Numpy.dot 给出相同的形状 (X, Y) (X,Y) 未对齐

Question

我正在尝试创建一个动态分配的输入神经元，在我的训练 model 中传递一个 numpy.array 作为神经元突触的输入。

主要的 function 获取numpy.array ，该数组会将其.shape提供给神经元构造函数，以便为第一次生成的偏置创建一个numpy.array 。

神经元对两个 arrays 使用numpy.dot操作，然后将其传递到 sigmoid function。

但是我得到的shapes (6,3) and (6,3) not aligned: 3 (dim 1) != 6 (dim 0) dot value error ，但对我来说这两个形状应该是相同的。

我对 numpy 阵列形状表格有什么误解？

主要.py：

# Training data for the small neuron
training_inputs = np.array([[1.7, 3.4, 5.0],
                            [1.95, 3.2, 3.6],
                            [2.38, 3.3, 2.75],
                            [1.44, 3.75, 7.5],
                            [1.54, 3.84, 7.0],
                            [1.54, 3.82, 6.9]])

neural_network = NeuralNetwork.NeuralNetwork(training_inputs.shape)

training_outputs = np.array([[0.5, 1, 0, 1, 1, 1]]).T

print("Synaptic Weights BEFORE training (random) for SMALL ")
print(neural_network.synaptic_weights)

neural_network.train(training_inputs, training_outputs, 10)
# Our model is now trained
print("Synaptic Weights AFTER Training for SMALL: ")
print(neural_network.synaptic_weights)
# User needs to input the testing values

user_input_1 = str(input("User Input 1: "))
user_input_2 = str(input("User Input 2: "))
user_input_3 = str(input("User Input 3: "))
# Neuron is now calculating

print("Neuron is calculating result for given input: ", user_input_1, user_input_2, user_input_3)
print("Trained output data: ")
print(neural_network.think(np.array([user_input_1, user_input_2, user_input_3])))

神经网络.py：

import numpy as np


# Class for our Neurons
class NeuralNetwork():

def __init__(self, shape):

    # Seeds random number generator that will be our bias
    np.random.seed(1)
    # Converting weights for our sensors to a 3 by 1 matrix
    # This bias needs to be fixed
    self.synaptic_weights = 2 * np.random.random(shape) - 1

# The sigmoid function that will have values from -1 to +1

# The sigmoid: f(x) = 1/[1 + e^(-x)]
@staticmethod
def sigmoid(x):
    # applying the sigmoid function
    return 1 / (1 + np.exp(-x))

# The derivative of the sigmoid: f'(x) = x*(1-x)
@staticmethod
def sigmoid_derivative(x):
    # computing derivative to the Sigmoid function
    return x * (1 - x)

# The training procedure of the neuron
def train(self, training_inputs, training_outputs, training_iterations):
    # The neuron is being trained #training_iteration times for the weights to be adjusted
    for iteration in range(training_iterations):

        # The output of the neuron that is being used as an adjustment to the next iteration
        output = self.think(training_inputs)

        # Error is being calculated for the back - propagation
        error = training_outputs - output

        # Adjusting the weights by multiplying the inputs * error * f'(output)
        adjustments = np.dot(training_inputs.T, self.sigmoid_derivative(output))

        # Adjusting the weights
        self.synaptic_weights += adjustments

# The thinking procedure of the neuron
def think(self, inputs):

    # Floating the values
    inputs = inputs.astype(float)

    # Neuron uses the inputs to produce its output
    output = self.sigmoid(np.dot(inputs, self.synaptic_weights))
    return output

Traceback 给出了这个：

> Traceback (most recent call last):   File
> "C:/../football_project/main.py", line 94
> in <module> main() 
> File "C:/../football_project/main.py", line 55
> in main
>     neural_network.train(training_inputs, training_outputs, 1)   
> File "C:\..\NeuralNetwork.py", line 35, in train
>     output = self.think(training_inputs)   File "C:..\football_project\NeuralNetwork.py", line 53, in think
>     output = self.sigmoid(np.dot(inputs, self.synaptic_weights))   
> File "<__array_function__ internals>", line 6, in dot ValueError:
> **shapes (6,3) and (6,3) not aligned: 3 (dim 1) != 6 (dim 0)**

Answer 1

点运算是矩阵乘法，所以A点B = C，其中dim (A) = (n, m)，dim (B) = (m, k)，dim (C) = (n, k)。 如果我理解您的概念（具有 3 个输入和 1 个输出的神经元），您需要将输入向量 (1, 3) 和权重矩阵 (3, 1) 相乘，并对所有输入重复。