#首先，需要导入PyTorch和相关的模块：
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
#接着，定义Transformer模型的编码器（Encoder）和解码器（Decoder）：
class Encoder(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_heads, dropout):
super(Encoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_heads = num_heads
self.dropout = dropout
self.embedding = nn.Embedding(input_size, hidden_size)
self.positional_encoding = PositionalEncoding(hidden_size, dropout)
self.encoders = nn.ModuleList([EncoderLayer(hidden_size, num_heads, dropout) for _ in range(num_layers)])
def forward(self, x):
x = self.embedding(x) * math.sqrt(self.hidden_size)
x = self.positional_encoding(x)
for layer in self.encoders:
x = layer(x)
return x
class Decoder(nn.Module):
def __init__(self, output_size, hidden_size, num_layers, num_heads, dropout):
super(Decoder, self).__init__()
self.output_size = output_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_heads = num_heads
self.dropout = dropout
self.embedding = nn.Embedding(output_size, hidden_size)
self.positional_encoding = PositionalEncoding(hidden_size, dropout)
self.decoders = nn.ModuleList([DecoderLayer(hidden_size, num_heads, dropout) for _ in range(num_layers)])
def forward(self, x, encoder_output):
x = self.embedding(x) * math.sqrt(self.hidden_size)
x = self.positional_encoding(x)
for layer in self.decoders:
x = layer(x, encoder_output)
return x
#在上述代码中，我们使用了PyTorch中的Embedding层和ModuleList容器，以及自定义的位置编码（PositionalEncoding）、编码器层（EncoderLayer）和解码器层（DecoderLayer）。
#接着，定义位置编码层：
class PositionalEncoding(nn.Module):
def __init__(self, hidden_size, dropout, max_length=5000):
super(PositionalEncoding, self).__init__()
self.hidden_size = hidden_size
self.dropout = nn.Dropout(p=dropout)
# 计算位置编码矩阵
pe = torch.zeros(max_length, hidden_size)
position = torch.arange(0, max_length, dtype=torch.float32).unsqueeze(1)
div = torch.exp(torch.arange(0, hidden_size, 2).float() * (-math.log(10000.0) / hidden_size))
pe[:, 0::2] = torch.sin(position * div)
pe[:, 1::2] = torch.cos(position * div)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
#这里使用了PyTorch中的Dropout层。
#接下来，定义编码器层（EncoderLayer）和解码器层（DecoderLayer）：
class EncoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(EncoderLayer, self).__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.dropout = dropout
self.self_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.feedforward = FeedForward(hidden_size, dropout)
self.layer_norm1 = nn.LayerNorm(hidden_size)
self.layer_norm2 = nn.LayerNorm(hidden_size)
def forward(self, x):
x = self.layer_norm1(x + self.self_attention(x))
x = self.layer_norm2(x + self.feedforward(x))
return x
class DecoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(DecoderLayer, self).__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.dropout = dropout
self.self_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.encoder_attention = MultiheadAttention(hidden_size, num_heads, dropout)
self.feedforward = FeedForward(hidden_size, dropout)
self.layer_norm1 = nn.LayerNorm(hidden_size)
self.layer_norm2 = nn.LayerNorm(hidden_size)
self.layer_norm3 = nn.LayerNorm(hidden_size)
def forward(self, x, encoder_output):
x = self.layer_norm1(x + self.self_attention(x))
x = self.layer_norm2(x + self.encoder_attention(x, encoder_output))
x = self.layer_norm3(x + self.feedforward(x))
return x
#在上述代码中，我们使用了自定义的多头注意力（MultiheadAttention）和前馈网络（FeedForward）。
#下面，定义多头注意力层和前馈网络层：
class MultiheadAttention(nn.Module):
def __init__(self, hidden_size, num_heads, dropout):
super(MultiheadAttention, self).__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.dropout = dropout
self.head_size = hidden_size // num_heads
self.query = nn.Linear(hidden_size, hidden_size)
self.key = nn.Linear(hidden_size, hidden_size)
self.value = nn.Linear(hidden_size, hidden_size)
self.fc = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x, enc_output=None):
batch_size = x.size(0)
q = self.query(x).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
k = self.key(x if enc_output is None else enc_output).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
v = self.value(x if enc_output is None else enc_output).view(batch_size, -1, self.num_heads, self.head_size).transpose(1, 2)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_size)
scores = F.softmax(scores, dim=-1)
scores = self.dropout(scores)
x = torch.matmul(scores, v)
x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.hidden_size)
x = self.fc(x)
x = self.dropout(x)
return x
class FeedForward(nn.Module):
def __init__(self, hidden_size, dropout):
super(FeedForward, self).__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.fc1 = nn.Linear(hidden_size, hidden_size * 4)
self.fc2 = nn.Linear(hidden_size * 4, hidden_size)
self.dropout = nn.Dropout(p=dropout)
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.dropout(x)
return x
#最后，定义完整的Transformer模型：
class Transformer(nn.Module):
def __init__(self, input_size, output_size, hidden_size, num_layers, num_heads, dropout):
super(Transformer, self).__init__()
self.encoder = Encoder(input_size, hidden_size, num_layers, num_heads, dropout)
self.decoder = Decoder(output_size, hidden_size, num_layers, num_heads, dropout)
self.output_size = output_size
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, input_seq, target_seq):
encoder_output = self.encoder(input_seq)
decoder_output = self.decoder(target_seq, encoder_output)
decoder_output = self.fc(decoder_output)
return decoder_output
#这里我们只定义了基本的Transformer模型，您可以根据具体的游戏场景进行个性化的修改和拓展，例如增加注意力权重控制等功能。同时，您也需要根据具体的游戏数据来定义数据预处理和模型训练的方式。
import random
import math
# 创建一些随机数据
def create_data(num_examples):
input_seqs = []
output_seqs = []
for _ in range(num_examples):
input_seq = [random.choice(range(1, 10)) for _ in range(5)]
output_seq = sorted(input_seq)
input_seqs.append(torch.tensor(input_seq, dtype=torch.long))
output_seqs.append(torch.tensor(output_seq, dtype=torch.long))
return input_seqs, output_seqs
# 定义训练函数
def train(model, data, epochs, device):
model.train()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
for epoch in range(epochs):
losses = []
for input_seq, target_seq in data:
input_seq = input_seq.to(device).unsqueeze(0) # 增加一个维度
target_seq = target_seq.to(device).unsqueeze(0) # 增加一个维度
optimizer.zero_grad()
output = model(input_seq, target_seq[:, :-1])
loss = criterion(output.view(-1, output.size(2)), target_seq[:, 1:].view(-1))
loss.backward()
optimizer.step()
losses.append(loss.item())
print(f'Epoch {epoch+1}/{epochs}, Loss: {sum(losses) / len(losses)}')
# 定义推理函数
def predict(model, input_seq, device):
model.eval()
input_seq = input_seq.to(device)
output_seq = [0]
for _ in range(5):
target_seq = torch.tensor(output_seq, dtype=torch.long).unsqueeze(0).to(device)
output = model(input_seq.unsqueeze(0), target_seq)
output = output.argmax(2)[:, -1].item()
if output != 0:
output_seq.append(output)
return output_seq[1:]
# 创建数据并将其分为训练和测试集
input_seqs, output_seqs = create_data(100)
train_data = list(zip(input_seqs[:-10], output_seqs[:-10]))
test_data = list(zip(input_seqs[-10:], output_seqs[-10:]))
# 初始化模型并将其移动到GPU（如有）
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Transformer(input_size=11, output_size=11, hidden_size=512, num_layers=6, num_heads=8, dropout=0.1).to(device)
# 训练模型
train(model, train_data, epochs=20, device=device)
# 测试模型
for input_seq, target_seq in test_data[:5]:
prediction = predict(model, input_seq, device)
print(f'Input: {input_seq.tolist()}')
print(f'True Output: {target_seq.tolist()}')
print(f'Predicted Output: {prediction}')
print()
#注意：上面的示例代码使用了一些随机生成的有序数据作为示例，实际应用中应该将其替换为特定游戏的数据。
