5  Einführung in Keras

Im praktischen Teil werden wir hauptsächlich mit Keras - innerhalb von TensorFlow - arbeiten. Wir lernen, wie man Feedforward-Netze in Keras erstellt, trainiert und evaluiert. Wir starten mit den Iris-Daten und einem sehr einfachen Netz. Dann lernen wir die MNIST-Daten zur Erkennung von handgeschriebenen Ziffern kennen und erstellen und trainieren ein FFN mit mehreren Schichten.

Lernziele, um Ihren Lernfortschritt zu prüfen

Nach Abschluss des Kapitels können Sie

• die Begriffe und Unterschiede von TensorFlow, Keras und PyTorch erklären
• Feedforwardnetze in Python mit Hilfe der Keras-Bibliothek erstellen, mit Daten aus Keras trainieren und den Trainingsverlauf visualisieren
• den MNIST-Datensatz beschreiben und das Training in Keras verwenden
• verschiedene Hyperparameter (Lernrate, Batchgröße etc.) benennen und ihre Bedeutung in konkreten Trainingssituationen beurteilen

Updates dieser Seite

• 05.05.2025: Sparse categorical crossentropy - Erklärung hinzugefügt
• 02.05.2025: kleine Codeanpassungen (z.B. Initializer raus, binary_crossentropy)
• 10.04.2025: Kapitel umstrukturiert

Datensatz

Name	Daten	Anz. Klassen	Klassen	Trainings-/Testdaten	Ort
Iris	Blütenblatt-Maße	3	Blumenklassen	150
MNIST	s/w-Bilder (28x28)	10	handgeschriebene Ziffern 0…9	60000/10000	5.4

Importe

import tensorflow
import math
from math import exp
import matplotlib.pyplot as plt
import numpy as np
import time

5.1 Hintergrund

5.1.1 TensorFlow

TensorFlow ist eine Open-Source-Bibliothek für Python (mit Bestandteilen in C++) für den Themenbereich des Maschinellen Lernens, inklusive Neuronaler Netze. TensorFlow wurde ursprünglich vom Google Brain Team für Google-eigenen Projekte entwickelt, wurde aber 2015 unter einer Open-Source-Lizenz (Apache License 2.0) für die Allgemeinheit frei gegeben. Mittlerweile dürfte TensorFlow das mit Abstand das wichtigste Framework im Bereich Maschinelles Lernen sein.

Noch ein paar Worte zum Entwicklungsteam hinter TensorFlow: Google Brain begann 2011 als ein sogenanntes “Google X”-Projekt als Kooperation zwischen Google-Fellow Jeff Dean und Stanford-Professor Andrew Ng. Seit 2013 ist Deep-Learning-Pioneer Geoffrey Hinton als leitender Wissenschaftler im Team. Weitere bekannte Wissenschaftler bei Google Brain sind Alex Krizhevsky und Ilya Sutskever (Autoren von AlexNet), Christopher Olah (bekannt durch seinen Blog) und Chris Lattner (Erfinder von Apple’s Programmiersprache Swift).

5.1.2 Keras

Keras ist ebenfalls eine Open-Source-Bibliothek für Python von François Chollet, die erstmals 2015 als Python-API für verschiedene “Backends” (u.a. TensorFlow) veröffentlicht wurde. Es ist ein objektorientiertes Framework für das Erstellen, Trainieren und Evaluieren Neuronaler Netze. Das Buch Deep Learning with Python (Chollet 2021) ist vom Erfinder von Keras und daher von besonderer Relevanz (einschränkend muss man sagen, dass die Theorie in dem Buch ein wenig zu kurz kommt). Eine Idee von Keras ist, dass es auch als Interface für verschiedene Backends in anderen Kontexten (z.B. Robotik oder mobile) genutzt werden kann. Daher liegt ein Fokus von Keras auf einer intuitiven, modularen und erweiterbaren Systematik.

Keras wurde unabhängig von TensorFlow entwickelt, wurde dann aber 2017 in TensorFlow 1.4 als Teil der TensorFlow Core API aufgenommen, d.h. alle Konzepte und Daten von Keras stehen in TensorFlow zur Verfügung. François Chollet arbeitet seit 2015 für Google und hat dort auch die Einbindung in TensorFlow unterstützt.

5.1.3 PyTorch

PyTorch ist - wie TensorFlow - eine Open-Source-Bibliothek für Maschinelles Lernen in Python. PyTorch wurde ursprünglich von der KI-Gruppe bei Facebook, genannt FAIR - Facebook AI Research (oder oft nur “Facebook AI”) - entwickelt. PyTorch ist in letzter Zeit immer populärer geworden, weil es an manchen Stellen einen direkteren Eingriff in die Abläufe zulässt. In diesem Buch wird hauptsächlich Keras verwendet, aber PyTorch wird in den Anhängen E und F zumindest angerissen.

5.2 Einfaches Neuronales Netz

Wir beginnen mit dem einfachsten möglichen Neuronales Netz und lernen, wie wir es erzeugen und trainieren (wir verzichten auf Testdaten).

In Keras heißt die Basisklasse für Neuronale Netze Sequential. Der Name weist darauf hin, dass ein Sequential-Objekt eine geordnete Reihe (Sequenz) von Schichten enthält.

Wir erstellen zunächst ein Neuronales Netz mit einer Schicht. Also stellen wir eine Instanz von Sequential her, dies wäre unser Modell.

from tensorflow.keras.models import Sequential

model = Sequential()

5.2.1 Schichten Input und Dense

Jetzt kann man Schichten als Objekte dem Modell hinzufügen. Es gibt verschiedene Arten von Schichten.

Als erstes fügen wir die Neuronen für die Eingabe als Objekt der Klasse Input hinzu. Diese “Schicht” hat keine Parameter, die zu lernen sind, und wird daher nicht als Schicht gezählt.

Unsere erste “richtige” Schicht ist ein Objekt vom Typ Dense. Diese Schicht verbindet sich mit allen Neuronen der Vorgängerschicht. In anderen Kontexten nennt man solche Schichten auch fully connected layer oder FC layer. Unsere Dense-Schicht ist gleichzeitig auch die Ausgabeschicht und hat daher nur ein Neuron (der erste Parameter).

Mit activation kann man die Aktivierungsfunktion spezifizieren. Wir wählen die Funktion sigmoid, also die logistische Funktion:

\[ g(z) = \frac{1}{1-e^{-z}} \]

Dies ist die typische Aktivierung für das Ausgabeneuron bei binärer Klassifikation.

from tensorflow.keras import Input
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Input(shape=(2,)))           # Zwei Eingabeneuronen
model.add(Dense(1,                     # Ein (Ausgabe-)Neuron in dieser Schicht
                activation='sigmoid')) # Aktivierungsfunktion am Ausgabeneuron

Unser Netz sieht derzeit so aus (das Bias-Neuron ist nicht grafisch repräsentiert):

Mit der Methode summary geben wir uns eine Zusammenfassung unserer Netzwerk-Architektur aus, was später bei komplexeren Netzen hilfreich ist.

model.summary()

Model: "sequential_1"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 1)              │             3 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 3 (12.00 B)

 Trainable params: 3 (12.00 B)

 Non-trainable params: 0 (0.00 B)

Wir haben drei Parameter: \(w_1\), \(w_2\) und \(b\).

5.2.2 Parameter

Man kann das Netzwerkobjekt auch programmatisch untersuchen. Die Eigenschaft layers enthält alle Schichtenobjekte in iterierbarer Form. Hier wenden wir die Methode get_config auf jede Schicht an, die uns ein Dictionary zurückgibt, das wir hier ausgeben.

for l in model.layers:
    for key in l.get_config():
        print(f'{key}: {l.get_config()[key]}')

name: dense
trainable: True
dtype: {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
units: 1
activation: sigmoid
use_bias: True
kernel_initializer: {'module': 'keras.initializers', 'class_name': 'GlorotUniform', 'config': {'seed': None}, 'registered_name': None}
bias_initializer: {'module': 'keras.initializers', 'class_name': 'Zeros', 'config': {}, 'registered_name': None}
kernel_regularizer: None
bias_regularizer: None
kernel_constraint: None
bias_constraint: None

Besonders interessant sind natürlich die Gewichte, zumindest bei kleineren Netzen (später werden die Gewichte nicht mehr handhabbar).

Mit der Methode get_weights kann man sich die Gewichte und Bias-Gewichte aller Schichten ausgeben lassen.

w, b = model.get_weights()
print(f'WEIGHTS {w[0]} {w[1]} BIAS {b}')

WEIGHTS [-1.058739] [-0.54168177] BIAS [0.]

Man sieht, dass die Gewichte auf Zufallswerte in einer bestimmten Wertespanne gesetzt werden. Unter Layer weight initializers finden Sie Infos zu den unterschiedlichen Methoden zur zufälligen Initialisierung der Gewichte.

5.2.3 Iris-Datensatz

Wenn Sie nicht genau wissen, was ein “Tensor” ist oder wie man mit Vektoren, Matrizen und Tensoren in Python/NumPy umgeht, sehen Sie sich Abschnitt 1.1.4 an.

Wir möchten jetzt noch das Experiment mit den Iris-Daten aus Kapitel 3 mit einem Keras-Netz wiederholen. Wir bereiten die Daten wieder für eine binäre Klassifikation vor, dass wir nur zwei Features haben und nur zwei Klassen; wie im Abschnitt 3.4.1.

Wir arbeiten hier nur mit Trainingsdaten und verzichten auf eine Trennung ist Test- und Trainingsdaten.

from sklearn import datasets

iris = datasets.load_iris()

Wir reduzieren die Features auf zwei Features.

Wir zeigen hier einen alternativen Weg, die Spalten 0 und 2 auszuwählen (und die anderen zu ignorieren). Der Doppelpunkt bei

iris.data[:, [0, 2]]

bedeutet, dass alle Zeilen ausgewählt werden.

x_iris = iris.data[:, [0, 2]]
x_iris[:5] # Testausgabe

array([[5.1, 1.4],
       [4.9, 1.4],
       [4.7, 1.3],
       [4.6, 1.5],
       [5. , 1.4]])

Wir reduzieren die Daten auf zwei Klassen, indem wir nur die vorderen 100 von den 150 Beispielen nehmen:

x_iris = x_iris[:100] # nur die ersten 100 Elemente (also zwei Klassen)
y_iris = iris.target[:100]
y_iris

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

Jetzt schauen wir uns die Daten nochmal an:

plt.scatter(x_iris[:50, 0], x_iris[:50, 1], color='red', marker='o', label='setosa')
plt.scatter(x_iris[50:, 0], x_iris[50:, 1], color='blue', marker='x', label='versicolor')
plt.xlabel('sepal length')
plt.ylabel('petal length')
plt.legend(loc='upper left')
plt.show()

5.2.4 Training

Wir trainieren das Netzwerk mit Backpropagation in 10 Epochen. Wir haben keine Testdaten, sondern lediglich einen Trainingsdatensatz mit 100 Beispielen. Damit man ein Keras-Netzwerk trainieren kann, muss es zunächst mit der Methode compile konfiguriert werden.

Konfiguration mit Compile

Mit compile legen wir Hyperparameter wie die Lernrate und die Optimierungsmethode fest, aber auch Metriken wie die Art der Zielfunktion und die Evaluationsmaße.

Wir verwenden hier stochastic gradient descent (SGD) als Optimierungsmethode, mit einer Lernrate von 0.1. Als Zielfunktion wählen wir MSE (gemittelte Fehlerquadrate) und als Evaluationsmaß Accuracy (dazu mehr im nächsten Kapitel, Abschnitt 6.2.9).

from tensorflow.keras.optimizers import SGD

sgd = SGD(learning_rate=0.1)

model.compile(optimizer=sgd, 
              loss='binary_crossentropy', 
              metrics=['acc'])

# Alternative:
# Hier benötigt man das Objekt in sgd nicht, kann allerdings auch
# nicht die Lernrate einstellen
#
# model_and.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['acc'])

Hinweis

Wie wir später noch sehen, muss man nicht unbedingt ein Objekt für den Optimizer herstellen, sondern kann auch einfach in der Methode compile optimizer=‘sgd’ angeben. Das Vorgehen hier benötigt man nur, wenn man den Optimizer konfigurieren will (wie hier mit der Lernrate).

Training mit Lernrate 0.1

Das eigentliche Training, also die Updates der Paramter mit Hilfe der Trainingsdaten, findet in der Methode fit statt. Hier geben wir die Featurevektoren an, die Zielwerte und die Anzahl der Epochen, also die Trainingsdauer.

history = model.fit(x_iris, 
                    y_iris, 
                    epochs=10)

Epoch 1/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 1s 446ms/step - acc: 0.3750 - loss: 5.2872

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - acc: 0.4625 - loss: 3.6283  

Epoch 2/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 17ms/step - acc: 0.9688 - loss: 0.6038

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 0.8719 - loss: 0.6046 

Epoch 3/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 1.0000 - loss: 0.4855

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 0.8499 - loss: 0.4894 

Epoch 4/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - acc: 0.6250 - loss: 0.5954

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - acc: 0.7686 - loss: 0.5391 

Epoch 5/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 0.5000 - loss: 0.5729

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 0.7527 - loss: 0.4784 

Epoch 6/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step - acc: 0.9688 - loss: 0.3438

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 0.9753 - loss: 0.3522 

Epoch 7/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 11ms/step - acc: 1.0000 - loss: 0.3081

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 1.0000 - loss: 0.3168 

Epoch 8/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 12ms/step - acc: 0.9688 - loss: 0.3646

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step - acc: 0.9845 - loss: 0.3214 

Epoch 9/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step - acc: 0.9688 - loss: 0.2891

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step - acc: 0.9845 - loss: 0.2769 

Epoch 10/10

1/4 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - acc: 1.0000 - loss: 0.2728

4/4 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step - acc: 1.0000 - loss: 0.2576 

Ergebnis

Wir sehen uns die Entwicklung von Loss und Accuracy an.

Dazu definieren wir eine Hilfsfunktion:

def plot_loss_acc(history):
    epochs = range(len(history.history['acc']))
    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,4))
    ax[0].plot(epochs, history.history['loss'], 'r', label='Loss')
    ax[0].set_xlabel('Epochen')
    ax[0].set_ylabel('Loss')
    ax[0].set_title('Loss')
    ax[1].plot(epochs, history.history['acc'], 'b', label='Accuracy')
    ax[1].set_xlabel('Epochen')
    ax[1].set_ylabel('Accuracy')
    ax[1].set_title('Accuracy')
    plt.show()

plot_loss_acc(history)

Man sieht hier, dass das Netz mit Lernrate 0.1 bereits nach 5 Epochen 100% Accuracy erreicht.

Wir können uns noch die Gewichte ausgeben:

w, b = model.get_weights()
print(f'WEIGHTS {w[0]} {w[1]} BIAS {b}')

WEIGHTS [-0.6526667] [1.1560209] BIAS [-0.0362203]

5.2.5 Vorhersage

Man kann mit predict auch Vorhersagen mit dem trainierten Netz berechnen. Hier für 10 Werte aus der ersten Hälfte der 100 Datenpunkte (Klasse 0):

pred = model.predict(x_iris[:10])
pred

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 16ms/step

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 24ms/step

array([[0.14850295],
       [0.16577686],
       [0.16785213],
       [0.2134161 ],
       [0.1569466 ],
       [0.16862759],
       [0.19465251],
       [0.17285565],
       [0.21593328],
       [0.18238737]], dtype=float32)

Da wir kontinuierliche Werte bekommen, legen wir eine Schwellwertfunktion an, um die Ausgabe auf die Werte {0, 1} zu zwingen.

[(1 if x>=0.5 else 0) for x in pred]

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Hier für Werte aus der “Mitte” der Daten (40 bis 59), wo die eine Hälfte noch zu Klasse 0 gehört, die andere zu Klasse 1.

pred = model.predict(x_iris[40:60])
[(1 if x>=0.5 else 0) for x in pred]

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 15ms/step

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 25ms/step

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

5.3 Feedforward-Netze

Unser erstes Netz hatte lediglich eine parametrisierte Schicht und ein Ausgabeneuron.

Jetzt sehen wir uns an, wie man FNN mit Zwischenschichten erstellt, das für die Klassifikation von 10 Klassen geeignet ist.

5.3.1 Verlustfunktion in Keras

Bevor man ein Modell in Keras mit fit trainiert, legt man mit compile die Verlustfunktion fest (siehe dazu auch Abschnitt 4.2.1), z.B. so:

model.compile(loss='categorical_crossenropy', optimizer='sgd')

Die Loss-Funktion ist in der Regel eine von diesen (die Begiffe sind mit der Keras-Dokumentation verlinkt):

• binary_crossentropy
• categorical_crossentropy
• sparse_categorical_crossentropy

Hier nochmal eine kurze Erklärung, wann Sie welche nehmen:

Sie verwenden binary_crossentropy für binäre Klassifikation, wo Ihre Labels als Liste mit Nullen und Einsen vorliegen.

Auf categorical_crossentropy greifen Sie bei der Klassifikation mit multiplen Klassen zurück (Beispiel MNIST mit Klassen 0 bis 9). Hier müssen die Labels in One-Hot-Encoding vorliegen.

Die sparse_categorical_crossentropy verwenden Sie ebenfalls bei der Klassifikation mit multiplen Klassen, aber hier liegen die Labels als Integer-Werte vor, also z.B. [1, 5, 3, 0] etc.

Wir wandeln die Labels in One-Hot-Encoding um und verwenden dann categorical_crossentropy.

Wie funktioniert sparse_categorical_crossentropy?

Nehmen wir an, dass wir 10 Klassen haben wie bei MNIST. In unseren Daten liegen die y-Werte als Zahlen zwischen 0 und 9 vor. Für categorical_crossentropy müssen wir diese y-Werte in Vektoren der Länge 10 umwandeln. Warum eigentlich? Wir brauchen die Vektorform für die Berechnung der Verlustfunktion (hier für ein Trainingsbeispiel):

\[ J = - \sum_{i=1}^m y_i\, log(\hat{y}_i) \]

Nehmen wir ein konkretes Beispiel: Wir haben ein Trainingsbeispiel, wo \(p \in {0,\ldots,9}\) der Zielwert ist (z.B. 5) und \(y\) der entsprechende One-Hot-Vektor der Länge 10 mit einer 1 an Position \(p\) (sonst 0). Dann ist der Verlust für dieses Trainingsbeispiel:

\[ J = - log(\hat{y}_p) \]

Es bleibt also nur der Summand mit dem Index \(p\) übrig. Dieser Index stand aber doch in den ursprünglichen y-Werte drin. Bei sparse_categorical_crossentropy berechnet man daher einfach den Verlust mit Hilfe des \(p\), ohne die Summe zu verwenden, wo im Grunde der One-Hot-Vektor nur als “Selektor” gebraucht wird. Ganz einfach also. Es handelt sich nicht um eine andere Berechnung, sondern um eine “Abkürzung”.

5.4 MNIST-Datensatz

MNIST kurz gefasst

Im MNIST-Datensatz geht es Erkennung von handgeschriebenen Ziffern von 0 bis 9. Es gibt zehn Klassen, die es vorherzusagen gilt: 0, 1, 2, … 9. Die Bilder haben die Auflösung 28x28 und liegen in Graustufen vor. Der Datensatz enthält 60000 Trainingsbeispiele und 10000 Testbeispiele.

Wir ziehen den Datensatz MNIST hinzu. MNIST steht für Modified National Institute of Standards and Technology (dataset). Siehe auch den Wikipedia-Artikel zu MNIST und die Keras-Doku zu MNIST.

Eine Einführung in die MNIST-Daten in Keras finden Sie im folgenden Video.

5.4.1 Daten einlesen

Die Daten kommen bereits separiert in Trainings- und Testdaten.

from tensorflow.keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

Wir sehen uns die Struktur an. Die Bilder sind als 28x28-Matrizen hinterlegt.

print('x_train: ', x_train.shape)
print('y_train: ', y_train.shape)
print('x_test: ', x_test.shape)
print('y_test: ', y_test.shape)

x_train:  (60000, 28, 28)
y_train:  (60000,)
x_test:  (10000, 28, 28)
y_test:  (10000,)

5.4.2 Daten visualisieren

Die Funktion imshow (image show) erlaubt uns das schnelle Betrachten der Bilder. Wir wählen als color map (cmap) Graustufen.

Erstes Trainingsbild:

plt.imshow(x_train[0], cmap='gray')

<matplotlib.image.AxesImage at 0x150dd31d0>

Zehntes Trainingsbild:

plt.imshow(x_train[9], cmap='gray')

<matplotlib.image.AxesImage at 0x151ba3110>

Hier geben wir von jeder Kategorie das erste Trainingsbeispiel aus:

fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True,)
ax = ax.flatten() 
for i in range(10):
    img = x_train[y_train == i][0]
    ax[i].imshow(img, cmap='gray')

ax[0].set_xticks([]) # keine Achsenmarkierungen
ax[0].set_yticks([])
plt.tight_layout()
plt.show()

5.4.3 Daten vorverarbeiten

Für unsere Zwecke möchten wir die Bildmatrizen linearisieren zu Vektoren der Länge 784 (= 28*28). Dazu verwenden wir reshape.

Außerdem möchten wir die Pixelwerte (0..255) auf das Interval [0, 1] normalisieren. Das erreichen wir, indem wir alle Werte durch 255.0 teilen.

x_train = x_train.reshape(60000, 784)/255.0
x_test = x_test.reshape(10000, 784)/255.0

Kleiner Check, ob die Linearisierung geklappt hat:

print('x_train: ', x_train.shape)
print('x_test: ', x_test.shape)

x_train:  (60000, 784)
x_test:  (10000, 784)

5.4.4 One-Hot Encoding

Um die zehn Ziffern 0, , 9 in der Ausgabe zu kodieren, benutzen wir One-Hot Encoding, d.h. jede Ziffer wird durch einen Vektor der Länge 10 repräsentiert:

\[ \left( \begin{array}{c} 1 \\0 \\ \vdots \\ 0\end{array} \right) \quad \left( \begin{array}{c} 0 \\1 \\ \vdots \\ 0\end{array} \right) \quad \ldots\quad \left( \begin{array}{c} 0 \\0 \\ \vdots \\ 1\end{array} \right) \]

In Keras gibt es dafür die Funktion to_categorical. Wir schreiben das Ergebnis in neue Variablen.

from tensorflow.keras.utils import to_categorical

y_train_1hot = to_categorical(y_train, 10)
y_test_1hot = to_categorical(y_test, 10)

Wir prüfen, ob die Label-Daten die richtige Form haben.

print("y_train: ", y_train_1hot.shape)
print("y_test: ", y_test_1hot.shape)

y_train:  (60000, 10)
y_test:  (10000, 10)

Das passt: Es sind Vektoren der Länge 10.

5.5 Netz mit zwei Schichten

Wir bauen jetzt ein Neuronales Netz mit einer “versteckten” Schicht (hidden layer) mit 60 Neuronen.

Das Netz benötigt für die MNIST-Daten eine Eingabeschicht mit 784 Neuronen und hat zwei parametrisierte Schichten:

• Versteckte Schicht mit 60 Neuronen
• Ausgabeschicht mit 10 Neuronen

Schematisch sieht das so aus (ohne Bias-Neuronen):

5.5.1 Modell

In Keras erstellen wir das Netz als Instanz der Klasse Sequential.

Neben der Eingabe fügen dem Objekt zwei parametrisierte Schichten vom Typ Dense (fully connected) hinzu. Wir wählen die Sigmoid-Funktion für die Aktivierung der versteckten Schicht. Bei der Ausgabeschicht geben wir an, dass wir die Softmax-Funktion anwenden möchten. Denken Sie daran, dass Sie bei Klassifikation mit drei und mehr Klassen immer Softmax an der Ausgabe verwenden müssen.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Input, Dense

model = Sequential()
model.add(Input(shape=(784,)))
model.add(Dense(60, 
                activation='sigmoid'))
model.add(Dense(10,
                activation='softmax'))

5.5.2 Anzahl der Parameter

Wie viele Parameter hat das Netz? Die Gewichtsmatrix \(W^{(1)}\) von Input- zu versteckter Schicht hat 784x60 Einträge:

784*60

47040

Es kommen 60 Gewichte für das Bias-Neuron dazu. Insgesamt also 47100 Parameter. Die Gewichtsmatrix \(W^{(2)}\) von versteckter zu Ausgabeschicht enthält 10x60 = 600 Einträge plus den 10 Gewichten für das Bias-Neuron zur Ausgabe, macht 610 Parameter. Alles in allem also 47710 Parameter.

Wir schauen uns das Netz in der Kurzübersicht mit Hilfe der Methode summary an. Dort steht auch nochmal die Anzahl der Parameter:

model.summary()

Model: "sequential_2"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_1 (Dense)                 │ (None, 60)             │        47,100 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 10)             │           610 │
└─────────────────────────────────┴────────────────────────┴───────────────┘

 Total params: 47,710 (186.37 KB)

 Trainable params: 47,710 (186.37 KB)

 Non-trainable params: 0 (0.00 B)

Einen etwas technischeren Blick auf Ihr Modell mit allen Schichten erhalten Sie mit get_config. Hier sehen Sie zum Beispiel verschiedene Einstellungen in den Schichten, z.B. zur Initialisierung der Gewichte.

model.get_config()

{'name': 'sequential_2',
 'trainable': True,
 'dtype': {'module': 'keras',
  'class_name': 'DTypePolicy',
  'config': {'name': 'float32'},
  'registered_name': None},
 'layers': [{'module': 'keras.layers',
   'class_name': 'InputLayer',
   'config': {'batch_shape': (None, 784),
    'dtype': 'float32',
    'sparse': False,
    'ragged': False,
    'name': 'input_layer_1'},
   'registered_name': None},
  {'module': 'keras.layers',
   'class_name': 'Dense',
   'config': {'name': 'dense_1',
    'trainable': True,
    'dtype': {'module': 'keras',
     'class_name': 'DTypePolicy',
     'config': {'name': 'float32'},
     'registered_name': None},
    'units': 60,
    'activation': 'sigmoid',
    'use_bias': True,
    'kernel_initializer': {'module': 'keras.initializers',
     'class_name': 'GlorotUniform',
     'config': {'seed': None},
     'registered_name': None},
    'bias_initializer': {'module': 'keras.initializers',
     'class_name': 'Zeros',
     'config': {},
     'registered_name': None},
    'kernel_regularizer': None,
    'bias_regularizer': None,
    'kernel_constraint': None,
    'bias_constraint': None},
   'registered_name': None,
   'build_config': {'input_shape': (None, 784)}},
  {'module': 'keras.layers',
   'class_name': 'Dense',
   'config': {'name': 'dense_2',
    'trainable': True,
    'dtype': {'module': 'keras',
     'class_name': 'DTypePolicy',
     'config': {'name': 'float32'},
     'registered_name': None},
    'units': 10,
    'activation': 'softmax',
    'use_bias': True,
    'kernel_initializer': {'module': 'keras.initializers',
     'class_name': 'GlorotUniform',
     'config': {'seed': None},
     'registered_name': None},
    'bias_initializer': {'module': 'keras.initializers',
     'class_name': 'Zeros',
     'config': {},
     'registered_name': None},
    'kernel_regularizer': None,
    'bias_regularizer': None,
    'kernel_constraint': None,
    'bias_constraint': None},
   'registered_name': None,
   'build_config': {'input_shape': (None, 60)}}],
 'build_input_shape': (None, 784)}

5.5.3 Gewichtsmatrizen und Biasvektoren

Mit get_weights können wir auf die Parameter des Netzwerks zugreifen. Für jede Schicht gibt es jeweils

• eine Gewichtsmatrix und
• einen Bias-Vektor.

Wir lassen uns hier in einer Schleife jeweils die Dimensionen der Gewichtsmatrix und des Bias-Vektor für unsere zwei Schichten (Hidden + Output) ausgeben:

weights = model.get_weights()
for w in weights:
    print(w.shape)

(784, 60)
(60,)
(60, 10)
(10,)

Wir sehen hier, dass die Gewichtsmatrix \(W^{(1)}\) von der Eingabeschicht zur versteckten Schicht die Dimensionen 784x60 hat. In unserer theoretischen Behandlung haben wir eine 60x784-Matrix verwendet, also die transponierte Version. Beide Varianten sind möglich und üblich.

Der Bias-Vektor von Input zu Hidden hat eine Länge von 60. Auch das ist plausibel, wenn Sie sich in der Abbildung oben ein Bias-Neuron in der Inputschicht vorstellen, dass mit allen 60 versteckten Neuronen verbunden ist. Dies muss offensichtlich 60 Gewichte haben.

5.5.4 Training

Beim Training wollen wir die ursprüngliche Variante des Backpropagation verwenden, wo alle Trainingsbeispiele vor einem Update durchlaufen werden. Wir nennen diese Variante Batchtraining (nicht zu verwechseln mit “Minibatch”, siehe unten).

In Keras spezifizieren wir dazu Stochastic Gradient Descent, kurz SGD, als Optimierungsmethode und geben als Batchgröße die Anzahl der Trainingsdaten an. Als Zielfunktion wählen wir wie im Theorieteil die Cross-Entropy-Funktion.

Lernrate

Damit wir die Lernrate einstellen können, erstellen wir für die Optimierungsmethode ein eigenes SGD-Objekt (stochastic gradient descent). Wir sehen uns mit get_config die Voreinstellungen an.

from tensorflow.keras.optimizers import SGD

opt = SGD()
opt.get_config()

{'name': 'SGD',
 'learning_rate': 0.009999999776482582,
 'weight_decay': None,
 'clipnorm': None,
 'global_clipnorm': None,
 'clipvalue': None,
 'use_ema': False,
 'ema_momentum': 0.99,
 'ema_overwrite_frequency': None,
 'loss_scale_factor': None,
 'gradient_accumulation_steps': None,
 'momentum': 0.0,
 'nesterov': False}

Die Standardeinstellung für die Lernrate ist also 0.01. Wir erstellen ein neues Objekt mit unserer gewünschten Lernrate:

opt = SGD(learning_rate = 0.1)
opt.get_config()

{'name': 'SGD',
 'learning_rate': 0.10000000149011612,
 'weight_decay': None,
 'clipnorm': None,
 'global_clipnorm': None,
 'clipvalue': None,
 'use_ema': False,
 'ema_momentum': 0.99,
 'ema_overwrite_frequency': None,
 'loss_scale_factor': None,
 'gradient_accumulation_steps': None,
 'momentum': 0.0,
 'nesterov': False}

5.5.5 Compile

Mit compile konfigurieren wir das Training zunächst nur und übergeben zum Beispiel die Optimiermethode als Objekt. Alternativ kann man als Parameter für optimizer den String sgd übergeben, dann werden für Lernrate etc. die Standardeinstellungen gewählt. Als Metrik für unsere Messungen wählen wir Accuracy, kurz acc.

model.compile(optimizer=opt,
              loss='categorical_crossentropy',
              metrics=['acc'])

5.5.6 Fit

Das eigentliche Training wird mit fit durchgeführt. Wichtig ist, dass fit den Verlauf der Metriken pro Epoche in einem History-Objekt zurückgibt. Will man die Fehler- und Performance-Entwicklung später visualisieren, muss man dieses Objekt speichern.

Die Methode hat außer den Daten und Epochen noch ein paar Parameter:

• mit batch_size können Sie die Größe der Minibatches angeben (Standardmäßig 32)
• mit validation_data übergibt man Daten, auf denen nach jeder Epoche das Modell evaluiert wird; wir benutzen hier die echten Testdaten und keine speziellen Validierungsdaten
• mit verbose kann man die Ausgabe steuern (z.B. mit verbose=0 ausschalten).

Um Batchtraining zu realisieren, wählen wir eine Batchgröße von 60000, und bilden damit das originale Backpropagation nach, wo in einer Epoche alle Trainingsbeispiele verarbeitet werden, bevor ein Update der Gewichte durchgeführt wird (siehe auch Abschnitt 3.6).

Wir unterdrücken die Ausgabe (verbose=0), messen aber die Trainingsdauer mit Hilfe der Funktion time aus dem gleichnamigen Paket (gibt die Sekunden seit 1.1.1970 0:00 Uhr zurück).

start_time = time.time() # Wir merken uns die Startzeit

history1 = model.fit(x_train, y_train_1hot, 
                     epochs=20,
                     batch_size=60000,
                     verbose=1,
                     validation_data = (x_test, y_test_1hot))

duration = time.time() - start_time # Wir berechnen die Dauer in Sek.
print(f'TRAININGSDAUER: {duration:.2f} Sek.')

Epoch 1/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 488ms/step - acc: 0.0878 - loss: 2.3608

1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 635ms/step - acc: 0.0878 - loss: 2.3608 - val_acc: 0.1003 - val_loss: 2.3329

Epoch 2/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step - acc: 0.0974 - loss: 2.3354

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 330ms/step - acc: 0.0974 - loss: 2.3354 - val_acc: 0.1104 - val_loss: 2.3127

Epoch 3/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 242ms/step - acc: 0.1099 - loss: 2.3150

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 281ms/step - acc: 0.1099 - loss: 2.3150 - val_acc: 0.1356 - val_loss: 2.2956

Epoch 4/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 158ms/step - acc: 0.1323 - loss: 2.2979

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 194ms/step - acc: 0.1323 - loss: 2.2979 - val_acc: 0.1705 - val_loss: 2.2806

Epoch 5/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 139ms/step - acc: 0.1662 - loss: 2.2830

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 177ms/step - acc: 0.1662 - loss: 2.2830 - val_acc: 0.2024 - val_loss: 2.2671

Epoch 6/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 132ms/step - acc: 0.2000 - loss: 2.2695

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 168ms/step - acc: 0.2000 - loss: 2.2695 - val_acc: 0.2308 - val_loss: 2.2545

Epoch 7/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 398ms/step - acc: 0.2287 - loss: 2.2569

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 442ms/step - acc: 0.2287 - loss: 2.2569 - val_acc: 0.2559 - val_loss: 2.2426

Epoch 8/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 218ms/step - acc: 0.2529 - loss: 2.2451

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 270ms/step - acc: 0.2529 - loss: 2.2451 - val_acc: 0.2773 - val_loss: 2.2311

Epoch 9/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 149ms/step - acc: 0.2751 - loss: 2.2338

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - acc: 0.2751 - loss: 2.2338 - val_acc: 0.2977 - val_loss: 2.2200

Epoch 10/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 256ms/step - acc: 0.2952 - loss: 2.2228

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 293ms/step - acc: 0.2952 - loss: 2.2228 - val_acc: 0.3170 - val_loss: 2.2091

Epoch 11/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 160ms/step - acc: 0.3145 - loss: 2.2120

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 203ms/step - acc: 0.3145 - loss: 2.2120 - val_acc: 0.3346 - val_loss: 2.1983

Epoch 12/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 123ms/step - acc: 0.3320 - loss: 2.2014

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 163ms/step - acc: 0.3320 - loss: 2.2014 - val_acc: 0.3553 - val_loss: 2.1877

Epoch 13/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 157ms/step - acc: 0.3509 - loss: 2.1909

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 193ms/step - acc: 0.3509 - loss: 2.1909 - val_acc: 0.3737 - val_loss: 2.1771

Epoch 14/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 126ms/step - acc: 0.3687 - loss: 2.1806

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 164ms/step - acc: 0.3687 - loss: 2.1806 - val_acc: 0.3900 - val_loss: 2.1667

Epoch 15/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 190ms/step - acc: 0.3857 - loss: 2.1703

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 230ms/step - acc: 0.3857 - loss: 2.1703 - val_acc: 0.4073 - val_loss: 2.1563

Epoch 16/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 156ms/step - acc: 0.4027 - loss: 2.1601

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 197ms/step - acc: 0.4027 - loss: 2.1601 - val_acc: 0.4253 - val_loss: 2.1460

Epoch 17/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 121ms/step - acc: 0.4178 - loss: 2.1500

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 157ms/step - acc: 0.4178 - loss: 2.1500 - val_acc: 0.4406 - val_loss: 2.1357

Epoch 18/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 151ms/step - acc: 0.4326 - loss: 2.1399

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - acc: 0.4326 - loss: 2.1399 - val_acc: 0.4521 - val_loss: 2.1255

Epoch 19/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 174ms/step - acc: 0.4463 - loss: 2.1299

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 217ms/step - acc: 0.4463 - loss: 2.1299 - val_acc: 0.4646 - val_loss: 2.1154

Epoch 20/20

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 157ms/step - acc: 0.4598 - loss: 2.1200

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 197ms/step - acc: 0.4598 - loss: 2.1200 - val_acc: 0.4778 - val_loss: 2.1053

TRAININGSDAUER: 5.37 Sek.

Die Trainingsdauer im Batchtraining ist mit 3 Sekunden für 20 Epochen extrem kurz.

5.5.7 Evaluation

Wir sehen uns die Entwicklung von Zielfunktion (Loss) und Accuracy an.

Dazu definieren wir vorab eine Hilfsfunktion.

def set_subplot(ax, y_label, traindata, testdata, ylim):
    e_range = range(1, len(traindata) + 1)
    ax.plot(e_range, traindata, 'b', label='Training')
    ax.plot(e_range, testdata, 'g', label='Test')
    ax.set_xlabel('Epochen')
    ax.set_ylabel(y_label)
    ax.legend()
    ax.grid()
    ax.set_ylim(ylim)
    ax.set_title(y_label)

Achten Sie bei den Plots immer auf die Grenzen der y-Achse (im Code set_ylim). Diese werden immer so gewählt, dass man die Kurve möglichst gut sieht. Hier haben wir das Interval 0 bis 0.6 für die Accuracy. Weiter unten haben wir ein anderes Interval. Dies muss man beim Vergleich der Kurven natürlich beachten.

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,4))

set_subplot(ax[0], 'Loss', history1.history['loss'], 
            history1.history['val_loss'], [0, 3])
set_subplot(ax[1], 'Accuracy', history1.history['acc'], 
            history1.history['val_acc'], [0, 0.6])

plt.show()

Mit evaluate können wir die Performanz unseres Modells auf den Testdaten messen. Wir bekommen ein Tupel mit Fehler und Accuracy zurück.

loss, acc = model.evaluate(x_test, y_test_1hot)
print(f"Evaluation auf den Testdaten:\n\nLoss = {loss:.3f}\nAccuracy = {acc:.3f}")

  1/313 ━━━━━━━━━━━━━━━━━━━━ 4s 16ms/step - acc: 0.6562 - loss: 2.0750

 80/313 ━━━━━━━━━━━━━━━━━━━━ 0s 637us/step - acc: 0.4362 - loss: 2.1287

158/313 ━━━━━━━━━━━━━━━━━━━━ 0s 640us/step - acc: 0.4327 - loss: 2.1291

256/313 ━━━━━━━━━━━━━━━━━━━━ 0s 592us/step - acc: 0.4412 - loss: 2.1246

313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 615us/step - acc: 0.4474 - loss: 2.1213

Evaluation auf den Testdaten:

Loss = 2.105
Accuracy = 0.478

Mit Batchtraining erhalten wir nach 20 Epochen Training eine enttäuschende Accuracy von 47% auf den Testdaten.

5.6 Einfluss der Batchgröße

Mit Batchtraining (Batchgröße = 60000) haben wir ein enttäuschendes Ergebnis erzielt. Wir sehen uns jetzt das reine SGD und eine Variante von Minibatch-Training an. Beides wurde in Abschnitt 3.6 eingeführt.

5.6.1 Reines SGD (Batchgröße = 1)

Wenn wir die Batchgröße auf 1 setzen, erreichen wir “reines” Stochastic Gradient Descent, wo die Gewichte nach jedem Trainingsbeispiel angepasst werden.

Wir erzeugen eine neue Instanz des Netzwerks mit den gleichen Eigenschaften (20 versteckte Neuronen, gleiche Aktivierungsfunktionen). Anschließend trainieren wir es unter gleichen Bedingungen. Der einzige Unterschied ist die Batchgröße, die wir hier auf 1 setzen.

model = Sequential()
model.add(Input(shape=(784,)))
model.add(Dense(60, 
                activation='sigmoid'))
model.add(Dense(10, activation='softmax'))

opt = SGD(learning_rate = 0.1)

model.compile(optimizer=opt,
              loss='categorical_crossentropy',
              metrics=['acc'])

start_time = time.time() 

history2 = model.fit(x_train, y_train_1hot, 
                     epochs=20,
                     batch_size=1,
                     verbose=1,
                     validation_data = (x_test, y_test_1hot))

duration = time.time() - start_time 
print(f'TRAININGSDAUER: {duration:.2f} Sek.')

Epoch 1/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 2:32:21 152ms/step - acc: 0.0000e+00 - loss: 3.2959

   91/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 556us/step - acc: 0.1307 - loss: 2.6126        

  194/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 518us/step - acc: 0.2005 - loss: 2.3502

  308/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 490us/step - acc: 0.2591 - loss: 2.1669

  408/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 493us/step - acc: 0.3020 - loss: 2.0336

  522/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 482us/step - acc: 0.3412 - loss: 1.9128

  636/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 474us/step - acc: 0.3749 - loss: 1.8123

  760/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 463us/step - acc: 0.4061 - loss: 1.7209

  872/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 461us/step - acc: 0.4301 - loss: 1.6497

  964/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 469us/step - acc: 0.4473 - loss: 1.5981

 1080/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 466us/step - acc: 0.4676 - loss: 1.5393

 1199/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 461us/step - acc: 0.4862 - loss: 1.4859

 1323/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 456us/step - acc: 0.5027 - loss: 1.4382

 1449/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 451us/step - acc: 0.5174 - loss: 1.3958

 1567/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 449us/step - acc: 0.5302 - loss: 1.3595

 1684/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 447us/step - acc: 0.5419 - loss: 1.3267

 1805/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 445us/step - acc: 0.5530 - loss: 1.2955

 1911/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 447us/step - acc: 0.5619 - loss: 1.2704

 2004/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 451us/step - acc: 0.5692 - loss: 1.2498

 2098/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 455us/step - acc: 0.5762 - loss: 1.2300

 2215/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 454us/step - acc: 0.5844 - loss: 1.2070

 2330/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 453us/step - acc: 0.5920 - loss: 1.1855

 2450/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 451us/step - acc: 0.5995 - loss: 1.1647

 2577/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 449us/step - acc: 0.6068 - loss: 1.1443

 2701/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 447us/step - acc: 0.6135 - loss: 1.1257

 2813/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 447us/step - acc: 0.6192 - loss: 1.1100

 2935/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 445us/step - acc: 0.6251 - loss: 1.0936

 3060/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 444us/step - acc: 0.6308 - loss: 1.0780

 3176/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 443us/step - acc: 0.6358 - loss: 1.0642

 3224/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 452us/step - acc: 0.6378 - loss: 1.0586

 3299/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 457us/step - acc: 0.6409 - loss: 1.0502

 3401/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 458us/step - acc: 0.6449 - loss: 1.0391

 3505/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 459us/step - acc: 0.6488 - loss: 1.0282

 3607/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 460us/step - acc: 0.6526 - loss: 1.0180

 3700/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 462us/step - acc: 0.6559 - loss: 1.0089

 3819/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 460us/step - acc: 0.6600 - loss: 0.9977

 3940/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 459us/step - acc: 0.6640 - loss: 0.9866

 4059/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 458us/step - acc: 0.6678 - loss: 0.9762

 4165/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 458us/step - acc: 0.6710 - loss: 0.9673

 4283/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 457us/step - acc: 0.6745 - loss: 0.9578

 4403/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 456us/step - acc: 0.6780 - loss: 0.9483

 4522/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 456us/step - acc: 0.6813 - loss: 0.9393

 4645/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 454us/step - acc: 0.6846 - loss: 0.9303

 4763/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 454us/step - acc: 0.6876 - loss: 0.9219

 4889/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 452us/step - acc: 0.6909 - loss: 0.9131

 5011/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 451us/step - acc: 0.6939 - loss: 0.9049

 5129/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 451us/step - acc: 0.6967 - loss: 0.8971

 5234/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 451us/step - acc: 0.6991 - loss: 0.8904

 5330/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 453us/step - acc: 0.7013 - loss: 0.8844

 5407/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 455us/step - acc: 0.7030 - loss: 0.8797

 5516/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 456us/step - acc: 0.7053 - loss: 0.8732

 5611/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 457us/step - acc: 0.7073 - loss: 0.8677

 5710/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 458us/step - acc: 0.7094 - loss: 0.8620

 5806/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 459us/step - acc: 0.7114 - loss: 0.8566

 5887/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 461us/step - acc: 0.7130 - loss: 0.8521

 5996/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 461us/step - acc: 0.7151 - loss: 0.8462

 6116/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 460us/step - acc: 0.7174 - loss: 0.8399

 6224/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 460us/step - acc: 0.7194 - loss: 0.8344

 6330/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 461us/step - acc: 0.7213 - loss: 0.8292

 6445/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 460us/step - acc: 0.7233 - loss: 0.8237

 6560/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 460us/step - acc: 0.7253 - loss: 0.8183

 6681/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 459us/step - acc: 0.7273 - loss: 0.8128

 6809/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 458us/step - acc: 0.7294 - loss: 0.8072

 6929/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 457us/step - acc: 0.7313 - loss: 0.8020

 7048/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 456us/step - acc: 0.7331 - loss: 0.7970

 7172/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 456us/step - acc: 0.7350 - loss: 0.7919

 7295/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 455us/step - acc: 0.7368 - loss: 0.7870

 7422/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 454us/step - acc: 0.7386 - loss: 0.7820

 7550/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 453us/step - acc: 0.7404 - loss: 0.7771

 7678/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 452us/step - acc: 0.7421 - loss: 0.7723

 7801/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 451us/step - acc: 0.7438 - loss: 0.7679

 7926/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 450us/step - acc: 0.7454 - loss: 0.7634

 8057/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 449us/step - acc: 0.7470 - loss: 0.7588

 8185/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 448us/step - acc: 0.7486 - loss: 0.7545

 8312/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 447us/step - acc: 0.7502 - loss: 0.7502

 8437/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 447us/step - acc: 0.7516 - loss: 0.7462

 8561/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 446us/step - acc: 0.7531 - loss: 0.7422

 8686/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 445us/step - acc: 0.7545 - loss: 0.7382

 8801/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 445us/step - acc: 0.7558 - loss: 0.7347

 8927/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 445us/step - acc: 0.7572 - loss: 0.7308

 9048/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 444us/step - acc: 0.7585 - loss: 0.7272

 9184/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 443us/step - acc: 0.7600 - loss: 0.7232

 9323/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 442us/step - acc: 0.7614 - loss: 0.7192

 9461/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 441us/step - acc: 0.7628 - loss: 0.7152

 9596/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 440us/step - acc: 0.7642 - loss: 0.7115

 9718/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 439us/step - acc: 0.7654 - loss: 0.7081

 9855/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 438us/step - acc: 0.7667 - loss: 0.7044

 9991/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 438us/step - acc: 0.7680 - loss: 0.7008

10117/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 437us/step - acc: 0.7692 - loss: 0.6976

10245/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 436us/step - acc: 0.7704 - loss: 0.6943

10379/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 436us/step - acc: 0.7716 - loss: 0.6910

10517/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 435us/step - acc: 0.7728 - loss: 0.6876

10654/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 434us/step - acc: 0.7740 - loss: 0.6843

10786/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 433us/step - acc: 0.7751 - loss: 0.6811

10910/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 433us/step - acc: 0.7761 - loss: 0.6782

11043/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 432us/step - acc: 0.7773 - loss: 0.6751

11166/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 432us/step - acc: 0.7783 - loss: 0.6724

11297/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 431us/step - acc: 0.7793 - loss: 0.6694

11433/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 431us/step - acc: 0.7804 - loss: 0.6664

11477/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 433us/step - acc: 0.7807 - loss: 0.6654

11612/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 433us/step - acc: 0.7818 - loss: 0.6625

11751/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 432us/step - acc: 0.7828 - loss: 0.6595

11884/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 431us/step - acc: 0.7838 - loss: 0.6567

12014/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 431us/step - acc: 0.7848 - loss: 0.6540

12148/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 430us/step - acc: 0.7858 - loss: 0.6512

12283/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 429us/step - acc: 0.7868 - loss: 0.6485

12414/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 429us/step - acc: 0.7877 - loss: 0.6459

12552/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 428us/step - acc: 0.7886 - loss: 0.6432

12679/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 428us/step - acc: 0.7895 - loss: 0.6407

12816/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 427us/step - acc: 0.7904 - loss: 0.6381

12952/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 427us/step - acc: 0.7913 - loss: 0.6356

13089/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 426us/step - acc: 0.7922 - loss: 0.6331

13224/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 425us/step - acc: 0.7931 - loss: 0.6306

13359/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 425us/step - acc: 0.7939 - loss: 0.6282

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43891/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8712 - loss: 0.4046

44010/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8714 - loss: 0.4042

44140/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8715 - loss: 0.4037

44267/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8716 - loss: 0.4033

44393/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8718 - loss: 0.4029

44522/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8719 - loss: 0.4025

44646/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8721 - loss: 0.4021

44777/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8722 - loss: 0.4016

44903/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8723 - loss: 0.4012

45032/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8725 - loss: 0.4008

45161/60000 ━━━━━━━━━━━━━━━━━━━━ 6s 405us/step - acc: 0.8726 - loss: 0.4004

45288/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 405us/step - acc: 0.8728 - loss: 0.4000

45421/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 405us/step - acc: 0.8729 - loss: 0.3995

45550/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 405us/step - acc: 0.8730 - loss: 0.3991

45672/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 405us/step - acc: 0.8732 - loss: 0.3987

45804/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 405us/step - acc: 0.8733 - loss: 0.3983

45936/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8735 - loss: 0.3979

46071/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8736 - loss: 0.3975

46208/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8737 - loss: 0.3971

46343/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8739 - loss: 0.3966

46478/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8740 - loss: 0.3962

46608/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8742 - loss: 0.3958

46745/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8743 - loss: 0.3954

46884/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8744 - loss: 0.3950

47018/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8746 - loss: 0.3946

47147/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8747 - loss: 0.3942

47271/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8748 - loss: 0.3938

47393/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8750 - loss: 0.3935

47518/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 404us/step - acc: 0.8751 - loss: 0.3931

47641/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8752 - loss: 0.3927

47767/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8753 - loss: 0.3923

47886/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8755 - loss: 0.3920

48009/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8756 - loss: 0.3916

48122/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8757 - loss: 0.3913

48231/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8758 - loss: 0.3910

48354/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8759 - loss: 0.3906

48478/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8760 - loss: 0.3902

48604/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8762 - loss: 0.3899

48721/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8763 - loss: 0.3895

48845/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8764 - loss: 0.3892

48971/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8765 - loss: 0.3888

49098/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8766 - loss: 0.3884

49225/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8768 - loss: 0.3881

49353/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8769 - loss: 0.3877

49478/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8770 - loss: 0.3874

49605/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8771 - loss: 0.3870

49735/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8772 - loss: 0.3866

49853/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8774 - loss: 0.3863

49976/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 404us/step - acc: 0.8775 - loss: 0.3860

50106/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8776 - loss: 0.3856

50237/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8777 - loss: 0.3852

50367/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8778 - loss: 0.3849

50493/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8780 - loss: 0.3845

50620/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8781 - loss: 0.3842

50744/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8782 - loss: 0.3839

50870/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8783 - loss: 0.3835

50992/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8784 - loss: 0.3832

51122/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8785 - loss: 0.3828

51250/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8787 - loss: 0.3825

51378/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8788 - loss: 0.3821

51507/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8789 - loss: 0.3818

51632/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8790 - loss: 0.3815

51757/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8791 - loss: 0.3811

51872/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8792 - loss: 0.3808

51992/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8793 - loss: 0.3805

52115/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8794 - loss: 0.3802

52242/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8795 - loss: 0.3798

52372/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8797 - loss: 0.3795

52496/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 404us/step - acc: 0.8798 - loss: 0.3792

52620/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8799 - loss: 0.3789

52746/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8800 - loss: 0.3785

52873/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8801 - loss: 0.3782

53000/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8802 - loss: 0.3779

53127/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8803 - loss: 0.3776

53254/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8804 - loss: 0.3772

53382/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8805 - loss: 0.3769

53507/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8806 - loss: 0.3766

53637/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8807 - loss: 0.3763

53763/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 404us/step - acc: 0.8809 - loss: 0.3759

53893/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8810 - loss: 0.3756

54020/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8811 - loss: 0.3753

54146/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8812 - loss: 0.3750

54273/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8813 - loss: 0.3747

54396/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8814 - loss: 0.3744

54521/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8815 - loss: 0.3741

54648/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8816 - loss: 0.3737

54775/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8817 - loss: 0.3734

54902/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8818 - loss: 0.3731

55033/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 403us/step - acc: 0.8819 - loss: 0.3728

55158/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8820 - loss: 0.3725

55290/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8821 - loss: 0.3722

55407/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8822 - loss: 0.3719

55540/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8823 - loss: 0.3716

55663/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8824 - loss: 0.3713

55798/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8825 - loss: 0.3710

55916/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8826 - loss: 0.3707

56045/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8827 - loss: 0.3704

56165/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8828 - loss: 0.3701

56293/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8829 - loss: 0.3698

56410/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8830 - loss: 0.3695

56535/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8831 - loss: 0.3692

56653/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8832 - loss: 0.3689

56783/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8833 - loss: 0.3686

56902/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8834 - loss: 0.3684

57027/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8835 - loss: 0.3681

57149/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8836 - loss: 0.3678

57287/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8837 - loss: 0.3675

57415/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 403us/step - acc: 0.8838 - loss: 0.3672

57553/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8839 - loss: 0.3668

57676/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8840 - loss: 0.3666

57806/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8841 - loss: 0.3663

57929/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8842 - loss: 0.3660

58067/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8843 - loss: 0.3657

58195/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8844 - loss: 0.3654

58333/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8845 - loss: 0.3651

58460/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8846 - loss: 0.3648

58583/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8847 - loss: 0.3645

58708/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8848 - loss: 0.3642

58841/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8849 - loss: 0.3639

58965/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8850 - loss: 0.3636

59100/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8851 - loss: 0.3633

59226/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8852 - loss: 0.3631

59362/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8853 - loss: 0.3628

59489/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8854 - loss: 0.3625

59627/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8855 - loss: 0.3622

59755/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 403us/step - acc: 0.8856 - loss: 0.3619

59891/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 402us/step - acc: 0.8857 - loss: 0.3616

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 464us/step - acc: 0.8857 - loss: 0.3614 - val_acc: 0.9560 - val_loss: 0.1414

Epoch 2/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 10:30 11ms/step - acc: 1.0000 - loss: 2.0669e-04

  132/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 384us/step - acc: 0.9738 - loss: 0.1039     

  263/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 383us/step - acc: 0.9721 - loss: 0.1148

  397/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9692 - loss: 0.1208

  523/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 385us/step - acc: 0.9668 - loss: 0.1274

  656/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 383us/step - acc: 0.9657 - loss: 0.1297

  784/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 384us/step - acc: 0.9650 - loss: 0.1301

  912/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9650 - loss: 0.1295

 1048/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 384us/step - acc: 0.9651 - loss: 0.1286

 1185/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 382us/step - acc: 0.9645 - loss: 0.1295

 1323/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9642 - loss: 0.1297

 1460/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 378us/step - acc: 0.9642 - loss: 0.1296

 1597/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 377us/step - acc: 0.9641 - loss: 0.1294

 1732/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9642 - loss: 0.1287

 1868/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 376us/step - acc: 0.9644 - loss: 0.1279

 2006/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9646 - loss: 0.1269

 2142/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9648 - loss: 0.1258

 2276/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9649 - loss: 0.1250

 2413/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9651 - loss: 0.1241

 2550/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9652 - loss: 0.1233

 2686/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9654 - loss: 0.1225

 2823/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 373us/step - acc: 0.9655 - loss: 0.1219

 2961/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 373us/step - acc: 0.9656 - loss: 0.1214

 3101/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 372us/step - acc: 0.9656 - loss: 0.1209

 3237/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 372us/step - acc: 0.9657 - loss: 0.1205

 3378/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 372us/step - acc: 0.9657 - loss: 0.1201

 3515/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 371us/step - acc: 0.9657 - loss: 0.1197

 3642/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 372us/step - acc: 0.9657 - loss: 0.1194

 3768/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 373us/step - acc: 0.9657 - loss: 0.1190

 3897/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9657 - loss: 0.1188

 4025/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9657 - loss: 0.1185

 4157/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9657 - loss: 0.1183

 4290/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9657 - loss: 0.1181

 4418/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9657 - loss: 0.1180

 4549/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9656 - loss: 0.1180

 4687/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9656 - loss: 0.1180

 4823/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9655 - loss: 0.1180

 4959/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9655 - loss: 0.1180

 5095/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9655 - loss: 0.1179

 5233/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9655 - loss: 0.1179

 5364/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9654 - loss: 0.1179

 5497/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9654 - loss: 0.1178

 5635/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9654 - loss: 0.1178

 5770/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9654 - loss: 0.1178

 5905/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9653 - loss: 0.1179

 6040/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9653 - loss: 0.1179

 6173/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9653 - loss: 0.1179

 6306/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9653 - loss: 0.1180

 6446/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 374us/step - acc: 0.9653 - loss: 0.1180

 6584/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9652 - loss: 0.1181

 6723/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9652 - loss: 0.1182

 6850/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9652 - loss: 0.1182

 6968/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9652 - loss: 0.1183

 7103/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9651 - loss: 0.1183

 7240/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9651 - loss: 0.1184

 7377/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9651 - loss: 0.1184

 7514/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9651 - loss: 0.1185

 7653/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9651 - loss: 0.1185

 7790/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 374us/step - acc: 0.9650 - loss: 0.1185

 7929/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9650 - loss: 0.1185

 8070/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9650 - loss: 0.1185

 8210/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9650 - loss: 0.1186

 8333/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9650 - loss: 0.1186

 8470/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9650 - loss: 0.1186

 8608/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9649 - loss: 0.1186

 8745/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9649 - loss: 0.1186

 8879/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9649 - loss: 0.1187

 9015/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 373us/step - acc: 0.9649 - loss: 0.1187

 9152/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9649 - loss: 0.1187

 9290/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9649 - loss: 0.1187

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 9697/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9648 - loss: 0.1189

 9828/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9648 - loss: 0.1189

 9961/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9648 - loss: 0.1190

10089/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 373us/step - acc: 0.9647 - loss: 0.1190

10196/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9647 - loss: 0.1191

10317/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 375us/step - acc: 0.9647 - loss: 0.1191

10455/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 375us/step - acc: 0.9647 - loss: 0.1192

10592/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9646 - loss: 0.1193

10730/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9646 - loss: 0.1193

10866/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9646 - loss: 0.1194

11003/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9645 - loss: 0.1195

11139/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9645 - loss: 0.1196

11277/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9645 - loss: 0.1196

11416/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9644 - loss: 0.1197

11554/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9644 - loss: 0.1198

11682/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9644 - loss: 0.1198

11806/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 374us/step - acc: 0.9643 - loss: 0.1199

11934/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9643 - loss: 0.1200

12068/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9643 - loss: 0.1200

12201/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9643 - loss: 0.1201

12337/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9642 - loss: 0.1201

12471/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9642 - loss: 0.1202

12608/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9642 - loss: 0.1203

12746/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9641 - loss: 0.1203

12879/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9641 - loss: 0.1204

13006/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9641 - loss: 0.1204

13142/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9641 - loss: 0.1205

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13413/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9640 - loss: 0.1206

13552/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9640 - loss: 0.1206

13686/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9640 - loss: 0.1207

13824/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9640 - loss: 0.1207

13963/60000 ━━━━━━━━━━━━━━━━━━━━ 17s 374us/step - acc: 0.9639 - loss: 0.1208

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14514/60000 ━━━━━━━━━━━━━━━━━━━━ 16s 373us/step - acc: 0.9638 - loss: 0.1210

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54487/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

54615/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

54752/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

54882/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

55015/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

55150/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 434us/step - acc: 0.9618 - loss: 0.1252

55286/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 433us/step - acc: 0.9618 - loss: 0.1253

55420/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

55554/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

55692/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

55830/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

55964/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

56101/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

56238/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 432us/step - acc: 0.9618 - loss: 0.1253

56378/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 432us/step - acc: 0.9618 - loss: 0.1253

56518/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 432us/step - acc: 0.9618 - loss: 0.1253

56653/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 432us/step - acc: 0.9618 - loss: 0.1253

56761/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 432us/step - acc: 0.9618 - loss: 0.1253

56788/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

56802/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 433us/step - acc: 0.9618 - loss: 0.1253

56812/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 434us/step - acc: 0.9618 - loss: 0.1253

56828/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 435us/step - acc: 0.9618 - loss: 0.1253

56888/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 435us/step - acc: 0.9618 - loss: 0.1253

56942/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57068/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57190/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57315/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57436/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57562/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57688/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 436us/step - acc: 0.9618 - loss: 0.1253

57820/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

57944/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58068/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58185/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58303/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58431/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58553/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1253

58672/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9618 - loss: 0.1254

58781/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

58906/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59025/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59151/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59274/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59395/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59511/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59638/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59744/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59857/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

59972/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 435us/step - acc: 0.9617 - loss: 0.1254

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 497us/step - acc: 0.9617 - loss: 0.1254 - val_acc: 0.9556 - val_loss: 0.1414

Epoch 3/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 11:06 11ms/step - acc: 1.0000 - loss: 0.0898

  136/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 371us/step - acc: 0.9977 - loss: 0.0427 

  265/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9953 - loss: 0.0502

  400/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 377us/step - acc: 0.9939 - loss: 0.0504

  522/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 385us/step - acc: 0.9926 - loss: 0.0534

  638/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 393us/step - acc: 0.9918 - loss: 0.0546

  770/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 391us/step - acc: 0.9907 - loss: 0.0564

  901/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 390us/step - acc: 0.9901 - loss: 0.0570

 1031/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 389us/step - acc: 0.9895 - loss: 0.0574

 1155/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 391us/step - acc: 0.9889 - loss: 0.0576

 1288/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 389us/step - acc: 0.9885 - loss: 0.0576

 1423/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9878 - loss: 0.0585

 1552/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9873 - loss: 0.0596

 1681/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9867 - loss: 0.0604

 1815/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9863 - loss: 0.0611

 1942/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9859 - loss: 0.0618

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 2198/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9851 - loss: 0.0629

 2322/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 389us/step - acc: 0.9848 - loss: 0.0634

 2453/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9844 - loss: 0.0640

 2587/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 388us/step - acc: 0.9840 - loss: 0.0647

 2723/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9835 - loss: 0.0653

 2860/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9831 - loss: 0.0660

 2996/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 385us/step - acc: 0.9827 - loss: 0.0668

 3134/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9823 - loss: 0.0676

 3270/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 383us/step - acc: 0.9819 - loss: 0.0683

 3396/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9815 - loss: 0.0690

 3526/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9812 - loss: 0.0697

 3658/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9808 - loss: 0.0704

 3789/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9805 - loss: 0.0712

 3923/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 384us/step - acc: 0.9801 - loss: 0.0720

 4055/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 383us/step - acc: 0.9798 - loss: 0.0728

 4189/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 383us/step - acc: 0.9795 - loss: 0.0736

 4328/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 382us/step - acc: 0.9792 - loss: 0.0743

 4452/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 383us/step - acc: 0.9789 - loss: 0.0750

 4588/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 383us/step - acc: 0.9787 - loss: 0.0757

 4725/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 382us/step - acc: 0.9784 - loss: 0.0763

 4859/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 382us/step - acc: 0.9782 - loss: 0.0768

 4996/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 382us/step - acc: 0.9780 - loss: 0.0774

 5132/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 381us/step - acc: 0.9778 - loss: 0.0780

 5267/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 381us/step - acc: 0.9775 - loss: 0.0785

 5404/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 381us/step - acc: 0.9773 - loss: 0.0790

 5542/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9771 - loss: 0.0795

 5681/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9770 - loss: 0.0799

 5811/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9768 - loss: 0.0803

 5941/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9766 - loss: 0.0807

 6075/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9765 - loss: 0.0810

 6211/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 380us/step - acc: 0.9763 - loss: 0.0814

 6348/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 379us/step - acc: 0.9762 - loss: 0.0817

 6484/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 379us/step - acc: 0.9761 - loss: 0.0820

 6621/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 379us/step - acc: 0.9760 - loss: 0.0823

 6756/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 379us/step - acc: 0.9759 - loss: 0.0825

 6893/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 379us/step - acc: 0.9758 - loss: 0.0828

 7029/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 378us/step - acc: 0.9757 - loss: 0.0830

 7165/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9756 - loss: 0.0832

 7302/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9755 - loss: 0.0835

 7441/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9754 - loss: 0.0837

 7571/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9753 - loss: 0.0839

 7701/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9752 - loss: 0.0841

 7829/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9751 - loss: 0.0843

 7959/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9751 - loss: 0.0845

 8092/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9750 - loss: 0.0847

 8229/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9749 - loss: 0.0850

 8363/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9748 - loss: 0.0852

 8496/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9747 - loss: 0.0854

 8626/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9747 - loss: 0.0856

 8760/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9746 - loss: 0.0858

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59710/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 386us/step - acc: 0.9698 - loss: 0.0994

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60000/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 447us/step - acc: 0.9698 - loss: 0.0994 - val_acc: 0.9598 - val_loss: 0.1290

Epoch 4/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 13:50 14ms/step - acc: 1.0000 - loss: 0.3703

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  224/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 449us/step - acc: 0.9702 - loss: 0.0943

  292/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 517us/step - acc: 0.9699 - loss: 0.0900

  407/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 495us/step - acc: 0.9710 - loss: 0.0849

  517/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 487us/step - acc: 0.9722 - loss: 0.0816

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52270/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 428us/step - acc: 0.9745 - loss: 0.0806

52407/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 428us/step - acc: 0.9745 - loss: 0.0806

52545/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 427us/step - acc: 0.9745 - loss: 0.0806

52687/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 427us/step - acc: 0.9745 - loss: 0.0806

52828/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 427us/step - acc: 0.9745 - loss: 0.0806

52966/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 427us/step - acc: 0.9745 - loss: 0.0806

53105/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 427us/step - acc: 0.9745 - loss: 0.0807

53244/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0807

53381/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0807

53518/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0807

53657/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0807

53793/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0807

53933/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0808

54058/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0808

54183/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0808

54310/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 425us/step - acc: 0.9745 - loss: 0.0808

54435/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 425us/step - acc: 0.9745 - loss: 0.0808

54534/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0808

54653/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0808

54765/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

54870/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

54964/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

55076/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

55179/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

55300/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 426us/step - acc: 0.9745 - loss: 0.0809

55412/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0809

55533/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0809

55660/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0809

55786/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0810

55917/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0810

56053/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0810

56191/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9745 - loss: 0.0810

56330/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0810

56470/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0810

56608/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0811

56740/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0811

56875/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0811

57009/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0811

57136/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9744 - loss: 0.0811

57274/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 424us/step - acc: 0.9744 - loss: 0.0811

57413/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 424us/step - acc: 0.9744 - loss: 0.0811

57551/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 424us/step - acc: 0.9744 - loss: 0.0812

57689/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 424us/step - acc: 0.9744 - loss: 0.0812

57827/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 424us/step - acc: 0.9744 - loss: 0.0812

57966/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 424us/step - acc: 0.9744 - loss: 0.0812

58106/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 424us/step - acc: 0.9744 - loss: 0.0812

58244/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0812

58384/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0812

58521/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0813

58659/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0813

58801/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0813

58939/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0813

59077/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9744 - loss: 0.0813

59216/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0813

59357/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0814

59494/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0814

59632/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0814

59772/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0814

59911/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9744 - loss: 0.0814

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 483us/step - acc: 0.9744 - loss: 0.0814 - val_acc: 0.9580 - val_loss: 0.1335

Epoch 5/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 18:10 18ms/step - acc: 1.0000 - loss: 6.1749e-05

  115/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 442us/step - acc: 0.9671 - loss: 0.1208     

  228/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 443us/step - acc: 0.9714 - loss: 0.1019

  326/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 464us/step - acc: 0.9747 - loss: 0.0908

  442/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 456us/step - acc: 0.9769 - loss: 0.0836

  545/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 462us/step - acc: 0.9785 - loss: 0.0791

  634/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 477us/step - acc: 0.9794 - loss: 0.0768

  682/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 518us/step - acc: 0.9797 - loss: 0.0760

  730/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 552us/step - acc: 0.9800 - loss: 0.0752

  797/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 569us/step - acc: 0.9802 - loss: 0.0746

  871/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 578us/step - acc: 0.9802 - loss: 0.0741

  953/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 581us/step - acc: 0.9800 - loss: 0.0742

 1059/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 570us/step - acc: 0.9797 - loss: 0.0744

 1166/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 561us/step - acc: 0.9796 - loss: 0.0745

 1281/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 550us/step - acc: 0.9796 - loss: 0.0745

 1393/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 542us/step - acc: 0.9797 - loss: 0.0742

 1504/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 535us/step - acc: 0.9798 - loss: 0.0740

 1624/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 527us/step - acc: 0.9798 - loss: 0.0738

 1737/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 521us/step - acc: 0.9797 - loss: 0.0737

 1856/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 515us/step - acc: 0.9796 - loss: 0.0738

 1952/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 515us/step - acc: 0.9796 - loss: 0.0739

 2016/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 524us/step - acc: 0.9795 - loss: 0.0741

 2081/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 532us/step - acc: 0.9795 - loss: 0.0742

 2106/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 549us/step - acc: 0.9795 - loss: 0.0743

 2166/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 557us/step - acc: 0.9794 - loss: 0.0744

 2222/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 566us/step - acc: 0.9794 - loss: 0.0746

 2300/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 569us/step - acc: 0.9793 - loss: 0.0749

 2382/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 570us/step - acc: 0.9793 - loss: 0.0751

 2467/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 571us/step - acc: 0.9792 - loss: 0.0752

 2563/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 569us/step - acc: 0.9792 - loss: 0.0753

 2652/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 569us/step - acc: 0.9792 - loss: 0.0754

 2750/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 567us/step - acc: 0.9791 - loss: 0.0755

 2852/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 564us/step - acc: 0.9791 - loss: 0.0756

 2966/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 559us/step - acc: 0.9790 - loss: 0.0757

 3037/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 563us/step - acc: 0.9790 - loss: 0.0757

 3097/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 568us/step - acc: 0.9790 - loss: 0.0757

 3175/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 570us/step - acc: 0.9790 - loss: 0.0757

 3267/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 570us/step - acc: 0.9790 - loss: 0.0757

 3348/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 571us/step - acc: 0.9789 - loss: 0.0757

 3466/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 566us/step - acc: 0.9789 - loss: 0.0757

 3583/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 561us/step - acc: 0.9789 - loss: 0.0757

 3700/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 557us/step - acc: 0.9788 - loss: 0.0757

 3819/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 553us/step - acc: 0.9787 - loss: 0.0757

 3942/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 548us/step - acc: 0.9787 - loss: 0.0757

 4065/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 544us/step - acc: 0.9786 - loss: 0.0758

 4193/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 540us/step - acc: 0.9785 - loss: 0.0758

 4325/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 535us/step - acc: 0.9785 - loss: 0.0758

 4433/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 533us/step - acc: 0.9785 - loss: 0.0758

 4521/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 534us/step - acc: 0.9784 - loss: 0.0758

 4606/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 535us/step - acc: 0.9784 - loss: 0.0757

 4713/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 534us/step - acc: 0.9784 - loss: 0.0757

 4831/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 531us/step - acc: 0.9784 - loss: 0.0756

 4950/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 528us/step - acc: 0.9784 - loss: 0.0755

 5070/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 526us/step - acc: 0.9784 - loss: 0.0755

 5172/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 525us/step - acc: 0.9784 - loss: 0.0754

 5298/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 522us/step - acc: 0.9784 - loss: 0.0753

 5425/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 519us/step - acc: 0.9784 - loss: 0.0753

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49651/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 424us/step - acc: 0.9769 - loss: 0.0741

49781/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 424us/step - acc: 0.9769 - loss: 0.0741

49916/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 424us/step - acc: 0.9769 - loss: 0.0742

50051/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 423us/step - acc: 0.9769 - loss: 0.0742

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50591/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 423us/step - acc: 0.9769 - loss: 0.0742

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51523/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0743

51656/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0743

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51908/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0743

52037/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0743

52172/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0744

52305/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 422us/step - acc: 0.9768 - loss: 0.0744

52436/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 421us/step - acc: 0.9768 - loss: 0.0744

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52948/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 421us/step - acc: 0.9768 - loss: 0.0744

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53727/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 421us/step - acc: 0.9768 - loss: 0.0745

53863/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 421us/step - acc: 0.9768 - loss: 0.0745

54001/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9768 - loss: 0.0745

54138/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9768 - loss: 0.0745

54270/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0745

54407/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0745

54534/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

54656/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

54770/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

54891/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

55015/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

55141/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 420us/step - acc: 0.9767 - loss: 0.0746

55277/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 420us/step - acc: 0.9767 - loss: 0.0746

55415/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 420us/step - acc: 0.9767 - loss: 0.0746

55552/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0746

55689/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0746

55825/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

55963/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

56099/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

56237/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

56380/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

56510/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 419us/step - acc: 0.9767 - loss: 0.0747

56644/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0747

56778/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0747

56909/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0747

57045/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0747

57181/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0747

57318/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0748

57450/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0748

57579/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 418us/step - acc: 0.9767 - loss: 0.0748

57700/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9767 - loss: 0.0748

57730/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9767 - loss: 0.0748

57822/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 419us/step - acc: 0.9767 - loss: 0.0748

57945/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 419us/step - acc: 0.9767 - loss: 0.0748

58081/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 419us/step - acc: 0.9767 - loss: 0.0748

58203/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 419us/step - acc: 0.9766 - loss: 0.0748

58339/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0748

58476/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0748

58613/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0748

58751/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0748

58887/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0749

59022/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0749

59155/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0749

59288/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0749

59412/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 418us/step - acc: 0.9766 - loss: 0.0749

59548/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 417us/step - acc: 0.9766 - loss: 0.0749

59684/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 417us/step - acc: 0.9766 - loss: 0.0749

59821/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 417us/step - acc: 0.9766 - loss: 0.0749

59958/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 417us/step - acc: 0.9766 - loss: 0.0749

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 477us/step - acc: 0.9766 - loss: 0.0749 - val_acc: 0.9679 - val_loss: 0.1087

Epoch 6/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 12:38 13ms/step - acc: 1.0000 - loss: 6.6757e-06

  118/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 428us/step - acc: 0.9673 - loss: 0.0435     

  240/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 419us/step - acc: 0.9732 - loss: 0.0373

  350/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 431us/step - acc: 0.9756 - loss: 0.0353

  439/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 457us/step - acc: 0.9770 - loss: 0.0345

  547/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 459us/step - acc: 0.9777 - loss: 0.0362

  648/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 465us/step - acc: 0.9781 - loss: 0.0373

  771/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 456us/step - acc: 0.9781 - loss: 0.0395

  894/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 449us/step - acc: 0.9782 - loss: 0.0414

 1011/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 447us/step - acc: 0.9783 - loss: 0.0425

 1071/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 469us/step - acc: 0.9784 - loss: 0.0430

 1138/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 485us/step - acc: 0.9785 - loss: 0.0433

 1168/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 516us/step - acc: 0.9786 - loss: 0.0435

 1216/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 538us/step - acc: 0.9787 - loss: 0.0438

 1266/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 556us/step - acc: 0.9788 - loss: 0.0439

 1297/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 581us/step - acc: 0.9788 - loss: 0.0440

 1363/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 590us/step - acc: 0.9789 - loss: 0.0442

 1443/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 592us/step - acc: 0.9791 - loss: 0.0443

 1496/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 605us/step - acc: 0.9791 - loss: 0.0444

 1588/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 601us/step - acc: 0.9793 - loss: 0.0445

 1649/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 610us/step - acc: 0.9794 - loss: 0.0444

 1715/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 615us/step - acc: 0.9795 - loss: 0.0444

 1810/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 611us/step - acc: 0.9796 - loss: 0.0443

 1909/60000 ━━━━━━━━━━━━━━━━━━━━ 35s 605us/step - acc: 0.9798 - loss: 0.0443

 2010/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 600us/step - acc: 0.9799 - loss: 0.0444

 2085/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 602us/step - acc: 0.9799 - loss: 0.0445

 2159/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 605us/step - acc: 0.9800 - loss: 0.0446

 2258/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 601us/step - acc: 0.9801 - loss: 0.0446

 2342/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 601us/step - acc: 0.9802 - loss: 0.0447

 2456/60000 ━━━━━━━━━━━━━━━━━━━━ 34s 593us/step - acc: 0.9803 - loss: 0.0447

 2572/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 586us/step - acc: 0.9804 - loss: 0.0448

 2687/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 579us/step - acc: 0.9805 - loss: 0.0451

 2809/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 572us/step - acc: 0.9805 - loss: 0.0453

 2876/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 576us/step - acc: 0.9805 - loss: 0.0455

 2989/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 571us/step - acc: 0.9805 - loss: 0.0458

 3102/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 567us/step - acc: 0.9805 - loss: 0.0461

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47795/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 421us/step - acc: 0.9800 - loss: 0.0600

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48062/60000 ━━━━━━━━━━━━━━━━━━━━ 5s 421us/step - acc: 0.9800 - loss: 0.0601

48183/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9800 - loss: 0.0601

48318/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9800 - loss: 0.0601

48450/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9800 - loss: 0.0601

48586/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9800 - loss: 0.0602

48716/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9799 - loss: 0.0602

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49317/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 421us/step - acc: 0.9799 - loss: 0.0603

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49580/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0603

49715/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0604

49847/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0604

49979/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0604

50109/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0604

50239/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0604

50374/60000 ━━━━━━━━━━━━━━━━━━━━ 4s 420us/step - acc: 0.9799 - loss: 0.0605

50510/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 420us/step - acc: 0.9799 - loss: 0.0605

50639/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 420us/step - acc: 0.9799 - loss: 0.0605

50766/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9799 - loss: 0.0605

50895/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9799 - loss: 0.0606

51029/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0606

51155/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0606

51271/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0606

51395/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0606

51522/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0607

51651/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0607

51774/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0607

51896/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0607

52019/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0607

52152/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0608

52281/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0608

52415/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0608

52546/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0608

52682/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 419us/step - acc: 0.9798 - loss: 0.0609

52811/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 418us/step - acc: 0.9798 - loss: 0.0609

52945/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9798 - loss: 0.0609

53081/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9798 - loss: 0.0609

53205/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0609

53322/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0610

53451/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0610

53573/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0610

53692/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0610

53804/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0610

53933/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0611

54064/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0611

54194/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0611

54326/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0611

54459/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0611

54595/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0612

54731/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 418us/step - acc: 0.9797 - loss: 0.0612

54864/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 417us/step - acc: 0.9797 - loss: 0.0612

54992/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 417us/step - acc: 0.9797 - loss: 0.0612

55124/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 417us/step - acc: 0.9797 - loss: 0.0612

55260/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9797 - loss: 0.0613

55396/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9797 - loss: 0.0613

55527/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9797 - loss: 0.0613

55660/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9796 - loss: 0.0613

55795/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9796 - loss: 0.0613

55932/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9796 - loss: 0.0614

56069/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 417us/step - acc: 0.9796 - loss: 0.0614

56204/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0614

56338/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0614

56471/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0614

56604/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0615

56739/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0615

56875/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0615

57011/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0615

57145/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0615

57280/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0616

57416/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 416us/step - acc: 0.9796 - loss: 0.0616

57553/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 415us/step - acc: 0.9796 - loss: 0.0616

57687/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9796 - loss: 0.0616

57822/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9796 - loss: 0.0617

57957/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9796 - loss: 0.0617

58093/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9796 - loss: 0.0617

58228/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9795 - loss: 0.0617

58362/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9795 - loss: 0.0617

58499/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9795 - loss: 0.0618

58630/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9795 - loss: 0.0618

58763/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 415us/step - acc: 0.9795 - loss: 0.0618

58898/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0618

59035/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0618

59168/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0619

59295/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0619

59426/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0619

59557/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0619

59690/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0619

59825/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0620

59958/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 414us/step - acc: 0.9795 - loss: 0.0620

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 474us/step - acc: 0.9795 - loss: 0.0620 - val_acc: 0.9648 - val_loss: 0.1224

Epoch 7/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 9:03 9ms/step - acc: 1.0000 - loss: 7.7483e-05

  133/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 381us/step - acc: 0.9889 - loss: 0.0230   

  266/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9914 - loss: 0.0220

  404/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 374us/step - acc: 0.9906 - loss: 0.0248

  541/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 372us/step - acc: 0.9902 - loss: 0.0254

  679/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 370us/step - acc: 0.9898 - loss: 0.0259

  817/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 369us/step - acc: 0.9892 - loss: 0.0279

  952/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 369us/step - acc: 0.9886 - loss: 0.0296

 1090/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9880 - loss: 0.0311

 1229/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9874 - loss: 0.0326

 1366/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9869 - loss: 0.0337

 1500/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9867 - loss: 0.0346

 1637/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9865 - loss: 0.0352

 1773/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9865 - loss: 0.0355

 1909/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9864 - loss: 0.0359

 2046/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9864 - loss: 0.0362

 2186/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9864 - loss: 0.0364

 2321/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9864 - loss: 0.0365

 2459/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 368us/step - acc: 0.9864 - loss: 0.0367

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55474/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 455us/step - acc: 0.9816 - loss: 0.0557

55607/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 455us/step - acc: 0.9816 - loss: 0.0557

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57822/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 453us/step - acc: 0.9816 - loss: 0.0559

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58211/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 453us/step - acc: 0.9815 - loss: 0.0560

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59032/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 452us/step - acc: 0.9815 - loss: 0.0561

59170/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 452us/step - acc: 0.9815 - loss: 0.0561

59298/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 451us/step - acc: 0.9815 - loss: 0.0561

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59538/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 451us/step - acc: 0.9815 - loss: 0.0561

59655/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 451us/step - acc: 0.9815 - loss: 0.0562

59771/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 451us/step - acc: 0.9815 - loss: 0.0562

59880/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 451us/step - acc: 0.9815 - loss: 0.0562

59907/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 452us/step - acc: 0.9815 - loss: 0.0562

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 517us/step - acc: 0.9815 - loss: 0.0562 - val_acc: 0.9710 - val_loss: 0.1043

Epoch 8/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 11:08 11ms/step - acc: 1.0000 - loss: 0.4715

  132/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 383us/step - acc: 0.9940 - loss: 0.0320 

  267/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 377us/step - acc: 0.9896 - loss: 0.0299

  404/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 373us/step - acc: 0.9890 - loss: 0.0291

  531/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 378us/step - acc: 0.9893 - loss: 0.0281

  663/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9894 - loss: 0.0281

  801/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9892 - loss: 0.0291

  939/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 374us/step - acc: 0.9887 - loss: 0.0306

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 1335/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9878 - loss: 0.0334

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 1862/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9868 - loss: 0.0368

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 3881/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9850 - loss: 0.0424

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 8078/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 379us/step - acc: 0.9843 - loss: 0.0472

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 8608/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 379us/step - acc: 0.9842 - loss: 0.0477

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10076/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 379us/step - acc: 0.9840 - loss: 0.0487

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60000/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 532us/step - acc: 0.9832 - loss: 0.0523 - val_acc: 0.9682 - val_loss: 0.1117

Epoch 9/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 18:21 18ms/step - acc: 1.0000 - loss: 0.0011

  128/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 394us/step - acc: 0.9972 - loss: 0.0303 

  265/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9935 - loss: 0.0339

  401/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 377us/step - acc: 0.9911 - loss: 0.0414

  535/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9903 - loss: 0.0435

  670/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9898 - loss: 0.0435

  797/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9894 - loss: 0.0436

  930/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9892 - loss: 0.0432

 1069/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9890 - loss: 0.0427

 1206/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 375us/step - acc: 0.9887 - loss: 0.0426

 1343/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9882 - loss: 0.0428

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 1894/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 372us/step - acc: 0.9870 - loss: 0.0439

 2019/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 373us/step - acc: 0.9869 - loss: 0.0441

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 2686/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9860 - loss: 0.0456

 2816/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9858 - loss: 0.0458

 2950/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9857 - loss: 0.0460

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 3334/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9853 - loss: 0.0463

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 3592/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 378us/step - acc: 0.9850 - loss: 0.0465

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 4261/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9844 - loss: 0.0472

 4400/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9842 - loss: 0.0474

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 5600/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9835 - loss: 0.0481

 5738/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9835 - loss: 0.0482

 5878/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9834 - loss: 0.0483

 6018/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9834 - loss: 0.0483

 6161/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9833 - loss: 0.0484

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 7348/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9830 - loss: 0.0489

 7479/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9830 - loss: 0.0489

 7617/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9830 - loss: 0.0489

 7758/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 375us/step - acc: 0.9830 - loss: 0.0490

 7891/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 375us/step - acc: 0.9830 - loss: 0.0490

 8026/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 375us/step - acc: 0.9829 - loss: 0.0490

 8151/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9829 - loss: 0.0490

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59433/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 411us/step - acc: 0.9823 - loss: 0.0525

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60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 470us/step - acc: 0.9823 - loss: 0.0525 - val_acc: 0.9714 - val_loss: 0.1028

Epoch 10/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 9:20 9ms/step - acc: 1.0000 - loss: 1.7881e-06

  135/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 373us/step - acc: 0.9830 - loss: 0.0461   

  272/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 369us/step - acc: 0.9849 - loss: 0.0435

  411/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 366us/step - acc: 0.9845 - loss: 0.0452

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58887/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 459us/step - acc: 0.9844 - loss: 0.0478

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59158/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 459us/step - acc: 0.9844 - loss: 0.0478

59291/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 459us/step - acc: 0.9844 - loss: 0.0478

59430/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 458us/step - acc: 0.9844 - loss: 0.0478

59555/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 458us/step - acc: 0.9844 - loss: 0.0478

59679/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 458us/step - acc: 0.9844 - loss: 0.0478

59813/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 458us/step - acc: 0.9844 - loss: 0.0478

59950/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 458us/step - acc: 0.9844 - loss: 0.0478

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 517us/step - acc: 0.9844 - loss: 0.0478 - val_acc: 0.9692 - val_loss: 0.1108

Epoch 11/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 10:25 10ms/step - acc: 1.0000 - loss: 9.5775e-04

  120/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 422us/step - acc: 0.9987 - loss: 0.0167     

  242/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9945 - loss: 0.0258

  360/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 420us/step - acc: 0.9934 - loss: 0.0275

  481/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9932 - loss: 0.0274

  603/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9935 - loss: 0.0264

  727/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 416us/step - acc: 0.9937 - loss: 0.0254

  863/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 408us/step - acc: 0.9938 - loss: 0.0247

  993/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 405us/step - acc: 0.9937 - loss: 0.0244

 1119/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 405us/step - acc: 0.9935 - loss: 0.0243

 1252/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 402us/step - acc: 0.9931 - loss: 0.0247

 1387/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 399us/step - acc: 0.9926 - loss: 0.0257

 1534/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 393us/step - acc: 0.9920 - loss: 0.0269

 1673/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 391us/step - acc: 0.9916 - loss: 0.0280

 1807/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 389us/step - acc: 0.9913 - loss: 0.0288

 1948/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9911 - loss: 0.0296

 2081/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9908 - loss: 0.0304

 2212/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9905 - loss: 0.0311

 2341/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9903 - loss: 0.0320

 2470/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9900 - loss: 0.0329

 2603/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9898 - loss: 0.0337

 2728/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9896 - loss: 0.0342

 2854/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 387us/step - acc: 0.9895 - loss: 0.0347

 2989/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9894 - loss: 0.0352

 3124/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 386us/step - acc: 0.9893 - loss: 0.0355

 3256/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 386us/step - acc: 0.9892 - loss: 0.0358

 3378/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 387us/step - acc: 0.9891 - loss: 0.0360

 3498/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9891 - loss: 0.0361

 3626/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9890 - loss: 0.0363

 3751/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9890 - loss: 0.0364

 3875/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 389us/step - acc: 0.9890 - loss: 0.0364

 4002/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 389us/step - acc: 0.9890 - loss: 0.0365

 4137/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9890 - loss: 0.0365

 4274/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9890 - loss: 0.0365

 4415/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 387us/step - acc: 0.9890 - loss: 0.0365

 4554/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 386us/step - acc: 0.9890 - loss: 0.0365

 4691/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 385us/step - acc: 0.9890 - loss: 0.0365

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53293/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

53413/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

53533/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

53655/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

53789/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

53916/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

54028/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

54140/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 376us/step - acc: 0.9874 - loss: 0.0402

54236/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 377us/step - acc: 0.9874 - loss: 0.0402

54341/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 377us/step - acc: 0.9874 - loss: 0.0403

54458/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 377us/step - acc: 0.9874 - loss: 0.0403

54564/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 377us/step - acc: 0.9874 - loss: 0.0403

54678/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 377us/step - acc: 0.9874 - loss: 0.0403

54796/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 377us/step - acc: 0.9874 - loss: 0.0403

54914/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 377us/step - acc: 0.9874 - loss: 0.0403

55028/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9874 - loss: 0.0403

55147/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9874 - loss: 0.0403

55267/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9874 - loss: 0.0403

55386/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

55493/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

55614/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

55737/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

55859/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

55984/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

56104/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

56229/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 378us/step - acc: 0.9873 - loss: 0.0403

56341/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0403

56457/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0403

56575/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

56685/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

56803/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

56920/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

57030/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

57140/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 379us/step - acc: 0.9873 - loss: 0.0404

57254/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 380us/step - acc: 0.9873 - loss: 0.0404

57365/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 380us/step - acc: 0.9873 - loss: 0.0404

57464/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9873 - loss: 0.0404

57577/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9873 - loss: 0.0404

57692/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9873 - loss: 0.0404

57807/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9873 - loss: 0.0404

57924/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9873 - loss: 0.0404

58033/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58145/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58272/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58389/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58506/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58618/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58729/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58846/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

58971/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9873 - loss: 0.0404

59067/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59168/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59272/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59392/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59510/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59629/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59717/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 382us/step - acc: 0.9873 - loss: 0.0405

59813/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9873 - loss: 0.0405

59932/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9873 - loss: 0.0405

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 443us/step - acc: 0.9873 - loss: 0.0405 - val_acc: 0.9717 - val_loss: 0.1089

Epoch 12/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 16:06 16ms/step - acc: 1.0000 - loss: 7.5102e-06

  117/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 434us/step - acc: 0.9788 - loss: 0.0681     

  238/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 425us/step - acc: 0.9778 - loss: 0.0802

  356/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 425us/step - acc: 0.9782 - loss: 0.0767

  475/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 424us/step - acc: 0.9783 - loss: 0.0735

  595/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 423us/step - acc: 0.9790 - loss: 0.0701

  720/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9798 - loss: 0.0672

  840/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9802 - loss: 0.0647

  961/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9805 - loss: 0.0627

 1080/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9808 - loss: 0.0614

 1199/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 420us/step - acc: 0.9810 - loss: 0.0604

 1311/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 422us/step - acc: 0.9813 - loss: 0.0595

 1418/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 426us/step - acc: 0.9815 - loss: 0.0586

 1523/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 429us/step - acc: 0.9817 - loss: 0.0577

 1633/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 431us/step - acc: 0.9819 - loss: 0.0568

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 1867/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 431us/step - acc: 0.9825 - loss: 0.0549

 1981/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 432us/step - acc: 0.9827 - loss: 0.0542

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 2196/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 435us/step - acc: 0.9831 - loss: 0.0529

 2314/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 435us/step - acc: 0.9832 - loss: 0.0522

 2431/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 434us/step - acc: 0.9834 - loss: 0.0517

 2547/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9835 - loss: 0.0512

 2664/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9835 - loss: 0.0508

 2774/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9836 - loss: 0.0504

 2889/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9837 - loss: 0.0499

 3006/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9838 - loss: 0.0494

 3118/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9839 - loss: 0.0490

 3242/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9840 - loss: 0.0486

 3365/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 433us/step - acc: 0.9841 - loss: 0.0483

 3477/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9842 - loss: 0.0480

 3601/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 433us/step - acc: 0.9843 - loss: 0.0477

 3708/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9844 - loss: 0.0474

 3816/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9844 - loss: 0.0472

 3930/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9845 - loss: 0.0469

 4048/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 434us/step - acc: 0.9846 - loss: 0.0465

 4150/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 436us/step - acc: 0.9847 - loss: 0.0463

 4271/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9848 - loss: 0.0460

 4394/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9849 - loss: 0.0456

 4502/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9850 - loss: 0.0454

 4620/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9851 - loss: 0.0451

 4738/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 435us/step - acc: 0.9852 - loss: 0.0448

 4851/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 435us/step - acc: 0.9853 - loss: 0.0445

 4970/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 435us/step - acc: 0.9853 - loss: 0.0443

 5088/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 435us/step - acc: 0.9854 - loss: 0.0440

 5192/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 436us/step - acc: 0.9855 - loss: 0.0438

 5310/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 435us/step - acc: 0.9855 - loss: 0.0436

 5421/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 436us/step - acc: 0.9856 - loss: 0.0434

 5525/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 437us/step - acc: 0.9857 - loss: 0.0432

 5644/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 436us/step - acc: 0.9857 - loss: 0.0430

 5767/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 436us/step - acc: 0.9858 - loss: 0.0428

 5891/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 435us/step - acc: 0.9859 - loss: 0.0426

 6021/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 434us/step - acc: 0.9859 - loss: 0.0424

 6147/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 433us/step - acc: 0.9860 - loss: 0.0422

 6261/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 433us/step - acc: 0.9860 - loss: 0.0421

 6364/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 434us/step - acc: 0.9861 - loss: 0.0419

 6478/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 434us/step - acc: 0.9861 - loss: 0.0418

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55132/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0373

55267/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

55404/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

55539/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

55676/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

55814/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

55951/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

56089/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

56229/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

56368/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

56506/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

56642/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9876 - loss: 0.0374

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56918/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9875 - loss: 0.0374

57054/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9875 - loss: 0.0374

57191/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9875 - loss: 0.0374

57329/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 381us/step - acc: 0.9875 - loss: 0.0374

57465/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0374

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58141/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0374

58274/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0374

58411/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0375

58550/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0375

58689/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0375

58829/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0375

58967/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 381us/step - acc: 0.9875 - loss: 0.0375

59106/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59243/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59379/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59510/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59636/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59773/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

59899/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 380us/step - acc: 0.9875 - loss: 0.0375

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 441us/step - acc: 0.9875 - loss: 0.0375 - val_acc: 0.9699 - val_loss: 0.1140

Epoch 13/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 19:07 19ms/step - acc: 1.0000 - loss: 5.9605e-07

  117/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 433us/step - acc: 1.0000 - loss: 0.0159     

  226/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 446us/step - acc: 0.9975 - loss: 0.0195

  345/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 438us/step - acc: 0.9971 - loss: 0.0188

  469/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 429us/step - acc: 0.9966 - loss: 0.0196

  599/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 420us/step - acc: 0.9965 - loss: 0.0191

  723/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 417us/step - acc: 0.9966 - loss: 0.0186

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  970/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 414us/step - acc: 0.9966 - loss: 0.0182

 1079/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9966 - loss: 0.0185

 1200/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 419us/step - acc: 0.9964 - loss: 0.0191

 1322/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9963 - loss: 0.0195

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 1933/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 416us/step - acc: 0.9954 - loss: 0.0214

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 2178/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 415us/step - acc: 0.9950 - loss: 0.0222

 2298/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 415us/step - acc: 0.9948 - loss: 0.0225

 2376/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 423us/step - acc: 0.9947 - loss: 0.0227

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 2957/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 425us/step - acc: 0.9939 - loss: 0.0239

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 3186/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 426us/step - acc: 0.9937 - loss: 0.0242

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 3528/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 427us/step - acc: 0.9934 - loss: 0.0248

 3646/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 427us/step - acc: 0.9933 - loss: 0.0250

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 4000/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 427us/step - acc: 0.9931 - loss: 0.0254

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 5200/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 425us/step - acc: 0.9926 - loss: 0.0264

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 5816/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 423us/step - acc: 0.9924 - loss: 0.0270

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 6354/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 419us/step - acc: 0.9922 - loss: 0.0275

 6486/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 418us/step - acc: 0.9922 - loss: 0.0276

 6618/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 417us/step - acc: 0.9921 - loss: 0.0277

 6755/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 416us/step - acc: 0.9921 - loss: 0.0278

 6892/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 415us/step - acc: 0.9920 - loss: 0.0279

 7028/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 414us/step - acc: 0.9920 - loss: 0.0280

 7165/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 413us/step - acc: 0.9919 - loss: 0.0281

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 7982/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 409us/step - acc: 0.9915 - loss: 0.0288

 8109/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 409us/step - acc: 0.9915 - loss: 0.0289

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 8498/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 408us/step - acc: 0.9913 - loss: 0.0292

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57795/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9894 - loss: 0.0331

57934/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9894 - loss: 0.0331

58070/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9894 - loss: 0.0331

58207/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9893 - loss: 0.0331

58347/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9893 - loss: 0.0331

58486/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9893 - loss: 0.0331

58626/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 384us/step - acc: 0.9893 - loss: 0.0331

58769/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

58912/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59054/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59197/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59340/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59483/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59628/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59770/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

59912/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 383us/step - acc: 0.9893 - loss: 0.0331

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 442us/step - acc: 0.9893 - loss: 0.0331 - val_acc: 0.9720 - val_loss: 0.1097

Epoch 14/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 10:18 10ms/step - acc: 1.0000 - loss: 1.1325e-05

  127/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 399us/step - acc: 0.9999 - loss: 0.0024     

  260/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 387us/step - acc: 0.9945 - loss: 0.0169

  393/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 384us/step - acc: 0.9932 - loss: 0.0192

  529/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9925 - loss: 0.0205

  669/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9921 - loss: 0.0210

  804/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 375us/step - acc: 0.9920 - loss: 0.0209

  940/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 374us/step - acc: 0.9920 - loss: 0.0210

 1077/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 373us/step - acc: 0.9919 - loss: 0.0212

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 1357/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 370us/step - acc: 0.9918 - loss: 0.0215

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 1865/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9918 - loss: 0.0223

 1989/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 379us/step - acc: 0.9918 - loss: 0.0226

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 2783/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 379us/step - acc: 0.9916 - loss: 0.0237

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 3197/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 377us/step - acc: 0.9916 - loss: 0.0238

 3335/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 376us/step - acc: 0.9916 - loss: 0.0238

 3476/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9916 - loss: 0.0239

 3617/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9916 - loss: 0.0240

 3757/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9916 - loss: 0.0240

 3886/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9916 - loss: 0.0241

 4015/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9916 - loss: 0.0242

 4144/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9916 - loss: 0.0244

 4273/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9916 - loss: 0.0245

 4401/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9916 - loss: 0.0246

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 4668/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9915 - loss: 0.0247

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 5615/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 375us/step - acc: 0.9915 - loss: 0.0252

 5743/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9915 - loss: 0.0252

 5873/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9915 - loss: 0.0253

 6005/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9915 - loss: 0.0253

 6132/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9915 - loss: 0.0253

 6264/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9915 - loss: 0.0254

 6395/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9915 - loss: 0.0255

 6530/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9915 - loss: 0.0256

 6666/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 377us/step - acc: 0.9914 - loss: 0.0256

 6807/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 376us/step - acc: 0.9914 - loss: 0.0257

 6933/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 377us/step - acc: 0.9914 - loss: 0.0258

 7049/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 377us/step - acc: 0.9914 - loss: 0.0258

 7178/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9914 - loss: 0.0258

 7310/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 378us/step - acc: 0.9914 - loss: 0.0259

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 7851/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 377us/step - acc: 0.9914 - loss: 0.0260

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 8131/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 377us/step - acc: 0.9914 - loss: 0.0261

 8270/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9914 - loss: 0.0262

 8413/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9914 - loss: 0.0262

 8554/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 376us/step - acc: 0.9914 - loss: 0.0263

 8695/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 375us/step - acc: 0.9914 - loss: 0.0263

 8834/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 375us/step - acc: 0.9913 - loss: 0.0264

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51718/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

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52148/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

52261/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

52381/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

52507/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

52625/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 479us/step - acc: 0.9899 - loss: 0.0312

52713/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 480us/step - acc: 0.9899 - loss: 0.0312

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53776/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0312

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54345/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0313

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54679/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0313

54785/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0313

54908/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0313

55031/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 479us/step - acc: 0.9899 - loss: 0.0313

55156/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 478us/step - acc: 0.9899 - loss: 0.0313

55285/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 478us/step - acc: 0.9899 - loss: 0.0313

55417/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 478us/step - acc: 0.9898 - loss: 0.0313

55548/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 478us/step - acc: 0.9898 - loss: 0.0313

55680/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 478us/step - acc: 0.9898 - loss: 0.0313

55809/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 477us/step - acc: 0.9898 - loss: 0.0313

55942/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 477us/step - acc: 0.9898 - loss: 0.0313

56071/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 477us/step - acc: 0.9898 - loss: 0.0313

56197/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 477us/step - acc: 0.9898 - loss: 0.0313

56317/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 477us/step - acc: 0.9898 - loss: 0.0313

56439/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

56548/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

56668/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

56793/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

56924/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

57047/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0313

57172/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 476us/step - acc: 0.9898 - loss: 0.0314

57295/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 475us/step - acc: 0.9898 - loss: 0.0314

57412/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 475us/step - acc: 0.9898 - loss: 0.0314

57530/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 475us/step - acc: 0.9898 - loss: 0.0314

57653/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 475us/step - acc: 0.9898 - loss: 0.0314

57773/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 475us/step - acc: 0.9898 - loss: 0.0314

57905/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 475us/step - acc: 0.9898 - loss: 0.0314

58029/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 475us/step - acc: 0.9898 - loss: 0.0314

58158/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9898 - loss: 0.0314

58292/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9898 - loss: 0.0314

58425/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9898 - loss: 0.0314

58565/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9898 - loss: 0.0314

58705/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9898 - loss: 0.0314

58841/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9898 - loss: 0.0314

58975/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9898 - loss: 0.0314

59111/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9898 - loss: 0.0314

59249/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9898 - loss: 0.0314

59383/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9898 - loss: 0.0314

59523/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9898 - loss: 0.0314

59656/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9898 - loss: 0.0314

59789/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9898 - loss: 0.0314

59922/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 471us/step - acc: 0.9898 - loss: 0.0314

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 530us/step - acc: 0.9898 - loss: 0.0314 - val_acc: 0.9733 - val_loss: 0.1062

Epoch 15/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 8:59 9ms/step - acc: 1.0000 - loss: 0.0010

  129/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 391us/step - acc: 1.0000 - loss: 0.0017

  260/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 387us/step - acc: 0.9985 - loss: 0.0062

  380/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 397us/step - acc: 0.9980 - loss: 0.0076

  510/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 394us/step - acc: 0.9979 - loss: 0.0084

  645/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 390us/step - acc: 0.9979 - loss: 0.0086

  782/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 385us/step - acc: 0.9977 - loss: 0.0091

  918/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 383us/step - acc: 0.9975 - loss: 0.0098

 1056/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9973 - loss: 0.0104

 1192/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9972 - loss: 0.0107

 1330/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 378us/step - acc: 0.9971 - loss: 0.0111

 1467/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9969 - loss: 0.0115

 1604/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 376us/step - acc: 0.9967 - loss: 0.0121

 1739/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 375us/step - acc: 0.9965 - loss: 0.0127

 1878/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9963 - loss: 0.0132

 2014/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 374us/step - acc: 0.9961 - loss: 0.0136

 2152/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 373us/step - acc: 0.9959 - loss: 0.0140

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53878/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 387us/step - acc: 0.9914 - loss: 0.0256

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54878/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 388us/step - acc: 0.9914 - loss: 0.0257

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55085/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 388us/step - acc: 0.9913 - loss: 0.0257

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55851/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 389us/step - acc: 0.9913 - loss: 0.0257

55968/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 389us/step - acc: 0.9913 - loss: 0.0257

56086/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 389us/step - acc: 0.9913 - loss: 0.0257

56174/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 390us/step - acc: 0.9913 - loss: 0.0257

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56417/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 390us/step - acc: 0.9913 - loss: 0.0258

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56649/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 391us/step - acc: 0.9913 - loss: 0.0258

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57696/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 396us/step - acc: 0.9913 - loss: 0.0258

57811/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 396us/step - acc: 0.9913 - loss: 0.0258

57931/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 396us/step - acc: 0.9913 - loss: 0.0258

58050/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 396us/step - acc: 0.9913 - loss: 0.0259

58167/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 396us/step - acc: 0.9913 - loss: 0.0259

58274/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

58383/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

58492/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

58611/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

58734/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

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58969/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59093/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59204/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59319/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59430/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59535/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0259

59646/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9913 - loss: 0.0259

59770/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9913 - loss: 0.0260

59903/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9913 - loss: 0.0260

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 458us/step - acc: 0.9913 - loss: 0.0260 - val_acc: 0.9713 - val_loss: 0.1090

Epoch 16/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 11:47 12ms/step - acc: 1.0000 - loss: 5.9604e-06

  133/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 1.0000 - loss: 0.0213     

  270/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 373us/step - acc: 0.9982 - loss: 0.0192

  393/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 384us/step - acc: 0.9969 - loss: 0.0189

  513/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 392us/step - acc: 0.9963 - loss: 0.0185

  642/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 391us/step - acc: 0.9959 - loss: 0.0184

  766/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 393us/step - acc: 0.9956 - loss: 0.0183

  893/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 394us/step - acc: 0.9956 - loss: 0.0181

 1010/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 398us/step - acc: 0.9953 - loss: 0.0185

 1132/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 399us/step - acc: 0.9951 - loss: 0.0191

 1255/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 400us/step - acc: 0.9949 - loss: 0.0196

 1377/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 401us/step - acc: 0.9948 - loss: 0.0199

 1497/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 402us/step - acc: 0.9947 - loss: 0.0202

 1614/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 404us/step - acc: 0.9946 - loss: 0.0204

 1719/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 409us/step - acc: 0.9945 - loss: 0.0206

 1838/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 410us/step - acc: 0.9945 - loss: 0.0207

 1952/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 412us/step - acc: 0.9944 - loss: 0.0209

 2066/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 414us/step - acc: 0.9944 - loss: 0.0211

 2185/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 414us/step - acc: 0.9943 - loss: 0.0213

 2302/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 415us/step - acc: 0.9942 - loss: 0.0214

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51017/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 402us/step - acc: 0.9911 - loss: 0.0274

51152/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 402us/step - acc: 0.9911 - loss: 0.0274

51287/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

51424/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

51561/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

51692/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

51823/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

51959/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

52097/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

52234/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

52373/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

52510/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 401us/step - acc: 0.9911 - loss: 0.0274

52645/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 401us/step - acc: 0.9910 - loss: 0.0274

52782/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 401us/step - acc: 0.9910 - loss: 0.0274

52920/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0274

53055/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0274

53192/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0274

53329/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

53461/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

53597/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

53736/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

53864/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

53997/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54125/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54249/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54373/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54508/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54643/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54781/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

54914/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 400us/step - acc: 0.9910 - loss: 0.0275

55052/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 400us/step - acc: 0.9910 - loss: 0.0275

55188/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

55326/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

55465/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

55602/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

55740/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

55877/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

56013/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

56147/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

56284/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

56422/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

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56698/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 399us/step - acc: 0.9910 - loss: 0.0275

56838/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 398us/step - acc: 0.9910 - loss: 0.0275

56974/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 398us/step - acc: 0.9910 - loss: 0.0275

57111/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 398us/step - acc: 0.9910 - loss: 0.0275

57248/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 398us/step - acc: 0.9910 - loss: 0.0275

57385/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 398us/step - acc: 0.9910 - loss: 0.0275

57521/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0275

57658/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

57795/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

57932/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

58071/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

58209/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

58348/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

58487/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

58625/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

58762/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

58899/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59035/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59173/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59309/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59447/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59519/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 397us/step - acc: 0.9910 - loss: 0.0276

59617/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

59738/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

59865/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

59981/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 398us/step - acc: 0.9910 - loss: 0.0276

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 461us/step - acc: 0.9910 - loss: 0.0276 - val_acc: 0.9723 - val_loss: 0.1081

Epoch 17/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 15:10 15ms/step - acc: 1.0000 - loss: 0.0019

  119/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 425us/step - acc: 0.9796 - loss: 0.0919 

  240/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 420us/step - acc: 0.9870 - loss: 0.0648

  360/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 419us/step - acc: 0.9894 - loss: 0.0539

  481/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9899 - loss: 0.0490

  602/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9900 - loss: 0.0466

  701/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 430us/step - acc: 0.9901 - loss: 0.0451

  795/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 443us/step - acc: 0.9901 - loss: 0.0438

  852/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 473us/step - acc: 0.9902 - loss: 0.0429

  907/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 500us/step - acc: 0.9903 - loss: 0.0422

  960/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 525us/step - acc: 0.9903 - loss: 0.0417

 1040/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 533us/step - acc: 0.9903 - loss: 0.0409

 1118/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 541us/step - acc: 0.9903 - loss: 0.0404

 1194/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 548us/step - acc: 0.9903 - loss: 0.0400

 1251/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 564us/step - acc: 0.9902 - loss: 0.0397

 1343/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 562us/step - acc: 0.9902 - loss: 0.0392

 1425/60000 ━━━━━━━━━━━━━━━━━━━━ 33s 565us/step - acc: 0.9902 - loss: 0.0388

 1523/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 562us/step - acc: 0.9903 - loss: 0.0382

 1630/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 556us/step - acc: 0.9903 - loss: 0.0377

 1735/60000 ━━━━━━━━━━━━━━━━━━━━ 32s 551us/step - acc: 0.9904 - loss: 0.0372

 1840/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 547us/step - acc: 0.9904 - loss: 0.0368

 1934/60000 ━━━━━━━━━━━━━━━━━━━━ 31s 547us/step - acc: 0.9905 - loss: 0.0364

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51509/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 418us/step - acc: 0.9907 - loss: 0.0264

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57650/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 413us/step - acc: 0.9907 - loss: 0.0265

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58476/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

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58876/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

59015/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

59152/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

59290/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

59428/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 412us/step - acc: 0.9907 - loss: 0.0266

59566/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 411us/step - acc: 0.9907 - loss: 0.0266

59708/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 411us/step - acc: 0.9907 - loss: 0.0266

59847/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 411us/step - acc: 0.9907 - loss: 0.0266

59982/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 411us/step - acc: 0.9907 - loss: 0.0266

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 471us/step - acc: 0.9907 - loss: 0.0266 - val_acc: 0.9705 - val_loss: 0.1160

Epoch 18/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 9:41 10ms/step - acc: 1.0000 - loss: 8.2609e-05

  136/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 371us/step - acc: 0.9925 - loss: 0.0176    

  270/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 374us/step - acc: 0.9902 - loss: 0.0218

  402/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 376us/step - acc: 0.9880 - loss: 0.0252

  534/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 377us/step - acc: 0.9875 - loss: 0.0261

  661/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9871 - loss: 0.0276

  794/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 379us/step - acc: 0.9871 - loss: 0.0286

  925/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 380us/step - acc: 0.9872 - loss: 0.0298

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58507/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

58622/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

58748/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

58869/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

58984/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

59101/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

59172/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 421us/step - acc: 0.9889 - loss: 0.0318

59264/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59365/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59458/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59549/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59653/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59760/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59848/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 422us/step - acc: 0.9889 - loss: 0.0318

59901/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9889 - loss: 0.0318

59980/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 423us/step - acc: 0.9889 - loss: 0.0318

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 30s 492us/step - acc: 0.9889 - loss: 0.0318 - val_acc: 0.9709 - val_loss: 0.1177

Epoch 19/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 14:31 15ms/step - acc: 1.0000 - loss: 7.1526e-07

  121/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 417us/step - acc: 1.0000 - loss: 0.0070     

  249/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 404us/step - acc: 1.0000 - loss: 0.0053

  385/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 392us/step - acc: 1.0000 - loss: 0.0050

  521/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9997 - loss: 0.0057

  658/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 382us/step - acc: 0.9994 - loss: 0.0067

  781/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9991 - loss: 0.0075

  913/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 386us/step - acc: 0.9989 - loss: 0.0082

 1016/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 396us/step - acc: 0.9987 - loss: 0.0088

 1134/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 399us/step - acc: 0.9984 - loss: 0.0098

 1265/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 397us/step - acc: 0.9981 - loss: 0.0107

 1392/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 397us/step - acc: 0.9978 - loss: 0.0114

 1514/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 398us/step - acc: 0.9975 - loss: 0.0120

 1627/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 401us/step - acc: 0.9973 - loss: 0.0126

 1745/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 403us/step - acc: 0.9970 - loss: 0.0131

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 1969/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 408us/step - acc: 0.9967 - loss: 0.0138

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 2195/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 412us/step - acc: 0.9963 - loss: 0.0147

 2312/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 413us/step - acc: 0.9961 - loss: 0.0151

 2414/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 416us/step - acc: 0.9960 - loss: 0.0154

 2533/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 417us/step - acc: 0.9958 - loss: 0.0157

 2656/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 416us/step - acc: 0.9956 - loss: 0.0160

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 2885/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 418us/step - acc: 0.9954 - loss: 0.0165

 3004/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 418us/step - acc: 0.9953 - loss: 0.0166

 3122/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 418us/step - acc: 0.9952 - loss: 0.0168

 3242/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 418us/step - acc: 0.9951 - loss: 0.0170

 3359/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 419us/step - acc: 0.9950 - loss: 0.0171

 3467/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 420us/step - acc: 0.9950 - loss: 0.0172

 3573/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 422us/step - acc: 0.9949 - loss: 0.0173

 3693/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 422us/step - acc: 0.9949 - loss: 0.0174

 3816/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 422us/step - acc: 0.9948 - loss: 0.0176

 3948/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 420us/step - acc: 0.9947 - loss: 0.0177

 4072/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 420us/step - acc: 0.9947 - loss: 0.0178

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 4332/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 418us/step - acc: 0.9946 - loss: 0.0179

 4455/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 417us/step - acc: 0.9946 - loss: 0.0180

 4579/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 417us/step - acc: 0.9945 - loss: 0.0180

 4710/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 416us/step - acc: 0.9945 - loss: 0.0181

 4839/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 415us/step - acc: 0.9945 - loss: 0.0182

 4980/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 414us/step - acc: 0.9944 - loss: 0.0183

 5112/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 413us/step - acc: 0.9944 - loss: 0.0183

 5247/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 412us/step - acc: 0.9944 - loss: 0.0184

 5372/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 412us/step - acc: 0.9944 - loss: 0.0184

 5507/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 411us/step - acc: 0.9943 - loss: 0.0185

 5647/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 409us/step - acc: 0.9943 - loss: 0.0185

 5782/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 408us/step - acc: 0.9942 - loss: 0.0186

 5918/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 407us/step - acc: 0.9942 - loss: 0.0186

 6051/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 407us/step - acc: 0.9942 - loss: 0.0187

 6180/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 406us/step - acc: 0.9941 - loss: 0.0188

 6311/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 406us/step - acc: 0.9941 - loss: 0.0188

 6442/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 405us/step - acc: 0.9941 - loss: 0.0189

 6568/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 405us/step - acc: 0.9940 - loss: 0.0190

 6696/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 405us/step - acc: 0.9940 - loss: 0.0190

 6826/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 405us/step - acc: 0.9940 - loss: 0.0191

 6953/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 405us/step - acc: 0.9939 - loss: 0.0192

 7083/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 404us/step - acc: 0.9939 - loss: 0.0192

 7221/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 403us/step - acc: 0.9939 - loss: 0.0193

 7348/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 403us/step - acc: 0.9938 - loss: 0.0193

 7485/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 403us/step - acc: 0.9938 - loss: 0.0194

 7623/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 402us/step - acc: 0.9938 - loss: 0.0195

 7758/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 401us/step - acc: 0.9937 - loss: 0.0196

 7893/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 401us/step - acc: 0.9937 - loss: 0.0197

 8028/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 400us/step - acc: 0.9937 - loss: 0.0198

 8162/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 400us/step - acc: 0.9936 - loss: 0.0199

 8291/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 400us/step - acc: 0.9936 - loss: 0.0200

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 8557/60000 ━━━━━━━━━━━━━━━━━━━━ 20s 399us/step - acc: 0.9935 - loss: 0.0202

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10027/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 396us/step - acc: 0.9932 - loss: 0.0209

10165/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 395us/step - acc: 0.9932 - loss: 0.0209

10301/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 395us/step - acc: 0.9932 - loss: 0.0210

10437/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 395us/step - acc: 0.9932 - loss: 0.0211

10574/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 394us/step - acc: 0.9931 - loss: 0.0211

10703/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 394us/step - acc: 0.9931 - loss: 0.0212

10841/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 394us/step - acc: 0.9931 - loss: 0.0213

10966/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 394us/step - acc: 0.9931 - loss: 0.0213

11092/60000 ━━━━━━━━━━━━━━━━━━━━ 19s 394us/step - acc: 0.9930 - loss: 0.0214

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11853/60000 ━━━━━━━━━━━━━━━━━━━━ 18s 394us/step - acc: 0.9929 - loss: 0.0216

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54856/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 424us/step - acc: 0.9919 - loss: 0.0246

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55080/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 424us/step - acc: 0.9919 - loss: 0.0246

55186/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 424us/step - acc: 0.9919 - loss: 0.0246

55296/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 424us/step - acc: 0.9919 - loss: 0.0246

55388/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9919 - loss: 0.0246

55481/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9919 - loss: 0.0246

55597/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 425us/step - acc: 0.9919 - loss: 0.0246

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57283/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 426us/step - acc: 0.9919 - loss: 0.0246

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58019/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 425us/step - acc: 0.9919 - loss: 0.0246

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58593/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 425us/step - acc: 0.9919 - loss: 0.0246

58699/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0246

58811/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0246

58877/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0246

58983/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0246

59094/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0246

59213/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59335/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59456/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59576/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59684/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59799/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

59923/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 426us/step - acc: 0.9919 - loss: 0.0247

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 29s 485us/step - acc: 0.9919 - loss: 0.0247 - val_acc: 0.9696 - val_loss: 0.1289

Epoch 20/20

    1/60000 ━━━━━━━━━━━━━━━━━━━━ 13:19 13ms/step - acc: 1.0000 - loss: 1.1921e-07

  112/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 452us/step - acc: 0.9661 - loss: 0.0607     

  222/60000 ━━━━━━━━━━━━━━━━━━━━ 27s 454us/step - acc: 0.9798 - loss: 0.0393

  344/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 438us/step - acc: 0.9857 - loss: 0.0315

  453/60000 ━━━━━━━━━━━━━━━━━━━━ 26s 444us/step - acc: 0.9885 - loss: 0.0280

  578/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 435us/step - acc: 0.9900 - loss: 0.0269

  711/60000 ━━━━━━━━━━━━━━━━━━━━ 25s 424us/step - acc: 0.9909 - loss: 0.0261

  834/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 422us/step - acc: 0.9913 - loss: 0.0255

  962/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 418us/step - acc: 0.9917 - loss: 0.0247

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 1194/60000 ━━━━━━━━━━━━━━━━━━━━ 24s 421us/step - acc: 0.9917 - loss: 0.0243

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 1729/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 407us/step - acc: 0.9919 - loss: 0.0232

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 1986/60000 ━━━━━━━━━━━━━━━━━━━━ 23s 404us/step - acc: 0.9921 - loss: 0.0225

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 2396/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 398us/step - acc: 0.9925 - loss: 0.0214

 2531/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 397us/step - acc: 0.9926 - loss: 0.0211

 2666/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 395us/step - acc: 0.9927 - loss: 0.0208

 2805/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 394us/step - acc: 0.9928 - loss: 0.0205

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 3330/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 392us/step - acc: 0.9931 - loss: 0.0198

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 3734/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 390us/step - acc: 0.9932 - loss: 0.0194

 3870/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 389us/step - acc: 0.9933 - loss: 0.0192

 4007/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9933 - loss: 0.0191

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 4661/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 388us/step - acc: 0.9936 - loss: 0.0185

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 5230/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 403us/step - acc: 0.9938 - loss: 0.0181

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 6005/60000 ━━━━━━━━━━━━━━━━━━━━ 22s 410us/step - acc: 0.9939 - loss: 0.0176

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 6483/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 410us/step - acc: 0.9940 - loss: 0.0175

 6605/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 411us/step - acc: 0.9940 - loss: 0.0175

 6725/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 411us/step - acc: 0.9940 - loss: 0.0175

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 7323/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 411us/step - acc: 0.9940 - loss: 0.0174

 7427/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 412us/step - acc: 0.9940 - loss: 0.0174

 7526/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 414us/step - acc: 0.9940 - loss: 0.0174

 7610/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 416us/step - acc: 0.9940 - loss: 0.0173

 7682/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 418us/step - acc: 0.9940 - loss: 0.0173

 7781/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 419us/step - acc: 0.9940 - loss: 0.0173

 7897/60000 ━━━━━━━━━━━━━━━━━━━━ 21s 420us/step - acc: 0.9940 - loss: 0.0173

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52616/60000 ━━━━━━━━━━━━━━━━━━━━ 3s 409us/step - acc: 0.9937 - loss: 0.0184

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52861/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

52997/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53132/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53267/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53404/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53543/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53680/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53814/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

53939/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

54076/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 409us/step - acc: 0.9937 - loss: 0.0184

54214/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0184

54353/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0184

54487/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0184

54626/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0184

54764/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0185

54903/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0185

55041/60000 ━━━━━━━━━━━━━━━━━━━━ 2s 408us/step - acc: 0.9937 - loss: 0.0185

55177/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 408us/step - acc: 0.9937 - loss: 0.0185

55314/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 408us/step - acc: 0.9937 - loss: 0.0185

55452/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

55592/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

55729/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

55865/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

55992/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56114/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56251/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56383/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56513/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56637/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56770/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

56909/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

57044/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

57181/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 407us/step - acc: 0.9937 - loss: 0.0185

57317/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 406us/step - acc: 0.9937 - loss: 0.0185

57455/60000 ━━━━━━━━━━━━━━━━━━━━ 1s 406us/step - acc: 0.9937 - loss: 0.0185

57590/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0185

57728/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0185

57864/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0185

57998/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0185

58136/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58274/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58414/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58551/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58672/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58804/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

58936/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 406us/step - acc: 0.9936 - loss: 0.0186

59066/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59190/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59318/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59443/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59572/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59700/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59830/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

59965/60000 ━━━━━━━━━━━━━━━━━━━━ 0s 405us/step - acc: 0.9936 - loss: 0.0186

60000/60000 ━━━━━━━━━━━━━━━━━━━━ 28s 464us/step - acc: 0.9936 - loss: 0.0186 - val_acc: 0.9688 - val_loss: 0.1185

TRAININGSDAUER: 575.09 Sek.

Wir sehen, dass das Training mit reinem SGD mit ca. 10 Minuten deutlich länger dauert als beim Batchtraining mit seinen 2 Sekunden - in beiden Fällen für 20 Epochen.

Das liegt daran, dass hier in jeder Epoche 60000 Mal sämtliche Gewichte angepasst werden, wohingegen vorher nur ein einziges Update pro Epoche stattfand.

Jetzt schauen wir uns die Performance an. Achten Sie auf den Wertebereich, der bei der Accuracy-Kurve zwischhen 0.9 und 1.0 liegt.

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,4))

set_subplot(ax[0], 'Loss', history2.history['loss'], 
            history2.history['val_loss'], [0, .5])
set_subplot(ax[1], 'Accuracy', history2.history['acc'], 
            history2.history['val_acc'], [0.9, 1])

plt.show()

Wir sehen einen typischen Verlauf der Accuracy: Auf den Trainingsdaten steigt die Kurve immer weiter an, aber auf den Testdaten bleibt die Kurve in einem bestimmten Bereich. Sobald die Kurve auf den Testdaten sich einpendelt, kann man das Training beenden, weil das Training anschließend Overfitting betreibt.

Wir schauen uns jetzt noch einmal den finalen Accuracy-Wert auf den Testdaten an:

loss, acc = model.evaluate(x_test, y_test_1hot)
print(f"Evaluation auf den Testdaten:\n\nLoss = {loss:.3f}\nAccuracy = {acc:.3f}")

  1/313 ━━━━━━━━━━━━━━━━━━━━ 37s 119ms/step - acc: 0.9688 - loss: 0.0292

 71/313 ━━━━━━━━━━━━━━━━━━━━ 0s 715us/step - acc: 0.9675 - loss: 0.1172 

158/313 ━━━━━━━━━━━━━━━━━━━━ 0s 637us/step - acc: 0.9619 - loss: 0.1438

246/313 ━━━━━━━━━━━━━━━━━━━━ 0s 613us/step - acc: 0.9622 - loss: 0.1429

313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 638us/step - acc: 0.9634 - loss: 0.1384

Evaluation auf den Testdaten:

Loss = 0.118
Accuracy = 0.969

Mit 97% Accuracy auf den Testdaten erreicht das mit reinem SGD trainierte Netz einen deutlich besseren Wert als das vorigen Netz, dass wir mit Batchtraining trainiert haben.

5.6.2 Minibatch (Batchgröße = 32)

Nachdem wir Batchtraining (alle Trainingsbeispiele pro Update) und reines SGD (Batchgröße = 1) ausprobiert haben, setzen wir jetzt die eigentliche Idee von Minibatch um, dass eine bestimmte Anzahl von (zufällig ausgewählten) Trainingsdaten durchlaufen wird, bevor dann ein Update durchgeführt wird. Es handelt sich also um einen Kompromiss zwischen Batchtraining und reinem SGD.

Im Keras ist bei der Methode SGD eine Batchgröße von 32 standardmäßig eingestellt. Diese probieren wir jetzt aus. Den Parameter batch_size lassen wir weg, damit die Standardgröße von 32 genommen wird. Alles andere bleibt wie in den beiden anderen Versuchen gleich.

model = Sequential()
model.add(Input(shape=(784,)))
model.add(Dense(60, 
                activation='sigmoid'))
model.add(Dense(10, activation='softmax'))

opt = SGD(learning_rate = 0.1)

model.compile(optimizer=opt,
              loss='categorical_crossentropy',
              metrics=['acc'])

start_time = time.time() 

history3 = model.fit(x_train, y_train_1hot, 
                     epochs=20,
                     verbose=1,
                     validation_data = (x_test, y_test_1hot))

duration = time.time() - start_time 
print(f'TRAININGSDAUER: {duration:.2f} Sek.')

Epoch 1/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 5:42 183ms/step - acc: 0.3125 - loss: 2.4901

  73/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 701us/step - acc: 0.3232 - loss: 2.1700  

 150/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 675us/step - acc: 0.4370 - loss: 1.9741

 234/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 648us/step - acc: 0.5073 - loss: 1.8116

 317/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 637us/step - acc: 0.5550 - loss: 1.6841

 400/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 632us/step - acc: 0.5900 - loss: 1.5805

 486/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 623us/step - acc: 0.6181 - loss: 1.4916

 573/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 616us/step - acc: 0.6408 - loss: 1.4163

 656/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 615us/step - acc: 0.6586 - loss: 1.3548

 687/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 664us/step - acc: 0.6645 - loss: 1.3339

 696/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 728us/step - acc: 0.6662 - loss: 1.3280

 764/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 729us/step - acc: 0.6779 - loss: 1.2860

 831/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 731us/step - acc: 0.6883 - loss: 1.2485

 887/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 742us/step - acc: 0.6961 - loss: 1.2198

 928/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 764us/step - acc: 0.7014 - loss: 1.2000

 982/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 773us/step - acc: 0.7079 - loss: 1.1756

1025/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 790us/step - acc: 0.7127 - loss: 1.1572

1078/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 798us/step - acc: 0.7183 - loss: 1.1359

1144/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 796us/step - acc: 0.7248 - loss: 1.1110

1206/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 797us/step - acc: 0.7304 - loss: 1.0892

1284/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 788us/step - acc: 0.7368 - loss: 1.0638

1338/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 793us/step - acc: 0.7409 - loss: 1.0474

1364/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 816us/step - acc: 0.7428 - loss: 1.0398

1411/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 824us/step - acc: 0.7461 - loss: 1.0265

1480/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 820us/step - acc: 0.7507 - loss: 1.0080

1547/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 818us/step - acc: 0.7549 - loss: 0.9912

1609/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 817us/step - acc: 0.7585 - loss: 0.9764

1679/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 813us/step - acc: 0.7624 - loss: 0.9605

1730/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 818us/step - acc: 0.7651 - loss: 0.9495

1808/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 811us/step - acc: 0.7690 - loss: 0.9335

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 1ms/step - acc: 0.7722 - loss: 0.9203 - val_acc: 0.9101 - val_loss: 0.3160

Epoch 2/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 23s 12ms/step - acc: 0.9688 - loss: 0.1811

  86/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 592us/step - acc: 0.9054 - loss: 0.3157

 161/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 630us/step - acc: 0.9057 - loss: 0.3214

 240/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 632us/step - acc: 0.9064 - loss: 0.3221

 319/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9068 - loss: 0.3229

 398/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 634us/step - acc: 0.9071 - loss: 0.3227

 482/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 627us/step - acc: 0.9075 - loss: 0.3219

 565/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 625us/step - acc: 0.9078 - loss: 0.3210

 641/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 631us/step - acc: 0.9081 - loss: 0.3201

 725/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 627us/step - acc: 0.9084 - loss: 0.3191

 811/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 623us/step - acc: 0.9087 - loss: 0.3183

 882/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 630us/step - acc: 0.9090 - loss: 0.3176

 964/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 628us/step - acc: 0.9092 - loss: 0.3167

1042/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 630us/step - acc: 0.9094 - loss: 0.3160

1120/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 631us/step - acc: 0.9096 - loss: 0.3154

1195/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9098 - loss: 0.3148

1261/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 641us/step - acc: 0.9099 - loss: 0.3142

1338/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 641us/step - acc: 0.9101 - loss: 0.3136

1413/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 643us/step - acc: 0.9103 - loss: 0.3131

1504/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 638us/step - acc: 0.9105 - loss: 0.3123

1595/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9108 - loss: 0.3116

1684/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 629us/step - acc: 0.9110 - loss: 0.3109

1767/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 628us/step - acc: 0.9112 - loss: 0.3103

1843/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 629us/step - acc: 0.9113 - loss: 0.3098

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 726us/step - acc: 0.9114 - loss: 0.3096 - val_acc: 0.9240 - val_loss: 0.2587

Epoch 3/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 20s 11ms/step - acc: 1.0000 - loss: 0.0591

  88/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 579us/step - acc: 0.9270 - loss: 0.2725

 167/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 607us/step - acc: 0.9262 - loss: 0.2665

 241/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 630us/step - acc: 0.9262 - loss: 0.2640

 328/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 616us/step - acc: 0.9258 - loss: 0.2633

 414/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 610us/step - acc: 0.9256 - loss: 0.2626

 489/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 619us/step - acc: 0.9253 - loss: 0.2625

 575/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 615us/step - acc: 0.9252 - loss: 0.2624

 660/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 612us/step - acc: 0.9251 - loss: 0.2624

 745/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 610us/step - acc: 0.9251 - loss: 0.2622

 816/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 619us/step - acc: 0.9251 - loss: 0.2620

 884/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 629us/step - acc: 0.9251 - loss: 0.2618

 937/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 647us/step - acc: 0.9251 - loss: 0.2616

 995/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 659us/step - acc: 0.9251 - loss: 0.2614

1068/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 661us/step - acc: 0.9251 - loss: 0.2610

1143/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 662us/step - acc: 0.9252 - loss: 0.2606

1217/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 663us/step - acc: 0.9252 - loss: 0.2603

1298/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 660us/step - acc: 0.9252 - loss: 0.2600

1377/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 659us/step - acc: 0.9252 - loss: 0.2597

1459/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 656us/step - acc: 0.9253 - loss: 0.2593

1542/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 654us/step - acc: 0.9253 - loss: 0.2590

1624/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 652us/step - acc: 0.9254 - loss: 0.2586

1707/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 650us/step - acc: 0.9254 - loss: 0.2583

1791/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 647us/step - acc: 0.9255 - loss: 0.2580

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 742us/step - acc: 0.9256 - loss: 0.2576 - val_acc: 0.9365 - val_loss: 0.2259

Epoch 4/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 19s 10ms/step - acc: 1.0000 - loss: 0.0727

  88/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 578us/step - acc: 0.9369 - loss: 0.2357

 167/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 607us/step - acc: 0.9344 - loss: 0.2387

 244/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 623us/step - acc: 0.9335 - loss: 0.2386

 327/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 619us/step - acc: 0.9331 - loss: 0.2372

 404/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 625us/step - acc: 0.9330 - loss: 0.2363

 486/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 624us/step - acc: 0.9329 - loss: 0.2354

 576/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 614us/step - acc: 0.9329 - loss: 0.2346

 665/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 608us/step - acc: 0.9330 - loss: 0.2340

 748/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 608us/step - acc: 0.9329 - loss: 0.2335

 830/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 608us/step - acc: 0.9329 - loss: 0.2331

 879/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 632us/step - acc: 0.9330 - loss: 0.2328

 962/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 629us/step - acc: 0.9331 - loss: 0.2323

1036/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9331 - loss: 0.2319

1116/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9333 - loss: 0.2314

1194/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 634us/step - acc: 0.9334 - loss: 0.2310

1277/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 632us/step - acc: 0.9335 - loss: 0.2305

1360/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 631us/step - acc: 0.9337 - loss: 0.2299

1441/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 630us/step - acc: 0.9338 - loss: 0.2295

1527/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 628us/step - acc: 0.9340 - loss: 0.2290

1611/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 626us/step - acc: 0.9341 - loss: 0.2286

1701/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 623us/step - acc: 0.9342 - loss: 0.2281

1786/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 621us/step - acc: 0.9343 - loss: 0.2277

1860/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 624us/step - acc: 0.9344 - loss: 0.2274

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 722us/step - acc: 0.9345 - loss: 0.2273 - val_acc: 0.9391 - val_loss: 0.2128

Epoch 5/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 20s 11ms/step - acc: 0.9375 - loss: 0.2328

  89/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 570us/step - acc: 0.9385 - loss: 0.2058

 182/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 555us/step - acc: 0.9386 - loss: 0.2057

 270/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 561us/step - acc: 0.9389 - loss: 0.2045

 357/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 564us/step - acc: 0.9392 - loss: 0.2035

 446/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 565us/step - acc: 0.9397 - loss: 0.2029

 535/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 565us/step - acc: 0.9401 - loss: 0.2026

 621/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 568us/step - acc: 0.9405 - loss: 0.2023

 710/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 568us/step - acc: 0.9408 - loss: 0.2022

 801/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 566us/step - acc: 0.9411 - loss: 0.2020

 885/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 570us/step - acc: 0.9413 - loss: 0.2018

 953/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 582us/step - acc: 0.9415 - loss: 0.2016

1038/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 583us/step - acc: 0.9417 - loss: 0.2015

1123/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 583us/step - acc: 0.9418 - loss: 0.2012

1210/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 583us/step - acc: 0.9420 - loss: 0.2010

1293/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 584us/step - acc: 0.9421 - loss: 0.2008

1367/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 590us/step - acc: 0.9422 - loss: 0.2006

1457/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 588us/step - acc: 0.9423 - loss: 0.2004

1544/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 587us/step - acc: 0.9424 - loss: 0.2003

1632/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 586us/step - acc: 0.9425 - loss: 0.2002

1720/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9425 - loss: 0.2001

1813/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 583us/step - acc: 0.9426 - loss: 0.2000

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 682us/step - acc: 0.9426 - loss: 0.1998 - val_acc: 0.9459 - val_loss: 0.1842

Epoch 6/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 17s 10ms/step - acc: 0.9688 - loss: 0.2947

  91/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 561us/step - acc: 0.9445 - loss: 0.1942

 185/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 548us/step - acc: 0.9442 - loss: 0.1973

 274/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 554us/step - acc: 0.9445 - loss: 0.1957

 370/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 546us/step - acc: 0.9452 - loss: 0.1939

 463/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9457 - loss: 0.1924

 556/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 544us/step - acc: 0.9462 - loss: 0.1906

 640/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - acc: 0.9466 - loss: 0.1893

 731/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - acc: 0.9471 - loss: 0.1879

 832/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9475 - loss: 0.1866

 932/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 540us/step - acc: 0.9477 - loss: 0.1857

1022/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9478 - loss: 0.1851

1109/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9479 - loss: 0.1846

1200/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9480 - loss: 0.1841

1294/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9481 - loss: 0.1837

1396/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 541us/step - acc: 0.9481 - loss: 0.1834

1496/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 538us/step - acc: 0.9482 - loss: 0.1831

1578/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9482 - loss: 0.1828

1673/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 541us/step - acc: 0.9483 - loss: 0.1825

1765/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9483 - loss: 0.1823

1857/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9484 - loss: 0.1820

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 633us/step - acc: 0.9484 - loss: 0.1820 - val_acc: 0.9513 - val_loss: 0.1696

Epoch 7/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 23s 13ms/step - acc: 0.9688 - loss: 0.0843

  91/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 557us/step - acc: 0.9503 - loss: 0.1575

 178/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 566us/step - acc: 0.9492 - loss: 0.1633

 274/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 552us/step - acc: 0.9493 - loss: 0.1670

 372/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9497 - loss: 0.1686

 442/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 569us/step - acc: 0.9498 - loss: 0.1693

 485/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 623us/step - acc: 0.9499 - loss: 0.1695

 572/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 616us/step - acc: 0.9500 - loss: 0.1697

 653/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 616us/step - acc: 0.9502 - loss: 0.1697

 715/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 633us/step - acc: 0.9502 - loss: 0.1695

 777/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 648us/step - acc: 0.9504 - loss: 0.1693

 832/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 665us/step - acc: 0.9504 - loss: 0.1691

 896/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 674us/step - acc: 0.9505 - loss: 0.1688

 974/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 671us/step - acc: 0.9507 - loss: 0.1685

1062/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 663us/step - acc: 0.9508 - loss: 0.1682

1149/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 656us/step - acc: 0.9509 - loss: 0.1678

1248/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 644us/step - acc: 0.9511 - loss: 0.1674

1345/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 635us/step - acc: 0.9512 - loss: 0.1671

1437/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 630us/step - acc: 0.9513 - loss: 0.1668

1538/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 621us/step - acc: 0.9515 - loss: 0.1665

1632/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 616us/step - acc: 0.9516 - loss: 0.1663

1741/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 606us/step - acc: 0.9517 - loss: 0.1660

1840/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 601us/step - acc: 0.9518 - loss: 0.1658

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 679us/step - acc: 0.9518 - loss: 0.1658 - val_acc: 0.9539 - val_loss: 0.1584

Epoch 8/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 24s 13ms/step - acc: 0.9688 - loss: 0.0964

 108/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 469us/step - acc: 0.9617 - loss: 0.1440

 213/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9583 - loss: 0.1511

 320/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9577 - loss: 0.1518

 423/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 476us/step - acc: 0.9573 - loss: 0.1524

 527/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9569 - loss: 0.1530

 636/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 475us/step - acc: 0.9567 - loss: 0.1532

 737/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9566 - loss: 0.1532

 812/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 496us/step - acc: 0.9565 - loss: 0.1531

 900/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 503us/step - acc: 0.9565 - loss: 0.1529

 997/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 505us/step - acc: 0.9565 - loss: 0.1528

1091/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 507us/step - acc: 0.9564 - loss: 0.1528

1187/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 508us/step - acc: 0.9564 - loss: 0.1527

1283/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9564 - loss: 0.1526

1384/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 509us/step - acc: 0.9564 - loss: 0.1525

1483/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 509us/step - acc: 0.9564 - loss: 0.1523

1579/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9564 - loss: 0.1521

1676/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9564 - loss: 0.1519

1775/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9564 - loss: 0.1518

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 587us/step - acc: 0.9564 - loss: 0.1517 - val_acc: 0.9563 - val_loss: 0.1485

Epoch 9/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 16s 9ms/step - acc: 0.9688 - loss: 0.1123

 108/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 467us/step - acc: 0.9598 - loss: 0.1407

 213/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9605 - loss: 0.1400

 291/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 521us/step - acc: 0.9606 - loss: 0.1393

 340/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 596us/step - acc: 0.9606 - loss: 0.1393

 425/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 595us/step - acc: 0.9605 - loss: 0.1395

 519/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 584us/step - acc: 0.9603 - loss: 0.1396

 620/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 570us/step - acc: 0.9601 - loss: 0.1396

 724/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 557us/step - acc: 0.9599 - loss: 0.1398

 833/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9597 - loss: 0.1403

 941/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 535us/step - acc: 0.9596 - loss: 0.1406

1051/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 527us/step - acc: 0.9596 - loss: 0.1407

1155/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 523us/step - acc: 0.9596 - loss: 0.1407

1249/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 524us/step - acc: 0.9597 - loss: 0.1407

1351/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 521us/step - acc: 0.9597 - loss: 0.1406

1457/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 518us/step - acc: 0.9598 - loss: 0.1405

1566/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 514us/step - acc: 0.9598 - loss: 0.1405

1674/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 511us/step - acc: 0.9598 - loss: 0.1404

1776/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9598 - loss: 0.1404

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 584us/step - acc: 0.9598 - loss: 0.1403 - val_acc: 0.9588 - val_loss: 0.1413

Epoch 10/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 16s 9ms/step - acc: 0.9688 - loss: 0.1366

 110/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 462us/step - acc: 0.9579 - loss: 0.1273

 215/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 471us/step - acc: 0.9587 - loss: 0.1293

 325/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 466us/step - acc: 0.9593 - loss: 0.1298

 434/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 465us/step - acc: 0.9597 - loss: 0.1303

 542/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 465us/step - acc: 0.9599 - loss: 0.1311

 653/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 462us/step - acc: 0.9601 - loss: 0.1315

 764/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 461us/step - acc: 0.9604 - loss: 0.1317

 864/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 466us/step - acc: 0.9605 - loss: 0.1319

 960/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9606 - loss: 0.1321

1068/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 471us/step - acc: 0.9607 - loss: 0.1323

1170/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 473us/step - acc: 0.9607 - loss: 0.1324

1271/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 475us/step - acc: 0.9608 - loss: 0.1325

1375/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 475us/step - acc: 0.9609 - loss: 0.1324

1484/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9610 - loss: 0.1324

1592/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 474us/step - acc: 0.9611 - loss: 0.1323

1702/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 472us/step - acc: 0.9612 - loss: 0.1321

1813/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 471us/step - acc: 0.9613 - loss: 0.1320

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 547us/step - acc: 0.9614 - loss: 0.1320 - val_acc: 0.9600 - val_loss: 0.1348

Epoch 11/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 17s 9ms/step - acc: 0.9375 - loss: 0.2580

  98/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 520us/step - acc: 0.9668 - loss: 0.1161

 180/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 564us/step - acc: 0.9665 - loss: 0.1176

 261/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 581us/step - acc: 0.9662 - loss: 0.1188

 348/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 581us/step - acc: 0.9660 - loss: 0.1194

 436/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 579us/step - acc: 0.9658 - loss: 0.1202

 520/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 581us/step - acc: 0.9657 - loss: 0.1210

 603/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9655 - loss: 0.1216

 690/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 584us/step - acc: 0.9654 - loss: 0.1220

 776/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 584us/step - acc: 0.9653 - loss: 0.1222

 861/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9653 - loss: 0.1224

 951/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 582us/step - acc: 0.9652 - loss: 0.1224

1032/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9652 - loss: 0.1224

1117/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 586us/step - acc: 0.9652 - loss: 0.1223

1205/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9652 - loss: 0.1221

1289/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 586us/step - acc: 0.9653 - loss: 0.1220

1372/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 587us/step - acc: 0.9653 - loss: 0.1219

1459/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 586us/step - acc: 0.9653 - loss: 0.1219

1543/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 587us/step - acc: 0.9653 - loss: 0.1218

1626/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 588us/step - acc: 0.9653 - loss: 0.1218

1720/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 585us/step - acc: 0.9653 - loss: 0.1217

1801/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 587us/step - acc: 0.9653 - loss: 0.1218

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 683us/step - acc: 0.9653 - loss: 0.1218 - val_acc: 0.9620 - val_loss: 0.1277

Epoch 12/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 22s 12ms/step - acc: 0.9688 - loss: 0.1040

  82/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 622us/step - acc: 0.9690 - loss: 0.1211

 172/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 588us/step - acc: 0.9694 - loss: 0.1175

 260/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 591us/step - acc: 0.9695 - loss: 0.1172

 346/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 590us/step - acc: 0.9694 - loss: 0.1175

 426/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 597us/step - acc: 0.9695 - loss: 0.1170

 508/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 600us/step - acc: 0.9694 - loss: 0.1167

 594/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 598us/step - acc: 0.9694 - loss: 0.1165

 681/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 597us/step - acc: 0.9694 - loss: 0.1163

 765/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 598us/step - acc: 0.9693 - loss: 0.1163

 856/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 593us/step - acc: 0.9691 - loss: 0.1163

 939/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 594us/step - acc: 0.9689 - loss: 0.1165

1011/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 602us/step - acc: 0.9688 - loss: 0.1166

1085/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 607us/step - acc: 0.9687 - loss: 0.1166

1152/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 615us/step - acc: 0.9686 - loss: 0.1167

1237/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 613us/step - acc: 0.9686 - loss: 0.1167

1326/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 610us/step - acc: 0.9685 - loss: 0.1167

1413/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 608us/step - acc: 0.9684 - loss: 0.1168

1498/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 607us/step - acc: 0.9684 - loss: 0.1169

1590/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 604us/step - acc: 0.9683 - loss: 0.1169

1678/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 602us/step - acc: 0.9683 - loss: 0.1169

1769/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 599us/step - acc: 0.9682 - loss: 0.1169

1864/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 596us/step - acc: 0.9682 - loss: 0.1169

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 681us/step - acc: 0.9682 - loss: 0.1169 - val_acc: 0.9629 - val_loss: 0.1252

Epoch 13/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 19s 10ms/step - acc: 0.9062 - loss: 0.3200

  93/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 545us/step - acc: 0.9720 - loss: 0.1054

 187/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 540us/step - acc: 0.9709 - loss: 0.1076

 279/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 542us/step - acc: 0.9707 - loss: 0.1078

 372/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 541us/step - acc: 0.9705 - loss: 0.1081

 468/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 537us/step - acc: 0.9703 - loss: 0.1083

 556/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 543us/step - acc: 0.9703 - loss: 0.1082

 654/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 538us/step - acc: 0.9702 - loss: 0.1081

 752/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 535us/step - acc: 0.9702 - loss: 0.1083

 833/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 543us/step - acc: 0.9701 - loss: 0.1083

 918/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 548us/step - acc: 0.9701 - loss: 0.1083

1005/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - acc: 0.9701 - loss: 0.1082

1094/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 552us/step - acc: 0.9701 - loss: 0.1082

1177/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 556us/step - acc: 0.9701 - loss: 0.1082

1266/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 557us/step - acc: 0.9701 - loss: 0.1082

1360/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 555us/step - acc: 0.9701 - loss: 0.1083

1443/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 558us/step - acc: 0.9701 - loss: 0.1083

1535/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 557us/step - acc: 0.9701 - loss: 0.1083

1632/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 555us/step - acc: 0.9701 - loss: 0.1083

1730/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 552us/step - acc: 0.9701 - loss: 0.1083

1815/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 554us/step - acc: 0.9700 - loss: 0.1083

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 650us/step - acc: 0.9700 - loss: 0.1083 - val_acc: 0.9633 - val_loss: 0.1231

Epoch 14/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - acc: 1.0000 - loss: 0.0707

  91/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 560us/step - acc: 0.9773 - loss: 0.0943

 199/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 508us/step - acc: 0.9734 - loss: 0.0985

 301/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 503us/step - acc: 0.9725 - loss: 0.0998

 405/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 498us/step - acc: 0.9720 - loss: 0.1005

 514/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 490us/step - acc: 0.9717 - loss: 0.1010

 608/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 497us/step - acc: 0.9716 - loss: 0.1014

 672/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 525us/step - acc: 0.9715 - loss: 0.1016

 761/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 530us/step - acc: 0.9714 - loss: 0.1018

 856/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 530us/step - acc: 0.9714 - loss: 0.1018

 957/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 526us/step - acc: 0.9714 - loss: 0.1019

1064/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 520us/step - acc: 0.9714 - loss: 0.1018

1165/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 518us/step - acc: 0.9714 - loss: 0.1017

1274/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 513us/step - acc: 0.9715 - loss: 0.1017

1379/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 511us/step - acc: 0.9715 - loss: 0.1018

1489/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 507us/step - acc: 0.9715 - loss: 0.1018

1596/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 504us/step - acc: 0.9715 - loss: 0.1019

1706/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 501us/step - acc: 0.9715 - loss: 0.1019

1811/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 500us/step - acc: 0.9715 - loss: 0.1020

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 585us/step - acc: 0.9714 - loss: 0.1021 - val_acc: 0.9641 - val_loss: 0.1198

Epoch 15/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 16s 9ms/step - acc: 1.0000 - loss: 0.0605

  57/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 896us/step - acc: 0.9708 - loss: 0.1037

 137/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 737us/step - acc: 0.9711 - loss: 0.1030

 245/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 617us/step - acc: 0.9718 - loss: 0.1025

 348/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 579us/step - acc: 0.9724 - loss: 0.1020

 457/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - acc: 0.9724 - loss: 0.1020

 567/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 533us/step - acc: 0.9724 - loss: 0.1019

 677/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 520us/step - acc: 0.9724 - loss: 0.1019

 785/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 513us/step - acc: 0.9724 - loss: 0.1018

 888/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9724 - loss: 0.1018

 986/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 510us/step - acc: 0.9724 - loss: 0.1017

1092/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 506us/step - acc: 0.9725 - loss: 0.1016

1198/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 503us/step - acc: 0.9726 - loss: 0.1013

1300/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 504us/step - acc: 0.9726 - loss: 0.1011

1386/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 509us/step - acc: 0.9726 - loss: 0.1009

1471/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 513us/step - acc: 0.9727 - loss: 0.1007

1572/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 514us/step - acc: 0.9727 - loss: 0.1004

1660/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 517us/step - acc: 0.9727 - loss: 0.1003

1755/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 518us/step - acc: 0.9727 - loss: 0.1002

1857/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 517us/step - acc: 0.9727 - loss: 0.1001

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 594us/step - acc: 0.9727 - loss: 0.1001 - val_acc: 0.9662 - val_loss: 0.1142

Epoch 16/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 16s 9ms/step - acc: 0.9688 - loss: 0.0966

 102/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 498us/step - acc: 0.9776 - loss: 0.0911

 211/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 479us/step - acc: 0.9762 - loss: 0.0906

 322/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 470us/step - acc: 0.9757 - loss: 0.0902

 432/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 467us/step - acc: 0.9752 - loss: 0.0906

 533/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 490us/step - acc: 0.9750 - loss: 0.0910

 589/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 531us/step - acc: 0.9748 - loss: 0.0911

 673/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 539us/step - acc: 0.9747 - loss: 0.0913

 743/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 556us/step - acc: 0.9746 - loss: 0.0914

 831/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 559us/step - acc: 0.9746 - loss: 0.0915

 928/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 555us/step - acc: 0.9746 - loss: 0.0917

1020/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 554us/step - acc: 0.9745 - loss: 0.0920

1114/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 553us/step - acc: 0.9744 - loss: 0.0922

1208/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 551us/step - acc: 0.9743 - loss: 0.0924

1289/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 556us/step - acc: 0.9743 - loss: 0.0925

1350/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 568us/step - acc: 0.9743 - loss: 0.0926

1438/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 568us/step - acc: 0.9742 - loss: 0.0927

1525/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 569us/step - acc: 0.9742 - loss: 0.0928

1622/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 566us/step - acc: 0.9742 - loss: 0.0930

1693/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 572us/step - acc: 0.9741 - loss: 0.0930

1771/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 575us/step - acc: 0.9741 - loss: 0.0931

1846/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 579us/step - acc: 0.9741 - loss: 0.0932

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 724us/step - acc: 0.9741 - loss: 0.0932 - val_acc: 0.9663 - val_loss: 0.1127

Epoch 17/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - acc: 1.0000 - loss: 0.0524

  67/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 774us/step - acc: 0.9712 - loss: 0.1065

 155/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 657us/step - acc: 0.9725 - loss: 0.1003

 253/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 601us/step - acc: 0.9736 - loss: 0.0960

 357/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 567us/step - acc: 0.9746 - loss: 0.0932

 467/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 541us/step - acc: 0.9752 - loss: 0.0913

 578/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 524us/step - acc: 0.9755 - loss: 0.0904

 680/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 519us/step - acc: 0.9757 - loss: 0.0900

 760/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 530us/step - acc: 0.9757 - loss: 0.0898

 850/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 533us/step - acc: 0.9758 - loss: 0.0896

 947/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 531us/step - acc: 0.9758 - loss: 0.0894

1051/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 527us/step - acc: 0.9758 - loss: 0.0893

1155/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 523us/step - acc: 0.9758 - loss: 0.0892

1263/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 517us/step - acc: 0.9757 - loss: 0.0893

1365/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - acc: 0.9757 - loss: 0.0893

1461/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - acc: 0.9756 - loss: 0.0893

1568/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 513us/step - acc: 0.9756 - loss: 0.0894

1660/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 515us/step - acc: 0.9755 - loss: 0.0895

1755/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - acc: 0.9754 - loss: 0.0895

1862/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 513us/step - acc: 0.9754 - loss: 0.0896

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 598us/step - acc: 0.9754 - loss: 0.0896 - val_acc: 0.9676 - val_loss: 0.1087

Epoch 18/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - acc: 1.0000 - loss: 0.0405

 104/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 489us/step - acc: 0.9795 - loss: 0.0796

 211/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 479us/step - acc: 0.9788 - loss: 0.0804

 317/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9782 - loss: 0.0815

 414/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9778 - loss: 0.0826

 518/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9775 - loss: 0.0837

 623/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 486us/step - acc: 0.9772 - loss: 0.0845

 727/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 485us/step - acc: 0.9770 - loss: 0.0851

 834/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 483us/step - acc: 0.9769 - loss: 0.0854

 932/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 486us/step - acc: 0.9768 - loss: 0.0855

1034/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9768 - loss: 0.0855

1137/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9768 - loss: 0.0855

1236/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 489us/step - acc: 0.9768 - loss: 0.0855

1340/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 488us/step - acc: 0.9768 - loss: 0.0855

1446/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9768 - loss: 0.0856

1549/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9767 - loss: 0.0858

1653/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9767 - loss: 0.0859

1756/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 487us/step - acc: 0.9767 - loss: 0.0860

1861/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 486us/step - acc: 0.9766 - loss: 0.0861

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 564us/step - acc: 0.9766 - loss: 0.0861 - val_acc: 0.9686 - val_loss: 0.1060

Epoch 19/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 20s 11ms/step - acc: 0.9688 - loss: 0.2103

 109/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 464us/step - acc: 0.9730 - loss: 0.1104

 219/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 460us/step - acc: 0.9744 - loss: 0.1014

 312/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 484us/step - acc: 0.9750 - loss: 0.0984

 414/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 486us/step - acc: 0.9755 - loss: 0.0958

 515/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 488us/step - acc: 0.9757 - loss: 0.0942

 611/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 494us/step - acc: 0.9759 - loss: 0.0929

 711/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 496us/step - acc: 0.9762 - loss: 0.0915

 807/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 499us/step - acc: 0.9764 - loss: 0.0904

 907/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 499us/step - acc: 0.9765 - loss: 0.0896

1001/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 502us/step - acc: 0.9766 - loss: 0.0890

1099/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 503us/step - acc: 0.9766 - loss: 0.0886

1193/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 505us/step - acc: 0.9766 - loss: 0.0883

1299/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 503us/step - acc: 0.9767 - loss: 0.0879

1405/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 501us/step - acc: 0.9767 - loss: 0.0875

1511/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 499us/step - acc: 0.9768 - loss: 0.0872

1612/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 499us/step - acc: 0.9768 - loss: 0.0870

1723/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 496us/step - acc: 0.9769 - loss: 0.0867

1833/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 494us/step - acc: 0.9769 - loss: 0.0865

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 569us/step - acc: 0.9769 - loss: 0.0864 - val_acc: 0.9684 - val_loss: 0.1040

Epoch 20/20

   1/1875 ━━━━━━━━━━━━━━━━━━━━ 18s 10ms/step - acc: 1.0000 - loss: 0.0234

  98/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 517us/step - acc: 0.9822 - loss: 0.0699

 196/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 516us/step - acc: 0.9820 - loss: 0.0708

 304/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 498us/step - acc: 0.9815 - loss: 0.0724

 413/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 489us/step - acc: 0.9814 - loss: 0.0728

 523/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 482us/step - acc: 0.9812 - loss: 0.0733

 630/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 480us/step - acc: 0.9809 - loss: 0.0739

 739/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9807 - loss: 0.0744

 847/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 476us/step - acc: 0.9806 - loss: 0.0747

 950/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9805 - loss: 0.0749

1056/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9804 - loss: 0.0751

1159/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9803 - loss: 0.0753

1265/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9801 - loss: 0.0756

1371/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9800 - loss: 0.0759

1474/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9799 - loss: 0.0761

1579/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 478us/step - acc: 0.9798 - loss: 0.0764

1687/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 477us/step - acc: 0.9797 - loss: 0.0766

1783/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 480us/step - acc: 0.9796 - loss: 0.0768

1875/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 566us/step - acc: 0.9796 - loss: 0.0770 - val_acc: 0.9688 - val_loss: 0.1030

TRAININGSDAUER: 26.25 Sek.

Minibatch liegt mit einer Dauer von ca. 22 Sekunden zwischen Batch-Training (3 Sek.) und reinem SGD (8 Min.). Interessant ist, dass es doch erheblich schneller ist als reines SGD.

Wir sehen uns wieder die Performance an:

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,4))

set_subplot(ax[0], 'Loss', history3.history['loss'], 
            history3.history['val_loss'], [0, 1])
set_subplot(ax[1], 'Accuracy', history3.history['acc'], 
            history3.history['val_acc'], [0.8, 1])

plt.show()

loss, acc = model.evaluate(x_test, y_test_1hot)
print(f"Evaluation auf den Testdaten:\n\nLoss = {loss:.3f}\nAccuracy = {acc:.3f}")

  1/313 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - acc: 0.9688 - loss: 0.1652

119/313 ━━━━━━━━━━━━━━━━━━━━ 0s 424us/step - acc: 0.9640 - loss: 0.1184

241/313 ━━━━━━━━━━━━━━━━━━━━ 0s 417us/step - acc: 0.9620 - loss: 0.1230

313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 439us/step - acc: 0.9634 - loss: 0.1188

Evaluation auf den Testdaten:

Loss = 0.103
Accuracy = 0.969

Die Performance für unser Minibatch-Training mit Batchgröße 32 ist mit einer 97% Accuracy auf den Testdaten praktisch gleich zur Performance mit reinem SGD.

5.6.3 Fazit

Schauen wir uns die Resultate nochmal im Vergleich an (unten sieht man auch die jeweilige Accuracy als Kurve über die Epochen).

	Trainingsdauer	Accuracy (Test)
Batchtraining	3 Sek	27%
Reines SGD	10 Min	97%
Minibatch (32)	22 Sek	97%

Es scheint - zumindest bei diesen Daten - so zu sein, dass man mit Minibatch-Training mit der voreingestellten Batchgröße am besten fährt, da man eine hohe Accuracy bei tolerierbarer Trainingsdauer erzielt.

Wir sehen uns nochmal die Kurven der drei Ansätze nebeneinander an.

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(15,4))

set_subplot(ax[0], 'Batchgröße 60000 / Accuracy', history1.history['acc'], history1.history['val_acc'], [0, 1])
set_subplot(ax[1], 'Batchgröße 1 / Accuracy', history2.history['acc'], history2.history['val_acc'], [0.85, 1])
set_subplot(ax[2], 'Batchgröße 32 / Accuracy', history3.history['acc'], history3.history['val_acc'], [0.85, 1])

plt.show()

Wichtig ist immer, sich die Rahmenbedingungen des obigen “Experiments” vor Augen zu führen. Wir haben die Optimierungsmethode Stochastic Gradient Descent mit einer Lernrate von 0.1 verwendet und für 20 Epochen trainiert. Wir hatten ein FNN mit einer versteckten Schicht mit 60 Neuronen und haben auf den MNIST-Daten gearbeitet. Sollte sich einer dieser Umstände ändern, kann das die Ergebnisse verändern.

Zudem müsste man jede der drei Variante mehrfach testen (z.B. 10x) und dann den Mittelwert von Dauer und Accuracy nehmen, um statistische Glaubwürdigkeit zu erreichen. In unserem Beispiel sind die Unterschiede allerdings so deutlich, dass wir uns das ersparen.

Chollet, François. 2021. Deep Learning with Python, 2nd Edition. 2nd ed. Manning.