=torch.float)
3.得到tensor的形狀
x.shape()
x.size()
4.tensor的運算
x = torch.rand(5,3) y = torch.rand(5,3)
x+y torch.add(x,y)
result = torch.empty(5,3) result = x+y
y.add_() #把結果保存到里面
5.numpy里面的indexing都可以在tensor使用
x[:,1:]
6.resizing(在numpy里面用reshape在torch里面用view)
x = torch.randn(4,4) y = x.view(16) z = x.view(-1,8)
7.如果只用一個元素的tensor 使用。item() 方法可以把里面的value 變成Python數值
x = torch.randn(1) x.data x.grad x.item() z.transpose(1,0)
8 .numpy和tensor 之間的轉化
a = torch.ones(5) b = a.numpy() #a，b共享內存空間
a = np.ones(5) b = torch.from_numpy(a) #a，b共享內存空間
9.cuda tensor
if torch.cuda.is_available():
device = torch.device("cuda")
y = torch.ones_like(x,device=device)
x = x.to(device)
y.cpu().data.numpy() y.to("cpu").data.numpy() model = model.cuda()
10. 用numpy 實現兩層神經網絡
N , D_in, H, D_out = 64,1000,100,10
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 t in range(500):
h = x.dot(w1) #(N,H)
h_relu = np.maxinum(h,0)
y_pred = h_relu.dot(w2)
#compute loss
loss = np.square(y_pred - y).sum()
print(t,loss)
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)
w1 -= learning_rate*grad_w1
w2 -=learning_rate*grad_w2
11.用tensors 實現兩層神經網絡
import torch
dtype = torch.float
device = torch.device("cpu")
# device = torch.device("cuda:0") # Uncomment this to run on GPU
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random input and output data
x = torch.randn(N, D_in, device=device, dtype=dtype)
y = torch.randn(N, D_out, device=device, dtype=dtype)
# Randomly initialize weights
w1 = torch.randn(D_in, H, device=device, dtype=dtype)
w2 = torch.randn(H, D_out, device=device, dtype=dtype)
learning_rate = 1e-6
for t in range(500):
# Forward pass: compute predicted y
h = x.mm(w1)
h_relu = h.clamp(min=0)
y_pred = h_relu.mm(w2)
# Compute and print loss
loss = (y_pred - y).pow(2).sum().item()
print(t, loss)
# Backprop to compute gradients of w1 and w2 with respect to loss
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 using gradient descent
w1 -= learning_rate * grad_w1
w2 -= learning_rate * grad_w2
autograd
import torch
dtype = torch.float
device = torch.device("cpu")
# device = torch.device("cuda:0") # Uncomment this to run on GPU
# N 是 batch size; D_in 是 input dimension;
# H 是 hidden dimension; D_out 是 output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# 創建隨機的Tensor來保存輸入和輸出
# 設定requires_grad=False表示在反向傳播的時候我們不需要計算gradient
x = torch.randn(N, D_in, device=device, dtype=dtype)
y = torch.randn(N, D_out, device=device, dtype=dtype)
# 創建隨機的Tensor和權重。
# 設置requires_grad=True表示我們希望反向傳播的時候計算Tensor的gradient
w1 = torch.randn(D_in, H, device=device, dtype=dtype, requires_grad=True)
w2 = torch.randn(H, D_out, device=device, dtype=dtype, requires_grad=True)
learning_rate = 1e-6
for t in range(500):
# 前向傳播:通過Tensor預測y；這個和普通的神經網絡的前向傳播沒有任何不同，
# 但是我們不需要保存網絡的中間運算結果，因為我們不需要手動計算反向傳播。
y_pred = x.mm(w1).clamp(min=0).mm(w2)
# 通過前向傳播計算loss
# loss是一個形狀為(1，)的Tensor
# loss.item()可以給我們返回一個loss的scalar
loss = (y_pred - y).pow(2).sum()
print(t, loss.item())
# PyTorch給我們提供了autograd的方法做反向傳播。如果一個Tensor的requires_grad=True，
# backward會自動計算loss相對于每個Tensor的gradient。在backward之后，
# w1.grad和w2.grad會包含兩個loss相對于兩個Tensor的gradient信息。
loss.backward()
# 我們可以手動做gradient descent(后面我們會介紹自動的方法)。
# 用torch.no_grad()包含以下statements，因為w1和w2都是requires_grad=True，
# 但是在更新weights之后我們并不需要再做autograd。
# 另一種方法是在weight.data和weight.grad.data上做操作，這樣就不會對grad產生影響。
# tensor.data會我們一個tensor，這個tensor和原來的tensor指向相同的內存空間，
# 但是不會記錄計算圖的歷史。
with torch.no_grad():
w1 -= learning_rate * w1.grad
w2 -= learning_rate * w2.grad
# Manually zero the gradients after updating weights
w1.grad.zero_()
w2.grad.zero_()
optim
import torch
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random Tensors to hold inputs and outputs
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
# Use the nn package to define our model and loss function.
model = torch.nn.Sequential(
torch.nn.Linear(D_in, H),
torch.nn.ReLU(),
torch.nn.Linear(H, D_out),
)
loss_fn = torch.nn.MSELoss(reduction='sum')
# Use the optim package to define an Optimizer that will update the weights of
# the model for us. Here we will use Adam; the optim package contains many other
# optimization algoriths. The first argument to the Adam constructor tells the
# optimizer which Tensors it should update.
learning_rate = 1e-4
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for t in range(500):
# Forward pass: compute predicted y by passing x to the model.
y_pred = model(x)
# Compute and print loss.
loss = loss_fn(y_pred, y)
print(t, loss.item())
# Before the backward pass, use the optimizer object to zero all of the
# gradients for the variables it will update (which are the learnable
# weights of the model). This is because by default, gradients are
# accumulated in buffers( i.e, not overwritten) whenever .backward()
# is called. Checkout docs of torch.autograd.backward for more details.
optimizer.zero_grad()
# Backward pass: compute gradient of the loss with respect to model
# parameters
loss.backward()
# Calling the step function on an Optimizer makes an update to its
# parameters
optimizer.step()
自定義的nn Modules
import torch
class TwoLayerNet(torch.nn.Module):
def __init__(self, D_in, H, D_out):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(TwoLayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.linear2 = torch.nn.Linear(H, D_out)
def forward(self, x):
"""
In the forward function we accept a Tensor of input data and we must return
a Tensor of output data. We can use Modules defined in the constructor as
well as arbitrary operators on Tensors.
"""
h_relu = self.linear1(x).clamp(min=0)
y_pred = self.linear2(h_relu)
return y_pred
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
# Create random Tensors to hold inputs and outputs
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
# Construct our model by instantiating the class defined above
model = TwoLayerNet(D_in, H, D_out)
# Construct our loss function and an Optimizer. The call to model.parameters()
# in the SGD constructor will contain the learnable parameters of the two
# nn.Linear modules which are members of the model.
criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
for t in range(500):
# Forward pass: Compute predicted y by passing x to the model
y_pred = model(x)
# Compute and print loss
loss = criterion(y_pred, y)
print(t, loss.item())
# Zero gradients, perform a backward pass, and update the weights.
optimizer.zero_grad()
loss.backward()
optimizer.step()
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