我这是预测Keras模型：
import numpy as np
import matplotlib.pyplot as plt
import pandas
import math
import datetime as dt
from datetime import datetime
from getDataFromPoloniex import get_data_from_poloniex
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
np.random.seed(7)
data2 = pandas.read_csv('data/BTC_ETH.csv')[::-1]
data2.columns = ['date','high','low','open','close','volume','quoteVolume','weightedAverage']
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
dataset = pandas.DataFrame(data2.close.ewm(span=14).mean())
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset))
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
look_back = 3
trainX, trainY = create_dataset(train, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 1)))
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)
trainPredict = model.predict(trainX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
plt.plot([dt.datetime.fromtimestamp(x) for x in data2.date], np.flip(trainPredict,0))
plt.show()
我有几个问题：