在获得太多帮助后，我最后一次来这里是我找不到解决方案的最后一个问题。
在我之前的question之后，一个用户指出了一个事实，即我对时间序列预测的不良结果可能是由于我的体系结构未收敛。
在查看并尝试了一些我在其他问题（设置权重，较低的学习率，更改优化器/激活）上找到的解决方法之后，我似乎无法获得更好的结果，始终将精度设为0（或0.0003），还不够好）。
我的代码：
import numpy
import numpy as np
import tflearn
from pandas import DataFrame
from pandas import Series
from pandas import concat
from pandas import read_csv
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from math import sqrt
import datetime
# Preprocessing function
from tflearn import Accuracy, Momentum
def preprocess(data):
return np.array(data, dtype=np.int32)
def parser(x):
return datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
# frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1):
df = DataFrame(data)
columns = [df.shift(i) for i in range(1, lag + 1)]
columns.append(df)
df = concat(columns, axis=1)
df.fillna(0, inplace=True)
return df
def difference(dataset, interval=1):
diff = list()
for i in range(interval, len(dataset)):
value = dataset[i] - dataset[i - interval]
diff.append(value)
return Series(diff)
# invert differenced value
def inverse_difference(history, yhat, interval=1):
return yhat + history[-interval]
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# inverse scaling for a forecasted value
def invert_scale(scaler, X, value):
new_row = [x for x in X] + [value]
array = numpy.array(new_row)
array = array.reshape(1, len(array))
inverted = scaler.inverse_transform(array)
return inverted[0, -1]
def fit_lstm(train, batch_size, nb_epoch, neurons):
X, y = train[0:-1], train[:, -1]
X = X[:, 0].reshape(len(X), 1, 1)
y = y.reshape(len(y), 1)
print (X.shape)
print (y.shape)
# Build neural network
net = tflearn.input_data(shape=[None, 1, 1])
tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0)
net = tflearn.dropout(net, 0.8)
net = tflearn.fully_connected(net, 1, activation='linear', weights_init=tnorm)
net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,
loss='mean_square')
# Define model
model = tflearn.DNN(net, tensorboard_verbose=3, best_val_accuracy=0.6)
model.fit(X, y, n_epoch=nb_epoch, batch_size=batch_size, shuffle=False, show_metric=True)
score = model.evaluate(X, y, batch_size=128)
print (score)
return model
# make a one-step forecast
def forecast_lstm(model, X):
X = X.reshape(len(X), 1, 1)
yhat = model.predict(X)
return yhat[0, 0]
# Load CSV file, indicate that the first column represents labels
data = read_csv('nowcastScaled.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)
# transform data to be stationary
raw_values = data.values
diff_values = difference(raw_values, 1)
# transform data to be supervised learning
supervised = timeseries_to_supervised(diff_values, 1)
supervised_values = supervised.values
# split data into train and test-sets
train, test = supervised_values[0:10000], supervised_values[10000:10100]
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
repeats = 1
for r in range(repeats):
# fit the model
lstm_model = fit_lstm(train_scaled, 128, 6, 1)
# forecast the entire training dataset to build up state for forecasting
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
print (lstm_model.predict(train_reshaped))
# walk-forward validation on the test data
predictions = list()
error_scores = list()
for i in range(len(test_scaled)):
# make one-step forecast
X, y = test_scaled[i, 0:-1], test_scaled[i, -1]
yhat = forecast_lstm(lstm_model, X)
# invert scaling
yhat = invert_scale(scaler, X, yhat)
# # invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
# store forecast
predictions.append(yhat)
rmse = sqrt(mean_squared_error(raw_values[10000:10100], predictions))
print('%d) Test RMSE: %.3f' % (1, rmse))
error_scores.append(rmse)
print predictions
print raw_values[10000:10100]
这是我从运行它得到的结果（提高纪元似乎并没有使它变得更好）：
Training Step: 472 | total loss: 0.00486 | time: 0.421s
| Adam | epoch: 006 | loss: 0.00486 - binary_acc: 0.0000 -- iter: 9856/9999
Training Step: 473 | total loss: 0.00453 | time: 0.427s
| Adam | epoch: 006 | loss: 0.00453 - binary_acc: 0.0000 -- iter: 9984/9999
Training Step: 474 | total loss: 0.00423 | time: 0.430s
| Adam | epoch: 006 | loss: 0.00423 - binary_acc: 0.0000 -- iter: 9999/9999
我试图降低/提高大多数设置，但是什么也没有。
这是data I'm using（单变量时间序列）的摘录，在训练中使用或多或少的数据也无济于事。
（注：我的代码主要来自this tutorial，由于我想尝试使用Tflearn，因此不得不对其进行一些更改）
最佳答案
您不能为回归问题定义accuracy。您只需跟踪预测的MSE和实际的MSE。您的训练损失似乎很低，因此，如果预测值不接近，则您的换算反比不正确或过度拟合。