input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.05 num_epochs=2000
时间: 2024-03-15 14:42:39 浏览: 93
这段代码是用来创建一个神经网络模型的,其中input_dim表示输入数据的维度,hidden_dim表示隐藏层的神经元个数,output_dim表示输出数据的维度。nn=NeuralNetwork(input_dim, hidden_dim, output_dim)表示创建一个NeuralNetwork类的对象nn,并且将输入维度、隐藏层神经元个数和输出维度作为参数传入。learning_rate表示学习率,是用来控制权重更新的速度,num_epochs表示训练的轮数。整个代码的作用是构建一个神经网络模型,并且使用给定的数据和超参数进行训练。
相关问题
加载数据 X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE') X_pred = data_pred.iloc[:, :-1].values y_pred = data_pred.iloc[:, -1:].values y_pred = nn.predict(X_pred)报错NameError: name 'data_pred' is not defined解决代码
你需要先定义 `data_pred` 变量并赋值,再执行 `X_pred = data_pred.iloc[:, :-1].values` 和 `y_pred = data_pred.iloc[:, -1:].values` 这两行代码。例如:
```python
import pandas as pd
# 加载数据
data = pd.read_csv('train_data.csv')
data_pred = pd.read_csv('predict_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1:].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练模型
input_dim = 13
hidden_dim = 25
output_dim = 1
nn = NeuralNetwork(input_dim, hidden_dim, output_dim)
learning_rate = 0.0016
num_epochs = 2000
loss_history = nn.train(X, y, learning_rate, num_epochs)
plt.plot(loss_history)
plt.title('loss')
plt.xlabel('Epoch')
plt.ylabel('MSE')
X_pred = data_pred.iloc[:, :-1].values
y_pred = data_pred.iloc[:, -1:].values
y_pred = nn.predict(X_pred)
```
# 加载数据 X = data.iloc[:, :-1].values y = data.iloc[:, -1:].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) data_pred = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') # 训练模型 input_dim=13 hidden_dim=25 output_dim=1 nn=NeuralNetwork(input_dim, hidden_dim, output_dim) learning_rate=0.0016 num_epochs=2000 loss_history=nn.train(X, y, learning_rate, num_epochs) plt.plot(loss_history) plt.title('loss') plt.xlabel('Epoch') plt.ylabel('MSE')根据此代码续写预测代码
# 预测代码
# 将数据预处理后进行预测
data_pred = data_pred.iloc[:, :-1].values
data_pred = (data_pred - np.mean(data_pred, axis=0)) / np.std(data_pred, axis=0)
y_pred = nn.forward(data_pred)
y_pred = y_pred * np.std(y_train) + np.mean(y_train)
print(y_pred)