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[英]TensorFlow Linear Regression - returning NaN for weights,bias and Inf for loss
[英]Linear regression getting NaN for loss
不能理解為什么keras線性回歸模型不起作用。 使用波士頓住房數據。將Loss作為nan
path='/Users/admin/Desktop/airfoil_self_noise.csv'
df=pd.read_csv(path,sep='\t',header=None)
y=df[5] #TARGET
df2=df.iloc[:,:-1]
X_train, X_test, y_train, y_test = train_test_split(df2, y, test_size=0.2)
p = Sequential()
p.add(Dense(units=20, activation='relu', input_dim=5))
p.add(Dense(units=20, activation='relu'))
p.add(Dense(units=1))
p.compile(loss='mean_squared_error',
optimizer='sgd')
p.fit(X_train, y_train, epochs=10, batch_size=32)
這個產量:
Epoch 1/10
1202/1202 [==============================] - 0s 172us/step - loss: nan
Epoch 2/10
1202/1202 [==============================] - 0s 37us/step - loss: nan
Epoch 3/10
1202/1202 [==============================] - 0s 38us/step - loss: nan
Epoch 4/10
1202/1202 [==============================] - 0s 36us/step - loss: nan
Epoch 5/10
1202/1202 [==============================] - 0s 36us/step - loss: nan
Epoch 6/10
1202/1202 [==============================] - 0s 40us/step - loss: nan
import pandas as pd
import keras
from keras.layers import Dense, Input
from keras import Sequential
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
#Grabbing these 2 lines from your example
path='/Users/admin/Desktop/airfoil_self_noise.csv'
df = pd.read_csv("airfoil_self_noise.csv", sep = '\t', header = None)
y = df[5]
df2 = df.iloc[:, :-1]
#preprocessing. Vectorization and Scaling
X_train, X_test, y_train, y_test = train_test_split(df2.values, y.values, test_size = 0.2)
X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test)
p = Sequential()
p.add(Dense(units = 20, activation ='relu', input_dim = 5))
p.add(Dense(units = 20, activation ='relu'))
p.add(Dense(units = 1))
p.compile(loss = 'mean_squared_error', optimizer = 'adam')
print(p.fit(X_train, y_train, epochs = 100, batch_size = 64))
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