python - 在 Tensorflow 2.2.0 中，我的 model.history.history 在拟合数据和 validation_data 后为空

Question

起初它工作正常，然后我尝试在创建模型时调整一些参数，之后，

print(model.history.history)

给我一本空字典。

如果有帮助，这是我的整个代码，

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import mean_absolute_error

df = pd.read_csv('TF_2_Notebooks_and_Data/DATA/kc_house_data.csv')
# print(df.columns)
'''prints
Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',
       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',
       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',
       'lat', 'long', 'sqft_living15', 'sqft_lot15'],
      dtype='object')'''
# if we want to see what data column has missing data point,
# print(df.isnull()) #will print 'True' if data is missing
'''
          id   date  price  bedrooms  ...    lat   long  sqft_living15  sqft_lot15
0      False  False  False     False  ...  False  False          False       False
1      False  False  False     False  ...  False  False          False       False
2      False  False  False     False  ...  False  False          False       False
3      False  False  False     False  ...  False  False          False       False
4      False  False  False     False  ...  False  False          False       False
...      ...    ...    ...       ...  ...    ...    ...            ...         ...
21592  False  False  False     False  ...  False  False          False       False
21593  False  False  False     False  ...  False  False          False       False
21594  False  False  False     False  ...  False  False          False       False
21595  False  False  False     False  ...  False  False          False       False
21596  False  False  False     False  ...  False  False          False       False
'''
# print(df.isnull().sum())
'''
id               0
date             0
price            0
bedrooms         0
bathrooms        0
sqft_living      0
sqft_lot         0
floors           0
waterfront       0
view             0
condition        0
grade            0
sqft_above       0
sqft_basement    0
yr_built         0
yr_renovated     0
zipcode          0
lat              0
long             0
sqft_living15    0
sqft_lot15       0
dtype: int64
'''

# describing the data set
# print(df.describe().transpose())

# let us see with histogram the prices of the houses
# sns.distplot(df['price'])

# counting bedrooms per house
# sns.countplot(df['bedrooms'])

# removing unwanted data
df = df.drop('id', axis=1)
# changing data style to yyyy-mm-dd
df['date'] = pd.to_datetime(df['date'])
# extracting year from date
df['year'] = df['date'].apply(lambda date: date.year)
df['month'] = df['date'].apply(lambda date: date.month)
# checking if prices are affected by year
# sns.scatterplot(x=df['price'],y=df['month'],hue=df['year'])
# or
# sns.boxplot('month','price',data=df)
# or
# print(df.groupby('month').mean()['price'].plot())

# removing date column
df = df.drop('date', axis=1)
# also drop zipcodes
df = df.drop('zipcode', axis=1)
# print(df['yr_renovated'].value_counts())

X = df.drop('price', axis=1).values
y = df['price'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

    # print(X_train.shape)
# prints (15117, 19)
model = Sequential()
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(19, activation='relu'))
model.add(Dense(1, activation=None))
model.compile(optimizer='adam', loss='mse')
# adding validation data will not affect the weights and the biases of the model, it is to get an idea of,
# over-fitting or under-fitting the data
#reducing the batch size will make the model more time to train but less over-fitting will occur
model.fit(X_train, y_train, validation_data=(X_test, y_test),
          batch_size=128, epochs=4,verbose=2)

predictions = model.predict(X_test)
# checking if we are over-fitting or no
print(f"model hist is : \n {model.history.history}")
losses = pd.DataFrame(model.history.history)
print(losses)
#losses.plot()
# NOTE: the line curve for loss must match for not over-fitting the data.
#plt.ylabel('losses')
#plt.xlabel('number of epochs')
off_by = mean_absolute_error(y_test, predictions)
print(f"the predictions are off by {off_by} dollars")
print(f"the mean of all the prices is {df['price'].mean()}")
plt.show()

output:

    Epoch 1/4
119/119 - 0s - loss: 430244003840.0000 - val_loss: 418937962496.0000
Epoch 2/4
119/119 - 0s - loss: 429396754432.0000 - val_loss: 415953223680.0000
Epoch 3/4
119/119 - 0s - loss: 417119928320.0000 - val_loss: 387559292928.0000
Epoch 4/4
119/119 - 0s - loss: 354640822272.0000 - val_loss: 283466629120.0000
model hist is : 
 {}
Empty DataFrame
Columns: []
Index: []
the predictions are off by 401518.14752604166 dollars
the mean of all the prices is 540296.5735055795

Process finished with exit code 0

我现在不知道该去哪里，
该行:

print(f"model hist is : \n {model.history.history}")

打印:

model hist is : 
{}

由于我需要分析损失和验证损失，我不能再进一步了

Answer 1

history = model.fit(...)
print(f"model hist is : \n {history.history}")