如何用MLP实现Minist分类？

1. 数据集 minist手写体数字数据集 2. 代码 ''' Description: Author: zhangyh Date: 2024-05-04 15:21:49 LastEditTime: 2024-05-04 22:36:26 LastEditors: zhangyh ''' import numpy as np class MlpClassifier: def __init__(self, input_size, hidden_size1, hidden_size2, output_size, learning_rate=0.01): self.input_size = input_size self.hidden_size1 = hidden_size1 self.hidden_size2 = hidden_size2 self.output_size = output_size self.learning_rate = learning_rate self.W1 = np.random.randn(input_size, hidden_size1) * 0.01 self.b1 = np.zeros((1, hidden_size1)) self.W2 = np.random.randn(hidden_size1, hidden_size2) * 0.01 self.b2 = np.zeros((1, hidden_size2)) self.W3 = np.random.randn(hidden_size2, output_size) * 0.01 self.b3 = np.zeros((1, output_size)) def softmax(self, x): exps = np.exp(x - np.max(x, axis=1, keepdims=True)) return exps / np.sum(exps, axis=1, keepdims=True) def relu(self, x): return np.maximum(x, 0) def relu_derivative(self, x): return np.where(x > 0, 1, 0) def cross_entropy_loss(self, y_true, y_pred): m = y_true.shape[0] return -np.sum(y_true * np.log(y_pred + 1e-8)) / m def forward(self, X): self.Z1 = np.dot(X, self.W1) + self.b1 self.A1 = self.relu(self.Z1) self.Z2 = np.dot(self.A1, self.W2) + self.b2 self.A2 = self.relu(self.Z2) self.Z3 = np.dot(self.A2, self.W3) + self.b3 self.A3 = self.softmax(self.Z3) return self.A3 def backward(self, X, y): m = X.shape[0] dZ3 = self.A3 - y dW3 = np.dot(self.A2.T, dZ3) / m db3 = np.sum(dZ3, axis=0, keepdims=True) / m dA2 = np.dot(dZ3, self.W3.T) dZ2 = dA2 * self.relu_derivative(self.Z2) dW2 = np.dot(self.A1.T, dZ2) / m db2 = np.sum(dZ2, axis=0, keepdims=True) / m dA1 = np.dot(dZ2, self.W2.T) dZ1 = dA