LeCun断言GPT活不过5年，Meta发布全新世界模型
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import tensorflow as tf
import math
import configparser
# 定义神经元类
class Neuron:
def __init__(self, position, derivative):
self.position = position
self.derivative = derivative
self.weight = np.random.randn() # 随机初始化权重
# 定义太阳类
class Sun:
def __init__(self, radius):
self.radius = radius
# 定义大气层类
class Atmosphere:
def __init__(self, radius, thickness):
self.radius = radius
self.thickness = thickness
# 定义松果类
class Pinecone:
def __init__(self, radius, scales, rotate_frequency):
self.radius = radius
self.scales = scales
self.rotate_frequency = rotate_frequency # 旋转频率
self.rotate_time = 0 # 初始化旋转时间
self.memory = [] # 初始化记忆列表
self.vertices = [] # 初始化三角形端点坐标
for i in range(3):
theta = 2 * math.pi * i / 3
x = self.radius * math.cos(theta)
y = self.radius * math.sin(theta)
z = self.scales * math.sin(self.rotate_time * self.rotate_frequency + theta)
self.vertices.append((x, y, z))
def rotate(self):
self.rotate_time += 1
for i in range(3):
theta = 2 * math.pi * i / 3
x = self.radius * math.cos(theta)
y = self.radius * math.sin(theta)
z = self.scales * math.sin(self.rotate_time * self.rotate_frequency + theta)
self.vertices[i] = (x, y, z)
# 定义系统模型类
class System:
def __init__(self, sun, atmosphere, pinecone, neurons):
self.sun = sun
self.atmosphere = atmosphere
self.pinecone = pinecone
self.neurons = neurons
def plot(self):
# 绘制太阳、大气层和松果的形状
# 具体实现略
pass
def simulate(self):
# 模拟系统的运动和变化
# 具体实现略
pass
# 定义神经网络模型类
class NeuralNetwork:
def __init__(self, levels):
self.levels = levels
self.weights = []
for i in range(len(levels) - 1):
weight = np.random.rand(levels[i], levels[i+1])
self.weights.append(weight)
def forward(self, input):
output = np.array(input)
for weight in self.weights:
output = np.dot(output, weight)
return output.tolist()
# 定义神经网络集合模型类
class NeuralNetworkGroup:
def __init__(self, levels_list):
self.levels_list = levels_list
self.weights = []
for i in range(len(levels_list) - 1):
weight = np.random.rand(sum(levels_list[i]), sum(levels_list[i+1]))
self.weights.append(weight)
def forward(self, input_list):
output_list = [np.array(input) for input in input_list]
for i in range(len(self.weights)):
weight = self.weights[i]
layer_input = np.concatenate(output_list[:sum(self.levels_list[i])], axis=0)
layer_output = np.dot(layer_input, weight)
output_list = np.split(layer_output, np.cumsum(self.levels_list[i+1])[:-1])
output_list = [output.tolist() for output in output_list]
return output_list
def interact(self, input_list):
output_list = [np.array(input) for input in input_list]
for i in range(len(self.weights)):
weight = self.weights[i]
layer_input = np.concatenate(output_list[:sum(self.levels_list[i])], axis=0)
layer_output = np.dot(layer_input, weight)
output_list = np.split(layer_output, np.cumsum(self.levels_list[i+1])[:-1])
output_list = [output.tolist() for output in output_list]
if i < len(self.weights) - 1:
output_list = [output / sum(output) for output in output_list]
output_list = [output * sum(input_list[sum(self.levels_list[i]):sum(self.levels_list[i+1])]) for output in output_list]
return output_list
# 读取配置文件
config = configparser.ConfigParser()
config.read('config.ini')
# 定义斐波那契数列
def fibonacci(n):
if n == 0:
return 0
elif n == 1:
return 1
else:
return fibonacci(n-1) + fibonacci(n-2)
# 计算神经元位置
def rotate_neuron(level, time, golden_ratio, radius):
theta = 2 * math.pi * golden_ratio
theta_n = theta * level
radius_n = radius * level
x = radius_n * math.cos(theta_n + time)
y = radius_n * math.sin(theta_n + time)
return (x, y)
# 生成神经元
def generate_neurons(level, rotation_time, leaf_size, golden_ratio, radius):
neurons = []
for j in range(level):
if level == 1:
size = 1
position = (0, 0)
else:
size = leaf_size
position = rotate_neuron(j, rotation_time, golden_ratio, radius)
connections = []
if j > 0:
connections.append(j-1)
if j < level-1:
connections.append(j+1)
neurons.append((position, size, connections))
return neurons
# 读取配置文件参数
golden_ratio = float(config['Fibonacci Spiral']['golden_ratio'])
radius = float(config['Fibonacci Spiral']['radius'])
levels = [int(level) for level in config['Neural Network']['levels'].split(',')]
# 生成神经元，并将其按照大小进行排序
neurons = []
for i in range(levels[-1]):
rotation_time = i * math.pi / fibonacci(levels[-1])
leaf_size = fibonacci(levels[-1] - i)
neurons_level = generate_neurons(levels[-1] - i, rotation_time, leaf_size, golden_ratio, radius)
neurons.extend(neurons_level)
neurons.sort(key=lambda x: x[1], reverse=True)
# 定义太阳、大气层和松果
sun = Sun(radius=100)
atmosphere = Atmosphere(radius=110, thickness=10)
pinecone = Pinecone(radius=5102410241024, scales=10102410241024, rotate_frequency=0.01) # 将松果的半径设置为5GB，规模设置为10GB，旋转频率设置为0.01
# 定义系统模型，并初始化所有神经元的权重
system = System(sun=sun, atmosphere=atmosphere, pinecone=pinecone, neurons=neurons)
input_size = 3
hidden_size = 16
output_size = 1
learning_rate = 0.01
inputs = np.array([neuron.position for neuron in neurons])
targets = np.array([neuron.weight for neuron in neurons])
inputs_placeholder = tf.placeholder(tf.float32, [None, input_size])
targets_placeholder = tf.placeholder(tf.float32, [None, output_size])
让AI按照我的想法写的，不过按照AI说的不够成熟，还需要继续完善，有一个留存记忆机制
