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ium_434788/IUM_5_434788_wersja_Jupyter.ipynb
Dominik Strzako fcc6e77ef0 Szybka poprawka zadania (dodanie zapisu predykcji)
2021-04-25 23:47:06 +02:00

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0. Imports and downloading the Data Frame

from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np

0.1. Wyczytanie pliku csv z mojego repo

!curl -OL https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv

wine=pd.read_csv('winequality-red.csv')
wine
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   98k  100   98k    0     0  66899      0  0:00:01  0:00:01 --:--:-- 66899
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5
1 7.8 0.880 0.00 2.6 0.098 25.0 67.0 0.99680 3.20 0.68 9.8 5
2 7.8 0.760 0.04 2.3 0.092 15.0 54.0 0.99700 3.26 0.65 9.8 5
3 11.2 0.280 0.56 1.9 0.075 17.0 60.0 0.99800 3.16 0.58 9.8 6
4 7.4 0.700 0.00 1.9 0.076 11.0 34.0 0.99780 3.51 0.56 9.4 5
... ... ... ... ... ... ... ... ... ... ... ... ...
1594 6.2 0.600 0.08 2.0 0.090 32.0 44.0 0.99490 3.45 0.58 10.5 5
1595 5.9 0.550 0.10 2.2 0.062 39.0 51.0 0.99512 3.52 0.76 11.2 6
1596 6.3 0.510 0.13 2.3 0.076 29.0 40.0 0.99574 3.42 0.75 11.0 6
1597 5.9 0.645 0.12 2.0 0.075 32.0 44.0 0.99547 3.57 0.71 10.2 5
1598 6.0 0.310 0.47 3.6 0.067 18.0 42.0 0.99549 3.39 0.66 11.0 6

1599 rows × 12 columns

1. Analiza zbioru

1.1. Heatmap by zbada korelacje. Z początku zastanawiałem się, czy nie wykorzystać tylko kolumn wysoko skorelowanych z 'Quality', jednak koniec końców model będzie się opierać o wszystkie kolumny

plt.figure(figsize=(10,6))
sns.heatmap(wine.corr(),annot=True)
plt.show()

2. Normalizacja i podział zbioru na Test/Train

2.1. 'y' to pojedyńcza kolumna z wartościami 'quality'

y = wine.quality
y.head()
0    5
1    5
2    5
3    6
4    5
Name: quality, dtype: int64

2.2. 'x' to wszystkie kolumny poza 'quality'

x = wine.drop(['quality'], axis= 1)
x.head()
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
0 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4
1 7.8 0.88 0.00 2.6 0.098 25.0 67.0 0.9968 3.20 0.68 9.8
2 7.8 0.76 0.04 2.3 0.092 15.0 54.0 0.9970 3.26 0.65 9.8
3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.9980 3.16 0.58 9.8
4 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4

2.3. Normalizacja wartości w x (do przedziału 0-1)

x=((x-x.min())/(x.max()-x.min()))
x.head()
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
0 0.247788 0.397260 0.00 0.068493 0.106845 0.140845 0.098940 0.567548 0.606299 0.137725 0.153846
1 0.283186 0.520548 0.00 0.116438 0.143573 0.338028 0.215548 0.494126 0.362205 0.209581 0.215385
2 0.283186 0.438356 0.04 0.095890 0.133556 0.197183 0.169611 0.508811 0.409449 0.191617 0.215385
3 0.584071 0.109589 0.56 0.068493 0.105175 0.225352 0.190813 0.582232 0.330709 0.149701 0.215385
4 0.247788 0.397260 0.00 0.068493 0.106845 0.140845 0.098940 0.567548 0.606299 0.137725 0.153846

2.4. Podział na zbiory testowe i treningowe (1:4)

x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
x_train.head()
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol
751 0.327434 0.363014 0.10 0.136986 0.128548 0.225352 0.120141 0.584435 0.433071 0.131737 0.169231
370 0.203540 0.441781 0.02 0.095890 0.085142 0.478873 0.201413 0.545521 0.653543 0.269461 0.230769
374 0.831858 0.198630 0.63 0.198630 0.128548 0.070423 0.144876 0.831865 0.212598 0.287425 0.369231
537 0.309735 0.482877 0.24 0.082192 0.120200 0.056338 0.024735 0.523495 0.496063 0.263473 0.353846
708 0.283186 0.291096 0.12 0.109589 0.093489 0.140845 0.102473 0.435389 0.472441 0.167665 0.492308

3. Model i jego trening (Tensorflow.Keras)

def regression_model():
    model = Sequential()
    model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
    model.add(Dense(64,activation = "relu"))
    model.add(Dense(1,activation = "relu"))
    
    model.compile(optimizer = "adam", loss = "mean_squared_error")
    return model
model = regression_model()
model.fit(x_train, y_train, epochs = 600, verbose = 1)
Epoch 1/600
40/40 [==============================] - 1s 1ms/step - loss: 27.8321
Epoch 2/600
40/40 [==============================] - 0s 1ms/step - loss: 7.2309
Epoch 3/600
40/40 [==============================] - 0s 1ms/step - loss: 1.0122
Epoch 4/600
40/40 [==============================] - 0s 1ms/step - loss: 0.8249
Epoch 5/600
40/40 [==============================] - 0s 1ms/step - loss: 0.8217
Epoch 6/600
40/40 [==============================] - 0s 1ms/step - loss: 0.7261
Epoch 7/600
40/40 [==============================] - 0s 2ms/step - loss: 0.6524
Epoch 8/600
40/40 [==============================] - 0s 1ms/step - loss: 0.6332
Epoch 9/600
40/40 [==============================] - 0s 1ms/step - loss: 0.6085
Epoch 10/600
40/40 [==============================] - 0s 2ms/step - loss: 0.5933
Epoch 11/600
40/40 [==============================] - 0s 2ms/step - loss: 0.5950
Epoch 12/600
40/40 [==============================] - 0s 1ms/step - loss: 0.6067
Epoch 13/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5047
Epoch 14/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5503
Epoch 15/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5120
Epoch 16/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5540
Epoch 17/600
40/40 [==============================] - 0s 2ms/step - loss: 0.5384
Epoch 18/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5129
Epoch 19/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4923
Epoch 20/600
40/40 [==============================] - 0s 1ms/step - loss: 0.5131
Epoch 21/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4585
Epoch 22/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4688
Epoch 23/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4497
Epoch 24/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4347
Epoch 25/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4830
Epoch 26/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4474
Epoch 27/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4599
Epoch 28/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4428
Epoch 29/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4375
Epoch 30/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4240
Epoch 31/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4524
Epoch 32/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4448
Epoch 33/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4163
Epoch 34/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4217
Epoch 35/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4117
Epoch 36/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4445
Epoch 37/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4249
Epoch 38/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4436
Epoch 39/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4290
Epoch 40/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4551
Epoch 41/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4315
Epoch 42/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3996
Epoch 43/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4504
Epoch 44/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4202
Epoch 45/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3944
Epoch 46/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3944
Epoch 47/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4092
Epoch 48/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4065
Epoch 49/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4056
Epoch 50/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4023
Epoch 51/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4188
Epoch 52/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3748
Epoch 53/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4079
Epoch 54/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3867
Epoch 55/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3920
Epoch 56/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4366
Epoch 57/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3996
Epoch 58/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3794
Epoch 59/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4243
Epoch 60/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4039
Epoch 61/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3504
Epoch 62/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4023
Epoch 63/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4076
Epoch 64/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4207
Epoch 65/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3727
Epoch 66/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4051
Epoch 67/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3956
Epoch 68/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3877
Epoch 69/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4053
Epoch 70/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3759
Epoch 71/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3773
Epoch 72/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3836
Epoch 73/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3573
Epoch 74/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4144
Epoch 75/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4208
Epoch 76/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3758
Epoch 77/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3989
Epoch 78/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3979
Epoch 79/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4047
Epoch 80/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4111
Epoch 81/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4047
Epoch 82/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4017
Epoch 83/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4451
Epoch 84/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3747
Epoch 85/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3734
Epoch 86/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4017
Epoch 87/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3886
Epoch 88/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4091
Epoch 89/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4020
Epoch 90/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4022
Epoch 91/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4048
Epoch 92/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3940
Epoch 93/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4150
Epoch 94/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4054
Epoch 95/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3523
Epoch 96/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3864
Epoch 97/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3790
Epoch 98/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3770
Epoch 99/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3783
Epoch 100/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3679
Epoch 101/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4155
Epoch 102/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3839
Epoch 103/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3859
Epoch 104/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3954
Epoch 105/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3609
Epoch 106/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4079
Epoch 107/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3977
Epoch 108/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3993
Epoch 109/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3991
Epoch 110/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3661
Epoch 111/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3566
Epoch 112/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3867
Epoch 113/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3705
Epoch 114/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3845
Epoch 115/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3792
Epoch 116/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3857
Epoch 117/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3865
Epoch 118/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3643
Epoch 119/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3673
Epoch 120/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4002
Epoch 121/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3690
Epoch 122/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3719
Epoch 123/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3939
Epoch 124/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4074
Epoch 125/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3835
Epoch 126/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4051
Epoch 127/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3629
Epoch 128/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3653
Epoch 129/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3473
Epoch 130/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3980
Epoch 131/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3647
Epoch 132/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3636
Epoch 133/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4010
Epoch 134/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3511
Epoch 135/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3899
Epoch 136/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3951
Epoch 137/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4010
Epoch 138/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3930
Epoch 139/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3711
Epoch 140/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3936
Epoch 141/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3907
Epoch 142/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3713
Epoch 143/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3592
Epoch 144/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3772
Epoch 145/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3981
Epoch 146/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3858
Epoch 147/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3546
Epoch 148/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3882
Epoch 149/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3635
Epoch 150/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3822
Epoch 151/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4120
Epoch 152/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3808
Epoch 153/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3656
Epoch 154/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3948
Epoch 155/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3483
Epoch 156/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3825
Epoch 157/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3450
Epoch 158/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3664
Epoch 159/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3530
Epoch 160/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3596
Epoch 161/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3659
Epoch 162/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3858
Epoch 163/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4161
Epoch 164/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3519
Epoch 165/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3834
Epoch 166/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3766
Epoch 167/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3909
Epoch 168/600
40/40 [==============================] - 0s 1ms/step - loss: 0.4038
Epoch 169/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3815
Epoch 170/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3823
Epoch 171/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3907
Epoch 172/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3637
Epoch 173/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3535
Epoch 174/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3484
Epoch 175/600
40/40 [==============================] - 0s 2ms/step - loss: 0.4050
Epoch 176/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3680
Epoch 177/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3710
Epoch 178/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3292
Epoch 179/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3750
Epoch 180/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3701
Epoch 181/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3613
Epoch 182/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3442
Epoch 183/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3786
Epoch 184/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3524
Epoch 185/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3703
Epoch 186/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3841
Epoch 187/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3517
Epoch 188/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3699
Epoch 189/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3567
Epoch 190/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3600
Epoch 191/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3606
Epoch 192/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3604
Epoch 193/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3902
Epoch 194/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3926
Epoch 195/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3976
Epoch 196/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3466
Epoch 197/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3872
Epoch 198/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3598
Epoch 199/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3616
Epoch 200/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3502
Epoch 201/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3880
Epoch 202/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3623
Epoch 203/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3724
Epoch 204/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3723
Epoch 205/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3580
Epoch 206/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3528
Epoch 207/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3621
Epoch 208/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3672
Epoch 209/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3613
Epoch 210/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3419
Epoch 211/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3778
Epoch 212/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3543
Epoch 213/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3638
Epoch 214/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3585
Epoch 215/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3508
Epoch 216/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3515
Epoch 217/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3430
Epoch 218/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3368
Epoch 219/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3448
Epoch 220/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3731
Epoch 221/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3654
Epoch 222/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3466
Epoch 223/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3498
Epoch 224/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3458
Epoch 225/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3740
Epoch 226/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3739
Epoch 227/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3356
Epoch 228/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3836
Epoch 229/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3621
Epoch 230/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3540
Epoch 231/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3454
Epoch 232/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3459
Epoch 233/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3736
Epoch 234/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3534
Epoch 235/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3818
Epoch 236/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3530
Epoch 237/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3725
Epoch 238/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3523
Epoch 239/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3500
Epoch 240/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3866
Epoch 241/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3375
Epoch 242/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3650
Epoch 243/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3788
Epoch 244/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3471
Epoch 245/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3462
Epoch 246/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3462
Epoch 247/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3628
Epoch 248/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3379
Epoch 249/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3776
Epoch 250/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3356
Epoch 251/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3577
Epoch 252/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3560
Epoch 253/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3786
Epoch 254/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3528
Epoch 255/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3808
Epoch 256/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3601
Epoch 257/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3587
Epoch 258/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3681
Epoch 259/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3386
Epoch 260/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3573
Epoch 261/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3669
Epoch 262/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3712
Epoch 263/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3560
Epoch 264/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3531
Epoch 265/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3293
Epoch 266/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3404
Epoch 267/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3455
Epoch 268/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3456
Epoch 269/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3568
Epoch 270/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3634
Epoch 271/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3354
Epoch 272/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3398
Epoch 273/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3537
Epoch 274/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3198
Epoch 275/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3313
Epoch 276/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3686
Epoch 277/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3463
Epoch 278/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3801
Epoch 279/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3593
Epoch 280/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3443
Epoch 281/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3401
Epoch 282/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3547
Epoch 283/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3571
Epoch 284/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3622
Epoch 285/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3451
Epoch 286/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3405
Epoch 287/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3550
Epoch 288/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3467
Epoch 289/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3271
Epoch 290/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3654
Epoch 291/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3436
Epoch 292/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3487
Epoch 293/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3580
Epoch 294/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3635
Epoch 295/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3222
Epoch 296/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3635
Epoch 297/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3755
Epoch 298/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3435
Epoch 299/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3492
Epoch 300/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3568
Epoch 301/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3517
Epoch 302/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3572
Epoch 303/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3537
Epoch 304/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3451
Epoch 305/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3365
Epoch 306/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3480
Epoch 307/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3329
Epoch 308/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3326
Epoch 309/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3350
Epoch 310/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3417
Epoch 311/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3374
Epoch 312/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3435
Epoch 313/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3292
Epoch 314/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3830
Epoch 315/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3492
Epoch 316/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3439
Epoch 317/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3609
Epoch 318/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3797
Epoch 319/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3569
Epoch 320/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3445
Epoch 321/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3296
Epoch 322/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3218
Epoch 323/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3325
Epoch 324/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3528
Epoch 325/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3351
Epoch 326/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3734
Epoch 327/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3268
Epoch 328/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3392
Epoch 329/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3393
Epoch 330/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3390
Epoch 331/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3291
Epoch 332/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3260
Epoch 333/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3345
Epoch 334/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3749
Epoch 335/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3545
Epoch 336/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3572
Epoch 337/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3491
Epoch 338/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3377
Epoch 339/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3574
Epoch 340/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3359
Epoch 341/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3532
Epoch 342/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3286
Epoch 343/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3675
Epoch 344/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3198
Epoch 345/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3613
Epoch 346/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3592
Epoch 347/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3624
Epoch 348/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3433
Epoch 349/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3433
Epoch 350/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3301
Epoch 351/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3418
Epoch 352/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3668
Epoch 353/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3247
Epoch 354/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3316
Epoch 355/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3367
Epoch 356/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3361
Epoch 357/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3343
Epoch 358/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3458
Epoch 359/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3431
Epoch 360/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3334
Epoch 361/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3458
Epoch 362/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3419
Epoch 363/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3463
Epoch 364/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3459
Epoch 365/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3305
Epoch 366/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3334
Epoch 367/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3300
Epoch 368/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3148
Epoch 369/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3328
Epoch 370/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3490
Epoch 371/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3349
Epoch 372/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3434
Epoch 373/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3631
Epoch 374/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3490
Epoch 375/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3435
Epoch 376/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3392
Epoch 377/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3440
Epoch 378/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3293
Epoch 379/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3160
Epoch 380/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3524
Epoch 381/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3347
Epoch 382/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3362
Epoch 383/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3412
Epoch 384/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3644
Epoch 385/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3231
Epoch 386/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3175
Epoch 387/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3360
Epoch 388/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3483
Epoch 389/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3323
Epoch 390/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3199
Epoch 391/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3390
Epoch 392/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3240
Epoch 393/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3285
Epoch 394/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3487
Epoch 395/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3266
Epoch 396/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3350
Epoch 397/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3294
Epoch 398/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3381
Epoch 399/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3154
Epoch 400/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3376
Epoch 401/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3182
Epoch 402/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3313
Epoch 403/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3382
Epoch 404/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3663
Epoch 405/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3355
Epoch 406/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3389
Epoch 407/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3132
Epoch 408/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3170
Epoch 409/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3150
Epoch 410/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3280
Epoch 411/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3339
Epoch 412/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3430
Epoch 413/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3446
Epoch 414/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3243
Epoch 415/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3232
Epoch 416/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3219
Epoch 417/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3019
Epoch 418/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3173
Epoch 419/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3165
Epoch 420/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3282
Epoch 421/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3027
Epoch 422/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3313
Epoch 423/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3453
Epoch 424/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3370
Epoch 425/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3364
Epoch 426/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3247
Epoch 427/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3346
Epoch 428/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3077
Epoch 429/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3195
Epoch 430/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2898
Epoch 431/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3086
Epoch 432/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3100
Epoch 433/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3305
Epoch 434/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3371
Epoch 435/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3308
Epoch 436/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2980
Epoch 437/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3129
Epoch 438/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3332
Epoch 439/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3368
Epoch 440/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3274
Epoch 441/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3208
Epoch 442/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3206
Epoch 443/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3280
Epoch 444/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3386
Epoch 445/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3297
Epoch 446/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3365
Epoch 447/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3030
Epoch 448/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3231
Epoch 449/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3107
Epoch 450/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3288
Epoch 451/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3194
Epoch 452/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3037
Epoch 453/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3168
Epoch 454/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2999
Epoch 455/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3104
Epoch 456/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2976
Epoch 457/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3572
Epoch 458/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3177
Epoch 459/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3102
Epoch 460/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3087
Epoch 461/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3039
Epoch 462/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3127
Epoch 463/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3300
Epoch 464/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3243
Epoch 465/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3106
Epoch 466/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2922
Epoch 467/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3300
Epoch 468/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3051
Epoch 469/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2999
Epoch 470/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3099
Epoch 471/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3122
Epoch 472/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3105
Epoch 473/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3167
Epoch 474/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3227
Epoch 475/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3039
Epoch 476/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3167
Epoch 477/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3083
Epoch 478/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3280
Epoch 479/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3070
Epoch 480/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3247
Epoch 481/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3205
Epoch 482/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3219
Epoch 483/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3158
Epoch 484/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3384
Epoch 485/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2816
Epoch 486/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3286
Epoch 487/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3357
Epoch 488/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3076
Epoch 489/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3206
Epoch 490/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3173
Epoch 491/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3251
Epoch 492/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3083
Epoch 493/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3154
Epoch 494/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3128
Epoch 495/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3146
Epoch 496/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3092
Epoch 497/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3007
Epoch 498/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3117
Epoch 499/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3241
Epoch 500/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3224
Epoch 501/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3187
Epoch 502/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3159
Epoch 503/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3144
Epoch 504/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3196
Epoch 505/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3058
Epoch 506/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3054
Epoch 507/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3240
Epoch 508/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3082
Epoch 509/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2909
Epoch 510/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3181
Epoch 511/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3180
Epoch 512/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3373
Epoch 513/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3207
Epoch 514/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3228
Epoch 515/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3178
Epoch 516/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3052
Epoch 517/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3281
Epoch 518/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3052
Epoch 519/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3119
Epoch 520/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2770
Epoch 521/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3306
Epoch 522/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3159
Epoch 523/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3191
Epoch 524/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3369
Epoch 525/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3080
Epoch 526/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3014
Epoch 527/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3012
Epoch 528/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3150
Epoch 529/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3105
Epoch 530/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3236
Epoch 531/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3102
Epoch 532/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3199
Epoch 533/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2913
Epoch 534/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2913
Epoch 535/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3063
Epoch 536/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3174
Epoch 537/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3165
Epoch 538/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3277
Epoch 539/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3098
Epoch 540/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3196
Epoch 541/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3007
Epoch 542/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3001
Epoch 543/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3018
Epoch 544/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2872
Epoch 545/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2685
Epoch 546/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3197
Epoch 547/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3055
Epoch 548/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3225
Epoch 549/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3167
Epoch 550/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3164
Epoch 551/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3104
Epoch 552/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3093
Epoch 553/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3062
Epoch 554/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3225
Epoch 555/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3169
Epoch 556/600
40/40 [==============================] - 0s 1ms/step - loss: 0.2989
Epoch 557/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2997
Epoch 558/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3211
Epoch 559/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3092
Epoch 560/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3128
Epoch 561/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3047
Epoch 562/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3100
Epoch 563/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3143
Epoch 564/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2766
Epoch 565/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3003
Epoch 566/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3040
Epoch 567/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2720
Epoch 568/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3156
Epoch 569/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3182
Epoch 570/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3047
Epoch 571/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3074
Epoch 572/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3250
Epoch 573/600
40/40 [==============================] - 0s 1ms/step - loss: 0.2953
Epoch 574/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2828
Epoch 575/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2882
Epoch 576/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2914
Epoch 577/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3143
Epoch 578/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2871
Epoch 579/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2677
Epoch 580/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3053
Epoch 581/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2921
Epoch 582/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3074
Epoch 583/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3053
Epoch 584/600
40/40 [==============================] - 0s 3ms/step - loss: 0.2888
Epoch 585/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3111
Epoch 586/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3032
Epoch 587/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2958
Epoch 588/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3009
Epoch 589/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3176
Epoch 590/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2913
Epoch 591/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2803
Epoch 592/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2720
Epoch 593/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2856
Epoch 594/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3113
Epoch 595/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2881
Epoch 596/600
40/40 [==============================] - 0s 2ms/step - loss: 0.3043
Epoch 597/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2897
Epoch 598/600
40/40 [==============================] - 0s 1ms/step - loss: 0.3105
Epoch 599/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2815
Epoch 600/600
40/40 [==============================] - 0s 2ms/step - loss: 0.2928

<tensorflow.python.keras.callbacks.History at 0x7fafd9388bd0>

4. Predykcje, Pokrycie, Precyzja i F-Score (+ Zapisanie y_pred)

y_pred = model.predict(x_test)

y_pred[:5]
array([[5.852079 ],
       [5.9662743],
       [5.219407 ],
       [5.5860786],
       [6.314252 ]], dtype=float32)
y_pred = np.around(y_pred, decimals=0)

y_pred[:5]

pd.DataFrame(y_pred).to_csv("preds.csv")
accuracy_score(y_test, y_pred)
0.603125
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred)) 
              precision    recall  f1-score   support

         1.0       0.00      0.00      0.00         0
         3.0       0.00      0.00      0.00         1
         4.0       0.00      0.00      0.00         6
         5.0       0.75      0.62      0.68       152
         6.0       0.49      0.70      0.58       115
         7.0       0.66      0.47      0.55        40
         8.0       0.00      0.00      0.00         6

    accuracy                           0.60       320
   macro avg       0.27      0.26      0.26       320
weighted avg       0.61      0.60      0.60       320


/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))