全连接神经网络numpy学习实现.zip

## 基于numpy实现的全连接深度学习工具 实现以下功能: - **激活函数**:relu,sigmoid,tanh,softmax - **损失函数**:平均绝对误差(MAE),交叉熵(cross_entropy) - **前向传播**:前向传播(forward),反向传播(backward 使用梯度下降优化器) - **权重矩阵**:权重矩阵形状推导和模型总结(summary) - **随机函数**:随机函数(shuffle) - **预测函数**:输出模型的预测结果(predict) - **验证损失**:使用模型预测并计算损失(verify) - **绘制曲线**:绘制损失变化曲线(plot) 在mnist数据集上的准确率为0.847 使用封装的fit训练函数训练 ``` python learning_rate = 1e-4 model = models() model.add(Dense(input_size=None, hidden_layer_num=128, activation=activations.relu)) model.add(Dense(input_size=None, hidden_layer_num=128, activation=activations.relu)) model.add(Dense(input_size=None, hidden_layer_num=10, activation=activations.softmax)) for i in range(30): print('step',i) model.fit(train_data=train_x, train_labels=train_y, epochs=1, batch_size=20, learning_rate=learning_rate, measure=measure.cross_entropy, shuffle=True) model.verify(test_data=test_x, test_labels=test_y, batch_size=20) model.plot() ``` 手动进行前向传播和反向传播训练 ``` python model = models() model.add(Dense(input_size=None, hidden_layer_num=32, activation=activations.relu)) model.add(Dense(input_size=None, hidden_layer_num=10, activation=activations.softmax)) model.batch_input = x model.batch_label = y model.summary() model.learning_rate = 0.0003 for i in range(10000): data = mnist.train.next_batch(batch_size) x = data[0] y = data[1] model.batch_input = x model.batch_label = y model.forward() model.backward() model.plot() ``` > demo提供了训练mnist数据的完整示例