bp神经网络双隐层 图像分割代码
时间: 2023-09-21 11:03:29 浏览: 46
以下是使用BP神经网络进行图像分割的Python代码,其中包含两个隐层:
```python
import numpy as np
import cv2
# BP神经网络类
class BPNeuralNetwork:
def __init__(self, input_size, hidden_size1, hidden_size2, output_size):
self.input_size = input_size
self.hidden_size1 = hidden_size1
self.hidden_size2 = hidden_size2
self.output_size = output_size
self.W1 = np.random.randn(self.input_size, self.hidden_size1)
self.W2 = np.random.randn(self.hidden_size1, self.hidden_size2)
self.W3 = np.random.randn(self.hidden_size2, self.output_size)
# sigmoid激活函数
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
# sigmoid函数的导数
def sigmoid_derivative(self, x):
return x * (1 - x)
# 训练函数
def train(self, X, y, learning_rate, num_epochs):
for epoch in range(num_epochs):
# 向前传播
hidden_layer1 = self.sigmoid(np.dot(X, self.W1))
hidden_layer2 = self.sigmoid(np.dot(hidden_layer1, self.W2))
output_layer = self.sigmoid(np.dot(hidden_layer2, self.W3))
# 计算误差
error = y - output_layer
# 后向传播
output_layer_delta = error * self.sigmoid_derivative(output_layer)
hidden_layer2_error = output_layer_delta.dot(self.W3.T)
hidden_layer2_delta = hidden_layer2_error * self.sigmoid_derivative(hidden_layer2)
hidden_layer1_error = hidden_layer2_delta.dot(self.W2.T)
hidden_layer1_delta = hidden_layer1_error * self.sigmoid_derivative(hidden_layer1)
# 更新权重
self.W3 += learning_rate * hidden_layer2.T.dot(output_layer_delta)
self.W2 += learning_rate * hidden_layer1.T.dot(hidden_layer2_delta)
self.W1 += learning_rate * X.T.dot(hidden_layer1_delta)
# 预测函数
def predict(self, X):
hidden_layer1 = self.sigmoid(np.dot(X, self.W1))
hidden_layer2 = self.sigmoid(np.dot(hidden_layer1, self.W2))
output_layer = self.sigmoid(np.dot(hidden_layer2, self.W3))
return output_layer
# 读取图像
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
height, width = img.shape
# 将图像转换为一维向量
X = np.reshape(img, (height * width, 1))
# 将像素值归一化到0-1之间
X = X / 255.0
# 标记图像
y = np.zeros((height * width, 1))
y[img > 127] = 1
# 创建BP神经网络
nn = BPNeuralNetwork(1, 256, 128, 1)
# 训练BP神经网络
nn.train(X, y, 0.1, 1000)
# 预测图像
y_pred = nn.predict(X)
# 将预测结果转换为图像
img_pred = np.reshape(y_pred, (height, width))
img_pred = img_pred * 255.0
img_pred = img_pred.astype(np.uint8)
# 显示预测结果
cv2.imshow('Predicted Image', img_pred)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
需要注意的是,该代码仅适用于二值图像分割,即将图像分为前景和背景两部分。如果需要分割多个类别的图像,需要对代码进行相应的修改。同时,该代码仅作为示例,实际应用中需要根据具体情况进行修改和优化。