优化print('Epoch: %d, Train Loss: %.4f' % (epoch, np.mean(np.abs(error))))
时间: 2023-11-27 12:54:57 浏览: 23
可以尝试使用f-string来简化代码,如下所示:
```
print(f"Epoch: {epoch}, Train Loss: {np.mean(np.abs(error)):.4f}")
```
这样可以更清晰地表达输出的信息,并且代码更加简洁易读。
相关问题
上面这串代码报错AttributeError: 'NeuralNetwork' object has no attribute 'predict'
抱歉,我的回答有误。你需要在 `NeuralNetwork` 类中添加 `predict` 方法,以便在训练模型后进行预测。下面是一个简单的实现:
```python
class NeuralNetwork:
def __init__(self, input_dim, hidden_dim, output_dim):
# 初始化权重和偏置
self.W1 = np.random.randn(input_dim, hidden_dim)
self.b1 = np.zeros((1, hidden_dim))
self.W2 = np.random.randn(hidden_dim, output_dim)
self.b2 = np.zeros((1, output_dim))
def sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def forward(self, X):
# 前向传播
self.z1 = np.dot(X, self.W1) + self.b1
self.a1 = self.sigmoid(self.z1)
self.z2 = np.dot(self.a1, self.W2) + self.b2
y_pred = self.z2
return y_pred
def mse_loss(self, y_pred, y_true):
# 计算均方误差
mse = np.mean((y_pred - y_true)**2)
return mse
def mse_loss_derivative(self, y_pred, y_true):
# 计算均方误差的导数
mse_derivative = 2 * (y_pred - y_true) / y_pred.size
return mse_derivative
def backward(self, X, y_true, y_pred, learning_rate):
# 反向传播
mse_derivative = self.mse_loss_derivative(y_pred, y_true)
dW2 = np.dot(self.a1.T, mse_derivative)
db2 = np.sum(mse_derivative, axis=0, keepdims=True)
da1 = np.dot(mse_derivative, self.W2.T)
dz1 = da1 * self.a1 * (1 - self.a1)
dW1 = np.dot(X.T, dz1)
db1 = np.sum(dz1, axis=0, keepdims=True)
# 更新参数
self.W2 -= learning_rate * dW2
self.b2 -= learning_rate * db2
self.W1 -= learning_rate * dW1
self.b1 -= learning_rate * db1
def train(self, X_train, y_train, learning_rate, num_epochs):
loss_history = []
for i in range(num_epochs):
# 前向传播
y_pred = self.forward(X_train)
# 计算损失
loss = self.mse_loss(y_pred, y_train)
# 反向传播更新参数
self.backward(X_train, y_train, y_pred, learning_rate)
# 记录损失历史
loss_history.append(loss)
# 打印损失
if i % 100 == 0:
print('Epoch: {}, Loss: {:.4f}'.format(i, loss))
return loss_history
def predict(self, X):
# 前向传播预测结果
y_pred = self.forward(X)
return y_pred
```
在上面的 `NeuralNetwork` 类中添加了 `predict` 方法,用于进行预测。你可以使用该方法进行预测,如下所示:
```python
# 预测
y_pred = nn.predict(X_test)
# 可视化预测结果和真实结果
plt.plot(y_test, label='true')
plt.plot(y_pred, label='pred')
plt.legend()
# 输出模型评估指标
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
print('MSE:', mse)
print('MAE:', mae)
```
Implement linear regression model and use autograd to optimize it by Pytorch.
Here is an example implementation of a linear regression model using PyTorch and Autograd for optimization:
```python
import torch
import numpy as np
# Generate some random data
np.random.seed(42)
x = np.random.rand(100, 1)
y = 2 + 3 * x + 0.1 * np.random.randn(100, 1)
# Convert data to PyTorch tensors
x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()
# Define the model
class LinearRegression(torch.nn.Module):
def __init__(self):
super(LinearRegression, self).__init__()
self.linear = torch.nn.Linear(1, 1)
def forward(self, x):
return self.linear(x)
model = LinearRegression()
# Define the loss function
criterion = torch.nn.MSELoss()
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Train the model
num_epochs = 1000
for epoch in range(num_epochs):
# Forward pass
y_pred = model(x_tensor)
loss = criterion(y_pred, y_tensor)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Print progress
if (epoch+1) % 100 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
# Print the learned parameters
w, b = model.parameters()
print('w =', w.item())
print('b =', b.item())
```
In this example, we define a linear regression model as a subclass of `torch.nn.Module`, with a single linear layer. We use the mean squared error loss function and stochastic gradient descent optimizer to train the model on the randomly generated data. The model parameters are learned through backpropagation using the `backward()` method, and are optimized using the `step()` method of the optimizer. After training, we print the learned values of the slope and intercept parameters.