写一个神经网络能判但预测结果以及预测准确率的代码
时间: 2023-04-10 07:03:40 浏览: 53
以下是一个简单的神经网络代码示例,可以用于分类和预测:
```python
import numpy as np
# 定义 sigmoid 函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# 定义神经网络类
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化权重
self.weights1 = np.random.randn(self.input_size, self.hidden_size)
self.weights2 = np.random.randn(self.hidden_size, self.output_size)
def forward(self, X):
# 前向传播
self.z = np.dot(X, self.weights1)
self.z2 = sigmoid(self.z)
self.z3 = np.dot(self.z2, self.weights2)
y_hat = sigmoid(self.z3)
return y_hat
def predict(self, X):
# 预测
y_hat = self.forward(X)
predictions = np.round(y_hat)
return predictions
def accuracy(self, X, y):
# 计算准确率
predictions = self.predict(X)
accuracy = np.mean(predictions == y)
return accuracy
# 测试代码
X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
y = np.array([[0], [1], [1], [0]])
nn = NeuralNetwork(3, 4, 1)
for i in range(10000):
# 反向传播
nn.forward(X)
nn.error = y - nn.z3
nn.delta3 = nn.error * sigmoid(nn.z3, derivative=True)
nn.weights2 += np.dot(nn.z2.T, nn.delta3)
nn.delta2 = np.dot(nn.delta3, nn.weights2.T) * sigmoid(nn.z2, derivative=True)
nn.weights1 += np.dot(X.T, nn.delta2)
print("预测结果:", nn.predict(X))
print("准确率:", nn.accuracy(X, y))
```
这个代码使用了一个简单的三层神经网络,包括一个输入层、一个隐藏层和一个输出层。它可以用于分类和预测任务,并且可以计算准确率。