用python和numpy写一个8个卷积层1个全连接层的神经网络模型
时间: 2023-04-06 12:02:02 浏览: 91
卷积神经网络实现手写数字识别(纯numpy实现)-python卷积神经网络代码.zip
你好,我可以回答这个问题。以下是一个使用 Python 和 Numpy 编写的 8 层卷积层和 1 层全连接层的神经网络模型的示例代码:
```python
import numpy as np
# 定义神经网络模型
class NeuralNetwork:
def __init__(self):
# 初始化权重和偏置
self.weights = [np.random.randn(3, 3, 3) for _ in range(8)]
self.biases = [np.zeros((1, 1, 3)) for _ in range(8)]
self.fc_weights = np.random.randn(27, 10)
self.fc_bias = np.zeros((1, 10))
def conv_layer(self, x, w, b):
# 卷积操作
z = np.zeros((x.shape[0] - w.shape[0] + 1, x.shape[1] - w.shape[1] + 1, w.shape[2]))
for i in range(z.shape[0]):
for j in range(z.shape[1]):
z[i, j] = np.sum(x[i:i+w.shape[0], j:j+w.shape[1]] * w, axis=(0, 1)) + b
return z
def relu(self, x):
# ReLU 激活函数
return np.maximum(x, 0)
def softmax(self, x):
# softmax 激活函数
exp_x = np.exp(x - np.max(x, axis=-1, keepdims=True))
return exp_x / np.sum(exp_x, axis=-1, keepdims=True)
def forward(self, x):
# 前向传播
for i in range(8):
x = self.conv_layer(x, self.weights[i], self.biases[i])
x = self.relu(x)
x = x.reshape(-1, 27)
x = np.dot(x, self.fc_weights) + self.fc_bias
x = self.softmax(x)
return x
# 测试模型
x = np.random.randn(32, 32, 3)
model = NeuralNetwork()
output = model.forward(x)
print(output)
```
希望这个示例代码能够帮助你理解如何使用 Python 和 Numpy 编写神经网络模型。
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