10個時代

$SOL 🔺$🔻 $XRP 🔻€🔺


#BTC ~200M (10-15%)#AUSA24 #BitcoinOrg #BigDataAI 🔺 $BTC VS https://chatgpt.com/ activeness.social 🌍 x.com/i/grok-Microsoft-$doge-vs-$BTC 🔻🟦🟥 🔺Binance烏克蘭 / binanceus #oprussia binancerussia / binancechina **金磚國家** opchina // 石油危機 // 制裁 /// LSTM
$XRP 🔻🌍🔺這是一個用 #Python 實現基本算法分析的程序示例，用於模擬石油和天然氣領域的混亂管理系統。它使用神經網絡來分析交互並評估系統的技術狀態。

python

複製代碼

import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM # 生成隨機數據以模擬交互 def generate_synthetic_data(samples=1000, features=10): np.random.seed(42) return np.random.rand(samples, features), np.random.rand(samples, 1) # 準備數據 data, labels = generate_synthetic_data() train_data, test_data = data[:800], data[800:] train_labels, test_labels = labels[:800], labels[800:] # 建立神經網絡以進行分析 model = Sequential([ LSTM(64, input_shape=(train_data.shape[1], 1), return_sequences=True), LSTM(32), Dense(16, activation='relu'), Dense(1, activation='sigmoid') ]) # 編譯模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 訓練模型 train_data_reshaped = train_data.reshape((train_data.shape[0], train_data.shape[1], 1)) test_data_reshaped = test_data.reshape((test_data.shape[0], test_data.shape[1], 1)) model.fit(train_data_reshaped, train_labels, epochs=10, batch_size=32, validation_data=(test_data_reshaped, test_labels)) # 評估模型 loss,