我最近开始在 YouTube 上学习 Siraj Raval 的深度学习教程，但当我尝试运行我的代码时出现错误。该代码来自他的系列“如何制作神经网络”的第二集。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)
我多次检查了他的代码，没有发现任何差异，甚至尝试从 GitHub 链接复制并粘贴他的代码。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]])))
即使复制并粘贴了在 Siraj 的剧集中有效的相同代码，我仍然遇到相同的错误。
我刚刚开始研究人工智能，不明白这个错误意味着什么。有人可以解释一下这意味着什么以及如何解决它吗？谢谢！
将
self.synaptic_weights += adjustmentself.synaptic_weights = self.synaptic_weights + adjustment
self.synaptic_weightsadjustmenta = 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)
使用 numpy.add 并指定
a>>> 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)
>>>
需要创建一个新的
a>>> a = a + b
>>> a
array([[10, 4, 7, 9],
[ 4, 7, 6, 10],
[ 9, 9, 8, 8]])
>>>
希望现在你一定已经执行了代码，但是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
在转置时不要忘记添加 2 个方括号，我对相同的代码遇到了同样的问题，这对我有用。 谢谢
我在重塑机器学习模型的预测结果并因此对其进行逆变换方面遇到了类似的挑战。我阅读了很多文档和建议，这些确实很有帮助，但并没有完全解决问题。我采取了反复试验的方法来解决这个问题。
这是我的代码片段：
根据需要调整变量和数字
复制数据以创建二维数组
solar_radiation_forecast_scaled = np.tile(solar_radiation_forecast_scaled, (9, 6))
用 9 行重塑数组
actual_solar_radiation_forecast_reshape = Solar_radiation_forecast_scaled.reshape(-1, 6)
实际预测
actual_solar_radiation_forecast = scaler.inverse_transform(actual_solar_radiation_forecast_reshape)
在第一个索引位置打印输出
打印（实际太阳能辐射预测[0]）
我发现我必须更好地理解 numpy 数组并相应地重塑。我建议您分解这些步骤并更好地了解您正在使用的阵列的形状。我希望这有帮助。