波士顿房价预测
Sklearn
# 导入机器学习库
from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, LogisticRegression, RidgeCV
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.externals import joblib
from sklearn.metrics import r2_score
from sklearn.neural_network import MLPRegressor
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# 波士顿房价数据集
lb = load_boston()
lb
{‘data’: array([[6.3200e-03, 1.8000e+01, 2.3100e+00, …, 1.5300e+01, 3.9690e+02,
4.9800e+00],
[2.7310e-02, 0.0000e+00, 7.0700e+00, …, 1.7800e+01, 3.9690e+02,
9.1400e+00],
[2.7290e-02, 0.0000e+00, 7.0700e+00, …, 1.7800e+01, 3.9283e+02,
4.0300e+00],
…,
[6.0760e-02, 0.0000e+00, 1.1930e+01, …, 2.1000e+01, 3.9690e+02,
5.6400e+00],
[1.0959e-01, 0.0000e+00, 1.1930e+01, …, 2.1000e+01, 3.9345e+02,
6.4800e+00],
[4.7410e-02, 0.0000e+00, 1.1930e+01, …, 2.1000e+01, 3.9690e+02,
7.8800e+00]]),
‘target’: array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,
18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,
15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,
13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,
21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,
35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,
19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,
20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,
23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,
33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,
21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,
20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,
23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,
15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,
17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,
25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,
23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,
32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,
34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,
20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,
26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,
31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,
22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,
42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,
36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,
32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,
20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,
20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,
22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,
21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,
19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,
32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,
18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,
16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,
13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,
7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,
12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,
27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,
8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,
9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,
10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,
15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,
19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,
29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,
20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,
23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),
‘feature_names’: array([‘CRIM’, ‘ZN’, ‘INDUS’, ‘CHAS’, ‘NOX’, ‘RM’, ‘AGE’, ‘DIS’, ‘RAD’,
‘TAX’, ‘PTRATIO’, ‘B’, ‘LSTAT’], dtype=‘<U7’),
‘DESCR’: “Boston House Prices dataset\n===========================\n\nNotes\n------\nData Set Characteristics: \n\n :Number of Instances: 506 \n\n :Number of Attributes: 13 numeric/categorical predictive\n \n :Median Value (attribute 14) is usually the target\n\n :Attribute Information (in order):\n - CRIM per capita crime rate by town\n - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n - INDUS proportion of non-retail business acres per town\n - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n - NOX nitric oxides concentration (parts per 10 million)\n - RM average number of rooms per dwelling\n - AGE proportion of owner-occupied units built prior to 1940\n - DIS weighted distances to five Boston employment centres\n - RAD index of accessibility to radial highways\n - TAX full-value property-tax rate per $10,000\n - PTRATIO pupil-teacher ratio by town\n - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n - LSTAT % lower status of the population\n - MEDV Median value of owner-occupied homes in $1000’s\n\n :Missing Attribute Values: None\n\n :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttp://archive.ics.uci.edu/ml/datasets/Housing\n\n\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. ‘Hedonic\nprices and the demand for clean air’, J. Environ. Economics & Management,\nvol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, ‘Regression diagnostics\n…’, Wiley, 1980. N.B. Various transformations are used in the table on\npages 244-261 of the latter.\n\nThe Boston house-price data has been used in many machine learning papers that address regression\nproblems. \n \nReferences\n\n - Belsley, Kuh & Welsch, ‘Regression diagnostics: Identifying Influential Data and Sources of Collinearity’, Wiley, 1980. 244-261.\n - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n”}
# 划分训练集与测试集
X_train, X_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size = 0.2)
X_train, X_test, y_train, y_test
(array([[6.72400e-02, 0.00000e+00, 3.24000e+00, …, 1.69000e+01,
3.75210e+02, 7.34000e+00],
[3.75780e-01, 0.00000e+00, 1.05900e+01, …, 1.86000e+01,
3.95240e+02, 2.39800e+01],
[1.39134e+01, 0.00000e+00, 1.81000e+01, …, 2.02000e+01,
1.00630e+02, 1.51700e+01],
…,
[2.89600e-01, 0.00000e+00, 9.69000e+00, …, 1.92000e+01,
3.96900e+02, 2.11400e+01],
[1.42310e-01, 0.00000e+00, 1.00100e+01, …, 1.78000e+01,
3.88740e+02, 1.04500e+01],
[1.75050e-01, 0.00000e+00, 5.96000e+00, …, 1.92000e+01,
3.93430e+02, 1.01300e+01]]),
array([[4.68400e-02, 0.00000e+00, 3.41000e+00, …, 1.78000e+01,
3.92180e+02, 8.81000e+00],
[1.00245e+00, 0.00000e+00, 8.14000e+00, …, 2.10000e+01,
3.80230e+02, 1.19800e+01],
[1.54450e-01, 2.50000e+01, 5.13000e+00, …, 1.97000e+01,
3.90680e+02, 6.86000e+00],
…,
[6.26300e-02, 0.00000e+00, 1.19300e+01, …, 2.10000e+01,
3.91990e+02, 9.67000e+00],
[5.82401e+00, 0.00000e+00, 1.81000e+01, …, 2.02000e+01,
3.96900e+02, 1.07400e+01],
[1.87000e-02, 8.50000e+01, 4.15000e+00, …, 1.79000e+01,
3.92430e+02, 6.36000e+00]]),
array([22.6, 19.3, 11.7, 20. , 24.3, 8.5, 14.6, 28.7, 15.4, 30.3, 21.4,
19.1, 20.5, 19.3, 50. , 36.2, 25. , 28.5, 17.8, 18.9, 23.9, 16.5,
30.8, 19.9, 23.2, 18.8, 32. , 29.4, 12. , 20.7, 44.8, 19.6, 19.3,
10.5, 14.1, 15.6, 23.8, 50. , 48.3, 23.7, 18.6, 33.8, 27.5, 20.1,
20.7, 35.1, 25. , 24.1, 32.7, 14.4, 13.5, 12.8, 33.2, 16.8, 21.6,
28. , 23.9, 20.4, 10.8, 22.9, 23.2, 34.7, 14.3, 22.6, 18.7, 21.5,
33.2, 22.2, 33. , 20.4, 27.9, 23.1, 19.1, 15.6, 29.6, 24.4, 23.5,
13.6, 19.2, 29.1, 35.2, 22.2, 17.2, 9.6, 18.7, 13.4, 18.8, 25.1,
15. , 21.1, 46.7, 18.4, 19.4, 17.2, 8.8, 14.5, 19.9, 38.7, 16.6,
24.8, 29.9, 20.2, 19.8, 23. , 33.1, 22. , 18.3, 12.7, 20.9, 11. ,
25. , 18.5, 45.4, 17.7, 21.8, 19.4, 30.7, 18.6, 23.2, 24.8, 22.6,
29.8, 27.5, 22.8, 29.6, 50. , 29. , 10.5, 21.4, 21.9, 10.4, 20.6,
28.7, 37. , 20.6, 31.1, 23.9, 19.1, 30.1, 13.1, 16.7, 23.9, 23.8,
19.3, 18.1, 13.8, 16.8, 50. , 24.5, 26.6, 50. , 32.4, 17.5, 25. ,
14.5, 43.8, 19.5, 18.5, 13.6, 19.4, 11.3, 18.5, 22.2, 34.6, 24. ,
50. , 22.2, 18.2, 11.7, 22.2, 18.7, 19.3, 21.7, 21.2, 9.5, 25. ,
6.3, 22. , 17.8, 10.4, 23.1, 30.5, 7.5, 13.1, 20.5, 21.8, 20.8,
19.5, 22.8, 19.6, 21.4, 13.5, 17.1, 36.5, 24.6, 7.2, 22.9, 33.4,
15.6, 7. , 16.6, 12.7, 26.2, 28.6, 34.9, 16.3, 31.5, 15.2, 10.9,
13.9, 12.1, 22.1, 31. , 19.6, 21.4, 41.3, 23.4, 50. , 41.7, 15.3,
15. , 17.1, 20. , 20.3, 23. , 24.2, 50. , 19.4, 35.4, 20.3, 8.3,
10.2, 17.2, 18. , 17.4, 32.9, 21.1, 20.1, 21.5, 24.3, 24.5, 48.5,
24.6, 20.8, 50. , 36.2, 14. , 21.7, 23.7, 26.6, 24.7, 24.5, 10.2,
36.4, 17.8, 19.9, 13.3, 25. , 13. , 8.4, 13.4, 26.5, 27.5, 17.6,
31.7, 32.2, 22.7, 10.2, 16.1, 20.4, 20. , 20.6, 16.7, 20.1, 19.5,
13.2, 21.2, 50. , 42.8, 8.5, 42.3, 19.8, 17.9, 24.4, 10.9, 16.1,
23.1, 50. , 23.1, 24.3, 22.3, 36.1, 22. , 17.3, 13.8, 15. , 50. ,
12.3, 9.7, 13.3, 24.8, 19.4, 39.8, 23.7, 12.6, 31.5, 21.7, 20.3,
13.1, 15.7, 19.6, 13.8, 22.5, 22. , 14.9, 20.2, 20.6, 18.9, 14.8,
21. , 18.4, 22. , 50. , 25.2, 19.8, 23.8, 14.1, 33.4, 12.5, 23.1,
24.7, 19.1, 21.4, 13.3, 13.8, 23.1, 27.5, 25.3, 50. , 23.7, 17.1,
5. , 43.5, 17.4, 8.3, 17.8, 18.4, 22.3, 24.8, 15.6, 16.2, 17.4,
28.2, 13.9, 17. , 31.2, 24.1, 32.5, 26.4, 46. , 17.8, 20.5, 16. ,
5. , 28.7, 30.1, 16.2, 29.8, 18.2, 20.6, 43.1, 21.2, 16.1, 21.2,
18.3, 21.9, 37.6, 50. , 8.8, 22. , 29. , 23.8, 15.1, 25. , 21.7,
14.5, 13.8, 23.6, 21.9, 17.5, 23. , 23.9, 22. , 22.5, 37.3, 31.6,
16.5, 27.1, 21.2, 19.9, 15.6, 19.7, 18.5, 24.7]),
array([22.6, 21. , 23.3, 7.4, 16.4, 30.1, 35.4, 27.9, 21.7, 15.4, 22.9,
20.9, 22.9, 33.3, 28.1, 8.4, 7.2, 36. , 22.5, 19.4, 33.1, 14.1,
14.9, 37.2, 14.4, 23.6, 8.1, 23.3, 24.4, 21.7, 28.4, 27.1, 20. ,
20.4, 15.2, 14.3, 19.7, 32. , 13.4, 20.6, 11.9, 48.8, 14.2, 18.9,
21.7, 20.1, 24. , 11.8, 19.6, 24.4, 13.1, 5.6, 50. , 11.9, 15.2,
29.1, 23.4, 34.9, 18.9, 22.8, 13.4, 44. , 7.2, 20.1, 22.4, 17.5,
20.8, 18.2, 22.7, 25. , 7. , 24.1, 26.6, 20.3, 19.5, 37.9, 21. ,
12.7, 26.7, 31.6, 28.4, 23.2, 23.3, 19. , 22.6, 19.2, 11.8, 22.8,
8.7, 26.4, 19. , 20. , 34.9, 27. , 23.3, 14.6, 11.5, 14.9, 21.6,
22.4, 23. , 23.1]))
# 为数据增加一个维度，相当于把[1, 5, 10]变成[[1, 5, 10]]
# 在新版sklearn中，所有数据都应该是二维矩阵，哪怕它只是单独一行或一列
y_train = y_train.reshape(-1,1)
y_test = y_test.reshape(-1,1)
# 进行标准化
std_x = StandardScaler()
X_train = std_x.fit_transform(X_train)
X_test = std_x.transform(X_test)
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train)
y_test = std_y.transform(y_test)
#线性回归器LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
print("r2 score of Linear regression is",r2_score(y_test,lr.predict(X_test)))
r2 score of Linear regression is 0.7778158147557528
#岭回归
cv = RidgeCV(alphas=np.logspace(-3, 2, 100))
cv.fit (X_train , y_train)
print("r2 score of Linear regression is",r2_score(y_test,cv.predict(X_test)))
r2 score of Linear regression is 0.7798009579941207
#线性回归器SGDRegressor
sgd = SGDRegressor()
sgd.fit(X_train, y_train)
print("r2 score of Linear regression is",r2_score(y_test,sgd.predict(X_test)))
r2 score of Linear regression is 0.77430200232832
Keras
# 使用Keras试试
from keras.models import Sequential
from keras.layers import Dense
#基准NN
#使用标准化后的数据
seq = Sequential()
#构建神经网络模型
#input_dim来隐含的指定输入数据shape
seq.add(Dense(64, activation='relu',input_dim=lb.data.shape[1]))
seq.add(Dense(64, activation='relu'))
seq.add(Dense(1, activation='relu'))
seq.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
seq.fit(X_train, y_train, epochs=300, batch_size = 16, shuffle = False)
score = seq.evaluate(X_test, y_test,batch_size=16) #loss value & metrics values
print("score:",score)
print('r2 score:',r2_score(y_test, seq.predict(X_test)))
Using TensorFlow backend.
WARNING:tensorflow:From /home/nlp/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
Epoch 1/300
404/404 [==============================] - 0s 688us/step - loss: 0.8272 - mae: 0.6820
Epoch 2/300
404/404 [==============================] - 0s 223us/step - loss: 0.5090 - mae: 0.5378
Epoch 3/300
404/404 [==============================] - 0s 114us/step - loss: 0.4726 - mae: 0.5096
Epoch 4/300
404/404 [==============================] - 0s 112us/step - loss: 0.4600 - mae: 0.4980
Epoch 5/300
404/404 [==============================] - 0s 299us/step - loss: 0.4517 - mae: 0.4884
Epoch 6/300
404/404 [==============================] - 0s 148us/step - loss: 0.4444 - mae: 0.4838
Epoch 7/300
404/404 [==============================] - 0s 109us/step - loss: 0.4388 - mae: 0.4784
Epoch 8/300
404/404 [==============================] - 0s 148us/step - loss: 0.4342 - mae: 0.4745
Epoch 9/300
404/404 [==============================] - 0s 166us/step - loss: 0.4316 - mae: 0.4721
Epoch 10/300
404/404 [==============================] - 0s 194us/step - loss: 0.4271 - mae: 0.4695
Epoch 11/300
404/404 [==============================] - 0s 110us/step - loss: 0.4242 - mae: 0.4667
Epoch 12/300
404/404 [==============================] - 0s 109us/step - loss: 0.4212 - mae: 0.4646
Epoch 13/300
404/404 [==============================] - 0s 190us/step - loss: 0.4190 - mae: 0.4630
Epoch 14/300
404/404 [==============================] - 0s 297us/step - loss: 0.4165 - mae: 0.4602
Epoch 15/300
404/404 [==============================] - 0s 129us/step - loss: 0.4142 - mae: 0.4584
Epoch 16/300
404/404 [==============================] - 0s 116us/step - loss: 0.4130 - mae: 0.4575
Epoch 17/300
404/404 [==============================] - 0s 167us/step - loss: 0.4098 - mae: 0.4558
Epoch 18/300
404/404 [==============================] - 0s 218us/step - loss: 0.4082 - mae: 0.4540
Epoch 19/300
404/404 [==============================] - 0s 253us/step - loss: 0.4072 - mae: 0.4529
Epoch 20/300
404/404 [==============================] - 0s 189us/step - loss: 0.4053 - mae: 0.4511
Epoch 21/300
404/404 [==============================] - 0s 235us/step - loss: 0.4042 - mae: 0.4501
Epoch 22/300
404/404 [==============================] - 0s 360us/step - loss: 0.4028 - mae: 0.4477
Epoch 23/300
404/404 [==============================] - 0s 184us/step - loss: 0.4020 - mae: 0.4463
Epoch 24/300
404/404 [==============================] - 0s 250us/step - loss: 0.4009 - mae: 0.4456
Epoch 25/300
404/404 [==============================] - 0s 331us/step - loss: 0.4002 - mae: 0.4446
Epoch 26/300
404/404 [==============================] - 0s 170us/step - loss: 0.3994 - mae: 0.4429
Epoch 27/300
404/404 [==============================] - 0s 159us/step - loss: 0.3982 - mae: 0.4430
Epoch 28/300
404/404 [==============================] - 0s 198us/step - loss: 0.3990 - mae: 0.4427
Epoch 29/300
404/404 [==============================] - 0s 224us/step - loss: 0.3970 - mae: 0.4412
Epoch 30/300
404/404 [==============================] - 0s 189us/step - loss: 0.3966 - mae: 0.4399
Epoch 31/300
404/404 [==============================] - 0s 165us/step - loss: 0.3894 - mae: 0.4406
Epoch 32/300
404/404 [==============================] - 0s 116us/step - loss: 0.3777 - mae: 0.4396
Epoch 33/300
404/404 [==============================] - 0s 87us/step - loss: 0.3740 - mae: 0.4369
Epoch 34/300
404/404 [==============================] - 0s 61us/step - loss: 0.3708 - mae: 0.4341
Epoch 35/300
404/404 [==============================] - 0s 130us/step - loss: 0.3690 - mae: 0.4330
Epoch 36/300
404/404 [==============================] - 0s 93us/step - loss: 0.3670 - mae: 0.4315
Epoch 37/300
404/404 [==============================] - ETA: 0s - loss: 0.3741 - mae: 0.447 - 0s 87us/step - loss: 0.3653 - mae: 0.4295
Epoch 38/300
404/404 [==============================] - 0s 64us/step - loss: 0.3633 - mae: 0.4279
Epoch 39/300
404/404 [==============================] - 0s 119us/step - loss: 0.3622 - mae: 0.4272
Epoch 40/300
404/404 [==============================] - 0s 76us/step - loss: 0.3604 - mae: 0.4254
Epoch 41/300
404/404 [==============================] - 0s 75us/step - loss: 0.3587 - mae: 0.4243
Epoch 42/300
404/404 [==============================] - 0s 66us/step - loss: 0.3578 - mae: 0.4238
Epoch 43/300
404/404 [==============================] - 0s 85us/step - loss: 0.3565 - mae: 0.4222
Epoch 44/300
404/404 [==============================] - 0s 81us/step - loss: 0.3557 - mae: 0.4221
Epoch 45/300
404/404 [==============================] - 0s 102us/step - loss: 0.3550 - mae: 0.4213
Epoch 46/300
404/404 [==============================] - 0s 68us/step - loss: 0.3542 - mae: 0.4202
Epoch 47/300
404/404 [==============================] - 0s 87us/step - loss: 0.3537 - mae: 0.4191
Epoch 48/300
404/404 [==============================] - 0s 64us/step - loss: 0.3553 - mae: 0.4193
Epoch 49/300
404/404 [==============================] - 0s 88us/step - loss: 0.3517 - mae: 0.4171
Epoch 50/300
404/404 [==============================] - 0s 79us/step - loss: 0.3523 - mae: 0.4172
Epoch 51/300
404/404 [==============================] - 0s 135us/step - loss: 0.3520 - mae: 0.4165
Epoch 52/300
404/404 [==============================] - 0s 90us/step - loss: 0.3510 - mae: 0.4154
Epoch 53/300
404/404 [==============================] - 0s 110us/step - loss: 0.3504 - mae: 0.4173
Epoch 54/300
404/404 [==============================] - 0s 112us/step - loss: 0.3501 - mae: 0.4145
Epoch 55/300
404/404 [==============================] - 0s 80us/step - loss: 0.3499 - mae: 0.4164
Epoch 56/300
404/404 [==============================] - 0s 230us/step - loss: 0.3492 - mae: 0.4129
Epoch 57/300
404/404 [==============================] - 0s 132us/step - loss: 0.3492 - mae: 0.4134
Epoch 58/300
404/404 [==============================] - 0s 102us/step - loss: 0.3508 - mae: 0.4135
Epoch 59/300
404/404 [==============================] - 0s 133us/step - loss: 0.3481 - mae: 0.4149
Epoch 60/300
404/404 [==============================] - 0s 50us/step - loss: 0.3488 - mae: 0.4151
Epoch 61/300
404/404 [==============================] - 0s 112us/step - loss: 0.3481 - mae: 0.4116
Epoch 62/300
404/404 [==============================] - 0s 286us/step - loss: 0.3477 - mae: 0.4123
Epoch 63/300
404/404 [==============================] - 0s 258us/step - loss: 0.3475 - mae: 0.4117
Epoch 64/300
404/404 [==============================] - 0s 227us/step - loss: 0.3470 - mae: 0.4110
Epoch 65/300
404/404 [==============================] - 0s 177us/step - loss: 0.3483 - mae: 0.4113
Epoch 66/300
404/404 [==============================] - 0s 170us/step - loss: 0.3472 - mae: 0.4116
Epoch 67/300
404/404 [==============================] - 0s 130us/step - loss: 0.3467 - mae: 0.4084
Epoch 68/300
404/404 [==============================] - 0s 209us/step - loss: 0.3467 - mae: 0.4108
Epoch 69/300
404/404 [==============================] - 0s 140us/step - loss: 0.3460 - mae: 0.4086
Epoch 70/300
404/404 [==============================] - 0s 196us/step - loss: 0.3459 - mae: 0.4100
Epoch 71/300
404/404 [==============================] - 0s 170us/step - loss: 0.3458 - mae: 0.4077
Epoch 72/300
404/404 [==============================] - 0s 182us/step - loss: 0.3467 - mae: 0.4111
Epoch 73/300
404/404 [==============================] - 0s 162us/step - loss: 0.3455 - mae: 0.4075
Epoch 74/300
404/404 [==============================] - 0s 223us/step - loss: 0.3456 - mae: 0.4085
Epoch 75/300
404/404 [==============================] - 0s 157us/step - loss: 0.3462 - mae: 0.4107
Epoch 76/300
404/404 [==============================] - 0s 120us/step - loss: 0.3450 - mae: 0.4069
Epoch 77/300
404/404 [==============================] - 0s 170us/step - loss: 0.3460 - mae: 0.4106
Epoch 78/300
404/404 [==============================] - 0s 149us/step - loss: 0.3452 - mae: 0.4070
Epoch 79/300
404/404 [==============================] - 0s 126us/step - loss: 0.3452 - mae: 0.4065
Epoch 80/300
404/404 [==============================] - 0s 85us/step - loss: 0.3451 - mae: 0.4104
Epoch 81/300
404/404 [==============================] - 0s 164us/step - loss: 0.3454 - mae: 0.4087
Epoch 82/300
404/404 [==============================] - 0s 113us/step - loss: 0.3453 - mae: 0.4077
Epoch 83/300
404/404 [==============================] - 0s 158us/step - loss: 0.3447 - mae: 0.4053
Epoch 84/300
404/404 [==============================] - 0s 155us/step - loss: 0.3443 - mae: 0.4057
Epoch 85/300
404/404 [==============================] - 0s 160us/step - loss: 0.3442 - mae: 0.4076
Epoch 86/300
404/404 [==============================] - 0s 221us/step - loss: 0.3451 - mae: 0.4076
Epoch 87/300
404/404 [==============================] - 0s 343us/step - loss: 0.3463 - mae: 0.4114
Epoch 88/300
404/404 [==============================] - 0s 218us/step - loss: 0.3442 - mae: 0.4031
Epoch 89/300
404/404 [==============================] - 0s 130us/step - loss: 0.3446 - mae: 0.4074
Epoch 90/300
404/404 [==============================] - 0s 210us/step - loss: 0.3449 - mae: 0.4073
Epoch 91/300
404/404 [==============================] - 0s 101us/step - loss: 0.3454 - mae: 0.4082
Epoch 92/300
404/404 [==============================] - 0s 187us/step - loss: 0.3435 - mae: 0.4025
Epoch 93/300
404/404 [==============================] - 0s 148us/step - loss: 0.3445 - mae: 0.4082
Epoch 94/300
404/404 [==============================] - 0s 155us/step - loss: 0.3430 - mae: 0.4022
Epoch 95/300
404/404 [==============================] - 0s 158us/step - loss: 0.3454 - mae: 0.4084
Epoch 96/300
404/404 [==============================] - 0s 191us/step - loss: 0.3449 - mae: 0.4074
Epoch 97/300
404/404 [==============================] - 0s 224us/step - loss: 0.3438 - mae: 0.4032
Epoch 98/300
404/404 [==============================] - 0s 122us/step - loss: 0.3455 - mae: 0.4057
Epoch 99/300
404/404 [==============================] - 0s 248us/step - loss: 0.3446 - mae: 0.4051
Epoch 100/300
404/404 [==============================] - 0s 223us/step - loss: 0.3438 - mae: 0.4053
Epoch 101/300
404/404 [==============================] - 0s 225us/step - loss: 0.3447 - mae: 0.4060
Epoch 102/300
404/404 [==============================] - 0s 255us/step - loss: 0.3439 - mae: 0.4032
Epoch 103/300
404/404 [==============================] - 0s 169us/step - loss: 0.3446 - mae: 0.4084
Epoch 104/300
404/404 [==============================] - 0s 196us/step - loss: 0.3450 - mae: 0.4062
Epoch 105/300
404/404 [==============================] - 0s 153us/step - loss: 0.3440 - mae: 0.4034
Epoch 106/300
404/404 [==============================] - 0s 64us/step - loss: 0.3442 - mae: 0.4056
Epoch 107/300
404/404 [==============================] - 0s 179us/step - loss: 0.3451 - mae: 0.4082
Epoch 108/300
404/404 [==============================] - 0s 163us/step - loss: 0.3438 - mae: 0.4055
Epoch 109/300
404/404 [==============================] - 0s 120us/step - loss: 0.3441 - mae: 0.4057
Epoch 110/300
404/404 [==============================] - 0s 221us/step - loss: 0.3448 - mae: 0.4066
Epoch 111/300
404/404 [==============================] - 0s 106us/step - loss: 0.3435 - mae: 0.4037
Epoch 112/300
404/404 [==============================] - 0s 103us/step - loss: 0.3428 - mae: 0.4026
Epoch 113/300
404/404 [==============================] - 0s 107us/step - loss: 0.3455 - mae: 0.4093
Epoch 114/300
404/404 [==============================] - 0s 204us/step - loss: 0.3444 - mae: 0.4060
Epoch 115/300
404/404 [==============================] - 0s 266us/step - loss: 0.3445 - mae: 0.4035
Epoch 116/300
404/404 [==============================] - 0s 223us/step - loss: 0.3436 - mae: 0.4035
Epoch 117/300
404/404 [==============================] - 0s 142us/step - loss: 0.3432 - mae: 0.4039
Epoch 118/300
404/404 [==============================] - 0s 277us/step - loss: 0.3447 - mae: 0.4073
Epoch 119/300
404/404 [==============================] - 0s 257us/step - loss: 0.3440 - mae: 0.4047
Epoch 120/300
404/404 [==============================] - 0s 203us/step - loss: 0.3432 - mae: 0.4035
Epoch 121/300
404/404 [==============================] - 0s 261us/step - loss: 0.3449 - mae: 0.4056
Epoch 122/300
404/404 [==============================] - 0s 161us/step - loss: 0.3430 - mae: 0.4031
Epoch 123/300
404/404 [==============================] - 0s 206us/step - loss: 0.3442 - mae: 0.4038
Epoch 124/300
404/404 [==============================] - 0s 151us/step - loss: 0.3433 - mae: 0.4024
Epoch 125/300
404/404 [==============================] - 0s 142us/step - loss: 0.3434 - mae: 0.4043
Epoch 126/300
404/404 [==============================] - 0s 116us/step - loss: 0.3438 - mae: 0.4036
Epoch 127/300
404/404 [==============================] - 0s 103us/step - loss: 0.3439 - mae: 0.4013
Epoch 128/300
404/404 [==============================] - 0s 218us/step - loss: 0.3440 - mae: 0.4071
Epoch 129/300
404/404 [==============================] - 0s 180us/step - loss: 0.3432 - mae: 0.4007
Epoch 130/300
404/404 [==============================] - 0s 122us/step - loss: 0.3442 - mae: 0.4040
Epoch 131/300
404/404 [==============================] - 0s 95us/step - loss: 0.3439 - mae: 0.4056
Epoch 132/300
404/404 [==============================] - 0s 91us/step - loss: 0.3439 - mae: 0.4049
Epoch 133/300
404/404 [==============================] - 0s 175us/step - loss: 0.3423 - mae: 0.3999
Epoch 134/300
404/404 [==============================] - 0s 125us/step - loss: 0.3445 - mae: 0.4068
Epoch 135/300
404/404 [==============================] - 0s 311us/step - loss: 0.3428 - mae: 0.4003
Epoch 136/300
404/404 [==============================] - 0s 177us/step - loss: 0.3446 - mae: 0.4046
Epoch 137/300
404/404 [==============================] - 0s 99us/step - loss: 0.3432 - mae: 0.4024
Epoch 138/300
404/404 [==============================] - 0s 74us/step - loss: 0.3443 - mae: 0.4050
Epoch 139/300
404/404 [==============================] - 0s 94us/step - loss: 0.3430 - mae: 0.4019
Epoch 140/300
404/404 [==============================] - 0s 123us/step - loss: 0.3442 - mae: 0.4045
Epoch 141/300
404/404 [==============================] - 0s 265us/step - loss: 0.3440 - mae: 0.4024
Epoch 142/300
404/404 [==============================] - 0s 246us/step - loss: 0.3428 - mae: 0.4014
Epoch 143/300
404/404 [==============================] - 0s 109us/step - loss: 0.3430 - mae: 0.4018
Epoch 144/300
404/404 [==============================] - 0s 129us/step - loss: 0.3444 - mae: 0.4061
Epoch 145/300
404/404 [==============================] - 0s 92us/step - loss: 0.3428 - mae: 0.4011
Epoch 146/300
404/404 [==============================] - 0s 80us/step - loss: 0.3426 - mae: 0.4000
Epoch 147/300
404/404 [==============================] - 0s 100us/step - loss: 0.3429 - mae: 0.4000
Epoch 148/300
404/404 [==============================] - 0s 119us/step - loss: 0.3445 - mae: 0.4033
Epoch 149/300
404/404 [==============================] - 0s 64us/step - loss: 0.3432 - mae: 0.4045
Epoch 150/300
404/404 [==============================] - 0s 83us/step - loss: 0.3430 - mae: 0.4018
Epoch 151/300
404/404 [==============================] - 0s 89us/step - loss: 0.3434 - mae: 0.4027
Epoch 152/300
404/404 [==============================] - 0s 88us/step - loss: 0.3433 - mae: 0.4020
Epoch 153/300
404/404 [==============================] - 0s 68us/step - loss: 0.3426 - mae: 0.3991
Epoch 154/300
404/404 [==============================] - 0s 93us/step - loss: 0.3437 - mae: 0.4046
Epoch 155/300
404/404 [==============================] - 0s 88us/step - loss: 0.3433 - mae: 0.4027
Epoch 156/300
404/404 [==============================] - 0s 219us/step - loss: 0.3433 - mae: 0.4023
Epoch 157/300
404/404 [==============================] - 0s 104us/step - loss: 0.3429 - mae: 0.4012
Epoch 158/300
404/404 [==============================] - 0s 117us/step - loss: 0.3438 - mae: 0.4029
Epoch 159/300
404/404 [==============================] - 0s 126us/step - loss: 0.3431 - mae: 0.4024
Epoch 160/300
404/404 [==============================] - 0s 89us/step - loss: 0.3436 - mae: 0.4018
Epoch 161/300
404/404 [==============================] - 0s 100us/step - loss: 0.3440 - mae: 0.4044
Epoch 162/300
404/404 [==============================] - 0s 181us/step - loss: 0.3428 - mae: 0.4013
Epoch 163/300
404/404 [==============================] - 0s 72us/step - loss: 0.3435 - mae: 0.4031
Epoch 164/300
404/404 [==============================] - 0s 76us/step - loss: 0.3426 - mae: 0.4001
Epoch 165/300
404/404 [==============================] - 0s 108us/step - loss: 0.3443 - mae: 0.4018
Epoch 166/300
404/404 [==============================] - 0s 77us/step - loss: 0.3425 - mae: 0.3993
Epoch 167/300
404/404 [==============================] - 0s 116us/step - loss: 0.3445 - mae: 0.4065
Epoch 168/300
404/404 [==============================] - 0s 109us/step - loss: 0.3427 - mae: 0.4014
Epoch 169/300
404/404 [==============================] - 0s 164us/step - loss: 0.3427 - mae: 0.3975
Epoch 170/300
404/404 [==============================] - 0s 208us/step - loss: 0.3428 - mae: 0.4019
Epoch 171/300
404/404 [==============================] - 0s 194us/step - loss: 0.3428 - mae: 0.4018
Epoch 172/300
404/404 [==============================] - 0s 122us/step - loss: 0.3434 - mae: 0.4012
Epoch 173/300
404/404 [==============================] - 0s 221us/step - loss: 0.3425 - mae: 0.4002
Epoch 174/300
404/404 [==============================] - 0s 98us/step - loss: 0.3443 - mae: 0.4053
Epoch 175/300
404/404 [==============================] - 0s 153us/step - loss: 0.3430 - mae: 0.3996
Epoch 176/300
404/404 [==============================] - 0s 106us/step - loss: 0.3435 - mae: 0.4020
Epoch 177/300
404/404 [==============================] - 0s 83us/step - loss: 0.3433 - mae: 0.4021
Epoch 178/300
404/404 [==============================] - 0s 158us/step - loss: 0.3434 - mae: 0.4018
Epoch 179/300
404/404 [==============================] - 0s 168us/step - loss: 0.3427 - mae: 0.4021
Epoch 180/300
404/404 [==============================] - 0s 189us/step - loss: 0.3435 - mae: 0.4024
Epoch 181/300
404/404 [==============================] - 0s 127us/step - loss: 0.3433 - mae: 0.4019
Epoch 182/300
404/404 [==============================] - 0s 180us/step - loss: 0.3431 - mae: 0.4014
Epoch 183/300
404/404 [==============================] - 0s 179us/step - loss: 0.3429 - mae: 0.3998
Epoch 184/300
404/404 [==============================] - 0s 62us/step - loss: 0.3430 - mae: 0.4006
Epoch 185/300
404/404 [==============================] - 0s 147us/step - loss: 0.3430 - mae: 0.4003
Epoch 186/300
404/404 [==============================] - 0s 86us/step - loss: 0.3428 - mae: 0.3997
Epoch 187/300
404/404 [==============================] - 0s 88us/step - loss: 0.3426 - mae: 0.3995
Epoch 188/300
404/404 [==============================] - 0s 185us/step - loss: 0.3436 - mae: 0.4005
Epoch 189/300
404/404 [==============================] - 0s 178us/step - loss: 0.3431 - mae: 0.4019
Epoch 190/300
404/404 [==============================] - 0s 108us/step - loss: 0.3419 - mae: 0.3982
Epoch 191/300
404/404 [==============================] - 0s 111us/step - loss: 0.3436 - mae: 0.4059
Epoch 192/300
404/404 [==============================] - 0s 117us/step - loss: 0.3431 - mae: 0.4012
Epoch 193/300
404/404 [==============================] - 0s 224us/step - loss: 0.3424 - mae: 0.3980
Epoch 194/300
404/404 [==============================] - 0s 117us/step - loss: 0.3430 - mae: 0.4022
Epoch 195/300
404/404 [==============================] - 0s 213us/step - loss: 0.3441 - mae: 0.4024
Epoch 196/300
404/404 [==============================] - 0s 135us/step - loss: 0.3419 - mae: 0.3966
Epoch 197/300
404/404 [==============================] - 0s 82us/step - loss: 0.3434 - mae: 0.4033
Epoch 198/300
404/404 [==============================] - 0s 129us/step - loss: 0.3423 - mae: 0.4000
Epoch 199/300
404/404 [==============================] - 0s 180us/step - loss: 0.3441 - mae: 0.4050
Epoch 200/300
404/404 [==============================] - 0s 88us/step - loss: 0.3428 - mae: 0.4001
Epoch 201/300
404/404 [==============================] - 0s 176us/step - loss: 0.3427 - mae: 0.4009
Epoch 202/300
404/404 [==============================] - 0s 103us/step - loss: 0.3422 - mae: 0.3985
Epoch 203/300
404/404 [==============================] - 0s 219us/step - loss: 0.3429 - mae: 0.4008
Epoch 204/300
404/404 [==============================] - 0s 82us/step - loss: 0.3419 - mae: 0.3975
Epoch 205/300
404/404 [==============================] - 0s 62us/step - loss: 0.3439 - mae: 0.4042
Epoch 206/300
404/404 [==============================] - 0s 117us/step - loss: 0.3418 - mae: 0.3973
Epoch 207/300
404/404 [==============================] - 0s 91us/step - loss: 0.3432 - mae: 0.4021
Epoch 208/300
404/404 [==============================] - 0s 60us/step - loss: 0.3436 - mae: 0.4039
Epoch 209/300
404/404 [==============================] - 0s 170us/step - loss: 0.3423 - mae: 0.3976
Epoch 210/300
404/404 [==============================] - 0s 84us/step - loss: 0.3429 - mae: 0.4025
Epoch 211/300
404/404 [==============================] - 0s 105us/step - loss: 0.3425 - mae: 0.4000
Epoch 212/300
404/404 [==============================] - 0s 88us/step - loss: 0.3429 - mae: 0.3990
Epoch 213/300
404/404 [==============================] - 0s 78us/step - loss: 0.3419 - mae: 0.3979
Epoch 214/300
404/404 [==============================] - 0s 107us/step - loss: 0.3431 - mae: 0.4028
Epoch 215/300
404/404 [==============================] - 0s 81us/step - loss: 0.3430 - mae: 0.3996
Epoch 216/300
404/404 [==============================] - 0s 88us/step - loss: 0.3425 - mae: 0.3988
Epoch 217/300
404/404 [==============================] - 0s 67us/step - loss: 0.3420 - mae: 0.3992
Epoch 218/300
404/404 [==============================] - 0s 87us/step - loss: 0.3425 - mae: 0.4016
Epoch 219/300
404/404 [==============================] - 0s 79us/step - loss: 0.3436 - mae: 0.4004
Epoch 220/300
404/404 [==============================] - 0s 123us/step - loss: 0.3418 - mae: 0.3983
Epoch 221/300
404/404 [==============================] - 0s 125us/step - loss: 0.3442 - mae: 0.4021
Epoch 222/300
404/404 [==============================] - 0s 62us/step - loss: 0.3423 - mae: 0.4005
Epoch 223/300
404/404 [==============================] - 0s 82us/step - loss: 0.3415 - mae: 0.3970
Epoch 224/300
404/404 [==============================] - 0s 78us/step - loss: 0.3432 - mae: 0.4030
Epoch 225/300
404/404 [==============================] - 0s 98us/step - loss: 0.3430 - mae: 0.4006
Epoch 226/300
404/404 [==============================] - 0s 111us/step - loss: 0.3429 - mae: 0.4007
Epoch 227/300
404/404 [==============================] - 0s 120us/step - loss: 0.3424 - mae: 0.4007
Epoch 228/300
404/404 [==============================] - 0s 72us/step - loss: 0.3426 - mae: 0.3984
Epoch 229/300
404/404 [==============================] - 0s 117us/step - loss: 0.3419 - mae: 0.3979
Epoch 230/300
404/404 [==============================] - 0s 98us/step - loss: 0.3436 - mae: 0.4034
Epoch 231/300
404/404 [==============================] - 0s 63us/step - loss: 0.3422 - mae: 0.3994
Epoch 232/300
404/404 [==============================] - 0s 107us/step - loss: 0.3430 - mae: 0.4020
Epoch 233/300
404/404 [==============================] - 0s 188us/step - loss: 0.3425 - mae: 0.3997
Epoch 234/300
404/404 [==============================] - 0s 100us/step - loss: 0.3426 - mae: 0.4005
Epoch 235/300
404/404 [==============================] - 0s 117us/step - loss: 0.3426 - mae: 0.3995
Epoch 236/300
404/404 [==============================] - 0s 175us/step - loss: 0.3421 - mae: 0.3980
Epoch 237/300
404/404 [==============================] - 0s 104us/step - loss: 0.3425 - mae: 0.4006
Epoch 238/300
404/404 [==============================] - 0s 71us/step - loss: 0.3423 - mae: 0.3982
Epoch 239/300
404/404 [==============================] - 0s 51us/step - loss: 0.3419 - mae: 0.4000
Epoch 240/300
404/404 [==============================] - 0s 74us/step - loss: 0.3440 - mae: 0.4016
Epoch 241/300
404/404 [==============================] - 0s 82us/step - loss: 0.3418 - mae: 0.3980
Epoch 242/300
404/404 [==============================] - 0s 122us/step - loss: 0.3416 - mae: 0.3971
Epoch 243/300
404/404 [==============================] - 0s 156us/step - loss: 0.3424 - mae: 0.3987
Epoch 244/300
404/404 [==============================] - 0s 58us/step - loss: 0.3424 - mae: 0.4002
Epoch 245/300
404/404 [==============================] - 0s 75us/step - loss: 0.3412 - mae: 0.3954
Epoch 246/300
404/404 [==============================] - 0s 80us/step - loss: 0.3421 - mae: 0.3997
Epoch 247/300
404/404 [==============================] - 0s 124us/step - loss: 0.3434 - mae: 0.4032
Epoch 248/300
404/404 [==============================] - 0s 109us/step - loss: 0.3424 - mae: 0.3991
Epoch 249/300
404/404 [==============================] - 0s 90us/step - loss: 0.3412 - mae: 0.3958
Epoch 250/300
404/404 [==============================] - 0s 77us/step - loss: 0.3427 - mae: 0.3999
Epoch 251/300
404/404 [==============================] - 0s 70us/step - loss: 0.3426 - mae: 0.4001
Epoch 252/300
404/404 [==============================] - 0s 80us/step - loss: 0.3418 - mae: 0.3980
Epoch 253/300
404/404 [==============================] - 0s 82us/step - loss: 0.3417 - mae: 0.3987
Epoch 254/300
404/404 [==============================] - 0s 207us/step - loss: 0.3431 - mae: 0.4014
Epoch 255/300
404/404 [==============================] - 0s 132us/step - loss: 0.3422 - mae: 0.3978
Epoch 256/300
404/404 [==============================] - 0s 128us/step - loss: 0.3416 - mae: 0.3966
Epoch 257/300
404/404 [==============================] - 0s 192us/step - loss: 0.3424 - mae: 0.3968
Epoch 258/300
404/404 [==============================] - 0s 99us/step - loss: 0.3418 - mae: 0.3971
Epoch 259/300
404/404 [==============================] - 0s 93us/step - loss: 0.3423 - mae: 0.3977
Epoch 260/300
404/404 [==============================] - 0s 84us/step - loss: 0.3422 - mae: 0.3994
Epoch 261/300
404/404 [==============================] - 0s 195us/step - loss: 0.3419 - mae: 0.3976
Epoch 262/300
404/404 [==============================] - 0s 121us/step - loss: 0.3413 - mae: 0.3943
Epoch 263/300
404/404 [==============================] - 0s 107us/step - loss: 0.3432 - mae: 0.4019
Epoch 264/300
404/404 [==============================] - 0s 56us/step - loss: 0.3414 - mae: 0.3955
Epoch 265/300
404/404 [==============================] - 0s 62us/step - loss: 0.3429 - mae: 0.3983
Epoch 266/300
404/404 [==============================] - 0s 87us/step - loss: 0.3422 - mae: 0.3986
Epoch 267/300
404/404 [==============================] - 0s 212us/step - loss: 0.3414 - mae: 0.3953
Epoch 268/300
404/404 [==============================] - 0s 176us/step - loss: 0.3423 - mae: 0.3989
Epoch 269/300
404/404 [==============================] - 0s 97us/step - loss: 0.3426 - mae: 0.3987
Epoch 270/300
404/404 [==============================] - 0s 175us/step - loss: 0.3409 - mae: 0.3932
Epoch 271/300
404/404 [==============================] - 0s 78us/step - loss: 0.3427 - mae: 0.4008
Epoch 272/300
404/404 [==============================] - 0s 74us/step - loss: 0.3423 - mae: 0.3999
Epoch 273/300
404/404 [==============================] - 0s 72us/step - loss: 0.3413 - mae: 0.3934
Epoch 274/300
404/404 [==============================] - 0s 111us/step - loss: 0.3429 - mae: 0.4001
Epoch 275/300
404/404 [==============================] - 0s 99us/step - loss: 0.3418 - mae: 0.3981
Epoch 276/300
404/404 [==============================] - 0s 138us/step - loss: 0.3424 - mae: 0.3986
Epoch 277/300
404/404 [==============================] - 0s 92us/step - loss: 0.3420 - mae: 0.3996
Epoch 278/300
404/404 [==============================] - 0s 99us/step - loss: 0.3430 - mae: 0.3979
Epoch 279/300
404/404 [==============================] - 0s 82us/step - loss: 0.3419 - mae: 0.3960
Epoch 280/300
404/404 [==============================] - 0s 96us/step - loss: 0.3416 - mae: 0.3967
Epoch 281/300
404/404 [==============================] - 0s 79us/step - loss: 0.3425 - mae: 0.3987
Epoch 282/300
404/404 [==============================] - 0s 89us/step - loss: 0.3420 - mae: 0.3963
Epoch 283/300
404/404 [==============================] - 0s 65us/step - loss: 0.3412 - mae: 0.3942
Epoch 284/300
404/404 [==============================] - 0s 78us/step - loss: 0.3416 - mae: 0.3955
Epoch 285/300
404/404 [==============================] - 0s 84us/step - loss: 0.3421 - mae: 0.3962
Epoch 286/300
404/404 [==============================] - 0s 88us/step - loss: 0.3418 - mae: 0.3975
Epoch 287/300
404/404 [==============================] - 0s 64us/step - loss: 0.3411 - mae: 0.3958
Epoch 288/300
404/404 [==============================] - 0s 105us/step - loss: 0.3429 - mae: 0.3992
Epoch 289/300
404/404 [==============================] - 0s 93us/step - loss: 0.3419 - mae: 0.3973
Epoch 290/300
404/404 [==============================] - 0s 129us/step - loss: 0.3409 - mae: 0.3931
Epoch 291/300
404/404 [==============================] - 0s 87us/step - loss: 0.3419 - mae: 0.3976
Epoch 292/300
404/404 [==============================] - 0s 128us/step - loss: 0.3416 - mae: 0.3982
Epoch 293/300
404/404 [==============================] - 0s 99us/step - loss: 0.3417 - mae: 0.3967
Epoch 294/300
404/404 [==============================] - 0s 93us/step - loss: 0.3413 - mae: 0.3946
Epoch 295/300
404/404 [==============================] - 0s 157us/step - loss: 0.3419 - mae: 0.3972
Epoch 296/300
404/404 [==============================] - 0s 113us/step - loss: 0.3426 - mae: 0.3958
Epoch 297/300
404/404 [==============================] - 0s 69us/step - loss: 0.3412 - mae: 0.3954
Epoch 298/300
404/404 [==============================] - 0s 75us/step - loss: 0.3419 - mae: 0.3972
Epoch 299/300
404/404 [==============================] - 0s 44us/step - loss: 0.3406 - mae: 0.3922
Epoch 300/300
404/404 [==============================] - 0s 54us/step - loss: 0.3423 - mae: 0.4000
102/102 [==============================] - 0s 309us/step
score: [0.44682052264026567, 0.4667647182941437]
r2 score: 0.45171405496274775