每次我运行神经网络代码时结果都会改变

问题描述

通过运行此链接神经网络–预测多个变量的值.我能够计算损失的准确性等.但是，每次运行此代码时，都会得到一个新结果.是否有可能获得相同(一致)的结果?

I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc.However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result?

推荐答案

代码随处都是 random.randint()！此外，权重在大多数情况下也是随机设置的，并且batch_size对结果也有影响(尽管影响很小).

The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result.

1. Y_train，X_test，X_train是随机生成的
2. 使用 adam 作为优化程序，这意味着您将执行随机梯度下降.以随机为起点进行迭代，以收敛.
3. batch_size为8表示您将运行由8个随机所选样本组成的批次.

1. Y_train, X_test, X_train are generated randomly
2. Using adam as optimizer, means you'll be performing stochastic gradient descent. With a random beginning point of the iterations in order to converge.
3. A batch_size of 8 means you will run batches consisting of 8 randomly selected samples.

解决方案:

1. 在代码中设置一个随机种子，以始终具有使用 np.random.seed()
生成的随机值
2. 尽管有很小的偏差，但不会产生太大的问题
3. 与2相同.

如果我找到一种方法来解决 batch_size / epoch 问题的一致性抽样方法，我将编辑我的答案.

If I find a way to have consistente sampling methods for the batch_size/epoch issue I will edit my answer.

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