一、nn.Modules
我们可以定义一个模型,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。
定义了__init__和 forward 两个方法,就实现了自定义的网络模型。
_init_(),定义模型架构,实现每个层的定义。
forward(),实现前向传播,返回y_pred
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, D_in, H, D_out = 64, 1000, 100, 10
x = torch.randn(N, D_in)
y = torch.randn(N, D_out)
model = TwoLayerNet(D_in, H, D_out)
criterion = torch.nn.MSELoss(reduction='sum')
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
for t in range(500):
y_pred = model(x)
loss = criterion(y_pred, y)
print(t, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
二、一个实例:FizzBuzz
FizzBuzz是一个简单的小游戏。游戏规则如下:从1开始往上数数,当遇到3的倍数的时候,说fizz,当遇到5的倍数,说buzz,当遇到15的倍数,就说fizzbuzz,其他情况下则正常数数。
# One-hot encode the desired outputs: [number, "fizz", "buzz", "fizzbuzz"]
def fizz_buzz_encode(i):
if i % 15 == 0: return 3
elif i % 5 == 0: return 2
elif i % 3 == 0: return 1
else: return 0
def fizz_buzz_decode(i, prediction):
return [str(i), "fizz", "buzz", "fizzbuzz"][prediction]
首先定义模型的输入与输出(训练数据)
import numpy as np
import torch
NUM_DIGITS = 10
# Represent each input by an array of its binary digits.
def binary_encode(i, num_digits):
return np.array([i >> d & 1 for d in range(num_digits)])[::-1] # 右移一位再和1做与运算。
# 右移动运算符:把">>"左边的运算数的各二进位全部右移若干位,>> 右边的数字指定了移动的位数
trX = torch.Tensor([binary_encode(i, NUM_DIGITS) for i in range(101, 2 ** NUM_DIGITS)])
trY = torch.LongTensor([fizz_buzz_encode(i) for i in range(101, 2 ** NUM_DIGITS)]) #因为表示类别,用LongTensor
然后用PyTorch定义模型,损失函数,优化器。
# Define the model
NUM_HIDDEN = 100
model = torch.nn.Sequential(
torch.nn.Linear(NUM_DIGITS, NUM_HIDDEN),
torch.nn.ReLU(),
torch.nn.Linear(NUM_HIDDEN, 4)
)
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.05)
以下是模型的训练代码
# Start training it
BATCH_SIZE = 128
for epoch in range(10000):
for start in range(0, len(trX), BATCH_SIZE):
end = start + BATCH_SIZE
batchX = trX[start:end]
batchY = trY[start:end]
y_pred = model(batchX)
loss = loss_fn(y_pred, batchY)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Find loss on training data
loss = loss_fn(model(trX), trY).item()
print('Epoch:', epoch, 'Loss:', loss)
本站文章如无特殊说明,均为本站原创,如若转载,请注明出处:pytorch(二) 自定义神经网络模型 - Python技术站