Question

两个月前，我开始与keras合作，以获得在其他软件中使用它的泵模式。

我不知道为什么我获得的模式与真实模式无关的原因。我试过在数据集中建立一些特征（输入），并且还有更多的输入，但它没有办法。结果如下：

here

其中：

蓝色：数据集（实际数据我试图“近似”）
橙色：预测

数据集是时间序列

是包含数据集

的csv文件

以下是代码：

import numpy
import matplotlib.pyplot as plt
import pandas
import math

from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.regularizers import l2, activity_l2


def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0:4] 
        dataX.append(a)
        dataY.append(dataset[i + look_back, 4]) 
    return numpy.array(dataX), numpy.array(dataY)



# fix random seed for reproducibility
seed=7
numpy.random.seed(seed)


# load dataset
dataframe = pandas.read_csv('datos_horarios.csv', engine='python') 
dataset = dataframe.values


# normalizar el dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

#split data into train data and test data
train_size = int(len(dataset) * 0.67) 
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]

# reshape to X=t y Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)  
testX, testY = create_dataset(test, look_back)

# reshape inputs to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], look_back, 4))
testX = numpy.reshape(testX, (testX.shape[0], look_back, 4))
# create and adjust LSTM network

model = Sequential()
model.add(Dropout(0.3, input_shape=(look_back,4))) 
model.add(LSTM(6, input_shape=(look_back,4), W_regularizer=l2(0.001))) 
model.add(Dense(10)) 
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam' ,momentum=0.99)
history= model.fit(trainX, trainY,validation_split=0.33, nb_epoch=250, batch_size=32)


# Plot
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epochs')
plt.legend(['training', 'validation'], loc='upper right')
plt.show()

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
print(trainPredict)

numero_inputs=4
inp=numero_inputs-1
# Get something which has as many features as dataset
trainPredict_extended = numpy.zeros((len(trainPredict),numero_inputs+1))
# Put the predictions there
trainPredict_extended[:,inp+1] = trainPredict[:,0]
# Inverse transform it and select the 3rd column.
trainPredict = scaler.inverse_transform(trainPredict_extended)[:,inp+1]

# Get something which has as many features as dataset
testPredict_extended = numpy.zeros((len(testPredict),numero_inputs+1))
# Put the predictions there
testPredict_extended[:,inp+1] = testPredict[:,0]
# Inverse transform it and select the 3rd column.
testPredict = scaler.inverse_transform(testPredict_extended)[:,inp+1]

trainY_extended = numpy.zeros((len(trainY),numero_inputs+1))
trainY_extended[:,inp+1]=trainY
trainY=scaler.inverse_transform(trainY_extended)[:,inp+1]

testY_extended = numpy.zeros((len(testY),numero_inputs+1))
testY_extended[:,inp+1]=testY
testY=scaler.inverse_transform(testY_extended)[:,inp+1]
# Calcular error medio cuadratico
trainScore = math.sqrt(mean_squared_error(trainY, trainPredict))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY, testPredict))
print('Test Score: %.2f RMSE' % (testScore))
# add train predictions to the plot

trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, 0] = trainPredict

# add test predictions to the plot
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, 0] = testPredict

# Plot real data and training and test predictions
serie,=plt.plot(scaler.inverse_transform(dataset)[:,numero_inputs])  #invierto muestras en formato (0,1) a valores reales y los ploteo
entrenamiento,=plt.plot(trainPredictPlot[:,0],linestyle='--')  #ploteo las predicciones de entrenamiento
prediccion_test,=plt.plot(testPredictPlot[:,0],linestyle='--')
plt.ylabel(' (m3)')
plt.xlabel('h')
plt.legend([serie,entrenamiento,prediccion_test],['Time series','Training','Prediction'], loc='upper right')
plt.show()

有关如何解决此问题的任何想法？或者，至少，问题是什么？

栏目输入：

一天中的时间（每半小时），转换为十进制。
星期几（1-Monday ... 7-sunday）
一年中的一个月（1-12）
每月的某一天（1-31）

输出：

抽水（m3）

修改使用@a_guest的代码，并更改一些参数，例如时期数或history值，结果非常好：

Answer 1

不是答案，但我在这里分享了我获得以下结果的代码：

注意，网络参数是任意选择的，即未优化。也就是说，通过改变这些参数，您最有可能获得更好的结果。同样改变history（或您的look_back）的值可能会对预测质量产生重大影响。

from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import numpy

numpy.random.seed(12)

history = 96


def generate_data():
    data = numpy.loadtxt('datos_horarios.csv', delimiter=',', dtype=float)
    # Normalize data.
    data[:, -1] /= numpy.max(data[:, -1])
    train_test_data = []
    for i in xrange(data.shape[0] - history - 1):
        # Include the reference value here, will be extracted later.
        train_test_data.append(data[i:i+history+1, -1].flatten())
    return numpy.array(train_test_data)

train_test_data = generate_data()
# Shuffle data set in order to randomly select training and test data.
numpy.random.shuffle(train_test_data)

n_samples = train_test_data.shape[0]
n_train_samples = int(0.8 * n_samples)

train_data = train_test_data[:n_train_samples, :-1]
train_data_reference = train_test_data[:n_train_samples, -1][:, None]

test_data = train_test_data[n_train_samples:, :-1]
test_data_reference = train_test_data[n_train_samples:, -1]

print 'Tranining data: ', train_data
print 'Reference values: ', train_data_reference


model = Sequential()
model.add(Dense(history, input_dim=history, activation='sigmoid'))
model.add(Dense(history/2, activation='sigmoid'))
model.add(Dense(history/4, activation='sigmoid'))
model.add(Dense(history/8, activation='sigmoid'))
model.add(Dense(history/16, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='mean_squared_error', metrics=['accuracy'])
model.summary()

model.fit(train_data, train_data_reference, shuffle=True, nb_epoch=200, batch_size=10)

# Use the complete data set to see the network performance.
# Regenerate data set because it was shuffled before.
train_test_data = generate_data()
test_data_predicted = model.predict(train_test_data[:, :-1]).flatten()
test_data_reference = train_test_data[:, -1]

relative_deviation = test_data_predicted/test_data_reference - 1.0
print 'Relative deviation: ', relative_deviation

plt.figure()
plt.plot(range(len(test_data_reference)), test_data_reference, 'b-', label='reference')
plt.plot(range(len(test_data_predicted)), test_data_predicted, 'r--', label='predicted')
plt.xlabel('test case #')
plt.ylabel('predictions')
plt.title('Reference values vs predicted values')
plt.legend()

plt.figure()
plt.plot(range(len(test_data_predicted)), relative_deviation, 'bx', label='relative deviation')
plt.xlabel('test case #')
plt.ylabel('relative deviation')
plt.title('Relative deviation of predicted values (predicted / reference - 1)')
plt.legend()

plt.show()

Keras没有做出好的预测

1 个答案: