% 定义LSTM回归网络 deepNetworkDesigner layers = [ ... sequenceInputLayer(1,"Name","input") lstmLayer(250,"Name","lstm") dropoutLayer(0.5,"Name","drop") fullyConnectedLayer(1,"Name","fc2") regressionLayer("Name","regressionoutput")]; % 定义训练选项 % InitialLearnRate: 初始学习率 options = trainingOptions('adam', ... 'MaxEpochs',350, ... 'GradientThreshold',0.2, ... 'InitialLearnRate',0.002, ... 'LearnRateSchedule','piecewise', ... 'LearnRateDropPeriod',150, ... 'LearnRateDropFactor',0.2, ... 'Verbose',0, ... 'Plots','training-progress'); % 训练LSTM网络 net = trainNetwork(XTrain,YTrain,layers,options);
时间: 2024-02-03 07:02:25 浏览: 120
这段代码使用MATLAB深度学习工具箱中的deepNetworkDesigner函数定义了一个LSTM回归网络,该网络由一个sequenceInputLayer、一个LSTM层、一个dropout层、一个全连接层和一个回归层组成。其中,sequenceInputLayer用于接收输入序列,LSTM层用于提取序列特征,dropout层用于防止过拟合,全连接层用于将特征映射到输出空间,回归层用于输出回归结果。
接下来,使用trainingOptions函数定义了训练选项,包括优化算法、最大迭代次数、梯度阈值、初始学习率、学习率调度策略、学习率下降周期、学习率下降因子、是否输出训练信息和是否可视化训练进度。
最后,使用trainNetwork函数对LSTM网络进行训练,其中XTrain和YTrain分别表示训练数据和训练标签。在训练过程中,MATLAB会自动调整学习率,通过反向传播算法更新网络参数,从而最小化损失函数。训练完成后,可以使用训练好的LSTM网络对新的数据进行预测。
相关问题
input_data = layers.Input(shape=(200, ), name = 'input_layer') word_2_vec = layers.Embedding(max_words, embeddings_dim, input_length=maxlen)(input_data) lstm_output = layers.Bidirectional(LSTM(200, input_shape=(200, 100), return_sequences=True), merge_mode='ave')(word_2_vec) feature_1 = layers.Conv1D(filters=100, kernel_size=3)(lstm_output) feature_1 = layers.MaxPool1D(pool_size=98)(feature_1) feature_2 = layers.Conv1D(filters=100, kernel_size=4)(lstm_output) feature_2 = layers.MaxPool1D(pool_size=97)(feature_2) feature_3 = layers.Conv1D(filters=100, kernel_size=5)(lstm_output) feature_3 = layers.MaxPool1D(pool_size=96)(feature_3) x = layers.concatenate([feature_1, feature_2, feature_3], axis=2) x = Self_Attention(300)(x) x = layers.Dense(64, activation='relu', input_shape=(300, ))(x) x = layers.Dense(16, activation='relu')(x) priority_pred = layers.Dense(1, name="priority", activation='softmax')(x) model = keras.Model(inputs = [input_data], outputs = [priority_pred]) #将预训练的词嵌入加入到Embedding层中# model.layers[1].set_weights([embeddings_matrix]) model.layers[1].trainable = False #训练与评估# model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc', keras.metrics.Recall(), km.f1_score()]) history = model.fit(x_train, y_train, epochs=30, batch_size=64, validation_data=(x_val, y_val)) 哪里出错了
在你提供的代码中,没有明显的错误。但是,有几个可能导致问题的地方需要注意:
1. 输入形状:你的输入层指定了形状为 (200,) 的输入数据,这意味着每个输入样本应该是一个长度为 200 的一维向量。确保你的输入数据与这个形状相匹配。
2. 目标值形状:你的模型输出层是一个具有 1 个单元的密集层,使用 softmax 激活函数。这意味着你的目标值应该是一个形状为 (None, 1) 的二维张量,其中每个元素表示一个二分类任务的标签。请确保你的目标值与这个形状相匹配。
3. 损失函数:你的模型使用了二元交叉熵作为损失函数。如果你的目标值是一个整数数组而不是二进制编码的标签,可能需要使用`sparse_categorical_crossentropy`作为损失函数。
请仔细检查你的输入数据、目标值以及损失函数,并确保它们与模型的期望形状相匹配。如果问题仍然存在,请提供更多错误信息或调试信息以便更具体地定位问题。
这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")
以下是加入 Test Loss 功能后的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
super().__init__()
self.device = device
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.output_size = output_size
self.num_directions = 1 # 单向LSTM
self.batch_size = batch_size
self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
self.linear = nn.Linear(65536, self.output_size)
def forward(self, input_seq):
h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
output, _ = self.lstm(input_seq, (h_0, c_0))
pred = self.linear(output.contiguous().view(self.batch_size, -1))
return pred
if __name__ == '__main__':
# 加载已保存的模型参数
saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
device = 'cuda:0'
lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
state_dict = torch.load(saved_model_path)
lstm_model.load_state_dict(state_dict)
# 加载测试数据集
test_dataset = ECGDataset(X_test_df.to_numpy())
test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
# 定义损失函数和优化器
loss_fn = nn.CrossEntropyLoss()
optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
# 开始训练
for epoch in range(200000):
print(f'epoch:{epoch}')
lstm_model.train()
epoch_bar = tqdm(dataloader)
for x, y in epoch_bar:
optimizer.zero_grad()
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
loss.backward()
epoch_bar.set_description(f'loss:{loss.item():.4f}')
optimizer.step()
# 计算测试集上的平均损失
test_loss = 0.0
lstm_model.eval()
with torch.no_grad():
for x, y in test_loader:
x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
loss = loss_fn(x_out, y.long().to(device))
test_loss += loss.item() * x.size(0)
test_loss /= len(test_dataset)
print(f'Test Loss: {test_loss:.4f}')
if epoch % 100 == 0 or epoch == epoch - 1:
torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
print("权重成功保存一次")
```
在这个代码中,我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后,我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式,并利用 `torch.no_grad()` 避免计算梯度,以加速计算。最后,我们输出测试集上的平均损失值。
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