def init_toy_data(): np.random.seed(1) X = 10 * np.random.randn(num_inputs, input_size) y = np.array([0, 1, 2, 2, 1]) return X, y
时间: 2024-04-17 17:29:22 浏览: 140
这是一个函数 `init_toy_data`,它用于生成一个玩具数据集。在函数中,首先设置随机种子,然后使用 `np.random.randn` 生成一个形状为 `(num_inputs, input_size)` 的随机数矩阵 `X`。接下来,创建一个形状为 `(5,)` 的数组 `y`,其中包含了标签数据。最后,函数返回生成的数据集 `X` 和标签 `y`。
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class TwoLayerNet: def __init__(self, input_size, hidden_size1,hidden_size2, output_size, weight_init_std=0.01): # 初始化权重 self.params = {} self.params['W1'] = weight_init_std * \ np.random.randn(input_size, hidden_size1) self.params['b1'] = np.zeros(hidden_size1) self.params['W2'] = weight_init_std * \ np.random.randn(hidden_size1, hidden_size2) self.params['b2'] = np.zeros(hidden_size2) self.params['W3'] = weight_init_std * \ np.random.randn(hidden_size2, output_size) self.params['b3'] = np.zeros(output_size) self.params['ConVW1']=0.4*np.random.normal(0,1,size=150).reshape(6,5,5,1).transpose(0, 3, 1, 2) self.params['ConVW2']=0.25*np.random.normal(0,1,size=2400).reshape(16,6,5,5) self.params['ConVB1']=np.zeros(6) self.params['ConVB2']=np.zeros(16)
这段代码定义了一个名为TwoLayerNet的类,用于构建一个具有两个隐藏层的神经网络模型。在初始化方法中,首先会初始化权重和偏置。权重W1和W2是通过随机生成服从高斯分布的数值来初始化的,而偏置b1和b2则被初始化为全零。然后,还定义了一些卷积层的参数,包括卷积权重和偏置。卷积权重ConVW1和ConVW2也是通过随机生成服从高斯分布的数值来初始化的,而偏置ConVB1和ConVB2则被初始化为全零。
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
好的,如果要加上损失函数的话,可以在backward方法中计算损失函数对网络输出的导数,然后将其与之前计算得到的delta2相乘得到最终的delta2。常见的损失函数有均方误差(MSE)和交叉熵(Cross Entropy)等,下面以MSE为例:
```
def mse_loss_derivative(self, y):
return 2*(self.y_hat - y) / self.output_dim
def backward(self, x, y, learning_rate):
error = self.y_hat - y
delta2 = self.mse_loss_derivative(y) * self.relu_derivative(self.z2) # 加上损失函数的导数
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
```
在这里,mse_loss_derivative方法计算了MSE损失函数对网络输出的导数,然后在计算delta2时将其与ReLU函数的导数相乘得到最终的delta2。接下来,该方法与之前的backward方法相同,计算梯度并更新参数。
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