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python - RNN 模型不是在学习。图中所附的预测值和实际值是一条平线

问题描述

我已经对时间序列预测 RNN 模型进行了重新缩放、规范化、初始化、丢弃，但它没有学习。图中显示的预测值和实际值为零处的平线。我无法弄清楚我的模型有什么问题。您的输入将不胜感激。这是整个代码

[Predicted and actual value graph[\]\[1\]][1]

# split into train and test sets
train_size = int(len(dataset) * 0.80)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:]
dataset[train_size:len(dataset),:]

scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
train_scaled = scaler.transform(train)
test_scaled = scaler.transform(test)  

# convert an array of values into a dataset matrix

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)  

# reshape into X=t and Y=t+1
look_back = 40
train_X, train_y = create_dataset(train_scaled, look_back)
test_X, test_y = create_dataset(test_scaled, look_back)

# reshape input to be [samples, time steps, features]
train_X= np.expand_dims(train_X, axis=1)
test_X = np.expand_dims(test_X, axis=1)

print ('Train_X shape', train_X.shape, 'Test_X.shape', test_X.shape)

model = Sequential()
model.add(SimpleRNN(64, input_shape=(1,window_size), 
          kernel_initializer = 'random_normal', 
          bias_initializer= 'random_normal'))
#model.add(Dropout(0.2))
model.add(Dense(32, kernel_initializer = 'random_normal',
          bias_initializer= 'random_normal'))
#model.add(Dropout(0.2))
model.add(Dense(1, activation = 'linear'))

optimizer = keras.optimizers.adam(lr=0.1)

model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
#fit network history =model.fit(train_X, train_y, epochs=2,         

batch_size=32, 
validation_data=(test_X, test_y), verbose=1, shuffle=False)

def predict_and_score(model, X, Y): # Make predictions on the     
original 
    scale of the data. pred =                 
scaler.inverse_transform(model.predict(X))  

    # Prepare Y data to also be on the original scale for   
interpretability. 

    orig_data = scaler.inverse_transform([Y]) # Calculate RMSE. score 
    = math.sqrt(mean_squared_error(orig_data[0], pred[:, 0]))  
return(score, pred)
    rmse_train, train_predict = predict_and_score(model, train_X,   
train_y)
    rmse_test, test_predict = predict_and_score(model, test_X, 
test_y)

    print("Training data score: %.2f RMSE" % rmse_train)
    print("Test data score: %.2f RMSE" % rmse_test)


Training data score: 151198.09 RMSE

Test data score: 125302.35 RMSE

[enter image description here][1]

标签： pythonkerasdeep-learning

解决方案

一个明显的问题是您的数据：它的范围绝对是荒谬的。虽然 NN 在理论上可以学习“任何东西”，但我想说实际上不可能学习对输出进行建模：（1）大部分时间为零，（2）然后范围高达 1e14。

虽然 (1) 可能是您的领域所固有的，但 (2) 似乎没有必要。

首先将您的数据缩小到范围 [0, 1]，这应该至少可以学习一些东西。

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