W3:Transfer learning

import os
import zipfile
import random
import shutil
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile
from os import getcwd

1 download data

1.1 download big cats-and-dogs.zip and we will random split into training and validation folder

os. getcwd()

import os
if not os.path.exists('tmp'):
    os.makedirs('tmp')

import urllib.request
urllib.request.urlretrieve("https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip", "./tmp/cats-and-dogs.zip")

import zipfile

local_zip = './tmp/cats-and-dogs.zip'
zip_ref   = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('./tmp')
zip_ref.close()

2 download model Download the inception v3 weights

import urllib.request
urllib.request.urlretrieve("https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5", "./tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5")

os.remove('./tmp/cats-and-dogs.zip')

os.listdir('./tmp/PetImages')

source_path = '/tmp/PetImages'

source_path_dogs = os.path.join(source_path, 'Dog')
source_path_cats = os.path.join(source_path, 'Cat')


# os.listdir returns a list containing all files under the given path
print(f"There are {len(os.listdir(source_path_dogs))} images of dogs.")
print(f"There are {len(os.listdir(source_path_cats))} images of cats.")

if not os.path.exists('./tmp/cats-v-dogs'):
  os.mkdir('./tmp/cats-v-dogs')
  
if not os.path.exists('./tmp/cats-v-dogs/training'):
  os.mkdir('./tmp/cats-v-dogs/training')
  
if not os.path.exists('./tmp/cats-v-dogs/testing'): 
  os.mkdir('./tmp/cats-v-dogs/testing')

if not os.path.exists('./tmp/cats-v-dogs/training/cats'):
  os.mkdir('./tmp/cats-v-dogs/training/cats')

if not os.path.exists('./tmp/cats-v-dogs/training/dogs'):
  os.mkdir('./tmp/cats-v-dogs/training/dogs')

if not os.path.exists('./tmp/cats-v-dogs/testing/cats'):
  os.mkdir('./tmp/cats-v-dogs/testing/cats')

if not os.path.exists('./tmp/cats-v-dogs/testing/dogs'):
  os.mkdir('./tmp/cats-v-dogs/testing/dogs')

def split_data(SOURCE, TRAINING, TESTING, SPLIT_SIZE):
# YOUR CODE STARTS HERE
    all_files = []
    
    for file_name in os.listdir(SOURCE):
        file_path = SOURCE + file_name

        if os.path.getsize(file_path):
            all_files.append(file_name)
        else:
            print('{} is zero length, so ignoring'.format(file_name))
    
    n_files = len(all_files)
    split_point = int(n_files * SPLIT_SIZE)
    
    shuffled = random.sample(all_files, n_files)
    
    train_set = shuffled[:split_point]
    test_set = shuffled[split_point:]
    
    for file_name in train_set:
        copyfile(SOURCE + file_name, TRAINING + file_name)
        
    for file_name in test_set:
        copyfile(SOURCE + file_name, TESTING + file_name)

split_size = .9
CAT_SOURCE_DIR = "./tmp/PetImages/Cat/"
TRAINING_CATS_DIR = "./tmp/cats-v-dogs/training/cats/"
TESTING_CATS_DIR = "./tmp/cats-v-dogs/testing/cats/"
DOG_SOURCE_DIR = "./tmp/PetImages/Dog/"
TRAINING_DOGS_DIR = "./tmp/cats-v-dogs/training/dogs/"
TESTING_DOGS_DIR = "./tmp/cats-v-dogs/testing/dogs/"

split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)
split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)

print(len(os.listdir('./tmp/cats-v-dogs/training/cats/')))
print(len(os.listdir('./tmp/cats-v-dogs/training/dogs/')))
print(len(os.listdir('./tmp/cats-v-dogs/testing/cats/')))
print(len(os.listdir('./tmp/cats-v-dogs/testing/dogs/')))

from PIL import Image

im = Image.open('./tmp/cats-v-dogs/training/cats/11.jpg')
im.size # (width,height) 

3 Load the data

train_dir='./tmp/cats-v-dogs/training/'
validation_dir='./tmp/cats-v-dogs/testing/'

ImageDataGenerator: All images will be resized to 150x150 and do augmentation to aviod over fiting

train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=40,
      width_shift_range=0.2,
      height_shift_range=0.2,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

test_datagen = ImageDataGenerator(rescale=1./255)

# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
        train_dir,  # This is the source directory for training images
        target_size=(150, 150),  # All images will be resized to 150x150
        batch_size=20,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

# Flow validation images in batches of 20 using test_datagen generator
validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=20,
        class_mode='binary')

Found 24998 images belonging to 2 classes.
Found 13013 images belonging to 2 classes.

4 pre_trained_model

# Import the inception model  
from tensorflow.keras.applications.inception_v3 import InceptionV3

# Create an instance of the inception model from the local pre-trained weights
local_weights_file = './tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'

pre_trained_model = InceptionV3(input_shape = (150, 150, 3),
                                  include_top = False, 
                                  weights = None) 


pre_trained_model.load_weights(local_weights_file)

# Make all the layers in the pre-trained model non-trainable
for layer in pre_trained_model.layers:
  layer.trainable = False

#pre_trained_model.summary()

total_params = pre_trained_model.count_params()
num_trainable_params = sum([w.shape.num_elements() for w in pre_trained_model.trainable_weights])

print(f"There are {total_params:,} total parameters in this model.")
print(f"There are {num_trainable_params:,} trainable parameters in this model.")

There are 21,802,784 total parameters in this model.
There are 0 trainable parameters in this model.

# Choose `mixed_7` as the last layer of your base model and not using layer after mixed7
last_layer = pre_trained_model.get_layer('mixed7')
#print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output

5 base model

add Dropout to improve over fitting issue.

from tensorflow.keras import layers
x = layers.Flatten()(last_output)
 # Add a fully connected layer with 1024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
  # Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)  
  # Add a final sigmoid layer for classification
x = layers.Dense(1, activation='sigmoid')(x) 

final model

# Append the dense network to the base model
from tensorflow.keras import Model
model = Model(pre_trained_model.input, x) 

# Print the model summary. See your dense network connected at the end.
#model.summary()

6 compile model

# v2.11+ optimizer `tf.keras.optimizers.RMSprop` runs slowly on M1/M2 Macs
from tensorflow.keras.optimizers import RMSprop

#from tensorflow.keras.optimizers.legacy import RMSprop


model.compile(loss='binary_crossentropy',
              optimizer=RMSprop(learning_rate=0.001),
              metrics=['accuracy'])

7 Callbacks

class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('accuracy')>0.95):
      print("\nReached 95% accuracy so cancelling training!")
      self.model.stop_training = True

# Instantiate class
callbacks = myCallback()

# Train the new model with augmentation
history = model.fit(
      train_generator,
      steps_per_epoch=100,  # 2000 images = batch_size * steps
      epochs=10,
      validation_data=validation_generator,
      validation_steps=50,  # 1000 images = batch_size * steps
      callbacks=[callbacks]
      )

Epoch 1/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 4:08 3s/step - accuracy: 0.5500 - loss: 0.7655  2/100 ━━━━━━━━━━━━━━━━━━━━ 26s 266ms/step - accuracy: 0.5625 - loss: 4.0972  3/100 ━━━━━━━━━━━━━━━━━━━━ 25s 267ms/step - accuracy: 0.5250 - loss: 8.6589  4/100 ━━━━━━━━━━━━━━━━━━━━ 24s 255ms/step - accuracy: 0.5188 - loss: 9.9921  5/100 ━━━━━━━━━━━━━━━━━━━━ 23s 251ms/step - accuracy: 0.5190 - loss: 10.3648  6/100 ━━━━━━━━━━━━━━━━━━━━ 23s 248ms/step - accuracy: 0.5283 - loss: 10.2944  7/100 ━━━━━━━━━━━━━━━━━━━━ 22s 246ms/step - accuracy: 0.5406 - loss: 10.0521  8/100 ━━━━━━━━━━━━━━━━━━━━ 22s 244ms/step - accuracy: 0.5519 - loss: 9.7533   9/100 ━━━━━━━━━━━━━━━━━━━━ 22s 243ms/step - accuracy: 0.5616 - loss: 9.4453 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 242ms/step - accuracy: 0.5709 - loss: 9.1355 11/100 ━━━━━━━━━━━━━━━━━━━━ 21s 241ms/step - accuracy: 0.5798 - loss: 8.8336 12/100 ━━━━━━━━━━━━━━━━━━━━ 21s 240ms/step - accuracy: 0.5884 - loss: 8.5434 13/100 ━━━━━━━━━━━━━━━━━━━━ 20s 240ms/step - accuracy: 0.5967 - loss: 8.2692 14/100 ━━━━━━━━━━━━━━━━━━━━ 20s 239ms/step - accuracy: 0.6046 - loss: 8.0107 15/100 ━━━━━━━━━━━━━━━━━━━━ 20s 239ms/step - accuracy: 0.6118 - loss: 7.7701 16/100 ━━━━━━━━━━━━━━━━━━━━ 20s 238ms/step - accuracy: 0.6189 - loss: 7.5440 17/100 ━━━━━━━━━━━━━━━━━━━━ 19s 238ms/step - accuracy: 0.6254 - loss: 7.3317 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 238ms/step - accuracy: 0.6316 - loss: 7.1320 19/100 ━━━━━━━━━━━━━━━━━━━━ 19s 237ms/step - accuracy: 0.6371 - loss: 6.9447 20/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.6424 - loss: 6.7687 21/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.6476 - loss: 6.6019 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.6526 - loss: 6.4441 23/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.6573 - loss: 6.2946 24/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.6617 - loss: 6.1537 25/100 ━━━━━━━━━━━━━━━━━━━━ 17s 236ms/step - accuracy: 0.6655 - loss: 6.0214 26/100 ━━━━━━━━━━━━━━━━━━━━ 17s 236ms/step - accuracy: 0.6690 - loss: 5.8967 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 236ms/step - accuracy: 0.6723 - loss: 5.7778 28/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.6756 - loss: 5.6642 29/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.6786 - loss: 5.5559 30/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.6815 - loss: 5.4523 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.6843 - loss: 5.3534 32/100 ━━━━━━━━━━━━━━━━━━━━ 16s 236ms/step - accuracy: 0.6869 - loss: 5.2587 33/100 ━━━━━━━━━━━━━━━━━━━━ 15s 237ms/step - accuracy: 0.6895 - loss: 5.1678 34/100 ━━━━━━━━━━━━━━━━━━━━ 15s 237ms/step - accuracy: 0.6918 - loss: 5.0808 35/100 ━━━━━━━━━━━━━━━━━━━━ 15s 237ms/step - accuracy: 0.6937 - loss: 4.9979 36/100 ━━━━━━━━━━━━━━━━━━━━ 15s 237ms/step - accuracy: 0.6956 - loss: 4.9188 37/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.6973 - loss: 4.8428 38/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.6989 - loss: 4.7696 39/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.7005 - loss: 4.6992 40/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.7020 - loss: 4.6314 41/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.7036 - loss: 4.5659 42/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.7051 - loss: 4.5025 43/100 ━━━━━━━━━━━━━━━━━━━━ 13s 236ms/step - accuracy: 0.7068 - loss: 4.4411 44/100 ━━━━━━━━━━━━━━━━━━━━ 13s 236ms/step - accuracy: 0.7084 - loss: 4.3816 45/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.7100 - loss: 4.3239 46/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.7117 - loss: 4.2681 47/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.7133 - loss: 4.2139 48/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.7149 - loss: 4.1612 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.7165 - loss: 4.1101 50/100 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - accuracy: 0.7180 - loss: 4.0606 51/100 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - accuracy: 0.7194 - loss: 4.0128 52/100 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - accuracy: 0.7208 - loss: 3.9663 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - accuracy: 0.7221 - loss: 3.9211 54/100 ━━━━━━━━━━━━━━━━━━━━ 10s 236ms/step - accuracy: 0.7234 - loss: 3.8773 55/100 ━━━━━━━━━━━━━━━━━━━━ 10s 236ms/step - accuracy: 0.7247 - loss: 3.8346 56/100 ━━━━━━━━━━━━━━━━━━━━ 10s 236ms/step - accuracy: 0.7259 - loss: 3.7931 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 236ms/step - accuracy: 0.7271 - loss: 3.7526 58/100 ━━━━━━━━━━━━━━━━━━━━ 9s 236ms/step - accuracy: 0.7283 - loss: 3.7133  59/100 ━━━━━━━━━━━━━━━━━━━━ 9s 236ms/step - accuracy: 0.7294 - loss: 3.6750 60/100 ━━━━━━━━━━━━━━━━━━━━ 9s 236ms/step - accuracy: 0.7306 - loss: 3.6377 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 236ms/step - accuracy: 0.7317 - loss: 3.6013 62/100 ━━━━━━━━━━━━━━━━━━━━ 8s 236ms/step - accuracy: 0.7328 - loss: 3.5658 63/100 ━━━━━━━━━━━━━━━━━━━━ 8s 236ms/step - accuracy: 0.7339 - loss: 3.5311 64/100 ━━━━━━━━━━━━━━━━━━━━ 8s 236ms/step - accuracy: 0.7349 - loss: 3.4973 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 236ms/step - accuracy: 0.7360 - loss: 3.4643 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 236ms/step - accuracy: 0.7371 - loss: 3.4321 67/100 ━━━━━━━━━━━━━━━━━━━━ 7s 236ms/step - accuracy: 0.7381 - loss: 3.4006 68/100 ━━━━━━━━━━━━━━━━━━━━ 7s 235ms/step - accuracy: 0.7392 - loss: 3.3698 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 235ms/step - accuracy: 0.7402 - loss: 3.3397 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 235ms/step - accuracy: 0.7411 - loss: 3.3102 71/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.7421 - loss: 3.2814 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.7431 - loss: 3.2532 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.7440 - loss: 3.2255 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.7450 - loss: 3.1984 75/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.7460 - loss: 3.1718 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.7469 - loss: 3.1458 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.7478 - loss: 3.1204 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.7486 - loss: 3.0955 79/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.7495 - loss: 3.0711 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.7503 - loss: 3.0472 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.7512 - loss: 3.0237 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.7520 - loss: 3.0007 83/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.7527 - loss: 2.9782 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.7535 - loss: 2.9560 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.7543 - loss: 2.9343 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.7551 - loss: 2.9130 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.7558 - loss: 2.8920 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.7566 - loss: 2.8713 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.7573 - loss: 2.8510 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.7581 - loss: 2.8311 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.7588 - loss: 2.8116 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.7595 - loss: 2.7924 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.7603 - loss: 2.7735 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.7610 - loss: 2.7550 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.7617 - loss: 2.7367 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.7624 - loss: 2.7188 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.7631 - loss: 2.7011 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.7637 - loss: 2.6837 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.7644 - loss: 2.6665100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.7651 - loss: 2.6497100/100 ━━━━━━━━━━━━━━━━━━━━ 33s 312ms/step - accuracy: 0.7657 - loss: 2.6332 - val_accuracy: 0.9090 - val_loss: 0.2641
Epoch 2/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 24s 249ms/step - accuracy: 1.0000 - loss: 0.1330  2/100 ━━━━━━━━━━━━━━━━━━━━ 25s 260ms/step - accuracy: 0.9500 - loss: 0.1873  3/100 ━━━━━━━━━━━━━━━━━━━━ 23s 244ms/step - accuracy: 0.9444 - loss: 0.1857  4/100 ━━━━━━━━━━━━━━━━━━━━ 22s 239ms/step - accuracy: 0.9458 - loss: 0.1761  5/100 ━━━━━━━━━━━━━━━━━━━━ 22s 237ms/step - accuracy: 0.9487 - loss: 0.1658  6/100 ━━━━━━━━━━━━━━━━━━━━ 22s 237ms/step - accuracy: 0.9503 - loss: 0.1619  7/100 ━━━━━━━━━━━━━━━━━━━━ 21s 236ms/step - accuracy: 0.9513 - loss: 0.1581  8/100 ━━━━━━━━━━━━━━━━━━━━ 21s 236ms/step - accuracy: 0.9503 - loss: 0.1572  9/100 ━━━━━━━━━━━━━━━━━━━━ 21s 235ms/step - accuracy: 0.9466 - loss: 0.1665 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 234ms/step - accuracy: 0.9414 - loss: 0.1818 11/100 ━━━━━━━━━━━━━━━━━━━━ 20s 235ms/step - accuracy: 0.9360 - loss: 0.1979 12/100 ━━━━━━━━━━━━━━━━━━━━ 20s 234ms/step - accuracy: 0.9313 - loss: 0.2106 13/100 ━━━━━━━━━━━━━━━━━━━━ 20s 234ms/step - accuracy: 0.9274 - loss: 0.2214 14/100 ━━━━━━━━━━━━━━━━━━━━ 20s 234ms/step - accuracy: 0.9244 - loss: 0.2296 15/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.9219 - loss: 0.2366 16/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.9197 - loss: 0.2422 17/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.9177 - loss: 0.2479 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.9158 - loss: 0.2532 19/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.9141 - loss: 0.2573 20/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.9128 - loss: 0.2606 21/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.9116 - loss: 0.2632 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 233ms/step - accuracy: 0.9104 - loss: 0.2657 23/100 ━━━━━━━━━━━━━━━━━━━━ 17s 233ms/step - accuracy: 0.9090 - loss: 0.2681 24/100 ━━━━━━━━━━━━━━━━━━━━ 17s 233ms/step - accuracy: 0.9077 - loss: 0.2703 25/100 ━━━━━━━━━━━━━━━━━━━━ 17s 233ms/step - accuracy: 0.9065 - loss: 0.2720 26/100 ━━━━━━━━━━━━━━━━━━━━ 17s 233ms/step - accuracy: 0.9055 - loss: 0.2733 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 233ms/step - accuracy: 0.9045 - loss: 0.2745 28/100 ━━━━━━━━━━━━━━━━━━━━ 16s 233ms/step - accuracy: 0.9036 - loss: 0.2756 29/100 ━━━━━━━━━━━━━━━━━━━━ 16s 233ms/step - accuracy: 0.9028 - loss: 0.2763 30/100 ━━━━━━━━━━━━━━━━━━━━ 16s 233ms/step - accuracy: 0.9022 - loss: 0.2771 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 233ms/step - accuracy: 0.9017 - loss: 0.2775 32/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.9013 - loss: 0.2778 33/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.9009 - loss: 0.2781 34/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.9006 - loss: 0.2785 35/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.9005 - loss: 0.2787 36/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.9003 - loss: 0.2789 37/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.9002 - loss: 0.2789 38/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.9001 - loss: 0.2787 39/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.9000 - loss: 0.2786 40/100 ━━━━━━━━━━━━━━━━━━━━ 13s 232ms/step - accuracy: 0.9000 - loss: 0.2784 41/100 ━━━━━━━━━━━━━━━━━━━━ 13s 232ms/step - accuracy: 0.8999 - loss: 0.2781 42/100 ━━━━━━━━━━━━━━━━━━━━ 13s 232ms/step - accuracy: 0.8999 - loss: 0.2778 43/100 ━━━━━━━━━━━━━━━━━━━━ 13s 232ms/step - accuracy: 0.9000 - loss: 0.2775 44/100 ━━━━━━━━━━━━━━━━━━━━ 13s 233ms/step - accuracy: 0.9000 - loss: 0.2774 45/100 ━━━━━━━━━━━━━━━━━━━━ 12s 232ms/step - accuracy: 0.9000 - loss: 0.2772 46/100 ━━━━━━━━━━━━━━━━━━━━ 12s 232ms/step - accuracy: 0.9000 - loss: 0.2769 47/100 ━━━━━━━━━━━━━━━━━━━━ 12s 232ms/step - accuracy: 0.9000 - loss: 0.2768 48/100 ━━━━━━━━━━━━━━━━━━━━ 12s 232ms/step - accuracy: 0.9000 - loss: 0.2767 49/100 ━━━━━━━━━━━━━━━━━━━━ 11s 232ms/step - accuracy: 0.9000 - loss: 0.2768 50/100 ━━━━━━━━━━━━━━━━━━━━ 11s 232ms/step - accuracy: 0.8999 - loss: 0.2769 51/100 ━━━━━━━━━━━━━━━━━━━━ 11s 232ms/step - accuracy: 0.8999 - loss: 0.2769 52/100 ━━━━━━━━━━━━━━━━━━━━ 11s 232ms/step - accuracy: 0.8999 - loss: 0.2769 53/100 ━━━━━━━━━━━━━━━━━━━━ 10s 232ms/step - accuracy: 0.9000 - loss: 0.2768 54/100 ━━━━━━━━━━━━━━━━━━━━ 10s 232ms/step - accuracy: 0.9000 - loss: 0.2767 55/100 ━━━━━━━━━━━━━━━━━━━━ 10s 232ms/step - accuracy: 0.9000 - loss: 0.2765 56/100 ━━━━━━━━━━━━━━━━━━━━ 10s 232ms/step - accuracy: 0.9001 - loss: 0.2763 57/100 ━━━━━━━━━━━━━━━━━━━━ 9s 232ms/step - accuracy: 0.9002 - loss: 0.2760  58/100 ━━━━━━━━━━━━━━━━━━━━ 9s 232ms/step - accuracy: 0.9003 - loss: 0.2757 59/100 ━━━━━━━━━━━━━━━━━━━━ 9s 232ms/step - accuracy: 0.9004 - loss: 0.2753 60/100 ━━━━━━━━━━━━━━━━━━━━ 9s 232ms/step - accuracy: 0.9006 - loss: 0.2750 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 232ms/step - accuracy: 0.9007 - loss: 0.2746 62/100 ━━━━━━━━━━━━━━━━━━━━ 8s 232ms/step - accuracy: 0.9008 - loss: 0.2745 63/100 ━━━━━━━━━━━━━━━━━━━━ 8s 232ms/step - accuracy: 0.9009 - loss: 0.2743 64/100 ━━━━━━━━━━━━━━━━━━━━ 8s 232ms/step - accuracy: 0.9010 - loss: 0.2741 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 232ms/step - accuracy: 0.9011 - loss: 0.2739 66/100 ━━━━━━━━━━━━━━━━━━━━ 7s 232ms/step - accuracy: 0.9012 - loss: 0.2737 67/100 ━━━━━━━━━━━━━━━━━━━━ 7s 232ms/step - accuracy: 0.9013 - loss: 0.2734 68/100 ━━━━━━━━━━━━━━━━━━━━ 7s 232ms/step - accuracy: 0.9014 - loss: 0.2732 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 232ms/step - accuracy: 0.9014 - loss: 0.2730 70/100 ━━━━━━━━━━━━━━━━━━━━ 6s 232ms/step - accuracy: 0.9015 - loss: 0.2727 71/100 ━━━━━━━━━━━━━━━━━━━━ 6s 232ms/step - accuracy: 0.9016 - loss: 0.2724 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 232ms/step - accuracy: 0.9017 - loss: 0.2721 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 232ms/step - accuracy: 0.9018 - loss: 0.2718 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 232ms/step - accuracy: 0.9019 - loss: 0.2716 75/100 ━━━━━━━━━━━━━━━━━━━━ 5s 233ms/step - accuracy: 0.9019 - loss: 0.2714 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 233ms/step - accuracy: 0.9020 - loss: 0.2713 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 233ms/step - accuracy: 0.9021 - loss: 0.2712 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 233ms/step - accuracy: 0.9021 - loss: 0.2710 79/100 ━━━━━━━━━━━━━━━━━━━━ 4s 233ms/step - accuracy: 0.9022 - loss: 0.2709 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 233ms/step - accuracy: 0.9022 - loss: 0.2708 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 233ms/step - accuracy: 0.9023 - loss: 0.2708 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 233ms/step - accuracy: 0.9023 - loss: 0.2707 83/100 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.9024 - loss: 0.2706 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.9024 - loss: 0.2705 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.9025 - loss: 0.2705 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.9025 - loss: 0.2704 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 233ms/step - accuracy: 0.9026 - loss: 0.2704 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 233ms/step - accuracy: 0.9026 - loss: 0.2703 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 233ms/step - accuracy: 0.9026 - loss: 0.2702 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 233ms/step - accuracy: 0.9027 - loss: 0.2701 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 233ms/step - accuracy: 0.9027 - loss: 0.2700 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 233ms/step - accuracy: 0.9027 - loss: 0.2699 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 233ms/step - accuracy: 0.9028 - loss: 0.2698 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 233ms/step - accuracy: 0.9028 - loss: 0.2696 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 233ms/step - accuracy: 0.9028 - loss: 0.2695 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.9028 - loss: 0.2693 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.9029 - loss: 0.2692 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.9029 - loss: 0.2690 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.9029 - loss: 0.2689100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 233ms/step - accuracy: 0.9029 - loss: 0.2687100/100 ━━━━━━━━━━━━━━━━━━━━ 30s 302ms/step - accuracy: 0.9029 - loss: 0.2686 - val_accuracy: 0.9320 - val_loss: 0.1914
Epoch 3/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 23s 239ms/step - accuracy: 0.9000 - loss: 0.4867  2/100 ━━━━━━━━━━━━━━━━━━━━ 22s 233ms/step - accuracy: 0.9125 - loss: 0.3922  3/100 ━━━━━━━━━━━━━━━━━━━━ 22s 236ms/step - accuracy: 0.8972 - loss: 0.3844  4/100 ━━━━━━━━━━━━━━━━━━━━ 22s 235ms/step - accuracy: 0.8854 - loss: 0.3772  5/100 ━━━━━━━━━━━━━━━━━━━━ 22s 235ms/step - accuracy: 0.8823 - loss: 0.3640  6/100 ━━━━━━━━━━━━━━━━━━━━ 21s 233ms/step - accuracy: 0.8783 - loss: 0.3591  7/100 ━━━━━━━━━━━━━━━━━━━━ 21s 234ms/step - accuracy: 0.8753 - loss: 0.3553  8/100 ━━━━━━━━━━━━━━━━━━━━ 21s 234ms/step - accuracy: 0.8745 - loss: 0.3536  9/100 ━━━━━━━━━━━━━━━━━━━━ 21s 234ms/step - accuracy: 0.8730 - loss: 0.3533 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 234ms/step - accuracy: 0.8727 - loss: 0.3516 11/100 ━━━━━━━━━━━━━━━━━━━━ 20s 234ms/step - accuracy: 0.8731 - loss: 0.3485 12/100 ━━━━━━━━━━━━━━━━━━━━ 20s 233ms/step - accuracy: 0.8740 - loss: 0.3456 13/100 ━━━━━━━━━━━━━━━━━━━━ 20s 233ms/step - accuracy: 0.8745 - loss: 0.3434 14/100 ━━━━━━━━━━━━━━━━━━━━ 20s 234ms/step - accuracy: 0.8740 - loss: 0.3447 15/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.8735 - loss: 0.3455 16/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.8734 - loss: 0.3453 17/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.8738 - loss: 0.3440 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 234ms/step - accuracy: 0.8743 - loss: 0.3423 19/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.8746 - loss: 0.3416 20/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.8747 - loss: 0.3413 21/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.8750 - loss: 0.3412 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.8755 - loss: 0.3406 23/100 ━━━━━━━━━━━━━━━━━━━━ 18s 234ms/step - accuracy: 0.8759 - loss: 0.3405 24/100 ━━━━━━━━━━━━━━━━━━━━ 17s 234ms/step - accuracy: 0.8765 - loss: 0.3400 25/100 ━━━━━━━━━━━━━━━━━━━━ 17s 234ms/step - accuracy: 0.8771 - loss: 0.3392 26/100 ━━━━━━━━━━━━━━━━━━━━ 17s 234ms/step - accuracy: 0.8778 - loss: 0.3385 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 234ms/step - accuracy: 0.8785 - loss: 0.3375 28/100 ━━━━━━━━━━━━━━━━━━━━ 16s 234ms/step - accuracy: 0.8792 - loss: 0.3366 29/100 ━━━━━━━━━━━━━━━━━━━━ 16s 234ms/step - accuracy: 0.8798 - loss: 0.3358 30/100 ━━━━━━━━━━━━━━━━━━━━ 16s 233ms/step - accuracy: 0.8805 - loss: 0.3349 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 234ms/step - accuracy: 0.8811 - loss: 0.3339 32/100 ━━━━━━━━━━━━━━━━━━━━ 15s 234ms/step - accuracy: 0.8817 - loss: 0.3330 33/100 ━━━━━━━━━━━━━━━━━━━━ 15s 234ms/step - accuracy: 0.8821 - loss: 0.3326 34/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.8825 - loss: 0.3320 35/100 ━━━━━━━━━━━━━━━━━━━━ 15s 233ms/step - accuracy: 0.8829 - loss: 0.3313 36/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.8833 - loss: 0.3305 37/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.8837 - loss: 0.3295 38/100 ━━━━━━━━━━━━━━━━━━━━ 14s 233ms/step - accuracy: 0.8840 - loss: 0.3286 39/100 ━━━━━━━━━━━━━━━━━━━━ 14s 234ms/step - accuracy: 0.8843 - loss: 0.3277 40/100 ━━━━━━━━━━━━━━━━━━━━ 14s 234ms/step - accuracy: 0.8846 - loss: 0.3266 41/100 ━━━━━━━━━━━━━━━━━━━━ 13s 233ms/step - accuracy: 0.8849 - loss: 0.3256 42/100 ━━━━━━━━━━━━━━━━━━━━ 13s 234ms/step - accuracy: 0.8852 - loss: 0.3248 43/100 ━━━━━━━━━━━━━━━━━━━━ 13s 234ms/step - accuracy: 0.8853 - loss: 0.3243 44/100 ━━━━━━━━━━━━━━━━━━━━ 13s 234ms/step - accuracy: 0.8856 - loss: 0.3236 45/100 ━━━━━━━━━━━━━━━━━━━━ 12s 234ms/step - accuracy: 0.8858 - loss: 0.3230 46/100 ━━━━━━━━━━━━━━━━━━━━ 12s 234ms/step - accuracy: 0.8859 - loss: 0.3224 47/100 ━━━━━━━━━━━━━━━━━━━━ 12s 234ms/step - accuracy: 0.8861 - loss: 0.3219 48/100 ━━━━━━━━━━━━━━━━━━━━ 12s 234ms/step - accuracy: 0.8862 - loss: 0.3213 49/100 ━━━━━━━━━━━━━━━━━━━━ 11s 234ms/step - accuracy: 0.8864 - loss: 0.3208 50/100 ━━━━━━━━━━━━━━━━━━━━ 11s 234ms/step - accuracy: 0.8865 - loss: 0.3202 51/100 ━━━━━━━━━━━━━━━━━━━━ 11s 234ms/step - accuracy: 0.8866 - loss: 0.3196 52/100 ━━━━━━━━━━━━━━━━━━━━ 11s 234ms/step - accuracy: 0.8867 - loss: 0.3190 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 234ms/step - accuracy: 0.8869 - loss: 0.3183 54/100 ━━━━━━━━━━━━━━━━━━━━ 10s 234ms/step - accuracy: 0.8871 - loss: 0.3176 55/100 ━━━━━━━━━━━━━━━━━━━━ 10s 234ms/step - accuracy: 0.8873 - loss: 0.3170 56/100 ━━━━━━━━━━━━━━━━━━━━ 10s 234ms/step - accuracy: 0.8874 - loss: 0.3163 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 234ms/step - accuracy: 0.8876 - loss: 0.3158 58/100 ━━━━━━━━━━━━━━━━━━━━ 9s 234ms/step - accuracy: 0.8877 - loss: 0.3153  59/100 ━━━━━━━━━━━━━━━━━━━━ 9s 234ms/step - accuracy: 0.8878 - loss: 0.3147 60/100 ━━━━━━━━━━━━━━━━━━━━ 9s 234ms/step - accuracy: 0.8880 - loss: 0.3142 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 234ms/step - accuracy: 0.8881 - loss: 0.3137 62/100 ━━━━━━━━━━━━━━━━━━━━ 8s 234ms/step - accuracy: 0.8882 - loss: 0.3131 63/100 ━━━━━━━━━━━━━━━━━━━━ 8s 234ms/step - accuracy: 0.8883 - loss: 0.3125 64/100 ━━━━━━━━━━━━━━━━━━━━ 8s 234ms/step - accuracy: 0.8885 - loss: 0.3119 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 234ms/step - accuracy: 0.8886 - loss: 0.3113 66/100 ━━━━━━━━━━━━━━━━━━━━ 7s 234ms/step - accuracy: 0.8888 - loss: 0.3107 67/100 ━━━━━━━━━━━━━━━━━━━━ 7s 234ms/step - accuracy: 0.8889 - loss: 0.3101 68/100 ━━━━━━━━━━━━━━━━━━━━ 7s 234ms/step - accuracy: 0.8891 - loss: 0.3094 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 234ms/step - accuracy: 0.8893 - loss: 0.3088 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 234ms/step - accuracy: 0.8895 - loss: 0.3081 71/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.8896 - loss: 0.3076 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.8898 - loss: 0.3070 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.8899 - loss: 0.3064 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 235ms/step - accuracy: 0.8901 - loss: 0.3058 75/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.8903 - loss: 0.3053 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.8904 - loss: 0.3048 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.8905 - loss: 0.3043 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 235ms/step - accuracy: 0.8906 - loss: 0.3039 79/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.8908 - loss: 0.3035 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.8909 - loss: 0.3030 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.8910 - loss: 0.3026 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 235ms/step - accuracy: 0.8912 - loss: 0.3021 83/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.8913 - loss: 0.3017 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.8914 - loss: 0.3013 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.8915 - loss: 0.3009 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.8916 - loss: 0.3004 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 235ms/step - accuracy: 0.8918 - loss: 0.3000 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.8919 - loss: 0.2996 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.8921 - loss: 0.2991 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.8922 - loss: 0.2986 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 235ms/step - accuracy: 0.8923 - loss: 0.2982 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.8925 - loss: 0.2977 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.8926 - loss: 0.2973 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.8927 - loss: 0.2968 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 235ms/step - accuracy: 0.8928 - loss: 0.2964 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.8930 - loss: 0.2959 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.8931 - loss: 0.2955 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.8932 - loss: 0.2951 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.8934 - loss: 0.2946100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - accuracy: 0.8935 - loss: 0.2942100/100 ━━━━━━━━━━━━━━━━━━━━ 31s 306ms/step - accuracy: 0.8936 - loss: 0.2938 - val_accuracy: 0.9570 - val_loss: 0.1224
Epoch 4/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 25s 253ms/step - accuracy: 0.9000 - loss: 0.1417  2/100 ━━━━━━━━━━━━━━━━━━━━ 23s 235ms/step - accuracy: 0.9125 - loss: 0.1968  3/100 ━━━━━━━━━━━━━━━━━━━━ 22s 234ms/step - accuracy: 0.9139 - loss: 0.2158  4/100 ━━━━━━━━━━━━━━━━━━━━ 22s 236ms/step - accuracy: 0.9167 - loss: 0.2190  5/100 ━━━━━━━━━━━━━━━━━━━━ 22s 237ms/step - accuracy: 0.9193 - loss: 0.2155  6/100 ━━━━━━━━━━━━━━━━━━━━ 22s 237ms/step - accuracy: 0.9203 - loss: 0.2141  7/100 ━━━━━━━━━━━━━━━━━━━━ 22s 237ms/step - accuracy: 0.9204 - loss: 0.2129  8/100 ━━━━━━━━━━━━━━━━━━━━ 21s 238ms/step - accuracy: 0.9210 - loss: 0.2119  9/100 ━━━━━━━━━━━━━━━━━━━━ 21s 237ms/step - accuracy: 0.9211 - loss: 0.2140 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 237ms/step - accuracy: 0.9210 - loss: 0.2159 11/100 ━━━━━━━━━━━━━━━━━━━━ 21s 237ms/step - accuracy: 0.9212 - loss: 0.2175 12/100 ━━━━━━━━━━━━━━━━━━━━ 20s 236ms/step - accuracy: 0.9212 - loss: 0.2193 13/100 ━━━━━━━━━━━━━━━━━━━━ 20s 236ms/step - accuracy: 0.9207 - loss: 0.2214 14/100 ━━━━━━━━━━━━━━━━━━━━ 20s 236ms/step - accuracy: 0.9200 - loss: 0.2228 15/100 ━━━━━━━━━━━━━━━━━━━━ 20s 236ms/step - accuracy: 0.9198 - loss: 0.2232 16/100 ━━━━━━━━━━━━━━━━━━━━ 19s 236ms/step - accuracy: 0.9193 - loss: 0.2235 17/100 ━━━━━━━━━━━━━━━━━━━━ 19s 236ms/step - accuracy: 0.9182 - loss: 0.2254 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 236ms/step - accuracy: 0.9169 - loss: 0.2272 19/100 ━━━━━━━━━━━━━━━━━━━━ 19s 236ms/step - accuracy: 0.9158 - loss: 0.2284 20/100 ━━━━━━━━━━━━━━━━━━━━ 18s 236ms/step - accuracy: 0.9149 - loss: 0.2291 21/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.9143 - loss: 0.2294 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.9137 - loss: 0.2302 23/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.9132 - loss: 0.2307 24/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.9128 - loss: 0.2309 25/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.9124 - loss: 0.2312 26/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.9121 - loss: 0.2314 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.9119 - loss: 0.2314 28/100 ━━━━━━━━━━━━━━━━━━━━ 17s 237ms/step - accuracy: 0.9116 - loss: 0.2313 29/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.9115 - loss: 0.2311 30/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.9114 - loss: 0.2307 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 237ms/step - accuracy: 0.9114 - loss: 0.2302 32/100 ━━━━━━━━━━━━━━━━━━━━ 16s 236ms/step - accuracy: 0.9113 - loss: 0.2298 33/100 ━━━━━━━━━━━━━━━━━━━━ 15s 236ms/step - accuracy: 0.9111 - loss: 0.2295 34/100 ━━━━━━━━━━━━━━━━━━━━ 15s 236ms/step - accuracy: 0.9109 - loss: 0.2292 35/100 ━━━━━━━━━━━━━━━━━━━━ 15s 237ms/step - accuracy: 0.9108 - loss: 0.2290 36/100 ━━━━━━━━━━━━━━━━━━━━ 15s 236ms/step - accuracy: 0.9105 - loss: 0.2290 37/100 ━━━━━━━━━━━━━━━━━━━━ 14s 236ms/step - accuracy: 0.9103 - loss: 0.2289 38/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.9101 - loss: 0.2289 39/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.9098 - loss: 0.2290 40/100 ━━━━━━━━━━━━━━━━━━━━ 14s 237ms/step - accuracy: 0.9097 - loss: 0.2289 41/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.9095 - loss: 0.2289 42/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.9093 - loss: 0.2290 43/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.9092 - loss: 0.2291 44/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.9091 - loss: 0.2290 45/100 ━━━━━━━━━━━━━━━━━━━━ 13s 237ms/step - accuracy: 0.9090 - loss: 0.2289 46/100 ━━━━━━━━━━━━━━━━━━━━ 12s 236ms/step - accuracy: 0.9089 - loss: 0.2287 47/100 ━━━━━━━━━━━━━━━━━━━━ 12s 237ms/step - accuracy: 0.9089 - loss: 0.2286 48/100 ━━━━━━━━━━━━━━━━━━━━ 12s 237ms/step - accuracy: 0.9088 - loss: 0.2285 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 237ms/step - accuracy: 0.9087 - loss: 0.2284 50/100 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - accuracy: 0.9086 - loss: 0.2283 51/100 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - accuracy: 0.9086 - loss: 0.2282 52/100 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - accuracy: 0.9087 - loss: 0.2279 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - accuracy: 0.9087 - loss: 0.2277 54/100 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - accuracy: 0.9087 - loss: 0.2275 55/100 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - accuracy: 0.9087 - loss: 0.2275 56/100 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - accuracy: 0.9086 - loss: 0.2276 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - accuracy: 0.9086 - loss: 0.2276 58/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.9085 - loss: 0.2277  59/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.9085 - loss: 0.2277 60/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.9085 - loss: 0.2277 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.9084 - loss: 0.2277 62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.9084 - loss: 0.2278 63/100 ━━━━━━━━━━━━━━━━━━━━ 8s 237ms/step - accuracy: 0.9084 - loss: 0.2279 64/100 ━━━━━━━━━━━━━━━━━━━━ 8s 238ms/step - accuracy: 0.9083 - loss: 0.2281 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 238ms/step - accuracy: 0.9083 - loss: 0.2282 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 239ms/step - accuracy: 0.9083 - loss: 0.2283 67/100 ━━━━━━━━━━━━━━━━━━━━ 7s 239ms/step - accuracy: 0.9082 - loss: 0.2284 68/100 ━━━━━━━━━━━━━━━━━━━━ 7s 239ms/step - accuracy: 0.9082 - loss: 0.2285 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 239ms/step - accuracy: 0.9082 - loss: 0.2285 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 239ms/step - accuracy: 0.9082 - loss: 0.2285 71/100 ━━━━━━━━━━━━━━━━━━━━ 6s 239ms/step - accuracy: 0.9082 - loss: 0.2286 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 239ms/step - accuracy: 0.9081 - loss: 0.2287 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 239ms/step - accuracy: 0.9080 - loss: 0.2287 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 239ms/step - accuracy: 0.9079 - loss: 0.2288 75/100 ━━━━━━━━━━━━━━━━━━━━ 5s 239ms/step - accuracy: 0.9078 - loss: 0.2289 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 238ms/step - accuracy: 0.9077 - loss: 0.2290 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 238ms/step - accuracy: 0.9076 - loss: 0.2291 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 238ms/step - accuracy: 0.9076 - loss: 0.2292 79/100 ━━━━━━━━━━━━━━━━━━━━ 4s 238ms/step - accuracy: 0.9075 - loss: 0.2293 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 238ms/step - accuracy: 0.9074 - loss: 0.2293 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 238ms/step - accuracy: 0.9073 - loss: 0.2294 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 238ms/step - accuracy: 0.9073 - loss: 0.2294 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 238ms/step - accuracy: 0.9072 - loss: 0.2295 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 238ms/step - accuracy: 0.9071 - loss: 0.2295 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 238ms/step - accuracy: 0.9071 - loss: 0.2295 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 237ms/step - accuracy: 0.9070 - loss: 0.2296 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 237ms/step - accuracy: 0.9070 - loss: 0.2296 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 237ms/step - accuracy: 0.9070 - loss: 0.2296 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 237ms/step - accuracy: 0.9070 - loss: 0.2296 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 237ms/step - accuracy: 0.9070 - loss: 0.2295 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 237ms/step - accuracy: 0.9070 - loss: 0.2295 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 237ms/step - accuracy: 0.9070 - loss: 0.2295 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 237ms/step - accuracy: 0.9070 - loss: 0.2294 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 237ms/step - accuracy: 0.9070 - loss: 0.2293 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 237ms/step - accuracy: 0.9070 - loss: 0.2292 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 237ms/step - accuracy: 0.9070 - loss: 0.2292 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 236ms/step - accuracy: 0.9071 - loss: 0.2291 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 236ms/step - accuracy: 0.9071 - loss: 0.2290 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 236ms/step - accuracy: 0.9071 - loss: 0.2290100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 236ms/step - accuracy: 0.9071 - loss: 0.2290100/100 ━━━━━━━━━━━━━━━━━━━━ 30s 305ms/step - accuracy: 0.9072 - loss: 0.2289 - val_accuracy: 0.9640 - val_loss: 0.1050
Epoch 5/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 23s 239ms/step - accuracy: 1.0000 - loss: 0.0107  2/100 ━━━━━━━━━━━━━━━━━━━━ 22s 234ms/step - accuracy: 0.9500 - loss: 0.1417  3/100 ━━━━━━━━━━━━━━━━━━━━ 22s 232ms/step - accuracy: 0.9333 - loss: 0.1711  4/100 ━━━━━━━━━━━━━━━━━━━━ 22s 231ms/step - accuracy: 0.9250 - loss: 0.1840  5/100 ━━━━━━━━━━━━━━━━━━━━ 21s 231ms/step - accuracy: 0.9160 - loss: 0.2029  6/100 ━━━━━━━━━━━━━━━━━━━━ 21s 232ms/step - accuracy: 0.9078 - loss: 0.2264  7/100 ━━━━━━━━━━━━━━━━━━━━ 21s 232ms/step - accuracy: 0.9046 - loss: 0.2362  8/100 ━━━━━━━━━━━━━━━━━━━━ 21s 233ms/step - accuracy: 0.9025 - loss: 0.2435  9/100 ━━━━━━━━━━━━━━━━━━━━ 22s 247ms/step - accuracy: 0.8985 - loss: 0.2556 10/100 ━━━━━━━━━━━━━━━━━━━━ 22s 250ms/step - accuracy: 0.8942 - loss: 0.2676 11/100 ━━━━━━━━━━━━━━━━━━━━ 22s 249ms/step - accuracy: 0.8901 - loss: 0.2775 12/100 ━━━━━━━━━━━━━━━━━━━━ 21s 248ms/step - accuracy: 0.8878 - loss: 0.2833 13/100 ━━━━━━━━━━━━━━━━━━━━ 21s 247ms/step - accuracy: 0.8867 - loss: 0.2864 14/100 ━━━━━━━━━━━━━━━━━━━━ 21s 246ms/step - accuracy: 0.8856 - loss: 0.2892 15/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.8850 - loss: 0.2908 16/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.8844 - loss: 0.2922 17/100 ━━━━━━━━━━━━━━━━━━━━ 20s 243ms/step - accuracy: 0.8841 - loss: 0.2928 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 243ms/step - accuracy: 0.8839 - loss: 0.2932 19/100 ━━━━━━━━━━━━━━━━━━━━ 19s 242ms/step - accuracy: 0.8839 - loss: 0.2931 20/100 ━━━━━━━━━━━━━━━━━━━━ 19s 242ms/step - accuracy: 0.8842 - loss: 0.2923 21/100 ━━━━━━━━━━━━━━━━━━━━ 19s 241ms/step - accuracy: 0.8844 - loss: 0.2918 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 241ms/step - accuracy: 0.8845 - loss: 0.2920 23/100 ━━━━━━━━━━━━━━━━━━━━ 18s 240ms/step - accuracy: 0.8847 - loss: 0.2917 24/100 ━━━━━━━━━━━━━━━━━━━━ 18s 240ms/step - accuracy: 0.8850 - loss: 0.2911 25/100 ━━━━━━━━━━━━━━━━━━━━ 17s 240ms/step - accuracy: 0.8854 - loss: 0.2902 26/100 ━━━━━━━━━━━━━━━━━━━━ 17s 239ms/step - accuracy: 0.8858 - loss: 0.2892 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 239ms/step - accuracy: 0.8863 - loss: 0.2881 28/100 ━━━━━━━━━━━━━━━━━━━━ 17s 239ms/step - accuracy: 0.8868 - loss: 0.2869 29/100 ━━━━━━━━━━━━━━━━━━━━ 16s 239ms/step - accuracy: 0.8872 - loss: 0.2859 30/100 ━━━━━━━━━━━━━━━━━━━━ 16s 239ms/step - accuracy: 0.8877 - loss: 0.2846 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 239ms/step - accuracy: 0.8881 - loss: 0.2836 32/100 ━━━━━━━━━━━━━━━━━━━━ 16s 239ms/step - accuracy: 0.8883 - loss: 0.2829 33/100 ━━━━━━━━━━━━━━━━━━━━ 15s 238ms/step - accuracy: 0.8886 - loss: 0.2823 34/100 ━━━━━━━━━━━━━━━━━━━━ 15s 238ms/step - accuracy: 0.8888 - loss: 0.2817 35/100 ━━━━━━━━━━━━━━━━━━━━ 15s 238ms/step - accuracy: 0.8892 - loss: 0.2809 36/100 ━━━━━━━━━━━━━━━━━━━━ 15s 238ms/step - accuracy: 0.8895 - loss: 0.2801 37/100 ━━━━━━━━━━━━━━━━━━━━ 15s 238ms/step - accuracy: 0.8899 - loss: 0.2792 38/100 ━━━━━━━━━━━━━━━━━━━━ 14s 238ms/step - accuracy: 0.8902 - loss: 0.2784 39/100 ━━━━━━━━━━━━━━━━━━━━ 14s 238ms/step - accuracy: 0.8905 - loss: 0.2776 40/100 ━━━━━━━━━━━━━━━━━━━━ 14s 238ms/step - accuracy: 0.8908 - loss: 0.2767 41/100 ━━━━━━━━━━━━━━━━━━━━ 14s 238ms/step - accuracy: 0.8912 - loss: 0.2759 42/100 ━━━━━━━━━━━━━━━━━━━━ 13s 238ms/step - accuracy: 0.8915 - loss: 0.2752 43/100 ━━━━━━━━━━━━━━━━━━━━ 13s 238ms/step - accuracy: 0.8918 - loss: 0.2744 44/100 ━━━━━━━━━━━━━━━━━━━━ 13s 238ms/step - accuracy: 0.8921 - loss: 0.2736 45/100 ━━━━━━━━━━━━━━━━━━━━ 13s 238ms/step - accuracy: 0.8924 - loss: 0.2728 46/100 ━━━━━━━━━━━━━━━━━━━━ 12s 238ms/step - accuracy: 0.8927 - loss: 0.2719 47/100 ━━━━━━━━━━━━━━━━━━━━ 12s 238ms/step - accuracy: 0.8930 - loss: 0.2711 48/100 ━━━━━━━━━━━━━━━━━━━━ 12s 238ms/step - accuracy: 0.8933 - loss: 0.2703 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 238ms/step - accuracy: 0.8936 - loss: 0.2694 50/100 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - accuracy: 0.8939 - loss: 0.2685 51/100 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - accuracy: 0.8942 - loss: 0.2676 52/100 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - accuracy: 0.8945 - loss: 0.2668 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - accuracy: 0.8948 - loss: 0.2659 54/100 ━━━━━━━━━━━━━━━━━━━━ 10s 238ms/step - accuracy: 0.8951 - loss: 0.2651 55/100 ━━━━━━━━━━━━━━━━━━━━ 10s 238ms/step - accuracy: 0.8954 - loss: 0.2643 56/100 ━━━━━━━━━━━━━━━━━━━━ 10s 238ms/step - accuracy: 0.8956 - loss: 0.2635 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 237ms/step - accuracy: 0.8959 - loss: 0.2627 58/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.8962 - loss: 0.2618  59/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.8965 - loss: 0.2610 60/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.8967 - loss: 0.2602 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.8970 - loss: 0.2594 62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 237ms/step - accuracy: 0.8973 - loss: 0.2586 63/100 ━━━━━━━━━━━━━━━━━━━━ 8s 237ms/step - accuracy: 0.8975 - loss: 0.2578 64/100 ━━━━━━━━━━━━━━━━━━━━ 8s 238ms/step - accuracy: 0.8978 - loss: 0.2570 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 237ms/step - accuracy: 0.8981 - loss: 0.2562 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 237ms/step - accuracy: 0.8983 - loss: 0.2554 67/100 ━━━━━━━━━━━━━━━━━━━━ 7s 237ms/step - accuracy: 0.8986 - loss: 0.2547 68/100 ━━━━━━━━━━━━━━━━━━━━ 7s 237ms/step - accuracy: 0.8988 - loss: 0.2540 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 237ms/step - accuracy: 0.8991 - loss: 0.2532 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 237ms/step - accuracy: 0.8993 - loss: 0.2525 71/100 ━━━━━━━━━━━━━━━━━━━━ 6s 237ms/step - accuracy: 0.8996 - loss: 0.2518 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 237ms/step - accuracy: 0.8998 - loss: 0.2511 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 237ms/step - accuracy: 0.9000 - loss: 0.2505 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 237ms/step - accuracy: 0.9002 - loss: 0.2500 75/100 ━━━━━━━━━━━━━━━━━━━━ 5s 237ms/step - accuracy: 0.9004 - loss: 0.2495 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 237ms/step - accuracy: 0.9006 - loss: 0.2490 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 238ms/step - accuracy: 0.9007 - loss: 0.2486 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 239ms/step - accuracy: 0.9009 - loss: 0.2481 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 240ms/step - accuracy: 0.9011 - loss: 0.2477 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 240ms/step - accuracy: 0.9012 - loss: 0.2473 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 240ms/step - accuracy: 0.9014 - loss: 0.2469 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 240ms/step - accuracy: 0.9015 - loss: 0.2464 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 240ms/step - accuracy: 0.9016 - loss: 0.2460 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 240ms/step - accuracy: 0.9018 - loss: 0.2456 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 240ms/step - accuracy: 0.9019 - loss: 0.2452 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 240ms/step - accuracy: 0.9021 - loss: 0.2448 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 240ms/step - accuracy: 0.9022 - loss: 0.2444 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 240ms/step - accuracy: 0.9024 - loss: 0.2440 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 240ms/step - accuracy: 0.9025 - loss: 0.2435 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 240ms/step - accuracy: 0.9027 - loss: 0.2432 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 240ms/step - accuracy: 0.9028 - loss: 0.2428 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 240ms/step - accuracy: 0.9029 - loss: 0.2425 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 240ms/step - accuracy: 0.9030 - loss: 0.2422 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 240ms/step - accuracy: 0.9031 - loss: 0.2419 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 240ms/step - accuracy: 0.9032 - loss: 0.2416 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9033 - loss: 0.2413 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9034 - loss: 0.2410 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9035 - loss: 0.2406 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9036 - loss: 0.2403100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 240ms/step - accuracy: 0.9037 - loss: 0.2400100/100 ━━━━━━━━━━━━━━━━━━━━ 31s 313ms/step - accuracy: 0.9038 - loss: 0.2397 - val_accuracy: 0.9650 - val_loss: 0.0877
Epoch 6/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 26s 268ms/step - accuracy: 0.9500 - loss: 0.1672  2/100 ━━━━━━━━━━━━━━━━━━━━ 26s 274ms/step - accuracy: 0.9625 - loss: 0.1346  3/100 ━━━━━━━━━━━━━━━━━━━━ 27s 281ms/step - accuracy: 0.9528 - loss: 0.1386  4/100 ━━━━━━━━━━━━━━━━━━━━ 26s 272ms/step - accuracy: 0.9490 - loss: 0.1405  5/100 ━━━━━━━━━━━━━━━━━━━━ 25s 272ms/step - accuracy: 0.9472 - loss: 0.1412  6/100 ━━━━━━━━━━━━━━━━━━━━ 26s 282ms/step - accuracy: 0.9476 - loss: 0.1381  7/100 ━━━━━━━━━━━━━━━━━━━━ 27s 293ms/step - accuracy: 0.9490 - loss: 0.1342  8/100 ━━━━━━━━━━━━━━━━━━━━ 26s 290ms/step - accuracy: 0.9499 - loss: 0.1328  9/100 ━━━━━━━━━━━━━━━━━━━━ 26s 288ms/step - accuracy: 0.9499 - loss: 0.1332 10/100 ━━━━━━━━━━━━━━━━━━━━ 25s 286ms/step - accuracy: 0.9494 - loss: 0.1358 11/100 ━━━━━━━━━━━━━━━━━━━━ 25s 282ms/step - accuracy: 0.9486 - loss: 0.1397 12/100 ━━━━━━━━━━━━━━━━━━━━ 24s 279ms/step - accuracy: 0.9481 - loss: 0.1428 13/100 ━━━━━━━━━━━━━━━━━━━━ 23s 276ms/step - accuracy: 0.9470 - loss: 0.1464 14/100 ━━━━━━━━━━━━━━━━━━━━ 23s 273ms/step - accuracy: 0.9462 - loss: 0.1489 15/100 ━━━━━━━━━━━━━━━━━━━━ 22s 270ms/step - accuracy: 0.9454 - loss: 0.1511 16/100 ━━━━━━━━━━━━━━━━━━━━ 22s 270ms/step - accuracy: 0.9447 - loss: 0.1527 17/100 ━━━━━━━━━━━━━━━━━━━━ 22s 268ms/step - accuracy: 0.9443 - loss: 0.1536 18/100 ━━━━━━━━━━━━━━━━━━━━ 21s 267ms/step - accuracy: 0.9440 - loss: 0.1545 19/100 ━━━━━━━━━━━━━━━━━━━━ 21s 266ms/step - accuracy: 0.9436 - loss: 0.1557 20/100 ━━━━━━━━━━━━━━━━━━━━ 21s 266ms/step - accuracy: 0.9433 - loss: 0.1565 21/100 ━━━━━━━━━━━━━━━━━━━━ 20s 266ms/step - accuracy: 0.9430 - loss: 0.1572 22/100 ━━━━━━━━━━━━━━━━━━━━ 20s 264ms/step - accuracy: 0.9428 - loss: 0.1575 23/100 ━━━━━━━━━━━━━━━━━━━━ 20s 264ms/step - accuracy: 0.9426 - loss: 0.1579 24/100 ━━━━━━━━━━━━━━━━━━━━ 20s 263ms/step - accuracy: 0.9426 - loss: 0.1582 25/100 ━━━━━━━━━━━━━━━━━━━━ 19s 262ms/step - accuracy: 0.9425 - loss: 0.1583 26/100 ━━━━━━━━━━━━━━━━━━━━ 19s 261ms/step - accuracy: 0.9425 - loss: 0.1588 27/100 ━━━━━━━━━━━━━━━━━━━━ 18s 260ms/step - accuracy: 0.9423 - loss: 0.1596 28/100 ━━━━━━━━━━━━━━━━━━━━ 18s 259ms/step - accuracy: 0.9420 - loss: 0.1609 29/100 ━━━━━━━━━━━━━━━━━━━━ 18s 258ms/step - accuracy: 0.9415 - loss: 0.1631 30/100 ━━━━━━━━━━━━━━━━━━━━ 18s 257ms/step - accuracy: 0.9411 - loss: 0.1651 31/100 ━━━━━━━━━━━━━━━━━━━━ 17s 256ms/step - accuracy: 0.9405 - loss: 0.1671 32/100 ━━━━━━━━━━━━━━━━━━━━ 17s 256ms/step - accuracy: 0.9400 - loss: 0.1689 33/100 ━━━━━━━━━━━━━━━━━━━━ 17s 256ms/step - accuracy: 0.9395 - loss: 0.1705 34/100 ━━━━━━━━━━━━━━━━━━━━ 16s 255ms/step - accuracy: 0.9391 - loss: 0.1719 35/100 ━━━━━━━━━━━━━━━━━━━━ 16s 255ms/step - accuracy: 0.9386 - loss: 0.1734 36/100 ━━━━━━━━━━━━━━━━━━━━ 16s 255ms/step - accuracy: 0.9383 - loss: 0.1748 37/100 ━━━━━━━━━━━━━━━━━━━━ 16s 255ms/step - accuracy: 0.9379 - loss: 0.1760 38/100 ━━━━━━━━━━━━━━━━━━━━ 15s 255ms/step - accuracy: 0.9376 - loss: 0.1770 39/100 ━━━━━━━━━━━━━━━━━━━━ 15s 255ms/step - accuracy: 0.9372 - loss: 0.1783 40/100 ━━━━━━━━━━━━━━━━━━━━ 15s 258ms/step - accuracy: 0.9368 - loss: 0.1795 41/100 ━━━━━━━━━━━━━━━━━━━━ 15s 259ms/step - accuracy: 0.9365 - loss: 0.1806 42/100 ━━━━━━━━━━━━━━━━━━━━ 14s 259ms/step - accuracy: 0.9361 - loss: 0.1817 43/100 ━━━━━━━━━━━━━━━━━━━━ 14s 259ms/step - accuracy: 0.9357 - loss: 0.1827 44/100 ━━━━━━━━━━━━━━━━━━━━ 14s 258ms/step - accuracy: 0.9354 - loss: 0.1836 45/100 ━━━━━━━━━━━━━━━━━━━━ 14s 258ms/step - accuracy: 0.9351 - loss: 0.1844 46/100 ━━━━━━━━━━━━━━━━━━━━ 13s 258ms/step - accuracy: 0.9347 - loss: 0.1852 47/100 ━━━━━━━━━━━━━━━━━━━━ 13s 258ms/step - accuracy: 0.9344 - loss: 0.1858 48/100 ━━━━━━━━━━━━━━━━━━━━ 13s 258ms/step - accuracy: 0.9341 - loss: 0.1865 49/100 ━━━━━━━━━━━━━━━━━━━━ 13s 259ms/step - accuracy: 0.9338 - loss: 0.1871 50/100 ━━━━━━━━━━━━━━━━━━━━ 12s 259ms/step - accuracy: 0.9336 - loss: 0.1876 51/100 ━━━━━━━━━━━━━━━━━━━━ 12s 258ms/step - accuracy: 0.9333 - loss: 0.1881 52/100 ━━━━━━━━━━━━━━━━━━━━ 12s 258ms/step - accuracy: 0.9330 - loss: 0.1886 53/100 ━━━━━━━━━━━━━━━━━━━━ 12s 259ms/step - accuracy: 0.9327 - loss: 0.1891 54/100 ━━━━━━━━━━━━━━━━━━━━ 11s 259ms/step - accuracy: 0.9325 - loss: 0.1895 55/100 ━━━━━━━━━━━━━━━━━━━━ 11s 259ms/step - accuracy: 0.9322 - loss: 0.1899 56/100 ━━━━━━━━━━━━━━━━━━━━ 11s 259ms/step - accuracy: 0.9320 - loss: 0.1904 57/100 ━━━━━━━━━━━━━━━━━━━━ 11s 259ms/step - accuracy: 0.9318 - loss: 0.1908 58/100 ━━━━━━━━━━━━━━━━━━━━ 10s 258ms/step - accuracy: 0.9315 - loss: 0.1911 59/100 ━━━━━━━━━━━━━━━━━━━━ 10s 258ms/step - accuracy: 0.9313 - loss: 0.1915 60/100 ━━━━━━━━━━━━━━━━━━━━ 10s 258ms/step - accuracy: 0.9311 - loss: 0.1918 61/100 ━━━━━━━━━━━━━━━━━━━━ 10s 258ms/step - accuracy: 0.9309 - loss: 0.1921 62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 258ms/step - accuracy: 0.9307 - loss: 0.1923  63/100 ━━━━━━━━━━━━━━━━━━━━ 9s 259ms/step - accuracy: 0.9306 - loss: 0.1925 64/100 ━━━━━━━━━━━━━━━━━━━━ 9s 260ms/step - accuracy: 0.9304 - loss: 0.1926 65/100 ━━━━━━━━━━━━━━━━━━━━ 9s 260ms/step - accuracy: 0.9302 - loss: 0.1928 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 260ms/step - accuracy: 0.9300 - loss: 0.1930 67/100 ━━━━━━━━━━━━━━━━━━━━ 8s 260ms/step - accuracy: 0.9299 - loss: 0.1932 68/100 ━━━━━━━━━━━━━━━━━━━━ 8s 259ms/step - accuracy: 0.9297 - loss: 0.1933 69/100 ━━━━━━━━━━━━━━━━━━━━ 8s 259ms/step - accuracy: 0.9295 - loss: 0.1935 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 259ms/step - accuracy: 0.9293 - loss: 0.1936 71/100 ━━━━━━━━━━━━━━━━━━━━ 7s 259ms/step - accuracy: 0.9292 - loss: 0.1938 72/100 ━━━━━━━━━━━━━━━━━━━━ 7s 259ms/step - accuracy: 0.9290 - loss: 0.1939 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9289 - loss: 0.1940 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9288 - loss: 0.1940 75/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9286 - loss: 0.1941 76/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9285 - loss: 0.1942 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 259ms/step - accuracy: 0.9284 - loss: 0.1943 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9283 - loss: 0.1943 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9282 - loss: 0.1943 80/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9281 - loss: 0.1944 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 258ms/step - accuracy: 0.9280 - loss: 0.1944 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9279 - loss: 0.1944 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9279 - loss: 0.1944 84/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9278 - loss: 0.1944 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 257ms/step - accuracy: 0.9277 - loss: 0.1944 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 257ms/step - accuracy: 0.9276 - loss: 0.1945 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 257ms/step - accuracy: 0.9275 - loss: 0.1946 88/100 ━━━━━━━━━━━━━━━━━━━━ 3s 256ms/step - accuracy: 0.9275 - loss: 0.1946 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 256ms/step - accuracy: 0.9274 - loss: 0.1947 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 256ms/step - accuracy: 0.9273 - loss: 0.1947 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 256ms/step - accuracy: 0.9272 - loss: 0.1947 92/100 ━━━━━━━━━━━━━━━━━━━━ 2s 256ms/step - accuracy: 0.9272 - loss: 0.1948 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9271 - loss: 0.1948 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9270 - loss: 0.1949 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9269 - loss: 0.1949 96/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9269 - loss: 0.1950 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 255ms/step - accuracy: 0.9268 - loss: 0.1951 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 255ms/step - accuracy: 0.9267 - loss: 0.1951 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 254ms/step - accuracy: 0.9267 - loss: 0.1952100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 254ms/step - accuracy: 0.9266 - loss: 0.1952100/100 ━━━━━━━━━━━━━━━━━━━━ 33s 327ms/step - accuracy: 0.9266 - loss: 0.1953 - val_accuracy: 0.9390 - val_loss: 0.1570
Epoch 7/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 25s 254ms/step - accuracy: 0.9000 - loss: 0.1678  2/100 ━━━━━━━━━━━━━━━━━━━━ 24s 253ms/step - accuracy: 0.8875 - loss: 0.2080  3/100 ━━━━━━━━━━━━━━━━━━━━ 25s 262ms/step - accuracy: 0.8917 - loss: 0.2037  4/100 ━━━━━━━━━━━━━━━━━━━━ 24s 256ms/step - accuracy: 0.9000 - loss: 0.1907  5/100 ━━━━━━━━━━━━━━━━━━━━ 23s 252ms/step - accuracy: 0.9060 - loss: 0.1810  6/100 ━━━━━━━━━━━━━━━━━━━━ 25s 267ms/step - accuracy: 0.9092 - loss: 0.1779  7/100 ━━━━━━━━━━━━━━━━━━━━ 25s 279ms/step - accuracy: 0.9079 - loss: 0.1822  8/100 ━━━━━━━━━━━━━━━━━━━━ 25s 278ms/step - accuracy: 0.9069 - loss: 0.1863  9/100 ━━━━━━━━━━━━━━━━━━━━ 25s 278ms/step - accuracy: 0.9061 - loss: 0.1925 10/100 ━━━━━━━━━━━━━━━━━━━━ 25s 281ms/step - accuracy: 0.9065 - loss: 0.1954 11/100 ━━━━━━━━━━━━━━━━━━━━ 25s 283ms/step - accuracy: 0.9063 - loss: 0.1999 12/100 ━━━━━━━━━━━━━━━━━━━━ 24s 282ms/step - accuracy: 0.9065 - loss: 0.2024 13/100 ━━━━━━━━━━━━━━━━━━━━ 24s 281ms/step - accuracy: 0.9066 - loss: 0.2046 14/100 ━━━━━━━━━━━━━━━━━━━━ 24s 281ms/step - accuracy: 0.9069 - loss: 0.2061 15/100 ━━━━━━━━━━━━━━━━━━━━ 23s 280ms/step - accuracy: 0.9069 - loss: 0.2075 16/100 ━━━━━━━━━━━━━━━━━━━━ 23s 280ms/step - accuracy: 0.9062 - loss: 0.2095 17/100 ━━━━━━━━━━━━━━━━━━━━ 23s 278ms/step - accuracy: 0.9059 - loss: 0.2112 18/100 ━━━━━━━━━━━━━━━━━━━━ 22s 280ms/step - accuracy: 0.9055 - loss: 0.2124 19/100 ━━━━━━━━━━━━━━━━━━━━ 22s 280ms/step - accuracy: 0.9050 - loss: 0.2135 20/100 ━━━━━━━━━━━━━━━━━━━━ 22s 279ms/step - accuracy: 0.9047 - loss: 0.2139 21/100 ━━━━━━━━━━━━━━━━━━━━ 21s 277ms/step - accuracy: 0.9044 - loss: 0.2151 22/100 ━━━━━━━━━━━━━━━━━━━━ 21s 275ms/step - accuracy: 0.9043 - loss: 0.2157 23/100 ━━━━━━━━━━━━━━━━━━━━ 21s 274ms/step - accuracy: 0.9044 - loss: 0.2159 24/100 ━━━━━━━━━━━━━━━━━━━━ 20s 272ms/step - accuracy: 0.9045 - loss: 0.2161 25/100 ━━━━━━━━━━━━━━━━━━━━ 20s 271ms/step - accuracy: 0.9044 - loss: 0.2164 26/100 ━━━━━━━━━━━━━━━━━━━━ 19s 270ms/step - accuracy: 0.9044 - loss: 0.2163 27/100 ━━━━━━━━━━━━━━━━━━━━ 19s 269ms/step - accuracy: 0.9045 - loss: 0.2165 28/100 ━━━━━━━━━━━━━━━━━━━━ 19s 267ms/step - accuracy: 0.9046 - loss: 0.2165 29/100 ━━━━━━━━━━━━━━━━━━━━ 18s 267ms/step - accuracy: 0.9046 - loss: 0.2165 30/100 ━━━━━━━━━━━━━━━━━━━━ 18s 266ms/step - accuracy: 0.9048 - loss: 0.2165 31/100 ━━━━━━━━━━━━━━━━━━━━ 18s 265ms/step - accuracy: 0.9049 - loss: 0.2164 32/100 ━━━━━━━━━━━━━━━━━━━━ 17s 264ms/step - accuracy: 0.9051 - loss: 0.2164 33/100 ━━━━━━━━━━━━━━━━━━━━ 17s 263ms/step - accuracy: 0.9052 - loss: 0.2165 34/100 ━━━━━━━━━━━━━━━━━━━━ 17s 262ms/step - accuracy: 0.9053 - loss: 0.2165 35/100 ━━━━━━━━━━━━━━━━━━━━ 17s 262ms/step - accuracy: 0.9055 - loss: 0.2164 36/100 ━━━━━━━━━━━━━━━━━━━━ 16s 261ms/step - accuracy: 0.9056 - loss: 0.2163 37/100 ━━━━━━━━━━━━━━━━━━━━ 16s 260ms/step - accuracy: 0.9058 - loss: 0.2161 38/100 ━━━━━━━━━━━━━━━━━━━━ 16s 260ms/step - accuracy: 0.9059 - loss: 0.2160 39/100 ━━━━━━━━━━━━━━━━━━━━ 15s 259ms/step - accuracy: 0.9060 - loss: 0.2158 40/100 ━━━━━━━━━━━━━━━━━━━━ 15s 259ms/step - accuracy: 0.9060 - loss: 0.2158 41/100 ━━━━━━━━━━━━━━━━━━━━ 15s 258ms/step - accuracy: 0.9060 - loss: 0.2158 42/100 ━━━━━━━━━━━━━━━━━━━━ 14s 258ms/step - accuracy: 0.9060 - loss: 0.2159 43/100 ━━━━━━━━━━━━━━━━━━━━ 14s 257ms/step - accuracy: 0.9060 - loss: 0.2158 44/100 ━━━━━━━━━━━━━━━━━━━━ 14s 257ms/step - accuracy: 0.9060 - loss: 0.2157 45/100 ━━━━━━━━━━━━━━━━━━━━ 14s 256ms/step - accuracy: 0.9061 - loss: 0.2157 46/100 ━━━━━━━━━━━━━━━━━━━━ 13s 256ms/step - accuracy: 0.9061 - loss: 0.2155 47/100 ━━━━━━━━━━━━━━━━━━━━ 13s 256ms/step - accuracy: 0.9063 - loss: 0.2153 48/100 ━━━━━━━━━━━━━━━━━━━━ 13s 255ms/step - accuracy: 0.9064 - loss: 0.2150 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 255ms/step - accuracy: 0.9064 - loss: 0.2150 50/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9065 - loss: 0.2149 51/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9066 - loss: 0.2148 52/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9067 - loss: 0.2147 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 254ms/step - accuracy: 0.9068 - loss: 0.2145 54/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9069 - loss: 0.2143 55/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9071 - loss: 0.2141 56/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9072 - loss: 0.2139 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 253ms/step - accuracy: 0.9073 - loss: 0.2137 58/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9074 - loss: 0.2136 59/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9076 - loss: 0.2133 60/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9077 - loss: 0.2131 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 252ms/step - accuracy: 0.9079 - loss: 0.2128  62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9080 - loss: 0.2125 63/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9082 - loss: 0.2123 64/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9083 - loss: 0.2120 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9085 - loss: 0.2117 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9086 - loss: 0.2114 67/100 ━━━━━━━━━━━━━━━━━━━━ 8s 250ms/step - accuracy: 0.9088 - loss: 0.2111 68/100 ━━━━━━━━━━━━━━━━━━━━ 8s 250ms/step - accuracy: 0.9089 - loss: 0.2107 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 250ms/step - accuracy: 0.9091 - loss: 0.2104 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 250ms/step - accuracy: 0.9093 - loss: 0.2101 71/100 ━━━━━━━━━━━━━━━━━━━━ 7s 250ms/step - accuracy: 0.9094 - loss: 0.2097 72/100 ━━━━━━━━━━━━━━━━━━━━ 6s 250ms/step - accuracy: 0.9096 - loss: 0.2094 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 249ms/step - accuracy: 0.9097 - loss: 0.2091 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 249ms/step - accuracy: 0.9099 - loss: 0.2088 75/100 ━━━━━━━━━━━━━━━━━━━━ 6s 249ms/step - accuracy: 0.9100 - loss: 0.2085 76/100 ━━━━━━━━━━━━━━━━━━━━ 5s 249ms/step - accuracy: 0.9101 - loss: 0.2083 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 249ms/step - accuracy: 0.9103 - loss: 0.2080 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 249ms/step - accuracy: 0.9104 - loss: 0.2077 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 248ms/step - accuracy: 0.9105 - loss: 0.2074 80/100 ━━━━━━━━━━━━━━━━━━━━ 4s 248ms/step - accuracy: 0.9107 - loss: 0.2071 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 248ms/step - accuracy: 0.9108 - loss: 0.2069 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 248ms/step - accuracy: 0.9109 - loss: 0.2066 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 248ms/step - accuracy: 0.9111 - loss: 0.2063 84/100 ━━━━━━━━━━━━━━━━━━━━ 3s 248ms/step - accuracy: 0.9112 - loss: 0.2060 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 247ms/step - accuracy: 0.9113 - loss: 0.2057 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 247ms/step - accuracy: 0.9114 - loss: 0.2055 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 247ms/step - accuracy: 0.9115 - loss: 0.2052 88/100 ━━━━━━━━━━━━━━━━━━━━ 2s 247ms/step - accuracy: 0.9117 - loss: 0.2050 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 247ms/step - accuracy: 0.9118 - loss: 0.2047 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 247ms/step - accuracy: 0.9119 - loss: 0.2044 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 247ms/step - accuracy: 0.9120 - loss: 0.2042 92/100 ━━━━━━━━━━━━━━━━━━━━ 1s 246ms/step - accuracy: 0.9121 - loss: 0.2039 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 246ms/step - accuracy: 0.9122 - loss: 0.2037 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 246ms/step - accuracy: 0.9124 - loss: 0.2034 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 246ms/step - accuracy: 0.9125 - loss: 0.2031 96/100 ━━━━━━━━━━━━━━━━━━━━ 0s 246ms/step - accuracy: 0.9126 - loss: 0.2029 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 246ms/step - accuracy: 0.9127 - loss: 0.2027 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 246ms/step - accuracy: 0.9129 - loss: 0.2024 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 246ms/step - accuracy: 0.9130 - loss: 0.2022100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 246ms/step - accuracy: 0.9131 - loss: 0.2020100/100 ━━━━━━━━━━━━━━━━━━━━ 32s 322ms/step - accuracy: 0.9132 - loss: 0.2019 - val_accuracy: 0.9650 - val_loss: 0.1167
Epoch 8/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 34s 352ms/step - accuracy: 0.8000 - loss: 0.2948  2/100 ━━━━━━━━━━━━━━━━━━━━ 24s 251ms/step - accuracy: 0.8500 - loss: 0.2281  3/100 ━━━━━━━━━━━━━━━━━━━━ 23s 242ms/step - accuracy: 0.8722 - loss: 0.1948  4/100 ━━━━━━━━━━━━━━━━━━━━ 23s 243ms/step - accuracy: 0.8854 - loss: 0.1758  5/100 ━━━━━━━━━━━━━━━━━━━━ 22s 240ms/step - accuracy: 0.8943 - loss: 0.1654  6/100 ━━━━━━━━━━━━━━━━━━━━ 22s 240ms/step - accuracy: 0.9008 - loss: 0.1623  7/100 ━━━━━━━━━━━━━━━━━━━━ 22s 239ms/step - accuracy: 0.9048 - loss: 0.1603  8/100 ━━━━━━━━━━━━━━━━━━━━ 22s 243ms/step - accuracy: 0.9073 - loss: 0.1605  9/100 ━━━━━━━━━━━━━━━━━━━━ 22s 244ms/step - accuracy: 0.9077 - loss: 0.1638 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 244ms/step - accuracy: 0.9085 - loss: 0.1665 11/100 ━━━━━━━━━━━━━━━━━━━━ 21s 246ms/step - accuracy: 0.9089 - loss: 0.1701 12/100 ━━━━━━━━━━━━━━━━━━━━ 21s 245ms/step - accuracy: 0.9092 - loss: 0.1747 13/100 ━━━━━━━━━━━━━━━━━━━━ 21s 247ms/step - accuracy: 0.9097 - loss: 0.1785 14/100 ━━━━━━━━━━━━━━━━━━━━ 21s 246ms/step - accuracy: 0.9098 - loss: 0.1821 15/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.9102 - loss: 0.1843 16/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.9108 - loss: 0.1856 17/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.9112 - loss: 0.1872 18/100 ━━━━━━━━━━━━━━━━━━━━ 20s 245ms/step - accuracy: 0.9113 - loss: 0.1889 19/100 ━━━━━━━━━━━━━━━━━━━━ 19s 245ms/step - accuracy: 0.9116 - loss: 0.1902 20/100 ━━━━━━━━━━━━━━━━━━━━ 19s 247ms/step - accuracy: 0.9117 - loss: 0.1915 21/100 ━━━━━━━━━━━━━━━━━━━━ 19s 248ms/step - accuracy: 0.9119 - loss: 0.1925 22/100 ━━━━━━━━━━━━━━━━━━━━ 19s 247ms/step - accuracy: 0.9120 - loss: 0.1935 23/100 ━━━━━━━━━━━━━━━━━━━━ 19s 247ms/step - accuracy: 0.9122 - loss: 0.1943 24/100 ━━━━━━━━━━━━━━━━━━━━ 18s 248ms/step - accuracy: 0.9123 - loss: 0.1951 25/100 ━━━━━━━━━━━━━━━━━━━━ 18s 247ms/step - accuracy: 0.9123 - loss: 0.1961 26/100 ━━━━━━━━━━━━━━━━━━━━ 18s 247ms/step - accuracy: 0.9123 - loss: 0.1970 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9124 - loss: 0.1976 28/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9125 - loss: 0.1981 29/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9126 - loss: 0.1988 30/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9125 - loss: 0.1995 31/100 ━━━━━━━━━━━━━━━━━━━━ 16s 246ms/step - accuracy: 0.9126 - loss: 0.2001 32/100 ━━━━━━━━━━━━━━━━━━━━ 16s 246ms/step - accuracy: 0.9125 - loss: 0.2009 33/100 ━━━━━━━━━━━━━━━━━━━━ 16s 246ms/step - accuracy: 0.9125 - loss: 0.2017 34/100 ━━━━━━━━━━━━━━━━━━━━ 16s 247ms/step - accuracy: 0.9124 - loss: 0.2024 35/100 ━━━━━━━━━━━━━━━━━━━━ 16s 247ms/step - accuracy: 0.9123 - loss: 0.2031 36/100 ━━━━━━━━━━━━━━━━━━━━ 15s 247ms/step - accuracy: 0.9122 - loss: 0.2037 37/100 ━━━━━━━━━━━━━━━━━━━━ 15s 249ms/step - accuracy: 0.9122 - loss: 0.2041 38/100 ━━━━━━━━━━━━━━━━━━━━ 15s 253ms/step - accuracy: 0.9121 - loss: 0.2046 39/100 ━━━━━━━━━━━━━━━━━━━━ 15s 254ms/step - accuracy: 0.9120 - loss: 0.2049 40/100 ━━━━━━━━━━━━━━━━━━━━ 15s 254ms/step - accuracy: 0.9119 - loss: 0.2054 41/100 ━━━━━━━━━━━━━━━━━━━━ 14s 254ms/step - accuracy: 0.9119 - loss: 0.2057 42/100 ━━━━━━━━━━━━━━━━━━━━ 14s 254ms/step - accuracy: 0.9118 - loss: 0.2060 43/100 ━━━━━━━━━━━━━━━━━━━━ 14s 254ms/step - accuracy: 0.9118 - loss: 0.2062 44/100 ━━━━━━━━━━━━━━━━━━━━ 14s 253ms/step - accuracy: 0.9118 - loss: 0.2063 45/100 ━━━━━━━━━━━━━━━━━━━━ 13s 254ms/step - accuracy: 0.9118 - loss: 0.2066 46/100 ━━━━━━━━━━━━━━━━━━━━ 13s 254ms/step - accuracy: 0.9118 - loss: 0.2068 47/100 ━━━━━━━━━━━━━━━━━━━━ 13s 254ms/step - accuracy: 0.9118 - loss: 0.2070 48/100 ━━━━━━━━━━━━━━━━━━━━ 13s 254ms/step - accuracy: 0.9118 - loss: 0.2072 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9118 - loss: 0.2073 50/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9119 - loss: 0.2073 51/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9119 - loss: 0.2074 52/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9119 - loss: 0.2075 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9120 - loss: 0.2075 54/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9120 - loss: 0.2076 55/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9120 - loss: 0.2077 56/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9120 - loss: 0.2078 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9120 - loss: 0.2079 58/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9121 - loss: 0.2080 59/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9121 - loss: 0.2081 60/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9122 - loss: 0.2082 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 253ms/step - accuracy: 0.9122 - loss: 0.2082  62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 253ms/step - accuracy: 0.9123 - loss: 0.2082 63/100 ━━━━━━━━━━━━━━━━━━━━ 9s 254ms/step - accuracy: 0.9123 - loss: 0.2082 64/100 ━━━━━━━━━━━━━━━━━━━━ 9s 254ms/step - accuracy: 0.9124 - loss: 0.2082 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 254ms/step - accuracy: 0.9124 - loss: 0.2082 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 254ms/step - accuracy: 0.9125 - loss: 0.2082 67/100 ━━━━━━━━━━━━━━━━━━━━ 8s 256ms/step - accuracy: 0.9125 - loss: 0.2081 68/100 ━━━━━━━━━━━━━━━━━━━━ 8s 257ms/step - accuracy: 0.9126 - loss: 0.2081 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 257ms/step - accuracy: 0.9127 - loss: 0.2080 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 257ms/step - accuracy: 0.9127 - loss: 0.2079 71/100 ━━━━━━━━━━━━━━━━━━━━ 7s 257ms/step - accuracy: 0.9128 - loss: 0.2078 72/100 ━━━━━━━━━━━━━━━━━━━━ 7s 258ms/step - accuracy: 0.9129 - loss: 0.2077 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9130 - loss: 0.2075 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9131 - loss: 0.2074 75/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9132 - loss: 0.2072 76/100 ━━━━━━━━━━━━━━━━━━━━ 6s 258ms/step - accuracy: 0.9132 - loss: 0.2072 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9133 - loss: 0.2071 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9134 - loss: 0.2071 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9135 - loss: 0.2070 80/100 ━━━━━━━━━━━━━━━━━━━━ 5s 258ms/step - accuracy: 0.9135 - loss: 0.2070 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 258ms/step - accuracy: 0.9136 - loss: 0.2070 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9136 - loss: 0.2071 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9136 - loss: 0.2071 84/100 ━━━━━━━━━━━━━━━━━━━━ 4s 257ms/step - accuracy: 0.9137 - loss: 0.2071 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 258ms/step - accuracy: 0.9137 - loss: 0.2071 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 259ms/step - accuracy: 0.9138 - loss: 0.2071 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 260ms/step - accuracy: 0.9138 - loss: 0.2071 88/100 ━━━━━━━━━━━━━━━━━━━━ 3s 260ms/step - accuracy: 0.9139 - loss: 0.2071 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 260ms/step - accuracy: 0.9139 - loss: 0.2072 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 261ms/step - accuracy: 0.9140 - loss: 0.2072 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 261ms/step - accuracy: 0.9140 - loss: 0.2072 92/100 ━━━━━━━━━━━━━━━━━━━━ 2s 261ms/step - accuracy: 0.9141 - loss: 0.2072 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 261ms/step - accuracy: 0.9141 - loss: 0.2072 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 261ms/step - accuracy: 0.9142 - loss: 0.2072 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 261ms/step - accuracy: 0.9142 - loss: 0.2071 96/100 ━━━━━━━━━━━━━━━━━━━━ 1s 261ms/step - accuracy: 0.9143 - loss: 0.2071 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 260ms/step - accuracy: 0.9143 - loss: 0.2071 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 260ms/step - accuracy: 0.9144 - loss: 0.2071 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 260ms/step - accuracy: 0.9144 - loss: 0.2070100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 260ms/step - accuracy: 0.9145 - loss: 0.2070100/100 ━━━━━━━━━━━━━━━━━━━━ 34s 337ms/step - accuracy: 0.9145 - loss: 0.2070 - val_accuracy: 0.9610 - val_loss: 0.1131
Epoch 9/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 31s 321ms/step - accuracy: 0.9000 - loss: 0.1581  2/100 ━━━━━━━━━━━━━━━━━━━━ 26s 268ms/step - accuracy: 0.8875 - loss: 0.2039  3/100 ━━━━━━━━━━━━━━━━━━━━ 24s 250ms/step - accuracy: 0.8861 - loss: 0.2339  4/100 ━━━━━━━━━━━━━━━━━━━━ 23s 249ms/step - accuracy: 0.8802 - loss: 0.2544  5/100 ━━━━━━━━━━━━━━━━━━━━ 23s 250ms/step - accuracy: 0.8802 - loss: 0.2589  6/100 ━━━━━━━━━━━━━━━━━━━━ 23s 248ms/step - accuracy: 0.8835 - loss: 0.2551  7/100 ━━━━━━━━━━━━━━━━━━━━ 22s 247ms/step - accuracy: 0.8879 - loss: 0.2485  8/100 ━━━━━━━━━━━━━━━━━━━━ 22s 245ms/step - accuracy: 0.8902 - loss: 0.2429  9/100 ━━━━━━━━━━━━━━━━━━━━ 22s 244ms/step - accuracy: 0.8931 - loss: 0.2362 10/100 ━━━━━━━━━━━━━━━━━━━━ 22s 245ms/step - accuracy: 0.8958 - loss: 0.2321 11/100 ━━━━━━━━━━━━━━━━━━━━ 21s 246ms/step - accuracy: 0.8983 - loss: 0.2284 12/100 ━━━━━━━━━━━━━━━━━━━━ 21s 248ms/step - accuracy: 0.9008 - loss: 0.2243 13/100 ━━━━━━━━━━━━━━━━━━━━ 21s 250ms/step - accuracy: 0.9031 - loss: 0.2209 14/100 ━━━━━━━━━━━━━━━━━━━━ 21s 255ms/step - accuracy: 0.9055 - loss: 0.2172 15/100 ━━━━━━━━━━━━━━━━━━━━ 21s 256ms/step - accuracy: 0.9078 - loss: 0.2133 16/100 ━━━━━━━━━━━━━━━━━━━━ 21s 256ms/step - accuracy: 0.9098 - loss: 0.2098 17/100 ━━━━━━━━━━━━━━━━━━━━ 21s 255ms/step - accuracy: 0.9118 - loss: 0.2063 18/100 ━━━━━━━━━━━━━━━━━━━━ 20s 255ms/step - accuracy: 0.9136 - loss: 0.2031 19/100 ━━━━━━━━━━━━━━━━━━━━ 20s 256ms/step - accuracy: 0.9151 - loss: 0.2004 20/100 ━━━━━━━━━━━━━━━━━━━━ 20s 257ms/step - accuracy: 0.9164 - loss: 0.1981 21/100 ━━━━━━━━━━━━━━━━━━━━ 20s 256ms/step - accuracy: 0.9176 - loss: 0.1957 22/100 ━━━━━━━━━━━━━━━━━━━━ 20s 257ms/step - accuracy: 0.9187 - loss: 0.1941 23/100 ━━━━━━━━━━━━━━━━━━━━ 19s 256ms/step - accuracy: 0.9196 - loss: 0.1927 24/100 ━━━━━━━━━━━━━━━━━━━━ 19s 255ms/step - accuracy: 0.9203 - loss: 0.1915 25/100 ━━━━━━━━━━━━━━━━━━━━ 19s 255ms/step - accuracy: 0.9209 - loss: 0.1907 26/100 ━━━━━━━━━━━━━━━━━━━━ 18s 254ms/step - accuracy: 0.9214 - loss: 0.1898 27/100 ━━━━━━━━━━━━━━━━━━━━ 18s 254ms/step - accuracy: 0.9219 - loss: 0.1888 28/100 ━━━━━━━━━━━━━━━━━━━━ 18s 253ms/step - accuracy: 0.9224 - loss: 0.1878 29/100 ━━━━━━━━━━━━━━━━━━━━ 17s 253ms/step - accuracy: 0.9229 - loss: 0.1868 30/100 ━━━━━━━━━━━━━━━━━━━━ 17s 253ms/step - accuracy: 0.9233 - loss: 0.1858 31/100 ━━━━━━━━━━━━━━━━━━━━ 17s 252ms/step - accuracy: 0.9238 - loss: 0.1848 32/100 ━━━━━━━━━━━━━━━━━━━━ 17s 252ms/step - accuracy: 0.9241 - loss: 0.1841 33/100 ━━━━━━━━━━━━━━━━━━━━ 16s 252ms/step - accuracy: 0.9244 - loss: 0.1834 34/100 ━━━━━━━━━━━━━━━━━━━━ 16s 252ms/step - accuracy: 0.9246 - loss: 0.1828 35/100 ━━━━━━━━━━━━━━━━━━━━ 16s 251ms/step - accuracy: 0.9248 - loss: 0.1823 36/100 ━━━━━━━━━━━━━━━━━━━━ 16s 251ms/step - accuracy: 0.9251 - loss: 0.1817 37/100 ━━━━━━━━━━━━━━━━━━━━ 15s 251ms/step - accuracy: 0.9253 - loss: 0.1812 38/100 ━━━━━━━━━━━━━━━━━━━━ 15s 253ms/step - accuracy: 0.9255 - loss: 0.1808 39/100 ━━━━━━━━━━━━━━━━━━━━ 15s 253ms/step - accuracy: 0.9257 - loss: 0.1804 40/100 ━━━━━━━━━━━━━━━━━━━━ 15s 253ms/step - accuracy: 0.9259 - loss: 0.1799 41/100 ━━━━━━━━━━━━━━━━━━━━ 14s 253ms/step - accuracy: 0.9260 - loss: 0.1797 42/100 ━━━━━━━━━━━━━━━━━━━━ 14s 253ms/step - accuracy: 0.9262 - loss: 0.1795 43/100 ━━━━━━━━━━━━━━━━━━━━ 14s 253ms/step - accuracy: 0.9263 - loss: 0.1793 44/100 ━━━━━━━━━━━━━━━━━━━━ 14s 253ms/step - accuracy: 0.9264 - loss: 0.1791 45/100 ━━━━━━━━━━━━━━━━━━━━ 13s 253ms/step - accuracy: 0.9265 - loss: 0.1788 46/100 ━━━━━━━━━━━━━━━━━━━━ 13s 252ms/step - accuracy: 0.9266 - loss: 0.1785 47/100 ━━━━━━━━━━━━━━━━━━━━ 13s 253ms/step - accuracy: 0.9267 - loss: 0.1783 48/100 ━━━━━━━━━━━━━━━━━━━━ 13s 254ms/step - accuracy: 0.9268 - loss: 0.1782 49/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9269 - loss: 0.1779 50/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9270 - loss: 0.1778 51/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9271 - loss: 0.1776 52/100 ━━━━━━━━━━━━━━━━━━━━ 12s 254ms/step - accuracy: 0.9272 - loss: 0.1775 53/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9272 - loss: 0.1774 54/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9272 - loss: 0.1775 55/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9273 - loss: 0.1776 56/100 ━━━━━━━━━━━━━━━━━━━━ 11s 253ms/step - accuracy: 0.9273 - loss: 0.1777 57/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9273 - loss: 0.1779 58/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9272 - loss: 0.1780 59/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9272 - loss: 0.1782 60/100 ━━━━━━━━━━━━━━━━━━━━ 10s 252ms/step - accuracy: 0.9272 - loss: 0.1783 61/100 ━━━━━━━━━━━━━━━━━━━━ 9s 252ms/step - accuracy: 0.9272 - loss: 0.1785  62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9272 - loss: 0.1786 63/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9272 - loss: 0.1787 64/100 ━━━━━━━━━━━━━━━━━━━━ 9s 251ms/step - accuracy: 0.9272 - loss: 0.1788 65/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9272 - loss: 0.1788 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9272 - loss: 0.1788 67/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9273 - loss: 0.1789 68/100 ━━━━━━━━━━━━━━━━━━━━ 8s 251ms/step - accuracy: 0.9273 - loss: 0.1789 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 251ms/step - accuracy: 0.9274 - loss: 0.1789 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 251ms/step - accuracy: 0.9274 - loss: 0.1789 71/100 ━━━━━━━━━━━━━━━━━━━━ 7s 251ms/step - accuracy: 0.9274 - loss: 0.1789 72/100 ━━━━━━━━━━━━━━━━━━━━ 7s 251ms/step - accuracy: 0.9274 - loss: 0.1789 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 251ms/step - accuracy: 0.9274 - loss: 0.1790 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 251ms/step - accuracy: 0.9274 - loss: 0.1790 75/100 ━━━━━━━━━━━━━━━━━━━━ 6s 251ms/step - accuracy: 0.9274 - loss: 0.1790 76/100 ━━━━━━━━━━━━━━━━━━━━ 6s 251ms/step - accuracy: 0.9274 - loss: 0.1791 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 251ms/step - accuracy: 0.9273 - loss: 0.1793 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 251ms/step - accuracy: 0.9273 - loss: 0.1794 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 251ms/step - accuracy: 0.9273 - loss: 0.1796 80/100 ━━━━━━━━━━━━━━━━━━━━ 5s 251ms/step - accuracy: 0.9273 - loss: 0.1797 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 251ms/step - accuracy: 0.9273 - loss: 0.1799 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 251ms/step - accuracy: 0.9273 - loss: 0.1800 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 251ms/step - accuracy: 0.9273 - loss: 0.1801 84/100 ━━━━━━━━━━━━━━━━━━━━ 4s 251ms/step - accuracy: 0.9273 - loss: 0.1802 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 251ms/step - accuracy: 0.9273 - loss: 0.1803 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 251ms/step - accuracy: 0.9272 - loss: 0.1804 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 251ms/step - accuracy: 0.9272 - loss: 0.1805 88/100 ━━━━━━━━━━━━━━━━━━━━ 3s 251ms/step - accuracy: 0.9272 - loss: 0.1806 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 251ms/step - accuracy: 0.9272 - loss: 0.1806 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 251ms/step - accuracy: 0.9272 - loss: 0.1807 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 251ms/step - accuracy: 0.9273 - loss: 0.1807 92/100 ━━━━━━━━━━━━━━━━━━━━ 2s 251ms/step - accuracy: 0.9273 - loss: 0.1807 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 251ms/step - accuracy: 0.9273 - loss: 0.1807 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 251ms/step - accuracy: 0.9273 - loss: 0.1807 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 250ms/step - accuracy: 0.9274 - loss: 0.1806 96/100 ━━━━━━━━━━━━━━━━━━━━ 1s 250ms/step - accuracy: 0.9274 - loss: 0.1806 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.9274 - loss: 0.1806 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.9275 - loss: 0.1806 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.9275 - loss: 0.1806100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 250ms/step - accuracy: 0.9275 - loss: 0.1806100/100 ━━━━━━━━━━━━━━━━━━━━ 33s 329ms/step - accuracy: 0.9276 - loss: 0.1805 - val_accuracy: 0.9540 - val_loss: 0.1467
Epoch 10/10
  1/100 ━━━━━━━━━━━━━━━━━━━━ 28s 283ms/step - accuracy: 0.8500 - loss: 0.1539  2/100 ━━━━━━━━━━━━━━━━━━━━ 24s 248ms/step - accuracy: 0.8875 - loss: 0.1378  3/100 ━━━━━━━━━━━━━━━━━━━━ 23s 240ms/step - accuracy: 0.8972 - loss: 0.1321  4/100 ━━━━━━━━━━━━━━━━━━━━ 22s 238ms/step - accuracy: 0.9073 - loss: 0.1220  5/100 ━━━━━━━━━━━━━━━━━━━━ 22s 241ms/step - accuracy: 0.9098 - loss: 0.1227  6/100 ━━━━━━━━━━━━━━━━━━━━ 22s 240ms/step - accuracy: 0.9137 - loss: 0.1202  7/100 ━━━━━━━━━━━━━━━━━━━━ 22s 239ms/step - accuracy: 0.9159 - loss: 0.1196  8/100 ━━━━━━━━━━━━━━━━━━━━ 22s 240ms/step - accuracy: 0.9178 - loss: 0.1193  9/100 ━━━━━━━━━━━━━━━━━━━━ 21s 241ms/step - accuracy: 0.9201 - loss: 0.1180 10/100 ━━━━━━━━━━━━━━━━━━━━ 21s 240ms/step - accuracy: 0.9221 - loss: 0.1166 11/100 ━━━━━━━━━━━━━━━━━━━━ 21s 239ms/step - accuracy: 0.9234 - loss: 0.1178 12/100 ━━━━━━━━━━━━━━━━━━━━ 21s 239ms/step - accuracy: 0.9242 - loss: 0.1190 13/100 ━━━━━━━━━━━━━━━━━━━━ 20s 239ms/step - accuracy: 0.9244 - loss: 0.1223 14/100 ━━━━━━━━━━━━━━━━━━━━ 20s 239ms/step - accuracy: 0.9247 - loss: 0.1249 15/100 ━━━━━━━━━━━━━━━━━━━━ 20s 239ms/step - accuracy: 0.9249 - loss: 0.1282 16/100 ━━━━━━━━━━━━━━━━━━━━ 20s 238ms/step - accuracy: 0.9253 - loss: 0.1306 17/100 ━━━━━━━━━━━━━━━━━━━━ 19s 238ms/step - accuracy: 0.9255 - loss: 0.1330 18/100 ━━━━━━━━━━━━━━━━━━━━ 19s 238ms/step - accuracy: 0.9258 - loss: 0.1348 19/100 ━━━━━━━━━━━━━━━━━━━━ 19s 238ms/step - accuracy: 0.9258 - loss: 0.1372 20/100 ━━━━━━━━━━━━━━━━━━━━ 18s 237ms/step - accuracy: 0.9257 - loss: 0.1398 21/100 ━━━━━━━━━━━━━━━━━━━━ 18s 238ms/step - accuracy: 0.9257 - loss: 0.1418 22/100 ━━━━━━━━━━━━━━━━━━━━ 18s 239ms/step - accuracy: 0.9255 - loss: 0.1444 23/100 ━━━━━━━━━━━━━━━━━━━━ 18s 239ms/step - accuracy: 0.9256 - loss: 0.1465 24/100 ━━━━━━━━━━━━━━━━━━━━ 18s 239ms/step - accuracy: 0.9254 - loss: 0.1487 25/100 ━━━━━━━━━━━━━━━━━━━━ 18s 243ms/step - accuracy: 0.9252 - loss: 0.1505 26/100 ━━━━━━━━━━━━━━━━━━━━ 18s 245ms/step - accuracy: 0.9252 - loss: 0.1520 27/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9252 - loss: 0.1532 28/100 ━━━━━━━━━━━━━━━━━━━━ 17s 246ms/step - accuracy: 0.9252 - loss: 0.1546 29/100 ━━━━━━━━━━━━━━━━━━━━ 17s 247ms/step - accuracy: 0.9254 - loss: 0.1557 30/100 ━━━━━━━━━━━━━━━━━━━━ 17s 252ms/step - accuracy: 0.9255 - loss: 0.1567 31/100 ━━━━━━━━━━━━━━━━━━━━ 17s 255ms/step - accuracy: 0.9256 - loss: 0.1576 32/100 ━━━━━━━━━━━━━━━━━━━━ 17s 258ms/step - accuracy: 0.9257 - loss: 0.1589 33/100 ━━━━━━━━━━━━━━━━━━━━ 17s 261ms/step - accuracy: 0.9256 - loss: 0.1604 34/100 ━━━━━━━━━━━━━━━━━━━━ 17s 261ms/step - accuracy: 0.9255 - loss: 0.1619 35/100 ━━━━━━━━━━━━━━━━━━━━ 16s 260ms/step - accuracy: 0.9254 - loss: 0.1631 36/100 ━━━━━━━━━━━━━━━━━━━━ 16s 260ms/step - accuracy: 0.9255 - loss: 0.1641 37/100 ━━━━━━━━━━━━━━━━━━━━ 16s 261ms/step - accuracy: 0.9254 - loss: 0.1652 38/100 ━━━━━━━━━━━━━━━━━━━━ 16s 261ms/step - accuracy: 0.9254 - loss: 0.1662 39/100 ━━━━━━━━━━━━━━━━━━━━ 15s 262ms/step - accuracy: 0.9255 - loss: 0.1670 40/100 ━━━━━━━━━━━━━━━━━━━━ 15s 263ms/step - accuracy: 0.9255 - loss: 0.1678 41/100 ━━━━━━━━━━━━━━━━━━━━ 15s 262ms/step - accuracy: 0.9255 - loss: 0.1685 42/100 ━━━━━━━━━━━━━━━━━━━━ 15s 262ms/step - accuracy: 0.9255 - loss: 0.1692 43/100 ━━━━━━━━━━━━━━━━━━━━ 14s 261ms/step - accuracy: 0.9255 - loss: 0.1698 44/100 ━━━━━━━━━━━━━━━━━━━━ 14s 262ms/step - accuracy: 0.9254 - loss: 0.1705 45/100 ━━━━━━━━━━━━━━━━━━━━ 14s 262ms/step - accuracy: 0.9254 - loss: 0.1712 46/100 ━━━━━━━━━━━━━━━━━━━━ 14s 262ms/step - accuracy: 0.9253 - loss: 0.1719 47/100 ━━━━━━━━━━━━━━━━━━━━ 13s 261ms/step - accuracy: 0.9253 - loss: 0.1724 48/100 ━━━━━━━━━━━━━━━━━━━━ 13s 262ms/step - accuracy: 0.9252 - loss: 0.1729 49/100 ━━━━━━━━━━━━━━━━━━━━ 13s 262ms/step - accuracy: 0.9252 - loss: 0.1734 50/100 ━━━━━━━━━━━━━━━━━━━━ 13s 263ms/step - accuracy: 0.9251 - loss: 0.1738 51/100 ━━━━━━━━━━━━━━━━━━━━ 12s 264ms/step - accuracy: 0.9251 - loss: 0.1742 52/100 ━━━━━━━━━━━━━━━━━━━━ 12s 264ms/step - accuracy: 0.9251 - loss: 0.1745 53/100 ━━━━━━━━━━━━━━━━━━━━ 12s 263ms/step - accuracy: 0.9250 - loss: 0.1748 54/100 ━━━━━━━━━━━━━━━━━━━━ 12s 263ms/step - accuracy: 0.9250 - loss: 0.1751 55/100 ━━━━━━━━━━━━━━━━━━━━ 11s 262ms/step - accuracy: 0.9249 - loss: 0.1753 56/100 ━━━━━━━━━━━━━━━━━━━━ 11s 262ms/step - accuracy: 0.9249 - loss: 0.1756 57/100 ━━━━━━━━━━━━━━━━━━━━ 11s 262ms/step - accuracy: 0.9249 - loss: 0.1759 58/100 ━━━━━━━━━━━━━━━━━━━━ 10s 261ms/step - accuracy: 0.9248 - loss: 0.1762 59/100 ━━━━━━━━━━━━━━━━━━━━ 10s 261ms/step - accuracy: 0.9248 - loss: 0.1765 60/100 ━━━━━━━━━━━━━━━━━━━━ 10s 261ms/step - accuracy: 0.9248 - loss: 0.1767 61/100 ━━━━━━━━━━━━━━━━━━━━ 10s 260ms/step - accuracy: 0.9247 - loss: 0.1769 62/100 ━━━━━━━━━━━━━━━━━━━━ 9s 260ms/step - accuracy: 0.9247 - loss: 0.1771  63/100 ━━━━━━━━━━━━━━━━━━━━ 9s 260ms/step - accuracy: 0.9247 - loss: 0.1773 64/100 ━━━━━━━━━━━━━━━━━━━━ 9s 259ms/step - accuracy: 0.9246 - loss: 0.1774 65/100 ━━━━━━━━━━━━━━━━━━━━ 9s 259ms/step - accuracy: 0.9246 - loss: 0.1776 66/100 ━━━━━━━━━━━━━━━━━━━━ 8s 259ms/step - accuracy: 0.9246 - loss: 0.1777 67/100 ━━━━━━━━━━━━━━━━━━━━ 8s 258ms/step - accuracy: 0.9246 - loss: 0.1779 68/100 ━━━━━━━━━━━━━━━━━━━━ 8s 258ms/step - accuracy: 0.9245 - loss: 0.1780 69/100 ━━━━━━━━━━━━━━━━━━━━ 7s 258ms/step - accuracy: 0.9245 - loss: 0.1782 70/100 ━━━━━━━━━━━━━━━━━━━━ 7s 258ms/step - accuracy: 0.9245 - loss: 0.1783 71/100 ━━━━━━━━━━━━━━━━━━━━ 7s 257ms/step - accuracy: 0.9244 - loss: 0.1785 72/100 ━━━━━━━━━━━━━━━━━━━━ 7s 257ms/step - accuracy: 0.9243 - loss: 0.1787 73/100 ━━━━━━━━━━━━━━━━━━━━ 6s 257ms/step - accuracy: 0.9243 - loss: 0.1789 74/100 ━━━━━━━━━━━━━━━━━━━━ 6s 257ms/step - accuracy: 0.9242 - loss: 0.1790 75/100 ━━━━━━━━━━━━━━━━━━━━ 6s 257ms/step - accuracy: 0.9242 - loss: 0.1792 76/100 ━━━━━━━━━━━━━━━━━━━━ 6s 257ms/step - accuracy: 0.9241 - loss: 0.1794 77/100 ━━━━━━━━━━━━━━━━━━━━ 5s 256ms/step - accuracy: 0.9240 - loss: 0.1796 78/100 ━━━━━━━━━━━━━━━━━━━━ 5s 256ms/step - accuracy: 0.9240 - loss: 0.1798 79/100 ━━━━━━━━━━━━━━━━━━━━ 5s 256ms/step - accuracy: 0.9239 - loss: 0.1800 80/100 ━━━━━━━━━━━━━━━━━━━━ 5s 256ms/step - accuracy: 0.9239 - loss: 0.1802 81/100 ━━━━━━━━━━━━━━━━━━━━ 4s 256ms/step - accuracy: 0.9238 - loss: 0.1804 82/100 ━━━━━━━━━━━━━━━━━━━━ 4s 255ms/step - accuracy: 0.9238 - loss: 0.1806 83/100 ━━━━━━━━━━━━━━━━━━━━ 4s 255ms/step - accuracy: 0.9237 - loss: 0.1808 84/100 ━━━━━━━━━━━━━━━━━━━━ 4s 255ms/step - accuracy: 0.9237 - loss: 0.1810 85/100 ━━━━━━━━━━━━━━━━━━━━ 3s 255ms/step - accuracy: 0.9236 - loss: 0.1811 86/100 ━━━━━━━━━━━━━━━━━━━━ 3s 255ms/step - accuracy: 0.9236 - loss: 0.1813 87/100 ━━━━━━━━━━━━━━━━━━━━ 3s 255ms/step - accuracy: 0.9235 - loss: 0.1814 88/100 ━━━━━━━━━━━━━━━━━━━━ 3s 255ms/step - accuracy: 0.9235 - loss: 0.1816 89/100 ━━━━━━━━━━━━━━━━━━━━ 2s 254ms/step - accuracy: 0.9235 - loss: 0.1817 90/100 ━━━━━━━━━━━━━━━━━━━━ 2s 254ms/step - accuracy: 0.9234 - loss: 0.1819 91/100 ━━━━━━━━━━━━━━━━━━━━ 2s 255ms/step - accuracy: 0.9234 - loss: 0.1820 92/100 ━━━━━━━━━━━━━━━━━━━━ 2s 255ms/step - accuracy: 0.9234 - loss: 0.1822 93/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9234 - loss: 0.1823 94/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9234 - loss: 0.1824 95/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9233 - loss: 0.1825 96/100 ━━━━━━━━━━━━━━━━━━━━ 1s 255ms/step - accuracy: 0.9233 - loss: 0.1826 97/100 ━━━━━━━━━━━━━━━━━━━━ 0s 254ms/step - accuracy: 0.9233 - loss: 0.1827 98/100 ━━━━━━━━━━━━━━━━━━━━ 0s 255ms/step - accuracy: 0.9233 - loss: 0.1828 99/100 ━━━━━━━━━━━━━━━━━━━━ 0s 255ms/step - accuracy: 0.9233 - loss: 0.1829100/100 ━━━━━━━━━━━━━━━━━━━━ 0s 254ms/step - accuracy: 0.9233 - loss: 0.1830100/100 ━━━━━━━━━━━━━━━━━━━━ 33s 328ms/step - accuracy: 0.9232 - loss: 0.1831 - val_accuracy: 0.9430 - val_loss: 0.1433

8 training result

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#------------------------------------------------
# Plot training and validation accuracy per epoch
#------------------------------------------------
plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')

plt.figure()

plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

9 resource:

https://coursera.org/learn/convolutional-neural-networks-tensorflow/home/

https://github.com/https-deeplearning-ai/tensorflow-1-public/tree/main/C2

https://www.kaggle.com/c/dogs-vs-cats