全連接神經網絡（FC）
全連接神經網絡是一種最基本的神經網絡結構，英文為Full Connection，所以一般簡稱FC。
FC的準則很簡單：神經網絡中除輸入層之外的每個節點都和上一層的所有節點有連接。
以上一次的MNIST為例
import torch
import torch.utils.data
from torch import optim
from torchvision import datasets
from torchvision.transforms import transforms
import torch.nn.functional as F
batch_size = 200
learning_rate = 0.001
epochs = 20
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('mnistdata', train=True, download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('mnistdata', train=False, download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
w1, b1 = torch.randn(200, 784, requires_grad=True), torch.zeros(200, requires_grad=True)
w2, b2 = torch.randn(200, 200, requires_grad=True), torch.zeros(200, requires_grad=True)
w3, b3 = torch.randn(10, 200, requires_grad=True), torch.zeros(10, requires_grad=True)
torch.nn.init.kaiming_normal_(w1)
torch.nn.init.kaiming_normal_(w2)
torch.nn.init.kaiming_normal_(w3)
def forward(x):
x = x@w1.t() + b1
x = F.relu(x)
x = x@w2.t() + b2
x = F.relu(x)
x = x@w3.t() + b3
x = F.relu(x)
return x
optimizer = optim.Adam([w1, b1, w2, b2, w3, b3], lr=learning_rate)
criteon = torch.nn.CrossEntropyLoss()
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
data = data.view(-1, 28*28)
logits = forward(data)
loss = criteon(logits, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch : {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx*len(data), len(train_loader.dataset),
100.*batch_idx/len(train_loader), loss.item()
))
test_loss = 0
correct = 0
for data, target in test_loader:
data = data.view(-1, 28*28)
logits = forward(data)
test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1]
correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set : Averge loss: {:.4f}, Accurancy: {}/{}({:.3f}%)'.format(
test_loss, correct, len(test_loader.dataset),
100.*correct/len(test_loader.dataset)
))
我們將每個w和b都進行了定義，并且自己寫了一個forward函數。如果我們采用了全連接層，那么整個代碼也會更加簡介明了。
首先，我們定義自己的網絡結構的類：
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.model = nn.Sequential(
nn.Linear(784, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 10),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
x = self.model(x)
return x
它繼承于nn.Moudle，并且自己定義里整個網絡結構。
其中inplace的作用是直接復用存儲空間，減少新開辟存儲空間。
除此之外，它可以直接進行運算，不需要手動定義參數和寫出運算語句，更加簡便。
同時我們還可以發現，它自動完成了初試化，不需要像之前一樣再手動寫一個初始化了。
區分nn.Relu和F.relu()
前者是一個類的接口，后者是一個函數式接口。
前者都是大寫的，并且調用的的時候需要先實例化才能使用，而后者是小寫的可以直接使用。
最重要的是后者的自由度更高，更適合做一些自己定義的操作。
完整代碼
import torch
import torch.utils.data
from torch import optim, nn
from torchvision import datasets
from torchvision.transforms import transforms
import torch.nn.functional as F
batch_size = 200
learning_rate = 0.001
epochs = 20
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('mnistdata', train=True, download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('mnistdata', train=False, download=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.model = nn.Sequential(
nn.Linear(784, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 200),
nn.LeakyReLU(inplace=True),
nn.Linear(200, 10),
nn.LeakyReLU(inplace=True)
)
def forward(self, x):
x = self.model(x)
return x
device = torch.device('cuda:0')
net = MLP().to(device)
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
criteon = nn.CrossEntropyLoss().to(device)
for epoch in range(epochs):
for batch_idx, (data, target) in enumerate(train_loader):
data = data.view(-1, 28*28)
data, target = data.to(device), target.to(device)
logits = net(data)
loss = criteon(logits, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch_idx % 100 == 0:
print('Train Epoch : {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx*len(data), len(train_loader.dataset),
100.*batch_idx/len(train_loader), loss.item()
))
test_loss = 0
correct = 0
for data, target in test_loader:
data = data.view(-1, 28*28)
data, target = data.to(device), target.to(device)
logits = net(data)
test_loss += criteon(logits, target).item()
pred = logits.data.max(1)[1]
correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset)
print('\nTest set : Averge loss: {:.4f}, Accurancy: {}/{}({:.3f}%)'.format(
test_loss, correct, len(test_loader.dataset),
100.*correct/len(test_loader.dataset)
))
補充：pytorch 實現一個隱層的全連接神經網絡
torch.nn 實現 模型的定義，網絡層的定義，損失函數的定義。
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 as a sequence of layers. nn.Sequential
# is a Module which contains other Modules, and applies them in sequence to
# produce its output. Each Linear Module computes output from input using a
# linear function, and holds internal Tensors for its weight and bias.
model = torch.nn.Sequential(
torch.nn.Linear(D_in, H),
torch.nn.ReLU(),
torch.nn.Linear(H, D_out),
)
# The nn package also contains definitions of popular loss functions; in this
# case we will use Mean Squared Error (MSE) as our loss function.
loss_fn = torch.nn.MSELoss(reduction='sum')
learning_rate = 1e-4
for t in range(500):
# Forward pass: compute predicted y by passing x to the model. Module objects
# override the __call__ operator so you can call them like functions. When
# doing so you pass a Tensor of input data to the Module and it produces
# a Tensor of output data.
y_pred = model(x)
# Compute and print loss. We pass Tensors containing the predicted and true
# values of y, and the loss function returns a Tensor containing the
# loss.
loss = loss_fn(y_pred, y)
print(t, loss.item())
# Zero the gradients before running the backward pass.
model.zero_grad()
# Backward pass: compute gradient of the loss with respect to all the learnable
# parameters of the model. Internally, the parameters of each Module are stored
# in Tensors with requires_grad=True, so this call will compute gradients for
# all learnable parameters in the model.
loss.backward()
# Update the weights using gradient descent. Each parameter is a Tensor, so
# we can access its gradients like we did before.
with torch.no_grad():
for param in model.parameters():
param -= learning_rate * param.grad
上面，我們使用parem= -= learning_rate* param.grad 手動更新參數。
使用torch.optim 自動優化參數。optim這個package提供了各種不同的模型優化方法，包括SGD+momentum, RMSProp, Adam等等。
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
for t in range(500):
y_pred = model(x)
loss = loss_fn(y_pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
以上為個人經驗，希望能給大家一個參考，也希望大家多多支持腳本之家。如有錯誤或未考慮完全的地方，望不吝賜教。
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