268 KiB
268 KiB
import numpy as np
import pandas as pd
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
def build_corpus(file_list):
documents = []
for file in file_list:
with open(file, 'r', encoding="utf8") as f:
for line in f:
processed_line = simple_preprocess(line)
documents.append(processed_line)
return documentsdef text_to_vector(text, model):
tokens = simple_preprocess(text)
word_vectors = [model.wv[token] for token in tokens if token in model.wv]
if word_vectors:
return np.mean(word_vectors, axis=0)
else:
return np.zeros(model.vector_size)def read_text_file(filepath):
lines = []
with open(filepath, 'r', encoding="utf8") as file:
for line in file:
lines.append(line.strip())
return linesdef write_predictions_to_file(predictions, filepath):
with open(filepath, 'w', encoding="utf8") as file:
for prediction in predictions:
file.write(f"{prediction[0]}\n")documents = build_corpus(['dev-0/in.tsv', 'test-A/in.tsv'])
word2vec_model = Word2Vec(sentences=documents, vector_size=100, window=5, min_count=1, workers=4)
word2vec_model.save("word2vec.model")
dev_texts = read_text_file('dev-0/in.tsv')
test_texts = read_text_file('test-A/in.tsv')
dev_features = np.array([text_to_vector(text, word2vec_model) for text in dev_texts])
test_features = np.array([text_to_vector(text, word2vec_model) for text in test_texts])
dev_labels = pd.read_csv('dev-0/expected.tsv', sep='\t', header=None).values.flatten()
X_train, X_valid, y_train, y_valid = train_test_split(dev_features, dev_labels, test_size=0.2, random_state=42)
neural_network = Sequential([
Dense(64, activation='relu', input_shape=(100,)),
Dense(32, activation='relu'),
Dense(1, activation='sigmoid')
])
neural_network.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
training_history = neural_network.fit(X_train, y_train, epochs=1000, batch_size=32, validation_data=(X_valid, y_valid))
dev_predictions_raw = neural_network.predict(dev_features)
test_predictions_raw = neural_network.predict(test_features)
dev_predictions = (dev_predictions_raw > 0.5).astype(int)
test_predictions = (test_predictions_raw > 0.5).astype(int)
write_predictions_to_file(dev_predictions, 'dev-0/out.tsv')
write_predictions_to_file(test_predictions, 'test-A/out.tsv')
Epoch 1/1000
c:\Users\mateu\anaconda3\lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
[1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 3ms/step - accuracy: 0.6695 - loss: 0.5541 - val_accuracy: 0.8258 - val_loss: 0.3704 Epoch 2/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8517 - loss: 0.3327 - val_accuracy: 0.8433 - val_loss: 0.3428 Epoch 3/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8496 - loss: 0.3206 - val_accuracy: 0.8313 - val_loss: 0.3492 Epoch 4/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8634 - loss: 0.2961 - val_accuracy: 0.8414 - val_loss: 0.3361 Epoch 5/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8571 - loss: 0.3020 - val_accuracy: 0.8442 - val_loss: 0.3439 Epoch 6/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8655 - loss: 0.2932 - val_accuracy: 0.8368 - val_loss: 0.3387 Epoch 7/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8744 - loss: 0.2790 - val_accuracy: 0.8313 - val_loss: 0.3474 Epoch 8/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8717 - loss: 0.2764 - val_accuracy: 0.8543 - val_loss: 0.3247 Epoch 9/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8669 - loss: 0.2731 - val_accuracy: 0.8478 - val_loss: 0.3360 Epoch 10/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8642 - loss: 0.2950 - val_accuracy: 0.8515 - val_loss: 0.3323 Epoch 11/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8670 - loss: 0.2797 - val_accuracy: 0.8579 - val_loss: 0.3148 Epoch 12/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8736 - loss: 0.2718 - val_accuracy: 0.8561 - val_loss: 0.3161 Epoch 13/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8650 - loss: 0.2835 - val_accuracy: 0.8552 - val_loss: 0.3130 Epoch 14/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8709 - loss: 0.2772 - val_accuracy: 0.8561 - val_loss: 0.3146 Epoch 15/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8787 - loss: 0.2575 - val_accuracy: 0.8313 - val_loss: 0.3488 Epoch 16/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8690 - loss: 0.2716 - val_accuracy: 0.8570 - val_loss: 0.3126 Epoch 17/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8757 - loss: 0.2791 - val_accuracy: 0.8579 - val_loss: 0.3060 Epoch 18/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8799 - loss: 0.2637 - val_accuracy: 0.8286 - val_loss: 0.3696 Epoch 19/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8711 - loss: 0.2741 - val_accuracy: 0.8588 - val_loss: 0.3126 Epoch 20/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8728 - loss: 0.2667 - val_accuracy: 0.8588 - val_loss: 0.3104 Epoch 21/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8786 - loss: 0.2623 - val_accuracy: 0.8625 - val_loss: 0.3045 Epoch 22/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8685 - loss: 0.2739 - val_accuracy: 0.8616 - val_loss: 0.3074 Epoch 23/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8856 - loss: 0.2596 - val_accuracy: 0.8607 - val_loss: 0.3026 Epoch 24/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8819 - loss: 0.2513 - val_accuracy: 0.8460 - val_loss: 0.3318 Epoch 25/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8788 - loss: 0.2670 - val_accuracy: 0.8598 - val_loss: 0.3004 Epoch 26/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8875 - loss: 0.2411 - val_accuracy: 0.8607 - val_loss: 0.3046 Epoch 27/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8728 - loss: 0.2773 - val_accuracy: 0.8570 - val_loss: 0.3078 Epoch 28/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8733 - loss: 0.2631 - val_accuracy: 0.8598 - val_loss: 0.2978 Epoch 29/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8817 - loss: 0.2624 - val_accuracy: 0.8570 - val_loss: 0.2989 Epoch 30/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8868 - loss: 0.2405 - val_accuracy: 0.8598 - val_loss: 0.3063 Epoch 31/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8782 - loss: 0.2612 - val_accuracy: 0.8662 - val_loss: 0.3172 Epoch 32/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8790 - loss: 0.2623 - val_accuracy: 0.8634 - val_loss: 0.2906 Epoch 33/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8743 - loss: 0.2586 - val_accuracy: 0.8607 - val_loss: 0.3055 Epoch 34/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8822 - loss: 0.2609 - val_accuracy: 0.8616 - val_loss: 0.3042 Epoch 35/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8830 - loss: 0.2508 - val_accuracy: 0.8607 - val_loss: 0.3090 Epoch 36/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8905 - loss: 0.2489 - val_accuracy: 0.8607 - val_loss: 0.2981 Epoch 37/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8826 - loss: 0.2505 - val_accuracy: 0.8625 - val_loss: 0.3003 Epoch 38/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8894 - loss: 0.2444 - val_accuracy: 0.8653 - val_loss: 0.2879 Epoch 39/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8826 - loss: 0.2530 - val_accuracy: 0.8598 - val_loss: 0.3056 Epoch 40/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8901 - loss: 0.2478 - val_accuracy: 0.8625 - val_loss: 0.2877 Epoch 41/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8918 - loss: 0.2420 - val_accuracy: 0.8671 - val_loss: 0.3057 Epoch 42/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8782 - loss: 0.2495 - val_accuracy: 0.8598 - val_loss: 0.3100 Epoch 43/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8762 - loss: 0.2696 - val_accuracy: 0.8671 - val_loss: 0.2878 Epoch 44/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8848 - loss: 0.2449 - val_accuracy: 0.8671 - val_loss: 0.2874 Epoch 45/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8815 - loss: 0.2522 - val_accuracy: 0.8726 - val_loss: 0.2847 Epoch 46/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8877 - loss: 0.2450 - val_accuracy: 0.8469 - val_loss: 0.3448 Epoch 47/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8884 - loss: 0.2631 - val_accuracy: 0.8561 - val_loss: 0.3236 Epoch 48/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8840 - loss: 0.2530 - val_accuracy: 0.8680 - val_loss: 0.2843 Epoch 49/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8868 - loss: 0.2437 - val_accuracy: 0.8662 - val_loss: 0.2856 Epoch 50/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8824 - loss: 0.2461 - val_accuracy: 0.8662 - val_loss: 0.2875 Epoch 51/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8826 - loss: 0.2454 - val_accuracy: 0.8671 - val_loss: 0.2905 Epoch 52/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8888 - loss: 0.2399 - val_accuracy: 0.8708 - val_loss: 0.2866 Epoch 53/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8856 - loss: 0.2471 - val_accuracy: 0.8717 - val_loss: 0.2832 Epoch 54/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8953 - loss: 0.2306 - val_accuracy: 0.8671 - val_loss: 0.2843 Epoch 55/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8931 - loss: 0.2271 - val_accuracy: 0.8708 - val_loss: 0.2883 Epoch 56/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8865 - loss: 0.2468 - val_accuracy: 0.8643 - val_loss: 0.2837 Epoch 57/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8905 - loss: 0.2391 - val_accuracy: 0.8708 - val_loss: 0.2781 Epoch 58/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8894 - loss: 0.2494 - val_accuracy: 0.8689 - val_loss: 0.2833 Epoch 59/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8942 - loss: 0.2285 - val_accuracy: 0.8708 - val_loss: 0.2843 Epoch 60/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8928 - loss: 0.2375 - val_accuracy: 0.8671 - val_loss: 0.2963 Epoch 61/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8939 - loss: 0.2299 - val_accuracy: 0.8680 - val_loss: 0.2898 Epoch 62/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8889 - loss: 0.2354 - val_accuracy: 0.8708 - val_loss: 0.2799 Epoch 63/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8889 - loss: 0.2359 - val_accuracy: 0.8671 - val_loss: 0.2820 Epoch 64/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8848 - loss: 0.2457 - val_accuracy: 0.8698 - val_loss: 0.2796 Epoch 65/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9009 - loss: 0.2236 - val_accuracy: 0.8781 - val_loss: 0.2799 Epoch 66/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8939 - loss: 0.2306 - val_accuracy: 0.8680 - val_loss: 0.2800 Epoch 67/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8848 - loss: 0.2428 - val_accuracy: 0.8708 - val_loss: 0.2762 Epoch 68/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8942 - loss: 0.2369 - val_accuracy: 0.8726 - val_loss: 0.2747 Epoch 69/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8931 - loss: 0.2317 - val_accuracy: 0.8708 - val_loss: 0.2821 Epoch 70/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8877 - loss: 0.2368 - val_accuracy: 0.8735 - val_loss: 0.2736 Epoch 71/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8896 - loss: 0.2465 - val_accuracy: 0.8708 - val_loss: 0.2758 Epoch 72/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8951 - loss: 0.2335 - val_accuracy: 0.8726 - val_loss: 0.2821 Epoch 73/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8914 - loss: 0.2334 - val_accuracy: 0.8680 - val_loss: 0.2973 Epoch 74/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8905 - loss: 0.2378 - val_accuracy: 0.8763 - val_loss: 0.2763 Epoch 75/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8928 - loss: 0.2287 - val_accuracy: 0.8763 - val_loss: 0.2735 Epoch 76/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8937 - loss: 0.2369 - val_accuracy: 0.8726 - val_loss: 0.2935 Epoch 77/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8804 - loss: 0.2561 - val_accuracy: 0.8753 - val_loss: 0.2910 Epoch 78/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8861 - loss: 0.2370 - val_accuracy: 0.8662 - val_loss: 0.2999 Epoch 79/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8879 - loss: 0.2394 - val_accuracy: 0.8735 - val_loss: 0.2887 Epoch 80/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8866 - loss: 0.2442 - val_accuracy: 0.8744 - val_loss: 0.2738 Epoch 81/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8819 - loss: 0.2450 - val_accuracy: 0.8735 - val_loss: 0.2935 Epoch 82/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8905 - loss: 0.2407 - val_accuracy: 0.8643 - val_loss: 0.2967 Epoch 83/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8804 - loss: 0.2424 - val_accuracy: 0.8717 - val_loss: 0.2720 Epoch 84/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8935 - loss: 0.2295 - val_accuracy: 0.8680 - val_loss: 0.2961 Epoch 85/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8884 - loss: 0.2331 - val_accuracy: 0.8698 - val_loss: 0.2751 Epoch 86/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8943 - loss: 0.2282 - val_accuracy: 0.8735 - val_loss: 0.2669 Epoch 87/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8954 - loss: 0.2188 - val_accuracy: 0.8726 - val_loss: 0.2698 Epoch 88/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9015 - loss: 0.2225 - val_accuracy: 0.8836 - val_loss: 0.2658 Epoch 89/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8878 - loss: 0.2319 - val_accuracy: 0.8799 - val_loss: 0.2670 Epoch 90/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8966 - loss: 0.2383 - val_accuracy: 0.8708 - val_loss: 0.2746 Epoch 91/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8940 - loss: 0.2270 - val_accuracy: 0.8744 - val_loss: 0.2786 Epoch 92/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8899 - loss: 0.2372 - val_accuracy: 0.8772 - val_loss: 0.2681 Epoch 93/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8937 - loss: 0.2258 - val_accuracy: 0.8753 - val_loss: 0.2687 Epoch 94/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8975 - loss: 0.2207 - val_accuracy: 0.8717 - val_loss: 0.2676 Epoch 95/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8967 - loss: 0.2250 - val_accuracy: 0.8753 - val_loss: 0.2756 Epoch 96/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8971 - loss: 0.2244 - val_accuracy: 0.8735 - val_loss: 0.2719 Epoch 97/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8945 - loss: 0.2208 - val_accuracy: 0.8726 - val_loss: 0.2784 Epoch 98/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9031 - loss: 0.2222 - val_accuracy: 0.8726 - val_loss: 0.2691 Epoch 99/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8954 - loss: 0.2350 - val_accuracy: 0.8735 - val_loss: 0.2868 Epoch 100/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8949 - loss: 0.2221 - val_accuracy: 0.8799 - val_loss: 0.2691 Epoch 101/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8899 - loss: 0.2273 - val_accuracy: 0.8763 - val_loss: 0.2775 Epoch 102/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8956 - loss: 0.2256 - val_accuracy: 0.8717 - val_loss: 0.2831 Epoch 103/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9027 - loss: 0.2257 - val_accuracy: 0.8763 - val_loss: 0.2738 Epoch 104/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9048 - loss: 0.2026 - val_accuracy: 0.8634 - val_loss: 0.2977 Epoch 105/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9056 - loss: 0.2171 - val_accuracy: 0.8689 - val_loss: 0.2877 Epoch 106/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9011 - loss: 0.2218 - val_accuracy: 0.8854 - val_loss: 0.2730 Epoch 107/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8927 - loss: 0.2302 - val_accuracy: 0.8772 - val_loss: 0.2653 Epoch 108/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9050 - loss: 0.2031 - val_accuracy: 0.8790 - val_loss: 0.2738 Epoch 109/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9087 - loss: 0.2103 - val_accuracy: 0.8836 - val_loss: 0.2688 Epoch 110/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9009 - loss: 0.2136 - val_accuracy: 0.8744 - val_loss: 0.2690 Epoch 111/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8956 - loss: 0.2290 - val_accuracy: 0.8735 - val_loss: 0.2859 Epoch 112/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9020 - loss: 0.2150 - val_accuracy: 0.8818 - val_loss: 0.2643 Epoch 113/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9015 - loss: 0.2180 - val_accuracy: 0.8808 - val_loss: 0.2858 Epoch 114/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9040 - loss: 0.2096 - val_accuracy: 0.8717 - val_loss: 0.2748 Epoch 115/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8998 - loss: 0.2145 - val_accuracy: 0.8772 - val_loss: 0.2629 Epoch 116/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9027 - loss: 0.2109 - val_accuracy: 0.8753 - val_loss: 0.2662 Epoch 117/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8993 - loss: 0.2232 - val_accuracy: 0.8799 - val_loss: 0.2606 Epoch 118/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8953 - loss: 0.2237 - val_accuracy: 0.8799 - val_loss: 0.2730 Epoch 119/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9001 - loss: 0.2300 - val_accuracy: 0.8845 - val_loss: 0.2627 Epoch 120/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9031 - loss: 0.2078 - val_accuracy: 0.8781 - val_loss: 0.2654 Epoch 121/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8929 - loss: 0.2277 - val_accuracy: 0.8818 - val_loss: 0.2690 Epoch 122/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8945 - loss: 0.2231 - val_accuracy: 0.8763 - val_loss: 0.2725 Epoch 123/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9029 - loss: 0.2125 - val_accuracy: 0.8781 - val_loss: 0.2716 Epoch 124/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9049 - loss: 0.2122 - val_accuracy: 0.8790 - val_loss: 0.2687 Epoch 125/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8994 - loss: 0.2090 - val_accuracy: 0.8680 - val_loss: 0.2960 Epoch 126/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8924 - loss: 0.2325 - val_accuracy: 0.8671 - val_loss: 0.3063 Epoch 127/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9012 - loss: 0.2226 - val_accuracy: 0.8781 - val_loss: 0.2700 Epoch 128/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9030 - loss: 0.2105 - val_accuracy: 0.8598 - val_loss: 0.2991 Epoch 129/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.8984 - loss: 0.2157 - val_accuracy: 0.8781 - val_loss: 0.2673 Epoch 130/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9007 - loss: 0.2159 - val_accuracy: 0.8845 - val_loss: 0.2697 Epoch 131/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9049 - loss: 0.2089 - val_accuracy: 0.8772 - val_loss: 0.2693 Epoch 132/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9036 - loss: 0.2157 - val_accuracy: 0.8799 - val_loss: 0.2741 Epoch 133/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9062 - loss: 0.2093 - val_accuracy: 0.8836 - val_loss: 0.2623 Epoch 134/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9022 - loss: 0.2077 - val_accuracy: 0.8827 - val_loss: 0.2680 Epoch 135/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9005 - loss: 0.2125 - val_accuracy: 0.8772 - val_loss: 0.2646 Epoch 136/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9108 - loss: 0.2043 - val_accuracy: 0.8827 - val_loss: 0.2763 Epoch 137/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9061 - loss: 0.2135 - val_accuracy: 0.8873 - val_loss: 0.2587 Epoch 138/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8985 - loss: 0.2229 - val_accuracy: 0.8781 - val_loss: 0.2951 Epoch 139/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8835 - loss: 0.2445 - val_accuracy: 0.8799 - val_loss: 0.2627 Epoch 140/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9022 - loss: 0.2191 - val_accuracy: 0.8808 - val_loss: 0.2653 Epoch 141/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8941 - loss: 0.2335 - val_accuracy: 0.8818 - val_loss: 0.2671 Epoch 142/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9021 - loss: 0.2220 - val_accuracy: 0.8845 - val_loss: 0.2617 Epoch 143/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9083 - loss: 0.2041 - val_accuracy: 0.8836 - val_loss: 0.2705 Epoch 144/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9046 - loss: 0.2086 - val_accuracy: 0.8854 - val_loss: 0.2607 Epoch 145/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9100 - loss: 0.2101 - val_accuracy: 0.8891 - val_loss: 0.2722 Epoch 146/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9048 - loss: 0.2144 - val_accuracy: 0.8836 - val_loss: 0.2664 Epoch 147/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9072 - loss: 0.2056 - val_accuracy: 0.8836 - val_loss: 0.2673 Epoch 148/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8990 - loss: 0.2224 - val_accuracy: 0.8836 - val_loss: 0.2617 Epoch 149/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8964 - loss: 0.2230 - val_accuracy: 0.8808 - val_loss: 0.2771 Epoch 150/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9038 - loss: 0.2052 - val_accuracy: 0.8900 - val_loss: 0.2670 Epoch 151/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9087 - loss: 0.2023 - val_accuracy: 0.8845 - val_loss: 0.2647 Epoch 152/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8963 - loss: 0.2146 - val_accuracy: 0.8909 - val_loss: 0.2637 Epoch 153/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9039 - loss: 0.2086 - val_accuracy: 0.8827 - val_loss: 0.2647 Epoch 154/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9065 - loss: 0.2134 - val_accuracy: 0.8772 - val_loss: 0.2786 Epoch 155/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8996 - loss: 0.2232 - val_accuracy: 0.8836 - val_loss: 0.2664 Epoch 156/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9088 - loss: 0.2097 - val_accuracy: 0.8689 - val_loss: 0.2887 Epoch 157/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9028 - loss: 0.2188 - val_accuracy: 0.8790 - val_loss: 0.2737 Epoch 158/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9083 - loss: 0.2065 - val_accuracy: 0.8763 - val_loss: 0.2722 Epoch 159/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9052 - loss: 0.2041 - val_accuracy: 0.8845 - val_loss: 0.2772 Epoch 160/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9087 - loss: 0.2130 - val_accuracy: 0.8854 - val_loss: 0.2727 Epoch 161/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9030 - loss: 0.2091 - val_accuracy: 0.8662 - val_loss: 0.3082 Epoch 162/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9025 - loss: 0.2149 - val_accuracy: 0.8818 - val_loss: 0.2666 Epoch 163/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9007 - loss: 0.2126 - val_accuracy: 0.8882 - val_loss: 0.2720 Epoch 164/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9036 - loss: 0.2210 - val_accuracy: 0.8891 - val_loss: 0.2630 Epoch 165/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8998 - loss: 0.2112 - val_accuracy: 0.8900 - val_loss: 0.2567 Epoch 166/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9036 - loss: 0.2241 - val_accuracy: 0.8873 - val_loss: 0.2666 Epoch 167/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9059 - loss: 0.2109 - val_accuracy: 0.8836 - val_loss: 0.2771 Epoch 168/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9026 - loss: 0.2143 - val_accuracy: 0.8827 - val_loss: 0.2680 Epoch 169/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9130 - loss: 0.1934 - val_accuracy: 0.8873 - val_loss: 0.2643 Epoch 170/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8920 - loss: 0.2272 - val_accuracy: 0.8863 - val_loss: 0.2647 Epoch 171/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9054 - loss: 0.2128 - val_accuracy: 0.8873 - val_loss: 0.2600 Epoch 172/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9070 - loss: 0.2026 - val_accuracy: 0.8900 - val_loss: 0.2581 Epoch 173/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9001 - loss: 0.2152 - val_accuracy: 0.8845 - val_loss: 0.2585 Epoch 174/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9101 - loss: 0.2082 - val_accuracy: 0.8781 - val_loss: 0.2773 Epoch 175/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9066 - loss: 0.2163 - val_accuracy: 0.8845 - val_loss: 0.2673 Epoch 176/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9156 - loss: 0.1942 - val_accuracy: 0.8818 - val_loss: 0.2736 Epoch 177/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9030 - loss: 0.2067 - val_accuracy: 0.8726 - val_loss: 0.2855 Epoch 178/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9015 - loss: 0.2270 - val_accuracy: 0.8735 - val_loss: 0.2828 Epoch 179/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9068 - loss: 0.2097 - val_accuracy: 0.8753 - val_loss: 0.2844 Epoch 180/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9093 - loss: 0.2045 - val_accuracy: 0.8781 - val_loss: 0.2797 Epoch 181/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9106 - loss: 0.2079 - val_accuracy: 0.8799 - val_loss: 0.2795 Epoch 182/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9077 - loss: 0.2081 - val_accuracy: 0.8808 - val_loss: 0.2617 Epoch 183/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8979 - loss: 0.2151 - val_accuracy: 0.8909 - val_loss: 0.2600 Epoch 184/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9060 - loss: 0.2027 - val_accuracy: 0.8928 - val_loss: 0.2595 Epoch 185/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9071 - loss: 0.1985 - val_accuracy: 0.8909 - val_loss: 0.2628 Epoch 186/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9030 - loss: 0.2134 - val_accuracy: 0.8790 - val_loss: 0.2716 Epoch 187/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9042 - loss: 0.1951 - val_accuracy: 0.8873 - val_loss: 0.2690 Epoch 188/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9136 - loss: 0.2058 - val_accuracy: 0.8653 - val_loss: 0.2986 Epoch 189/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8865 - loss: 0.2381 - val_accuracy: 0.8863 - val_loss: 0.2595 Epoch 190/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9119 - loss: 0.1963 - val_accuracy: 0.8854 - val_loss: 0.2672 Epoch 191/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9038 - loss: 0.2089 - val_accuracy: 0.8909 - val_loss: 0.2556 Epoch 192/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9003 - loss: 0.2082 - val_accuracy: 0.8918 - val_loss: 0.2642 Epoch 193/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9007 - loss: 0.2127 - val_accuracy: 0.8891 - val_loss: 0.2597 Epoch 194/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9075 - loss: 0.2076 - val_accuracy: 0.8918 - val_loss: 0.2618 Epoch 195/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9022 - loss: 0.2064 - val_accuracy: 0.8946 - val_loss: 0.2659 Epoch 196/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9048 - loss: 0.2214 - val_accuracy: 0.8818 - val_loss: 0.2917 Epoch 197/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9068 - loss: 0.1978 - val_accuracy: 0.8900 - val_loss: 0.2594 Epoch 198/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9101 - loss: 0.2071 - val_accuracy: 0.8900 - val_loss: 0.2639 Epoch 199/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8983 - loss: 0.2111 - val_accuracy: 0.8854 - val_loss: 0.2678 Epoch 200/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9115 - loss: 0.2000 - val_accuracy: 0.8928 - val_loss: 0.2615 Epoch 201/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9095 - loss: 0.1994 - val_accuracy: 0.8882 - val_loss: 0.2639 Epoch 202/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9065 - loss: 0.2018 - val_accuracy: 0.8918 - val_loss: 0.2713 Epoch 203/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9022 - loss: 0.2078 - val_accuracy: 0.8863 - val_loss: 0.2617 Epoch 204/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.2048 - val_accuracy: 0.8827 - val_loss: 0.2697 Epoch 205/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9006 - loss: 0.2129 - val_accuracy: 0.8799 - val_loss: 0.2750 Epoch 206/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9088 - loss: 0.2063 - val_accuracy: 0.8836 - val_loss: 0.2707 Epoch 207/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9111 - loss: 0.2016 - val_accuracy: 0.8854 - val_loss: 0.2706 Epoch 208/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9011 - loss: 0.2117 - val_accuracy: 0.8882 - val_loss: 0.2597 Epoch 209/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9015 - loss: 0.2017 - val_accuracy: 0.8891 - val_loss: 0.2687 Epoch 210/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9120 - loss: 0.2009 - val_accuracy: 0.8808 - val_loss: 0.2769 Epoch 211/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9110 - loss: 0.2000 - val_accuracy: 0.8836 - val_loss: 0.2698 Epoch 212/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9090 - loss: 0.1953 - val_accuracy: 0.8900 - val_loss: 0.2643 Epoch 213/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9139 - loss: 0.1932 - val_accuracy: 0.8808 - val_loss: 0.2778 Epoch 214/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9136 - loss: 0.1944 - val_accuracy: 0.8909 - val_loss: 0.2571 Epoch 215/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9066 - loss: 0.2005 - val_accuracy: 0.8854 - val_loss: 0.2720 Epoch 216/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9091 - loss: 0.2024 - val_accuracy: 0.8882 - val_loss: 0.2636 Epoch 217/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9154 - loss: 0.1851 - val_accuracy: 0.8882 - val_loss: 0.2775 Epoch 218/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9021 - loss: 0.2115 - val_accuracy: 0.8845 - val_loss: 0.2704 Epoch 219/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9098 - loss: 0.1989 - val_accuracy: 0.8891 - val_loss: 0.2642 Epoch 220/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9120 - loss: 0.1992 - val_accuracy: 0.8478 - val_loss: 0.3517 Epoch 221/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.8967 - loss: 0.2336 - val_accuracy: 0.8918 - val_loss: 0.2637 Epoch 222/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9099 - loss: 0.2053 - val_accuracy: 0.8818 - val_loss: 0.2788 Epoch 223/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9084 - loss: 0.2008 - val_accuracy: 0.8818 - val_loss: 0.2726 Epoch 224/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9027 - loss: 0.2092 - val_accuracy: 0.8882 - val_loss: 0.2743 Epoch 225/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9177 - loss: 0.1943 - val_accuracy: 0.8909 - val_loss: 0.2514 Epoch 226/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9106 - loss: 0.2034 - val_accuracy: 0.8918 - val_loss: 0.2650 Epoch 227/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9195 - loss: 0.1890 - val_accuracy: 0.8873 - val_loss: 0.2711 Epoch 228/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9181 - loss: 0.1900 - val_accuracy: 0.8937 - val_loss: 0.2612 Epoch 229/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9069 - loss: 0.1987 - val_accuracy: 0.8772 - val_loss: 0.2905 Epoch 230/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9096 - loss: 0.2033 - val_accuracy: 0.8863 - val_loss: 0.2744 Epoch 231/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9075 - loss: 0.2025 - val_accuracy: 0.8955 - val_loss: 0.2615 Epoch 232/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9139 - loss: 0.1935 - val_accuracy: 0.8781 - val_loss: 0.2783 Epoch 233/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9013 - loss: 0.2027 - val_accuracy: 0.8873 - val_loss: 0.2662 Epoch 234/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9092 - loss: 0.2061 - val_accuracy: 0.8827 - val_loss: 0.2689 Epoch 235/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9071 - loss: 0.1968 - val_accuracy: 0.8937 - val_loss: 0.2618 Epoch 236/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9180 - loss: 0.1938 - val_accuracy: 0.8744 - val_loss: 0.2797 Epoch 237/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9110 - loss: 0.1996 - val_accuracy: 0.8891 - val_loss: 0.2645 Epoch 238/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9163 - loss: 0.1893 - val_accuracy: 0.8827 - val_loss: 0.2781 Epoch 239/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9150 - loss: 0.1966 - val_accuracy: 0.8964 - val_loss: 0.2651 Epoch 240/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9150 - loss: 0.1859 - val_accuracy: 0.8937 - val_loss: 0.2538 Epoch 241/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9144 - loss: 0.1942 - val_accuracy: 0.8928 - val_loss: 0.2537 Epoch 242/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9212 - loss: 0.1837 - val_accuracy: 0.8946 - val_loss: 0.2595 Epoch 243/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9122 - loss: 0.1936 - val_accuracy: 0.8790 - val_loss: 0.2756 Epoch 244/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9114 - loss: 0.1958 - val_accuracy: 0.8918 - val_loss: 0.2546 Epoch 245/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9113 - loss: 0.1983 - val_accuracy: 0.8799 - val_loss: 0.2716 Epoch 246/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9166 - loss: 0.1890 - val_accuracy: 0.8937 - val_loss: 0.2635 Epoch 247/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9139 - loss: 0.1872 - val_accuracy: 0.8882 - val_loss: 0.2686 Epoch 248/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9061 - loss: 0.2019 - val_accuracy: 0.8891 - val_loss: 0.2707 Epoch 249/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9105 - loss: 0.1968 - val_accuracy: 0.8955 - val_loss: 0.2592 Epoch 250/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9211 - loss: 0.1891 - val_accuracy: 0.8790 - val_loss: 0.2843 Epoch 251/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9147 - loss: 0.1973 - val_accuracy: 0.8909 - val_loss: 0.2687 Epoch 252/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9117 - loss: 0.1970 - val_accuracy: 0.8900 - val_loss: 0.2699 Epoch 253/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9163 - loss: 0.1909 - val_accuracy: 0.8882 - val_loss: 0.2775 Epoch 254/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9085 - loss: 0.1998 - val_accuracy: 0.8873 - val_loss: 0.2670 Epoch 255/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9214 - loss: 0.1795 - val_accuracy: 0.8818 - val_loss: 0.2792 Epoch 256/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9130 - loss: 0.1912 - val_accuracy: 0.8808 - val_loss: 0.2709 Epoch 257/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9109 - loss: 0.1958 - val_accuracy: 0.8909 - val_loss: 0.2670 Epoch 258/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9100 - loss: 0.2043 - val_accuracy: 0.8891 - val_loss: 0.2737 Epoch 259/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9126 - loss: 0.1978 - val_accuracy: 0.8937 - val_loss: 0.2584 Epoch 260/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9165 - loss: 0.1918 - val_accuracy: 0.8845 - val_loss: 0.2720 Epoch 261/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9159 - loss: 0.1933 - val_accuracy: 0.8964 - val_loss: 0.2534 Epoch 262/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9131 - loss: 0.1979 - val_accuracy: 0.8983 - val_loss: 0.2582 Epoch 263/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9109 - loss: 0.1932 - val_accuracy: 0.8900 - val_loss: 0.2768 Epoch 264/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9151 - loss: 0.1920 - val_accuracy: 0.8900 - val_loss: 0.2587 Epoch 265/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9077 - loss: 0.2053 - val_accuracy: 0.8882 - val_loss: 0.2654 Epoch 266/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9191 - loss: 0.1924 - val_accuracy: 0.8937 - val_loss: 0.2573 Epoch 267/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9187 - loss: 0.1875 - val_accuracy: 0.8873 - val_loss: 0.2564 Epoch 268/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9049 - loss: 0.2044 - val_accuracy: 0.8891 - val_loss: 0.2667 Epoch 269/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9160 - loss: 0.1881 - val_accuracy: 0.8918 - val_loss: 0.2642 Epoch 270/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9170 - loss: 0.1873 - val_accuracy: 0.8873 - val_loss: 0.2709 Epoch 271/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9082 - loss: 0.1996 - val_accuracy: 0.8973 - val_loss: 0.2614 Epoch 272/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9144 - loss: 0.1837 - val_accuracy: 0.8946 - val_loss: 0.2696 Epoch 273/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9162 - loss: 0.1885 - val_accuracy: 0.8909 - val_loss: 0.2612 Epoch 274/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.1880 - val_accuracy: 0.8836 - val_loss: 0.2797 Epoch 275/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9132 - loss: 0.1954 - val_accuracy: 0.8946 - val_loss: 0.2595 Epoch 276/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9139 - loss: 0.1946 - val_accuracy: 0.8781 - val_loss: 0.2963 Epoch 277/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9129 - loss: 0.1891 - val_accuracy: 0.8955 - val_loss: 0.2585 Epoch 278/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9171 - loss: 0.1834 - val_accuracy: 0.8836 - val_loss: 0.2670 Epoch 279/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9171 - loss: 0.1872 - val_accuracy: 0.8909 - val_loss: 0.2630 Epoch 280/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9105 - loss: 0.1957 - val_accuracy: 0.8928 - val_loss: 0.2722 Epoch 281/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9123 - loss: 0.1975 - val_accuracy: 0.8772 - val_loss: 0.2844 Epoch 282/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9104 - loss: 0.1956 - val_accuracy: 0.8937 - val_loss: 0.2634 Epoch 283/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9134 - loss: 0.2015 - val_accuracy: 0.8928 - val_loss: 0.2621 Epoch 284/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9191 - loss: 0.1942 - val_accuracy: 0.8863 - val_loss: 0.2584 Epoch 285/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9146 - loss: 0.1868 - val_accuracy: 0.8928 - val_loss: 0.2639 Epoch 286/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9144 - loss: 0.1940 - val_accuracy: 0.8863 - val_loss: 0.2699 Epoch 287/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9134 - loss: 0.1835 - val_accuracy: 0.8946 - val_loss: 0.2606 Epoch 288/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9152 - loss: 0.1875 - val_accuracy: 0.8918 - val_loss: 0.2598 Epoch 289/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9170 - loss: 0.1899 - val_accuracy: 0.8909 - val_loss: 0.2749 Epoch 290/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9101 - loss: 0.1968 - val_accuracy: 0.8873 - val_loss: 0.2709 Epoch 291/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9189 - loss: 0.1904 - val_accuracy: 0.8863 - val_loss: 0.2785 Epoch 292/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9015 - loss: 0.2084 - val_accuracy: 0.8863 - val_loss: 0.2656 Epoch 293/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9171 - loss: 0.1920 - val_accuracy: 0.8955 - val_loss: 0.2690 Epoch 294/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9153 - loss: 0.1972 - val_accuracy: 0.8891 - val_loss: 0.2651 Epoch 295/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9087 - loss: 0.1923 - val_accuracy: 0.8946 - val_loss: 0.2717 Epoch 296/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9122 - loss: 0.1853 - val_accuracy: 0.8863 - val_loss: 0.2659 Epoch 297/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9147 - loss: 0.1973 - val_accuracy: 0.8873 - val_loss: 0.2749 Epoch 298/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9227 - loss: 0.1757 - val_accuracy: 0.8909 - val_loss: 0.2722 Epoch 299/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9123 - loss: 0.1874 - val_accuracy: 0.8937 - val_loss: 0.2739 Epoch 300/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9161 - loss: 0.1928 - val_accuracy: 0.8790 - val_loss: 0.2913 Epoch 301/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9096 - loss: 0.1962 - val_accuracy: 0.8918 - val_loss: 0.2637 Epoch 302/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9210 - loss: 0.1793 - val_accuracy: 0.8937 - val_loss: 0.2716 Epoch 303/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9213 - loss: 0.1840 - val_accuracy: 0.8900 - val_loss: 0.2614 Epoch 304/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9093 - loss: 0.1934 - val_accuracy: 0.8973 - val_loss: 0.2669 Epoch 305/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9137 - loss: 0.1864 - val_accuracy: 0.8900 - val_loss: 0.2693 Epoch 306/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9125 - loss: 0.1951 - val_accuracy: 0.8937 - val_loss: 0.2631 Epoch 307/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9084 - loss: 0.1979 - val_accuracy: 0.8882 - val_loss: 0.2851 Epoch 308/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9075 - loss: 0.2001 - val_accuracy: 0.8909 - val_loss: 0.2660 Epoch 309/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9140 - loss: 0.1943 - val_accuracy: 0.8973 - val_loss: 0.2706 Epoch 310/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9189 - loss: 0.1899 - val_accuracy: 0.8891 - val_loss: 0.2674 Epoch 311/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9211 - loss: 0.1845 - val_accuracy: 0.8946 - val_loss: 0.2632 Epoch 312/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9183 - loss: 0.1814 - val_accuracy: 0.8955 - val_loss: 0.2632 Epoch 313/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9196 - loss: 0.1854 - val_accuracy: 0.8790 - val_loss: 0.2963 Epoch 314/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9196 - loss: 0.1841 - val_accuracy: 0.8928 - val_loss: 0.2621 Epoch 315/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9229 - loss: 0.1781 - val_accuracy: 0.8900 - val_loss: 0.2776 Epoch 316/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9146 - loss: 0.1921 - val_accuracy: 0.8900 - val_loss: 0.2648 Epoch 317/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9178 - loss: 0.1841 - val_accuracy: 0.8882 - val_loss: 0.2689 Epoch 318/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9208 - loss: 0.1828 - val_accuracy: 0.8827 - val_loss: 0.2782 Epoch 319/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9143 - loss: 0.1968 - val_accuracy: 0.8818 - val_loss: 0.2779 Epoch 320/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9119 - loss: 0.1843 - val_accuracy: 0.8955 - val_loss: 0.2582 Epoch 321/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9164 - loss: 0.1809 - val_accuracy: 0.8827 - val_loss: 0.2871 Epoch 322/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9112 - loss: 0.1941 - val_accuracy: 0.8955 - val_loss: 0.2687 Epoch 323/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9186 - loss: 0.1860 - val_accuracy: 0.8854 - val_loss: 0.2672 Epoch 324/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9093 - loss: 0.1874 - val_accuracy: 0.8863 - val_loss: 0.2694 Epoch 325/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9190 - loss: 0.1780 - val_accuracy: 0.8827 - val_loss: 0.2782 Epoch 326/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9173 - loss: 0.1872 - val_accuracy: 0.8937 - val_loss: 0.2651 Epoch 327/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9239 - loss: 0.1755 - val_accuracy: 0.8992 - val_loss: 0.2613 Epoch 328/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9154 - loss: 0.1847 - val_accuracy: 0.8818 - val_loss: 0.2890 Epoch 329/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9176 - loss: 0.1740 - val_accuracy: 0.8918 - val_loss: 0.2681 Epoch 330/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9130 - loss: 0.1836 - val_accuracy: 0.8900 - val_loss: 0.2656 Epoch 331/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9247 - loss: 0.1757 - val_accuracy: 0.8946 - val_loss: 0.2655 Epoch 332/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9216 - loss: 0.1791 - val_accuracy: 0.8928 - val_loss: 0.2607 Epoch 333/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9200 - loss: 0.1857 - val_accuracy: 0.8900 - val_loss: 0.2716 Epoch 334/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9154 - loss: 0.1951 - val_accuracy: 0.8882 - val_loss: 0.2673 Epoch 335/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9166 - loss: 0.1845 - val_accuracy: 0.8882 - val_loss: 0.2708 Epoch 336/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9187 - loss: 0.1796 - val_accuracy: 0.8909 - val_loss: 0.2647 Epoch 337/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9202 - loss: 0.1813 - val_accuracy: 0.8882 - val_loss: 0.2793 Epoch 338/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9106 - loss: 0.1875 - val_accuracy: 0.8900 - val_loss: 0.2738 Epoch 339/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9195 - loss: 0.1848 - val_accuracy: 0.8863 - val_loss: 0.2897 Epoch 340/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9179 - loss: 0.1860 - val_accuracy: 0.8928 - val_loss: 0.2644 Epoch 341/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9189 - loss: 0.1770 - val_accuracy: 0.8909 - val_loss: 0.2730 Epoch 342/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9190 - loss: 0.1915 - val_accuracy: 0.8937 - val_loss: 0.2692 Epoch 343/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9202 - loss: 0.1779 - val_accuracy: 0.8918 - val_loss: 0.2676 Epoch 344/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9300 - loss: 0.1715 - val_accuracy: 0.8781 - val_loss: 0.3034 Epoch 345/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9123 - loss: 0.1927 - val_accuracy: 0.8955 - val_loss: 0.2652 Epoch 346/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9237 - loss: 0.1709 - val_accuracy: 0.8937 - val_loss: 0.2662 Epoch 347/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9265 - loss: 0.1722 - val_accuracy: 0.8937 - val_loss: 0.2603 Epoch 348/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9167 - loss: 0.1753 - val_accuracy: 0.8928 - val_loss: 0.2621 Epoch 349/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9225 - loss: 0.1701 - val_accuracy: 0.8763 - val_loss: 0.3072 Epoch 350/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9182 - loss: 0.1874 - val_accuracy: 0.8836 - val_loss: 0.2894 Epoch 351/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9082 - loss: 0.1998 - val_accuracy: 0.8937 - val_loss: 0.2686 Epoch 352/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9193 - loss: 0.1738 - val_accuracy: 0.8827 - val_loss: 0.2784 Epoch 353/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9261 - loss: 0.1719 - val_accuracy: 0.8818 - val_loss: 0.2777 Epoch 354/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9273 - loss: 0.1744 - val_accuracy: 0.8873 - val_loss: 0.2646 Epoch 355/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.1903 - val_accuracy: 0.8918 - val_loss: 0.2658 Epoch 356/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9189 - loss: 0.1761 - val_accuracy: 0.8937 - val_loss: 0.2669 Epoch 357/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9202 - loss: 0.1738 - val_accuracy: 0.8983 - val_loss: 0.2662 Epoch 358/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9179 - loss: 0.1865 - val_accuracy: 0.8900 - val_loss: 0.2667 Epoch 359/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9076 - loss: 0.1909 - val_accuracy: 0.8873 - val_loss: 0.2774 Epoch 360/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9116 - loss: 0.2014 - val_accuracy: 0.8753 - val_loss: 0.2935 Epoch 361/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9174 - loss: 0.1836 - val_accuracy: 0.8863 - val_loss: 0.2735 Epoch 362/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9198 - loss: 0.1891 - val_accuracy: 0.8818 - val_loss: 0.2900 Epoch 363/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9218 - loss: 0.1801 - val_accuracy: 0.8918 - val_loss: 0.2927 Epoch 364/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9143 - loss: 0.1837 - val_accuracy: 0.8882 - val_loss: 0.2793 Epoch 365/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9025 - loss: 0.1954 - val_accuracy: 0.8946 - val_loss: 0.2636 Epoch 366/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9254 - loss: 0.1699 - val_accuracy: 0.8753 - val_loss: 0.3099 Epoch 367/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9123 - loss: 0.1921 - val_accuracy: 0.8873 - val_loss: 0.2853 Epoch 368/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9119 - loss: 0.1952 - val_accuracy: 0.8928 - val_loss: 0.2648 Epoch 369/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9132 - loss: 0.1804 - val_accuracy: 0.8946 - val_loss: 0.2622 Epoch 370/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9169 - loss: 0.1834 - val_accuracy: 0.8909 - val_loss: 0.2687 Epoch 371/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9277 - loss: 0.1642 - val_accuracy: 0.8891 - val_loss: 0.2620 Epoch 372/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9187 - loss: 0.1736 - val_accuracy: 0.8873 - val_loss: 0.2770 Epoch 373/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9151 - loss: 0.1849 - val_accuracy: 0.8854 - val_loss: 0.2725 Epoch 374/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9183 - loss: 0.1787 - val_accuracy: 0.8818 - val_loss: 0.2777 Epoch 375/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9174 - loss: 0.1827 - val_accuracy: 0.8863 - val_loss: 0.2708 Epoch 376/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9164 - loss: 0.1845 - val_accuracy: 0.8863 - val_loss: 0.2676 Epoch 377/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9265 - loss: 0.1642 - val_accuracy: 0.8964 - val_loss: 0.2635 Epoch 378/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9253 - loss: 0.1659 - val_accuracy: 0.8928 - val_loss: 0.2666 Epoch 379/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9205 - loss: 0.1747 - val_accuracy: 0.8900 - val_loss: 0.2674 Epoch 380/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9231 - loss: 0.1700 - val_accuracy: 0.8964 - val_loss: 0.2746 Epoch 381/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9160 - loss: 0.1855 - val_accuracy: 0.8900 - val_loss: 0.2591 Epoch 382/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9164 - loss: 0.1820 - val_accuracy: 0.8827 - val_loss: 0.2763 Epoch 383/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9259 - loss: 0.1793 - val_accuracy: 0.8937 - val_loss: 0.2619 Epoch 384/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9253 - loss: 0.1805 - val_accuracy: 0.9001 - val_loss: 0.2604 Epoch 385/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9160 - loss: 0.1796 - val_accuracy: 0.8983 - val_loss: 0.2756 Epoch 386/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9225 - loss: 0.1738 - val_accuracy: 0.8928 - val_loss: 0.2662 Epoch 387/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9203 - loss: 0.1715 - val_accuracy: 0.8808 - val_loss: 0.2755 Epoch 388/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9088 - loss: 0.2025 - val_accuracy: 0.8873 - val_loss: 0.2770 Epoch 389/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9225 - loss: 0.1776 - val_accuracy: 0.8946 - val_loss: 0.2594 Epoch 390/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9279 - loss: 0.1661 - val_accuracy: 0.8790 - val_loss: 0.2996 Epoch 391/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9174 - loss: 0.1845 - val_accuracy: 0.8818 - val_loss: 0.2801 Epoch 392/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9228 - loss: 0.1632 - val_accuracy: 0.8955 - val_loss: 0.2709 Epoch 393/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9250 - loss: 0.1640 - val_accuracy: 0.8955 - val_loss: 0.2722 Epoch 394/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9199 - loss: 0.1733 - val_accuracy: 0.8937 - val_loss: 0.2641 Epoch 395/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9196 - loss: 0.1717 - val_accuracy: 0.8662 - val_loss: 0.3393 Epoch 396/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9066 - loss: 0.2030 - val_accuracy: 0.8854 - val_loss: 0.2809 Epoch 397/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9153 - loss: 0.1896 - val_accuracy: 0.8937 - val_loss: 0.2790 Epoch 398/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9146 - loss: 0.1798 - val_accuracy: 0.8937 - val_loss: 0.2630 Epoch 399/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9218 - loss: 0.1757 - val_accuracy: 0.8873 - val_loss: 0.2754 Epoch 400/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9131 - loss: 0.1857 - val_accuracy: 0.8928 - val_loss: 0.2782 Epoch 401/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9239 - loss: 0.1722 - val_accuracy: 0.8909 - val_loss: 0.2667 Epoch 402/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9212 - loss: 0.1724 - val_accuracy: 0.8827 - val_loss: 0.2883 Epoch 403/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9217 - loss: 0.1683 - val_accuracy: 0.8845 - val_loss: 0.2727 Epoch 404/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9225 - loss: 0.1781 - val_accuracy: 0.8909 - val_loss: 0.2670 Epoch 405/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9222 - loss: 0.1760 - val_accuracy: 0.8891 - val_loss: 0.2797 Epoch 406/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9269 - loss: 0.1662 - val_accuracy: 0.8634 - val_loss: 0.3358 Epoch 407/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9077 - loss: 0.2009 - val_accuracy: 0.8781 - val_loss: 0.3029 Epoch 408/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9223 - loss: 0.1699 - val_accuracy: 0.8937 - val_loss: 0.2715 Epoch 409/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9200 - loss: 0.1767 - val_accuracy: 0.8836 - val_loss: 0.2745 Epoch 410/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9154 - loss: 0.1819 - val_accuracy: 0.8946 - val_loss: 0.2694 Epoch 411/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9169 - loss: 0.1761 - val_accuracy: 0.8818 - val_loss: 0.2958 Epoch 412/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9167 - loss: 0.1773 - val_accuracy: 0.8983 - val_loss: 0.2691 Epoch 413/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9246 - loss: 0.1637 - val_accuracy: 0.8836 - val_loss: 0.2742 Epoch 414/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9187 - loss: 0.1806 - val_accuracy: 0.8900 - val_loss: 0.2727 Epoch 415/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9246 - loss: 0.1657 - val_accuracy: 0.8845 - val_loss: 0.2809 Epoch 416/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9340 - loss: 0.1608 - val_accuracy: 0.8909 - val_loss: 0.2727 Epoch 417/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9211 - loss: 0.1746 - val_accuracy: 0.8937 - val_loss: 0.2638 Epoch 418/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9232 - loss: 0.1713 - val_accuracy: 0.8882 - val_loss: 0.2870 Epoch 419/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9218 - loss: 0.1775 - val_accuracy: 0.8799 - val_loss: 0.2932 Epoch 420/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9025 - loss: 0.2156 - val_accuracy: 0.8983 - val_loss: 0.2720 Epoch 421/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9202 - loss: 0.1733 - val_accuracy: 0.8854 - val_loss: 0.2812 Epoch 422/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9222 - loss: 0.1702 - val_accuracy: 0.8790 - val_loss: 0.2967 Epoch 423/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9113 - loss: 0.1816 - val_accuracy: 0.8928 - val_loss: 0.2691 Epoch 424/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9260 - loss: 0.1752 - val_accuracy: 0.8891 - val_loss: 0.2713 Epoch 425/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9302 - loss: 0.1649 - val_accuracy: 0.8634 - val_loss: 0.3383 Epoch 426/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9172 - loss: 0.1882 - val_accuracy: 0.8763 - val_loss: 0.2788 Epoch 427/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9304 - loss: 0.1692 - val_accuracy: 0.8873 - val_loss: 0.2817 Epoch 428/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9226 - loss: 0.1709 - val_accuracy: 0.8946 - val_loss: 0.2686 Epoch 429/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9233 - loss: 0.1663 - val_accuracy: 0.8863 - val_loss: 0.2620 Epoch 430/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9272 - loss: 0.1653 - val_accuracy: 0.8964 - val_loss: 0.2716 Epoch 431/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9283 - loss: 0.1599 - val_accuracy: 0.8735 - val_loss: 0.2948 Epoch 432/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9316 - loss: 0.1698 - val_accuracy: 0.8863 - val_loss: 0.2808 Epoch 433/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9214 - loss: 0.1693 - val_accuracy: 0.8882 - val_loss: 0.2770 Epoch 434/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9220 - loss: 0.1660 - val_accuracy: 0.8836 - val_loss: 0.2670 Epoch 435/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9161 - loss: 0.1801 - val_accuracy: 0.8900 - val_loss: 0.2652 Epoch 436/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9341 - loss: 0.1580 - val_accuracy: 0.8983 - val_loss: 0.2774 Epoch 437/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9266 - loss: 0.1750 - val_accuracy: 0.8854 - val_loss: 0.2837 Epoch 438/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9194 - loss: 0.1714 - val_accuracy: 0.8891 - val_loss: 0.2694 Epoch 439/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9262 - loss: 0.1681 - val_accuracy: 0.8937 - val_loss: 0.2700 Epoch 440/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9222 - loss: 0.1728 - val_accuracy: 0.8909 - val_loss: 0.2720 Epoch 441/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9119 - loss: 0.1870 - val_accuracy: 0.8790 - val_loss: 0.2962 Epoch 442/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9176 - loss: 0.1722 - val_accuracy: 0.8918 - val_loss: 0.2772 Epoch 443/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9194 - loss: 0.1782 - val_accuracy: 0.8808 - val_loss: 0.2941 Epoch 444/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9185 - loss: 0.1818 - val_accuracy: 0.8918 - val_loss: 0.2755 Epoch 445/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9263 - loss: 0.1628 - val_accuracy: 0.8983 - val_loss: 0.2670 Epoch 446/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9311 - loss: 0.1581 - val_accuracy: 0.8955 - val_loss: 0.2647 Epoch 447/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9273 - loss: 0.1581 - val_accuracy: 0.9001 - val_loss: 0.2663 Epoch 448/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9210 - loss: 0.1741 - val_accuracy: 0.8854 - val_loss: 0.2822 Epoch 449/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9183 - loss: 0.1766 - val_accuracy: 0.8827 - val_loss: 0.2773 Epoch 450/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9233 - loss: 0.1657 - val_accuracy: 0.8891 - val_loss: 0.2788 Epoch 451/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9241 - loss: 0.1615 - val_accuracy: 0.8937 - val_loss: 0.2714 Epoch 452/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9242 - loss: 0.1706 - val_accuracy: 0.8891 - val_loss: 0.2748 Epoch 453/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9190 - loss: 0.1798 - val_accuracy: 0.8900 - val_loss: 0.2927 Epoch 454/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9177 - loss: 0.1718 - val_accuracy: 0.8790 - val_loss: 0.2897 Epoch 455/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9217 - loss: 0.1685 - val_accuracy: 0.8818 - val_loss: 0.2676 Epoch 456/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9187 - loss: 0.1789 - val_accuracy: 0.8863 - val_loss: 0.2734 Epoch 457/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9271 - loss: 0.1665 - val_accuracy: 0.8964 - val_loss: 0.2666 Epoch 458/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9245 - loss: 0.1619 - val_accuracy: 0.8781 - val_loss: 0.2971 Epoch 459/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9298 - loss: 0.1634 - val_accuracy: 0.8882 - val_loss: 0.2706 Epoch 460/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9208 - loss: 0.1681 - val_accuracy: 0.8973 - val_loss: 0.2758 Epoch 461/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9254 - loss: 0.1778 - val_accuracy: 0.8808 - val_loss: 0.2834 Epoch 462/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9203 - loss: 0.1705 - val_accuracy: 0.8799 - val_loss: 0.2810 Epoch 463/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.1889 - val_accuracy: 0.8873 - val_loss: 0.2778 Epoch 464/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9314 - loss: 0.1521 - val_accuracy: 0.8854 - val_loss: 0.2727 Epoch 465/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9310 - loss: 0.1634 - val_accuracy: 0.8909 - val_loss: 0.2793 Epoch 466/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9230 - loss: 0.1724 - val_accuracy: 0.8845 - val_loss: 0.2714 Epoch 467/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9257 - loss: 0.1646 - val_accuracy: 0.8735 - val_loss: 0.3140 Epoch 468/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9260 - loss: 0.1709 - val_accuracy: 0.8827 - val_loss: 0.2801 Epoch 469/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9238 - loss: 0.1675 - val_accuracy: 0.8854 - val_loss: 0.2995 Epoch 470/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9271 - loss: 0.1668 - val_accuracy: 0.8643 - val_loss: 0.3328 Epoch 471/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9149 - loss: 0.1882 - val_accuracy: 0.8891 - val_loss: 0.2900 Epoch 472/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9240 - loss: 0.1747 - val_accuracy: 0.8946 - val_loss: 0.2960 Epoch 473/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9209 - loss: 0.1677 - val_accuracy: 0.8955 - val_loss: 0.2858 Epoch 474/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9288 - loss: 0.1574 - val_accuracy: 0.8836 - val_loss: 0.2890 Epoch 475/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9158 - loss: 0.1795 - val_accuracy: 0.8918 - val_loss: 0.2735 Epoch 476/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9274 - loss: 0.1673 - val_accuracy: 0.8891 - val_loss: 0.2705 Epoch 477/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9268 - loss: 0.1714 - val_accuracy: 0.8937 - val_loss: 0.2735 Epoch 478/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9223 - loss: 0.1696 - val_accuracy: 0.8918 - val_loss: 0.2685 Epoch 479/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9349 - loss: 0.1565 - val_accuracy: 0.8900 - val_loss: 0.2847 Epoch 480/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9264 - loss: 0.1638 - val_accuracy: 0.8808 - val_loss: 0.2734 Epoch 481/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9271 - loss: 0.1614 - val_accuracy: 0.8873 - val_loss: 0.2818 Epoch 482/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9164 - loss: 0.1768 - val_accuracy: 0.8863 - val_loss: 0.2902 Epoch 483/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9364 - loss: 0.1453 - val_accuracy: 0.8937 - val_loss: 0.2852 Epoch 484/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9181 - loss: 0.1732 - val_accuracy: 0.8946 - val_loss: 0.2641 Epoch 485/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9275 - loss: 0.1635 - val_accuracy: 0.8854 - val_loss: 0.2916 Epoch 486/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9213 - loss: 0.1747 - val_accuracy: 0.8799 - val_loss: 0.3200 Epoch 487/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9164 - loss: 0.1699 - val_accuracy: 0.8882 - val_loss: 0.2738 Epoch 488/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9317 - loss: 0.1636 - val_accuracy: 0.8863 - val_loss: 0.2799 Epoch 489/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9215 - loss: 0.1669 - val_accuracy: 0.8928 - val_loss: 0.2770 Epoch 490/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9192 - loss: 0.1732 - val_accuracy: 0.8873 - val_loss: 0.2826 Epoch 491/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9248 - loss: 0.1677 - val_accuracy: 0.8799 - val_loss: 0.2793 Epoch 492/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9266 - loss: 0.1582 - val_accuracy: 0.8818 - val_loss: 0.2843 Epoch 493/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9177 - loss: 0.1823 - val_accuracy: 0.8863 - val_loss: 0.2772 Epoch 494/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9235 - loss: 0.1729 - val_accuracy: 0.8717 - val_loss: 0.2747 Epoch 495/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9257 - loss: 0.1711 - val_accuracy: 0.8781 - val_loss: 0.3104 Epoch 496/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9230 - loss: 0.1633 - val_accuracy: 0.8818 - val_loss: 0.2897 Epoch 497/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9263 - loss: 0.1640 - val_accuracy: 0.8735 - val_loss: 0.2944 Epoch 498/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9282 - loss: 0.1590 - val_accuracy: 0.8909 - val_loss: 0.2772 Epoch 499/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9251 - loss: 0.1611 - val_accuracy: 0.8873 - val_loss: 0.2931 Epoch 500/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9259 - loss: 0.1632 - val_accuracy: 0.8918 - val_loss: 0.2766 Epoch 501/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9228 - loss: 0.1631 - val_accuracy: 0.8882 - val_loss: 0.2741 Epoch 502/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9266 - loss: 0.1606 - val_accuracy: 0.8781 - val_loss: 0.2770 Epoch 503/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9217 - loss: 0.1703 - val_accuracy: 0.8946 - val_loss: 0.2816 Epoch 504/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9271 - loss: 0.1706 - val_accuracy: 0.8937 - val_loss: 0.2726 Epoch 505/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9362 - loss: 0.1553 - val_accuracy: 0.8900 - val_loss: 0.2780 Epoch 506/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9290 - loss: 0.1646 - val_accuracy: 0.8863 - val_loss: 0.3070 Epoch 507/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9256 - loss: 0.1696 - val_accuracy: 0.8827 - val_loss: 0.2871 Epoch 508/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9272 - loss: 0.1526 - val_accuracy: 0.8882 - val_loss: 0.2747 Epoch 509/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9284 - loss: 0.1602 - val_accuracy: 0.8891 - val_loss: 0.2868 Epoch 510/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9367 - loss: 0.1571 - val_accuracy: 0.8891 - val_loss: 0.2827 Epoch 511/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9368 - loss: 0.1538 - val_accuracy: 0.8873 - val_loss: 0.2810 Epoch 512/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9132 - loss: 0.1806 - val_accuracy: 0.8900 - val_loss: 0.2780 Epoch 513/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9290 - loss: 0.1563 - val_accuracy: 0.8763 - val_loss: 0.2864 Epoch 514/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9337 - loss: 0.1545 - val_accuracy: 0.8946 - val_loss: 0.2681 Epoch 515/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9284 - loss: 0.1597 - val_accuracy: 0.8882 - val_loss: 0.3104 Epoch 516/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9246 - loss: 0.1679 - val_accuracy: 0.8827 - val_loss: 0.2827 Epoch 517/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9312 - loss: 0.1570 - val_accuracy: 0.8836 - val_loss: 0.3059 Epoch 518/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9310 - loss: 0.1530 - val_accuracy: 0.8845 - val_loss: 0.2879 Epoch 519/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9237 - loss: 0.1588 - val_accuracy: 0.8928 - val_loss: 0.2730 Epoch 520/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9310 - loss: 0.1605 - val_accuracy: 0.8918 - val_loss: 0.2883 Epoch 521/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9315 - loss: 0.1538 - val_accuracy: 0.8891 - val_loss: 0.2723 Epoch 522/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9340 - loss: 0.1479 - val_accuracy: 0.8818 - val_loss: 0.3009 Epoch 523/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9167 - loss: 0.1856 - val_accuracy: 0.8827 - val_loss: 0.2994 Epoch 524/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9296 - loss: 0.1632 - val_accuracy: 0.9019 - val_loss: 0.2692 Epoch 525/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9319 - loss: 0.1609 - val_accuracy: 0.8845 - val_loss: 0.2820 Epoch 526/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9338 - loss: 0.1531 - val_accuracy: 0.8818 - val_loss: 0.3103 Epoch 527/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9285 - loss: 0.1634 - val_accuracy: 0.8882 - val_loss: 0.2911 Epoch 528/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9205 - loss: 0.1673 - val_accuracy: 0.8891 - val_loss: 0.2849 Epoch 529/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9313 - loss: 0.1530 - val_accuracy: 0.8891 - val_loss: 0.2872 Epoch 530/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9277 - loss: 0.1636 - val_accuracy: 0.8900 - val_loss: 0.2914 Epoch 531/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9279 - loss: 0.1591 - val_accuracy: 0.8854 - val_loss: 0.3099 Epoch 532/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9273 - loss: 0.1593 - val_accuracy: 0.8900 - val_loss: 0.2793 Epoch 533/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9316 - loss: 0.1615 - val_accuracy: 0.8909 - val_loss: 0.2852 Epoch 534/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9212 - loss: 0.1643 - val_accuracy: 0.8873 - val_loss: 0.2975 Epoch 535/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9258 - loss: 0.1588 - val_accuracy: 0.8928 - val_loss: 0.2819 Epoch 536/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9287 - loss: 0.1605 - val_accuracy: 0.8845 - val_loss: 0.2799 Epoch 537/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9299 - loss: 0.1586 - val_accuracy: 0.8882 - val_loss: 0.2786 Epoch 538/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9333 - loss: 0.1617 - val_accuracy: 0.8900 - val_loss: 0.2765 Epoch 539/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9330 - loss: 0.1533 - val_accuracy: 0.8836 - val_loss: 0.2910 Epoch 540/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9340 - loss: 0.1475 - val_accuracy: 0.8882 - val_loss: 0.2860 Epoch 541/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9292 - loss: 0.1558 - val_accuracy: 0.8854 - val_loss: 0.2797 Epoch 542/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9323 - loss: 0.1532 - val_accuracy: 0.8900 - val_loss: 0.2856 Epoch 543/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9266 - loss: 0.1613 - val_accuracy: 0.8928 - val_loss: 0.2961 Epoch 544/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9303 - loss: 0.1543 - val_accuracy: 0.8827 - val_loss: 0.2922 Epoch 545/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9296 - loss: 0.1534 - val_accuracy: 0.8900 - val_loss: 0.2846 Epoch 546/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9258 - loss: 0.1573 - val_accuracy: 0.8863 - val_loss: 0.2995 Epoch 547/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9339 - loss: 0.1533 - val_accuracy: 0.8763 - val_loss: 0.3194 Epoch 548/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9250 - loss: 0.1712 - val_accuracy: 0.8818 - val_loss: 0.2984 Epoch 549/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9253 - loss: 0.1650 - val_accuracy: 0.8753 - val_loss: 0.3172 Epoch 550/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9293 - loss: 0.1519 - val_accuracy: 0.8726 - val_loss: 0.3133 Epoch 551/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9297 - loss: 0.1517 - val_accuracy: 0.8873 - val_loss: 0.2847 Epoch 552/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9327 - loss: 0.1543 - val_accuracy: 0.8873 - val_loss: 0.2824 Epoch 553/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9322 - loss: 0.1536 - val_accuracy: 0.8863 - val_loss: 0.2951 Epoch 554/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9324 - loss: 0.1560 - val_accuracy: 0.8836 - val_loss: 0.2676 Epoch 555/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9298 - loss: 0.1574 - val_accuracy: 0.8845 - val_loss: 0.2925 Epoch 556/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9340 - loss: 0.1603 - val_accuracy: 0.8909 - val_loss: 0.2850 Epoch 557/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9393 - loss: 0.1435 - val_accuracy: 0.8928 - val_loss: 0.2741 Epoch 558/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9286 - loss: 0.1555 - val_accuracy: 0.8808 - val_loss: 0.3057 Epoch 559/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9329 - loss: 0.1522 - val_accuracy: 0.8863 - val_loss: 0.2996 Epoch 560/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9410 - loss: 0.1477 - val_accuracy: 0.8854 - val_loss: 0.2960 Epoch 561/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9315 - loss: 0.1535 - val_accuracy: 0.8928 - val_loss: 0.2884 Epoch 562/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9379 - loss: 0.1481 - val_accuracy: 0.8891 - val_loss: 0.2836 Epoch 563/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9296 - loss: 0.1558 - val_accuracy: 0.8900 - val_loss: 0.2872 Epoch 564/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9336 - loss: 0.1477 - val_accuracy: 0.8854 - val_loss: 0.2841 Epoch 565/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9293 - loss: 0.1517 - val_accuracy: 0.8918 - val_loss: 0.2878 Epoch 566/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9411 - loss: 0.1399 - val_accuracy: 0.8891 - val_loss: 0.2959 Epoch 567/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9372 - loss: 0.1555 - val_accuracy: 0.8735 - val_loss: 0.3099 Epoch 568/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9357 - loss: 0.1455 - val_accuracy: 0.8918 - val_loss: 0.2857 Epoch 569/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9246 - loss: 0.1620 - val_accuracy: 0.8799 - val_loss: 0.2721 Epoch 570/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9296 - loss: 0.1587 - val_accuracy: 0.8662 - val_loss: 0.3653 Epoch 571/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9188 - loss: 0.1709 - val_accuracy: 0.8827 - val_loss: 0.3067 Epoch 572/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9355 - loss: 0.1392 - val_accuracy: 0.8808 - val_loss: 0.2815 Epoch 573/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9341 - loss: 0.1541 - val_accuracy: 0.8863 - val_loss: 0.3098 Epoch 574/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9323 - loss: 0.1502 - val_accuracy: 0.8873 - val_loss: 0.2869 Epoch 575/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9326 - loss: 0.1566 - val_accuracy: 0.8918 - val_loss: 0.2872 Epoch 576/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9333 - loss: 0.1461 - val_accuracy: 0.8964 - val_loss: 0.2912 Epoch 577/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9389 - loss: 0.1479 - val_accuracy: 0.8836 - val_loss: 0.3026 Epoch 578/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9277 - loss: 0.1577 - val_accuracy: 0.8836 - val_loss: 0.2852 Epoch 579/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9296 - loss: 0.1598 - val_accuracy: 0.8891 - val_loss: 0.2744 Epoch 580/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9209 - loss: 0.1721 - val_accuracy: 0.8891 - val_loss: 0.2855 Epoch 581/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9375 - loss: 0.1456 - val_accuracy: 0.8836 - val_loss: 0.3053 Epoch 582/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9392 - loss: 0.1429 - val_accuracy: 0.8946 - val_loss: 0.2817 Epoch 583/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9333 - loss: 0.1465 - val_accuracy: 0.8964 - val_loss: 0.2887 Epoch 584/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9335 - loss: 0.1458 - val_accuracy: 0.8753 - val_loss: 0.2920 Epoch 585/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9368 - loss: 0.1407 - val_accuracy: 0.8882 - val_loss: 0.3125 Epoch 586/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9344 - loss: 0.1492 - val_accuracy: 0.8799 - val_loss: 0.2928 Epoch 587/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9347 - loss: 0.1453 - val_accuracy: 0.8863 - val_loss: 0.2988 Epoch 588/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9352 - loss: 0.1473 - val_accuracy: 0.8808 - val_loss: 0.2932 Epoch 589/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9334 - loss: 0.1487 - val_accuracy: 0.8799 - val_loss: 0.2879 Epoch 590/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9256 - loss: 0.1658 - val_accuracy: 0.8799 - val_loss: 0.2889 Epoch 591/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9407 - loss: 0.1307 - val_accuracy: 0.8873 - val_loss: 0.2815 Epoch 592/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9250 - loss: 0.1556 - val_accuracy: 0.8955 - val_loss: 0.2813 Epoch 593/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9298 - loss: 0.1464 - val_accuracy: 0.8845 - val_loss: 0.2893 Epoch 594/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9349 - loss: 0.1487 - val_accuracy: 0.8900 - val_loss: 0.3035 Epoch 595/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9361 - loss: 0.1515 - val_accuracy: 0.8845 - val_loss: 0.2872 Epoch 596/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9360 - loss: 0.1499 - val_accuracy: 0.8891 - val_loss: 0.3052 Epoch 597/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9332 - loss: 0.1500 - val_accuracy: 0.8928 - val_loss: 0.2972 Epoch 598/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9314 - loss: 0.1533 - val_accuracy: 0.8882 - val_loss: 0.2906 Epoch 599/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9436 - loss: 0.1373 - val_accuracy: 0.8863 - val_loss: 0.2943 Epoch 600/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9380 - loss: 0.1416 - val_accuracy: 0.8790 - val_loss: 0.2876 Epoch 601/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9255 - loss: 0.1662 - val_accuracy: 0.8863 - val_loss: 0.3211 Epoch 602/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9398 - loss: 0.1368 - val_accuracy: 0.8836 - val_loss: 0.2927 Epoch 603/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9364 - loss: 0.1411 - val_accuracy: 0.8836 - val_loss: 0.2995 Epoch 604/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9345 - loss: 0.1413 - val_accuracy: 0.8873 - val_loss: 0.3031 Epoch 605/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9304 - loss: 0.1546 - val_accuracy: 0.8891 - val_loss: 0.2863 Epoch 606/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9363 - loss: 0.1395 - val_accuracy: 0.8845 - val_loss: 0.3053 Epoch 607/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9348 - loss: 0.1397 - val_accuracy: 0.8818 - val_loss: 0.2859 Epoch 608/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9305 - loss: 0.1534 - val_accuracy: 0.8900 - val_loss: 0.2851 Epoch 609/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9327 - loss: 0.1485 - val_accuracy: 0.8836 - val_loss: 0.2880 Epoch 610/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9403 - loss: 0.1331 - val_accuracy: 0.8763 - val_loss: 0.3202 Epoch 611/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9290 - loss: 0.1565 - val_accuracy: 0.8790 - val_loss: 0.2885 Epoch 612/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9291 - loss: 0.1601 - val_accuracy: 0.8845 - val_loss: 0.2901 Epoch 613/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9308 - loss: 0.1520 - val_accuracy: 0.8873 - val_loss: 0.2849 Epoch 614/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9373 - loss: 0.1397 - val_accuracy: 0.8818 - val_loss: 0.3000 Epoch 615/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9359 - loss: 0.1421 - val_accuracy: 0.8753 - val_loss: 0.2919 Epoch 616/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9428 - loss: 0.1386 - val_accuracy: 0.8873 - val_loss: 0.2903 Epoch 617/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9340 - loss: 0.1425 - val_accuracy: 0.8873 - val_loss: 0.3149 Epoch 618/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9317 - loss: 0.1607 - val_accuracy: 0.8818 - val_loss: 0.3069 Epoch 619/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9273 - loss: 0.1570 - val_accuracy: 0.8900 - val_loss: 0.2999 Epoch 620/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9363 - loss: 0.1342 - val_accuracy: 0.8863 - val_loss: 0.3042 Epoch 621/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9354 - loss: 0.1394 - val_accuracy: 0.8882 - val_loss: 0.2950 Epoch 622/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9410 - loss: 0.1393 - val_accuracy: 0.8836 - val_loss: 0.2996 Epoch 623/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9336 - loss: 0.1501 - val_accuracy: 0.8836 - val_loss: 0.2993 Epoch 624/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9375 - loss: 0.1366 - val_accuracy: 0.8928 - val_loss: 0.2911 Epoch 625/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9323 - loss: 0.1394 - val_accuracy: 0.8827 - val_loss: 0.2924 Epoch 626/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1460 - val_accuracy: 0.8772 - val_loss: 0.3091 Epoch 627/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9365 - loss: 0.1484 - val_accuracy: 0.8717 - val_loss: 0.3624 Epoch 628/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9245 - loss: 0.1601 - val_accuracy: 0.8909 - val_loss: 0.2882 Epoch 629/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9362 - loss: 0.1422 - val_accuracy: 0.8836 - val_loss: 0.3317 Epoch 630/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9350 - loss: 0.1428 - val_accuracy: 0.8772 - val_loss: 0.3202 Epoch 631/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9379 - loss: 0.1369 - val_accuracy: 0.8863 - val_loss: 0.3095 Epoch 632/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9418 - loss: 0.1344 - val_accuracy: 0.8726 - val_loss: 0.3360 Epoch 633/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9350 - loss: 0.1567 - val_accuracy: 0.8891 - val_loss: 0.3001 Epoch 634/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9421 - loss: 0.1406 - val_accuracy: 0.8708 - val_loss: 0.3126 Epoch 635/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9336 - loss: 0.1387 - val_accuracy: 0.8726 - val_loss: 0.3712 Epoch 636/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9316 - loss: 0.1502 - val_accuracy: 0.8790 - val_loss: 0.3169 Epoch 637/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9370 - loss: 0.1279 - val_accuracy: 0.8763 - val_loss: 0.3077 Epoch 638/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9378 - loss: 0.1419 - val_accuracy: 0.8827 - val_loss: 0.3079 Epoch 639/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9358 - loss: 0.1377 - val_accuracy: 0.8781 - val_loss: 0.3154 Epoch 640/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9324 - loss: 0.1461 - val_accuracy: 0.8882 - val_loss: 0.2976 Epoch 641/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9309 - loss: 0.1492 - val_accuracy: 0.8882 - val_loss: 0.3215 Epoch 642/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9309 - loss: 0.1490 - val_accuracy: 0.8799 - val_loss: 0.3067 Epoch 643/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9519 - loss: 0.1291 - val_accuracy: 0.8818 - val_loss: 0.3128 Epoch 644/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9218 - loss: 0.1600 - val_accuracy: 0.8882 - val_loss: 0.3101 Epoch 645/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9386 - loss: 0.1365 - val_accuracy: 0.8790 - val_loss: 0.2885 Epoch 646/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9374 - loss: 0.1374 - val_accuracy: 0.8863 - val_loss: 0.2979 Epoch 647/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9432 - loss: 0.1313 - val_accuracy: 0.8891 - val_loss: 0.3047 Epoch 648/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9412 - loss: 0.1387 - val_accuracy: 0.8689 - val_loss: 0.3361 Epoch 649/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9333 - loss: 0.1386 - val_accuracy: 0.8772 - val_loss: 0.3070 Epoch 650/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9343 - loss: 0.1473 - val_accuracy: 0.8863 - val_loss: 0.3214 Epoch 651/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9295 - loss: 0.1515 - val_accuracy: 0.8882 - val_loss: 0.2925 Epoch 652/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9381 - loss: 0.1304 - val_accuracy: 0.8918 - val_loss: 0.2918 Epoch 653/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1285 - val_accuracy: 0.8900 - val_loss: 0.3128 Epoch 654/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9306 - loss: 0.1431 - val_accuracy: 0.8900 - val_loss: 0.2896 Epoch 655/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9420 - loss: 0.1375 - val_accuracy: 0.8928 - val_loss: 0.2964 Epoch 656/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9386 - loss: 0.1367 - val_accuracy: 0.8854 - val_loss: 0.2862 Epoch 657/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9348 - loss: 0.1439 - val_accuracy: 0.8900 - val_loss: 0.3327 Epoch 658/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9264 - loss: 0.1585 - val_accuracy: 0.8882 - val_loss: 0.3002 Epoch 659/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9318 - loss: 0.1437 - val_accuracy: 0.8873 - val_loss: 0.2985 Epoch 660/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9407 - loss: 0.1364 - val_accuracy: 0.8836 - val_loss: 0.2986 Epoch 661/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9361 - loss: 0.1370 - val_accuracy: 0.8845 - val_loss: 0.3029 Epoch 662/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9304 - loss: 0.1414 - val_accuracy: 0.8808 - val_loss: 0.2986 Epoch 663/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9305 - loss: 0.1486 - val_accuracy: 0.8854 - val_loss: 0.3053 Epoch 664/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9373 - loss: 0.1388 - val_accuracy: 0.8808 - val_loss: 0.3104 Epoch 665/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9307 - loss: 0.1464 - val_accuracy: 0.8845 - val_loss: 0.3004 Epoch 666/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9435 - loss: 0.1374 - val_accuracy: 0.8799 - val_loss: 0.3053 Epoch 667/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9425 - loss: 0.1290 - val_accuracy: 0.8873 - val_loss: 0.2988 Epoch 668/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9435 - loss: 0.1244 - val_accuracy: 0.8836 - val_loss: 0.3227 Epoch 669/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9317 - loss: 0.1394 - val_accuracy: 0.8818 - val_loss: 0.3293 Epoch 670/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9349 - loss: 0.1468 - val_accuracy: 0.8873 - val_loss: 0.3126 Epoch 671/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9329 - loss: 0.1397 - val_accuracy: 0.8763 - val_loss: 0.3372 Epoch 672/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 3ms/step - accuracy: 0.9314 - loss: 0.1516 - val_accuracy: 0.8845 - val_loss: 0.2993 Epoch 673/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9369 - loss: 0.1423 - val_accuracy: 0.8836 - val_loss: 0.3192 Epoch 674/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9319 - loss: 0.1428 - val_accuracy: 0.8827 - val_loss: 0.3149 Epoch 675/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9441 - loss: 0.1319 - val_accuracy: 0.8808 - val_loss: 0.3016 Epoch 676/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1277 - val_accuracy: 0.8836 - val_loss: 0.3008 Epoch 677/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9400 - loss: 0.1317 - val_accuracy: 0.8854 - val_loss: 0.3097 Epoch 678/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9364 - loss: 0.1385 - val_accuracy: 0.8726 - val_loss: 0.3223 Epoch 679/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9432 - loss: 0.1257 - val_accuracy: 0.8845 - val_loss: 0.3090 Epoch 680/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9406 - loss: 0.1320 - val_accuracy: 0.8854 - val_loss: 0.3079 Epoch 681/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9411 - loss: 0.1330 - val_accuracy: 0.8818 - val_loss: 0.3077 Epoch 682/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9394 - loss: 0.1345 - val_accuracy: 0.8808 - val_loss: 0.3201 Epoch 683/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9422 - loss: 0.1291 - val_accuracy: 0.8836 - val_loss: 0.3221 Epoch 684/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9445 - loss: 0.1260 - val_accuracy: 0.8799 - val_loss: 0.3119 Epoch 685/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9420 - loss: 0.1342 - val_accuracy: 0.8854 - val_loss: 0.3244 Epoch 686/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9456 - loss: 0.1264 - val_accuracy: 0.8854 - val_loss: 0.3067 Epoch 687/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9418 - loss: 0.1391 - val_accuracy: 0.8836 - val_loss: 0.3162 Epoch 688/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9391 - loss: 0.1333 - val_accuracy: 0.8781 - val_loss: 0.3063 Epoch 689/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9398 - loss: 0.1343 - val_accuracy: 0.8818 - val_loss: 0.3113 Epoch 690/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9412 - loss: 0.1339 - val_accuracy: 0.8808 - val_loss: 0.3191 Epoch 691/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9489 - loss: 0.1275 - val_accuracy: 0.8744 - val_loss: 0.3190 Epoch 692/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9435 - loss: 0.1286 - val_accuracy: 0.8854 - val_loss: 0.3204 Epoch 693/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9402 - loss: 0.1348 - val_accuracy: 0.8808 - val_loss: 0.3052 Epoch 694/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9380 - loss: 0.1381 - val_accuracy: 0.8799 - val_loss: 0.3067 Epoch 695/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9460 - loss: 0.1261 - val_accuracy: 0.8763 - val_loss: 0.3100 Epoch 696/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9383 - loss: 0.1380 - val_accuracy: 0.8735 - val_loss: 0.3271 Epoch 697/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9470 - loss: 0.1329 - val_accuracy: 0.8799 - val_loss: 0.3102 Epoch 698/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9405 - loss: 0.1279 - val_accuracy: 0.8818 - val_loss: 0.3283 Epoch 699/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9380 - loss: 0.1349 - val_accuracy: 0.8818 - val_loss: 0.3195 Epoch 700/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9432 - loss: 0.1323 - val_accuracy: 0.8772 - val_loss: 0.2982 Epoch 701/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9437 - loss: 0.1270 - val_accuracy: 0.8763 - val_loss: 0.3265 Epoch 702/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9360 - loss: 0.1333 - val_accuracy: 0.8863 - val_loss: 0.3028 Epoch 703/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9476 - loss: 0.1193 - val_accuracy: 0.8818 - val_loss: 0.3300 Epoch 704/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9391 - loss: 0.1322 - val_accuracy: 0.8781 - val_loss: 0.3052 Epoch 705/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9410 - loss: 0.1304 - val_accuracy: 0.8772 - val_loss: 0.3267 Epoch 706/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9303 - loss: 0.1451 - val_accuracy: 0.8790 - val_loss: 0.3464 Epoch 707/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9331 - loss: 0.1419 - val_accuracy: 0.8808 - val_loss: 0.3013 Epoch 708/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9268 - loss: 0.1479 - val_accuracy: 0.8882 - val_loss: 0.3317 Epoch 709/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9402 - loss: 0.1342 - val_accuracy: 0.8799 - val_loss: 0.3104 Epoch 710/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9476 - loss: 0.1256 - val_accuracy: 0.8873 - val_loss: 0.3118 Epoch 711/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9394 - loss: 0.1327 - val_accuracy: 0.8900 - val_loss: 0.3075 Epoch 712/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9382 - loss: 0.1354 - val_accuracy: 0.8845 - val_loss: 0.3096 Epoch 713/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9351 - loss: 0.1376 - val_accuracy: 0.8799 - val_loss: 0.3393 Epoch 714/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9425 - loss: 0.1265 - val_accuracy: 0.8882 - val_loss: 0.3086 Epoch 715/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1365 - val_accuracy: 0.8836 - val_loss: 0.3190 Epoch 716/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9387 - loss: 0.1340 - val_accuracy: 0.8808 - val_loss: 0.3389 Epoch 717/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1336 - val_accuracy: 0.8918 - val_loss: 0.3246 Epoch 718/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9433 - loss: 0.1355 - val_accuracy: 0.8818 - val_loss: 0.3173 Epoch 719/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9367 - loss: 0.1417 - val_accuracy: 0.8873 - val_loss: 0.3200 Epoch 720/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9469 - loss: 0.1229 - val_accuracy: 0.8818 - val_loss: 0.3192 Epoch 721/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9407 - loss: 0.1385 - val_accuracy: 0.8836 - val_loss: 0.3156 Epoch 722/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9421 - loss: 0.1357 - val_accuracy: 0.8827 - val_loss: 0.3394 Epoch 723/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9402 - loss: 0.1305 - val_accuracy: 0.8836 - val_loss: 0.3056 Epoch 724/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1235 - val_accuracy: 0.8845 - val_loss: 0.3306 Epoch 725/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9432 - loss: 0.1266 - val_accuracy: 0.8845 - val_loss: 0.3117 Epoch 726/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9406 - loss: 0.1314 - val_accuracy: 0.8836 - val_loss: 0.3376 Epoch 727/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9512 - loss: 0.1272 - val_accuracy: 0.8909 - val_loss: 0.3231 Epoch 728/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9451 - loss: 0.1279 - val_accuracy: 0.8735 - val_loss: 0.3387 Epoch 729/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1455 - val_accuracy: 0.8799 - val_loss: 0.3202 Epoch 730/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9455 - loss: 0.1326 - val_accuracy: 0.8845 - val_loss: 0.3135 Epoch 731/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9389 - loss: 0.1332 - val_accuracy: 0.8827 - val_loss: 0.3286 Epoch 732/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9365 - loss: 0.1382 - val_accuracy: 0.8763 - val_loss: 0.3393 Epoch 733/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1233 - val_accuracy: 0.8863 - val_loss: 0.3259 Epoch 734/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9419 - loss: 0.1349 - val_accuracy: 0.8845 - val_loss: 0.3510 Epoch 735/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1354 - val_accuracy: 0.8799 - val_loss: 0.3447 Epoch 736/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9397 - loss: 0.1418 - val_accuracy: 0.8753 - val_loss: 0.3170 Epoch 737/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9391 - loss: 0.1294 - val_accuracy: 0.8799 - val_loss: 0.3207 Epoch 738/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9432 - loss: 0.1322 - val_accuracy: 0.8790 - val_loss: 0.3411 Epoch 739/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9311 - loss: 0.1462 - val_accuracy: 0.8863 - val_loss: 0.3101 Epoch 740/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9390 - loss: 0.1311 - val_accuracy: 0.8753 - val_loss: 0.3227 Epoch 741/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9419 - loss: 0.1335 - val_accuracy: 0.8818 - val_loss: 0.3357 Epoch 742/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9450 - loss: 0.1292 - val_accuracy: 0.8845 - val_loss: 0.3269 Epoch 743/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9372 - loss: 0.1343 - val_accuracy: 0.8836 - val_loss: 0.3220 Epoch 744/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9442 - loss: 0.1265 - val_accuracy: 0.8808 - val_loss: 0.3227 Epoch 745/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9334 - loss: 0.1394 - val_accuracy: 0.8882 - val_loss: 0.3156 Epoch 746/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9421 - loss: 0.1285 - val_accuracy: 0.8882 - val_loss: 0.3114 Epoch 747/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9487 - loss: 0.1157 - val_accuracy: 0.8799 - val_loss: 0.3216 Epoch 748/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9441 - loss: 0.1298 - val_accuracy: 0.8882 - val_loss: 0.3410 Epoch 749/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9353 - loss: 0.1474 - val_accuracy: 0.8763 - val_loss: 0.3477 Epoch 750/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9399 - loss: 0.1337 - val_accuracy: 0.8900 - val_loss: 0.3124 Epoch 751/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9427 - loss: 0.1324 - val_accuracy: 0.8726 - val_loss: 0.3387 Epoch 752/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9418 - loss: 0.1259 - val_accuracy: 0.8799 - val_loss: 0.3163 Epoch 753/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9439 - loss: 0.1244 - val_accuracy: 0.8836 - val_loss: 0.3248 Epoch 754/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9512 - loss: 0.1167 - val_accuracy: 0.8854 - val_loss: 0.3085 Epoch 755/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9387 - loss: 0.1323 - val_accuracy: 0.8726 - val_loss: 0.3808 Epoch 756/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9379 - loss: 0.1266 - val_accuracy: 0.8772 - val_loss: 0.3421 Epoch 757/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9450 - loss: 0.1236 - val_accuracy: 0.8845 - val_loss: 0.3329 Epoch 758/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9457 - loss: 0.1269 - val_accuracy: 0.8854 - val_loss: 0.3141 Epoch 759/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9489 - loss: 0.1175 - val_accuracy: 0.8882 - val_loss: 0.3306 Epoch 760/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9506 - loss: 0.1174 - val_accuracy: 0.8818 - val_loss: 0.3226 Epoch 761/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9469 - loss: 0.1239 - val_accuracy: 0.8579 - val_loss: 0.4195 Epoch 762/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9285 - loss: 0.1516 - val_accuracy: 0.8790 - val_loss: 0.3428 Epoch 763/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9441 - loss: 0.1295 - val_accuracy: 0.8763 - val_loss: 0.3332 Epoch 764/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9419 - loss: 0.1309 - val_accuracy: 0.8891 - val_loss: 0.3375 Epoch 765/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9405 - loss: 0.1332 - val_accuracy: 0.8607 - val_loss: 0.4154 Epoch 766/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9297 - loss: 0.1504 - val_accuracy: 0.8790 - val_loss: 0.3271 Epoch 767/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9472 - loss: 0.1207 - val_accuracy: 0.8873 - val_loss: 0.3392 Epoch 768/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9485 - loss: 0.1249 - val_accuracy: 0.8818 - val_loss: 0.3190 Epoch 769/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9399 - loss: 0.1357 - val_accuracy: 0.8909 - val_loss: 0.3220 Epoch 770/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9507 - loss: 0.1188 - val_accuracy: 0.8799 - val_loss: 0.3462 Epoch 771/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9506 - loss: 0.1163 - val_accuracy: 0.8781 - val_loss: 0.3189 Epoch 772/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9406 - loss: 0.1335 - val_accuracy: 0.8753 - val_loss: 0.3451 Epoch 773/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9468 - loss: 0.1231 - val_accuracy: 0.8753 - val_loss: 0.3340 Epoch 774/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9416 - loss: 0.1386 - val_accuracy: 0.8781 - val_loss: 0.3442 Epoch 775/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9481 - loss: 0.1227 - val_accuracy: 0.8808 - val_loss: 0.3338 Epoch 776/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9377 - loss: 0.1323 - val_accuracy: 0.8781 - val_loss: 0.3339 Epoch 777/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9422 - loss: 0.1292 - val_accuracy: 0.8790 - val_loss: 0.3234 Epoch 778/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9445 - loss: 0.1206 - val_accuracy: 0.8836 - val_loss: 0.3439 Epoch 779/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1152 - val_accuracy: 0.8753 - val_loss: 0.3489 Epoch 780/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9475 - loss: 0.1172 - val_accuracy: 0.8744 - val_loss: 0.3424 Epoch 781/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9469 - loss: 0.1283 - val_accuracy: 0.8717 - val_loss: 0.3462 Epoch 782/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9511 - loss: 0.1249 - val_accuracy: 0.8882 - val_loss: 0.3344 Epoch 783/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9460 - loss: 0.1282 - val_accuracy: 0.8836 - val_loss: 0.3359 Epoch 784/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9523 - loss: 0.1194 - val_accuracy: 0.8863 - val_loss: 0.3435 Epoch 785/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9453 - loss: 0.1256 - val_accuracy: 0.8818 - val_loss: 0.3393 Epoch 786/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9466 - loss: 0.1191 - val_accuracy: 0.8753 - val_loss: 0.3582 Epoch 787/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9433 - loss: 0.1279 - val_accuracy: 0.8909 - val_loss: 0.3191 Epoch 788/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9519 - loss: 0.1158 - val_accuracy: 0.8744 - val_loss: 0.3344 Epoch 789/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9512 - loss: 0.1142 - val_accuracy: 0.8827 - val_loss: 0.3471 Epoch 790/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9463 - loss: 0.1272 - val_accuracy: 0.8827 - val_loss: 0.3505 Epoch 791/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9340 - loss: 0.1461 - val_accuracy: 0.8753 - val_loss: 0.3447 Epoch 792/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9444 - loss: 0.1229 - val_accuracy: 0.8827 - val_loss: 0.3225 Epoch 793/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9517 - loss: 0.1147 - val_accuracy: 0.8753 - val_loss: 0.3423 Epoch 794/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9437 - loss: 0.1349 - val_accuracy: 0.8854 - val_loss: 0.3520 Epoch 795/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9451 - loss: 0.1210 - val_accuracy: 0.8818 - val_loss: 0.3294 Epoch 796/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9479 - loss: 0.1221 - val_accuracy: 0.8671 - val_loss: 0.3699 Epoch 797/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9463 - loss: 0.1265 - val_accuracy: 0.8863 - val_loss: 0.3447 Epoch 798/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9477 - loss: 0.1215 - val_accuracy: 0.8863 - val_loss: 0.3457 Epoch 799/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9458 - loss: 0.1298 - val_accuracy: 0.8753 - val_loss: 0.3232 Epoch 800/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9448 - loss: 0.1277 - val_accuracy: 0.8827 - val_loss: 0.3468 Epoch 801/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9451 - loss: 0.1197 - val_accuracy: 0.8818 - val_loss: 0.3373 Epoch 802/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9498 - loss: 0.1165 - val_accuracy: 0.8744 - val_loss: 0.3489 Epoch 803/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9553 - loss: 0.1058 - val_accuracy: 0.8863 - val_loss: 0.3723 Epoch 804/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9379 - loss: 0.1317 - val_accuracy: 0.8827 - val_loss: 0.3248 Epoch 805/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9438 - loss: 0.1277 - val_accuracy: 0.8882 - val_loss: 0.3455 Epoch 806/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9430 - loss: 0.1236 - val_accuracy: 0.8882 - val_loss: 0.3254 Epoch 807/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9458 - loss: 0.1220 - val_accuracy: 0.8799 - val_loss: 0.3416 Epoch 808/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1292 - val_accuracy: 0.8790 - val_loss: 0.3524 Epoch 809/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9460 - loss: 0.1209 - val_accuracy: 0.8772 - val_loss: 0.3410 Epoch 810/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9487 - loss: 0.1271 - val_accuracy: 0.8863 - val_loss: 0.3485 Epoch 811/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9487 - loss: 0.1118 - val_accuracy: 0.8781 - val_loss: 0.3547 Epoch 812/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9460 - loss: 0.1283 - val_accuracy: 0.8781 - val_loss: 0.3516 Epoch 813/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9515 - loss: 0.1110 - val_accuracy: 0.8772 - val_loss: 0.3443 Epoch 814/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9458 - loss: 0.1192 - val_accuracy: 0.8772 - val_loss: 0.3470 Epoch 815/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9313 - loss: 0.1403 - val_accuracy: 0.8873 - val_loss: 0.3427 Epoch 816/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9458 - loss: 0.1252 - val_accuracy: 0.8753 - val_loss: 0.3468 Epoch 817/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9398 - loss: 0.1321 - val_accuracy: 0.8909 - val_loss: 0.3539 Epoch 818/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9513 - loss: 0.1120 - val_accuracy: 0.8772 - val_loss: 0.3525 Epoch 819/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9417 - loss: 0.1268 - val_accuracy: 0.8653 - val_loss: 0.3714 Epoch 820/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9508 - loss: 0.1175 - val_accuracy: 0.8863 - val_loss: 0.3388 Epoch 821/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9474 - loss: 0.1209 - val_accuracy: 0.8808 - val_loss: 0.3466 Epoch 822/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9504 - loss: 0.1155 - val_accuracy: 0.8808 - val_loss: 0.3402 Epoch 823/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9399 - loss: 0.1267 - val_accuracy: 0.8781 - val_loss: 0.3440 Epoch 824/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9428 - loss: 0.1277 - val_accuracy: 0.8863 - val_loss: 0.3367 Epoch 825/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9437 - loss: 0.1291 - val_accuracy: 0.8854 - val_loss: 0.3322 Epoch 826/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9498 - loss: 0.1158 - val_accuracy: 0.8781 - val_loss: 0.3475 Epoch 827/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9525 - loss: 0.1141 - val_accuracy: 0.8808 - val_loss: 0.3315 Epoch 828/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9513 - loss: 0.1145 - val_accuracy: 0.8827 - val_loss: 0.3492 Epoch 829/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9507 - loss: 0.1164 - val_accuracy: 0.8836 - val_loss: 0.3392 Epoch 830/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1146 - val_accuracy: 0.8616 - val_loss: 0.4139 Epoch 831/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9478 - loss: 0.1190 - val_accuracy: 0.8689 - val_loss: 0.3568 Epoch 832/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9416 - loss: 0.1297 - val_accuracy: 0.8735 - val_loss: 0.3364 Epoch 833/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9488 - loss: 0.1196 - val_accuracy: 0.8854 - val_loss: 0.3420 Epoch 834/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9368 - loss: 0.1442 - val_accuracy: 0.8808 - val_loss: 0.3349 Epoch 835/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9547 - loss: 0.1139 - val_accuracy: 0.8735 - val_loss: 0.3993 Epoch 836/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9463 - loss: 0.1235 - val_accuracy: 0.8671 - val_loss: 0.3501 Epoch 837/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9524 - loss: 0.1162 - val_accuracy: 0.8937 - val_loss: 0.3394 Epoch 838/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9425 - loss: 0.1257 - val_accuracy: 0.8616 - val_loss: 0.4545 Epoch 839/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9410 - loss: 0.1393 - val_accuracy: 0.8808 - val_loss: 0.3491 Epoch 840/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9469 - loss: 0.1153 - val_accuracy: 0.8763 - val_loss: 0.3554 Epoch 841/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9565 - loss: 0.1056 - val_accuracy: 0.8799 - val_loss: 0.3614 Epoch 842/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9457 - loss: 0.1218 - val_accuracy: 0.8781 - val_loss: 0.3602 Epoch 843/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9479 - loss: 0.1134 - val_accuracy: 0.8717 - val_loss: 0.3680 Epoch 844/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9415 - loss: 0.1257 - val_accuracy: 0.8808 - val_loss: 0.3475 Epoch 845/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9480 - loss: 0.1192 - val_accuracy: 0.8744 - val_loss: 0.3457 Epoch 846/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9508 - loss: 0.1121 - val_accuracy: 0.8781 - val_loss: 0.3673 Epoch 847/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1115 - val_accuracy: 0.8625 - val_loss: 0.3666 Epoch 848/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9406 - loss: 0.1408 - val_accuracy: 0.8790 - val_loss: 0.3443 Epoch 849/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9402 - loss: 0.1285 - val_accuracy: 0.8854 - val_loss: 0.3504 Epoch 850/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9548 - loss: 0.1094 - val_accuracy: 0.8616 - val_loss: 0.3906 Epoch 851/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9408 - loss: 0.1330 - val_accuracy: 0.8827 - val_loss: 0.3809 Epoch 852/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9394 - loss: 0.1339 - val_accuracy: 0.8845 - val_loss: 0.3599 Epoch 853/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9440 - loss: 0.1138 - val_accuracy: 0.8772 - val_loss: 0.3529 Epoch 854/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9426 - loss: 0.1225 - val_accuracy: 0.8790 - val_loss: 0.3734 Epoch 855/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9514 - loss: 0.1288 - val_accuracy: 0.8873 - val_loss: 0.3431 Epoch 856/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9466 - loss: 0.1262 - val_accuracy: 0.8781 - val_loss: 0.3609 Epoch 857/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9471 - loss: 0.1132 - val_accuracy: 0.8698 - val_loss: 0.3577 Epoch 858/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9455 - loss: 0.1229 - val_accuracy: 0.8753 - val_loss: 0.3541 Epoch 859/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9506 - loss: 0.1186 - val_accuracy: 0.8735 - val_loss: 0.3856 Epoch 860/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9622 - loss: 0.1005 - val_accuracy: 0.8845 - val_loss: 0.3655 Epoch 861/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9506 - loss: 0.1141 - val_accuracy: 0.8818 - val_loss: 0.3565 Epoch 862/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9538 - loss: 0.1052 - val_accuracy: 0.8735 - val_loss: 0.3479 Epoch 863/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9562 - loss: 0.1068 - val_accuracy: 0.8763 - val_loss: 0.3640 Epoch 864/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9598 - loss: 0.1000 - val_accuracy: 0.8753 - val_loss: 0.3714 Epoch 865/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9437 - loss: 0.1289 - val_accuracy: 0.8726 - val_loss: 0.3554 Epoch 866/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9505 - loss: 0.1085 - val_accuracy: 0.8808 - val_loss: 0.3437 Epoch 867/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9533 - loss: 0.1079 - val_accuracy: 0.8946 - val_loss: 0.3623 Epoch 868/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9514 - loss: 0.1126 - val_accuracy: 0.8579 - val_loss: 0.3930 Epoch 869/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9350 - loss: 0.1482 - val_accuracy: 0.8854 - val_loss: 0.3508 Epoch 870/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9309 - loss: 0.1685 - val_accuracy: 0.8863 - val_loss: 0.3581 Epoch 871/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9523 - loss: 0.1139 - val_accuracy: 0.8744 - val_loss: 0.3709 Epoch 872/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9496 - loss: 0.1149 - val_accuracy: 0.8799 - val_loss: 0.3720 Epoch 873/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9475 - loss: 0.1241 - val_accuracy: 0.8698 - val_loss: 0.3860 Epoch 874/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9434 - loss: 0.1232 - val_accuracy: 0.8744 - val_loss: 0.3644 Epoch 875/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9493 - loss: 0.1170 - val_accuracy: 0.8717 - val_loss: 0.3571 Epoch 876/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9490 - loss: 0.1183 - val_accuracy: 0.8818 - val_loss: 0.3530 Epoch 877/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9526 - loss: 0.1092 - val_accuracy: 0.8799 - val_loss: 0.3569 Epoch 878/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9579 - loss: 0.1063 - val_accuracy: 0.8873 - val_loss: 0.3543 Epoch 879/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9530 - loss: 0.1157 - val_accuracy: 0.8698 - val_loss: 0.3622 Epoch 880/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9498 - loss: 0.1121 - val_accuracy: 0.8891 - val_loss: 0.3410 Epoch 881/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9476 - loss: 0.1226 - val_accuracy: 0.8671 - val_loss: 0.3937 Epoch 882/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9477 - loss: 0.1253 - val_accuracy: 0.8827 - val_loss: 0.3450 Epoch 883/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9575 - loss: 0.1036 - val_accuracy: 0.8790 - val_loss: 0.3556 Epoch 884/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9537 - loss: 0.1117 - val_accuracy: 0.8689 - val_loss: 0.3839 Epoch 885/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9528 - loss: 0.1097 - val_accuracy: 0.8854 - val_loss: 0.3629 Epoch 886/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9444 - loss: 0.1212 - val_accuracy: 0.8698 - val_loss: 0.3646 Epoch 887/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1126 - val_accuracy: 0.8818 - val_loss: 0.3873 Epoch 888/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9431 - loss: 0.1222 - val_accuracy: 0.8863 - val_loss: 0.3591 Epoch 889/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9603 - loss: 0.0978 - val_accuracy: 0.8808 - val_loss: 0.3576 Epoch 890/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9542 - loss: 0.1059 - val_accuracy: 0.8882 - val_loss: 0.3638 Epoch 891/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9489 - loss: 0.1217 - val_accuracy: 0.8744 - val_loss: 0.4065 Epoch 892/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9389 - loss: 0.1318 - val_accuracy: 0.8790 - val_loss: 0.3766 Epoch 893/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9502 - loss: 0.1159 - val_accuracy: 0.8744 - val_loss: 0.3491 Epoch 894/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9542 - loss: 0.1091 - val_accuracy: 0.8744 - val_loss: 0.3442 Epoch 895/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9407 - loss: 0.1319 - val_accuracy: 0.8763 - val_loss: 0.3637 Epoch 896/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9419 - loss: 0.1256 - val_accuracy: 0.8836 - val_loss: 0.3568 Epoch 897/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9526 - loss: 0.1112 - val_accuracy: 0.8698 - val_loss: 0.3622 Epoch 898/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9502 - loss: 0.1140 - val_accuracy: 0.8827 - val_loss: 0.3431 Epoch 899/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1087 - val_accuracy: 0.8753 - val_loss: 0.3746 Epoch 900/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9518 - loss: 0.1068 - val_accuracy: 0.8698 - val_loss: 0.3618 Epoch 901/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9536 - loss: 0.1149 - val_accuracy: 0.8698 - val_loss: 0.3579 Epoch 902/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9446 - loss: 0.1258 - val_accuracy: 0.8708 - val_loss: 0.3862 Epoch 903/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9443 - loss: 0.1235 - val_accuracy: 0.8726 - val_loss: 0.3596 Epoch 904/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9536 - loss: 0.1078 - val_accuracy: 0.8781 - val_loss: 0.3543 Epoch 905/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9485 - loss: 0.1180 - val_accuracy: 0.8726 - val_loss: 0.3703 Epoch 906/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9534 - loss: 0.1170 - val_accuracy: 0.8753 - val_loss: 0.3871 Epoch 907/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9522 - loss: 0.1147 - val_accuracy: 0.8772 - val_loss: 0.3704 Epoch 908/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9535 - loss: 0.1177 - val_accuracy: 0.8863 - val_loss: 0.3643 Epoch 909/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9519 - loss: 0.1124 - val_accuracy: 0.8726 - val_loss: 0.3753 Epoch 910/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9499 - loss: 0.1129 - val_accuracy: 0.8854 - val_loss: 0.3650 Epoch 911/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9522 - loss: 0.1098 - val_accuracy: 0.8680 - val_loss: 0.4154 Epoch 912/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9458 - loss: 0.1136 - val_accuracy: 0.8808 - val_loss: 0.3600 Epoch 913/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9492 - loss: 0.1207 - val_accuracy: 0.8827 - val_loss: 0.3607 Epoch 914/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9555 - loss: 0.1009 - val_accuracy: 0.8836 - val_loss: 0.3696 Epoch 915/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9521 - loss: 0.1169 - val_accuracy: 0.8772 - val_loss: 0.3675 Epoch 916/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9481 - loss: 0.1107 - val_accuracy: 0.8799 - val_loss: 0.3648 Epoch 917/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9514 - loss: 0.1176 - val_accuracy: 0.8579 - val_loss: 0.4192 Epoch 918/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9450 - loss: 0.1278 - val_accuracy: 0.8689 - val_loss: 0.3715 Epoch 919/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9552 - loss: 0.1062 - val_accuracy: 0.8781 - val_loss: 0.3626 Epoch 920/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9541 - loss: 0.1045 - val_accuracy: 0.8698 - val_loss: 0.3619 Epoch 921/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9493 - loss: 0.1190 - val_accuracy: 0.8753 - val_loss: 0.3713 Epoch 922/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9447 - loss: 0.1210 - val_accuracy: 0.8763 - val_loss: 0.3873 Epoch 923/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9479 - loss: 0.1185 - val_accuracy: 0.8836 - val_loss: 0.3723 Epoch 924/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9464 - loss: 0.1175 - val_accuracy: 0.8744 - val_loss: 0.3806 Epoch 925/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9578 - loss: 0.1018 - val_accuracy: 0.8799 - val_loss: 0.3779 Epoch 926/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9531 - loss: 0.1117 - val_accuracy: 0.8616 - val_loss: 0.4099 Epoch 927/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9400 - loss: 0.1369 - val_accuracy: 0.8845 - val_loss: 0.3643 Epoch 928/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9532 - loss: 0.1028 - val_accuracy: 0.8717 - val_loss: 0.3882 Epoch 929/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9469 - loss: 0.1175 - val_accuracy: 0.8863 - val_loss: 0.3822 Epoch 930/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9505 - loss: 0.1064 - val_accuracy: 0.8744 - val_loss: 0.3988 Epoch 931/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9474 - loss: 0.1174 - val_accuracy: 0.8790 - val_loss: 0.3603 Epoch 932/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9526 - loss: 0.1128 - val_accuracy: 0.8698 - val_loss: 0.3879 Epoch 933/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9530 - loss: 0.1043 - val_accuracy: 0.8698 - val_loss: 0.3573 Epoch 934/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9545 - loss: 0.1059 - val_accuracy: 0.8845 - val_loss: 0.3823 Epoch 935/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9442 - loss: 0.1268 - val_accuracy: 0.8790 - val_loss: 0.3738 Epoch 936/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9566 - loss: 0.1050 - val_accuracy: 0.8818 - val_loss: 0.3742 Epoch 937/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9533 - loss: 0.1085 - val_accuracy: 0.8689 - val_loss: 0.3850 Epoch 938/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9553 - loss: 0.1007 - val_accuracy: 0.8799 - val_loss: 0.3710 Epoch 939/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9514 - loss: 0.1158 - val_accuracy: 0.8808 - val_loss: 0.3881 Epoch 940/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9533 - loss: 0.1061 - val_accuracy: 0.8634 - val_loss: 0.4248 Epoch 941/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9509 - loss: 0.1129 - val_accuracy: 0.8744 - val_loss: 0.3946 Epoch 942/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9552 - loss: 0.1084 - val_accuracy: 0.8726 - val_loss: 0.3590 Epoch 943/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9551 - loss: 0.1067 - val_accuracy: 0.8836 - val_loss: 0.3730 Epoch 944/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9439 - loss: 0.1287 - val_accuracy: 0.8735 - val_loss: 0.3690 Epoch 945/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9509 - loss: 0.1149 - val_accuracy: 0.8781 - val_loss: 0.3866 Epoch 946/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9509 - loss: 0.1155 - val_accuracy: 0.8799 - val_loss: 0.3892 Epoch 947/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9618 - loss: 0.1046 - val_accuracy: 0.8808 - val_loss: 0.3704 Epoch 948/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9491 - loss: 0.1115 - val_accuracy: 0.8763 - val_loss: 0.3681 Epoch 949/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9437 - loss: 0.1230 - val_accuracy: 0.8836 - val_loss: 0.3625 Epoch 950/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9512 - loss: 0.1109 - val_accuracy: 0.8717 - val_loss: 0.3631 Epoch 951/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9602 - loss: 0.1048 - val_accuracy: 0.8717 - val_loss: 0.3707 Epoch 952/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9537 - loss: 0.1082 - val_accuracy: 0.8845 - val_loss: 0.3697 Epoch 953/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9533 - loss: 0.1110 - val_accuracy: 0.8735 - val_loss: 0.4001 Epoch 954/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9448 - loss: 0.1216 - val_accuracy: 0.8753 - val_loss: 0.3954 Epoch 955/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9520 - loss: 0.1090 - val_accuracy: 0.8799 - val_loss: 0.3935 Epoch 956/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9476 - loss: 0.1127 - val_accuracy: 0.8744 - val_loss: 0.3929 Epoch 957/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9526 - loss: 0.1028 - val_accuracy: 0.8799 - val_loss: 0.3741 Epoch 958/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9479 - loss: 0.1066 - val_accuracy: 0.8836 - val_loss: 0.3754 Epoch 959/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9512 - loss: 0.1052 - val_accuracy: 0.8689 - val_loss: 0.3619 Epoch 960/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9590 - loss: 0.1040 - val_accuracy: 0.8772 - val_loss: 0.3578 Epoch 961/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9535 - loss: 0.1082 - val_accuracy: 0.8882 - val_loss: 0.3726 Epoch 962/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9481 - loss: 0.1130 - val_accuracy: 0.8753 - val_loss: 0.3845 Epoch 963/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9556 - loss: 0.1067 - val_accuracy: 0.8772 - val_loss: 0.3907 Epoch 964/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9544 - loss: 0.1105 - val_accuracy: 0.8680 - val_loss: 0.3859 Epoch 965/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9518 - loss: 0.1129 - val_accuracy: 0.8726 - val_loss: 0.4016 Epoch 966/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9517 - loss: 0.1123 - val_accuracy: 0.8753 - val_loss: 0.4160 Epoch 967/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9519 - loss: 0.1057 - val_accuracy: 0.8735 - val_loss: 0.3864 Epoch 968/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9538 - loss: 0.1060 - val_accuracy: 0.8753 - val_loss: 0.3817 Epoch 969/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9553 - loss: 0.1117 - val_accuracy: 0.8753 - val_loss: 0.3748 Epoch 970/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9572 - loss: 0.1043 - val_accuracy: 0.8799 - val_loss: 0.3654 Epoch 971/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9516 - loss: 0.1096 - val_accuracy: 0.8772 - val_loss: 0.3756 Epoch 972/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9550 - loss: 0.1066 - val_accuracy: 0.8616 - val_loss: 0.4797 Epoch 973/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9479 - loss: 0.1214 - val_accuracy: 0.8735 - val_loss: 0.3808 Epoch 974/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9584 - loss: 0.1004 - val_accuracy: 0.8680 - val_loss: 0.3958 Epoch 975/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9504 - loss: 0.1210 - val_accuracy: 0.8680 - val_loss: 0.4856 Epoch 976/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9412 - loss: 0.1393 - val_accuracy: 0.8772 - val_loss: 0.4309 Epoch 977/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9545 - loss: 0.1070 - val_accuracy: 0.8781 - val_loss: 0.4104 Epoch 978/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9447 - loss: 0.1352 - val_accuracy: 0.8781 - val_loss: 0.3633 Epoch 979/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9485 - loss: 0.1155 - val_accuracy: 0.8726 - val_loss: 0.4027 Epoch 980/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9441 - loss: 0.1234 - val_accuracy: 0.8735 - val_loss: 0.3668 Epoch 981/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9550 - loss: 0.1070 - val_accuracy: 0.8790 - val_loss: 0.3914 Epoch 982/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9563 - loss: 0.1001 - val_accuracy: 0.8763 - val_loss: 0.3797 Epoch 983/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9519 - loss: 0.1102 - val_accuracy: 0.8799 - val_loss: 0.3767 Epoch 984/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9617 - loss: 0.0968 - val_accuracy: 0.8744 - val_loss: 0.3871 Epoch 985/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9492 - loss: 0.1192 - val_accuracy: 0.8808 - val_loss: 0.3760 Epoch 986/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9507 - loss: 0.1138 - val_accuracy: 0.8836 - val_loss: 0.3957 Epoch 987/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9557 - loss: 0.1014 - val_accuracy: 0.8708 - val_loss: 0.3938 Epoch 988/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9592 - loss: 0.0977 - val_accuracy: 0.8790 - val_loss: 0.3815 Epoch 989/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9491 - loss: 0.1213 - val_accuracy: 0.8854 - val_loss: 0.3666 Epoch 990/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9565 - loss: 0.1018 - val_accuracy: 0.8763 - val_loss: 0.3965 Epoch 991/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9619 - loss: 0.0954 - val_accuracy: 0.8808 - val_loss: 0.3932 Epoch 992/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step - accuracy: 0.9502 - loss: 0.1148 - val_accuracy: 0.8708 - val_loss: 0.3778 Epoch 993/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9605 - loss: 0.0996 - val_accuracy: 0.8763 - val_loss: 0.4069 Epoch 994/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9473 - loss: 0.1161 - val_accuracy: 0.8763 - val_loss: 0.3945 Epoch 995/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9588 - loss: 0.0959 - val_accuracy: 0.8744 - val_loss: 0.4135 Epoch 996/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9625 - loss: 0.0901 - val_accuracy: 0.8662 - val_loss: 0.4634 Epoch 997/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9454 - loss: 0.1246 - val_accuracy: 0.8708 - val_loss: 0.4124 Epoch 998/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9574 - loss: 0.1060 - val_accuracy: 0.8836 - val_loss: 0.3864 Epoch 999/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9543 - loss: 0.1107 - val_accuracy: 0.8845 - val_loss: 0.3957 Epoch 1000/1000 [1m137/137[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 2ms/step - accuracy: 0.9496 - loss: 0.1262 - val_accuracy: 0.8708 - val_loss: 0.3969 [1m171/171[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 1ms/step [1m171/171[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 831us/step
dev_pred_labels = pd.read_csv('dev-0/out.tsv', header=None).values.flatten()
expected_labels = dev_labels
accuracy = accuracy_score(expected_labels, dev_pred_labels)
report = classification_report(expected_labels, dev_pred_labels)
print("=== Evaluation Results ===")
print(f"Accuracy: {accuracy:.4f}")
print("\nClassification Report:\n")
print(report)
print("==========================\n")=== Evaluation Results ===
Accuracy: 0.9364
Classification Report:
precision recall f1-score support
0 0.93 0.89 0.91 1983
1 0.94 0.96 0.95 3469
accuracy 0.94 5452
macro avg 0.93 0.93 0.93 5452
weighted avg 0.94 0.94 0.94 5452
==========================