校正张量流中ANN的NaN值/损失 [英] Correcting NaN values/loss for ANN in tensorflow
本文介绍了校正张量流中ANN的NaN值/损失的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
我正在使用张量流运行搅动模型,并遇到NaN损失.仔细阅读,发现 print(np.any(np.isnan(X_test)))
证实了我的数据中可能存在一些NaN值.
I am running a churn model using tensorflow and running into a NaN loss. Reading around, I found that I probably had some NaN values in my data as was confirmed by print(np.any(np.isnan(X_test)))
.
我尝试使用
def standardize(train, test):
mean = np.mean(train, axis=0)
std = np.std(train, axis=0)+0.000001
X_train = (train - mean) / std
X_test = (test - mean) /std
return X_train, X_test
但是仍然想出NaN值.
But still coming up with NaN values.
如果有帮助,这里是完整的代码:
Here's the full code if it helps:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
dataset = pd.read_excel('CHURN DATA.xlsx')
X = dataset.iloc[:, 2:45].values
y = dataset.iloc[:, 45].values
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
X[:, 1] = le.fit_transform(X[:,1])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[0])], remainder = 'passthrough')
X = np.array(ct.fit_transform(X))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
ann = tf.keras.models.Sequential()
ann.add(tf.keras.layers.Dense(units = 43, activation = 'relu'))
ann.add(tf.keras.layers.Dense(units = 43, activation = 'relu'))
ann.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))
ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
ann.fit(X_train, y_train, batch_size = 256, epochs = 50)
推荐答案
您尚未替换 nan
值.而且您的数据中可能还会有一些 inf
和 -inf
值.您可以将它们都替换为 0
You havent replaced the nan
values. And it’s likely that you have some inf
and -inf
values also in your data. You can replace both of them with 0
对于数据框
X.replace([np.inf, -np.inf], np.nan, inplace=True)
X = X.fillna(0)
或者如果您的数据位于numpy数组中
or if your data is in a numpy array
X[np.isnan(X)] = 0
X[X == np.inf] = 0
X[X == -np.inf] = 0
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