我是TensorFlow框架的新手，我正在尝试应用Tensorflow来基于这个泰坦尼克号数据集预测幸存者：https://www.kaggle.com/c/titanic/data。

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
#%%
titanictrain = pd.read_csv('train.csv')
titanictest = pd.read_csv('test.csv')
df = pd.concat([titanictrain,titanictest],join='outer',keys='PassengerId',sort=False,ignore_index=True).drop(['Name'],1)

#%%

def preprocess(df):
    df['Fare'].fillna(value=df.groupby('Pclass')['Fare'].transform('median'),inplace=True)
    df['Fare'] = df['Fare'].map(lambda x: np.log(x) if x>0 else 0)
    df['Embarked'].fillna(value=df['Embarked'].mode()[0],inplace=True)
    df['CabinAlphabet'] = df['Cabin'].str[0]
    categories_to_one_hot = ['Pclass','Sex','Embarked','CabinAlphabet']
    df = pd.get_dummies(df,columns=categories_to_one_hot,drop_first=True)
    return df
df = preprocess(df)

df = df.drop(['PassengerId','Ticket','Cabin','Survived'],1)

titanic_trainandval = df.iloc[:len(titanictrain)]
titanic_test = df.iloc[len(titanictrain):] #test after preprocessing

titanic_test.head()

#  split train into training and validation set
labels = titanictrain['Survived']
y = labels.values

test = titanic_test.copy() # real test sets
print(len(test), 'test examples')

这里我尝试对数据进行预处理：
1.删除名称列，并在列车和测试集上进行一次热编码
2.删除['PassengerId'，'Ticket'，'Cabin'，'Survived']以简化操作。
1.拆分列车并按原顺序进行试验

这张图片显示了训练集的外观。

"""# model training"""

from tensorflow.keras.layers import Input, Dense, Activation,Dropout
from tensorflow.keras.models import Model

X = titanic_trainandval.copy()
input_layer = Input(shape=(X.shape[1],))
dense_layer_1 = Dense(10, activation='relu')(input_layer)
dense_layer_2 = Dense(5, activation='relu')(dense_layer_1)
output = Dense(1, activation='softmax',name = 'predictions')(dense_layer_2)

model = Model(inputs=input_layer, outputs=output)
base_learning_rate = 0.0001

model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate), metrics=['acc'])

history = model.fit(X, y, batch_size=5, epochs=20, verbose=2, validation_split=0.1,shuffle = False)

submission = pd.DataFrame()
submission['PassengerId'] = titanictest['PassengerId']

然后，我将训练集X放入模型中，以获得结果。然而，历史显示了以下结果：

无论我如何改变学习率和批量大小，结果都不会改变，损失总是“nan”，基于测试集的预测也总是“nan”。
有人能解释一下问题出在哪里，并给予一些可能的解决方案吗？