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python - Kaggle Titanic-Machine Learning From Disaster with TensorFlow：模型训练无法获得损失值

问题描述

我刚刚开始使用 TensorFlow 学习机器学习，我认为通过进入Kaggle上的Titanic-Machine Learning from Disaster来测试我欠发达的技能是一个很好的方法。本次比赛的数据可以在这里找到。

为简单起见，我删除了除 之外的所有字符串值，我将Sex其映射为1formale和0for female。

但是在模型训练过程中，所有 epoch 的损失值都是nan. 我不知道为什么会这样，如果有人能告诉我问题出在哪里，那就太好了。

我当前的代码：

import numpy as np
import pandas as pd

train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

train_data['Sex'] = train_data['Sex'].map({'male':1,'female':0})

train_data = train_data.drop('PassengerId', axis=1)
train_data = train_data.drop('Name', axis=1)
train_data = train_data.drop('Ticket', axis=1)
train_data = train_data.drop('Cabin', axis=1)
train_data = train_data.drop('Embarked', axis=1)
train_data = train_data.drop('Fare', axis=1)

test_data = test_data.drop('PassengerId', axis=1)
test_data = test_data.drop('Name', axis=1)
test_data = test_data.drop('Ticket', axis=1)
test_data = test_data.drop('Cabin', axis=1)
test_data = test_data.drop('Embarked', axis=1)
test_data = test_data.drop('Fare', axis=1)

X = train_data.drop('Survived',axis=1).values
y = train_data['Survived'].values

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 MinMaxScaler
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.constraints import max_norm

model = Sequential()
model.add(Dense(6, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')

model.fit(x=X_train, 
      y=y_train, 
      epochs=25,
      batch_size=256,
      validation_data=(X_test, y_test), 
      )

输出：

Epoch 1/25
3/3 [==============================] - 1s 102ms/step - loss: nan - val_loss: nan
Epoch 2/25
3/3 [==============================] - 0s 15ms/step - loss: nan - val_loss: nan
Epoch 3/25
3/3 [==============================] - 0s 14ms/step - loss: nan - val_loss: nan
Epoch 4/25
3/3 [==============================] - 0s 19ms/step - loss: nan - val_loss: nan
Epoch 5/25
3/3 [==============================] - 0s 22ms/step - loss: nan - val_loss: nan
Epoch 6/25
3/3 [==============================] - 0s 22ms/step - loss: nan - val_loss: nan
Epoch 7/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 8/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 9/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 10/25
3/3 [==============================] - 0s 20ms/step - loss: nan - val_loss: nan
Epoch 11/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 12/25
3/3 [==============================] - 0s 19ms/step - loss: nan - val_loss: nan
Epoch 13/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 14/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 15/25
3/3 [==============================] - 0s 18ms/step - loss: nan - val_loss: nan
Epoch 16/25
3/3 [==============================] - 0s 17ms/step - loss: nan - val_loss: nan
Epoch 17/25
3/3 [==============================] - 0s 15ms/step - loss: nan - val_loss: nan
Epoch 18/25
3/3 [==============================] - 0s 18ms/step - loss: nan - val_loss: nan
Epoch 19/25
3/3 [==============================] - 0s 19ms/step - loss: nan - val_loss: nan
Epoch 20/25
3/3 [==============================] - 0s 16ms/step - loss: nan - val_loss: nan
Epoch 21/25
3/3 [==============================] - 0s 19ms/step - loss: nan - val_loss: nan
Epoch 22/25
3/3 [==============================] - 0s 20ms/step - loss: nan - val_loss: nan
Epoch 23/25
3/3 [==============================] - 0s 18ms/step - loss: nan - val_loss: nan
Epoch 24/25
3/3 [==============================] - 0s 13ms/step - loss: nan - val_loss: nan
Epoch 25/25
3/3 [==============================] - 0s 18ms/step - loss: nan - val_loss: nan
<tensorflow.python.keras.callbacks.History at 0x18bc9160dc0>

标签： pythonpandasdataframetensorflowmachine-learning

解决方案

因为在这个数据集中，该Age列有一些空值。这就是为什么你得到的损失为nan.

您可以删除Age列或清理数据，使其不包含空值。

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