问题描述
我正在尝试为我的数据集采用标准的波士顿住房问题，不同之处在于我在数据集中有负值并且想要预测输出中的负值。
当我在 StackOverflow 中读到预测负值时，我应该在输出层上使用 than 激活函数。另外，我知道我应该将我的数据集标准化到 -1,1 范围。
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在第二个变体中，我的神经网络预测值类似于“0.9”，但我的数据集值类似于“24”。我认为这是因为此代码中没有适当的规范化。请告诉我如何实施规范化。
我在 Keras 方面的经验很差，在 Python 方面也没有那么强的技能，所以我只是尝试从不同的地方组装一段代码。
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense
#read in training data
train_df = pd.read_csv('train.csv',index_col='ID')
train_df.head()
target = 'medv'
scaler = MinMaxScaler(feature_range=(-1,1)) ## tut byl 0,1
scaled_train = scaler.fit_transform(train_df)
# Print out the adjustment that the scaler applied to the total_earnings column of data
print("Note: median values were scaled by multiplying by {:.10f} and adding {:.6f}".format(scaler.scale_[13],scaler.min_[13]))
multiplied_by = scaler.scale_[13]
added = scaler.min_[13]
scaled_train_df = pd.DataFrame(scaled_train,columns=train_df.columns.values)
#build our model
model = Sequential()
model.add(Dense(50,activation='relu'))
model.add(Dense(100,activation='relu'))
model.add(Dense(50,activation='relu'))
model.add(Dense(1,activation='tanh')) #tut nichego
model.compile(loss='mean_squared_error',optimizer='adam')
X = scaled_train_df.drop(target,axis=1).values
Y = scaled_train_df[[target]].values
# Train the model
model.fit(
X[10:],Y[10:],epochs=100,shuffle=True,verbose=2
)
#inference
prediction = model.predict(X[:4])
y_0 = prediction[0][0]
print('Prediction with scaling - {}',format(y_0))
y_0 -= added
y_0 /= multiplied_by
print("Housing Price Prediction - ${}".format(y_0))
Prediction with scaling - {} -0.1745799034833908
Housing Price Prediction - $23.571952171623707
第二个代码变体：
# Regression Example With Boston Dataset: Standardized and Larger
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy
# load dataset
dataframe = read_csv("housing.csv",delim_whitespace=True,header=None)
dataset = dataframe.values
# split into input (X) and output (Y) variables
X = dataset[:,0:13]
Y = dataset[:,13]
# define the model
def larger_model():
# create model
model = Sequential()
model.add(Dense(13,input_dim=13,kernel_initializer='normal',activation='relu'))
model.add(Dense(6,activation='relu'))
model.add(Dense(1,activation='tanh'))
# Compile model
model.compile(loss='mean_squared_error',optimizer='adam')
return model
# evaluate model with standardized dataset
estimators = []
estimators.append(('standardize',StandardScaler()))
estimators.append(('mlp',KerasRegressor(build_fn=larger_model,epochs=50,batch_size=5,verbose=1)))
pipeline = Pipeline(estimators)
kfold = KFold(n_splits=10)
results = cross_val_score(pipeline,X,Y,cv=kfold)
print("Larger: %.2f (%.2f) MSE" % (results.mean(),results.std()))
pipeline.fit(X,Y)
#prediction = pipeline.predict(numpy.array([[0.0273,0.,7.07,0.469,6.421,78.9,4.9671,2.,242.,17.8,396.9,9.14]]))
prediction = pipeline.predict(numpy.array([[0.7258,8.14,0.538,5.727,69.5,3.7965,4.,307.,21.0,390.95,11.28]]))
print(prediction)
结果：
......
......
102/102 [==============================] - 0s 927us/step - loss: 548.0819
Epoch 50/50
102/102 [==============================] - 0s 912us/step - loss: 548.0818
1/1 [==============================] - 0s 0s/step
0.99998754
解决方法
在最终输出层中，您使用的是 tanh 激活，这是一个问题。tanh 激活函数将提供从 -1 到 +1 的输出，您可以尝试使用线性激活函数代替 tanh