predict-coin/train_gift.py

 
from  keras.models import Sequential
from  keras.layers import Dense, Dropout, Embedding
from  keras.layers import InputLayer, Activation
from  keras.layers import LSTM
from  keras import backend 

import pymysql
import pickle
import os
import numpy

from data import load_gift_data

if __name__ == "__main__":

    x_train, y_train, tx_train, ty_train, input_shape = load_gift_data()

    model = Sequential()
    units = 400 
    
    model.add(LSTM(units, activation='relu', input_shape=input_shape ))
    model.add(Dropout(0.2))
 
    model.add(Dense(1))
    model.summary()
    
    model.compile(loss='mse', optimizer='adam')

    model.fit(x_train, y_train, batch_size=128, epochs=1500)
    model.save("./predict_gift")

    p_data = model.predict(tx_train)
    for i in range(len(p_data)):
        comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
        print(comp, p_data[i][0], ty_train[i])
        if abs(comp) >= 0.1:
            print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])