Python 简单的神经网络无法做出正确的预测。我在哪里犯了错误？

我正在尝试建立一个简单的神经网络，有3个输入，3个输出，没有深层，它将执行梯度下降来调整其权重，并能够将输入数组转换为输出数组。出于某种原因，它不起作用（预测在几次迭代后停止改变），我似乎不明白为什么。你能帮我吗

当我有1个输入和3个输出，或者3个输出和1个输入时，类似的代码工作。我曾经这样计算导数：

导数=输入*pure_error

。但是现在我需要对权重矩阵进行这样的处理，我使用的是

导数=np.outer（输入，纯错误）

，这是我所做的唯一更改

代码：

import numpy as np 

inputs = np.array([3, 5, 1])
weights = np.array([[0.1,0.2,0.05],
                    [1.0,0.53,0.5],
                    [1.7,2.3,1.2]])
target_predictions = np.array([1.5, 10, 93])
learning_rate = 0.05

def ann(inputs, weights):
    predictions = np.dot(weights, inputs)
    return predictions

# Initial Predictions
predictions = ann(inputs, weights)
errors = (predictions - target_predictions) ** 2
print("Predictions:",predictions, "Errors:",errors)

for i in range(350):
    predictions = ann(inputs, weights)
    errors = (predictions - target_predictions) ** 2

    print("Predictions:",predictions, "Error:",errors)

    pure_errors = predictions - target_predictions
    derivatives = np.outer(inputs, pure_errors)
    weight_updates = derivatives * learning_rate

    weights -= weight_updates

Predictions: [ 1.35  6.15 17.8 ] Error: [2.25000e-02 1.48225e+01 5.65504e+03]
Predictions: [15.585 29.875 22.545] Error: [ 198.387225  395.015625 4963.907025]
Predictions: [ 4.90875 12.08125 18.98625] Error: [1.16195766e+01 4.33160156e+00 5.47803519e+03]
.......
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]

输出：

import numpy as np 

inputs = np.array([3, 5, 1])
weights = np.array([[0.1,0.2,0.05],
                    [1.0,0.53,0.5],
                    [1.7,2.3,1.2]])
target_predictions = np.array([1.5, 10, 93])
learning_rate = 0.05

def ann(inputs, weights):
    predictions = np.dot(weights, inputs)
    return predictions

# Initial Predictions
predictions = ann(inputs, weights)
errors = (predictions - target_predictions) ** 2
print("Predictions:",predictions, "Errors:",errors)

for i in range(350):
    predictions = ann(inputs, weights)
    errors = (predictions - target_predictions) ** 2

    print("Predictions:",predictions, "Error:",errors)

    pure_errors = predictions - target_predictions
    derivatives = np.outer(inputs, pure_errors)
    weight_updates = derivatives * learning_rate

    weights -= weight_updates

Predictions: [ 1.35  6.15 17.8 ] Error: [2.25000e-02 1.48225e+01 5.65504e+03]
Predictions: [15.585 29.875 22.545] Error: [ 198.387225  395.015625 4963.907025]
Predictions: [ 4.90875 12.08125 18.98625] Error: [1.16195766e+01 4.33160156e+00 5.47803519e+03]
.......
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]
Predictions: [ 9.48428571 19.70714286 20.51142857] Error: [  63.74881837   94.22862245 5254.59298776]

---

找到了错误。而不是：

derivatives = np.outer(inputs, pure_errors)

我需要做：

derivatives = np.outer(pure_errors, inputs)

---

以错误的顺序传递变量会改变结果，以某种方式交换数字并更新错误的权重。

您有3个大小为1的输入，因此您只需要一个权重和一个偏差。