Python ValueError：具有形状 (3,1) 的不可广播输出操作数与广播形状 (3,4) 不匹配

ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

提问by dpopp783

I recently started to follow along with Siraj Raval's Deep Learning tutorials on YouTube, but I an error came up when I tried to run my code. The code is from the second episode of his series, How To Make A Neural Network. When I ran the code I got the error:

我最近开始在 YouTube 上学习 Siraj Raval 的深度学习教程，但是当我尝试运行我的代码时出现错误。代码来自他的系列的第二集，如何制作神经网络。当我运行代码时，我收到错误：

Traceback (most recent call last):
File "C:\Users\dpopp\Documents\Machine Learning\first_neural_net.py", line 66, in <module>
neural_network.train(training_set_inputs, training_set_outputs, 10000)
File "C:\Users\dpopp\Documents\Machine Learning\first_neural_net.py", line 44, in train
self.synaptic_weights += adjustment
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

I checked multiple times with his code and couldn't find any differences, and even tried copying and pasting his code from the GitHub link. This is the code I have now:

我用他的代码检查了多次，没有发现任何差异，甚至尝试从 GitHub 链接复制和粘贴他的代码。这是我现在的代码：

from numpy import exp, array, random, dot

class NeuralNetwork():
    def __init__(self):
        # Seed the random number generator, so it generates the same numbers
        # every time the program runs.
        random.seed(1)

        # We model a single neuron, with 3 input connections and 1 output connection.
        # We assign random weights to a 3 x 1 matrix, with values in the range -1 to 1
        # and mean 0.
        self.synaptic_weights = 2 * random.random((3, 1)) - 1

    # The Sigmoid function, which describes an S shaped curve.
    # We pass the weighted sum of the inputs through this function to
    # normalise them between 0 and 1.
    def __sigmoid(self, x):
        return 1 / (1 + exp(-x))

    # The derivative of the Sigmoid function.
    # This is the gradient of the Sigmoid curve.
    # It indicates how confident we are about the existing weight.
    def __sigmoid_derivative(self, x):
        return x * (1 - x)

    # We train the neural network through a process of trial and error.
    # Adjusting the synaptic weights each time.
    def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
        for iteration in range(number_of_training_iterations):
            # Pass the training set through our neural network (a single neuron).
            output = self.think(training_set_inputs)

            # Calculate the error (The difference between the desired output
            # and the predicted output).
            error = training_set_outputs - output

            # Multiply the error by the input and again by the gradient of the Sigmoid curve.
            # This means less confident weights are adjusted more.
            # This means inputs, which are zero, do not cause changes to the weights.
            adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))

            # Adjust the weights.
            self.synaptic_weights += adjustment

    # The neural network thinks.
    def think(self, inputs):
        # Pass inputs through our neural network (our single neuron).
        return self.__sigmoid(dot(inputs, self.synaptic_weights))

if __name__ == '__main__':

    # Initialize a single neuron neural network
    neural_network = NeuralNetwork()

    print("Random starting synaptic weights:")
    print(neural_network.synaptic_weights)

    # The training set. We have 4 examples, each consisting of 3 input values
    # and 1 output value.
    training_set_inputs = array([[0, 0, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]])
    training_set_outputs = array([[0, 1, 1, 0]])

    # Train the neural network using a training set
    # Do it 10,000 times and make small adjustments each time
    neural_network.train(training_set_inputs, training_set_outputs, 10000)

    print("New Synaptic weights after training:")
    print(neural_network.synaptic_weights)

    # Test the neural net with a new situation
    print("Considering new situation [1, 0, 0] -> ?:")
    print(neural_network.think(array([[1, 0, 0]])))

Even after copying and pasting the same code that worked in Siraj's episode, I'm still getting the same error.

即使复制并粘贴了在 Siraj 的剧集中工作的相同代码，我仍然遇到同样的错误。

I just started out look into artificial intelligence, and don't understand what the error means. Could someone please explain what it means and how to fix it? Thanks!

我刚开始研究人工智能，不明白错误意味着什么。有人可以解释它的含义以及如何解决它吗？谢谢！

回答by wwii

Change self.synaptic_weights += adjustmentto

更改self.synaptic_weights += adjustment为

self.synaptic_weights = self.synaptic_weights + adjustment

---

---

self.synaptic_weightsmust have a shape of (3,1) and adjustmentmust have a shape of (3,4). While the shapes are broadcastablenumpy must not like trying to assign the result with shape (3,4) to an array of shape (3,1)

self.synaptic_weights必须具有 (3,1)adjustment的形状并且必须具有 (3,4) 的形状。虽然形状是可广播的numpy 一定不喜欢尝试将形状 (3,4) 的结果分配给形状 (3,1) 的数组

a = np.ones((3,1))
b = np.random.randint(1,10, (3,4))

>>> a
array([[1],
       [1],
       [1]])
>>> b
array([[8, 2, 5, 7],
       [2, 5, 4, 8],
       [7, 7, 6, 6]])

>>> a + b
array([[9, 3, 6, 8],
       [3, 6, 5, 9],
       [8, 8, 7, 7]])

>>> b += a
>>> b
array([[9, 3, 6, 8],
       [3, 6, 5, 9],
       [8, 8, 7, 7]])
>>> a
array([[1],
       [1],
       [1]])

>>> a += b
Traceback (most recent call last):
  File "<pyshell#24>", line 1, in <module>
    a += b
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)

The same error occurs when using numpy.addand specifying aas the output array

使用numpy.add并指定a为输出数组时发生相同的错误

>>> np.add(a,b, out = a)
Traceback (most recent call last):
  File "<pyshell#31>", line 1, in <module>
    np.add(a,b, out = a)
ValueError: non-broadcastable output operand with shape (3,1) doesn't match the broadcast shape (3,4)
>>> 

A new aneeds to be created

一个新的a需要被创造

>>> a = a + b
>>> a
array([[10,  4,  7,  9],
       [ 4,  7,  6, 10],
       [ 9,  9,  8,  8]])
>>> 

回答by user10864598

Hopefully, by now you must have executed the code, but the problem between his code and your code is this line:

希望现在你一定已经执行了代码，但他的代码和你的代码之间的问题是这一行：

training_output = np.array([[0,1,1,0]]).T  

While transposing don't forget to add 2 square brackets, I had the same problem for the same code, this worked for me. Thanks

转置时不要忘记添加 2 个方括号，对于相同的代码，我遇到了同样的问题，这对我有用。谢谢