Googleads unity plugin Chinese tutorial latest version

2023-01-23   ES  

mnist dataset
0 ~ 9 handwriting pictures, the data has divided the data into training sets and test sets by default. There are 60,000 pictures in the training set, and 10,000 pictures are available in the test set.

Import the necessary library

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
print(tf.__version__)

download data file

DATA_URL = "https: // storage.go` Here inserted a code piece` ogleapis.com/TensorFlow/tf- keys-datasets/mnist.npz "" "" "
path = keras.utils.get_file("mnist.npz",origin=DATA_URL)

with np.load(path) as data:
	train_data = data['x_train']
	train_labels = data['y_train']
	test_data = data['x_test']
	test_labels = data['y_test']

Use tf.data.dataset to load Numpy data
We passed the image data array and the corresponding label array as the metal group to tf.data.dataSet.from_Tensor_slice to create tf.data.dataSet.

train_dataset = tf.data.Dataset.from_tensor_slices((train_data,train_labels))
test_dataset = tf.data.Dataset.from_tensor_slices((test_data,test_labels))shuffle and batch data for training before training 
 BATCH_SIZE= 64
SHUFFLE_BUFFER_SIZE = 100
 
train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

model construction and training

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
 
model.compile(optimizer=tf.keras.optimizers.RMSprop(),
                loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
 
model.fit(train_dataset,epochs=10)

Train for 938 steps
Epoch 1/10

1/938 […] – ETA: 11:24 – loss: 111.1329 – sparse_categorical_accuracy: 0.1406
20/938 […] – ETA: 35s – loss: 32.7100 – sparse_categorical_accuracy: 0.5633
40/938 [>…] – ETA: 18s – loss: 21.6791 – sparse_categorical_accuracy: 0.6703
60/938 [>…] – ETA: 12s – loss: 17.2033 – sparse_categorical_accuracy: 0.7174
80/938 [=>…] – ETA: 10s – loss: 14.2442 – sparse_categorical_accuracy: 0.7480
100/938 [>…] – ETA: 8s – loss: 12.2833 – sparse_categorical_accuracy: 0.7709
120/938 [
>…] – ETA: 7s – loss: 11.0870 – sparse_categorical_accuracy: 0.7819
140/938 [=>…] – ETA: 6s – loss: 10.1944 – sparse_categorical_accuracy: 0.7903
159/938 [
>…] – ETA: 5s – loss: 9.4267 – sparse_categorical_accuracy: 0.7991
179/938 [
>…] – ETA: 5s – loss: 8.6361 – sparse_categorical_accuracy: 0.8105
199/938 [
=>…] – ETA: 4s – loss: 8.1802 – sparse_categorical_accuracy: 0.8142
219/938 [
>…] – ETA: 4s – loss: 7.7128 – sparse_categorical_accuracy: 0.8176
238/938 [
>…] – ETA: 3s – loss: 7.3328 – sparse_categorical_accuracy: 0.8210
258/938 [
=>…] – ETA: 3s – loss: 6.9488 – sparse_categorical_accuracy: 0.8249
278/938 [
=>…] – ETA: 3s – loss: 6.5940 – sparse_categorical_accuracy: 0.8287
298/938 [
>…] – ETA: 3s – loss: 6.2636 – sparse_categorical_accuracy: 0.8335
318/938 [
=>…] – ETA: 3s – loss: 5.9963 – sparse_categorical_accuracy: 0.8364
339/938 [
=>…] – ETA: 2s – loss: 5.7277 – sparse_categorical_accuracy: 0.8406
360/938 [
>…] – ETA: 2s – loss: 5.5116 – sparse_categorical_accuracy: 0.8433
380/938 [
=>…] – ETA: 2s – loss: 5.3110 – sparse_categorical_accuracy: 0.8459
400/938 [
=>…] – ETA: 2s – loss: 5.1290 – sparse_categorical_accuracy: 0.8478
419/938 [
>…] – ETA: 2s – loss: 4.9773 – sparse_categorical_accuracy: 0.8498
438/938 [
=>…] – ETA: 2s – loss: 4.8220 – sparse_categorical_accuracy: 0.8516
458/938 [
=>…] – ETA: 2s – loss: 4.6737 – sparse_categorical_accuracy: 0.8526
478/938 [
>…] – ETA: 1s – loss: 4.5308 – sparse_categorical_accuracy: 0.8528
498/938 [
>…] – ETA: 1s – loss: 4.4027 – sparse_categorical_accuracy: 0.8532
518/938 [
=>…] – ETA: 1s – loss: 4.2768 – sparse_categorical_accuracy: 0.8540
538/938 [
>…] – ETA: 1s – loss: 4.1556 – sparse_categorical_accuracy: 0.8555
558/938 [
>…] – ETA: 1s – loss: 4.0413 – sparse_categorical_accuracy: 0.8565
578/938 [
=>…] – ETA: 1s – loss: 3.9355 – sparse_categorical_accuracy: 0.8574
598/938 [
>…] – ETA: 1s – loss: 3.8393 – sparse_categorical_accuracy: 0.8584
617/938 [
>…] – ETA: 1s – loss: 3.7514 – sparse_categorical_accuracy: 0.8594
636/938 [
=>…] – ETA: 1s – loss: 3.6633 – sparse_categorical_accuracy: 0.8602
656/938 [
=>…] – ETA: 1s – loss: 3.5780 – sparse_categorical_accuracy: 0.8609
676/938 [
>…] – ETA: 0s – loss: 3.4987 – sparse_categorical_accuracy: 0.8612
696/938 [
=>…] – ETA: 0s – loss: 3.4163 – sparse_categorical_accuracy: 0.8624
716/938 [
=>…] – ETA: 0s – loss: 3.3439 – sparse_categorical_accuracy: 0.8634
736/938 [
>…] – ETA: 0s – loss: 3.2719 – sparse_categorical_accuracy: 0.8642
756/938 [
=>…] – ETA: 0s – loss: 3.2039 – sparse_categorical_accuracy: 0.8651
776/938 [
=>…] – ETA: 0s – loss: 3.1422 – sparse_categorical_accuracy: 0.8658
797/938 [
>…] – ETA: 0s – loss: 3.0772 – sparse_categorical_accuracy: 0.8661
817/938 [
=>…] – ETA: 0s – loss: 3.0207 – sparse_categorical_accuracy: 0.8671
838/938 [
=>…] – ETA: 0s – loss: 2.9621 – sparse_categorical_accuracy: 0.8677
858/938 [
>…] – ETA: 0s – loss: 2.9080 – sparse_categorical_accuracy: 0.8685
878/938 [
=>…] – ETA: 0s – loss: 2.8545 – sparse_categorical_accuracy: 0.8695
898/938 [
=>…] – ETA: 0s – loss: 2.8005 – sparse_categorical_accuracy: 0.8708
918/938 [
>.] – ETA: 0s – loss: 2.7483 – sparse_categorical_accuracy: 0.8722
938/938 [
================] – 3s 3ms/step – loss: 2.7024 – sparse_categorical_accuracy: 0.8735
Epoch 2/10

1/938 […] – ETA: 14s – loss: 0.5318 – sparse_categorical_accuracy: 0.9219
20/938 […] – ETA: 3s – loss: 0.7503 – sparse_categorical_accuracy: 0.9016
40/938 [>…] – ETA: 2s – loss: 0.6746 – sparse_categorical_accuracy: 0.9086
60/938 [>…] – ETA: 2s – loss: 0.6212 – sparse_categorical_accuracy: 0.9180
80/938 [=>…] – ETA: 2s – loss: 0.5782 – sparse_categorical_accuracy: 0.9229
100/938 [>…] – ETA: 2s – loss: 0.5659 – sparse_categorical_accuracy: 0.9219
120/938 [
>…] – ETA: 2s – loss: 0.5556 – sparse_categorical_accuracy: 0.9204
140/938 [=>…] – ETA: 2s – loss: 0.5685 – sparse_categorical_accuracy: 0.9186
160/938 [
>…] – ETA: 2s – loss: 0.5496 – sparse_categorical_accuracy: 0.9188
180/938 [
>…] – ETA: 2s – loss: 0.5370 – sparse_categorical_accuracy: 0.9201
200/938 [
=>…] – ETA: 1s – loss: 0.5417 – sparse_categorical_accuracy: 0.9184
220/938 [
>…] – ETA: 1s – loss: 0.5521 – sparse_categorical_accuracy: 0.9168
240/938 [
>…] – ETA: 1s – loss: 0.5637 – sparse_categorical_accuracy: 0.9160
260/938 [
=>…] – ETA: 1s – loss: 0.5576 – sparse_categorical_accuracy: 0.9162
280/938 [
=>…] – ETA: 1s – loss: 0.5578 – sparse_categorical_accuracy: 0.9165
300/938 [
>…] – ETA: 1s – loss: 0.5529 – sparse_categorical_accuracy: 0.9178
320/938 [
=>…] – ETA: 1s – loss: 0.5610 – sparse_categorical_accuracy: 0.9179
340/938 [
=>…] – ETA: 1s – loss: 0.5577 – sparse_categorical_accuracy: 0.9182
360/938 [
>…] – ETA: 1s – loss: 0.5496 – sparse_categorical_accuracy: 0.9185
380/938 [
=>…] – ETA: 1s – loss: 0.5465 – sparse_categorical_accuracy: 0.9197
400/938 [
=>…] – ETA: 1s – loss: 0.5471 – sparse_categorical_accuracy: 0.9198
420/938 [
>…] – ETA: 1s – loss: 0.5545 – sparse_categorical_accuracy: 0.9195
440/938 [
=>…] – ETA: 1s – loss: 0.5542 – sparse_categorical_accuracy: 0.9198
459/938 [
=>…] – ETA: 1s – loss: 0.5504 – sparse_categorical_accuracy: 0.9198
479/938 [
>…] – ETA: 1s – loss: 0.5454 – sparse_categorical_accuracy: 0.9204
499/938 [
>…] – ETA: 1s – loss: 0.5435 – sparse_categorical_accuracy: 0.9204
519/938 [
=>…] – ETA: 1s – loss: 0.5452 – sparse_categorical_accuracy: 0.9202
539/938 [
>…] – ETA: 1s – loss: 0.5422 – sparse_categorical_accuracy: 0.9203
559/938 [
>…] – ETA: 0s – loss: 0.5406 – sparse_categorical_accuracy: 0.9207
579/938 [
=>…] – ETA: 0s – loss: 0.5392 – sparse_categorical_accuracy: 0.9210
599/938 [
>…] – ETA: 0s – loss: 0.5400 – sparse_categorical_accuracy: 0.9209
619/938 [
>…] – ETA: 0s – loss: 0.5374 – sparse_categorical_accuracy: 0.9211
640/938 [
=>…] – ETA: 0s – loss: 0.5358 – sparse_categorical_accuracy: 0.9214
660/938 [
>…] – ETA: 0s – loss: 0.5331 – sparse_categorical_accuracy: 0.9214
679/938 [
>…] – ETA: 0s – loss: 0.5339 – sparse_categorical_accuracy: 0.9216
699/938 [
=>…] – ETA: 0s – loss: 0.5281 – sparse_categorical_accuracy: 0.9221
719/938 [
=>…] – ETA: 0s – loss: 0.5263 – sparse_categorical_accuracy: 0.9223
739/938 [
>…] – ETA: 0s – loss: 0.5233 – sparse_categorical_accuracy: 0.9223
759/938 [
=>…] – ETA: 0s – loss: 0.5206 – sparse_categorical_accuracy: 0.9226
779/938 [
=>…] – ETA: 0s – loss: 0.5235 – sparse_categorical_accuracy: 0.9224
798/938 [
>…] – ETA: 0s – loss: 0.5202 – sparse_categorical_accuracy: 0.9225
818/938 [
=>…] – ETA: 0s – loss: 0.5193 – sparse_categorical_accuracy: 0.9229
838/938 [
=>…] – ETA: 0s – loss: 0.5162 – sparse_categorical_accuracy: 0.9230
858/938 [
>…] – ETA: 0s – loss: 0.5116 – sparse_categorical_accuracy: 0.9234
878/938 [
=>…] – ETA: 0s – loss: 0.5074 – sparse_categorical_accuracy: 0.9239
898/938 [
=>…] – ETA: 0s – loss: 0.5023 – sparse_categorical_accuracy: 0.9246
918/938 [
>.] – ETA: 0s – loss: 0.4956 – sparse_categorical_accuracy: 0.9253
936/938 [
>.] – ETA: 0s – loss: 0.4938 – sparse_categorical_accuracy: 0.9258
938/938 [
==============] – 2s 3ms/step – loss: 0.4945 – sparse_categorical_accuracy: 0.9258
Epoch 3/10

1/938 […] – ETA: 16s – loss: 0.1087 – sparse_categorical_accuracy: 0.9688
20/938 […] – ETA: 3s – loss: 0.5862 – sparse_categorical_accuracy: 0.9328
40/938 [>…] – ETA: 2s – loss: 0.5121 – sparse_categorical_accuracy: 0.9371
54/938 [>…] – ETA: 2s – loss: 0.4385 – sparse_categorical_accuracy: 0.9436
69/938 [=>…] – ETA: 2s – loss: 0.4167 – sparse_categorical_accuracy: 0.9470
85/938 [=>…] – ETA: 2s – loss: 0.3965 – sparse_categorical_accuracy: 0.9460
99/938 [>…] – ETA: 2s – loss: 0.3815 – sparse_categorical_accuracy: 0.9463
118/938 [
>…] – ETA: 2s – loss: 0.3754 – sparse_categorical_accuracy: 0.9460
138/938 [=>…] – ETA: 2s – loss: 0.3972 – sparse_categorical_accuracy: 0.9444
158/938 [
>…] – ETA: 2s – loss: 0.3866 – sparse_categorical_accuracy: 0.9451
178/938 [
>…] – ETA: 2s – loss: 0.3799 – sparse_categorical_accuracy: 0.9445
198/938 [
=>…] – ETA: 2s – loss: 0.3837 – sparse_categorical_accuracy: 0.9441
218/938 [
=>…] – ETA: 2s – loss: 0.3909 – sparse_categorical_accuracy: 0.9440
238/938 [
>…] – ETA: 2s – loss: 0.3994 – sparse_categorical_accuracy: 0.9436
256/938 [
=>…] – ETA: 1s – loss: 0.3934 – sparse_categorical_accuracy: 0.9436
276/938 [
=>…] – ETA: 1s – loss: 0.3963 – sparse_categorical_accuracy: 0.9440
296/938 [
>…] – ETA: 1s – loss: 0.3962 – sparse_categorical_accuracy: 0.9444
316/938 [
=>…] – ETA: 1s – loss: 0.4018 – sparse_categorical_accuracy: 0.9439
336/938 [
=>…] – ETA: 1s – loss: 0.3980 – sparse_categorical_accuracy: 0.9441
356/938 [
>…] – ETA: 1s – loss: 0.3938 – sparse_categorical_accuracy: 0.9443
376/938 [
=>…] – ETA: 1s – loss: 0.3945 – sparse_categorical_accuracy: 0.9446
395/938 [
=>…] – ETA: 1s – loss: 0.3958 – sparse_categorical_accuracy: 0.9443
415/938 [
>…] – ETA: 1s – loss: 0.4061 – sparse_categorical_accuracy: 0.9434
434/938 [
>…] – ETA: 1s – loss: 0.4087 – sparse_categorical_accuracy: 0.9430
454/938 [
=>…] – ETA: 1s – loss: 0.4035 – sparse_categorical_accuracy: 0.9434
474/938 [
>…] – ETA: 1s – loss: 0.4022 – sparse_categorical_accuracy: 0.9433
493/938 [
>…] – ETA: 1s – loss: 0.4020 – sparse_categorical_accuracy: 0.9434
513/938 [
=>…] – ETA: 1s – loss: 0.4012 – sparse_categorical_accuracy: 0.9430
532/938 [
>…] – ETA: 1s – loss: 0.4022 – sparse_categorical_accuracy: 0.9432
552/938 [
>…] – ETA: 1s – loss: 0.3987 – sparse_categorical_accuracy: 0.9433
572/938 [
=>…] – ETA: 0s – loss: 0.3967 – sparse_categorical_accuracy: 0.9438
592/938 [
=>…] – ETA: 0s – loss: 0.3985 – sparse_categorical_accuracy: 0.9437
612/938 [
>…] – ETA: 0s – loss: 0.3969 – sparse_categorical_accuracy: 0.9437
631/938 [
=>…] – ETA: 0s – loss: 0.4010 – sparse_categorical_accuracy: 0.9432
648/938 [
=>…] – ETA: 0s – loss: 0.4017 – sparse_categorical_accuracy: 0.9432
664/938 [
>…] – ETA: 0s – loss: 0.4044 – sparse_categorical_accuracy: 0.9430
682/938 [
>…] – ETA: 0s – loss: 0.4036 – sparse_categorical_accuracy: 0.9430
701/938 [
=>…] – ETA: 0s – loss: 0.4005 – sparse_categorical_accuracy: 0.9434
719/938 [
=>…] – ETA: 0s – loss: 0.3999 – sparse_categorical_accuracy: 0.9435
737/938 [
>…] – ETA: 0s – loss: 0.3985 – sparse_categorical_accuracy: 0.9436
756/938 [
=>…] – ETA: 0s – loss: 0.3978 – sparse_categorical_accuracy: 0.9434
776/938 [
=>…] – ETA: 0s – loss: 0.3998 – sparse_categorical_accuracy: 0.9432
795/938 [
>…] – ETA: 0s – loss: 0.4008 – sparse_categorical_accuracy: 0.9427
814/938 [
=>…] – ETA: 0s – loss: 0.4012 – sparse_categorical_accuracy: 0.9429
833/938 [
=>…] – ETA: 0s – loss: 0.4017 – sparse_categorical_accuracy: 0.9426
852/938 [
>…] – ETA: 0s – loss: 0.3980 – sparse_categorical_accuracy: 0.9427
872/938 [
>…] – ETA: 0s – loss: 0.3960 – sparse_categorical_accuracy: 0.9429
891/938 [
=>…] – ETA: 0s – loss: 0.3929 – sparse_categorical_accuracy: 0.9430
911/938 [
>.] – ETA: 0s – loss: 0.3891 – sparse_categorical_accuracy: 0.9433
928/938 [
>.] – ETA: 0s – loss: 0.3846 – sparse_categorical_accuracy: 0.9438
938/938 [
================] – 3s 3ms/step – loss: 0.3855 – sparse_categorical_accuracy: 0.9441
Epoch 4/10

1/938 […] – ETA: 14s – loss: 0.2610 – sparse_categorical_accuracy: 0.9531
9/938 […] – ETA: 7s – loss: 0.3007 – sparse_categorical_accuracy: 0.9583
22/938 […] – ETA: 5s – loss: 0.5099 – sparse_categorical_accuracy: 0.9368
41/938 [>…] – ETA: 3s – loss: 0.4120 – sparse_categorical_accuracy: 0.9417
59/938 [>…] – ETA: 3s – loss: 0.3770 – sparse_categorical_accuracy: 0.9468
79/938 [=>…] – ETA: 3s – loss: 0.3286 – sparse_categorical_accuracy: 0.9517
99/938 [>…] – ETA: 2s – loss: 0.3215 – sparse_categorical_accuracy: 0.9519
119/938 [
>…] – ETA: 2s – loss: 0.3062 – sparse_categorical_accuracy: 0.9533
139/938 [=>…] – ETA: 2s – loss: 0.3224 – sparse_categorical_accuracy: 0.9523
159/938 [
>…] – ETA: 2s – loss: 0.3189 – sparse_categorical_accuracy: 0.9524
179/938 [
>…] – ETA: 2s – loss: 0.3118 – sparse_categorical_accuracy: 0.9530
199/938 [
=>…] – ETA: 2s – loss: 0.3121 – sparse_categorical_accuracy: 0.9525
219/938 [
>…] – ETA: 2s – loss: 0.3220 – sparse_categorical_accuracy: 0.9526
239/938 [
>…] – ETA: 2s – loss: 0.3346 – sparse_categorical_accuracy: 0.9521
260/938 [
=>…] – ETA: 1s – loss: 0.3342 – sparse_categorical_accuracy: 0.9523
281/938 [
=>…] – ETA: 1s – loss: 0.3488 – sparse_categorical_accuracy: 0.9520
301/938 [
>…] – ETA: 1s – loss: 0.3435 – sparse_categorical_accuracy: 0.9522
321/938 [
=>…] – ETA: 1s – loss: 0.3462 – sparse_categorical_accuracy: 0.9523
341/938 [
=>…] – ETA: 1s – loss: 0.3429 – sparse_categorical_accuracy: 0.9522
361/938 [
>…] – ETA: 1s – loss: 0.3400 – sparse_categorical_accuracy: 0.9518
381/938 [
=>…] – ETA: 1s – loss: 0.3404 – sparse_categorical_accuracy: 0.9521
401/938 [
=>…] – ETA: 1s – loss: 0.3433 – sparse_categorical_accuracy: 0.9524
421/938 [
>…] – ETA: 1s – loss: 0.3487 – sparse_categorical_accuracy: 0.9519
441/938 [
=>…] – ETA: 1s – loss: 0.3478 – sparse_categorical_accuracy: 0.9522
461/938 [
=>…] – ETA: 1s – loss: 0.3468 – sparse_categorical_accuracy: 0.9520
481/938 [
>…] – ETA: 1s – loss: 0.3425 – sparse_categorical_accuracy: 0.9522
501/938 [
=>…] – ETA: 1s – loss: 0.3423 – sparse_categorical_accuracy: 0.9521
521/938 [
=>…] – ETA: 1s – loss: 0.3451 – sparse_categorical_accuracy: 0.9520
541/938 [
>…] – ETA: 1s – loss: 0.3415 – sparse_categorical_accuracy: 0.9523
561/938 [
>…] – ETA: 1s – loss: 0.3405 – sparse_categorical_accuracy: 0.9526
581/938 [
=>…] – ETA: 0s – loss: 0.3396 – sparse_categorical_accuracy: 0.9527
601/938 [
>…] – ETA: 0s – loss: 0.3414 – sparse_categorical_accuracy: 0.9523
621/938 [
>…] – ETA: 0s – loss: 0.3409 – sparse_categorical_accuracy: 0.9525
641/938 [
=>…] – ETA: 0s – loss: 0.3417 – sparse_categorical_accuracy: 0.9526
661/938 [
>…] – ETA: 0s – loss: 0.3422 – sparse_categorical_accuracy: 0.9522
681/938 [
>…] – ETA: 0s – loss: 0.3463 – sparse_categorical_accuracy: 0.9521
701/938 [
=>…] – ETA: 0s – loss: 0.3428 – sparse_categorical_accuracy: 0.9524
721/938 [
>…] – ETA: 0s – loss: 0.3392 – sparse_categorical_accuracy: 0.9527
741/938 [
>…] – ETA: 0s – loss: 0.3382 – sparse_categorical_accuracy: 0.9525
761/938 [
=>…] – ETA: 0s – loss: 0.3360 – sparse_categorical_accuracy: 0.9525
781/938 [
=>…] – ETA: 0s – loss: 0.3393 – sparse_categorical_accuracy: 0.9520
801/938 [
>…] – ETA: 0s – loss: 0.3398 – sparse_categorical_accuracy: 0.9518
820/938 [
=>…] – ETA: 0s – loss: 0.3395 – sparse_categorical_accuracy: 0.9520
840/938 [
=>…] – ETA: 0s – loss: 0.3397 – sparse_categorical_accuracy: 0.9518
860/938 [
>…] – ETA: 0s – loss: 0.3365 – sparse_categorical_accuracy: 0.9521
880/938 [
=>…] – ETA: 0s – loss: 0.3350 – sparse_categorical_accuracy: 0.9520
900/938 [
=>…] – ETA: 0s – loss: 0.3333 – sparse_categorical_accuracy: 0.9522
920/938 [
>.] – ETA: 0s – loss: 0.3300 – sparse_categorical_accuracy: 0.9526
938/938 [
================] – 2s 3ms/step – loss: 0.3288 – sparse_categorical_accuracy: 0.9528
Epoch 5/10

1/938 […] – ETA: 13s – loss: 0.2853 – sparse_categorical_accuracy: 0.9375
20/938 […] – ETA: 2s – loss: 0.4085 – sparse_categorical_accuracy: 0.9563
40/938 [>…] – ETA: 2s – loss: 0.2935 – sparse_categorical_accuracy: 0.9594
60/938 [>…] – ETA: 2s – loss: 0.2836 – sparse_categorical_accuracy: 0.9563
80/938 [=>…] – ETA: 2s – loss: 0.2747 – sparse_categorical_accuracy: 0.9570
100/938 [>…] – ETA: 2s – loss: 0.2865 – sparse_categorical_accuracy: 0.9563
120/938 [
>…] – ETA: 2s – loss: 0.2887 – sparse_categorical_accuracy: 0.9570
140/938 [=>…] – ETA: 2s – loss: 0.3004 – sparse_categorical_accuracy: 0.9554
160/938 [
>…] – ETA: 2s – loss: 0.2936 – sparse_categorical_accuracy: 0.9563
181/938 [
>…] – ETA: 2s – loss: 0.2874 – sparse_categorical_accuracy: 0.9570
201/938 [
=>…] – ETA: 1s – loss: 0.2852 – sparse_categorical_accuracy: 0.9569
221/938 [
>…] – ETA: 1s – loss: 0.2943 – sparse_categorical_accuracy: 0.9569
241/938 [
>…] – ETA: 1s – loss: 0.3028 – sparse_categorical_accuracy: 0.9573
260/938 [
=>…] – ETA: 1s – loss: 0.2938 – sparse_categorical_accuracy: 0.9577
281/938 [
=>…] – ETA: 1s – loss: 0.3141 – sparse_categorical_accuracy: 0.9577
301/938 [
>…] – ETA: 1s – loss: 0.3119 – sparse_categorical_accuracy: 0.9580
321/938 [
=>…] – ETA: 1s – loss: 0.3159 – sparse_categorical_accuracy: 0.9573
341/938 [
=>…] – ETA: 1s – loss: 0.3143 – sparse_categorical_accuracy: 0.9576
361/938 [
>…] – ETA: 1s – loss: 0.3094 – sparse_categorical_accuracy: 0.9578
381/938 [
=>…] – ETA: 1s – loss: 0.3078 – sparse_categorical_accuracy: 0.9581
401/938 [
=>…] – ETA: 1s – loss: 0.3096 – sparse_categorical_accuracy: 0.9581
421/938 [
>…] – ETA: 1s – loss: 0.3170 – sparse_categorical_accuracy: 0.9578
440/938 [
=>…] – ETA: 1s – loss: 0.3174 – sparse_categorical_accuracy: 0.9576
460/938 [
=>…] – ETA: 1s – loss: 0.3144 – sparse_categorical_accuracy: 0.9579
480/938 [
>…] – ETA: 1s – loss: 0.3121 – sparse_categorical_accuracy: 0.9580
500/938 [
>…] – ETA: 1s – loss: 0.3116 – sparse_categorical_accuracy: 0.9578
520/938 [
=>…] – ETA: 1s – loss: 0.3162 – sparse_categorical_accuracy: 0.9575
540/938 [
>…] – ETA: 1s – loss: 0.3132 – sparse_categorical_accuracy: 0.9579
560/938 [
>…] – ETA: 0s – loss: 0.3134 – sparse_categorical_accuracy: 0.9580
580/938 [
=>…] – ETA: 0s – loss: 0.3143 – sparse_categorical_accuracy: 0.9580
600/938 [
>…] – ETA: 0s – loss: 0.3161 – sparse_categorical_accuracy: 0.9578
620/938 [
>…] – ETA: 0s – loss: 0.3188 – sparse_categorical_accuracy: 0.9576
640/938 [
=>…] – ETA: 0s – loss: 0.3181 – sparse_categorical_accuracy: 0.9573
660/938 [
>…] – ETA: 0s – loss: 0.3185 – sparse_categorical_accuracy: 0.9574
680/938 [
>…] – ETA: 0s – loss: 0.3209 – sparse_categorical_accuracy: 0.9574
700/938 [
=>…] – ETA: 0s – loss: 0.3183 – sparse_categorical_accuracy: 0.9576
720/938 [
>…] – ETA: 0s – loss: 0.3170 – sparse_categorical_accuracy: 0.9576
740/938 [
>…] – ETA: 0s – loss: 0.3176 – sparse_categorical_accuracy: 0.9576
760/938 [
=>…] – ETA: 0s – loss: 0.3182 – sparse_categorical_accuracy: 0.9577
780/938 [
=>…] – ETA: 0s – loss: 0.3201 – sparse_categorical_accuracy: 0.9573
800/938 [
>…] – ETA: 0s – loss: 0.3192 – sparse_categorical_accuracy: 0.9573
820/938 [
=>…] – ETA: 0s – loss: 0.3206 – sparse_categorical_accuracy: 0.9572
840/938 [
=>…] – ETA: 0s – loss: 0.3209 – sparse_categorical_accuracy: 0.9570
860/938 [
>…] – ETA: 0s – loss: 0.3182 – sparse_categorical_accuracy: 0.9573
880/938 [
=>…] – ETA: 0s – loss: 0.3152 – sparse_categorical_accuracy: 0.9575
900/938 [
=>…] – ETA: 0s – loss: 0.3124 – sparse_categorical_accuracy: 0.9577
920/938 [
>.] – ETA: 0s – loss: 0.3096 – sparse_categorical_accuracy: 0.9579
938/938 [
================] – 2s 3ms/step – loss: 0.3096 – sparse_categorical_accuracy: 0.9582
Epoch 6/10

1/938 […] – ETA: 13s – loss: 0.0905 – sparse_categorical_accuracy: 0.9688
20/938 […] – ETA: 2s – loss: 0.3884 – sparse_categorical_accuracy: 0.9555
40/938 [>…] – ETA: 2s – loss: 0.3029 – sparse_categorical_accuracy: 0.9617
60/938 [>…] – ETA: 2s – loss: 0.2930 – sparse_categorical_accuracy: 0.9617
80/938 [=>…] – ETA: 2s – loss: 0.2771 – sparse_categorical_accuracy: 0.9635
100/938 [>…] – ETA: 2s – loss: 0.2690 – sparse_categorical_accuracy: 0.9634
120/938 [
>…] – ETA: 2s – loss: 0.2731 – sparse_categorical_accuracy: 0.9641
141/938 [=>…] – ETA: 2s – loss: 0.2968 – sparse_categorical_accuracy: 0.9618
161/938 [
>…] – ETA: 2s – loss: 0.2975 – sparse_categorical_accuracy: 0.9607
181/938 [
>…] – ETA: 1s – loss: 0.2889 – sparse_categorical_accuracy: 0.9628
200/938 [
=>…] – ETA: 1s – loss: 0.2900 – sparse_categorical_accuracy: 0.9628
218/938 [
=>…] – ETA: 1s – loss: 0.2925 – sparse_categorical_accuracy: 0.9624
238/938 [
>…] – ETA: 1s – loss: 0.3037 – sparse_categorical_accuracy: 0.9615
259/938 [
=>…] – ETA: 1s – loss: 0.2987 – sparse_categorical_accuracy: 0.9617
279/938 [
=>…] – ETA: 1s – loss: 0.3022 – sparse_categorical_accuracy: 0.9621
299/938 [
>…] – ETA: 1s – loss: 0.2986 – sparse_categorical_accuracy: 0.9623
319/938 [
=>…] – ETA: 1s – loss: 0.3040 – sparse_categorical_accuracy: 0.9620
339/938 [
=>…] – ETA: 1s – loss: 0.3027 – sparse_categorical_accuracy: 0.9620
359/938 [
>…] – ETA: 1s – loss: 0.2983 – sparse_categorical_accuracy: 0.9625
380/938 [
=>…] – ETA: 1s – loss: 0.2968 – sparse_categorical_accuracy: 0.9625
400/938 [
=>…] – ETA: 1s – loss: 0.2942 – sparse_categorical_accuracy: 0.9627
420/938 [
>…] – ETA: 1s – loss: 0.2923 – sparse_categorical_accuracy: 0.9625
440/938 [
=>…] – ETA: 1s – loss: 0.2966 – sparse_categorical_accuracy: 0.9622
459/938 [
=>…] – ETA: 1s – loss: 0.2955 – sparse_categorical_accuracy: 0.9621
479/938 [
>…] – ETA: 1s – loss: 0.2928 – sparse_categorical_accuracy: 0.9623
499/938 [
>…] – ETA: 1s – loss: 0.2918 – sparse_categorical_accuracy: 0.9623
519/938 [
=>…] – ETA: 1s – loss: 0.2968 – sparse_categorical_accuracy: 0.9623
539/938 [
>…] – ETA: 1s – loss: 0.2967 – sparse_categorical_accuracy: 0.9625
559/938 [
>…] – ETA: 0s – loss: 0.2965 – sparse_categorical_accuracy: 0.9623
572/938 [
=>…] – ETA: 0s – loss: 0.2980 – sparse_categorical_accuracy: 0.9624
592/938 [
=>…] – ETA: 0s – loss: 0.3026 – sparse_categorical_accuracy: 0.9621
612/938 [
>…] – ETA: 0s – loss: 0.3015 – sparse_categorical_accuracy: 0.9623
632/938 [
=>…] – ETA: 0s – loss: 0.3034 – sparse_categorical_accuracy: 0.9619
652/938 [
=>…] – ETA: 0s – loss: 0.3012 – sparse_categorical_accuracy: 0.9617
659/938 [
>…] – ETA: 0s – loss: 0.3007 – sparse_categorical_accuracy: 0.9618
669/938 [
>…] – ETA: 0s – loss: 0.3052 – sparse_categorical_accuracy: 0.9617
683/938 [
>…] – ETA: 0s – loss: 0.3036 – sparse_categorical_accuracy: 0.9618
698/938 [
=>…] – ETA: 0s – loss: 0.3034 – sparse_categorical_accuracy: 0.9618
715/938 [
=>…] – ETA: 0s – loss: 0.3023 – sparse_categorical_accuracy: 0.9619
735/938 [
>…] – ETA: 0s – loss: 0.3019 – sparse_categorical_accuracy: 0.9619
755/938 [
=>…] – ETA: 0s – loss: 0.3008 – sparse_categorical_accuracy: 0.9617
775/938 [
=>…] – ETA: 0s – loss: 0.3032 – sparse_categorical_accuracy: 0.9614
795/938 [
>…] – ETA: 0s – loss: 0.3049 – sparse_categorical_accuracy: 0.9610
815/938 [
=>…] – ETA: 0s – loss: 0.3035 – sparse_categorical_accuracy: 0.9612
835/938 [
=>…] – ETA: 0s – loss: 0.3045 – sparse_categorical_accuracy: 0.9611
855/938 [
>…] – ETA: 0s – loss: 0.3031 – sparse_categorical_accuracy: 0.9613
875/938 [
>…] – ETA: 0s – loss: 0.3006 – sparse_categorical_accuracy: 0.9613
895/938 [
=>…] – ETA: 0s – loss: 0.2981 – sparse_categorical_accuracy: 0.9615
915/938 [
>.] – ETA: 0s – loss: 0.2965 – sparse_categorical_accuracy: 0.9618
934/938 [
>.] – ETA: 0s – loss: 0.2934 – sparse_categorical_accuracy: 0.9622
938/938 [
================] – 3s 3ms/step – loss: 0.2942 – sparse_categorical_accuracy: 0.9622
Epoch 7/10

1/938 […] – ETA: 14s – loss: 0.2891 – sparse_categorical_accuracy: 0.9531
20/938 […] – ETA: 3s – loss: 0.5150 – sparse_categorical_accuracy: 0.9516
39/938 [>…] – ETA: 2s – loss: 0.3791 – sparse_categorical_accuracy: 0.9607
58/938 [>…] – ETA: 2s – loss: 0.3416 – sparse_categorical_accuracy: 0.9628
78/938 [=>…] – ETA: 2s – loss: 0.3253 – sparse_categorical_accuracy: 0.9647
95/938 [>…] – ETA: 2s – loss: 0.3040 – sparse_categorical_accuracy: 0.9650
113/938 [
>…] – ETA: 2s – loss: 0.3009 – sparse_categorical_accuracy: 0.9653
131/938 [=>…] – ETA: 2s – loss: 0.2967 – sparse_categorical_accuracy: 0.9658
150/938 [
=>…] – ETA: 2s – loss: 0.3071 – sparse_categorical_accuracy: 0.9642
170/938 [>…] – ETA: 2s – loss: 0.3032 – sparse_categorical_accuracy: 0.9632
190/938 [
=>…] – ETA: 2s – loss: 0.2886 – sparse_categorical_accuracy: 0.9641
210/938 [=>…] – ETA: 2s – loss: 0.2835 – sparse_categorical_accuracy: 0.9641
230/938 [
>…] – ETA: 1s – loss: 0.2799 – sparse_categorical_accuracy: 0.9637
250/938 [
>…] – ETA: 1s – loss: 0.2875 – sparse_categorical_accuracy: 0.9633
269/938 [
=>…] – ETA: 1s – loss: 0.2835 – sparse_categorical_accuracy: 0.9637
289/938 [
>…] – ETA: 1s – loss: 0.2860 – sparse_categorical_accuracy: 0.9642
308/938 [
>…] – ETA: 1s – loss: 0.2822 – sparse_categorical_accuracy: 0.9645
328/938 [
=>…] – ETA: 1s – loss: 0.2818 – sparse_categorical_accuracy: 0.9648
348/938 [
>…] – ETA: 1s – loss: 0.2754 – sparse_categorical_accuracy: 0.9652
368/938 [
>…] – ETA: 1s – loss: 0.2699 – sparse_categorical_accuracy: 0.9656
388/938 [
=>…] – ETA: 1s – loss: 0.2685 – sparse_categorical_accuracy: 0.9656
408/938 [
>…] – ETA: 1s – loss: 0.2645 – sparse_categorical_accuracy: 0.9661
427/938 [
>…] – ETA: 1s – loss: 0.2708 – sparse_categorical_accuracy: 0.9659
447/938 [
=>…] – ETA: 1s – loss: 0.2707 – sparse_categorical_accuracy: 0.9659
467/938 [
=>…] – ETA: 1s – loss: 0.2683 – sparse_categorical_accuracy: 0.9659
487/938 [
>…] – ETA: 1s – loss: 0.2759 – sparse_categorical_accuracy: 0.9654
507/938 [
=>…] – ETA: 1s – loss: 0.2790 – sparse_categorical_accuracy: 0.9650
527/938 [
=>…] – ETA: 1s – loss: 0.2810 – sparse_categorical_accuracy: 0.9655
547/938 [
>…] – ETA: 1s – loss: 0.2770 – sparse_categorical_accuracy: 0.9658
568/938 [
=>…] – ETA: 0s – loss: 0.2746 – sparse_categorical_accuracy: 0.9658
587/938 [
=>…] – ETA: 0s – loss: 0.2731 – sparse_categorical_accuracy: 0.9660
607/938 [
>…] – ETA: 0s – loss: 0.2742 – sparse_categorical_accuracy: 0.9658
627/938 [
=>…] – ETA: 0s – loss: 0.2732 – sparse_categorical_accuracy: 0.9657
646/938 [
=>…] – ETA: 0s – loss: 0.2742 – sparse_categorical_accuracy: 0.9655
666/938 [
>…] – ETA: 0s – loss: 0.2761 – sparse_categorical_accuracy: 0.9654
686/938 [
>…] – ETA: 0s – loss: 0.2751 – sparse_categorical_accuracy: 0.9656
706/938 [
=>…] – ETA: 0s – loss: 0.2755 – sparse_categorical_accuracy: 0.9657
726/938 [
>…] – ETA: 0s – loss: 0.2738 – sparse_categorical_accuracy: 0.9659
746/938 [
>…] – ETA: 0s – loss: 0.2716 – sparse_categorical_accuracy: 0.9658
766/938 [
=>…] – ETA: 0s – loss: 0.2711 – sparse_categorical_accuracy: 0.9658
786/938 [
>…] – ETA: 0s – loss: 0.2748 – sparse_categorical_accuracy: 0.9655
806/938 [
>…] – ETA: 0s – loss: 0.2741 – sparse_categorical_accuracy: 0.9653
826/938 [
=>…] – ETA: 0s – loss: 0.2735 – sparse_categorical_accuracy: 0.9655
846/938 [
>…] – ETA: 0s – loss: 0.2721 – sparse_categorical_accuracy: 0.9654
866/938 [
>…] – ETA: 0s – loss: 0.2708 – sparse_categorical_accuracy: 0.9656
885/938 [
=>…] – ETA: 0s – loss: 0.2688 – sparse_categorical_accuracy: 0.9657
905/938 [
=>…] – ETA: 0s – loss: 0.2675 – sparse_categorical_accuracy: 0.9658
925/938 [
>.] – ETA: 0s – loss: 0.2653 – sparse_categorical_accuracy: 0.9661
938/938 [
============] – 2s 3ms/step – loss: 0.2671 – sparse_categorical_accuracy: 0.9661
Epoch 8/10

1/938 […] – ETA: 13s – loss: 0.4572 – sparse_categorical_accuracy: 0.9531
20/938 […] – ETA: 2s – loss: 0.2865 – sparse_categorical_accuracy: 0.9570
40/938 [>…] – ETA: 2s – loss: 0.2358 – sparse_categorical_accuracy: 0.9652
60/938 [>…] – ETA: 2s – loss: 0.2004 – sparse_categorical_accuracy: 0.9698
80/938 [=>…] – ETA: 2s – loss: 0.2039 – sparse_categorical_accuracy: 0.9682
99/938 [>…] – ETA: 2s – loss: 0.2027 – sparse_categorical_accuracy: 0.9684
119/938 [
>…] – ETA: 2s – loss: 0.2185 – sparse_categorical_accuracy: 0.9685
139/938 [=>…] – ETA: 2s – loss: 0.2562 – sparse_categorical_accuracy: 0.9666
159/938 [
>…] – ETA: 2s – loss: 0.2403 – sparse_categorical_accuracy: 0.9674
179/938 [
>…] – ETA: 2s – loss: 0.2248 – sparse_categorical_accuracy: 0.9681
199/938 [
=>…] – ETA: 1s – loss: 0.2244 – sparse_categorical_accuracy: 0.9680
219/938 [
>…] – ETA: 1s – loss: 0.2178 – sparse_categorical_accuracy: 0.9684
239/938 [
>…] – ETA: 1s – loss: 0.2221 – sparse_categorical_accuracy: 0.9682
259/938 [
=>…] – ETA: 1s – loss: 0.2192 – sparse_categorical_accuracy: 0.9683
279/938 [
=>…] – ETA: 1s – loss: 0.2232 – sparse_categorical_accuracy: 0.9682
300/938 [
>…] – ETA: 1s – loss: 0.2235 – sparse_categorical_accuracy: 0.9686
320/938 [
=>…] – ETA: 1s – loss: 0.2234 – sparse_categorical_accuracy: 0.9689
339/938 [
=>…] – ETA: 1s – loss: 0.2204 – sparse_categorical_accuracy: 0.9692
358/938 [
>…] – ETA: 1s – loss: 0.2159 – sparse_categorical_accuracy: 0.9696
377/938 [
=>…] – ETA: 1s – loss: 0.2145 – sparse_categorical_accuracy: 0.9695
387/938 [
=>…] – ETA: 1s – loss: 0.2137 – sparse_categorical_accuracy: 0.9694
391/938 [
=>…] – ETA: 1s – loss: 0.2159 – sparse_categorical_accuracy: 0.9693
401/938 [
=>…] – ETA: 1s – loss: 0.2172 – sparse_categorical_accuracy: 0.9692
416/938 [
>…] – ETA: 1s – loss: 0.2170 – sparse_categorical_accuracy: 0.9691
434/938 [
>…] – ETA: 1s – loss: 0.2176 – sparse_categorical_accuracy: 0.9689
454/938 [
=>…] – ETA: 1s – loss: 0.2156 – sparse_categorical_accuracy: 0.9690
474/938 [
>…] – ETA: 1s – loss: 0.2162 – sparse_categorical_accuracy: 0.9687
495/938 [
>…] – ETA: 1s – loss: 0.2145 – sparse_categorical_accuracy: 0.9688
515/938 [
=>…] – ETA: 1s – loss: 0.2161 – sparse_categorical_accuracy: 0.9686
535/938 [
>…] – ETA: 1s – loss: 0.2154 – sparse_categorical_accuracy: 0.9690
555/938 [
>…] – ETA: 1s – loss: 0.2159 – sparse_categorical_accuracy: 0.9692
575/938 [
=>…] – ETA: 1s – loss: 0.2141 – sparse_categorical_accuracy: 0.9693
595/938 [
>…] – ETA: 0s – loss: 0.2155 – sparse_categorical_accuracy: 0.9690
615/938 [
>…] – ETA: 0s – loss: 0.2167 – sparse_categorical_accuracy: 0.9691
636/938 [
=>…] – ETA: 0s – loss: 0.2177 – sparse_categorical_accuracy: 0.9690
656/938 [
=>…] – ETA: 0s – loss: 0.2194 – sparse_categorical_accuracy: 0.9689
676/938 [
>…] – ETA: 0s – loss: 0.2231 – sparse_categorical_accuracy: 0.9688
696/938 [
=>…] – ETA: 0s – loss: 0.2221 – sparse_categorical_accuracy: 0.9690
716/938 [
=>…] – ETA: 0s – loss: 0.2217 – sparse_categorical_accuracy: 0.9691
736/938 [
>…] – ETA: 0s – loss: 0.2206 – sparse_categorical_accuracy: 0.9692
755/938 [
=>…] – ETA: 0s – loss: 0.2203 – sparse_categorical_accuracy: 0.9692
775/938 [
=>…] – ETA: 0s – loss: 0.2223 – sparse_categorical_accuracy: 0.9690
795/938 [
>…] – ETA: 0s – loss: 0.2256 – sparse_categorical_accuracy: 0.9687
814/938 [
=>…] – ETA: 0s – loss: 0.2252 – sparse_categorical_accuracy: 0.9687
834/938 [
=>…] – ETA: 0s – loss: 0.2269 – sparse_categorical_accuracy: 0.9684
854/938 [
>…] – ETA: 0s – loss: 0.2258 – sparse_categorical_accuracy: 0.9686
874/938 [
>…] – ETA: 0s – loss: 0.2245 – sparse_categorical_accuracy: 0.9686
894/938 [
=>…] – ETA: 0s – loss: 0.2233 – sparse_categorical_accuracy: 0.9687
913/938 [
>.] – ETA: 0s – loss: 0.2245 – sparse_categorical_accuracy: 0.9689
933/938 [
>.] – ETA: 0s – loss: 0.2232 – sparse_categorical_accuracy: 0.9690
938/938 [
================] – 3s 3ms/step – loss: 0.2250 – sparse_categorical_accuracy: 0.9690
Epoch 9/10

1/938 […] – ETA: 14s – loss: 0.5676 – sparse_categorical_accuracy: 0.9688
19/938 […] – ETA: 3s – loss: 0.3787 – sparse_categorical_accuracy: 0.9704
39/938 [>…] – ETA: 2s – loss: 0.2861 – sparse_categorical_accuracy: 0.9704
58/938 [>…] – ETA: 2s – loss: 0.2416 – sparse_categorical_accuracy: 0.9739
78/938 [=>…] – ETA: 2s – loss: 0.2319 – sparse_categorical_accuracy: 0.9722
98/938 [>…] – ETA: 2s – loss: 0.2357 – sparse_categorical_accuracy: 0.9721
117/938 [
>…] – ETA: 2s – loss: 0.2361 – sparse_categorical_accuracy: 0.9728
137/938 [=>…] – ETA: 2s – loss: 0.2492 – sparse_categorical_accuracy: 0.9708
158/938 [
>…] – ETA: 2s – loss: 0.2434 – sparse_categorical_accuracy: 0.9704
177/938 [
>…] – ETA: 2s – loss: 0.2318 – sparse_categorical_accuracy: 0.9704
197/938 [
=>…] – ETA: 2s – loss: 0.2241 – sparse_categorical_accuracy: 0.9714
217/938 [
=>…] – ETA: 1s – loss: 0.2207 – sparse_categorical_accuracy: 0.9718
237/938 [
>…] – ETA: 1s – loss: 0.2289 – sparse_categorical_accuracy: 0.9708
257/938 [
=>…] – ETA: 1s – loss: 0.2276 – sparse_categorical_accuracy: 0.9701
277/938 [
=>…] – ETA: 1s – loss: 0.2316 – sparse_categorical_accuracy: 0.9700
297/938 [
>…] – ETA: 1s – loss: 0.2343 – sparse_categorical_accuracy: 0.9702
317/938 [
=>…] – ETA: 1s – loss: 0.2365 – sparse_categorical_accuracy: 0.9701
337/938 [
=>…] – ETA: 1s – loss: 0.2391 – sparse_categorical_accuracy: 0.9702
357/938 [
>…] – ETA: 1s – loss: 0.2335 – sparse_categorical_accuracy: 0.9702
378/938 [
=>…] – ETA: 1s – loss: 0.2302 – sparse_categorical_accuracy: 0.9707
398/938 [
=>…] – ETA: 1s – loss: 0.2274 – sparse_categorical_accuracy: 0.9708
418/938 [
>…] – ETA: 1s – loss: 0.2302 – sparse_categorical_accuracy: 0.9704
438/938 [
=>…] – ETA: 1s – loss: 0.2325 – sparse_categorical_accuracy: 0.9704
458/938 [
=>…] – ETA: 1s – loss: 0.2327 – sparse_categorical_accuracy: 0.9706
478/938 [
>…] – ETA: 1s – loss: 0.2289 – sparse_categorical_accuracy: 0.9706
498/938 [
>…] – ETA: 1s – loss: 0.2289 – sparse_categorical_accuracy: 0.9707
518/938 [
=>…] – ETA: 1s – loss: 0.2293 – sparse_categorical_accuracy: 0.9706
538/938 [
>…] – ETA: 1s – loss: 0.2310 – sparse_categorical_accuracy: 0.9707
558/938 [
>…] – ETA: 0s – loss: 0.2323 – sparse_categorical_accuracy: 0.9707
578/938 [
=>…] – ETA: 0s – loss: 0.2300 – sparse_categorical_accuracy: 0.9708
598/938 [
>…] – ETA: 0s – loss: 0.2308 – sparse_categorical_accuracy: 0.9705
618/938 [
>…] – ETA: 0s – loss: 0.2370 – sparse_categorical_accuracy: 0.9703
638/938 [
=>…] – ETA: 0s – loss: 0.2393 – sparse_categorical_accuracy: 0.9702
659/938 [
>…] – ETA: 0s – loss: 0.2435 – sparse_categorical_accuracy: 0.9700
679/938 [
>…] – ETA: 0s – loss: 0.2435 – sparse_categorical_accuracy: 0.9700
699/938 [
=>…] – ETA: 0s – loss: 0.2425 – sparse_categorical_accuracy: 0.9701
719/938 [
=>…] – ETA: 0s – loss: 0.2432 – sparse_categorical_accuracy: 0.9700
739/938 [
>…] – ETA: 0s – loss: 0.2412 – sparse_categorical_accuracy: 0.9701
759/938 [
=>…] – ETA: 0s – loss: 0.2423 – sparse_categorical_accuracy: 0.9700
779/938 [
=>…] – ETA: 0s – loss: 0.2462 – sparse_categorical_accuracy: 0.9698
799/938 [
>…] – ETA: 0s – loss: 0.2459 – sparse_categorical_accuracy: 0.9696
819/938 [
=>…] – ETA: 0s – loss: 0.2442 – sparse_categorical_accuracy: 0.9697
839/938 [
=>…] – ETA: 0s – loss: 0.2433 – sparse_categorical_accuracy: 0.9698
859/938 [
>…] – ETA: 0s – loss: 0.2424 – sparse_categorical_accuracy: 0.9698
879/938 [
=>…] – ETA: 0s – loss: 0.2405 – sparse_categorical_accuracy: 0.9699
899/938 [
=>…] – ETA: 0s – loss: 0.2390 – sparse_categorical_accuracy: 0.9700
919/938 [
>.] – ETA: 0s – loss: 0.2380 – sparse_categorical_accuracy: 0.9702
938/938 [
================] – 2s 3ms/step – loss: 0.2385 – sparse_categorical_accuracy: 0.9703
Epoch 10/10

1/938 […] – ETA: 14s – loss: 0.0725 – sparse_categorical_accuracy: 0.9844
20/938 […] – ETA: 3s – loss: 0.3122 – sparse_categorical_accuracy: 0.9680
40/938 [>…] – ETA: 2s – loss: 0.2266 – sparse_categorical_accuracy: 0.9727
60/938 [>…] – ETA: 2s – loss: 0.2137 – sparse_categorical_accuracy: 0.9747
80/938 [=>…] – ETA: 2s – loss: 0.2240 – sparse_categorical_accuracy: 0.9740
100/938 [>…] – ETA: 2s – loss: 0.2429 – sparse_categorical_accuracy: 0.9730
120/938 [
>…] – ETA: 2s – loss: 0.2390 – sparse_categorical_accuracy: 0.9732
140/938 [=>…] – ETA: 2s – loss: 0.2509 – sparse_categorical_accuracy: 0.9714
160/938 [
>…] – ETA: 2s – loss: 0.2450 – sparse_categorical_accuracy: 0.9709
180/938 [
>…] – ETA: 2s – loss: 0.2335 – sparse_categorical_accuracy: 0.9713
200/938 [
=>…] – ETA: 1s – loss: 0.2335 – sparse_categorical_accuracy: 0.9720
220/938 [
>…] – ETA: 1s – loss: 0.2226 – sparse_categorical_accuracy: 0.9723
240/938 [
>…] – ETA: 1s – loss: 0.2288 – sparse_categorical_accuracy: 0.9720
260/938 [
=>…] – ETA: 1s – loss: 0.2262 – sparse_categorical_accuracy: 0.9726
280/938 [
=>…] – ETA: 1s – loss: 0.2322 – sparse_categorical_accuracy: 0.9722
299/938 [
>…] – ETA: 1s – loss: 0.2260 – sparse_categorical_accuracy: 0.9725
315/938 [
=>…] – ETA: 1s – loss: 0.2245 – sparse_categorical_accuracy: 0.9729
335/938 [
=>…] – ETA: 1s – loss: 0.2242 – sparse_categorical_accuracy: 0.9727
355/938 [
>…] – ETA: 1s – loss: 0.2235 – sparse_categorical_accuracy: 0.9728
362/938 [
>…] – ETA: 1s – loss: 0.2231 – sparse_categorical_accuracy: 0.9728
375/938 [
>…] – ETA: 1s – loss: 0.2236 – sparse_categorical_accuracy: 0.9727
389/938 [
=>…] – ETA: 1s – loss: 0.2228 – sparse_categorical_accuracy: 0.9728
404/938 [
=>…] – ETA: 1s – loss: 0.2269 – sparse_categorical_accuracy: 0.9726
421/938 [
>…] – ETA: 1s – loss: 0.2309 – sparse_categorical_accuracy: 0.9725
438/938 [
=>…] – ETA: 1s – loss: 0.2292 – sparse_categorical_accuracy: 0.9726
456/938 [
=>…] – ETA: 1s – loss: 0.2315 – sparse_categorical_accuracy: 0.9724
475/938 [
>…] – ETA: 1s – loss: 0.2302 – sparse_categorical_accuracy: 0.9722
495/938 [
>…] – ETA: 1s – loss: 0.2279 – sparse_categorical_accuracy: 0.9725
515/938 [
=>…] – ETA: 1s – loss: 0.2301 – sparse_categorical_accuracy: 0.9722
534/938 [
>…] – ETA: 1s – loss: 0.2293 – sparse_categorical_accuracy: 0.9724
554/938 [
>…] – ETA: 1s – loss: 0.2276 – sparse_categorical_accuracy: 0.9726
574/938 [
=>…] – ETA: 1s – loss: 0.2264 – sparse_categorical_accuracy: 0.9726
594/938 [
=>…] – ETA: 0s – loss: 0.2265 – sparse_categorical_accuracy: 0.9724
614/938 [
>…] – ETA: 0s – loss: 0.2267 – sparse_categorical_accuracy: 0.9725
634/938 [
=>…] – ETA: 0s – loss: 0.2255 – sparse_categorical_accuracy: 0.9726
654/938 [
=>…] – ETA: 0s – loss: 0.2259 – sparse_categorical_accuracy: 0.9728
673/938 [
>…] – ETA: 0s – loss: 0.2284 – sparse_categorical_accuracy: 0.9727
693/938 [
=>…] – ETA: 0s – loss: 0.2280 – sparse_categorical_accuracy: 0.9728
711/938 [
=>…] – ETA: 0s – loss: 0.2263 – sparse_categorical_accuracy: 0.9729
728/938 [
>…] – ETA: 0s – loss: 0.2262 – sparse_categorical_accuracy: 0.9729
745/938 [
>…] – ETA: 0s – loss: 0.2286 – sparse_categorical_accuracy: 0.9728
764/938 [
=>…] – ETA: 0s – loss: 0.2269 – sparse_categorical_accuracy: 0.9728
783/938 [
>…] – ETA: 0s – loss: 0.2305 – sparse_categorical_accuracy: 0.9728
803/938 [
>…] – ETA: 0s – loss: 0.2327 – sparse_categorical_accuracy: 0.9726
823/938 [
=>…] – ETA: 0s – loss: 0.2306 – sparse_categorical_accuracy: 0.9727
843/938 [
=>…] – ETA: 0s – loss: 0.2311 – sparse_categorical_accuracy: 0.9726
863/938 [
>…] – ETA: 0s – loss: 0.2303 – sparse_categorical_accuracy: 0.9727
883/938 [
=>…] – ETA: 0s – loss: 0.2281 – sparse_categorical_accuracy: 0.9728
902/938 [
=>…] – ETA: 0s – loss: 0.2261 – sparse_categorical_accuracy: 0.9730
922/938 [
>.] – ETA: 0s – loss: 0.2235 – sparse_categorical_accuracy: 0.9732
938/938 [
==============] – 3s 3ms/step – loss: 0.2235 – sparse_categorical_accuracy: 0.9733

model evaluation

model.evaluate(test_dataset)

1/157 […] – ETA: 21s – loss: 2.3587e-04 – sparse_categorical_accuracy: 1.0000
34/157 [=>…] – ETA: 0s – loss: 0.7066 – sparse_categorical_accuracy: 0.9453
69/157 [
>…] – ETA: 0s – loss: 0.8308 – sparse_categorical_accuracy: 0.9443
81/157 [
>…] – ETA: 0s – loss: 0.8148 – sparse_categorical_accuracy: 0.9441
89/157 [
>…] – ETA: 0s – loss: 0.7783 – sparse_categorical_accuracy: 0.9473
110/157 [
>…] – ETA: 0s – loss: 0.6985 – sparse_categorical_accuracy: 0.9540
141/157 [
=>…] – ETA: 0s – loss: 0.5986 – sparse_categorical_accuracy: 0.9580
157/157 [
================] – 0s 3ms/step – loss: 0.5758 – sparse_categorical_accuracy: 0.9590
[0.5758481910577826, 0.959]

source

Related Posts

Android Ijkplayer compile and import iJKPlayer-EXAMPLE example

Report using related knowledge and skills FM78

FRESCO’s packaging and use instructions and obtaining the Bitmap object in the cache

[CSS] Border frame Dora

Googleads unity plugin Chinese tutorial latest version

Random Posts

HDU 6053 Reversing DP or Mobius Reverse

SpringBoot @ConfigurationProperties use and garbled problem

2021-03-02 COMP9021 Fifth Lesson Note

PHP converts the network picture into Base64 format to solve the cross -domain problem of HTML2CANVAS pictures

The Road to Domestic Exploration — Winning Kirin System (VMware Virtual Machine Installation + Real Machine Installation)