以下哪个选项描述的是固定神经网络架构情景中的学习流程？

Which option below describes the process of learning in the context of a fixed neural network architecture?

Question

以下哪个选项描述的是固定神经网络架构情景中的学习流程？

Which option below describes the process of learning in the context of a fixed neural network architecture?

BlackTom AI · Accepted Answer

In a fixed neural network architecture, learning proceeds by adjusting the network’s weights and biases based on examples of inputs paired with desired outputs.

1. Relevant knowledge points and concepts
- Fixed architecture: number of layers, neurons, and connections are held constant during learning. 
- Learning (training): changing parameter values (weights and biases) to reduce a loss function that measures discrepancy between predicted and target outputs. 
- Optimization methods: gradient descent and its variants compute gradients of the loss with respect to weights and update weights iteratively.

2. Step-by-step reasoning process
- Given an input x and a target y, the network computes a prediction y_pred (forward pass).
- Compute loss L(y_pred, y) measuring error (e.g., squared error).
- Compute gradients ∂L/∂w for each weight w (backpropagation).
- Update weights using a rule such as w_new = w_old − η * ∂L/∂w, where η is the learning rate.
- Repeat across many input–output pairs so weights converge to values that generalize well.

3. Example calculation (all intermediate steps)
- Suppose a simple single-weight model y_pred = w * x, with w = 0.4, x = 2, target y = 1.
- Prediction: y_pred = 0.4 * 2 = 0.8. Error e = y_pred − y = −0.2.
- Loss (MSE half form): L = 0.5 * e^2 = 0.5 * 0.04 = 0.02.
- Gradient: ∂L/∂w = e * x = (−0.2) * 2 = −0.4.
- With η = 0.1, update: w_new = 0.4 − 0.1*(−0.4) = 0.44. Weight moved to reduce future error.

4. Why the correct answer is correct
- The process described above is precisely “adapting the weights in response to different input–output pairs.” In fixed architectures, only parameters change; connectivity does not. Learning adjusts weights to reduce loss across many examples.

5. Why other options are incorrect
- “Manually updating the various weights”: manual changes are not the typical automated learning process; learning uses algorithmic gradient-based updates, not manual tuning. 
- “Finding new connections in the neural network architecture”: that describes architecture search or structural learning (changing topology), not learning within a fixed architecture. 
- “Adapting the input to fit the desired output”: modifying inputs is data manipulation or adversarial/example engineering, not the network’s learning process.

6. Conclusion / Final answer confirmation
- Therefore the correct description is: 应对不同的输入-输出对，调整权重Adapting the weights in response to different input output pairs.

以下哪个选项描述的是固定神经网络架构情景中的学习流程？ Which option below describes the process of learning in the context of a fixed neural network architecture?单项选择题

类似问题

You own a bond that pays $64 in interest annually. The face value is $1,000 and the current market price is $1,021.61. The bond matures in 11 years. What is the yield to maturity?

Which of the following statements is CORRECT?

It will cost $9,600 to acquire an ice cream cart that is expected to produce cash inflows of $3,600 a year for three years. After the three years, the cart is expected to be worthless. What is the payback period?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单