Kaggle泰坦尼克-基于TensorFlow的灾难机器学习:模型训练无法获得损失值

Question

我刚刚开始用TensorFlow学习机器学习，我认为通过在Kaggle上输入泰坦尼克-灾难机器学习来测试我欠发达的技能将是一个很好的方法。本次比赛的数据可以在这里找到。

为简单起见，我删除了除了Sex以外的所有字符串值，我将CC_1映射为1，将female映射为0。

但在模型训练过程中，所有时代的损失值都是nan。我不知道为什么会这样，如果有人能告诉我是什么问题，那就太好了。

我的当前代码:

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>

Answer 1

因为在这个数据集中Age列有一些空值。这就是为什么你得到nan的损失。

您可以删除Age列或清除数据，使其没有空值。

Answer 2

这里是更正这是因为你在编译中忘记了一个非常重要的参数，那就是度量参数。

代码

model.compile(loss='binary_crossentropy', optimizer='adam')

<<p>修正/strong>

model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])

你可以根据最适合你的条件传递不同类型的度量

Answer 3

我认为这个问题可能是由validation_data=(X_test, y_test)引起的。

我认为火车数据需要分成三组来使用该选项。也许在你最初的分割之后这样做，并实现任何扩展到有效集合的事情也会有所帮助:

X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.2)

然后改成validation_data=(X_valid, y_valid)