Wyjaśnialność modeli - XAI - tutorial

Wstęp

Rozwój uczenia maszynowego oraz większe moce obliczeniowe, którymi dzisiaj dysponujemy doprowadziły do powstania modeli z ilością parametrów niemozliwą do objęcią umysłem przez człowieka, wciąż jednak potrzebujemy rozumiec działanie modeli. Zadanie to, łatwe gdy myślimy o regresji liniowej, gdzie przyrost zmiennej objaśniającej o jednostkę powoduje liniowy wzrost zmiennej odpowiedzi o wartość współczynnika przeradza się w poważne wyzwanie gdy chcemy zrozumieć wpływ danego piksela na sieć neuronową klasyfikującą obrazy, która ma miliony parametrów.

To wyzwanie oczywiście musimy podjąć z kilku przyczyn. Pierwszą z nich jest biznesowa konieczność zrozumienia działania modelu. Jeśli mamy w zamiarze wykorzystywać model do wspierania decyzji o biznesowym znaczeniu, musimy rozumieć dlaczego predykcje dawane przez nasz model są takie a nie inne. Zwiększa do także zaufanie docelowych użytkowników modelu. Kolejną ważną kwestia jest debugowanie modelu. Wyjaśnialność mówi nam jakie cechy powodują że predykcje sa jakie są, a to pozwala nam rozwiązać ewentualne niewłaściwe działanie modelu.

W tym module pochylimy się nad kwestią wyjaśnialności modeli uczenia maszynowego.

Modele white box i black box

Modele uczenie maszynowego możemy podzielić na modele czarnopudełkowe (ang. black box) oraz modele białopudełkowe (ang. white box). Podstawową różnicą między modelami czarnopudełkowymi a tymi biało jest nasza zdolność wejrzenia w wewnętrzności modelu. W przypadku modelu białopudełkowego łatwo możemy zrozumieć co w danym modelu odpowiada za taką a nie inną predykcję. Dobrym przykładem moga być współczynniki w regresji liniowej, gdzie wzrost o jednostkę zmiennej objaśniającej powoduje wzrost przedykcji o wartość współczynnika przy wspomnianej zmiennej objaśniającej. Bardziej obrazowo wyobraźmy sobie prosty model regresji linowej prognozujący cenę \(m^{2}\) mieszkania w zalezności od odległości od centrum. Jeżeli współczynnik przy odległości od centrum to -300, wtedy przyw zroście odległości o kilometr prognoza ceny spadnie o 300 zł. W modelu białopudełkowym widzimy dokładnie jak jego elementy wpływaja na ostateczną predykcję.

Na rysunku przedstawiono schemat modelu white box. Jego wewnętrzności są dla nas przejrzyste, stąd nazwa.

Przejdźmy teraz do modelu czarnopudełkowych. Podstawową różnicą między nimi a omawianymi uprzednio modelami białopudełkowymi jest brak łatwego wejrzenia w wenętrzne działanie modelu czarnopudełkowego. Niekoniecznie chodzi tutaj o brak teoretycznego zrozumienia działania owego czarnopudełkowego, a raczej o brak wyjaśnienia jak współczynniki wpływaja na końcowe predykcje. Mamy zatem dane na wejściu, mamy prognoze na wyjściu, ale nie wiemy co jest w środku naszego pudełka.

Na rysunku przedstawiono schemat modelu white box. Jego wewnętrzności są zasłonięte przed naszym wejrzeniem, stąd nazwa.

Uwaga!

Warto podkreślić że, gdy myślimy o modelach biało i czarnopudełkowych, nie mamy na myśli dwóch rozłącznych kategorii, a raczej kontinuum wyjaśnialności, od bardziej białych łatwo wyjasnialnych modeli, do tych bardziej czarnych, trudnych w wyjaśnieniu

Na rysunku widzimy schematyczne przedstaweinie kontinuum czarnopudełkowości modelu w data science.

Przejdźmy do przykładowych modeli, które możemy zaklasyfikowac do grupy czarno lub biało pudełkowych. Najbardziej znane i czesto używane znajdziemy w tabeli poniżej. Jak możemy zobaczyć wśród obu typów modeli o których mówimy w tym rozdziale znajdziemy zarówno klasyfikatory jak i regresory.

Modele białopudełkowe	Modele czarnopudełkowe
Regresja liniowa	Maszyna wektorów nośnych
Regresja logistyczna	Sieć neuronowa
Drzewo decyzyjne	Drzewa wzmacniane
	Las losowy

Wyjaśnialność

W poprzednim rozdziale wprowadziliśmy podział na modele czarno i biało pudełkowo. O ile w przypadku modeli białopudełkowych tak jak juz wspomniano wyjasnienie predykcji otrzymywanych przez model nie jest problematyczne o tyle dla modeli czarnopudełkowych zastosowanie metod wyjaśnialności jest konieczne dla zrozumienia i interpretacji modelu. Gdy mówimy o wyjasnialności powinniśmy rozróżnić jej dwa rodzaje. Pierwszym jej typem jest wyjaśnialność lokalna - chcemy wiedzieć jakie cechy powodują określoną wartość predykcji, kolejnymi są wyjaśnialność kohorty czyli wyjaśnienia dla podzbioru danych cechujących się określoną charakterystyką zaś ostatnim wyjaśnialność globalna, gdzie chcemy ogólne znaczenie cech na przestrzeni wszystkich predykcji. Zobarazujmy tą różnice na ilustracji. Wyobraźmy że stworzylismy model . Niebieskim otoczono wszystkie przypadki - ich dotyczy wyjasnialność globalna. Na fioletowo zaznaczono jedna subklasę - jej dotyczyłaby wyjaśnialność kohorty, a na żółto pojedynczy przypadek stylokoloru

Schemat wyjasnialność globalna/kohorty/lokalna

Na początku przyjrzymy się metodą globalnej wyjaśnialności

Wykres cząstkowej zależności (ang. partial dependence plot)

Wykres cząstkowej zależności pokazuje nam marginalny wpływ jednej bądź też dwóch zmiennych na prognozowany wynik modelu uczenia maszynowego. Tego typu wykres pozwala nam zwizualizowac relację pomiędzy zmmienną celu a predyktorami. Wprowadźmy sobie funkcję cząstkowej zależności (ang. partial dependence function) \(f_{S}\).

Funkcja cząstkowej zależności wyraża się wzorem

\[ f_{S}(x_{S}) = E_{X_{C}} [f(x_{S}, X_{C})] = \int f(x_{S},x_{C})d\rho(x_{C}) \]

gdzie \(x_{S}\) to wartość cech dla których chcemy stworzyc wykres zalezności cząstkowej, natomiast zmienna losowa \(X_{C}\), to inne cechy użyte w modelu. Przez f oznaczamy funkcje modelu uczenia maszynowego, natomiast \(\rho\) to miara rozkładu prawdopodobieństwa dla \(X_{C}\) W ten sposób otrzymujemy zależnośc tylko między interesujacymi nas wartościami cech S, a wartościa predykcji modelu. Estymujemy funkcję \(f_{S}\) metodą Monte Carlo. Wzór na tą estymację \(\hat{f}_{S}\), to:

\[ \hat{f}_{S}(x_{S}) = \frac{1}{n}\sum_{i=1}{n}f(x_{S}, x_{C}^{i}) \]

gdzie n to ilość obserwacji w naszym zbiorze treningowym, naotmiast \(x_{C}^{i}\), to rzeczywiste wartości zmiennych objaśniających \(x_{S}\) w zbiorze treningowym

Demonstracja na przykładowym zbiorze

Dzaiłanie wykresu cząstkowej zależności sprawdzimy na wbudowanym w pakiet scikit-learn zbiorze California housing, który zawiera dane z amerykańskiego spisu powszechnego z roku 1990. Dane są na poziomie bloku - najmniejszej geograficznej jesdostki dla której amerykański urząd stastystyczny publikuje próbki danych. Taki blok zazwyczaj zmieszkuje w granicach 600 do 3000 osób.

W zbiorze mamy nastepujące zmienne objaśniające:

• MedInc mediana dochodu w bloku

• HouseAge mediana wieku domu w bloku

• AveRooms średnia ilość pokojów na gospodarstwo domowe

• AveBedrms średnia ilość sypialni na gospodarstwo domowe

• AveOccup średnia ilość członków gospodarstwa domowego

• Latitude szerokość geograficzna

• Longitude długość geograficzna

a zmienną przewidywaną jest mediana wartości domu w bloku wyrażona w setkach tysięcy dolarów

Przygotujmy dane:

import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split


cal_housing = fetch_california_housing()
X = pd.DataFrame(cal_housing.data, columns=cal_housing.feature_names)
y = cal_housing.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

Prezentują sie one w następujący sposób

X_train.head()

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude
2255	3.1250	16.0	5.380071	1.058201	3407.0	3.004409	36.80	-119.83
17341	2.0508	11.0	4.993884	1.064220	504.0	1.541284	34.86	-120.40
11589	5.1061	26.0	6.714765	1.013423	836.0	2.805369	33.78	-118.03
13635	2.3750	38.0	4.307065	0.937500	1347.0	3.660326	34.09	-117.32
693	2.1552	23.0	3.812641	1.040632	828.0	1.869074	37.70	-122.11

y_train[:5]

array([0.808, 2.75 , 2.575, 0.753, 1.614])

Następnie stwórzmy nieinterpretowalny model. Wykorzystamy sieci neuronowe.

from sklearn.preprocessing import QuantileTransformer
from sklearn.pipeline import Pipeline
from sklearn.neural_network import MLPRegressor

pipe = Pipeline([('qtran', QuantileTransformer()), ('mlpreg', MLPRegressor(
        hidden_layer_sizes=(50, 50), learning_rate_init=0.01, early_stopping=True
    ))])

pipe.fit(X_train, y_train)

Pipeline(steps=[('qtran', QuantileTransformer()),
                ('mlpreg',
                 MLPRegressor(early_stopping=True, hidden_layer_sizes=(50, 50),
                              learning_rate_init=0.01))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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from sklearn.metrics import mean_squared_error

pred = pipe.predict(X_test)

print(f'Błąd średniokwadratowy dla modelu sieci neuronowej to {mean_squared_error(pred, y_test):.2f}')

Błąd średniokwadratowy dla modelu sieci neuronowej to 0.28

import numpy as np
import plotly.graph_objects as go

unique_vals = sorted(np.unique(X_train['HouseAge'].values))
X_train_copy = X_train.copy(deep=True)
y = []
for val in unique_vals:
    X_train_copy['HouseAge'] = val
    y.append(np.average(pipe.predict(X_train_copy)))

scatter = go.Scatter(x=unique_vals, y=y)

fig = go.Figure(data=[scatter], layout=go.Layout(title={'text': 'Wykres cząstkowej zależności'},
                                                 xaxis={'title': 'Wiek domu'}, yaxis={'title': 'Wartość domu'}))

fig.show()

Na wykresie widzimy że według wykresu cząstkowej zależności wiek domu dodatnio wpływa na jego wartość Wykres cząstkowej zalezności pozwala nam na bardzo intuicyjne zwizualizowanie zależności predykowanych przez nasz model. Ma też jednak swoje wady, naczelną z nich jest założenie o niezalezności zmiennej objasniającej. W rzeczywistości niekoniecznie tak może być. Na przykład w miejscach o okreslonej szerokości i długości geograficznej może nie być zadnego nowego budownictwa. Wielkość domu może być skorelowana z jego wiekiem. Widzimy zatem, że do wykresów cząstkowej zależności trzeba podchodzic z pewną rezerwą.

Local interpretable model-agnostic explanation (LIME)

Przejdźmy teraz do kolejnej metody objaśniania modeli. Tym razem użyjemy metody LIME. Jest to metoda lokalnego objaśniania modeli, która wyjaśnia przyczyny otrzymania takiej prognozy dla określonego punktu. Metoda LIME opiera się o modele zastępcze. Oznacza to że w otoczeniu punktu który chcelibyśmy wyjaśnić generujemy dodatkowe punkty i uczymy na nich prostszy interpetowalny model, czyli np. regrsję wielomianową, bądź też drzewo decyzyjne, który wyjaśni nam lokalne działanie naszego modelu, czyli będzie lokalnie wierny. Matematycznie wzór na takie wyjaśnienie \(\xi\) w punkcie x wyraża się wzorem:

\[ \xi(x) = argmin_{g \in G} L(f,g, \pi_{x}) + \Omega(g) \]

gdzie \(\xi\) to funkcja z rodziny funkcji G (np. wielomiany co najwyżej drugiego stopnia), L to funkcja wierności odwzorowania, na przykład możemy tu użyć błędu średniokwadratowego, natomiast \(\Omega\) jest miarą złożoności funkcji - np może to być stopień wielomianu funkcji pomocniczej, \(\pi_{x}\) zaś jesto otoczeniem punktu \(x\) dla którego chcemy uzyskac objaśnienie

Przepis na trenowanie lokalnych modeli pomocniczych wygląda nastepująco

• wybranie przypadku dla którego chcielibysmy wyjasnić prognozy modelu czarnopudełkowego

• wygeneruj nowe punkty w pobliżu punktu \(x\)

• wyznacz wagi dla nowych punktów w zalezności od ich odległości

• wytenoważ ważony interpretowalny model na zbiorze danych z wylosowanymi punktami

• objaśnij predykcje iterpretując lokalny model

Dobrze to ilustruje wykres zamieszczony za Interpretable Machine Learning Christopha Molnara.

Problem przedstawiony na wykresie powyżej to problem klasyfikacji binarnej ze złożoną granicą decyzji oddzielającą od siebie kategorie. Na podwykresie A widzimy wspomiana granice decyzyjną. W kroku B generujemy losowe punkty. W korku C nadajemy im wagi odpowiednie do odległośc od punktu dla którego szukamy wyjasnienia (żółty punkty). W D widzimy wynik działania LIME - wierny lokalnie model liniowy objaśniający predykcje.

Przykład

Przejzmy teraz do przykładu uzycia LIME. Spróbujemy wyznaczyć wyjaśnienia prognoz dla problemu klasyfikacji zdjęć, korzystająć ze zbioru danych cifar 10 wbudowanego w pakiet służący do budowy m.in sieci neuronowych keras. Cifar 10 zawiera 60 tysięcy zdjęć z 10 klas, po 6 tysięcy na klasę. Zbiór jest podzielony na trening (50 tysięcy zdjęć) oraz test (10 tysięcy zdjęć). Wśród nich mamy następujące klasy: samolot, samochód, ptak, kot, sarna, pies, żaba, koń,statek, ciężarówka. Stwórzmy wpierw obiekty z danymi.

from tensorflow.keras.datasets import cifar10
from tensorflow import keras
(X_train, y_train), (X_test, y_test) = cifar10.load_data() # ładujemy dane

C:\Users\knajmajer\AppData\Local\anaconda3\envs\jbook\Lib\site-packages\keras\src\export\tf2onnx_lib.py:8: FutureWarning:

In the future `np.object` will be defined as the corresponding NumPy scalar.

cifar_10_cats_dict = {0: 'samolot',
                      1: 'samochód',
                      2: 'ptak',
                      3: 'kot',
                      4: 'sarna',
                      5: 'pies',
                      6: 'żaba',
                      7: 'koń',
                      8: 'statek',
                      9: 'ciężarówka'}

Zobaczmy jak wygląda przykładowy obraz ze zbioru danych

import plotly.express as px
print(f' Na wykresie mamy zdjęcie kategorii: {cifar_10_cats_dict[y_train[1][0]]}')
fig = px.imshow(X_train[1], width = 400, height = 400)
fig.show()

 Na wykresie mamy zdjęcie kategorii: ciężarówka

Przygotujmy dane do uczenia modelu

X_train = X_train.astype("float32") / 255
X_test = X_test.astype("float32") / 255

y_train = keras.utils.to_categorical(y_train, 10) # mamy 10 kategorii
y_test = keras.utils.to_categorical(y_test, 10)

Uczymy model. Używamy sekwencyjnego API kerasa w celu wyuczenia konwolucyjnej sieci neuronowej

model = keras.Sequential(
    [keras.Input(shape=(32, 32, 3)),
        keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        keras.layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Flatten(),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(10, activation="softmax"),
     ]) # definicja modelu

batch_size = 128
epochs = 15

model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])

model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1); # trening modelu

Epoch 1/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 26:37 5s/step - accuracy: 0.1016 - loss: 2.3163


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176/352 ━━━━━━━━━━━━━━━━━━━━ 21s 125ms/step - accuracy: 0.2352 - loss: 2.0396


178/352 ━━━━━━━━━━━━━━━━━━━━ 21s 124ms/step - accuracy: 0.2361 - loss: 2.0375


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186/352 ━━━━━━━━━━━━━━━━━━━━ 20s 121ms/step - accuracy: 0.2395 - loss: 2.0293


187/352 ━━━━━━━━━━━━━━━━━━━━ 19s 120ms/step - accuracy: 0.2399 - loss: 2.0283


189/352 ━━━━━━━━━━━━━━━━━━━━ 19s 120ms/step - accuracy: 0.2407 - loss: 2.0263


190/352 ━━━━━━━━━━━━━━━━━━━━ 19s 119ms/step - accuracy: 0.2411 - loss: 2.0253


192/352 ━━━━━━━━━━━━━━━━━━━━ 18s 119ms/step - accuracy: 0.2419 - loss: 2.0234


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196/352 ━━━━━━━━━━━━━━━━━━━━ 18s 117ms/step - accuracy: 0.2436 - loss: 2.0195


198/352 ━━━━━━━━━━━━━━━━━━━━ 17s 116ms/step - accuracy: 0.2443 - loss: 2.0176


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202/352 ━━━━━━━━━━━━━━━━━━━━ 17s 115ms/step - accuracy: 0.2459 - loss: 2.0138


204/352 ━━━━━━━━━━━━━━━━━━━━ 16s 114ms/step - accuracy: 0.2467 - loss: 2.0119


206/352 ━━━━━━━━━━━━━━━━━━━━ 16s 114ms/step - accuracy: 0.2474 - loss: 2.0101


207/352 ━━━━━━━━━━━━━━━━━━━━ 16s 113ms/step - accuracy: 0.2478 - loss: 2.0092


209/352 ━━━━━━━━━━━━━━━━━━━━ 16s 113ms/step - accuracy: 0.2486 - loss: 2.0074


210/352 ━━━━━━━━━━━━━━━━━━━━ 15s 112ms/step - accuracy: 0.2490 - loss: 2.0065


212/352 ━━━━━━━━━━━━━━━━━━━━ 15s 112ms/step - accuracy: 0.2497 - loss: 2.0047


214/352 ━━━━━━━━━━━━━━━━━━━━ 15s 111ms/step - accuracy: 0.2504 - loss: 2.0029


216/352 ━━━━━━━━━━━━━━━━━━━━ 15s 111ms/step - accuracy: 0.2512 - loss: 2.0011


217/352 ━━━━━━━━━━━━━━━━━━━━ 14s 110ms/step - accuracy: 0.2515 - loss: 2.0003


219/352 ━━━━━━━━━━━━━━━━━━━━ 14s 110ms/step - accuracy: 0.2523 - loss: 1.9985


220/352 ━━━━━━━━━━━━━━━━━━━━ 14s 109ms/step - accuracy: 0.2526 - loss: 1.9976


222/352 ━━━━━━━━━━━━━━━━━━━━ 14s 109ms/step - accuracy: 0.2533 - loss: 1.9959


224/352 ━━━━━━━━━━━━━━━━━━━━ 13s 108ms/step - accuracy: 0.2541 - loss: 1.9942


226/352 ━━━━━━━━━━━━━━━━━━━━ 13s 108ms/step - accuracy: 0.2548 - loss: 1.9925


227/352 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - accuracy: 0.2551 - loss: 1.9916


228/352 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - accuracy: 0.2555 - loss: 1.9908


230/352 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - accuracy: 0.2562 - loss: 1.9891


232/352 ━━━━━━━━━━━━━━━━━━━━ 12s 106ms/step - accuracy: 0.2569 - loss: 1.9874


233/352 ━━━━━━━━━━━━━━━━━━━━ 12s 106ms/step - accuracy: 0.2572 - loss: 1.9866


235/352 ━━━━━━━━━━━━━━━━━━━━ 12s 105ms/step - accuracy: 0.2579 - loss: 1.9849


237/352 ━━━━━━━━━━━━━━━━━━━━ 12s 105ms/step - accuracy: 0.2586 - loss: 1.9833


239/352 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - accuracy: 0.2592 - loss: 1.9817


240/352 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - accuracy: 0.2596 - loss: 1.9809


241/352 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - accuracy: 0.2599 - loss: 1.9801


243/352 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - accuracy: 0.2606 - loss: 1.9785


244/352 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - accuracy: 0.2609 - loss: 1.9777


245/352 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - accuracy: 0.2612 - loss: 1.9769


246/352 ━━━━━━━━━━━━━━━━━━━━ 10s 103ms/step - accuracy: 0.2615 - loss: 1.9761


247/352 ━━━━━━━━━━━━━━━━━━━━ 10s 103ms/step - accuracy: 0.2619 - loss: 1.9753


249/352 ━━━━━━━━━━━━━━━━━━━━ 10s 102ms/step - accuracy: 0.2625 - loss: 1.9738


251/352 ━━━━━━━━━━━━━━━━━━━━ 10s 102ms/step - accuracy: 0.2631 - loss: 1.9722


253/352 ━━━━━━━━━━━━━━━━━━━━ 10s 101ms/step - accuracy: 0.2638 - loss: 1.9707


255/352 ━━━━━━━━━━━━━━━━━━━━ 9s 101ms/step - accuracy: 0.2644 - loss: 1.9692 


257/352 ━━━━━━━━━━━━━━━━━━━━ 9s 101ms/step - accuracy: 0.2650 - loss: 1.9677


259/352 ━━━━━━━━━━━━━━━━━━━━ 9s 100ms/step - accuracy: 0.2656 - loss: 1.9662


260/352 ━━━━━━━━━━━━━━━━━━━━ 9s 100ms/step - accuracy: 0.2659 - loss: 1.9654


262/352 ━━━━━━━━━━━━━━━━━━━━ 8s 100ms/step - accuracy: 0.2665 - loss: 1.9640


263/352 ━━━━━━━━━━━━━━━━━━━━ 8s 99ms/step - accuracy: 0.2668 - loss: 1.9632 


265/352 ━━━━━━━━━━━━━━━━━━━━ 8s 99ms/step - accuracy: 0.2674 - loss: 1.9618


266/352 ━━━━━━━━━━━━━━━━━━━━ 8s 99ms/step - accuracy: 0.2677 - loss: 1.9610


268/352 ━━━━━━━━━━━━━━━━━━━━ 8s 99ms/step - accuracy: 0.2683 - loss: 1.9596


270/352 ━━━━━━━━━━━━━━━━━━━━ 8s 98ms/step - accuracy: 0.2689 - loss: 1.9581


271/352 ━━━━━━━━━━━━━━━━━━━━ 7s 98ms/step - accuracy: 0.2692 - loss: 1.9574


272/352 ━━━━━━━━━━━━━━━━━━━━ 7s 98ms/step - accuracy: 0.2695 - loss: 1.9567


274/352 ━━━━━━━━━━━━━━━━━━━━ 7s 98ms/step - accuracy: 0.2701 - loss: 1.9553


275/352 ━━━━━━━━━━━━━━━━━━━━ 7s 97ms/step - accuracy: 0.2704 - loss: 1.9546


276/352 ━━━━━━━━━━━━━━━━━━━━ 7s 97ms/step - accuracy: 0.2707 - loss: 1.9539


277/352 ━━━━━━━━━━━━━━━━━━━━ 7s 97ms/step - accuracy: 0.2710 - loss: 1.9532


278/352 ━━━━━━━━━━━━━━━━━━━━ 7s 97ms/step - accuracy: 0.2713 - loss: 1.9525


279/352 ━━━━━━━━━━━━━━━━━━━━ 7s 97ms/step - accuracy: 0.2715 - loss: 1.9518


281/352 ━━━━━━━━━━━━━━━━━━━━ 6s 96ms/step - accuracy: 0.2721 - loss: 1.9504


283/352 ━━━━━━━━━━━━━━━━━━━━ 6s 96ms/step - accuracy: 0.2727 - loss: 1.9490


284/352 ━━━━━━━━━━━━━━━━━━━━ 6s 96ms/step - accuracy: 0.2730 - loss: 1.9483


286/352 ━━━━━━━━━━━━━━━━━━━━ 6s 96ms/step - accuracy: 0.2735 - loss: 1.9469


287/352 ━━━━━━━━━━━━━━━━━━━━ 6s 95ms/step - accuracy: 0.2738 - loss: 1.9463


288/352 ━━━━━━━━━━━━━━━━━━━━ 6s 95ms/step - accuracy: 0.2741 - loss: 1.9456


290/352 ━━━━━━━━━━━━━━━━━━━━ 5s 95ms/step - accuracy: 0.2746 - loss: 1.9442


291/352 ━━━━━━━━━━━━━━━━━━━━ 5s 95ms/step - accuracy: 0.2749 - loss: 1.9435


292/352 ━━━━━━━━━━━━━━━━━━━━ 5s 95ms/step - accuracy: 0.2752 - loss: 1.9429


293/352 ━━━━━━━━━━━━━━━━━━━━ 5s 95ms/step - accuracy: 0.2755 - loss: 1.9422


295/352 ━━━━━━━━━━━━━━━━━━━━ 5s 94ms/step - accuracy: 0.2760 - loss: 1.9408


296/352 ━━━━━━━━━━━━━━━━━━━━ 5s 94ms/step - accuracy: 0.2763 - loss: 1.9402


298/352 ━━━━━━━━━━━━━━━━━━━━ 5s 94ms/step - accuracy: 0.2769 - loss: 1.9388


299/352 ━━━━━━━━━━━━━━━━━━━━ 4s 94ms/step - accuracy: 0.2771 - loss: 1.9382


300/352 ━━━━━━━━━━━━━━━━━━━━ 4s 94ms/step - accuracy: 0.2774 - loss: 1.9375


301/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2777 - loss: 1.9368


303/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2782 - loss: 1.9355


304/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2785 - loss: 1.9349


305/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2788 - loss: 1.9343


307/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2793 - loss: 1.9330


308/352 ━━━━━━━━━━━━━━━━━━━━ 4s 93ms/step - accuracy: 0.2796 - loss: 1.9323


310/352 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - accuracy: 0.2801 - loss: 1.9311


312/352 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - accuracy: 0.2806 - loss: 1.9298


314/352 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - accuracy: 0.2812 - loss: 1.9285


315/352 ━━━━━━━━━━━━━━━━━━━━ 3s 92ms/step - accuracy: 0.2814 - loss: 1.9279


317/352 ━━━━━━━━━━━━━━━━━━━━ 3s 91ms/step - accuracy: 0.2819 - loss: 1.9267


318/352 ━━━━━━━━━━━━━━━━━━━━ 3s 91ms/step - accuracy: 0.2822 - loss: 1.9260


320/352 ━━━━━━━━━━━━━━━━━━━━ 2s 91ms/step - accuracy: 0.2827 - loss: 1.9248


322/352 ━━━━━━━━━━━━━━━━━━━━ 2s 91ms/step - accuracy: 0.2832 - loss: 1.9236


323/352 ━━━━━━━━━━━━━━━━━━━━ 2s 91ms/step - accuracy: 0.2835 - loss: 1.9230


324/352 ━━━━━━━━━━━━━━━━━━━━ 2s 91ms/step - accuracy: 0.2837 - loss: 1.9223


325/352 ━━━━━━━━━━━━━━━━━━━━ 2s 90ms/step - accuracy: 0.2840 - loss: 1.9217


326/352 ━━━━━━━━━━━━━━━━━━━━ 2s 90ms/step - accuracy: 0.2842 - loss: 1.9211


327/352 ━━━━━━━━━━━━━━━━━━━━ 2s 90ms/step - accuracy: 0.2845 - loss: 1.9205


328/352 ━━━━━━━━━━━━━━━━━━━━ 2s 90ms/step - accuracy: 0.2847 - loss: 1.9199


330/352 ━━━━━━━━━━━━━━━━━━━━ 1s 90ms/step - accuracy: 0.2852 - loss: 1.9187


331/352 ━━━━━━━━━━━━━━━━━━━━ 1s 90ms/step - accuracy: 0.2855 - loss: 1.9181


333/352 ━━━━━━━━━━━━━━━━━━━━ 1s 90ms/step - accuracy: 0.2860 - loss: 1.9169


335/352 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.2865 - loss: 1.9157


337/352 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.2870 - loss: 1.9145


338/352 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.2872 - loss: 1.9139


339/352 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.2875 - loss: 1.9133


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 89ms/step - accuracy: 0.2877 - loss: 1.9127


341/352 ━━━━━━━━━━━━━━━━━━━━ 0s 89ms/step - accuracy: 0.2880 - loss: 1.9121


343/352 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step - accuracy: 0.2884 - loss: 1.9110


344/352 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step - accuracy: 0.2887 - loss: 1.9104


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step - accuracy: 0.2892 - loss: 1.9092


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step - accuracy: 0.2896 - loss: 1.9081


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 88ms/step - accuracy: 0.2901 - loss: 1.9069


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 87ms/step - accuracy: 0.2906 - loss: 1.9058


352/352 ━━━━━━━━━━━━━━━━━━━━ 36s 90ms/step - accuracy: 0.3733 - loss: 1.7065 - val_accuracy: 0.4728 - val_loss: 1.4360

Epoch 2/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 24s 69ms/step - accuracy: 0.3438 - loss: 1.6121


  2/352 ━━━━━━━━━━━━━━━━━━━━ 22s 64ms/step - accuracy: 0.3555 - loss: 1.6514


  4/352 ━━━━━━━━━━━━━━━━━━━━ 18s 53ms/step - accuracy: 0.3792 - loss: 1.6278


  6/352 ━━━━━━━━━━━━━━━━━━━━ 17s 51ms/step - accuracy: 0.3989 - loss: 1.5962


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259/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4768 - loss: 1.4338


260/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4769 - loss: 1.4336


261/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4769 - loss: 1.4335


262/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4770 - loss: 1.4333


263/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4771 - loss: 1.4332


264/352 ━━━━━━━━━━━━━━━━━━━━ 14s 161ms/step - accuracy: 0.4771 - loss: 1.4330


265/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4772 - loss: 1.4329


266/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4773 - loss: 1.4327


267/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4773 - loss: 1.4326


268/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4774 - loss: 1.4325


269/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4775 - loss: 1.4323


270/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4775 - loss: 1.4322


271/352 ━━━━━━━━━━━━━━━━━━━━ 13s 161ms/step - accuracy: 0.4776 - loss: 1.4320


272/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4777 - loss: 1.4319


273/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4777 - loss: 1.4318


274/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4778 - loss: 1.4316


275/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4779 - loss: 1.4315


276/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4779 - loss: 1.4313


277/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.4780 - loss: 1.4312


278/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4781 - loss: 1.4311


279/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4781 - loss: 1.4309


280/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4782 - loss: 1.4308


281/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4783 - loss: 1.4307


282/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4783 - loss: 1.4305


283/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.4784 - loss: 1.4304


284/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4785 - loss: 1.4302


285/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4785 - loss: 1.4301


286/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4786 - loss: 1.4300


287/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4787 - loss: 1.4298


288/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4787 - loss: 1.4297


289/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.4788 - loss: 1.4296


290/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4789 - loss: 1.4294 


291/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4789 - loss: 1.4293


292/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4790 - loss: 1.4291


293/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4790 - loss: 1.4290


294/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4791 - loss: 1.4289


295/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4792 - loss: 1.4287


296/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.4792 - loss: 1.4286


297/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4793 - loss: 1.4285


298/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4794 - loss: 1.4283


299/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4794 - loss: 1.4282


300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4795 - loss: 1.4280


301/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4796 - loss: 1.4279


302/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.4796 - loss: 1.4278


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4797 - loss: 1.4276


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4798 - loss: 1.4275


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4798 - loss: 1.4274


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4799 - loss: 1.4272


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4800 - loss: 1.4271


308/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.4800 - loss: 1.4269


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4801 - loss: 1.4268


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4801 - loss: 1.4267


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4802 - loss: 1.4265


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4803 - loss: 1.4264


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4803 - loss: 1.4263


314/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.4804 - loss: 1.4261


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4805 - loss: 1.4260


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4805 - loss: 1.4258


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4806 - loss: 1.4257


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4807 - loss: 1.4256


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4807 - loss: 1.4254


320/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.4808 - loss: 1.4253


321/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4809 - loss: 1.4251


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4809 - loss: 1.4250


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4810 - loss: 1.4249


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4811 - loss: 1.4247


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4811 - loss: 1.4246


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4812 - loss: 1.4244


327/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.4812 - loss: 1.4243


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4813 - loss: 1.4241


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4814 - loss: 1.4240


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4814 - loss: 1.4239


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4815 - loss: 1.4237


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4816 - loss: 1.4236


333/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.4816 - loss: 1.4234


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4817 - loss: 1.4233


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4818 - loss: 1.4231


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4818 - loss: 1.4230


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4819 - loss: 1.4229


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4820 - loss: 1.4227


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.4820 - loss: 1.4226


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4821 - loss: 1.4224


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4822 - loss: 1.4223


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4822 - loss: 1.4222


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4823 - loss: 1.4220


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4823 - loss: 1.4219


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.4824 - loss: 1.4217


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - accuracy: 0.4825 - loss: 1.4216


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - accuracy: 0.4825 - loss: 1.4215


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.4826 - loss: 1.4213


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.4827 - loss: 1.4212


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - accuracy: 0.4827 - loss: 1.4210


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.4828 - loss: 1.4209


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - accuracy: 0.4829 - loss: 1.4208


352/352 ━━━━━━━━━━━━━━━━━━━━ 59s 167ms/step - accuracy: 0.5047 - loss: 1.3728 - val_accuracy: 0.5626 - val_loss: 1.2552

Epoch 3/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 2:16:35 23s/step - accuracy: 0.5391 - loss: 1.2689


  2/352 ━━━━━━━━━━━━━━━━━━━━ 22s 63ms/step - accuracy: 0.5469 - loss: 1.2894   


  3/352 ━━━━━━━━━━━━━━━━━━━━ 24s 71ms/step - accuracy: 0.5486 - loss: 1.2848


  4/352 ━━━━━━━━━━━━━━━━━━━━ 23s 68ms/step - accuracy: 0.5492 - loss: 1.2824


  5/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5509 - loss: 1.2819


  6/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5498 - loss: 1.2843


  7/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5492 - loss: 1.2847


  8/352 ━━━━━━━━━━━━━━━━━━━━ 22s 66ms/step - accuracy: 0.5488 - loss: 1.2852


  9/352 ━━━━━━━━━━━━━━━━━━━━ 22s 64ms/step - accuracy: 0.5489 - loss: 1.2842


 10/352 ━━━━━━━━━━━━━━━━━━━━ 21s 64ms/step - accuracy: 0.5489 - loss: 1.2834


 11/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5489 - loss: 1.2830


 12/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5491 - loss: 1.2831


 13/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5490 - loss: 1.2842


 14/352 ━━━━━━━━━━━━━━━━━━━━ 22s 65ms/step - accuracy: 0.5490 - loss: 1.2851


 15/352 ━━━━━━━━━━━━━━━━━━━━ 21s 65ms/step - accuracy: 0.5488 - loss: 1.2860


 16/352 ━━━━━━━━━━━━━━━━━━━━ 21s 64ms/step - accuracy: 0.5486 - loss: 1.2864


 17/352 ━━━━━━━━━━━━━━━━━━━━ 21s 65ms/step - accuracy: 0.5486 - loss: 1.2866


 18/352 ━━━━━━━━━━━━━━━━━━━━ 21s 64ms/step - accuracy: 0.5485 - loss: 1.2868


 19/352 ━━━━━━━━━━━━━━━━━━━━ 21s 65ms/step - accuracy: 0.5486 - loss: 1.2870


 20/352 ━━━━━━━━━━━━━━━━━━━━ 21s 65ms/step - accuracy: 0.5488 - loss: 1.2867


 21/352 ━━━━━━━━━━━━━━━━━━━━ 22s 69ms/step - accuracy: 0.5490 - loss: 1.2863


 22/352 ━━━━━━━━━━━━━━━━━━━━ 24s 75ms/step - accuracy: 0.5492 - loss: 1.2858


 23/352 ━━━━━━━━━━━━━━━━━━━━ 26s 81ms/step - accuracy: 0.5495 - loss: 1.2856


 24/352 ━━━━━━━━━━━━━━━━━━━━ 28s 86ms/step - accuracy: 0.5495 - loss: 1.2855


 25/352 ━━━━━━━━━━━━━━━━━━━━ 29s 89ms/step - accuracy: 0.5497 - loss: 1.2853


 26/352 ━━━━━━━━━━━━━━━━━━━━ 29s 92ms/step - accuracy: 0.5499 - loss: 1.2852


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277/352 ━━━━━━━━━━━━━━━━━━━━ 12s 161ms/step - accuracy: 0.5557 - loss: 1.2595


278/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5557 - loss: 1.2594


279/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5557 - loss: 1.2593


280/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5557 - loss: 1.2592


281/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5558 - loss: 1.2591


282/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5558 - loss: 1.2590


283/352 ━━━━━━━━━━━━━━━━━━━━ 11s 161ms/step - accuracy: 0.5558 - loss: 1.2589


284/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5558 - loss: 1.2588


285/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5559 - loss: 1.2587


286/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5559 - loss: 1.2586


287/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5559 - loss: 1.2585


288/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5559 - loss: 1.2584


289/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5560 - loss: 1.2583


290/352 ━━━━━━━━━━━━━━━━━━━━ 10s 161ms/step - accuracy: 0.5560 - loss: 1.2582


291/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5560 - loss: 1.2581 


292/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5560 - loss: 1.2581


293/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5561 - loss: 1.2580


294/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5561 - loss: 1.2579


295/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5561 - loss: 1.2578


296/352 ━━━━━━━━━━━━━━━━━━━━ 9s 161ms/step - accuracy: 0.5561 - loss: 1.2577


297/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5562 - loss: 1.2576


298/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5562 - loss: 1.2575


299/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5562 - loss: 1.2574


300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5562 - loss: 1.2573


301/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5563 - loss: 1.2572


302/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5563 - loss: 1.2571


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5563 - loss: 1.2570


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5563 - loss: 1.2569


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5564 - loss: 1.2568


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5564 - loss: 1.2567


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5564 - loss: 1.2566


308/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5564 - loss: 1.2565


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.5565 - loss: 1.2565


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.5565 - loss: 1.2564


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.5565 - loss: 1.2563


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 161ms/step - accuracy: 0.5566 - loss: 1.2562


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 160ms/step - accuracy: 0.5566 - loss: 1.2561


314/352 ━━━━━━━━━━━━━━━━━━━━ 6s 160ms/step - accuracy: 0.5566 - loss: 1.2560


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5566 - loss: 1.2559


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5567 - loss: 1.2558


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5567 - loss: 1.2557


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5567 - loss: 1.2556


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5567 - loss: 1.2555


320/352 ━━━━━━━━━━━━━━━━━━━━ 5s 160ms/step - accuracy: 0.5568 - loss: 1.2555


321/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5568 - loss: 1.2554


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5568 - loss: 1.2553


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5568 - loss: 1.2552


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5569 - loss: 1.2551


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5569 - loss: 1.2550


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 160ms/step - accuracy: 0.5569 - loss: 1.2549


327/352 ━━━━━━━━━━━━━━━━━━━━ 4s 161ms/step - accuracy: 0.5570 - loss: 1.2548


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 161ms/step - accuracy: 0.5570 - loss: 1.2547


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 161ms/step - accuracy: 0.5570 - loss: 1.2546


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 161ms/step - accuracy: 0.5570 - loss: 1.2545


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 161ms/step - accuracy: 0.5571 - loss: 1.2545


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 160ms/step - accuracy: 0.5571 - loss: 1.2544


333/352 ━━━━━━━━━━━━━━━━━━━━ 3s 161ms/step - accuracy: 0.5571 - loss: 1.2543


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 161ms/step - accuracy: 0.5572 - loss: 1.2542


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 161ms/step - accuracy: 0.5572 - loss: 1.2541


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 161ms/step - accuracy: 0.5572 - loss: 1.2540


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.5572 - loss: 1.2539


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.5573 - loss: 1.2538


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 160ms/step - accuracy: 0.5573 - loss: 1.2537


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.5573 - loss: 1.2537


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.5573 - loss: 1.2536


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 160ms/step - accuracy: 0.5574 - loss: 1.2535


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 161ms/step - accuracy: 0.5574 - loss: 1.2534


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 161ms/step - accuracy: 0.5574 - loss: 1.2533


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 161ms/step - accuracy: 0.5575 - loss: 1.2532


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5575 - loss: 1.2531


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5575 - loss: 1.2530


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5575 - loss: 1.2529


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5576 - loss: 1.2528


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5576 - loss: 1.2528


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5576 - loss: 1.2527


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.5576 - loss: 1.2526


352/352 ━━━━━━━━━━━━━━━━━━━━ 82s 168ms/step - accuracy: 0.5675 - loss: 1.2209 - val_accuracy: 0.6168 - val_loss: 1.1072

Epoch 4/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:35 273ms/step - accuracy: 0.5781 - loss: 1.2486


  2/352 ━━━━━━━━━━━━━━━━━━━━ 51s 147ms/step - accuracy: 0.5664 - loss: 1.2765 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 58s 168ms/step - accuracy: 0.5642 - loss: 1.2661


  4/352 ━━━━━━━━━━━━━━━━━━━━ 55s 160ms/step - accuracy: 0.5628 - loss: 1.2636


  5/352 ━━━━━━━━━━━━━━━━━━━━ 56s 163ms/step - accuracy: 0.5631 - loss: 1.2576


  6/352 ━━━━━━━━━━━━━━━━━━━━ 56s 164ms/step - accuracy: 0.5641 - loss: 1.2512


  7/352 ━━━━━━━━━━━━━━━━━━━━ 56s 163ms/step - accuracy: 0.5646 - loss: 1.2454


  8/352 ━━━━━━━━━━━━━━━━━━━━ 55s 161ms/step - accuracy: 0.5651 - loss: 1.2388


  9/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.5650 - loss: 1.2336


 10/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.5646 - loss: 1.2294


 11/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.5646 - loss: 1.2260


 12/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.5648 - loss: 1.2234


 13/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.5652 - loss: 1.2207


 14/352 ━━━━━━━━━━━━━━━━━━━━ 54s 162ms/step - accuracy: 0.5661 - loss: 1.2178


 15/352 ━━━━━━━━━━━━━━━━━━━━ 54s 161ms/step - accuracy: 0.5671 - loss: 1.2148


 16/352 ━━━━━━━━━━━━━━━━━━━━ 53s 160ms/step - accuracy: 0.5681 - loss: 1.2116


 17/352 ━━━━━━━━━━━━━━━━━━━━ 53s 160ms/step - accuracy: 0.5689 - loss: 1.2087


 18/352 ━━━━━━━━━━━━━━━━━━━━ 53s 159ms/step - accuracy: 0.5696 - loss: 1.2060


 19/352 ━━━━━━━━━━━━━━━━━━━━ 53s 160ms/step - accuracy: 0.5705 - loss: 1.2031


 20/352 ━━━━━━━━━━━━━━━━━━━━ 52s 160ms/step - accuracy: 0.5714 - loss: 1.2004


 21/352 ━━━━━━━━━━━━━━━━━━━━ 53s 161ms/step - accuracy: 0.5721 - loss: 1.1980


 22/352 ━━━━━━━━━━━━━━━━━━━━ 53s 161ms/step - accuracy: 0.5729 - loss: 1.1955


 23/352 ━━━━━━━━━━━━━━━━━━━━ 53s 162ms/step - accuracy: 0.5736 - loss: 1.1934


 24/352 ━━━━━━━━━━━━━━━━━━━━ 53s 163ms/step - accuracy: 0.5744 - loss: 1.1912


 25/352 ━━━━━━━━━━━━━━━━━━━━ 53s 163ms/step - accuracy: 0.5752 - loss: 1.1891


 26/352 ━━━━━━━━━━━━━━━━━━━━ 53s 163ms/step - accuracy: 0.5758 - loss: 1.1872


 27/352 ━━━━━━━━━━━━━━━━━━━━ 53s 164ms/step - accuracy: 0.5764 - loss: 1.1855


 28/352 ━━━━━━━━━━━━━━━━━━━━ 53s 165ms/step - accuracy: 0.5767 - loss: 1.1842


 29/352 ━━━━━━━━━━━━━━━━━━━━ 53s 167ms/step - accuracy: 0.5770 - loss: 1.1832


 30/352 ━━━━━━━━━━━━━━━━━━━━ 54s 168ms/step - accuracy: 0.5773 - loss: 1.1821


 31/352 ━━━━━━━━━━━━━━━━━━━━ 54s 169ms/step - accuracy: 0.5777 - loss: 1.1811


 32/352 ━━━━━━━━━━━━━━━━━━━━ 53s 168ms/step - accuracy: 0.5780 - loss: 1.1800


 33/352 ━━━━━━━━━━━━━━━━━━━━ 53s 168ms/step - accuracy: 0.5784 - loss: 1.1789


 34/352 ━━━━━━━━━━━━━━━━━━━━ 53s 167ms/step - accuracy: 0.5787 - loss: 1.1780


 35/352 ━━━━━━━━━━━━━━━━━━━━ 52s 167ms/step - accuracy: 0.5791 - loss: 1.1771


 36/352 ━━━━━━━━━━━━━━━━━━━━ 52s 168ms/step - accuracy: 0.5795 - loss: 1.1762


 37/352 ━━━━━━━━━━━━━━━━━━━━ 52s 168ms/step - accuracy: 0.5798 - loss: 1.1754


 38/352 ━━━━━━━━━━━━━━━━━━━━ 52s 168ms/step - accuracy: 0.5801 - loss: 1.1746


 39/352 ━━━━━━━━━━━━━━━━━━━━ 52s 168ms/step - accuracy: 0.5804 - loss: 1.1740


 40/352 ━━━━━━━━━━━━━━━━━━━━ 52s 169ms/step - accuracy: 0.5806 - loss: 1.1734


 41/352 ━━━━━━━━━━━━━━━━━━━━ 52s 169ms/step - accuracy: 0.5809 - loss: 1.1727


 42/352 ━━━━━━━━━━━━━━━━━━━━ 52s 169ms/step - accuracy: 0.5811 - loss: 1.1721


 43/352 ━━━━━━━━━━━━━━━━━━━━ 52s 169ms/step - accuracy: 0.5813 - loss: 1.1715


 44/352 ━━━━━━━━━━━━━━━━━━━━ 52s 169ms/step - accuracy: 0.5815 - loss: 1.1709


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301/352 ━━━━━━━━━━━━━━━━━━━━ 8s 161ms/step - accuracy: 0.5989 - loss: 1.1330


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - accuracy: 0.5990 - loss: 1.1328


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 160ms/step - accuracy: 0.5991 - loss: 1.1327


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 159ms/step - accuracy: 0.5991 - loss: 1.1326


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 158ms/step - accuracy: 0.5992 - loss: 1.1324


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 158ms/step - accuracy: 0.5992 - loss: 1.1323


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 157ms/step - accuracy: 0.5993 - loss: 1.1322


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.5993 - loss: 1.1320


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.5994 - loss: 1.1319


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 155ms/step - accuracy: 0.5994 - loss: 1.1318


320/352 ━━━━━━━━━━━━━━━━━━━━ 4s 154ms/step - accuracy: 0.5995 - loss: 1.1317


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 154ms/step - accuracy: 0.5995 - loss: 1.1315


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 153ms/step - accuracy: 0.5996 - loss: 1.1314


326/352 ━━━━━━━━━━━━━━━━━━━━ 3s 152ms/step - accuracy: 0.5996 - loss: 1.1313


327/352 ━━━━━━━━━━━━━━━━━━━━ 3s 152ms/step - accuracy: 0.5997 - loss: 1.1312


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 151ms/step - accuracy: 0.5997 - loss: 1.1311


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 151ms/step - accuracy: 0.5998 - loss: 1.1309


333/352 ━━━━━━━━━━━━━━━━━━━━ 2s 150ms/step - accuracy: 0.5998 - loss: 1.1308


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 150ms/step - accuracy: 0.5999 - loss: 1.1307


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 149ms/step - accuracy: 0.5999 - loss: 1.1305


339/352 ━━━━━━━━━━━━━━━━━━━━ 1s 148ms/step - accuracy: 0.6000 - loss: 1.1304


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 148ms/step - accuracy: 0.6000 - loss: 1.1302


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 147ms/step - accuracy: 0.6001 - loss: 1.1301


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 147ms/step - accuracy: 0.6001 - loss: 1.1300


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 146ms/step - accuracy: 0.6002 - loss: 1.1299


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 146ms/step - accuracy: 0.6002 - loss: 1.1298


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 145ms/step - accuracy: 0.6003 - loss: 1.1296


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 145ms/step - accuracy: 0.6003 - loss: 1.1295


352/352 ━━━━━━━━━━━━━━━━━━━━ 52s 147ms/step - accuracy: 0.6094 - loss: 1.1059 - val_accuracy: 0.6556 - val_loss: 0.9964

Epoch 5/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 27s 79ms/step - accuracy: 0.6250 - loss: 0.9519


  3/352 ━━━━━━━━━━━━━━━━━━━━ 17s 49ms/step - accuracy: 0.6220 - loss: 1.0001


  5/352 ━━━━━━━━━━━━━━━━━━━━ 16s 48ms/step - accuracy: 0.6289 - loss: 1.0079


  7/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6330 - loss: 1.0090


  9/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6369 - loss: 1.0082


 11/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6395 - loss: 1.0076


 13/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6411 - loss: 1.0072


 14/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6416 - loss: 1.0072


 16/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6417 - loss: 1.0095


 18/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6417 - loss: 1.0112


 20/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6415 - loss: 1.0127


 22/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6409 - loss: 1.0145


 24/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6405 - loss: 1.0159


 26/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6398 - loss: 1.0180


 27/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6394 - loss: 1.0191


 28/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6389 - loss: 1.0203


 30/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6381 - loss: 1.0226


 32/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6373 - loss: 1.0246


 34/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6367 - loss: 1.0263


 36/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6361 - loss: 1.0279


 38/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6355 - loss: 1.0293


 40/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6350 - loss: 1.0306


 42/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6346 - loss: 1.0317


 44/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6342 - loss: 1.0328


 46/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6338 - loss: 1.0337


 47/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6336 - loss: 1.0343


 48/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6334 - loss: 1.0348


 50/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6330 - loss: 1.0358


 52/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6327 - loss: 1.0367


 53/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6325 - loss: 1.0372


 55/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6323 - loss: 1.0381


 57/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6320 - loss: 1.0388


 59/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6318 - loss: 1.0394


 60/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6318 - loss: 1.0397


 61/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6317 - loss: 1.0399


 63/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6316 - loss: 1.0403


 64/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6315 - loss: 1.0405


 66/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6314 - loss: 1.0409


 68/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6312 - loss: 1.0414


 70/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6310 - loss: 1.0418


 72/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6309 - loss: 1.0421


 74/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6307 - loss: 1.0424


 76/352 ━━━━━━━━━━━━━━━━━━━━ 13s 47ms/step - accuracy: 0.6306 - loss: 1.0426


 78/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6305 - loss: 1.0429


 80/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6304 - loss: 1.0431


 81/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6303 - loss: 1.0432


 82/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6303 - loss: 1.0433


 83/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6302 - loss: 1.0434


 85/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6301 - loss: 1.0437


 87/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6301 - loss: 1.0439


 89/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6300 - loss: 1.0441


 91/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6299 - loss: 1.0444


 92/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6299 - loss: 1.0445


 94/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6298 - loss: 1.0447


 96/352 ━━━━━━━━━━━━━━━━━━━━ 12s 47ms/step - accuracy: 0.6297 - loss: 1.0450


 98/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6296 - loss: 1.0453


 99/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6296 - loss: 1.0454


101/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6295 - loss: 1.0457


103/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6294 - loss: 1.0459


104/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6294 - loss: 1.0461


106/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6293 - loss: 1.0463


107/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6293 - loss: 1.0464


109/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6293 - loss: 1.0466


111/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6292 - loss: 1.0467


113/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6292 - loss: 1.0469


115/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6292 - loss: 1.0470


116/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6292 - loss: 1.0470


118/352 ━━━━━━━━━━━━━━━━━━━━ 11s 47ms/step - accuracy: 0.6292 - loss: 1.0471


120/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6292 - loss: 1.0471


122/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6292 - loss: 1.0472


124/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6293 - loss: 1.0472


126/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6293 - loss: 1.0472


127/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6293 - loss: 1.0472


129/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6293 - loss: 1.0472


131/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6294 - loss: 1.0472


133/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6294 - loss: 1.0472


135/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6294 - loss: 1.0472


136/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6294 - loss: 1.0472


137/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6295 - loss: 1.0472


139/352 ━━━━━━━━━━━━━━━━━━━━ 10s 47ms/step - accuracy: 0.6295 - loss: 1.0472


141/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6295 - loss: 1.0471 


143/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6295 - loss: 1.0471


145/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6296 - loss: 1.0470


147/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6296 - loss: 1.0469


148/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6296 - loss: 1.0469


150/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6297 - loss: 1.0468


152/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6297 - loss: 1.0467


154/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6297 - loss: 1.0466


156/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6298 - loss: 1.0465


158/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6298 - loss: 1.0464


159/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6299 - loss: 1.0464


161/352 ━━━━━━━━━━━━━━━━━━━━ 9s 47ms/step - accuracy: 0.6299 - loss: 1.0463


163/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6299 - loss: 1.0462


165/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6300 - loss: 1.0461


167/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6300 - loss: 1.0460


169/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6300 - loss: 1.0460


171/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6301 - loss: 1.0459


172/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6301 - loss: 1.0459


174/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6301 - loss: 1.0458


176/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6301 - loss: 1.0458


178/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6302 - loss: 1.0457


180/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6302 - loss: 1.0457


182/352 ━━━━━━━━━━━━━━━━━━━━ 8s 47ms/step - accuracy: 0.6302 - loss: 1.0456


184/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6303 - loss: 1.0455


186/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6303 - loss: 1.0455


188/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6303 - loss: 1.0454


189/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6304 - loss: 1.0454


191/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6304 - loss: 1.0453


193/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6304 - loss: 1.0452


195/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6304 - loss: 1.0452


197/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6305 - loss: 1.0451


199/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6305 - loss: 1.0450


201/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6305 - loss: 1.0450


203/352 ━━━━━━━━━━━━━━━━━━━━ 7s 47ms/step - accuracy: 0.6305 - loss: 1.0449


205/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6306 - loss: 1.0448


207/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6306 - loss: 1.0447


209/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6306 - loss: 1.0447


211/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6307 - loss: 1.0446


213/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6307 - loss: 1.0445


215/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6307 - loss: 1.0445


217/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6307 - loss: 1.0444


219/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6308 - loss: 1.0443


221/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6308 - loss: 1.0443


223/352 ━━━━━━━━━━━━━━━━━━━━ 6s 47ms/step - accuracy: 0.6308 - loss: 1.0442


225/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6308 - loss: 1.0441


227/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6309 - loss: 1.0441


229/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6309 - loss: 1.0440


231/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6309 - loss: 1.0439


233/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6309 - loss: 1.0439


235/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0438


237/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0437


239/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0437


240/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0437


242/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0436


243/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6310 - loss: 1.0436


245/352 ━━━━━━━━━━━━━━━━━━━━ 5s 47ms/step - accuracy: 0.6311 - loss: 1.0435


247/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6311 - loss: 1.0435


249/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6311 - loss: 1.0434


251/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6311 - loss: 1.0434


253/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6311 - loss: 1.0433


255/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6312 - loss: 1.0433


257/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6312 - loss: 1.0432


259/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6312 - loss: 1.0431


261/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6312 - loss: 1.0431


263/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6313 - loss: 1.0430


265/352 ━━━━━━━━━━━━━━━━━━━━ 4s 47ms/step - accuracy: 0.6313 - loss: 1.0429


267/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6313 - loss: 1.0429


269/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6314 - loss: 1.0428


271/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6314 - loss: 1.0427


273/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6314 - loss: 1.0427


275/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6314 - loss: 1.0426


277/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6315 - loss: 1.0425


279/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6315 - loss: 1.0425


281/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6315 - loss: 1.0424


283/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6316 - loss: 1.0423


285/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6316 - loss: 1.0423


286/352 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step - accuracy: 0.6316 - loss: 1.0422


288/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6317 - loss: 1.0422


290/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6317 - loss: 1.0421


292/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6317 - loss: 1.0420


294/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6317 - loss: 1.0420


296/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6318 - loss: 1.0419


298/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6318 - loss: 1.0418


300/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6318 - loss: 1.0418


302/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6319 - loss: 1.0417


304/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6319 - loss: 1.0416


306/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6319 - loss: 1.0415


308/352 ━━━━━━━━━━━━━━━━━━━━ 2s 47ms/step - accuracy: 0.6320 - loss: 1.0415


310/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6320 - loss: 1.0414


312/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6320 - loss: 1.0413


314/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6321 - loss: 1.0412


316/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6321 - loss: 1.0412


318/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6321 - loss: 1.0411


320/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6322 - loss: 1.0410


322/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6322 - loss: 1.0409


324/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6322 - loss: 1.0408


326/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6323 - loss: 1.0408


328/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6323 - loss: 1.0407


330/352 ━━━━━━━━━━━━━━━━━━━━ 1s 47ms/step - accuracy: 0.6324 - loss: 1.0406


331/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6324 - loss: 1.0406


333/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6324 - loss: 1.0405


335/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6324 - loss: 1.0404


337/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6325 - loss: 1.0403


339/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6325 - loss: 1.0402


341/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6325 - loss: 1.0401


343/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6326 - loss: 1.0400


344/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6326 - loss: 1.0400


345/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6326 - loss: 1.0400


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6326 - loss: 1.0399


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6327 - loss: 1.0399


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6327 - loss: 1.0398


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 47ms/step - accuracy: 0.6327 - loss: 1.0397


352/352 ━━━━━━━━━━━━━━━━━━━━ 17s 49ms/step - accuracy: 0.6380 - loss: 1.0266 - val_accuracy: 0.6748 - val_loss: 0.9432

Epoch 6/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 27s 78ms/step - accuracy: 0.7266 - loss: 0.8078


  3/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6853 - loss: 0.9115


  5/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6755 - loss: 0.9459


  7/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6720 - loss: 0.9515


  9/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6707 - loss: 0.9512


 11/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6711 - loss: 0.9488


 13/352 ━━━━━━━━━━━━━━━━━━━━ 16s 47ms/step - accuracy: 0.6696 - loss: 0.9499


 15/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6680 - loss: 0.9509


 17/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6664 - loss: 0.9522


 19/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6654 - loss: 0.9529


 20/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6651 - loss: 0.9533


 22/352 ━━━━━━━━━━━━━━━━━━━━ 15s 46ms/step - accuracy: 0.6644 - loss: 0.9542


 24/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6638 - loss: 0.9550


 26/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6633 - loss: 0.9557


 27/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6631 - loss: 0.9559


 29/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6625 - loss: 0.9565


 31/352 ━━━━━━━━━━━━━━━━━━━━ 15s 47ms/step - accuracy: 0.6618 - loss: 0.9571


 33/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6613 - loss: 0.9578


 34/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6611 - loss: 0.9581


 36/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6607 - loss: 0.9586


 38/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6603 - loss: 0.9593


 40/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6598 - loss: 0.9603


 42/352 ━━━━━━━━━━━━━━━━━━━━ 14s 47ms/step - accuracy: 0.6595 - loss: 0.9611


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252/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6603 - loss: 0.9627


254/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6604 - loss: 0.9626


256/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6604 - loss: 0.9626


258/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6604 - loss: 0.9625


260/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6604 - loss: 0.9625


262/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6605 - loss: 0.9625


264/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6605 - loss: 0.9624


266/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6605 - loss: 0.9624


268/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6605 - loss: 0.9624


269/352 ━━━━━━━━━━━━━━━━━━━━ 4s 48ms/step - accuracy: 0.6605 - loss: 0.9624


271/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6606 - loss: 0.9624


273/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6606 - loss: 0.9623


275/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6606 - loss: 0.9623


276/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6606 - loss: 0.9623


277/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6606 - loss: 0.9623


278/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6607 - loss: 0.9623


280/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6607 - loss: 0.9622


282/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6607 - loss: 0.9622


283/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6607 - loss: 0.9622


284/352 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step - accuracy: 0.6607 - loss: 0.9622


285/352 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - accuracy: 0.6607 - loss: 0.9622


286/352 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - accuracy: 0.6607 - loss: 0.9621


287/352 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step - accuracy: 0.6608 - loss: 0.9621


288/352 ━━━━━━━━━━━━━━━━━━━━ 3s 50ms/step - accuracy: 0.6608 - loss: 0.9621


289/352 ━━━━━━━━━━━━━━━━━━━━ 3s 50ms/step - accuracy: 0.6608 - loss: 0.9621


290/352 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.6608 - loss: 0.9621


291/352 ━━━━━━━━━━━━━━━━━━━━ 3s 51ms/step - accuracy: 0.6608 - loss: 0.9621


292/352 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.6608 - loss: 0.9621


293/352 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.6608 - loss: 0.9620


294/352 ━━━━━━━━━━━━━━━━━━━━ 3s 52ms/step - accuracy: 0.6608 - loss: 0.9620


295/352 ━━━━━━━━━━━━━━━━━━━━ 3s 53ms/step - accuracy: 0.6609 - loss: 0.9620


296/352 ━━━━━━━━━━━━━━━━━━━━ 2s 53ms/step - accuracy: 0.6609 - loss: 0.9620


297/352 ━━━━━━━━━━━━━━━━━━━━ 2s 54ms/step - accuracy: 0.6609 - loss: 0.9620


298/352 ━━━━━━━━━━━━━━━━━━━━ 2s 54ms/step - accuracy: 0.6609 - loss: 0.9620


299/352 ━━━━━━━━━━━━━━━━━━━━ 2s 54ms/step - accuracy: 0.6609 - loss: 0.9620


300/352 ━━━━━━━━━━━━━━━━━━━━ 2s 55ms/step - accuracy: 0.6609 - loss: 0.9620


301/352 ━━━━━━━━━━━━━━━━━━━━ 2s 55ms/step - accuracy: 0.6609 - loss: 0.9619


302/352 ━━━━━━━━━━━━━━━━━━━━ 2s 55ms/step - accuracy: 0.6609 - loss: 0.9619


303/352 ━━━━━━━━━━━━━━━━━━━━ 2s 56ms/step - accuracy: 0.6610 - loss: 0.9619


304/352 ━━━━━━━━━━━━━━━━━━━━ 2s 56ms/step - accuracy: 0.6610 - loss: 0.9619


305/352 ━━━━━━━━━━━━━━━━━━━━ 2s 56ms/step - accuracy: 0.6610 - loss: 0.9619


306/352 ━━━━━━━━━━━━━━━━━━━━ 2s 57ms/step - accuracy: 0.6610 - loss: 0.9619


307/352 ━━━━━━━━━━━━━━━━━━━━ 2s 57ms/step - accuracy: 0.6610 - loss: 0.9619


308/352 ━━━━━━━━━━━━━━━━━━━━ 2s 57ms/step - accuracy: 0.6610 - loss: 0.9619


309/352 ━━━━━━━━━━━━━━━━━━━━ 2s 57ms/step - accuracy: 0.6610 - loss: 0.9619


310/352 ━━━━━━━━━━━━━━━━━━━━ 2s 58ms/step - accuracy: 0.6610 - loss: 0.9619


311/352 ━━━━━━━━━━━━━━━━━━━━ 2s 58ms/step - accuracy: 0.6610 - loss: 0.9619


312/352 ━━━━━━━━━━━━━━━━━━━━ 2s 58ms/step - accuracy: 0.6610 - loss: 0.9619


313/352 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.6610 - loss: 0.9619


314/352 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.6610 - loss: 0.9619


315/352 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.6611 - loss: 0.9619


316/352 ━━━━━━━━━━━━━━━━━━━━ 2s 59ms/step - accuracy: 0.6611 - loss: 0.9619


317/352 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.6611 - loss: 0.9618


318/352 ━━━━━━━━━━━━━━━━━━━━ 2s 60ms/step - accuracy: 0.6611 - loss: 0.9618


319/352 ━━━━━━━━━━━━━━━━━━━━ 1s 60ms/step - accuracy: 0.6611 - loss: 0.9618


320/352 ━━━━━━━━━━━━━━━━━━━━ 1s 61ms/step - accuracy: 0.6611 - loss: 0.9618


321/352 ━━━━━━━━━━━━━━━━━━━━ 1s 61ms/step - accuracy: 0.6611 - loss: 0.9618


322/352 ━━━━━━━━━━━━━━━━━━━━ 1s 61ms/step - accuracy: 0.6611 - loss: 0.9618


323/352 ━━━━━━━━━━━━━━━━━━━━ 1s 62ms/step - accuracy: 0.6611 - loss: 0.9618


324/352 ━━━━━━━━━━━━━━━━━━━━ 1s 62ms/step - accuracy: 0.6611 - loss: 0.9618


325/352 ━━━━━━━━━━━━━━━━━━━━ 1s 62ms/step - accuracy: 0.6611 - loss: 0.9618


326/352 ━━━━━━━━━━━━━━━━━━━━ 1s 63ms/step - accuracy: 0.6612 - loss: 0.9618


327/352 ━━━━━━━━━━━━━━━━━━━━ 1s 63ms/step - accuracy: 0.6612 - loss: 0.9618


328/352 ━━━━━━━━━━━━━━━━━━━━ 1s 63ms/step - accuracy: 0.6612 - loss: 0.9618


329/352 ━━━━━━━━━━━━━━━━━━━━ 1s 63ms/step - accuracy: 0.6612 - loss: 0.9618


330/352 ━━━━━━━━━━━━━━━━━━━━ 1s 64ms/step - accuracy: 0.6612 - loss: 0.9618


331/352 ━━━━━━━━━━━━━━━━━━━━ 1s 64ms/step - accuracy: 0.6612 - loss: 0.9618


332/352 ━━━━━━━━━━━━━━━━━━━━ 1s 64ms/step - accuracy: 0.6612 - loss: 0.9617


333/352 ━━━━━━━━━━━━━━━━━━━━ 1s 64ms/step - accuracy: 0.6612 - loss: 0.9617


334/352 ━━━━━━━━━━━━━━━━━━━━ 1s 65ms/step - accuracy: 0.6612 - loss: 0.9617


335/352 ━━━━━━━━━━━━━━━━━━━━ 1s 65ms/step - accuracy: 0.6612 - loss: 0.9617


336/352 ━━━━━━━━━━━━━━━━━━━━ 1s 65ms/step - accuracy: 0.6612 - loss: 0.9617


337/352 ━━━━━━━━━━━━━━━━━━━━ 0s 65ms/step - accuracy: 0.6613 - loss: 0.9617


338/352 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.6613 - loss: 0.9617


339/352 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.6613 - loss: 0.9617


340/352 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.6613 - loss: 0.9617


341/352 ━━━━━━━━━━━━━━━━━━━━ 0s 66ms/step - accuracy: 0.6613 - loss: 0.9617


342/352 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.6613 - loss: 0.9617


343/352 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.6613 - loss: 0.9617


344/352 ━━━━━━━━━━━━━━━━━━━━ 0s 67ms/step - accuracy: 0.6613 - loss: 0.9616


345/352 ━━━━━━━━━━━━━━━━━━━━ 0s 68ms/step - accuracy: 0.6613 - loss: 0.9616


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 68ms/step - accuracy: 0.6613 - loss: 0.9616


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 68ms/step - accuracy: 0.6614 - loss: 0.9616


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 68ms/step - accuracy: 0.6614 - loss: 0.9616


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 68ms/step - accuracy: 0.6614 - loss: 0.9616


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 69ms/step - accuracy: 0.6614 - loss: 0.9616


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 69ms/step - accuracy: 0.6614 - loss: 0.9616


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 69ms/step - accuracy: 0.6614 - loss: 0.9616


352/352 ━━━━━━━━━━━━━━━━━━━━ 27s 76ms/step - accuracy: 0.6651 - loss: 0.9579 - val_accuracy: 0.6976 - val_loss: 0.8708

Epoch 7/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:37 277ms/step - accuracy: 0.6719 - loss: 0.8510


  2/352 ━━━━━━━━━━━━━━━━━━━━ 56s 160ms/step - accuracy: 0.6758 - loss: 0.8446 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 58s 169ms/step - accuracy: 0.6771 - loss: 0.8447


  4/352 ━━━━━━━━━━━━━━━━━━━━ 1:00 173ms/step - accuracy: 0.6787 - loss: 0.8461


  5/352 ━━━━━━━━━━━━━━━━━━━━ 58s 169ms/step - accuracy: 0.6770 - loss: 0.8543 


  6/352 ━━━━━━━━━━━━━━━━━━━━ 56s 165ms/step - accuracy: 0.6777 - loss: 0.8568


  7/352 ━━━━━━━━━━━━━━━━━━━━ 55s 161ms/step - accuracy: 0.6786 - loss: 0.8584


  8/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.6800 - loss: 0.8594


  9/352 ━━━━━━━━━━━━━━━━━━━━ 54s 160ms/step - accuracy: 0.6805 - loss: 0.8613


 10/352 ━━━━━━━━━━━━━━━━━━━━ 54s 159ms/step - accuracy: 0.6812 - loss: 0.8621


 11/352 ━━━━━━━━━━━━━━━━━━━━ 53s 157ms/step - accuracy: 0.6818 - loss: 0.8630


 12/352 ━━━━━━━━━━━━━━━━━━━━ 53s 156ms/step - accuracy: 0.6820 - loss: 0.8650


 13/352 ━━━━━━━━━━━━━━━━━━━━ 52s 154ms/step - accuracy: 0.6818 - loss: 0.8679


 14/352 ━━━━━━━━━━━━━━━━━━━━ 52s 154ms/step - accuracy: 0.6821 - loss: 0.8694


 15/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.6825 - loss: 0.8709


 16/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6828 - loss: 0.8719


 17/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6827 - loss: 0.8736


 18/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.6826 - loss: 0.8751


 19/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.6827 - loss: 0.8759


 20/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.6827 - loss: 0.8766


 21/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.6827 - loss: 0.8771


 22/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.6826 - loss: 0.8778


 23/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.6825 - loss: 0.8788


 24/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.6822 - loss: 0.8799


 25/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.6820 - loss: 0.8810


 26/352 ━━━━━━━━━━━━━━━━━━━━ 49s 151ms/step - accuracy: 0.6819 - loss: 0.8821


 27/352 ━━━━━━━━━━━━━━━━━━━━ 49s 151ms/step - accuracy: 0.6817 - loss: 0.8832


 28/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.6815 - loss: 0.8842


 29/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.6814 - loss: 0.8851


 30/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.6812 - loss: 0.8859


 31/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.6811 - loss: 0.8866


 32/352 ━━━━━━━━━━━━━━━━━━━━ 48s 150ms/step - accuracy: 0.6810 - loss: 0.8873


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283/352 ━━━━━━━━━━━━━━━━━━━━ 11s 162ms/step - accuracy: 0.6813 - loss: 0.9083


284/352 ━━━━━━━━━━━━━━━━━━━━ 11s 162ms/step - accuracy: 0.6813 - loss: 0.9083


285/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9083


286/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9083


287/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9083


288/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9084


289/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9084


290/352 ━━━━━━━━━━━━━━━━━━━━ 10s 162ms/step - accuracy: 0.6813 - loss: 0.9084


291/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6813 - loss: 0.9084 


292/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6814 - loss: 0.9084


293/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6814 - loss: 0.9084


294/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6814 - loss: 0.9084


295/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6814 - loss: 0.9084


296/352 ━━━━━━━━━━━━━━━━━━━━ 9s 162ms/step - accuracy: 0.6814 - loss: 0.9084


297/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


298/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


299/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


301/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


302/352 ━━━━━━━━━━━━━━━━━━━━ 8s 162ms/step - accuracy: 0.6814 - loss: 0.9084


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6814 - loss: 0.9084


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6814 - loss: 0.9084


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6814 - loss: 0.9084


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6814 - loss: 0.9084


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6814 - loss: 0.9084


308/352 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - accuracy: 0.6815 - loss: 0.9085


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


314/352 ━━━━━━━━━━━━━━━━━━━━ 6s 162ms/step - accuracy: 0.6815 - loss: 0.9085


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


320/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


321/352 ━━━━━━━━━━━━━━━━━━━━ 5s 162ms/step - accuracy: 0.6815 - loss: 0.9085


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9085


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9085


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9085


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9085


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9085


327/352 ━━━━━━━━━━━━━━━━━━━━ 4s 162ms/step - accuracy: 0.6815 - loss: 0.9086


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


333/352 ━━━━━━━━━━━━━━━━━━━━ 3s 162ms/step - accuracy: 0.6815 - loss: 0.9086


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6815 - loss: 0.9086


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6815 - loss: 0.9086


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6815 - loss: 0.9086


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6815 - loss: 0.9086


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6816 - loss: 0.9086


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.6816 - loss: 0.9086


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9086


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9086


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9086


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9086


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9086


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.6816 - loss: 0.9087


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.6816 - loss: 0.9087


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.6816 - loss: 0.9087


352/352 ━━━━━━━━━━━━━━━━━━━━ 59s 168ms/step - accuracy: 0.6831 - loss: 0.9101 - val_accuracy: 0.7096 - val_loss: 0.8546

Epoch 8/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:28 251ms/step - accuracy: 0.6250 - loss: 1.0522


  2/352 ━━━━━━━━━━━━━━━━━━━━ 54s 155ms/step - accuracy: 0.6367 - loss: 1.0347 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 52s 152ms/step - accuracy: 0.6458 - loss: 1.0083


  4/352 ━━━━━━━━━━━━━━━━━━━━ 52s 150ms/step - accuracy: 0.6562 - loss: 0.9869


  5/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.6650 - loss: 0.9709


  6/352 ━━━━━━━━━━━━━━━━━━━━ 52s 150ms/step - accuracy: 0.6696 - loss: 0.9585


  7/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.6749 - loss: 0.9463


  8/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.6791 - loss: 0.9361


  9/352 ━━━━━━━━━━━━━━━━━━━━ 52s 153ms/step - accuracy: 0.6817 - loss: 0.9284


 10/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.6835 - loss: 0.9226


 11/352 ━━━━━━━━━━━━━━━━━━━━ 52s 153ms/step - accuracy: 0.6847 - loss: 0.9180


 12/352 ━━━━━━━━━━━━━━━━━━━━ 52s 154ms/step - accuracy: 0.6856 - loss: 0.9134


 13/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6861 - loss: 0.9098


 14/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6865 - loss: 0.9068


 15/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6871 - loss: 0.9036


 16/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.6876 - loss: 0.9008


 17/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.6880 - loss: 0.8981


 18/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.6883 - loss: 0.8959


 19/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.6886 - loss: 0.8940


 20/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.6890 - loss: 0.8923


 21/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.6894 - loss: 0.8905


 22/352 ━━━━━━━━━━━━━━━━━━━━ 50s 154ms/step - accuracy: 0.6898 - loss: 0.8888


 23/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6904 - loss: 0.8870


 24/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6910 - loss: 0.8851


 25/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6916 - loss: 0.8833


 26/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6923 - loss: 0.8815


 27/352 ━━━━━━━━━━━━━━━━━━━━ 50s 156ms/step - accuracy: 0.6928 - loss: 0.8799


 28/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6934 - loss: 0.8783


 29/352 ━━━━━━━━━━━━━━━━━━━━ 50s 156ms/step - accuracy: 0.6939 - loss: 0.8770


 30/352 ━━━━━━━━━━━━━━━━━━━━ 50s 155ms/step - accuracy: 0.6943 - loss: 0.8760


 31/352 ━━━━━━━━━━━━━━━━━━━━ 49s 155ms/step - accuracy: 0.6947 - loss: 0.8751


 32/352 ━━━━━━━━━━━━━━━━━━━━ 49s 156ms/step - accuracy: 0.6951 - loss: 0.8742


 33/352 ━━━━━━━━━━━━━━━━━━━━ 49s 155ms/step - accuracy: 0.6955 - loss: 0.8734


 34/352 ━━━━━━━━━━━━━━━━━━━━ 49s 155ms/step - accuracy: 0.6959 - loss: 0.8726


 35/352 ━━━━━━━━━━━━━━━━━━━━ 49s 155ms/step - accuracy: 0.6963 - loss: 0.8719


 36/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.6967 - loss: 0.8712


 37/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.6969 - loss: 0.8707


 38/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.6972 - loss: 0.8702


 39/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.6974 - loss: 0.8699


 40/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.6976 - loss: 0.8697


 41/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.6977 - loss: 0.8695


 42/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6979 - loss: 0.8693


 43/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6980 - loss: 0.8691


 44/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6982 - loss: 0.8688


 45/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6984 - loss: 0.8685


 46/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6985 - loss: 0.8683


 47/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.6986 - loss: 0.8681


 48/352 ━━━━━━━━━━━━━━━━━━━━ 46s 154ms/step - accuracy: 0.6987 - loss: 0.8678


 49/352 ━━━━━━━━━━━━━━━━━━━━ 46s 154ms/step - accuracy: 0.6988 - loss: 0.8676


 50/352 ━━━━━━━━━━━━━━━━━━━━ 46s 154ms/step - accuracy: 0.6989 - loss: 0.8674


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300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 165ms/step - accuracy: 0.7004 - loss: 0.8616


301/352 ━━━━━━━━━━━━━━━━━━━━ 8s 165ms/step - accuracy: 0.7004 - loss: 0.8616


302/352 ━━━━━━━━━━━━━━━━━━━━ 8s 165ms/step - accuracy: 0.7004 - loss: 0.8616


303/352 ━━━━━━━━━━━━━━━━━━━━ 8s 165ms/step - accuracy: 0.7004 - loss: 0.8617


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 165ms/step - accuracy: 0.7004 - loss: 0.8617


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 165ms/step - accuracy: 0.7004 - loss: 0.8617


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 165ms/step - accuracy: 0.7004 - loss: 0.8617


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 165ms/step - accuracy: 0.7004 - loss: 0.8617


308/352 ━━━━━━━━━━━━━━━━━━━━ 7s 166ms/step - accuracy: 0.7004 - loss: 0.8617


309/352 ━━━━━━━━━━━━━━━━━━━━ 7s 166ms/step - accuracy: 0.7003 - loss: 0.8618


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 166ms/step - accuracy: 0.7003 - loss: 0.8618


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 166ms/step - accuracy: 0.7003 - loss: 0.8618


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 167ms/step - accuracy: 0.7003 - loss: 0.8618


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 168ms/step - accuracy: 0.7003 - loss: 0.8618


314/352 ━━━━━━━━━━━━━━━━━━━━ 6s 168ms/step - accuracy: 0.7003 - loss: 0.8619


315/352 ━━━━━━━━━━━━━━━━━━━━ 6s 168ms/step - accuracy: 0.7003 - loss: 0.8619


316/352 ━━━━━━━━━━━━━━━━━━━━ 6s 169ms/step - accuracy: 0.7003 - loss: 0.8619


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 169ms/step - accuracy: 0.7003 - loss: 0.8619


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 170ms/step - accuracy: 0.7003 - loss: 0.8619


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 170ms/step - accuracy: 0.7003 - loss: 0.8619


320/352 ━━━━━━━━━━━━━━━━━━━━ 5s 171ms/step - accuracy: 0.7003 - loss: 0.8620


321/352 ━━━━━━━━━━━━━━━━━━━━ 5s 171ms/step - accuracy: 0.7002 - loss: 0.8620


322/352 ━━━━━━━━━━━━━━━━━━━━ 5s 172ms/step - accuracy: 0.7002 - loss: 0.8620


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8620


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8620


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8621


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8621


327/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8621


328/352 ━━━━━━━━━━━━━━━━━━━━ 4s 172ms/step - accuracy: 0.7002 - loss: 0.8621


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 172ms/step - accuracy: 0.7002 - loss: 0.8621


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 172ms/step - accuracy: 0.7002 - loss: 0.8621


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 172ms/step - accuracy: 0.7002 - loss: 0.8622


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 172ms/step - accuracy: 0.7002 - loss: 0.8622


333/352 ━━━━━━━━━━━━━━━━━━━━ 3s 171ms/step - accuracy: 0.7001 - loss: 0.8622


334/352 ━━━━━━━━━━━━━━━━━━━━ 3s 171ms/step - accuracy: 0.7001 - loss: 0.8622


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8622


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8622


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8623


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8623


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8623


340/352 ━━━━━━━━━━━━━━━━━━━━ 2s 171ms/step - accuracy: 0.7001 - loss: 0.8623


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7001 - loss: 0.8623


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7001 - loss: 0.8624


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7001 - loss: 0.8624


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7000 - loss: 0.8624


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7000 - loss: 0.8624


346/352 ━━━━━━━━━━━━━━━━━━━━ 1s 171ms/step - accuracy: 0.7000 - loss: 0.8624


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8624


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8625


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8625


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8625


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8625


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 171ms/step - accuracy: 0.7000 - loss: 0.8625


352/352 ━━━━━━━━━━━━━━━━━━━━ 63s 179ms/step - accuracy: 0.6973 - loss: 0.8673 - val_accuracy: 0.7222 - val_loss: 0.7918

Epoch 9/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:21 232ms/step - accuracy: 0.7266 - loss: 0.7507


  2/352 ━━━━━━━━━━━━━━━━━━━━ 50s 144ms/step - accuracy: 0.7305 - loss: 0.7440 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 53s 152ms/step - accuracy: 0.7257 - loss: 0.7569


  4/352 ━━━━━━━━━━━━━━━━━━━━ 52s 152ms/step - accuracy: 0.7230 - loss: 0.7673


  5/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7237 - loss: 0.7721


  6/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7235 - loss: 0.7792


  7/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7225 - loss: 0.7833


  8/352 ━━━━━━━━━━━━━━━━━━━━ 50s 148ms/step - accuracy: 0.7210 - loss: 0.7878


  9/352 ━━━━━━━━━━━━━━━━━━━━ 51s 149ms/step - accuracy: 0.7197 - loss: 0.7910


 10/352 ━━━━━━━━━━━━━━━━━━━━ 51s 149ms/step - accuracy: 0.7181 - loss: 0.7957


 11/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7170 - loss: 0.7986


 12/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7159 - loss: 0.8017


 13/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7147 - loss: 0.8052


 14/352 ━━━━━━━━━━━━━━━━━━━━ 50s 151ms/step - accuracy: 0.7135 - loss: 0.8086


 15/352 ━━━━━━━━━━━━━━━━━━━━ 50s 151ms/step - accuracy: 0.7127 - loss: 0.8112


 16/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7120 - loss: 0.8134


 17/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7116 - loss: 0.8148


 18/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7114 - loss: 0.8155


 19/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7112 - loss: 0.8160


 20/352 ━━━━━━━━━━━━━━━━━━━━ 49s 150ms/step - accuracy: 0.7110 - loss: 0.8164


 21/352 ━━━━━━━━━━━━━━━━━━━━ 49s 151ms/step - accuracy: 0.7108 - loss: 0.8170


 22/352 ━━━━━━━━━━━━━━━━━━━━ 49s 150ms/step - accuracy: 0.7107 - loss: 0.8176


 23/352 ━━━━━━━━━━━━━━━━━━━━ 49s 150ms/step - accuracy: 0.7106 - loss: 0.8184


 24/352 ━━━━━━━━━━━━━━━━━━━━ 49s 150ms/step - accuracy: 0.7104 - loss: 0.8191


 25/352 ━━━━━━━━━━━━━━━━━━━━ 49s 151ms/step - accuracy: 0.7101 - loss: 0.8201


 26/352 ━━━━━━━━━━━━━━━━━━━━ 48s 150ms/step - accuracy: 0.7098 - loss: 0.8211


 27/352 ━━━━━━━━━━━━━━━━━━━━ 48s 150ms/step - accuracy: 0.7095 - loss: 0.8220


 28/352 ━━━━━━━━━━━━━━━━━━━━ 48s 150ms/step - accuracy: 0.7093 - loss: 0.8229


 29/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.7090 - loss: 0.8237


 30/352 ━━━━━━━━━━━━━━━━━━━━ 48s 150ms/step - accuracy: 0.7088 - loss: 0.8244


 31/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.7086 - loss: 0.8251


 32/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.7085 - loss: 0.8256


 33/352 ━━━━━━━━━━━━━━━━━━━━ 48s 151ms/step - accuracy: 0.7083 - loss: 0.8261


 34/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7082 - loss: 0.8266


 35/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7080 - loss: 0.8271


 36/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7079 - loss: 0.8275


 37/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7077 - loss: 0.8279


 38/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7076 - loss: 0.8283


 39/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7074 - loss: 0.8288


 40/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7072 - loss: 0.8292


 41/352 ━━━━━━━━━━━━━━━━━━━━ 47s 151ms/step - accuracy: 0.7070 - loss: 0.8296


 42/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7069 - loss: 0.8299


 43/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7068 - loss: 0.8302


 44/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7067 - loss: 0.8305


 45/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7066 - loss: 0.8309


 46/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7066 - loss: 0.8312


 47/352 ━━━━━━━━━━━━━━━━━━━━ 46s 151ms/step - accuracy: 0.7065 - loss: 0.8315


 48/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7064 - loss: 0.8317


 49/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7064 - loss: 0.8319


 50/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7063 - loss: 0.8321


 51/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7063 - loss: 0.8322


 52/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7062 - loss: 0.8322


 53/352 ━━━━━━━━━━━━━━━━━━━━ 45s 151ms/step - accuracy: 0.7062 - loss: 0.8323


 54/352 ━━━━━━━━━━━━━━━━━━━━ 44s 151ms/step - accuracy: 0.7061 - loss: 0.8325


 55/352 ━━━━━━━━━━━━━━━━━━━━ 44s 151ms/step - accuracy: 0.7061 - loss: 0.8326


 56/352 ━━━━━━━━━━━━━━━━━━━━ 44s 152ms/step - accuracy: 0.7060 - loss: 0.8327


 57/352 ━━━━━━━━━━━━━━━━━━━━ 44s 151ms/step - accuracy: 0.7059 - loss: 0.8329


 58/352 ━━━━━━━━━━━━━━━━━━━━ 44s 151ms/step - accuracy: 0.7058 - loss: 0.8331


 59/352 ━━━━━━━━━━━━━━━━━━━━ 44s 151ms/step - accuracy: 0.7058 - loss: 0.8333


 60/352 ━━━━━━━━━━━━━━━━━━━━ 44s 152ms/step - accuracy: 0.7057 - loss: 0.8335


 61/352 ━━━━━━━━━━━━━━━━━━━━ 44s 152ms/step - accuracy: 0.7056 - loss: 0.8337


 62/352 ━━━━━━━━━━━━━━━━━━━━ 44s 152ms/step - accuracy: 0.7055 - loss: 0.8339


 63/352 ━━━━━━━━━━━━━━━━━━━━ 43s 152ms/step - accuracy: 0.7055 - loss: 0.8341


 64/352 ━━━━━━━━━━━━━━━━━━━━ 43s 152ms/step - accuracy: 0.7054 - loss: 0.8343


 65/352 ━━━━━━━━━━━━━━━━━━━━ 43s 153ms/step - accuracy: 0.7054 - loss: 0.8345


 66/352 ━━━━━━━━━━━━━━━━━━━━ 43s 152ms/step - accuracy: 0.7053 - loss: 0.8346


 67/352 ━━━━━━━━━━━━━━━━━━━━ 43s 152ms/step - accuracy: 0.7053 - loss: 0.8348


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317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.7050 - loss: 0.8376


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.7050 - loss: 0.8375


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.7050 - loss: 0.8375


320/352 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.7050 - loss: 0.8375


321/352 ━━━━━━━━━━━━━━━━━━━━ 5s 163ms/step - accuracy: 0.7050 - loss: 0.8375


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8375


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8375


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8374


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8374


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8374


327/352 ━━━━━━━━━━━━━━━━━━━━ 4s 163ms/step - accuracy: 0.7051 - loss: 0.8374


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8374


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8374


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8373


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8373


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8373


333/352 ━━━━━━━━━━━━━━━━━━━━ 3s 163ms/step - accuracy: 0.7051 - loss: 0.8373


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7051 - loss: 0.8373


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7051 - loss: 0.8373


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7052 - loss: 0.8373


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7052 - loss: 0.8372


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7052 - loss: 0.8372


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 162ms/step - accuracy: 0.7052 - loss: 0.8372


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8372


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8372


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8372


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8371


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8371


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 162ms/step - accuracy: 0.7052 - loss: 0.8371


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7052 - loss: 0.8371


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7052 - loss: 0.8371


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7053 - loss: 0.8370


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7053 - loss: 0.8370


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7053 - loss: 0.8370


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7053 - loss: 0.8370


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7053 - loss: 0.8370


352/352 ━━━━━━━━━━━━━━━━━━━━ 60s 169ms/step - accuracy: 0.7086 - loss: 0.8298 - val_accuracy: 0.7418 - val_loss: 0.7552

Epoch 10/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 2:13:55 23s/step - accuracy: 0.7656 - loss: 0.6774


  2/352 ━━━━━━━━━━━━━━━━━━━━ 59s 171ms/step - accuracy: 0.7422 - loss: 0.7234  


  3/352 ━━━━━━━━━━━━━━━━━━━━ 59s 169ms/step - accuracy: 0.7361 - loss: 0.7355


  4/352 ━━━━━━━━━━━━━━━━━━━━ 56s 163ms/step - accuracy: 0.7357 - loss: 0.7372


  5/352 ━━━━━━━━━━━━━━━━━━━━ 55s 160ms/step - accuracy: 0.7323 - loss: 0.7438


  6/352 ━━━━━━━━━━━━━━━━━━━━ 56s 162ms/step - accuracy: 0.7294 - loss: 0.7489


  7/352 ━━━━━━━━━━━━━━━━━━━━ 56s 164ms/step - accuracy: 0.7266 - loss: 0.7562


  8/352 ━━━━━━━━━━━━━━━━━━━━ 55s 163ms/step - accuracy: 0.7255 - loss: 0.7583


  9/352 ━━━━━━━━━━━━━━━━━━━━ 55s 162ms/step - accuracy: 0.7250 - loss: 0.7607


 10/352 ━━━━━━━━━━━━━━━━━━━━ 55s 163ms/step - accuracy: 0.7242 - loss: 0.7631


 11/352 ━━━━━━━━━━━━━━━━━━━━ 55s 164ms/step - accuracy: 0.7233 - loss: 0.7661


 12/352 ━━━━━━━━━━━━━━━━━━━━ 55s 163ms/step - accuracy: 0.7227 - loss: 0.7686


 13/352 ━━━━━━━━━━━━━━━━━━━━ 54s 161ms/step - accuracy: 0.7222 - loss: 0.7710


 14/352 ━━━━━━━━━━━━━━━━━━━━ 54s 161ms/step - accuracy: 0.7222 - loss: 0.7724


 15/352 ━━━━━━━━━━━━━━━━━━━━ 53s 160ms/step - accuracy: 0.7221 - loss: 0.7735


 16/352 ━━━━━━━━━━━━━━━━━━━━ 53s 159ms/step - accuracy: 0.7223 - loss: 0.7740


 17/352 ━━━━━━━━━━━━━━━━━━━━ 53s 159ms/step - accuracy: 0.7225 - loss: 0.7745


 18/352 ━━━━━━━━━━━━━━━━━━━━ 53s 159ms/step - accuracy: 0.7227 - loss: 0.7749


 19/352 ━━━━━━━━━━━━━━━━━━━━ 52s 159ms/step - accuracy: 0.7229 - loss: 0.7751


 20/352 ━━━━━━━━━━━━━━━━━━━━ 52s 159ms/step - accuracy: 0.7230 - loss: 0.7755


 21/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7231 - loss: 0.7757


 22/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7232 - loss: 0.7759


 23/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7232 - loss: 0.7761


 24/352 ━━━━━━━━━━━━━━━━━━━━ 51s 158ms/step - accuracy: 0.7233 - loss: 0.7763


 25/352 ━━━━━━━━━━━━━━━━━━━━ 51s 158ms/step - accuracy: 0.7234 - loss: 0.7763


 26/352 ━━━━━━━━━━━━━━━━━━━━ 52s 160ms/step - accuracy: 0.7234 - loss: 0.7764


 27/352 ━━━━━━━━━━━━━━━━━━━━ 51s 159ms/step - accuracy: 0.7235 - loss: 0.7764


 28/352 ━━━━━━━━━━━━━━━━━━━━ 51s 159ms/step - accuracy: 0.7236 - loss: 0.7763


 29/352 ━━━━━━━━━━━━━━━━━━━━ 51s 159ms/step - accuracy: 0.7237 - loss: 0.7763


 30/352 ━━━━━━━━━━━━━━━━━━━━ 51s 159ms/step - accuracy: 0.7238 - loss: 0.7763


 31/352 ━━━━━━━━━━━━━━━━━━━━ 50s 158ms/step - accuracy: 0.7239 - loss: 0.7764


 32/352 ━━━━━━━━━━━━━━━━━━━━ 50s 158ms/step - accuracy: 0.7240 - loss: 0.7763


 33/352 ━━━━━━━━━━━━━━━━━━━━ 50s 157ms/step - accuracy: 0.7242 - loss: 0.7762


 34/352 ━━━━━━━━━━━━━━━━━━━━ 50s 157ms/step - accuracy: 0.7243 - loss: 0.7761


 35/352 ━━━━━━━━━━━━━━━━━━━━ 49s 158ms/step - accuracy: 0.7245 - loss: 0.7763


 36/352 ━━━━━━━━━━━━━━━━━━━━ 49s 158ms/step - accuracy: 0.7245 - loss: 0.7764


 37/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7247 - loss: 0.7765


 38/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7248 - loss: 0.7766


 39/352 ━━━━━━━━━━━━━━━━━━━━ 49s 158ms/step - accuracy: 0.7248 - loss: 0.7767


 40/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7249 - loss: 0.7769


 41/352 ━━━━━━━━━━━━━━━━━━━━ 49s 158ms/step - accuracy: 0.7249 - loss: 0.7772


 42/352 ━━━━━━━━━━━━━━━━━━━━ 49s 158ms/step - accuracy: 0.7250 - loss: 0.7774


 43/352 ━━━━━━━━━━━━━━━━━━━━ 49s 159ms/step - accuracy: 0.7250 - loss: 0.7777


 44/352 ━━━━━━━━━━━━━━━━━━━━ 49s 160ms/step - accuracy: 0.7250 - loss: 0.7780


 45/352 ━━━━━━━━━━━━━━━━━━━━ 49s 160ms/step - accuracy: 0.7249 - loss: 0.7785


 46/352 ━━━━━━━━━━━━━━━━━━━━ 48s 159ms/step - accuracy: 0.7248 - loss: 0.7789


 47/352 ━━━━━━━━━━━━━━━━━━━━ 48s 159ms/step - accuracy: 0.7247 - loss: 0.7794


 48/352 ━━━━━━━━━━━━━━━━━━━━ 48s 159ms/step - accuracy: 0.7246 - loss: 0.7799


 49/352 ━━━━━━━━━━━━━━━━━━━━ 48s 158ms/step - accuracy: 0.7245 - loss: 0.7803


 50/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7244 - loss: 0.7808


 51/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7243 - loss: 0.7812


 52/352 ━━━━━━━━━━━━━━━━━━━━ 47s 158ms/step - accuracy: 0.7242 - loss: 0.7816


 53/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7241 - loss: 0.7821


 54/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7240 - loss: 0.7824


 55/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7239 - loss: 0.7827


 56/352 ━━━━━━━━━━━━━━━━━━━━ 47s 159ms/step - accuracy: 0.7238 - loss: 0.7830


 57/352 ━━━━━━━━━━━━━━━━━━━━ 47s 160ms/step - accuracy: 0.7238 - loss: 0.7832


 58/352 ━━━━━━━━━━━━━━━━━━━━ 47s 160ms/step - accuracy: 0.7237 - loss: 0.7835


 59/352 ━━━━━━━━━━━━━━━━━━━━ 47s 160ms/step - accuracy: 0.7237 - loss: 0.7837


 60/352 ━━━━━━━━━━━━━━━━━━━━ 47s 161ms/step - accuracy: 0.7236 - loss: 0.7839


 61/352 ━━━━━━━━━━━━━━━━━━━━ 46s 161ms/step - accuracy: 0.7236 - loss: 0.7841


 62/352 ━━━━━━━━━━━━━━━━━━━━ 46s 162ms/step - accuracy: 0.7236 - loss: 0.7843


 63/352 ━━━━━━━━━━━━━━━━━━━━ 46s 162ms/step - accuracy: 0.7235 - loss: 0.7845


 64/352 ━━━━━━━━━━━━━━━━━━━━ 46s 162ms/step - accuracy: 0.7235 - loss: 0.7847


 65/352 ━━━━━━━━━━━━━━━━━━━━ 46s 162ms/step - accuracy: 0.7235 - loss: 0.7848


 66/352 ━━━━━━━━━━━━━━━━━━━━ 46s 163ms/step - accuracy: 0.7235 - loss: 0.7850


 67/352 ━━━━━━━━━━━━━━━━━━━━ 46s 163ms/step - accuracy: 0.7234 - loss: 0.7851


 68/352 ━━━━━━━━━━━━━━━━━━━━ 46s 163ms/step - accuracy: 0.7234 - loss: 0.7852


 69/352 ━━━━━━━━━━━━━━━━━━━━ 46s 163ms/step - accuracy: 0.7234 - loss: 0.7853


 70/352 ━━━━━━━━━━━━━━━━━━━━ 46s 164ms/step - accuracy: 0.7234 - loss: 0.7854


 71/352 ━━━━━━━━━━━━━━━━━━━━ 46s 164ms/step - accuracy: 0.7234 - loss: 0.7856


 72/352 ━━━━━━━━━━━━━━━━━━━━ 46s 165ms/step - accuracy: 0.7234 - loss: 0.7857


 73/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7233 - loss: 0.7859


 74/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7233 - loss: 0.7860


 75/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7233 - loss: 0.7861


 76/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7233 - loss: 0.7863


 77/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7232 - loss: 0.7864


 78/352 ━━━━━━━━━━━━━━━━━━━━ 45s 165ms/step - accuracy: 0.7232 - loss: 0.7865


 79/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7232 - loss: 0.7866


 80/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7231 - loss: 0.7867


 81/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7231 - loss: 0.7869


 82/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7230 - loss: 0.7870


 83/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7230 - loss: 0.7872


 84/352 ━━━━━━━━━━━━━━━━━━━━ 44s 165ms/step - accuracy: 0.7229 - loss: 0.7873


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334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7986


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7986


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7987


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7987


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7987


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 159ms/step - accuracy: 0.7200 - loss: 0.7987


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7200 - loss: 0.7987


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7200 - loss: 0.7988


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7200 - loss: 0.7988


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7200 - loss: 0.7988


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7199 - loss: 0.7988


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 159ms/step - accuracy: 0.7199 - loss: 0.7988


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7988


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 159ms/step - accuracy: 0.7199 - loss: 0.7989


352/352 ━━━━━━━━━━━━━━━━━━━━ 81s 167ms/step - accuracy: 0.7189 - loss: 0.8060 - val_accuracy: 0.7418 - val_loss: 0.7453

Epoch 11/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 2:17:58 24s/step - accuracy: 0.7656 - loss: 0.6529


  2/352 ━━━━━━━━━━━━━━━━━━━━ 54s 155ms/step - accuracy: 0.7461 - loss: 0.6805  


  3/352 ━━━━━━━━━━━━━━━━━━━━ 53s 153ms/step - accuracy: 0.7361 - loss: 0.7007


  4/352 ━━━━━━━━━━━━━━━━━━━━ 55s 160ms/step - accuracy: 0.7298 - loss: 0.7211


  5/352 ━━━━━━━━━━━━━━━━━━━━ 55s 159ms/step - accuracy: 0.7270 - loss: 0.7338


  6/352 ━━━━━━━━━━━━━━━━━━━━ 54s 157ms/step - accuracy: 0.7258 - loss: 0.7402


  7/352 ━━━━━━━━━━━━━━━━━━━━ 54s 157ms/step - accuracy: 0.7242 - loss: 0.7464


  8/352 ━━━━━━━━━━━━━━━━━━━━ 54s 157ms/step - accuracy: 0.7230 - loss: 0.7516


  9/352 ━━━━━━━━━━━━━━━━━━━━ 54s 159ms/step - accuracy: 0.7217 - loss: 0.7549


 10/352 ━━━━━━━━━━━━━━━━━━━━ 54s 159ms/step - accuracy: 0.7215 - loss: 0.7559


 11/352 ━━━━━━━━━━━━━━━━━━━━ 54s 159ms/step - accuracy: 0.7212 - loss: 0.7579


 12/352 ━━━━━━━━━━━━━━━━━━━━ 53s 158ms/step - accuracy: 0.7210 - loss: 0.7591


 13/352 ━━━━━━━━━━━━━━━━━━━━ 53s 158ms/step - accuracy: 0.7209 - loss: 0.7602


 14/352 ━━━━━━━━━━━━━━━━━━━━ 53s 157ms/step - accuracy: 0.7210 - loss: 0.7607


 15/352 ━━━━━━━━━━━━━━━━━━━━ 53s 157ms/step - accuracy: 0.7215 - loss: 0.7608


 16/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7217 - loss: 0.7610


 17/352 ━━━━━━━━━━━━━━━━━━━━ 52s 157ms/step - accuracy: 0.7218 - loss: 0.7611


 18/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7221 - loss: 0.7610


 19/352 ━━━━━━━━━━━━━━━━━━━━ 52s 157ms/step - accuracy: 0.7224 - loss: 0.7610


 20/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7227 - loss: 0.7608


 21/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7229 - loss: 0.7609


 22/352 ━━━━━━━━━━━━━━━━━━━━ 52s 158ms/step - accuracy: 0.7230 - loss: 0.7611


 23/352 ━━━━━━━━━━━━━━━━━━━━ 51s 157ms/step - accuracy: 0.7231 - loss: 0.7612


 24/352 ━━━━━━━━━━━━━━━━━━━━ 51s 157ms/step - accuracy: 0.7231 - loss: 0.7616


 25/352 ━━━━━━━━━━━━━━━━━━━━ 51s 157ms/step - accuracy: 0.7231 - loss: 0.7618


 26/352 ━━━━━━━━━━━━━━━━━━━━ 51s 157ms/step - accuracy: 0.7231 - loss: 0.7619


 27/352 ━━━━━━━━━━━━━━━━━━━━ 51s 157ms/step - accuracy: 0.7231 - loss: 0.7621


 28/352 ━━━━━━━━━━━━━━━━━━━━ 50s 157ms/step - accuracy: 0.7231 - loss: 0.7623


 29/352 ━━━━━━━━━━━━━━━━━━━━ 50s 157ms/step - accuracy: 0.7232 - loss: 0.7625


 30/352 ━━━━━━━━━━━━━━━━━━━━ 50s 156ms/step - accuracy: 0.7232 - loss: 0.7625


 31/352 ━━━━━━━━━━━━━━━━━━━━ 50s 156ms/step - accuracy: 0.7233 - loss: 0.7625


 32/352 ━━━━━━━━━━━━━━━━━━━━ 50s 156ms/step - accuracy: 0.7234 - loss: 0.7627


 33/352 ━━━━━━━━━━━━━━━━━━━━ 50s 157ms/step - accuracy: 0.7234 - loss: 0.7629


 34/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7234 - loss: 0.7631


 35/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7234 - loss: 0.7633


 36/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7234 - loss: 0.7634


 37/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7234 - loss: 0.7637


 38/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7233 - loss: 0.7641


 39/352 ━━━━━━━━━━━━━━━━━━━━ 49s 157ms/step - accuracy: 0.7233 - loss: 0.7644


 40/352 ━━━━━━━━━━━━━━━━━━━━ 48s 156ms/step - accuracy: 0.7233 - loss: 0.7646


 41/352 ━━━━━━━━━━━━━━━━━━━━ 48s 156ms/step - accuracy: 0.7232 - loss: 0.7648


 42/352 ━━━━━━━━━━━━━━━━━━━━ 48s 156ms/step - accuracy: 0.7232 - loss: 0.7650


 43/352 ━━━━━━━━━━━━━━━━━━━━ 48s 156ms/step - accuracy: 0.7231 - loss: 0.7652


 44/352 ━━━━━━━━━━━━━━━━━━━━ 48s 156ms/step - accuracy: 0.7231 - loss: 0.7654


 45/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7231 - loss: 0.7657


 46/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7230 - loss: 0.7658


 47/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7230 - loss: 0.7660


 48/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7230 - loss: 0.7661


 49/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7229 - loss: 0.7662


 50/352 ━━━━━━━━━━━━━━━━━━━━ 47s 156ms/step - accuracy: 0.7229 - loss: 0.7662


 51/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7663


 52/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7664


 53/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7665


 54/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7666


 55/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7667


 56/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7229 - loss: 0.7668


 57/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7229 - loss: 0.7669


 58/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7230 - loss: 0.7670


 59/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7230 - loss: 0.7670


 60/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7230 - loss: 0.7671


 61/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7230 - loss: 0.7670


 62/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7231 - loss: 0.7670


 63/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7231 - loss: 0.7670


 64/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7232 - loss: 0.7669


 65/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7232 - loss: 0.7669


 66/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7233 - loss: 0.7669


 67/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7233 - loss: 0.7668


 68/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7234 - loss: 0.7668


 69/352 ━━━━━━━━━━━━━━━━━━━━ 44s 156ms/step - accuracy: 0.7234 - loss: 0.7667


 70/352 ━━━━━━━━━━━━━━━━━━━━ 43s 156ms/step - accuracy: 0.7234 - loss: 0.7667


 71/352 ━━━━━━━━━━━━━━━━━━━━ 43s 156ms/step - accuracy: 0.7234 - loss: 0.7667


 72/352 ━━━━━━━━━━━━━━━━━━━━ 43s 156ms/step - accuracy: 0.7235 - loss: 0.7667


 73/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7235 - loss: 0.7667


 74/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7235 - loss: 0.7666


 75/352 ━━━━━━━━━━━━━━━━━━━━ 43s 156ms/step - accuracy: 0.7235 - loss: 0.7666


 76/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7236 - loss: 0.7666


 77/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7236 - loss: 0.7666


 78/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7236 - loss: 0.7666


 79/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7236 - loss: 0.7666


 80/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7237 - loss: 0.7667


 81/352 ━━━━━━━━━━━━━━━━━━━━ 42s 156ms/step - accuracy: 0.7237 - loss: 0.7667


 82/352 ━━━━━━━━━━━━━━━━━━━━ 41s 156ms/step - accuracy: 0.7237 - loss: 0.7668


 83/352 ━━━━━━━━━━━━━━━━━━━━ 41s 155ms/step - accuracy: 0.7237 - loss: 0.7668


 84/352 ━━━━━━━━━━━━━━━━━━━━ 41s 156ms/step - accuracy: 0.7238 - loss: 0.7669


 85/352 ━━━━━━━━━━━━━━━━━━━━ 41s 155ms/step - accuracy: 0.7238 - loss: 0.7669


 86/352 ━━━━━━━━━━━━━━━━━━━━ 41s 155ms/step - accuracy: 0.7238 - loss: 0.7670


 87/352 ━━━━━━━━━━━━━━━━━━━━ 41s 155ms/step - accuracy: 0.7238 - loss: 0.7671


 88/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7238 - loss: 0.7671


 89/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7239 - loss: 0.7672


 90/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7239 - loss: 0.7673


 91/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7239 - loss: 0.7673


 92/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7239 - loss: 0.7674


 93/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7239 - loss: 0.7675


 94/352 ━━━━━━━━━━━━━━━━━━━━ 40s 155ms/step - accuracy: 0.7240 - loss: 0.7675


 95/352 ━━━━━━━━━━━━━━━━━━━━ 39s 156ms/step - accuracy: 0.7240 - loss: 0.7676


 96/352 ━━━━━━━━━━━━━━━━━━━━ 39s 156ms/step - accuracy: 0.7240 - loss: 0.7677


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100/352 ━━━━━━━━━━━━━━━━━━━━ 39s 156ms/step - accuracy: 0.7241 - loss: 0.7679


101/352 ━━━━━━━━━━━━━━━━━━━━ 39s 156ms/step - accuracy: 0.7241 - loss: 0.7679


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351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 162ms/step - accuracy: 0.7257 - loss: 0.7737


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 161ms/step - accuracy: 0.7257 - loss: 0.7737


352/352 ━━━━━━━━━━━━━━━━━━━━ 83s 169ms/step - accuracy: 0.7251 - loss: 0.7815 - val_accuracy: 0.7448 - val_loss: 0.7345

Epoch 12/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:26 247ms/step - accuracy: 0.7344 - loss: 0.7088


  2/352 ━━━━━━━━━━━━━━━━━━━━ 51s 148ms/step - accuracy: 0.7344 - loss: 0.7195 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 51s 148ms/step - accuracy: 0.7335 - loss: 0.7205


  4/352 ━━━━━━━━━━━━━━━━━━━━ 52s 150ms/step - accuracy: 0.7318 - loss: 0.7252


  5/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7310 - loss: 0.7280


  6/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7307 - loss: 0.7280


  7/352 ━━━━━━━━━━━━━━━━━━━━ 51s 148ms/step - accuracy: 0.7306 - loss: 0.7281


  8/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7307 - loss: 0.7269


  9/352 ━━━━━━━━━━━━━━━━━━━━ 51s 149ms/step - accuracy: 0.7304 - loss: 0.7281


 10/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7299 - loss: 0.7303


 11/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7296 - loss: 0.7322


 12/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7292 - loss: 0.7340


 13/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7292 - loss: 0.7350


 14/352 ━━━━━━━━━━━━━━━━━━━━ 50s 150ms/step - accuracy: 0.7294 - loss: 0.7357


 15/352 ━━━━━━━━━━━━━━━━━━━━ 50s 151ms/step - accuracy: 0.7295 - loss: 0.7362


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 20/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.7294 - loss: 0.7381


 21/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.7295 - loss: 0.7384


 22/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.7296 - loss: 0.7387


 23/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.7297 - loss: 0.7389


 24/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7299 - loss: 0.7392


 25/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7300 - loss: 0.7393


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 28/352 ━━━━━━━━━━━━━━━━━━━━ 49s 153ms/step - accuracy: 0.7303 - loss: 0.7401


 29/352 ━━━━━━━━━━━━━━━━━━━━ 49s 153ms/step - accuracy: 0.7303 - loss: 0.7403


 30/352 ━━━━━━━━━━━━━━━━━━━━ 49s 153ms/step - accuracy: 0.7304 - loss: 0.7404


 31/352 ━━━━━━━━━━━━━━━━━━━━ 49s 153ms/step - accuracy: 0.7306 - loss: 0.7404


 32/352 ━━━━━━━━━━━━━━━━━━━━ 48s 153ms/step - accuracy: 0.7307 - loss: 0.7404


 33/352 ━━━━━━━━━━━━━━━━━━━━ 48s 153ms/step - accuracy: 0.7309 - loss: 0.7403


 34/352 ━━━━━━━━━━━━━━━━━━━━ 48s 153ms/step - accuracy: 0.7311 - loss: 0.7403


 35/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.7312 - loss: 0.7404


 36/352 ━━━━━━━━━━━━━━━━━━━━ 48s 154ms/step - accuracy: 0.7313 - loss: 0.7405


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 38/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.7317 - loss: 0.7406


 39/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.7318 - loss: 0.7408


 40/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.7320 - loss: 0.7408


 41/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.7322 - loss: 0.7408


 42/352 ━━━━━━━━━━━━━━━━━━━━ 48s 155ms/step - accuracy: 0.7323 - loss: 0.7409


 43/352 ━━━━━━━━━━━━━━━━━━━━ 47s 155ms/step - accuracy: 0.7325 - loss: 0.7410


 44/352 ━━━━━━━━━━━━━━━━━━━━ 47s 155ms/step - accuracy: 0.7327 - loss: 0.7410


 45/352 ━━━━━━━━━━━━━━━━━━━━ 47s 155ms/step - accuracy: 0.7328 - loss: 0.7409


 46/352 ━━━━━━━━━━━━━━━━━━━━ 47s 155ms/step - accuracy: 0.7330 - loss: 0.7409


 47/352 ━━━━━━━━━━━━━━━━━━━━ 47s 154ms/step - accuracy: 0.7332 - loss: 0.7407


 48/352 ━━━━━━━━━━━━━━━━━━━━ 47s 155ms/step - accuracy: 0.7334 - loss: 0.7406


 49/352 ━━━━━━━━━━━━━━━━━━━━ 46s 155ms/step - accuracy: 0.7335 - loss: 0.7405


 50/352 ━━━━━━━━━━━━━━━━━━━━ 46s 155ms/step - accuracy: 0.7337 - loss: 0.7405


 51/352 ━━━━━━━━━━━━━━━━━━━━ 46s 154ms/step - accuracy: 0.7338 - loss: 0.7404


 52/352 ━━━━━━━━━━━━━━━━━━━━ 46s 155ms/step - accuracy: 0.7340 - loss: 0.7403


 53/352 ━━━━━━━━━━━━━━━━━━━━ 46s 155ms/step - accuracy: 0.7341 - loss: 0.7403


 54/352 ━━━━━━━━━━━━━━━━━━━━ 46s 155ms/step - accuracy: 0.7342 - loss: 0.7403


 55/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7343 - loss: 0.7403


 56/352 ━━━━━━━━━━━━━━━━━━━━ 46s 156ms/step - accuracy: 0.7343 - loss: 0.7404


 57/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7344 - loss: 0.7405


 58/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7345 - loss: 0.7406


 59/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7345 - loss: 0.7407


 60/352 ━━━━━━━━━━━━━━━━━━━━ 45s 155ms/step - accuracy: 0.7346 - loss: 0.7407


 61/352 ━━━━━━━━━━━━━━━━━━━━ 45s 156ms/step - accuracy: 0.7346 - loss: 0.7408


 62/352 ━━━━━━━━━━━━━━━━━━━━ 45s 155ms/step - accuracy: 0.7347 - loss: 0.7409


 63/352 ━━━━━━━━━━━━━━━━━━━━ 44s 155ms/step - accuracy: 0.7347 - loss: 0.7410


 64/352 ━━━━━━━━━━━━━━━━━━━━ 44s 155ms/step - accuracy: 0.7348 - loss: 0.7411


 65/352 ━━━━━━━━━━━━━━━━━━━━ 44s 155ms/step - accuracy: 0.7348 - loss: 0.7412


 66/352 ━━━━━━━━━━━━━━━━━━━━ 44s 155ms/step - accuracy: 0.7349 - loss: 0.7413


 67/352 ━━━━━━━━━━━━━━━━━━━━ 44s 155ms/step - accuracy: 0.7349 - loss: 0.7414


 68/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7349 - loss: 0.7415


 69/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7350 - loss: 0.7416


 70/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7350 - loss: 0.7418


 71/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7350 - loss: 0.7419


 72/352 ━━━━━━━━━━━━━━━━━━━━ 43s 154ms/step - accuracy: 0.7351 - loss: 0.7420


 73/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7351 - loss: 0.7422


 74/352 ━━━━━━━━━━━━━━━━━━━━ 43s 155ms/step - accuracy: 0.7351 - loss: 0.7423


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 81/352 ━━━━━━━━━━━━━━━━━━━━ 41s 155ms/step - accuracy: 0.7352 - loss: 0.7432


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 85/352 ━━━━━━━━━━━━━━━━━━━━ 41s 154ms/step - accuracy: 0.7352 - loss: 0.7438


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249/352 ━━━━━━━━━━━━━━━━━━━━ 15s 155ms/step - accuracy: 0.7347 - loss: 0.7522


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253/352 ━━━━━━━━━━━━━━━━━━━━ 15s 155ms/step - accuracy: 0.7347 - loss: 0.7523


254/352 ━━━━━━━━━━━━━━━━━━━━ 15s 155ms/step - accuracy: 0.7347 - loss: 0.7524


255/352 ━━━━━━━━━━━━━━━━━━━━ 15s 155ms/step - accuracy: 0.7347 - loss: 0.7524


256/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7347 - loss: 0.7524


257/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7347 - loss: 0.7525


258/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7347 - loss: 0.7525


259/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7347 - loss: 0.7525


260/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7346 - loss: 0.7526


261/352 ━━━━━━━━━━━━━━━━━━━━ 14s 155ms/step - accuracy: 0.7346 - loss: 0.7526


262/352 ━━━━━━━━━━━━━━━━━━━━ 13s 155ms/step - accuracy: 0.7346 - loss: 0.7526


263/352 ━━━━━━━━━━━━━━━━━━━━ 13s 155ms/step - accuracy: 0.7346 - loss: 0.7526


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267/352 ━━━━━━━━━━━━━━━━━━━━ 13s 155ms/step - accuracy: 0.7346 - loss: 0.7528


268/352 ━━━━━━━━━━━━━━━━━━━━ 13s 155ms/step - accuracy: 0.7346 - loss: 0.7528


269/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7346 - loss: 0.7528


270/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7346 - loss: 0.7528


271/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7346 - loss: 0.7529


272/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7346 - loss: 0.7529


273/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7345 - loss: 0.7529


274/352 ━━━━━━━━━━━━━━━━━━━━ 12s 155ms/step - accuracy: 0.7345 - loss: 0.7529


275/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7530


276/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7530


277/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7530


278/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7530


279/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7531


280/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7531


281/352 ━━━━━━━━━━━━━━━━━━━━ 11s 155ms/step - accuracy: 0.7345 - loss: 0.7531


282/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7531


283/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7532


284/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7532


285/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7532


286/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7532


287/352 ━━━━━━━━━━━━━━━━━━━━ 10s 155ms/step - accuracy: 0.7345 - loss: 0.7533


288/352 ━━━━━━━━━━━━━━━━━━━━ 9s 155ms/step - accuracy: 0.7344 - loss: 0.7533 


289/352 ━━━━━━━━━━━━━━━━━━━━ 9s 155ms/step - accuracy: 0.7344 - loss: 0.7533


290/352 ━━━━━━━━━━━━━━━━━━━━ 9s 156ms/step - accuracy: 0.7344 - loss: 0.7533


291/352 ━━━━━━━━━━━━━━━━━━━━ 9s 156ms/step - accuracy: 0.7344 - loss: 0.7534


292/352 ━━━━━━━━━━━━━━━━━━━━ 9s 156ms/step - accuracy: 0.7344 - loss: 0.7534


293/352 ━━━━━━━━━━━━━━━━━━━━ 9s 156ms/step - accuracy: 0.7344 - loss: 0.7534


294/352 ━━━━━━━━━━━━━━━━━━━━ 9s 156ms/step - accuracy: 0.7344 - loss: 0.7534


295/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7535


296/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7535


297/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7535


298/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7535


299/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7536


300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 156ms/step - accuracy: 0.7344 - loss: 0.7536


301/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7344 - loss: 0.7536


302/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7536


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7536


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7537


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7537


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7537


307/352 ━━━━━━━━━━━━━━━━━━━━ 7s 156ms/step - accuracy: 0.7343 - loss: 0.7537


308/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7537


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7537


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7538


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7538


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7538


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 156ms/step - accuracy: 0.7343 - loss: 0.7538


314/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7538


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7538


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7539


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7539


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7539


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 156ms/step - accuracy: 0.7343 - loss: 0.7539


320/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7539


321/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7539


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7539


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7343 - loss: 0.7540


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7540


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7540


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 156ms/step - accuracy: 0.7343 - loss: 0.7540


327/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7540


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7541


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7541


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7541


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7541


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 156ms/step - accuracy: 0.7342 - loss: 0.7541


333/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7541


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 156ms/step - accuracy: 0.7342 - loss: 0.7542


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 156ms/step - accuracy: 0.7342 - loss: 0.7543


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - accuracy: 0.7342 - loss: 0.7544


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 155ms/step - accuracy: 0.7342 - loss: 0.7545


352/352 ━━━━━━━━━━━━━━━━━━━━ 57s 162ms/step - accuracy: 0.7331 - loss: 0.7597 - val_accuracy: 0.7440 - val_loss: 0.7319

Epoch 13/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:24 240ms/step - accuracy: 0.7188 - loss: 0.7124


  2/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7266 - loss: 0.7233 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 51s 148ms/step - accuracy: 0.7248 - loss: 0.7408


  4/352 ━━━━━━━━━━━━━━━━━━━━ 52s 150ms/step - accuracy: 0.7228 - loss: 0.7493


  5/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7204 - loss: 0.7548


  6/352 ━━━━━━━━━━━━━━━━━━━━ 51s 150ms/step - accuracy: 0.7180 - loss: 0.7571


  7/352 ━━━━━━━━━━━━━━━━━━━━ 51s 149ms/step - accuracy: 0.7171 - loss: 0.7571


  8/352 ━━━━━━━━━━━━━━━━━━━━ 50s 148ms/step - accuracy: 0.7167 - loss: 0.7568


  9/352 ━━━━━━━━━━━━━━━━━━━━ 51s 149ms/step - accuracy: 0.7168 - loss: 0.7558


 10/352 ━━━━━━━━━━━━━━━━━━━━ 51s 151ms/step - accuracy: 0.7174 - loss: 0.7539


 11/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7179 - loss: 0.7530


 12/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7183 - loss: 0.7520


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263/352 ━━━━━━━━━━━━━━━━━━━━ 13s 154ms/step - accuracy: 0.7368 - loss: 0.7430


264/352 ━━━━━━━━━━━━━━━━━━━━ 13s 154ms/step - accuracy: 0.7368 - loss: 0.7430


265/352 ━━━━━━━━━━━━━━━━━━━━ 13s 154ms/step - accuracy: 0.7368 - loss: 0.7430


266/352 ━━━━━━━━━━━━━━━━━━━━ 13s 154ms/step - accuracy: 0.7368 - loss: 0.7430


267/352 ━━━━━━━━━━━━━━━━━━━━ 13s 154ms/step - accuracy: 0.7368 - loss: 0.7430


268/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


269/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


270/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


271/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


272/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


273/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


274/352 ━━━━━━━━━━━━━━━━━━━━ 12s 154ms/step - accuracy: 0.7368 - loss: 0.7430


275/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7430


276/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7430


277/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7431


278/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7431


279/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7431


280/352 ━━━━━━━━━━━━━━━━━━━━ 11s 154ms/step - accuracy: 0.7369 - loss: 0.7431


281/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


282/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


283/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


284/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


285/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


286/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


287/352 ━━━━━━━━━━━━━━━━━━━━ 10s 154ms/step - accuracy: 0.7369 - loss: 0.7431


288/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431 


289/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431


290/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431


291/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431


292/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431


293/352 ━━━━━━━━━━━━━━━━━━━━ 9s 154ms/step - accuracy: 0.7369 - loss: 0.7431


294/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


295/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


296/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


297/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


298/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


299/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


300/352 ━━━━━━━━━━━━━━━━━━━━ 8s 154ms/step - accuracy: 0.7370 - loss: 0.7431


301/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


302/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


303/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


304/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


305/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


306/352 ━━━━━━━━━━━━━━━━━━━━ 7s 154ms/step - accuracy: 0.7370 - loss: 0.7431


307/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7431


308/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7431


309/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7432


310/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7432


311/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7432


312/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7432


313/352 ━━━━━━━━━━━━━━━━━━━━ 6s 154ms/step - accuracy: 0.7370 - loss: 0.7432


314/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


315/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


316/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


317/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


318/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


319/352 ━━━━━━━━━━━━━━━━━━━━ 5s 154ms/step - accuracy: 0.7370 - loss: 0.7432


320/352 ━━━━━━━━━━━━━━━━━━━━ 4s 154ms/step - accuracy: 0.7371 - loss: 0.7432


321/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


322/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


323/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


324/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


325/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


326/352 ━━━━━━━━━━━━━━━━━━━━ 4s 155ms/step - accuracy: 0.7371 - loss: 0.7432


327/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


328/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


329/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


330/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


331/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


332/352 ━━━━━━━━━━━━━━━━━━━━ 3s 155ms/step - accuracy: 0.7371 - loss: 0.7432


333/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


336/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


337/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


338/352 ━━━━━━━━━━━━━━━━━━━━ 2s 155ms/step - accuracy: 0.7371 - loss: 0.7432


339/352 ━━━━━━━━━━━━━━━━━━━━ 2s 154ms/step - accuracy: 0.7371 - loss: 0.7433


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


345/352 ━━━━━━━━━━━━━━━━━━━━ 1s 154ms/step - accuracy: 0.7371 - loss: 0.7433


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7371 - loss: 0.7433


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7371 - loss: 0.7433


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7371 - loss: 0.7433


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7372 - loss: 0.7433


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7372 - loss: 0.7433


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7372 - loss: 0.7433


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 154ms/step - accuracy: 0.7372 - loss: 0.7433


352/352 ━━━━━━━━━━━━━━━━━━━━ 57s 161ms/step - accuracy: 0.7384 - loss: 0.7454 - val_accuracy: 0.7638 - val_loss: 0.6975

Epoch 14/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:22 234ms/step - accuracy: 0.7734 - loss: 0.6553


  2/352 ━━━━━━━━━━━━━━━━━━━━ 53s 153ms/step - accuracy: 0.7578 - loss: 0.6885 


  3/352 ━━━━━━━━━━━━━━━━━━━━ 53s 153ms/step - accuracy: 0.7561 - loss: 0.7005


  4/352 ━━━━━━━━━━━━━━━━━━━━ 52s 150ms/step - accuracy: 0.7550 - loss: 0.7046


  5/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7540 - loss: 0.7117


  6/352 ━━━━━━━━━━━━━━━━━━━━ 52s 151ms/step - accuracy: 0.7536 - loss: 0.7149


  7/352 ━━━━━━━━━━━━━━━━━━━━ 52s 153ms/step - accuracy: 0.7534 - loss: 0.7181


  8/352 ━━━━━━━━━━━━━━━━━━━━ 52s 152ms/step - accuracy: 0.7532 - loss: 0.7197


  9/352 ━━━━━━━━━━━━━━━━━━━━ 51s 151ms/step - accuracy: 0.7534 - loss: 0.7196


 10/352 ━━━━━━━━━━━━━━━━━━━━ 51s 151ms/step - accuracy: 0.7532 - loss: 0.7194


 11/352 ━━━━━━━━━━━━━━━━━━━━ 51s 152ms/step - accuracy: 0.7534 - loss: 0.7186


 12/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.7533 - loss: 0.7180


 13/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.7532 - loss: 0.7182


 14/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.7531 - loss: 0.7183


 15/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.7531 - loss: 0.7181


 16/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.7533 - loss: 0.7178


 17/352 ━━━━━━━━━━━━━━━━━━━━ 51s 153ms/step - accuracy: 0.7534 - loss: 0.7176


 18/352 ━━━━━━━━━━━━━━━━━━━━ 51s 154ms/step - accuracy: 0.7535 - loss: 0.7172


 19/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.7536 - loss: 0.7167


 20/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.7537 - loss: 0.7161


 21/352 ━━━━━━━━━━━━━━━━━━━━ 50s 153ms/step - accuracy: 0.7538 - loss: 0.7157


 22/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.7539 - loss: 0.7154


 23/352 ━━━━━━━━━━━━━━━━━━━━ 50s 152ms/step - accuracy: 0.7538 - loss: 0.7150


 24/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7537 - loss: 0.7147


 25/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7537 - loss: 0.7143


 26/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7537 - loss: 0.7139


 27/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7538 - loss: 0.7134


 28/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7538 - loss: 0.7129


 29/352 ━━━━━━━━━━━━━━━━━━━━ 49s 152ms/step - accuracy: 0.7539 - loss: 0.7125


 30/352 ━━━━━━━━━━━━━━━━━━━━ 48s 152ms/step - accuracy: 0.7539 - loss: 0.7121


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292/352 ━━━━━━━━━━━━━━━━━━━━ 7s 133ms/step - accuracy: 0.7506 - loss: 0.7136


293/352 ━━━━━━━━━━━━━━━━━━━━ 7s 132ms/step - accuracy: 0.7506 - loss: 0.7136


294/352 ━━━━━━━━━━━━━━━━━━━━ 7s 132ms/step - accuracy: 0.7506 - loss: 0.7136


295/352 ━━━━━━━━━━━━━━━━━━━━ 7s 132ms/step - accuracy: 0.7505 - loss: 0.7137


296/352 ━━━━━━━━━━━━━━━━━━━━ 7s 132ms/step - accuracy: 0.7505 - loss: 0.7137


297/352 ━━━━━━━━━━━━━━━━━━━━ 7s 131ms/step - accuracy: 0.7505 - loss: 0.7137


299/352 ━━━━━━━━━━━━━━━━━━━━ 6s 131ms/step - accuracy: 0.7505 - loss: 0.7138


300/352 ━━━━━━━━━━━━━━━━━━━━ 6s 131ms/step - accuracy: 0.7505 - loss: 0.7138


302/352 ━━━━━━━━━━━━━━━━━━━━ 6s 130ms/step - accuracy: 0.7505 - loss: 0.7139


304/352 ━━━━━━━━━━━━━━━━━━━━ 6s 130ms/step - accuracy: 0.7504 - loss: 0.7140


306/352 ━━━━━━━━━━━━━━━━━━━━ 5s 129ms/step - accuracy: 0.7504 - loss: 0.7140


308/352 ━━━━━━━━━━━━━━━━━━━━ 5s 129ms/step - accuracy: 0.7504 - loss: 0.7141


310/352 ━━━━━━━━━━━━━━━━━━━━ 5s 128ms/step - accuracy: 0.7504 - loss: 0.7142


311/352 ━━━━━━━━━━━━━━━━━━━━ 5s 128ms/step - accuracy: 0.7504 - loss: 0.7142


312/352 ━━━━━━━━━━━━━━━━━━━━ 5s 128ms/step - accuracy: 0.7503 - loss: 0.7142


314/352 ━━━━━━━━━━━━━━━━━━━━ 4s 127ms/step - accuracy: 0.7503 - loss: 0.7143


315/352 ━━━━━━━━━━━━━━━━━━━━ 4s 127ms/step - accuracy: 0.7503 - loss: 0.7143


316/352 ━━━━━━━━━━━━━━━━━━━━ 4s 127ms/step - accuracy: 0.7503 - loss: 0.7144


318/352 ━━━━━━━━━━━━━━━━━━━━ 4s 126ms/step - accuracy: 0.7503 - loss: 0.7144


320/352 ━━━━━━━━━━━━━━━━━━━━ 4s 126ms/step - accuracy: 0.7503 - loss: 0.7145


322/352 ━━━━━━━━━━━━━━━━━━━━ 3s 125ms/step - accuracy: 0.7502 - loss: 0.7146


324/352 ━━━━━━━━━━━━━━━━━━━━ 3s 125ms/step - accuracy: 0.7502 - loss: 0.7146


326/352 ━━━━━━━━━━━━━━━━━━━━ 3s 124ms/step - accuracy: 0.7502 - loss: 0.7147


328/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7502 - loss: 0.7147


329/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7502 - loss: 0.7148


330/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7501 - loss: 0.7148


331/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7501 - loss: 0.7148


332/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7501 - loss: 0.7149


333/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7501 - loss: 0.7149


334/352 ━━━━━━━━━━━━━━━━━━━━ 2s 124ms/step - accuracy: 0.7501 - loss: 0.7149


335/352 ━━━━━━━━━━━━━━━━━━━━ 2s 125ms/step - accuracy: 0.7501 - loss: 0.7149


336/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7501 - loss: 0.7150


337/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7501 - loss: 0.7150


338/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7501 - loss: 0.7150


339/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7501 - loss: 0.7150


340/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7500 - loss: 0.7151


341/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7500 - loss: 0.7151


342/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7500 - loss: 0.7151


343/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7500 - loss: 0.7152


344/352 ━━━━━━━━━━━━━━━━━━━━ 1s 125ms/step - accuracy: 0.7500 - loss: 0.7152


345/352 ━━━━━━━━━━━━━━━━━━━━ 0s 125ms/step - accuracy: 0.7500 - loss: 0.7152


346/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7152


347/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7153


348/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7153


349/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7153


350/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7153


351/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7500 - loss: 0.7153


352/352 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - accuracy: 0.7499 - loss: 0.7154


352/352 ━━━━━━━━━━━━━━━━━━━━ 47s 133ms/step - accuracy: 0.7475 - loss: 0.7222 - val_accuracy: 0.7642 - val_loss: 0.6979

Epoch 15/15

  1/352 ━━━━━━━━━━━━━━━━━━━━ 1:41 288ms/step - accuracy: 0.7031 - loss: 0.7295


  2/352 ━━━━━━━━━━━━━━━━━━━━ 1:20 229ms/step - accuracy: 0.7168 - loss: 0.7293


  3/352 ━━━━━━━━━━━━━━━━━━━━ 1:11 204ms/step - accuracy: 0.7227 - loss: 0.7219


  4/352 ━━━━━━━━━━━━━━━━━━━━ 1:06 190ms/step - accuracy: 0.7271 - loss: 0.7101


  5/352 ━━━━━━━━━━━━━━━━━━━━ 1:03 184ms/step - accuracy: 0.7273 - loss: 0.7124


  6/352 ━━━━━━━━━━━━━━━━━━━━ 1:03 183ms/step - accuracy: 0.7276 - loss: 0.7125


  7/352 ━━━━━━━━━━━━━━━━━━━━ 1:03 185ms/step - accuracy: 0.7286 - loss: 0.7125


  8/352 ━━━━━━━━━━━━━━━━━━━━ 1:03 184ms/step - accuracy: 0.7296 - loss: 0.7130


  9/352 ━━━━━━━━━━━━━━━━━━━━ 1:02 182ms/step - accuracy: 0.7303 - loss: 0.7137


 10/352 ━━━━━━━━━━━━━━━━━━━━ 1:01 181ms/step - accuracy: 0.7311 - loss: 0.7135


 11/352 ━━━━━━━━━━━━━━━━━━━━ 1:01 181ms/step - accuracy: 0.7321 - loss: 0.7129


 12/352 ━━━━━━━━━━━━━━━━━━━━ 1:01 182ms/step - accuracy: 0.7330 - loss: 0.7132


 13/352 ━━━━━━━━━━━━━━━━━━━━ 1:01 181ms/step - accuracy: 0.7340 - loss: 0.7129


 14/352 ━━━━━━━━━━━━━━━━━━━━ 1:01 181ms/step - accuracy: 0.7352 - loss: 0.7118


 15/352 ━━━━━━━━━━━━━━━━━━━━ 1:00 179ms/step - accuracy: 0.7359 - loss: 0.7115


 16/352 ━━━━━━━━━━━━━━━━━━━━ 59s 178ms/step - accuracy: 0.7366 - loss: 0.7113 


 17/352 ━━━━━━━━━━━━━━━━━━━━ 59s 177ms/step - accuracy: 0.7372 - loss: 0.7110


 18/352 ━━━━━━━━━━━━━━━━━━━━ 58s 176ms/step - accuracy: 0.7378 - loss: 0.7108


 19/352 ━━━━━━━━━━━━━━━━━━━━ 58s 176ms/step - accuracy: 0.7383 - loss: 0.7104


 20/352 ━━━━━━━━━━━━━━━━━━━━ 58s 175ms/step - accuracy: 0.7388 - loss: 0.7100


 21/352 ━━━━━━━━━━━━━━━━━━━━ 57s 174ms/step - accuracy: 0.7394 - loss: 0.7099


 22/352 ━━━━━━━━━━━━━━━━━━━━ 57s 174ms/step - accuracy: 0.7398 - loss: 0.7098


 23/352 ━━━━━━━━━━━━━━━━━━━━ 57s 174ms/step - accuracy: 0.7402 - loss: 0.7099


 24/352 ━━━━━━━━━━━━━━━━━━━━ 57s 174ms/step - accuracy: 0.7405 - loss: 0.7101


 25/352 ━━━━━━━━━━━━━━━━━━━━ 57s 176ms/step - accuracy: 0.7408 - loss: 0.7103


 26/352 ━━━━━━━━━━━━━━━━━━━━ 57s 177ms/step - accuracy: 0.7410 - loss: 0.7103


 27/352 ━━━━━━━━━━━━━━━━━━━━ 57s 176ms/step - accuracy: 0.7413 - loss: 0.7101


 28/352 ━━━━━━━━━━━━━━━━━━━━ 56s 175ms/step - accuracy: 0.7416 - loss: 0.7099


 29/352 ━━━━━━━━━━━━━━━━━━━━ 56s 175ms/step - accuracy: 0.7418 - loss: 0.7097


 30/352 ━━━━━━━━━━━━━━━━━━━━ 56s 175ms/step - accuracy: 0.7421 - loss: 0.7094


 31/352 ━━━━━━━━━━━━━━━━━━━━ 56s 175ms/step - accuracy: 0.7423 - loss: 0.7091


 32/352 ━━━━━━━━━━━━━━━━━━━━ 56s 175ms/step - accuracy: 0.7425 - loss: 0.7090


 33/352 ━━━━━━━━━━━━━━━━━━━━ 55s 175ms/step - accuracy: 0.7427 - loss: 0.7089


 34/352 ━━━━━━━━━━━━━━━━━━━━ 55s 175ms/step - accuracy: 0.7429 - loss: 0.7089


 35/352 ━━━━━━━━━━━━━━━━━━━━ 55s 175ms/step - accuracy: 0.7432 - loss: 0.7088


 36/352 ━━━━━━━━━━━━━━━━━━━━ 55s 175ms/step - accuracy: 0.7434 - loss: 0.7088


 37/352 ━━━━━━━━━━━━━━━━━━━━ 54s 175ms/step - accuracy: 0.7436 - loss: 0.7087


 38/352 ━━━━━━━━━━━━━━━━━━━━ 54s 174ms/step - accuracy: 0.7439 - loss: 0.7086


 39/352 ━━━━━━━━━━━━━━━━━━━━ 54s 174ms/step - accuracy: 0.7440 - loss: 0.7086


 40/352 ━━━━━━━━━━━━━━━━━━━━ 54s 174ms/step - accuracy: 0.7442 - loss: 0.7086


 41/352 ━━━━━━━━━━━━━━━━━━━━ 53s 173ms/step - accuracy: 0.7443 - loss: 0.7087


 42/352 ━━━━━━━━━━━━━━━━━━━━ 53s 174ms/step - accuracy: 0.7445 - loss: 0.7087


 43/352 ━━━━━━━━━━━━━━━━━━━━ 53s 173ms/step - accuracy: 0.7447 - loss: 0.7087


 44/352 ━━━━━━━━━━━━━━━━━━━━ 53s 173ms/step - accuracy: 0.7449 - loss: 0.7087


 45/352 ━━━━━━━━━━━━━━━━━━━━ 53s 173ms/step - accuracy: 0.7451 - loss: 0.7087


 46/352 ━━━━━━━━━━━━━━━━━━━━ 52s 173ms/step - accuracy: 0.7453 - loss: 0.7087


 47/352 ━━━━━━━━━━━━━━━━━━━━ 52s 173ms/step - accuracy: 0.7454 - loss: 0.7087


 48/352 ━━━━━━━━━━━━━━━━━━━━ 52s 173ms/step - accuracy: 0.7456 - loss: 0.7087


 49/352 ━━━━━━━━━━━━━━━━━━━━ 52s 173ms/step - accuracy: 0.7458 - loss: 0.7087


 50/352 ━━━━━━━━━━━━━━━━━━━━ 52s 174ms/step - accuracy: 0.7459 - loss: 0.7086


 51/352 ━━━━━━━━━━━━━━━━━━━━ 52s 174ms/step - accuracy: 0.7461 - loss: 0.7086


 52/352 ━━━━━━━━━━━━━━━━━━━━ 52s 174ms/step - accuracy: 0.7463 - loss: 0.7085


 53/352 ━━━━━━━━━━━━━━━━━━━━ 52s 174ms/step - accuracy: 0.7465 - loss: 0.7085


 54/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7466 - loss: 0.7084


 55/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7468 - loss: 0.7083


 56/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7470 - loss: 0.7082


 57/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7472 - loss: 0.7082


 58/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7473 - loss: 0.7081


 59/352 ━━━━━━━━━━━━━━━━━━━━ 51s 174ms/step - accuracy: 0.7475 - loss: 0.7080


 60/352 ━━━━━━━━━━━━━━━━━━━━ 50s 174ms/step - accuracy: 0.7476 - loss: 0.7079


 61/352 ━━━━━━━━━━━━━━━━━━━━ 51s 175ms/step - accuracy: 0.7478 - loss: 0.7079


 62/352 ━━━━━━━━━━━━━━━━━━━━ 50s 175ms/step - accuracy: 0.7479 - loss: 0.7078


 63/352 ━━━━━━━━━━━━━━━━━━━━ 50s 175ms/step - accuracy: 0.7481 - loss: 0.7078


 64/352 ━━━━━━━━━━━━━━━━━━━━ 50s 175ms/step - accuracy: 0.7482 - loss: 0.7078


 65/352 ━━━━━━━━━━━━━━━━━━━━ 50s 175ms/step - accuracy: 0.7483 - loss: 0.7079


 66/352 ━━━━━━━━━━━━━━━━━━━━ 49s 174ms/step - accuracy: 0.7484 - loss: 0.7079


 67/352 ━━━━━━━━━━━━━━━━━━━━ 49s 174ms/step - accuracy: 0.7485 - loss: 0.7079


 68/352 ━━━━━━━━━━━━━━━━━━━━ 49s 174ms/step - accuracy: 0.7486 - loss: 0.7080


 69/352 ━━━━━━━━━━━━━━━━━━━━ 49s 174ms/step - accuracy: 0.7487 - loss: 0.7080


 70/352 ━━━━━━━━━━━━━━━━━━━━ 48s 174ms/step - accuracy: 0.7488 - loss: 0.7081


 71/352 ━━━━━━━━━━━━━━━━━━━━ 48s 173ms/step - accuracy: 0.7489 - loss: 0.7081


 72/352 ━━━━━━━━━━━━━━━━━━━━ 48s 173ms/step - accuracy: 0.7490 - loss: 0.7081


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score = model.evaluate(X_test, y_test, verbose=0)
print(f'Strata modelu wyniosła {score[0]:.2f}')
print(f'Skuteczność modelu wyniosła {score[1]:.2f}')

Strata modelu wyniosła 0.69
Skuteczność modelu wyniosła 0.76

Zobaczmy przykładową predykcję dla obrazu testowego

print(f'Na wykresie mamy zdjęcie kategorii: {cifar_10_cats_dict[np.argmax(y_test[1])]}')
prediction = model.predict(np.expand_dims(X_test[1], axis = 0)) # predict spodziewa się batcha, zminiamy wymiar
prob = np.max(prediction)
index = np.argmax(prediction)
print(f'Prognoza to {cifar_10_cats_dict[index]} z prawdopodobieństwem {prob:.2f}')
fig = px.imshow(X_test[1], width = 400, height = 400)
fig.show()

Na wykresie mamy zdjęcie kategorii: statek

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1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 355ms/step

Prognoza to statek z prawdopodobieństwem 0.99

Otrzymujemy poprawną predykcję statku

Wykorzystajmy metode wyjasniania predykcji LIME do zdiagnozowania co przyczyniło się do takiej, a niej innej prognozy

from lime.wrappers.scikit_image import SegmentationAlgorithm

from lime import lime_image

explainer = lime_image.LimeImageExplainer(random_state=0)


segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=2,
                                        max_dist=5, ratio=0.2,
                                        random_seed=0)

explanation = explainer.explain_instance(X_test[1].astype('double'), model.predict,
                                         top_labels=3, num_samples=100, segmentation_fn=segmentation_fn)

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Zwizualizujmy wyniki działania wyjaśniarki. Na wykresach widzimy z lewej piksele które przyczyniły się do predykcji statku, natomaist z lewej te które obniżały prawdopodbieństwo tego że mamy do czynienia ze statkiem.

import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries

temp_1, mask_1 = explanation.get_image_and_mask(
    explanation.top_labels[0], positive_only=True, hide_rest=True)
temp_2, mask_2 = explanation.get_image_and_mask(
    explanation.top_labels[0], positive_only=False, negative_only=True, num_features=10, hide_rest=True)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 15))
ax1.imshow(mark_boundaries(temp_1, mask_1))
ax2.imshow(mark_boundaries(temp_2, mask_2));

Podsumowanie

Wyjaśnialność pozwala nam zrozumiec czym kieruje się model w swoich decyzjach, co jest kluczowe dla oceny poprawności tych decyzji. W przedstawionym przykładzie użylismy obrazów ale LIME jest wszechstronną metoda używaną także do wyjasniania. Jakie są jej korzyści? Ważna jest to że nie jest ona inherentna dla konkretnego modelu, lecz agnostyczna wobec modelu którego używamy. W efekcie w przypadku zmiany modelu wyjaśniarka oparta o LIME wciąż będzie działała. Oprócz niewątpilwych zalet LIME ma także wady, którymi jest niestabilność oraz metoda próbkowania nie uwzględniająca korelacji między zmiennymi objaśniającymi.

Shap

Data science jest szeroką dziedziną w której zastosowanie znajdują liczne dziedziny matematyki. Jedną z dziedzin matematyki, która jest powszechnie stosowana jest teoria gier. Właśnie z teorii gier wywodzi się metoda wyjaśniania o której opowiemy w tym dziale.

Wartośc Shapleya wywowdzi się z teorii gier kooperacyjncyh. Swoja nazwe zawdzięcza amerykańskiemu matematykowi LLoydowi Shapleyowi, który utrzymał za nią Nagrodę Nobla. Najpierw przyjrzyjmy sie definicji gier kooperacyjnych.

Definicja

Grą koalicyjną deifniujemy jako zawierającą nastepujące elementy. Zbiór \(N\) oraz funkcję \(v\) mamupjąca podzbiory graczy na liczby rzeczywiste \(v:2^{n} \rightarrow \mathbb{R}\), gdzie \(v(\varnothing) = 0\). Funkcję \(v\) nazywamy funkcją charakterystyczną.

Funkcję v możemy rozumieć w następujący sposób. Jeżel \(S\) jest koalicją graczy, wtedy \(v(S)\) stawoi oczekiwaną wartość zysku jaki człokowie \(S\) moga uzyskać przez kooperację. Wprowadźmy wzór na wartość shap.

\[ \phi_{i}(v) = \sum_{S\subseteq N - \{i\}} \frac{|S|!(|N|-|S|-1)!}{n!}(v(S \cup \{i\})-v(S)) \]

Jak możemy to rozumieć? Otóż wartość Shapley dal gracza i to suma róznic między wartościami funkcji charakterystycznej dla takich samych koalicji z dodatkowo zawartym graczem i lub nie. Wartość ta jest skalowana przez ilość graczy oraz ilość koalicji takiego samego rozmiaru (dla każdego elementu sumy, bez uwzględniania gracza i). Zobaczmy na ilustracjach podstawową intuicję kryjącą się za wartościami shap

Na pierwszej ilustracji widzimy na czym polega porównanie udziału danego gracza w koalicji. Dla jeden z koalicji wyliczamy zysk z obecnym graczem oraz bez niego. Następnie liczymy w ten sposób zyski dla wszystkich mozliwych koalicji z graczem lub bez niego.

Tak jak widzimy na kolejnej ilustracji, dla wszystkich mozliwych koalicji liczymy zyski (wartości funkcji charakterystyczej z oraz bez badanego gracza). Te wartości posłuża nam do wyliczenia wartości Shapley według wzoru.

Implementacja

Metodą uczenia maszynowego opartą o wartości Shapleya do wyjaśniania wpływu zmiennych na predykcje jest SHAP (SHapley Additive EXplanation). AUtorzy algorytmu zaproponowali metodę estymacji wartości SHAP oraz metody globalnej iterpretacji oparte o agregacje wartości Shapleya. Pakietem implementującym SHAP w pythonie jest shap. Zobaczmy działanie shap na przykładzie danych tabularycznych, używanym już uprzednio California Housing Dataset

Dokumentacja SHAP: https://shap.readthedocs.io/en/latest/index.html#

import shap

Wgrajmy jeszcze raz i podzielmy na test i trening zbiór danych california housing data. Tym razem stworzymy model drzew wzmacnianych Light Gradient Boosting Machine i wyjaśnimy jego predykcje za pomocą shap.

import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split


cal_housing = fetch_california_housing()
X = pd.DataFrame(cal_housing.data, columns=cal_housing.feature_names)
y = cal_housing.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)

from sklearn.preprocessing import QuantileTransformer
from sklearn.pipeline import Pipeline
from sklearn.neural_network import MLPRegressor
from lightgbm import LGBMRegressor

lgb_reg = LGBMRegressor() # używamy domyślnych parametów

lgb_reg.fit(X_train, y_train)

[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.008137 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1838
[LightGBM] [Info] Number of data points in the train set: 18576, number of used features: 8
[LightGBM] [Info] Start training from score 2.069685

LGBMRegressor()

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from sklearn.metrics import mean_squared_error

pred = lgb_reg.predict(X_test)

print(f'Błąd średniokwadratowy dla modelu LightGBM to {mean_squared_error(pred, y_test):.2f}')

Błąd średniokwadratowy dla modelu LightGBM to 0.21

Zwizualizujmy wyniki dla przykładowego rekordu.

explainer = shap.TreeExplainer(lgb_reg)
shap_values = explainer.shap_values(X_train)

W ten sposób prezentują sie rzeczywiste dane dla przykładu, dla które chcemy stworzyć wyjasnienie

X_train.iloc[0]

MedInc           3.125000
HouseAge        16.000000
AveRooms         5.380071
AveBedrms        1.058201
Population    3407.000000
AveOccup         3.004409
Latitude        36.800000
Longitude     -119.830000
Name: 2255, dtype: float64

y_test[0]

1.369

shap.initjs()
shap.force_plot(explainer.expected_value, shap_values[0], X_train.iloc[0])

Visualization omitted, Javascript library not loaded!
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Na wykresie widzimy istotne znaczenie jakie ma położenie geograficzne dla wartości mieszkania. Otrzymaliśmy proste do zrozumienia wyjasnienie czarnopudełkowego modelu. Shap pozwala nam także na zrozumienie globalnego znaczenia zmiennych. Jesto wyliczane w prosty sposób, jako średnia wartości Shapleya dla poszczególnych rekordów dla danej cechy

shap.summary_plot(shap_values, X_test, plot_type="bar")

Na wykresie widzimy cechy mające największa wagę dla cen nieruchomości. W naszym przypadku będzie to dochód oraz położenie. Populacja zaś nie ma szczególnego wpływu na wartość mieszkania wg naszego modelu Light GBM.

Zadanie

Przygotuj krótkie analizy wyjaśnialności dla modeli stworzonych w zadaniach z notatników o klasyfikacji i regresji - dla każdego modelu wyznacz globalną istotność zmiennych (summary plot), wpływ każdej cechy na ogólne predykcje (dependence plots) oraz wpływ każdej cechy na trzy dowolnie wybrane, konkretne predykcje (waterfall plot). Skorzystaj z SHAP. Zinterpretuj uzyskane wyniki.

Podsumowanie

Shap jest najbardziej dojrzałym z algorytmów wyjaśniania, jest mocno zakorzeniony w teorii gier i ma silne podstawy teoretyczne. Jest to wskazana metoda wyjaśniania modeli. Kolejną zaleta jest możliwość uzyskania wyjaśnień globalnych.

Zakończenie

Wyjaśnialność jest istotnym i rozwijającym się tematem w dziedzinie uczenie maszynowego. Nie ma w tym nic dziwnego. W czasach gdy sztuczna inteligencja automatyzuje coraz liczenijsze elemnty naszego życia, a prognozy przez nią dawane mają wielkie znaczenie, musimy je rozumieć żeby budować zaufanie do tychże metod.