self.weights1 = np.random.randn(input_dim, hidden_dim) self.bias1 = np.zeros((1, hidden_dim)) self.weights2 = np.random.randn(hidden_dim, output_dim) self.bias2 = np.zeros((1, output_dim))
时间: 2024-04-26 12:21:10 浏览: 15
这段代码是神经网络类构造函数中的一部分,它用于初始化神经网络的参数。其中,`self.weights1` 和 `self.weights2` 分别是输入层到隐藏层和隐藏层到输出层之间的权重矩阵,使用 `np.random.randn` 函数随机生成。`self.bias1` 和 `self.bias2` 分别是隐藏层和输出层的偏置向量,使用 `np.zeros` 函数初始化为全零向量。这些参数用于计算神经网络的前向传播过程,从而生成预测结果。
相关问题
class NeuralNetwork: def init(self, input_dim, hidden_dim, output_dim): self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.weights1 = np.random.randn(input_dim, hidden_dim) self.bias1 = np.zeros((1, hidden_dim)) self.weights2 = np.random.randn(hidden_dim, output_dim) self.bias2 = np.zeros((1, output_dim)) def relu(self, x): return np.maximum(0, x) def relu_derivative(self, x): return np.where(x >= 0, 1, 0) def forward(self, x): self.z1 = np.dot(x, self.weights1) + self.bias1 self.a1 = self.relu(self.z1) self.z2 = np.dot(self.a1, self.weights2) + self.bias2 self.y_hat = self.z2 return self.y_hat def backward(self, x, y, learning_rate): error = self.y_hat - y delta2 = error delta1 = np.dot(delta2, self.weights2.T) * self.relu_derivative(self.a1) grad_weights2 = np.dot(self.a1.T, delta2) grad_bias2 = np.sum(delta2, axis=0, keepdims=True) grad_weights1 = np.dot(x.T, delta1) grad_bias1 = np.sum(delta1, axis=0) self.weights2 -= learning_rate * grad_weights2 self.bias2 -= learning_rate * grad_bias2 self.weights1 -= learning_rate * grad_weights1 根据代码加上损失函数和优化
函数,可以将这个神经网络变为完整的模型。一种常见的损失函数是均方误差(Mean Squared Error),可以用于回归问题:
```python
class NeuralNetwork:
def __init__(self, input_dim, hidden_dim, output_dim):
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.weights1 = np.random.randn(input_dim, hidden_dim)
self.bias1 = np.zeros((1, hidden_dim))
self.weights2 = np.random.randn(hidden_dim, output_dim)
self.bias2 = np.zeros((1, output_dim))
def relu(self, x):
return np.maximum(0, x)
def relu_derivative(self, x):
return np.where(x >= 0, 1, 0)
def forward(self, x):
self.z1 = np.dot(x, self.weights1) + self.bias1
self.a1 = self.relu(self.z1)
self.z2 = np.dot(self.a1, self.weights2) + self.bias2
self.y_hat = self.z2
return self.y_hat
def backward(self, x, y, learning_rate):
error = self.y_hat - y
delta2 = error
delta1 = np.dot(delta2, self.weights2.T) * self.relu_derivative(self.a1)
grad_weights2 = np.dot(self.a1.T, delta2)
grad_bias2 = np.sum(delta2, axis=0, keepdims=True)
grad_weights1 = np.dot(x.T, delta1)
grad_bias1 = np.sum(delta1, axis=0)
self.weights2 -= learning_rate * grad_weights2
self.bias2 -= learning_rate * grad_bias2
self.weights1 -= learning_rate * grad_weights1
self.bias1 -= learning_rate * grad_bias1
def mse_loss(self, y, y_hat):
return np.mean((y - y_hat)**2)
def sgd_optimizer(self, x, y, learning_rate):
y_hat = self.forward(x)
loss = self.mse_loss(y, y_hat)
self.backward(x, y, learning_rate)
return loss
```
在这个模型中,我们添加了 `mse_loss` 函数,用于计算均方误差,同时添加了 `sgd_optimizer` 函数,用于执行随机梯度下降优化算法。在每次迭代中,我们计算预测值 `y_hat`,然后计算损失值并执行反向传播算法更新神经网络的权重和偏置。最后,我们返回损失值作为当前迭代的结果。根据需要,我们可以使用其他损失函数和优化器来训练这个神经网络。
多层感知机python实现
以下是一个简单的多层感知机的 Python 实现,使用 Numpy 库来进行矩阵运算:
```python
import numpy as np
class MLP:
def __init__(self, input_dim, hidden_dim, output_dim):
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
# 随机初始化权重
self.weights1 = np.random.randn(input_dim, hidden_dim)
self.bias1 = np.zeros((1, hidden_dim))
self.weights2 = np.random.randn(hidden_dim, output_dim)
self.bias2 = np.zeros((1, output_dim))
def forward(self, X):
# 前向传播计算输出
self.z1 = np.dot(X, self.weights1) + self.bias1
self.a1 = np.tanh(self.z1)
self.z2 = np.dot(self.a1, self.weights2) + self.bias2
self.a2 = self.softmax(self.z2)
return self.a2
def backward(self, X, y, lr=0.1):
# 计算输出误差
delta2 = self.a2 - y
# 计算隐藏层误差
delta1 = (1 - np.power(self.a1, 2)) * np.dot(delta2, self.weights2.T)
# 更新权重和偏置
self.weights2 -= lr * np.dot(self.a1.T, delta2)
self.bias2 -= lr * np.sum(delta2, axis=0, keepdims=True)
self.weights1 -= lr * np.dot(X.T, delta1)
self.bias1 -= lr * np.sum(delta1, axis=0)
def softmax(self, x):
exp_x = np.exp(x)
return exp_x / np.sum(exp_x, axis=1, keepdims=True)
```
以上实现了一个单隐藏层的 MLP,输入层神经元数为 `input_dim`,隐藏层神经元数为 `hidden_dim`,输出层神经元数为 `output_dim`。在初始化时随机初始化权重,使用 tanh 作为激活函数,softmax 作为输出函数。
`forward` 方法用于前向传播计算输出。`backward` 方法用于根据输出误差反向传播更新权重和偏置。`softmax` 方法用于计算输出的概率分布。
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