我最近开始关注YouTube上Siraj Raval的深度学习教程,但是当我尝试运行代码时出现了错误。该代码来自他的系列第二集“如何制作神经网络”。运行代码时出现错误:
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,没有发现任何区别,甚至尝试从GitHub链接复制和粘贴他的代码。这是我现在拥有的代码:
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Siraj剧集中的相同代码,我仍然遇到相同的错误。
我刚开始研究人工智能,却不了解错误的含义。有人可以解释一下这是什么意思以及如何解决吗?谢谢!
更改self.synaptic_weights += adjustment
为
self.synaptic_weights = self.synaptic_weights + adjustment
self.synaptic_weights
必须具有(3,1)的形状,并且adjustment
必须具有(3,4)的形状。虽然形状是可广播的,但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)
使用numpy.add并将其指定a
为输出数组时,会发生相同的错误
>>> 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
需要创建一个新的
>>> a = a + b
>>> a
array([[10, 4, 7, 9],
[ 4, 7, 6, 10],
[ 9, 9, 8, 8]])
>>>
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