legend_opt

Legend_opt通常是指图形中的图例优化设置，它涉及到如何更好地呈现数据系列或变量在图表上。在各种数据可视化工具中，如Matplotlib、Seaborn或Plotly等，legend_opt是对图例的位置、大小、标签样式、可见性等进行调整的选项。通过合理的legend_opt设置，可以使图例更加清晰易读，帮助观众更快地理解图表信息。

例如，在Python的Matplotlib库中，你可以使用`legend()`函数，并通过`loc`参数控制图例位置，使用`frameon`或`framealpha`改变图例框的显示效果。具体设置可能会包括：

plt.legend(loc='upper right', fontsize=10, frameon=False)

在这里，`loc='upper right'`指定了图例位于右上角，`fontsize=10`设置了字体大小，`frameon=False`表示不显示边框。

相关问题

return data, label def __len__(self): return len(self.data)train_dataset = MyDataset(train, y[:split_boundary].values, time_steps, output_steps, target_index)test_ds = MyDataset(test, y[split_boundary:].values, time_steps, output_steps, target_index)class MyLSTMModel(nn.Module): def __init__(self): super(MyLSTMModel, self).__init__() self.rnn = nn.LSTM(input_dim, 16, 1, batch_first=True) self.flatten = nn.Flatten() self.fc1 = nn.Linear(16 * time_steps, 120) self.relu = nn.PReLU() self.fc2 = nn.Linear(120, output_steps) def forward(self, input): out, (h, c) = self.rnn(input) out = self.flatten(out) out = self.fc1(out) out = self.relu(out) out = self.fc2(out) return outepoch_num = 50batch_size = 128learning_rate = 0.001def train(): print('训练开始') model = MyLSTMModel() model.train() opt = optim.Adam(model.parameters(), lr=learning_rate) mse_loss = nn.MSELoss() data_reader = DataLoader(train_dataset, batch_size=batch_size, drop_last=True) history_loss = [] iter_epoch = [] for epoch in range(epoch_num): for data, label in data_reader: # 验证数据和标签的形状是否满足期望，如果不满足，则跳过这个批次 if data.shape[0] != batch_size or label.shape[0] != batch_size: continue train_ds = data.float() train_lb = label.float() out = model(train_ds) avg_loss = mse_loss(out, train_lb) avg_loss.backward() opt.step() opt.zero_grad() print('epoch {}, loss {}'.format(epoch, avg_loss.item())) iter_epoch.append(epoch) history_loss.append(avg_loss.item()) plt.plot(iter_epoch, history_loss, label='loss') plt.legend() plt.xlabel('iters') plt.ylabel('Loss') plt.show() torch.save(model.state_dict(), 'model_1')train()param_dict = torch.load('model_1')model = MyLSTMModel()model.load_state_dict(param_dict)model.eval()data_reader1 = DataLoader(test_ds, batch_size=batch_size, drop_last=True)res = []res1 = []# 在模型预测时，label 的处理for data, label in data_reader1: data = data.float() label = label.float() out = model(data) res.extend(out.detach().numpy().reshape(data.shape[0]).tolist()) res1.extend(label.numpy().tolist()) # 由于预测一步，所以无需 reshape，直接转为 list 即可title = "t321"plt.title(title, fontsize=24)plt.xlabel("time", fontsize=14)plt.ylabel("irr", fontsize=14)plt.plot(res, color='g', label='predict')plt.plot(res1, color='red', label='real')plt.legend()plt.grid()plt.show()的运算过程

这段代码是用 PyTorch 实现的一个 LSTM 模型，用于时间序列预测。首先定义了一个自定义的数据集类 MyDataset，用于加载训练数据和测试数据。然后定义了一个包含 LSTM 层的 MyLSTMModel 模型，并在 train 函数中使用 DataLoader 加载训练数据集，采用 Adam 优化器和 MSE 损失函数进行训练。训练过程中记录了每个 epoch 的损失值，并在训练结束后保存了模型参数。最后，使用加载的模型参数对测试数据进行预测，并将预测结果和真实值可视化展示出来。

S_inputs = Input(shape=(11,), dtype='int32') #(None,600) O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128) cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq) cnn1 = MaxPooling1D(pool_size=3)(cnn1) cnn = cnn1 O_seq = GlobalAveragePooling1D()(cnn) #(None,128) print(O_seq.shape) O_seq = Dropout(0.9)(O_seq) outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq) model = Model(inputs=S_inputs, outputs=outputs) opt = SGD(learning_rate=0.1, decay=0.00001) loss = 'categorical_crossentropy' model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy']) print('Train...') h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5) plt.plot(h.history["loss"], label="train_loss") plt.plot(h.history["val_loss"], label="test_loss") plt.legend() plt.show()给这段代码加注释

# 导入模块
from tensorflow.keras.layers import Input, Embedding, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Dropout, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import SGD
import tensorflow as tf
import matplotlib.pyplot as plt

# 定义输入层
S_inputs = Input(shape=(11,), dtype='int32') #(None,600)

# 创建嵌入层
O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128)

# 创建卷积层并进行池化操作
cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq)
cnn1 = MaxPooling1D(pool_size=3)(cnn1)
cnn = cnn1

# 全局平均池化
O_seq = GlobalAveragePooling1D()(cnn) #(None,128)

# 添加 dropout 层
O_seq = Dropout(0.9)(O_seq)

# 创建输出层
outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq)

# 定义模型并进行编译
model = Model(inputs=S_inputs, outputs=outputs)
opt = SGD(learning_rate=0.1, decay=0.00001)
loss = 'categorical_crossentropy'
model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy'])

# 输出模型结构
model.summary()

# 训练模型
print('Train...')
h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5)

# 绘制损失函数曲线
plt.plot(h.history["loss"], label="train_loss")
plt.plot(h.history["val_loss"], label="test_loss")
plt.legend()
plt.show()