“You Jump，I Jump”语出经典爱情电影《泰坦尼克号》经典台词，女主角Rose在船首即将跳入海里，站在旁边的男主Jack为挽救女主，便说出经典台词“You Jump，I Jump”。当一个陌生男人肯为一个陌生女人没理由地去死的时候，毫无缘由的，女主对男主产生了爱的情愫。
当然这跟我这篇教程关系不大，这里我们将会通过AI预测Jack和Rose的存活率，国庆没断更，属实不易，需要数据集可以私聊本人or加学习群。谢谢大家支持！

一、数据集

1.读取数据集

import pandas as pddf = pd.read_excel('titanic3.xls')
df.describe()
pclass survived age sibsp parch fare body
count 1309.000000 1309.000000 1046.000000 1309.000000 1309.000000 1308.000000 121.000000
mean 2.294882 0.381971 29.881135 0.498854 0.385027 33.295479 160.809917
std 0.837836 0.486055 14.413500 1.041658 0.865560 51.758668 97.696922
min 1.000000 0.000000 0.166700 0.000000 0.000000 0.000000 1.000000
25% 2.000000 0.000000 21.000000 0.000000 0.000000 7.895800 72.000000
50% 3.000000 0.000000 28.000000 0.000000 0.000000 14.454200 155.000000
75% 3.000000 1.000000 39.000000 1.000000 0.000000 31.275000 256.000000
max 3.000000 1.000000 80.000000 8.000000 9.000000 512.329200 328.000000 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1309 entries, 0 to 1308
Data columns (total 14 columns):#   Column     Non-Null Count  Dtype
---  ------     --------------  -----  0   pclass     1309 non-null   int64  1   survived   1309 non-null   int64  2   name       1309 non-null   object 3   sex        1309 non-null   object 4   age        1046 non-null   float645   sibsp      1309 non-null   int64  6   parch      1309 non-null   int64  7   ticket     1309 non-null   object 8   fare       1308 non-null   float649   cabin      295 non-null    object 10  embarked   1307 non-null   object 11  boat       486 non-null    object 12  body       121 non-null    float6413  home.dest  745 non-null    object
dtypes: float64(3), int64(4), object(7)
memory usage: 143.3+ KB

df.head()
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
0 1 1 Allen, Miss. Elisabeth Walton female 29.0000 0 0 24160 211.3375 B5 S 2 NaN St Louis, MO
1 1 1 Allison, Master. Hudson Trevor male 0.9167 1 2 113781 151.5500 C22 C26 S 11 NaN Montreal, PQ / Chesterville, ON
2 1 0 Allison, Miss. Helen Loraine female 2.0000 1 2 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.0000 1 2 113781 151.5500 C22 C26 S NaN 135.0 Montreal, PQ / Chesterville, ON
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0000 1 2 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON

2.处理数据集

  • 提取字段
  • 处理缺失值
  • 转换编码
  • 删除name列
# 筛选需要提取的字段
selected_cols = ['survived','name','pclass','sex','age','sibsp','parch','fare','embarked']
df_selected = df[selected_cols]
df = df[selected_cols] # 默认按列取值
df.head()
survived name pclass sex age sibsp parch fare embarked
0 1 Allen, Miss. Elisabeth Walton 1 female 29.0000 0 0 211.3375 S
1 1 Allison, Master. Hudson Trevor 1 male 0.9167 1 2 151.5500 S
2 0 Allison, Miss. Helen Loraine 1 female 2.0000 1 2 151.5500 S
3 0 Allison, Mr. Hudson Joshua Creighton 1 male 30.0000 1 2 151.5500 S
4 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 1 female 25.0000 1 2 151.5500 S 
# 找出有null值的字段
df.isnull().any()

survived    False
name        False
pclass      False
sex         False
age          True
sibsp       False
parch       False
fare         True
embarked     True
dtype: bool

# 统计各个列有多少个空值
df.isnull().sum()

survived      0
name          0
pclass        0
sex           0
age         263
sibsp         0
parch         0
fare          1
embarked      2
dtype: int64

# 确定缺失值的位置
df[df.isnull().values==True]
survived name pclass sex age sibsp parch fare embarked
15 0 Baumann, Mr. John D 1 male NaN 0 0 25.9250 S
37 1 Bradley, Mr. George ("George Arthur Brayton") 1 male NaN 0 0 26.5500 S
40 0 Brewe, Dr. Arthur Jackson 1 male NaN 0 0 39.6000 C
46 0 Cairns, Mr. Alexander 1 male NaN 0 0 31.0000 S
59 1 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genev... 1 female NaN 0 0 27.7208 C
... ... ... ... ... ... ... ... ... ...
1293 0 Williams, Mr. Howard Hugh "Harry" 3 male NaN 0 0 8.0500 S
1297 0 Wiseman, Mr. Phillippe 3 male NaN 0 0 7.2500 S
1302 0 Yousif, Mr. Wazli 3 male NaN 0 0 7.2250 C
1303 0 Yousseff, Mr. Gerious 3 male NaN 0 0 14.4583 C
1305 0 Zabour, Miss. Thamine 3 female NaN 1 0 14.4542 C

266 rows × 9 columns

# 将age空的字段改为平均值
age_mean = df['age'].mean()
df['age'] = df['age'].fillna(age_mean)
df['age'].isnull().any() # 但凡有空值就返回True

False

# 将fare空的字段改为平均值
fare_mean = df['fare'].mean()
df['fare'] = df['fare'].fillna(age_mean)# 为确实embarked记录填充值
df['embarked'] = df['embarked'].fillna('S')

df.isnull().any()

survived    False
name        False
pclass      False
sex         False
age         False
sibsp       False
parch       False
fare        False
embarked    False
dtype: bool

# 转换编码
# 性别sex由字符串转换为数字编码
df['sex'] = df['sex'].map({'female':0,'male':1}).astype(int)
# 港口embarked由字母表示转换为数字编码
df['embarked'] = df['embarked'].map({'C':0,'Q':1,'S':2}).astype(int)

# 删除name字段
df = df.drop(['name'],axis=1) # 0行1列
df.head()
survived pclass sex age sibsp parch fare embarked
0 1 1 0 29.0000 0 0 211.3375 2
1 1 1 1 0.9167 1 2 151.5500 2
2 0 1 0 2.0000 1 2 151.5500 2
3 0 1 1 30.0000 1 2 151.5500 2
4 0 1 0 25.0000 1 2 151.5500 2

3.划分特征值和标签值

# 分离特征值和标签值
data = df.values# 后七列是特征值
features = data[:,1:] # ndarray默认取行,dataframe默认取列
# 第零列是标签值
labels = data[:,0]

labels.shape

(1309,)

4.定义数据预处理函数

def prepare_data(df):# 删除name列df = df.drop(['name'],axis=1) # 将age空的字段改为平均值age_mean = df['age'].mean()df['age'] = df['age'].fillna(age_mean)# 将fare空的字段改为平均值fare_mean = df['fare'].mean()df['fare'] = df['fare'].fillna(age_mean)# 为确实embarked记录填充值df['embarked'] = df['embarked'].fillna('S')# 性别sex由字符串转换为数字编码df['sex'] = df['sex'].map({'female':0,'male':1}).astype(int)# 港口embarked由字母表示转换为数字编码df['embarked'] = df['embarked'].map({'C':0,'Q':1,'S':2}).astype(int)print(df.isnull().any())# 分离特征值和标签值data = df.values# 后七列是特征值features = data[:,1:] # ndarray默认取行,dataframe默认取列# 第零列是标签值labels = data[:,0]return features,labels

5.划分训练集和测试集

shuffle_df = df_selected.sample(frac=1) # 打乱数据顺序，为后面训练做准备，frac为百分比，df保持不变

x_data,y_data = prepare_data(shuffle_df)
x_data.shape,y_data.shape

survived    False
pclass      False
sex         False
age         False
sibsp       False
parch       False
fare        False
embarked    False
dtype: bool((1309, 7), (1309,))

shuffle_df.head()
survived name pclass sex age sibsp parch fare embarked
58 0 Case, Mr. Howard Brown 1 male 49.0 0 0 26.0000 S
666 0 Barbara, Mrs. (Catherine David) 3 female 45.0 0 1 14.4542 C
781 0 Drazenoic, Mr. Jozef 3 male 33.0 0 0 7.8958 C
480 0 Laroche, Mr. Joseph Philippe Lemercier 2 male 25.0 1 2 41.5792 C
459 0 Jacobsohn, Mr. Sidney Samuel 2 male 42.0 1 0 27.0000 S 
test_split = 0.2
train_num = int((1 - test_split) * x_data.shape[0])
# 训练集
x_train = x_data[:train_num]
y_trian = y_data[:train_num]
# 测试集
x_test = x_data[train_num:]
y_test = y_data[train_num:]

6.归一化

from sklearn import preprocessingminmax_scale = preprocessing.MinMaxScaler(feature_range=(0,1))
x_train = minmax_scale.fit_transform(x_train) # 特征值标准化
x_test = minmax_scale.fit_transform(x_test)

二、模型

import tensorflow as tf
tf.__version__

'2.6.0'

1.建立序列模型

model = tf.keras.models.Sequential()

2.添加隐藏层

model.add(tf.keras.layers.Dense(units=64,use_bias=True,activation='relu',input_dim=7, # 也可以用input_shape=(7,)bias_initializer='zeros',kernel_initializer='normal'))

model.add(tf.keras.layers.Dropout(rate=0.2)) # 丢弃层，rate代表丢弃前一层的神经元的比例，防止过拟合

model.add(tf.keras.layers.Dense(units=32,activation='sigmoid',input_shape=(64,), # 也可以用input_dim=64bias_initializer='zeros',kernel_initializer='uniform'))

model.add(tf.keras.layers.Dropout(rate=0.2)) # 丢弃层，rate代表丢弃前一层的神经元的比例，防止过拟合

3.添加输出层

model.add(tf.keras.layers.Dense(units=1,activation='sigmoid',input_dim=32, # 也可以用input_shape=(7,)bias_initializer='zeros',kernel_initializer='uniform'))

model.summary()

Model: "sequential_23"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_68 (Dense)             (None, 64)                512
_________________________________________________________________
dropout_6 (Dropout)          (None, 64)                0
_________________________________________________________________
dense_69 (Dense)             (None, 32)                2080
_________________________________________________________________
dropout_7 (Dropout)          (None, 32)                0
_________________________________________________________________
dense_70 (Dense)             (None, 1)                 33
=================================================================
Total params: 2,625
Trainable params: 2,625
Non-trainable params: 0
_________________________________________________________________

三、训练

1.训练

# 定义训练模式
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.003),loss='binary_crossentropy',metrics=['accuracy'])

# 设置训练参数
train_epochs = 100
batch_size = 40

train_history = model.fit(x=x_train,#训练特征值y=y_trian,#训练集的标签validation_split=0.2,#验证集的比例epochs=train_epochs,#训练的次数batch_size=batch_size,#批量的大小verbose=2) #训练过程的日志信息显示，一个epoch输出一行记录

Epoch 1/100
21/21 - 1s - loss: 0.6780 - accuracy: 0.5854 - val_loss: 0.6464 - val_accuracy: 0.6429
Epoch 2/100
21/21 - 0s - loss: 0.6623 - accuracy: 0.6057 - val_loss: 0.6293 - val_accuracy: 0.6429
Epoch 3/100
21/21 - 0s - loss: 0.6306 - accuracy: 0.6069 - val_loss: 0.5861 - val_accuracy: 0.6667
Epoch 4/100
21/21 - 0s - loss: 0.5771 - accuracy: 0.7336 - val_loss: 0.5199 - val_accuracy: 0.7905
Epoch 5/100
21/21 - 0s - loss: 0.5364 - accuracy: 0.7646 - val_loss: 0.4939 - val_accuracy: 0.7952
Epoch 6/100
21/21 - 0s - loss: 0.5200 - accuracy: 0.7670 - val_loss: 0.4847 - val_accuracy: 0.8143
Epoch 7/100
21/21 - 0s - loss: 0.5118 - accuracy: 0.7718 - val_loss: 0.4771 - val_accuracy: 0.8143
Epoch 8/100
21/21 - 0s - loss: 0.5060 - accuracy: 0.7766 - val_loss: 0.4738 - val_accuracy: 0.8095
Epoch 9/100
21/21 - 0s - loss: 0.4934 - accuracy: 0.7861 - val_loss: 0.4670 - val_accuracy: 0.7952
Epoch 10/100
21/21 - 0s - loss: 0.4966 - accuracy: 0.7814 - val_loss: 0.4637 - val_accuracy: 0.8000
Epoch 11/100
21/21 - 0s - loss: 0.4928 - accuracy: 0.7766 - val_loss: 0.4635 - val_accuracy: 0.7905
Epoch 12/100
21/21 - 0s - loss: 0.4995 - accuracy: 0.7670 - val_loss: 0.4691 - val_accuracy: 0.7905
Epoch 13/100
21/21 - 0s - loss: 0.4886 - accuracy: 0.7957 - val_loss: 0.4620 - val_accuracy: 0.8095
Epoch 14/100
21/21 - 0s - loss: 0.4790 - accuracy: 0.7838 - val_loss: 0.4565 - val_accuracy: 0.8095
Epoch 15/100
21/21 - 0s - loss: 0.4877 - accuracy: 0.7766 - val_loss: 0.4576 - val_accuracy: 0.8095
Epoch 16/100
21/21 - 0s - loss: 0.4839 - accuracy: 0.7897 - val_loss: 0.4560 - val_accuracy: 0.8095
Epoch 17/100
21/21 - 0s - loss: 0.4813 - accuracy: 0.7814 - val_loss: 0.4614 - val_accuracy: 0.8095
Epoch 18/100
21/21 - 0s - loss: 0.4812 - accuracy: 0.7742 - val_loss: 0.4553 - val_accuracy: 0.8095
Epoch 19/100
21/21 - 0s - loss: 0.4762 - accuracy: 0.7885 - val_loss: 0.4554 - val_accuracy: 0.8048
Epoch 20/100
21/21 - 0s - loss: 0.4784 - accuracy: 0.7802 - val_loss: 0.4567 - val_accuracy: 0.8000
Epoch 21/100
21/21 - 0s - loss: 0.4794 - accuracy: 0.7885 - val_loss: 0.4626 - val_accuracy: 0.8000
Epoch 22/100
21/21 - 0s - loss: 0.4824 - accuracy: 0.7838 - val_loss: 0.4567 - val_accuracy: 0.7857
Epoch 23/100
21/21 - 0s - loss: 0.4786 - accuracy: 0.7849 - val_loss: 0.4553 - val_accuracy: 0.8048
Epoch 24/100
21/21 - 0s - loss: 0.4801 - accuracy: 0.7742 - val_loss: 0.4735 - val_accuracy: 0.7905
Epoch 25/100
21/21 - 0s - loss: 0.4752 - accuracy: 0.7849 - val_loss: 0.4571 - val_accuracy: 0.7905
Epoch 26/100
21/21 - 0s - loss: 0.4688 - accuracy: 0.7909 - val_loss: 0.4597 - val_accuracy: 0.8000
Epoch 27/100
21/21 - 0s - loss: 0.4624 - accuracy: 0.7873 - val_loss: 0.4577 - val_accuracy: 0.8048
Epoch 28/100
21/21 - 0s - loss: 0.4656 - accuracy: 0.7993 - val_loss: 0.4602 - val_accuracy: 0.8000
Epoch 29/100
21/21 - 0s - loss: 0.4649 - accuracy: 0.7969 - val_loss: 0.4546 - val_accuracy: 0.8000
Epoch 30/100
21/21 - 0s - loss: 0.4645 - accuracy: 0.7849 - val_loss: 0.4638 - val_accuracy: 0.8000
Epoch 31/100
21/21 - 0s - loss: 0.4635 - accuracy: 0.7921 - val_loss: 0.4603 - val_accuracy: 0.7952
Epoch 32/100
21/21 - 0s - loss: 0.4646 - accuracy: 0.7909 - val_loss: 0.4567 - val_accuracy: 0.7952
Epoch 33/100
21/21 - 0s - loss: 0.4664 - accuracy: 0.7909 - val_loss: 0.4583 - val_accuracy: 0.7952
Epoch 34/100
21/21 - 0s - loss: 0.4661 - accuracy: 0.7921 - val_loss: 0.4575 - val_accuracy: 0.8000
Epoch 35/100
21/21 - 0s - loss: 0.4660 - accuracy: 0.7838 - val_loss: 0.4582 - val_accuracy: 0.7952
Epoch 36/100
21/21 - 0s - loss: 0.4577 - accuracy: 0.8005 - val_loss: 0.4567 - val_accuracy: 0.8000
Epoch 37/100
21/21 - 0s - loss: 0.4648 - accuracy: 0.7909 - val_loss: 0.4585 - val_accuracy: 0.7952
Epoch 38/100
21/21 - 0s - loss: 0.4613 - accuracy: 0.7921 - val_loss: 0.4569 - val_accuracy: 0.7952
Epoch 39/100
21/21 - 0s - loss: 0.4643 - accuracy: 0.7921 - val_loss: 0.4687 - val_accuracy: 0.8000
Epoch 40/100
21/21 - 0s - loss: 0.4696 - accuracy: 0.7814 - val_loss: 0.4601 - val_accuracy: 0.8048
Epoch 41/100
21/21 - 0s - loss: 0.4589 - accuracy: 0.7933 - val_loss: 0.4562 - val_accuracy: 0.7952
Epoch 42/100
21/21 - 0s - loss: 0.4587 - accuracy: 0.7885 - val_loss: 0.4594 - val_accuracy: 0.8000
Epoch 43/100
21/21 - 0s - loss: 0.4601 - accuracy: 0.7981 - val_loss: 0.4563 - val_accuracy: 0.7905
Epoch 44/100
21/21 - 0s - loss: 0.4639 - accuracy: 0.7897 - val_loss: 0.4594 - val_accuracy: 0.8048
Epoch 45/100
21/21 - 0s - loss: 0.4569 - accuracy: 0.7957 - val_loss: 0.4587 - val_accuracy: 0.8000
Epoch 46/100
21/21 - 0s - loss: 0.4619 - accuracy: 0.7957 - val_loss: 0.4556 - val_accuracy: 0.8048
Epoch 47/100
21/21 - 0s - loss: 0.4661 - accuracy: 0.7861 - val_loss: 0.4563 - val_accuracy: 0.8000
Epoch 48/100
21/21 - 0s - loss: 0.4550 - accuracy: 0.7969 - val_loss: 0.4538 - val_accuracy: 0.8000
Epoch 49/100
21/21 - 0s - loss: 0.4550 - accuracy: 0.7873 - val_loss: 0.4572 - val_accuracy: 0.8048
Epoch 50/100
21/21 - 0s - loss: 0.4603 - accuracy: 0.7909 - val_loss: 0.4584 - val_accuracy: 0.8000
Epoch 51/100
21/21 - 0s - loss: 0.4575 - accuracy: 0.7957 - val_loss: 0.4531 - val_accuracy: 0.8095
Epoch 52/100
21/21 - 0s - loss: 0.4568 - accuracy: 0.8029 - val_loss: 0.4584 - val_accuracy: 0.8048
Epoch 53/100
21/21 - 0s - loss: 0.4594 - accuracy: 0.7909 - val_loss: 0.4558 - val_accuracy: 0.8000
Epoch 54/100
21/21 - 0s - loss: 0.4588 - accuracy: 0.8065 - val_loss: 0.4523 - val_accuracy: 0.8000
Epoch 55/100
21/21 - 0s - loss: 0.4532 - accuracy: 0.8029 - val_loss: 0.4593 - val_accuracy: 0.8048
Epoch 56/100
21/21 - 0s - loss: 0.4578 - accuracy: 0.8100 - val_loss: 0.4614 - val_accuracy: 0.8048
Epoch 57/100
21/21 - 0s - loss: 0.4549 - accuracy: 0.8041 - val_loss: 0.4580 - val_accuracy: 0.8095
Epoch 58/100
21/21 - 0s - loss: 0.4568 - accuracy: 0.7909 - val_loss: 0.4597 - val_accuracy: 0.8095
Epoch 59/100
21/21 - 0s - loss: 0.4567 - accuracy: 0.7981 - val_loss: 0.4532 - val_accuracy: 0.8095
Epoch 60/100
21/21 - 0s - loss: 0.4532 - accuracy: 0.7993 - val_loss: 0.4569 - val_accuracy: 0.7952
Epoch 61/100
21/21 - 0s - loss: 0.4543 - accuracy: 0.7969 - val_loss: 0.4555 - val_accuracy: 0.8000
Epoch 62/100
21/21 - 0s - loss: 0.4472 - accuracy: 0.8053 - val_loss: 0.4543 - val_accuracy: 0.8048
Epoch 63/100
21/21 - 0s - loss: 0.4458 - accuracy: 0.8100 - val_loss: 0.4534 - val_accuracy: 0.8095
Epoch 64/100
21/21 - 0s - loss: 0.4497 - accuracy: 0.8005 - val_loss: 0.4593 - val_accuracy: 0.8000
Epoch 65/100
21/21 - 0s - loss: 0.4511 - accuracy: 0.8053 - val_loss: 0.4522 - val_accuracy: 0.8095
Epoch 66/100
21/21 - 0s - loss: 0.4506 - accuracy: 0.8005 - val_loss: 0.4592 - val_accuracy: 0.7952
Epoch 67/100
21/21 - 0s - loss: 0.4533 - accuracy: 0.8005 - val_loss: 0.4545 - val_accuracy: 0.8000
Epoch 68/100
21/21 - 0s - loss: 0.4481 - accuracy: 0.7909 - val_loss: 0.4545 - val_accuracy: 0.7952
Epoch 69/100
21/21 - 0s - loss: 0.4555 - accuracy: 0.7981 - val_loss: 0.4551 - val_accuracy: 0.8000
Epoch 70/100
21/21 - 0s - loss: 0.4440 - accuracy: 0.8029 - val_loss: 0.4552 - val_accuracy: 0.7952
Epoch 71/100
21/21 - 0s - loss: 0.4584 - accuracy: 0.8029 - val_loss: 0.4530 - val_accuracy: 0.7952
Epoch 72/100
21/21 - 0s - loss: 0.4480 - accuracy: 0.7933 - val_loss: 0.4549 - val_accuracy: 0.8048
Epoch 73/100
21/21 - 0s - loss: 0.4554 - accuracy: 0.7981 - val_loss: 0.4536 - val_accuracy: 0.7952
Epoch 74/100
21/21 - 0s - loss: 0.4438 - accuracy: 0.8029 - val_loss: 0.4532 - val_accuracy: 0.7952
Epoch 75/100
21/21 - 0s - loss: 0.4483 - accuracy: 0.8053 - val_loss: 0.4515 - val_accuracy: 0.8095
Epoch 76/100
21/21 - 0s - loss: 0.4408 - accuracy: 0.8041 - val_loss: 0.4554 - val_accuracy: 0.8048
Epoch 77/100
21/21 - 0s - loss: 0.4470 - accuracy: 0.8017 - val_loss: 0.4531 - val_accuracy: 0.8000
Epoch 78/100
21/21 - 0s - loss: 0.4484 - accuracy: 0.8053 - val_loss: 0.4549 - val_accuracy: 0.8048
Epoch 79/100
21/21 - 0s - loss: 0.4456 - accuracy: 0.8053 - val_loss: 0.4526 - val_accuracy: 0.8048
Epoch 80/100
21/21 - 0s - loss: 0.4459 - accuracy: 0.8100 - val_loss: 0.4573 - val_accuracy: 0.7952
Epoch 81/100
21/21 - 0s - loss: 0.4496 - accuracy: 0.7981 - val_loss: 0.4573 - val_accuracy: 0.8095
Epoch 82/100
21/21 - 0s - loss: 0.4515 - accuracy: 0.8053 - val_loss: 0.4502 - val_accuracy: 0.8095
Epoch 83/100
21/21 - 0s - loss: 0.4503 - accuracy: 0.8100 - val_loss: 0.4546 - val_accuracy: 0.7952
Epoch 84/100
21/21 - 0s - loss: 0.4386 - accuracy: 0.8065 - val_loss: 0.4540 - val_accuracy: 0.8048
Epoch 85/100
21/21 - 0s - loss: 0.4371 - accuracy: 0.8088 - val_loss: 0.4552 - val_accuracy: 0.8095
Epoch 86/100
21/21 - 0s - loss: 0.4420 - accuracy: 0.8053 - val_loss: 0.4553 - val_accuracy: 0.8048
Epoch 87/100
21/21 - 0s - loss: 0.4437 - accuracy: 0.8112 - val_loss: 0.4550 - val_accuracy: 0.7952
Epoch 88/100
21/21 - 0s - loss: 0.4432 - accuracy: 0.7969 - val_loss: 0.4565 - val_accuracy: 0.8095
Epoch 89/100
21/21 - 0s - loss: 0.4396 - accuracy: 0.8065 - val_loss: 0.4552 - val_accuracy: 0.8000
Epoch 90/100
21/21 - 0s - loss: 0.4477 - accuracy: 0.8088 - val_loss: 0.4554 - val_accuracy: 0.8048
Epoch 91/100
21/21 - 0s - loss: 0.4412 - accuracy: 0.8017 - val_loss: 0.4507 - val_accuracy: 0.8048
Epoch 92/100
21/21 - 0s - loss: 0.4484 - accuracy: 0.7957 - val_loss: 0.4531 - val_accuracy: 0.8048
Epoch 93/100
21/21 - 0s - loss: 0.4433 - accuracy: 0.8017 - val_loss: 0.4519 - val_accuracy: 0.8048
Epoch 94/100
21/21 - 0s - loss: 0.4415 - accuracy: 0.7957 - val_loss: 0.4524 - val_accuracy: 0.8095
Epoch 95/100
21/21 - 0s - loss: 0.4399 - accuracy: 0.8065 - val_loss: 0.4549 - val_accuracy: 0.8048
Epoch 96/100
21/21 - 0s - loss: 0.4387 - accuracy: 0.8065 - val_loss: 0.4546 - val_accuracy: 0.8095
Epoch 97/100
21/21 - 0s - loss: 0.4463 - accuracy: 0.7945 - val_loss: 0.4542 - val_accuracy: 0.8048
Epoch 98/100
21/21 - 0s - loss: 0.4447 - accuracy: 0.7993 - val_loss: 0.4542 - val_accuracy: 0.8143
Epoch 99/100
21/21 - 0s - loss: 0.4368 - accuracy: 0.8041 - val_loss: 0.4551 - val_accuracy: 0.8048
Epoch 100/100
21/21 - 0s - loss: 0.4395 - accuracy: 0.8053 - val_loss: 0.4501 - val_accuracy: 0.8095

2.训练过程可视化

# 训练过程可视化
import matplotlib.pyplot as pltdef show_train_history(trian_history,train_metric,validation_metric):plt.plot(trian_history[train_metric])plt.plot(trian_history[validation_metric])plt.title('Train History')plt.ylabel(train_metric)plt.xlabel('epoch')plt.legend(['train','validation'],loc='upper left')plt.show()

show_train_history(train_history.history,'loss','val_loss')


show_train_history(train_history.history,'accuracy','val_accuracy')

3.评估模型

loss,acc = model.evaluate(x_test,y_test)

9/9 [==============================] - 0s 2ms/step - loss: 0.3703 - accuracy: 0.8435

loss,acc

(0.3702643811702728, 0.8435114622116089)

四.预测

#@title
Jack_info = [0,'Jack',3,'male',23,1,0,5.0000,'S']
Rose_info = [1,'Rose',1,'female',20,1,0,100.0000,'S']

x_pre = pd.DataFrame([Jack_info,Rose_info],columns=selected_cols)
x_pre
survived name pclass sex age sibsp parch fare embarked
0 0 Jack 3 male 23 1 0 5.0 S
1 1 Rose 1 female 20 1 0 100.0 S 
x_pre_features,y = prepare_data(x_pre)
from sklearn import preprocessingminmax_scale = preprocessing.MinMaxScaler(feature_range=(0,1))
x_pre_features = minmax_scale.fit_transform(x_pre_features) # 特征值标准化
y_pre = model.predict(x_pre_features)

survived    False
pclass      False
sex         False
age         False
sibsp       False
parch       False
fare        False
embarked    False
dtype: bool

x_pre.insert(len(x_pre.columns),'surv_probabilty',y_pre)

x_pre
survived name pclass sex age sibsp parch fare embarked surv_probabilty
0 0 Jack 3 male 23 1 0 5.0 S 0.058498
1 1 Rose 1 female 20 1 0 100.0 S 0.975978

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