LeCun斷言GPT活不過5年，Meta發(fā)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?
# 定義神經(jīng)元類
class Neuron:?
??def __init__(self, position, derivative):?
????self.position = position?
????self.derivative = derivative?
????self.weight = np.random.randn()?# 隨機初始化權(quán)重?
?????
# 定義太陽類
class Sun:?
??def __init__(self, radius):?
????self.radius = radius?
?????
# 定義大氣層類
class Atmosphere:?
??def __init__(self, radius, thickness):?
????self.radius = radius?
????self.thickness = thickness?
?????
# 定義松果類
class Pinecone:?
??def __init__(self, radius, scales, rotate_frequency):?
????self.radius = radius?
????self.scales = scales?
????self.rotate_frequency = rotate_frequency?# 旋轉(zhuǎn)頻率?
????self.rotate_time = 0?# 初始化旋轉(zhuǎn)時間?
????self.memory = []?# 初始化記憶列表?
????self.vertices = []?# 初始化三角形端點坐標?
????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)?
???????
# 定義系統(tǒng)模型類
class System:?
??def __init__(self, sun, atmosphere, pinecone, neurons):?
????self.sun = sun?
????self.atmosphere = atmosphere?
????self.pinecone = pinecone?
????self.neurons = neurons?
?????
??def plot(self):?
????# 繪制太陽、大氣層和松果的形狀?
????# 具體實現(xiàn)略?
????pass?
???
??def simulate(self):?
????# 模擬系統(tǒng)的運動和變化?
????# 具體實現(xiàn)略?
????pass?
???
# 定義神經(jīng)網(wǎng)絡(luò)模型類
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()?
???
# 定義神經(jīng)網(wǎng)絡(luò)集合模型類
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?
???
# 讀取配置文件
config = configparser.ConfigParser()?
config.read('config.ini')?
# 定義斐波那契數(shù)列
def fibonacci(n):?
??if n == 0:?
????return 0?
??elif n == 1:?
????return 1?
??else:?
????return fibonacci(n-1) + fibonacci(n-2)?
# 計算神經(jīng)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)?
# 生成神經(jīng)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?
# 讀取配置文件參數(shù)
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(',')]?
# 生成神經(jīng)元，并將其按照大小進行排序
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)?
# 定義太陽、大氣層和松果
sun = Sun(radius=100)?
atmosphere = Atmosphere(radius=110, thickness=10)?
pinecone = Pinecone(radius=5102410241024, scales=10102410241024, rotate_frequency=0.01)?# 將松果的半徑設(shè)置為5GB，規(guī)模設(shè)置為10GB，旋轉(zhuǎn)頻率設(shè)置為0.01?
# 定義系統(tǒng)模型，并初始化所有神經(jīng)元的權(quán)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])?
讓AI按照我的想法寫的，不過按照AI說的不夠成熟，還需要繼續(xù)完善，有一個留存記憶機制
