线性回归使NaN损失

Question

Cant understand why keras linear regression model is not working. Using Boston Housing data.Get Loss as 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)

this yeilds:

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

Answer 1

Just to get you started, building on the top of NaN loss when training regression network

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))