Ví dụ này triển khai mô hình Vision Transformer (ViT) của Alexey Dosovitskiy để phân loại hình ảnh và thể hiện mô hình đó trên tập dữ liệu CIFAR-100. Mô hình ViT áp dụng kiến trúc Transformer với khả năng tự điều chỉnh đến chuỗi các bản vá hình ảnh mà không cần sử dụng các lớp tích chập.

Ví dụ này yêu cầu TensorFlow 2.4 trở lên, cũng như TensorFlow Addons, có thể được cài đặt bằng lệnh sau:

pip install -U tensorflow-addons

Cài đặt thư viện cần thiết

import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa

Chuẩn bị data

num_classes = 100
input_shape = (32, 32, 3)

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()

print(f"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}")
print(f"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}")

x_train shape: (50000, 32, 32, 3) – y_train shape: (50000, 1)

x_test shape: (10000, 32, 32, 3) – y_test shape: (10000, 1)

Cấu hình các siêu tham số (hyperparameters)

learning_rate = 0.001
weight_decay = 0.0001
batch_size = 256
num_epochs = 100
image_size = 72  
patch_size = 6  
num_patches = (image_size // patch_size) ** 2
projection_dim = 64
num_heads = 4
transformer_units = [
    projection_dim * 2,
    projection_dim,
]  
transformer_layers = 8
mlp_head_units = [2048, 1024]  

Tăng cường dữ liệu

data_augmentation = keras.Sequential(
    [
        layers.Normalization(),
        layers.Resizing(image_size, image_size),
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(factor=0.02),
        layers.RandomZoom(
            height_factor=0.2, width_factor=0.2
        ),
    ],
    name="data_augmentation",
)
data_augmentation.layers[0].adapt(x_train)

Triển khai Perceptron đa lớp (MLP)

def mlp(x, hidden_units, dropout_rate):
    for units in hidden_units:
        x = layers.Dense(units, activation=tf.nn.gelu)(x)
        x = layers.Dropout(dropout_rate)(x)
    return x

Triển khai tạo Patch dưới dạng một lớp

class Patches(layers.Layer):
    def __init__(self, patch_size):
        super().__init__()
        self.patch_size = patch_size

    def call(self, images):
        batch_size = tf.shape(images)[0]
        patches = tf.image.extract_patches(
            images=images,
            sizes=[1, self.patch_size, self.patch_size, 1],
            strides=[1, self.patch_size, self.patch_size, 1],
            rates=[1, 1, 1, 1],
            padding="VALID",
        )
        patch_dims = patches.shape[-1]
        patches = tf.reshape(patches, [batch_size, -1, patch_dims])
        return patches

Hãy hiển thị các patches cho một hình ảnh mẫu:

import matplotlib.pyplot as plt

plt.figure(figsize=(4, 4))
image = x_train[np.random.choice(range(x_train.shape[0]))]
plt.imshow(image.astype("uint8"))
plt.axis("off")

resized_image = tf.image.resize(
    tf.convert_to_tensor([image]), size=(image_size, image_size)
)
patches = Patches(patch_size)(resized_image)
print(f"Image size: {image_size} X {image_size}")
print(f"Patch size: {patch_size} X {patch_size}")
print(f"Patches per image: {patches.shape[1]}")
print(f"Elements per patch: {patches.shape[-1]}")

n = int(np.sqrt(patches.shape[1]))
plt.figure(figsize=(4, 4))
for i, patch in enumerate(patches[0]):
    ax = plt.subplot(n, n, i + 1)
    patch_img = tf.reshape(patch, (patch_size, patch_size, 3))
    plt.imshow(patch_img.numpy().astype("uint8"))
    plt.axis("off")

Image size: 72 X 72

Patch size: 6 X 6

Patches per image: 144

Elements per patch: 108

Triển khai lớp mã hóa Patch

Lớp PatchEncoder sẽ chuyển đổi tuyến tính một patch bằng cách chiếu nó vào một vectơ có kích thước là projector_dim. Ngoài ra, nó thêm một vị trí nhúng có thể học được vào vectơ được chiếu.

class PatchEncoder(layers.Layer):
    def __init__(self, num_patches, projection_dim):
        super().__init__()
        self.num_patches = num_patches
        self.projection = layers.Dense(units=projection_dim)
        self.position_embedding = layers.Embedding(
            input_dim=num_patches, output_dim=projection_dim
        )

    def call(self, patch):
        positions = tf.range(start=0, limit=self.num_patches, delta=1)
        encoded = self.projection(patch) + self.position_embedding(positions)
        return encoded

Xây dựng mô hình ViT

Mô hình ViT bao gồm nhiều khối Transformer sử dụng các lớp. Lớp MultiHeadAttention như một cơ chế tự chú ý được áp dụng cho chuỗi các bản vá lỗi. Các khối Transformer tạo ra một tensor [batch_size, num_patches, projector_dim], được xử lý thông qua một đầu phân loại với softmax để tạo ra đầu ra xác suất của lớp cuối cùng.

Trong đó thêm một phần nhúng có thể học được vào chuỗi các patch được mã hóa để dùng làm biểu diễn hình ảnh, tất cả đầu ra của khối Transformer cuối cùng được định hình lại bằng các lớp. Flatten() và được sử dụng làm đầu vào biểu diễn hình ảnh cho đầu phân loại. Lưu ý rằng lớp layers.GlobalAveragePooling1D cũng có thể được sử dụng thay thế để tổng hợp các đầu ra của khối Transformer, đặc biệt khi số lượng bản vá và kích thước hình chiếu lớn.

def create_vit_classifier():
    inputs = layers.Input(shape=input_shape)
    
    augmented = data_augmentation(inputs)
    
    patches = Patches(patch_size)(augmented)
   
    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)

    # Create multiple layers of the Transformer block.
    for _ in range(transformer_layers):
       
        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
        
        attention_output = layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=projection_dim, dropout=0.1
        )(x1, x1)
        
        x2 = layers.Add()([attention_output, encoded_patches])
        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
       
        encoded_patches = layers.Add()([x3, x2])

    
    representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
    representation = layers.Flatten()(representation)
    representation = layers.Dropout(0.5)(representation)
    
    features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.5)
   
    logits = layers.Dense(num_classes)(features)
    
    model = keras.Model(inputs=inputs, outputs=logits)
    return model

Biên dịch, đào tạo và đánh giá

def run_experiment(model):
    optimizer = tfa.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )

    model.compile(
        optimizer=optimizer,
        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=[
            keras.metrics.SparseCategoricalAccuracy(name="accuracy"),
            keras.metrics.SparseTopKCategoricalAccuracy(5, name="top-5-accuracy"),
        ],
    )

    checkpoint_filepath = "/tmp/checkpoint"
    checkpoint_callback = keras.callbacks.ModelCheckpoint(
        checkpoint_filepath,
        monitor="val_accuracy",
        save_best_only=True,
        save_weights_only=True,
    )

    history = model.fit(
        x=x_train,
        y=y_train,
        batch_size=batch_size,
        epochs=num_epochs,
        validation_split=0.1,
        callbacks=[checkpoint_callback],
    )

    model.load_weights(checkpoint_filepath)
    _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
    print(f"Test accuracy: {round(accuracy * 100, 2)}%")
    print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%")

    return history


vit_classifier = create_vit_classifier()
history = run_experiment(vit_classifier)

Epoch 1/100

176/176 [==============================] – 33s 136ms/step – loss: 4.8863 – accuracy: 0.0294 – top-5-accuracy: 0.1117 – val_loss: 3.9661 – val_accuracy: 0.0992 – val_top-5-accuracy: 0.3056

Epoch 2/100

176/176 [==============================] – 22s 127ms/step – loss: 4.0162 – accuracy: 0.0865 – top-5-accuracy: 0.2683 – val_loss: 3.5691 – val_accuracy: 0.1630 – val_top-5-accuracy: 0.4226

Epoch 3/100

176/176 [==============================] – 22s 127ms/step – loss: 3.7313 – accuracy: 0.1254 – top-5-accuracy: 0.3535 – val_loss: 3.3455 – val_accuracy: 0.1976 – val_top-5-accuracy: 0.4756

Epoch 4/100

176/176 [==============================] – 23s 128ms/step – loss: 3.5411 – accuracy: 0.1541 – top-5-accuracy: 0.4121 – val_loss: 3.1925 – val_accuracy: 0.2274 – val_top-5-accuracy: 0.5126

Epoch 5/100

176/176 [==============================] – 22s 127ms/step – loss: 3.3749 – accuracy: 0.1847 – top-5-accuracy: 0.4572 – val_loss: 3.1043 – val_accuracy: 0.2388 – val_top-5-accuracy: 0.5320

Epoch 6/100

176/176 [==============================] – 22s 127ms/step – loss: 3.2589 – accuracy: 0.2057 – top-5-accuracy: 0.4906 – val_loss: 2.9319 – val_accuracy: 0.2782 – val_top-5-accuracy: 0.5756

Epoch 7/100

176/176 [==============================] – 22s 127ms/step – loss: 3.1165 – accuracy: 0.2331 – top-5-accuracy: 0.5273 – val_loss: 2.8072 – val_accuracy: 0.2972 – val_top-5-accuracy: 0.5946

Epoch 8/100

176/176 [==============================] – 22s 127ms/step – loss: 2.9902 – accuracy: 0.2563 – top-5-accuracy: 0.5556 – val_loss: 2.7207 – val_accuracy: 0.3188 – val_top-5-accuracy: 0.6258

Epoch 9/100

176/176 [==============================] – 22s 127ms/step – loss: 2.8828 – accuracy: 0.2800 – top-5-accuracy: 0.5827 – val_loss: 2.6396 – val_accuracy: 0.3244 – val_top-5-accuracy: 0.6402

Epoch 10/100

176/176 [==============================] – 23s 128ms/step – loss: 2.7824 – accuracy: 0.2997 – top-5-accuracy: 0.6110 – val_loss: 2.5580 – val_accuracy: 0.3494 – val_top-5-accuracy: 0.6568

Epoch 11/100

176/176 [==============================] – 23s 130ms/step – loss: 2.6743 – accuracy: 0.3209 – top-5-accuracy: 0.6333 – val_loss: 2.5000 – val_accuracy: 0.3594 – val_top-5-accuracy: 0.6726

Epoch 12/100

176/176 [==============================] – 23s 130ms/step – loss: 2.5800 – accuracy: 0.3431 – top-5-accuracy: 0.6522 – val_loss: 2.3900 – val_accuracy: 0.3798 – val_top-5-accuracy: 0.6878

Epoch 13/100

176/176 [==============================] – 23s 128ms/step – loss: 2.5019 – accuracy: 0.3559 – top-5-accuracy: 0.6671 – val_loss: 2.3464 – val_accuracy: 0.3960 – val_top-5-accuracy: 0.7002

Epoch 14/100

176/176 [==============================] – 22s 128ms/step – loss: 2.4207 – accuracy: 0.3728 – top-5-accuracy: 0.6905 – val_loss: 2.3130 – val_accuracy: 0.4032 – val_top-5-accuracy: 0.7040

Epoch 15/100

176/176 [==============================] – 23s 128ms/step – loss: 2.3371 – accuracy: 0.3932 – top-5-accuracy: 0.7093 – val_loss: 2.2447 – val_accuracy: 0.4136 – val_top-5-accuracy: 0.7202

Epoch 16/100

176/176 [==============================] – 23s 128ms/step – loss: 2.2650 – accuracy: 0.4077 – top-5-accuracy: 0.7201 – val_loss: 2.2101 – val_accuracy: 0.4222 – val_top-5-accuracy: 0.7246

Epoch 17/100

176/176 [==============================] – 22s 127ms/step – loss: 2.1822 – accuracy: 0.4204 – top-5-accuracy: 0.7376 – val_loss: 2.1446 – val_accuracy: 0.4344 – val_top-5-accuracy: 0.7416

Epoch 18/100

176/176 [==============================] – 22s 128ms/step – loss: 2.1485 – accuracy: 0.4284 – top-5-accuracy: 0.7476 – val_loss: 2.1094 – val_accuracy: 0.4432 – val_top-5-accuracy: 0.7454

Epoch 19/100

176/176 [==============================] – 22s 128ms/step – loss: 2.0717 – accuracy: 0.4464 – top-5-accuracy: 0.7618 – val_loss: 2.0718 – val_accuracy: 0.4584 – val_top-5-accuracy: 0.7570

Epoch 20/100

176/176 [==============================] – 22s 127ms/step – loss: 2.0031 – accuracy: 0.4605 – top-5-accuracy: 0.7731 – val_loss: 2.0286 – val_accuracy: 0.4610 – val_top-5-accuracy: 0.7654

Epoch 21/100

176/176 [==============================] – 22s 127ms/step – loss: 1.9650 – accuracy: 0.4700 – top-5-accuracy: 0.7820 – val_loss: 2.0225 – val_accuracy: 0.4642 – val_top-5-accuracy: 0.7628

Epoch 22/100

176/176 [==============================] – 22s 127ms/step – loss: 1.9066 – accuracy: 0.4839 – top-5-accuracy: 0.7904 – val_loss: 1.9961 – val_accuracy: 0.4746 – val_top-5-accuracy: 0.7656

Epoch 23/100

176/176 [==============================] – 22s 127ms/step – loss: 1.8564 – accuracy: 0.4952 – top-5-accuracy: 0.8030 – val_loss: 1.9769 – val_accuracy: 0.4828 – val_top-5-accuracy: 0.7742

Epoch 24/100

176/176 [==============================] – 22s 128ms/step – loss: 1.8167 – accuracy: 0.5034 – top-5-accuracy: 0.8099 – val_loss: 1.9730 – val_accuracy: 0.4766 – val_top-5-accuracy: 0.7728

Epoch 25/100

176/176 [==============================] – 22s 128ms/step – loss: 1.7788 – accuracy: 0.5124 – top-5-accuracy: 0.8174 – val_loss: 1.9187 – val_accuracy: 0.4926 – val_top-5-accuracy: 0.7854

Epoch 26/100

176/176 [==============================] – 23s 128ms/step – loss: 1.7437 – accuracy: 0.5187 – top-5-accuracy: 0.8206 – val_loss: 1.9732 – val_accuracy: 0.4792 – val_top-5-accuracy: 0.7772

Epoch 27/100

176/176 [==============================] – 23s 128ms/step – loss: 1.6929 – accuracy: 0.5300 – top-5-accuracy: 0.8287 – val_loss: 1.9109 – val_accuracy: 0.4928 – val_top-5-accuracy: 0.7912

Epoch 28/100

176/176 [==============================] – 23s 129ms/step – loss: 1.6647 – accuracy: 0.5400 – top-5-accuracy: 0.8362 – val_loss: 1.9031 – val_accuracy: 0.4984 – val_top-5-accuracy: 0.7824

Epoch 29/100

176/176 [==============================] – 23s 129ms/step – loss: 1.6295 – accuracy: 0.5488 – top-5-accuracy: 0.8402 – val_loss: 1.8744 – val_accuracy: 0.4982 – val_top-5-accuracy: 0.7910

Epoch 30/100

176/176 [==============================] – 22s 128ms/step – loss: 1.5860 – accuracy: 0.5548 – top-5-accuracy: 0.8504 – val_loss: 1.8551 – val_accuracy: 0.5108 – val_top-5-accuracy: 0.7946

Epoch 31/100

176/176 [==============================] – 22s 127ms/step – loss: 1.5666 – accuracy: 0.5614 – top-5-accuracy: 0.8548 – val_loss: 1.8720 – val_accuracy: 0.5076 – val_top-5-accuracy: 0.7960

Epoch 32/100

176/176 [==============================] – 22s 127ms/step – loss: 1.5272 – accuracy: 0.5712 – top-5-accuracy: 0.8596 – val_loss: 1.8840 – val_accuracy: 0.5106 – val_top-5-accuracy: 0.7966

Epoch 33/100

176/176 [==============================] – 22s 128ms/step – loss: 1.4995 – accuracy: 0.5779 – top-5-accuracy: 0.8651 – val_loss: 1.8660 – val_accuracy: 0.5116 – val_top-5-accuracy: 0.7904

Epoch 34/100

176/176 [==============================] – 22s 128ms/step – loss: 1.4686 – accuracy: 0.5849 – top-5-accuracy: 0.8685 – val_loss: 1.8544 – val_accuracy: 0.5126 – val_top-5-accuracy: 0.7954

Epoch 35/100

176/176 [==============================] – 22s 127ms/step – loss: 1.4276 – accuracy: 0.5992 – top-5-accuracy: 0.8743 – val_loss: 1.8497 – val_accuracy: 0.5164 – val_top-5-accuracy: 0.7990

Epoch 36/100

176/176 [==============================] – 22s 127ms/step – loss: 1.4102 – accuracy: 0.5970 – top-5-accuracy: 0.8768 – val_loss: 1.8496 – val_accuracy: 0.5198 – val_top-5-accuracy: 0.7948

Epoch 37/100

176/176 [==============================] – 22s 126ms/step – loss: 1.3800 – accuracy: 0.6112 – top-5-accuracy: 0.8814 – val_loss: 1.8033 – val_accuracy: 0.5284 – val_top-5-accuracy: 0.8068

Epoch 38/100

176/176 [==============================] – 22s 126ms/step – loss: 1.3500 – accuracy: 0.6103 – top-5-accuracy: 0.8862 – val_loss: 1.8092 – val_accuracy: 0.5214 – val_top-5-accuracy: 0.8128

Epoch 39/100

176/176 [==============================] – 22s 127ms/step – loss: 1.3575 – accuracy: 0.6127 – top-5-accuracy: 0.8857 – val_loss: 1.8175 – val_accuracy: 0.5198 – val_top-5-accuracy: 0.8086

Epoch 40/100

176/176 [==============================] – 22s 126ms/step – loss: 1.3030 – accuracy: 0.6283 – top-5-accuracy: 0.8927 – val_loss: 1.8361 – val_accuracy: 0.5170 – val_top-5-accuracy: 0.8056

Epoch 41/100

176/176 [==============================] – 22s 125ms/step – loss: 1.3160 – accuracy: 0.6247 – top-5-accuracy: 0.8923 – val_loss: 1.8074 – val_accuracy: 0.5260 – val_top-5-accuracy: 0.8082

Epoch 42/100

176/176 [==============================] – 22s 126ms/step – loss: 1.2679 – accuracy: 0.6329 – top-5-accuracy: 0.9002 – val_loss: 1.8430 – val_accuracy: 0.5244 – val_top-5-accuracy: 0.8100

Epoch 43/100

176/176 [==============================] – 22s 126ms/step – loss: 1.2514 – accuracy: 0.6375 – top-5-accuracy: 0.9034 – val_loss: 1.8318 – val_accuracy: 0.5196 – val_top-5-accuracy: 0.8034

Epoch 44/100

176/176 [==============================] – 22s 126ms/step – loss: 1.2311 – accuracy: 0.6431 – top-5-accuracy: 0.9067 – val_loss: 1.8283 – val_accuracy: 0.5218 – val_top-5-accuracy: 0.8050

Epoch 45/100

176/176 [==============================] – 22s 125ms/step – loss: 1.2073 – accuracy: 0.6484 – top-5-accuracy: 0.9098 – val_loss: 1.8384 – val_accuracy: 0.5302 – val_top-5-accuracy: 0.8056

Epoch 46/100

176/176 [==============================] – 22s 125ms/step – loss: 1.1775 – accuracy: 0.6558 – top-5-accuracy: 0.9117 – val_loss: 1.8409 – val_accuracy: 0.5294 – val_top-5-accuracy: 0.8078

Epoch 47/100

176/176 [==============================] – 22s 126ms/step – loss: 1.1891 – accuracy: 0.6563 – top-5-accuracy: 0.9103 – val_loss: 1.8167 – val_accuracy: 0.5346 – val_top-5-accuracy: 0.8142

Epoch 48/100

176/176 [==============================] – 22s 127ms/step – loss: 1.1586 – accuracy: 0.6621 – top-5-accuracy: 0.9161 – val_loss: 1.8285 – val_accuracy: 0.5314 – val_top-5-accuracy: 0.8086

Epoch 49/100

176/176 [==============================] – 22s 126ms/step – loss: 1.1586 – accuracy: 0.6634 – top-5-accuracy: 0.9154 – val_loss: 1.8189 – val_accuracy: 0.5366 – val_top-5-accuracy: 0.8134

Epoch 50/100

176/176 [==============================] – 22s 126ms/step – loss: 1.1306 – accuracy: 0.6682 – top-5-accuracy: 0.9199 – val_loss: 1.8442 – val_accuracy: 0.5254 – val_top-5-accuracy: 0.8096

Epoch 51/100

176/176 [==============================] – 22s 126ms/step – loss: 1.1175 – accuracy: 0.6708 – top-5-accuracy: 0.9227 – val_loss: 1.8513 – val_accuracy: 0.5230 – val_top-5-accuracy: 0.8104

Epoch 52/100

176/176 [==============================] – 22s 126ms/step – loss: 1.1104 – accuracy: 0.6743 – top-5-accuracy: 0.9226 – val_loss: 1.8041 – val_accuracy: 0.5332 – val_top-5-accuracy: 0.8142

Epoch 53/100

176/176 [==============================] – 22s 127ms/step – loss: 1.0914 – accuracy: 0.6809 – top-5-accuracy: 0.9236 – val_loss: 1.8213 – val_accuracy: 0.5342 – val_top-5-accuracy: 0.8094

Epoch 54/100

176/176 [==============================] – 22s 126ms/step – loss: 1.0681 – accuracy: 0.6856 – top-5-accuracy: 0.9270 – val_loss: 1.8429 – val_accuracy: 0.5328 – val_top-5-accuracy: 0.8086

Epoch 55/100

176/176 [==============================] – 22s 126ms/step – loss: 1.0625 – accuracy: 0.6862 – top-5-accuracy: 0.9301 – val_loss: 1.8316 – val_accuracy: 0.5364 – val_top-5-accuracy: 0.8090

Epoch 56/100

176/176 [==============================] – 22s 127ms/step – loss: 1.0474 – accuracy: 0.6920 – top-5-accuracy: 0.9308 – val_loss: 1.8310 – val_accuracy: 0.5440 – val_top-5-accuracy: 0.8132

Epoch 57/100

176/176 [==============================] – 22s 127ms/step – loss: 1.0381 – accuracy: 0.6974 – top-5-accuracy: 0.9297 – val_loss: 1.8447 – val_accuracy: 0.5368 – val_top-5-accuracy: 0.8126

Epoch 58/100

176/176 [==============================] – 22s 126ms/step – loss: 1.0230 – accuracy: 0.7011 – top-5-accuracy: 0.9341 – val_loss: 1.8241 – val_accuracy: 0.5418 – val_top-5-accuracy: 0.8094

Epoch 59/100

176/176 [==============================] – 22s 127ms/step – loss: 1.0113 – accuracy: 0.7023 – top-5-accuracy: 0.9361 – val_loss: 1.8216 – val_accuracy: 0.5380 – val_top-5-accuracy: 0.8134

Epoch 60/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9953 – accuracy: 0.7031 – top-5-accuracy: 0.9386 – val_loss: 1.8356 – val_accuracy: 0.5422 – val_top-5-accuracy: 0.8122

Epoch 61/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9928 – accuracy: 0.7084 – top-5-accuracy: 0.9375 – val_loss: 1.8514 – val_accuracy: 0.5342 – val_top-5-accuracy: 0.8182

Epoch 62/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9740 – accuracy: 0.7121 – top-5-accuracy: 0.9387 – val_loss: 1.8674 – val_accuracy: 0.5366 – val_top-5-accuracy: 0.8092

Epoch 63/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9742 – accuracy: 0.7112 – top-5-accuracy: 0.9413 – val_loss: 1.8274 – val_accuracy: 0.5414 – val_top-5-accuracy: 0.8144

Epoch 64/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9633 – accuracy: 0.7147 – top-5-accuracy: 0.9393 – val_loss: 1.8250 – val_accuracy: 0.5434 – val_top-5-accuracy: 0.8180

Epoch 65/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9407 – accuracy: 0.7221 – top-5-accuracy: 0.9444 – val_loss: 1.8456 – val_accuracy: 0.5424 – val_top-5-accuracy: 0.8120

Epoch 66/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9410 – accuracy: 0.7194 – top-5-accuracy: 0.9447 – val_loss: 1.8559 – val_accuracy: 0.5460 – val_top-5-accuracy: 0.8144

Epoch 67/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9359 – accuracy: 0.7252 – top-5-accuracy: 0.9421 – val_loss: 1.8352 – val_accuracy: 0.5458 – val_top-5-accuracy: 0.8110

Epoch 68/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9232 – accuracy: 0.7254 – top-5-accuracy: 0.9460 – val_loss: 1.8479 – val_accuracy: 0.5444 – val_top-5-accuracy: 0.8132

Epoch 69/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9138 – accuracy: 0.7283 – top-5-accuracy: 0.9456 – val_loss: 1.8697 – val_accuracy: 0.5312 – val_top-5-accuracy: 0.8052

Epoch 70/100

176/176 [==============================] – 22s 126ms/step – loss: 0.9095 – accuracy: 0.7295 – top-5-accuracy: 0.9478 – val_loss: 1.8550 – val_accuracy: 0.5376 – val_top-5-accuracy: 0.8170

Epoch 71/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8945 – accuracy: 0.7332 – top-5-accuracy: 0.9504 – val_loss: 1.8286 – val_accuracy: 0.5436 – val_top-5-accuracy: 0.8198

Epoch 72/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8936 – accuracy: 0.7344 – top-5-accuracy: 0.9479 – val_loss: 1.8727 – val_accuracy: 0.5438 – val_top-5-accuracy: 0.8182

Epoch 73/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8775 – accuracy: 0.7355 – top-5-accuracy: 0.9510 – val_loss: 1.8522 – val_accuracy: 0.5404 – val_top-5-accuracy: 0.8170

Epoch 74/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8660 – accuracy: 0.7390 – top-5-accuracy: 0.9513 – val_loss: 1.8432 – val_accuracy: 0.5448 – val_top-5-accuracy: 0.8156

Epoch 75/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8583 – accuracy: 0.7441 – top-5-accuracy: 0.9532 – val_loss: 1.8419 – val_accuracy: 0.5462 – val_top-5-accuracy: 0.8226

Epoch 76/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8549 – accuracy: 0.7443 – top-5-accuracy: 0.9529 – val_loss: 1.8757 – val_accuracy: 0.5454 – val_top-5-accuracy: 0.8086

Epoch 77/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8578 – accuracy: 0.7384 – top-5-accuracy: 0.9531 – val_loss: 1.9051 – val_accuracy: 0.5462 – val_top-5-accuracy: 0.8136

Epoch 78/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8530 – accuracy: 0.7442 – top-5-accuracy: 0.9526 – val_loss: 1.8496 – val_accuracy: 0.5384 – val_top-5-accuracy: 0.8124

Epoch 79/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8403 – accuracy: 0.7485 – top-5-accuracy: 0.9542 – val_loss: 1.8701 – val_accuracy: 0.5550 – val_top-5-accuracy: 0.8228

Epoch 80/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8410 – accuracy: 0.7491 – top-5-accuracy: 0.9538 – val_loss: 1.8737 – val_accuracy: 0.5502 – val_top-5-accuracy: 0.8150

Epoch 81/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8275 – accuracy: 0.7547 – top-5-accuracy: 0.9532 – val_loss: 1.8391 – val_accuracy: 0.5534 – val_top-5-accuracy: 0.8156

Epoch 82/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8221 – accuracy: 0.7528 – top-5-accuracy: 0.9562 – val_loss: 1.8775 – val_accuracy: 0.5428 – val_top-5-accuracy: 0.8120

Epoch 83/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8270 – accuracy: 0.7526 – top-5-accuracy: 0.9550 – val_loss: 1.8464 – val_accuracy: 0.5468 – val_top-5-accuracy: 0.8148

Epoch 84/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8080 – accuracy: 0.7551 – top-5-accuracy: 0.9576 – val_loss: 1.8789 – val_accuracy: 0.5486 – val_top-5-accuracy: 0.8204

Epoch 85/100

176/176 [==============================] – 22s 125ms/step – loss: 0.8058 – accuracy: 0.7593 – top-5-accuracy: 0.9573 – val_loss: 1.8691 – val_accuracy: 0.5446 – val_top-5-accuracy: 0.8156

Epoch 86/100

176/176 [==============================] – 22s 126ms/step – loss: 0.8092 – accuracy: 0.7564 – top-5-accuracy: 0.9560 – val_loss: 1.8588 – val_accuracy: 0.5524 – val_top-5-accuracy: 0.8172

Epoch 87/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7897 – accuracy: 0.7613 – top-5-accuracy: 0.9604 – val_loss: 1.8649 – val_accuracy: 0.5490 – val_top-5-accuracy: 0.8166

Epoch 88/100

176/176 [==============================] – 22s 126ms/step – loss: 0.7890 – accuracy: 0.7635 – top-5-accuracy: 0.9598 – val_loss: 1.9060 – val_accuracy: 0.5446 – val_top-5-accuracy: 0.8112

Epoch 89/100

176/176 [==============================] – 22s 126ms/step – loss: 0.7682 – accuracy: 0.7687 – top-5-accuracy: 0.9620 – val_loss: 1.8645 – val_accuracy: 0.5474 – val_top-5-accuracy: 0.8150

Epoch 90/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7958 – accuracy: 0.7617 – top-5-accuracy: 0.9600 – val_loss: 1.8549 – val_accuracy: 0.5496 – val_top-5-accuracy: 0.8140

Epoch 91/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7978 – accuracy: 0.7603 – top-5-accuracy: 0.9590 – val_loss: 1.9169 – val_accuracy: 0.5440 – val_top-5-accuracy: 0.8140

Epoch 92/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7898 – accuracy: 0.7630 – top-5-accuracy: 0.9594 – val_loss: 1.9015 – val_accuracy: 0.5540 – val_top-5-accuracy: 0.8174

Epoch 93/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7550 – accuracy: 0.7722 – top-5-accuracy: 0.9622 – val_loss: 1.9219 – val_accuracy: 0.5410 – val_top-5-accuracy: 0.8098

Epoch 94/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7692 – accuracy: 0.7689 – top-5-accuracy: 0.9599 – val_loss: 1.8928 – val_accuracy: 0.5506 – val_top-5-accuracy: 0.8184

Epoch 95/100

176/176 [==============================] – 22s 126ms/step – loss: 0.7783 – accuracy: 0.7661 – top-5-accuracy: 0.9597 – val_loss: 1.8646 – val_accuracy: 0.5490 – val_top-5-accuracy: 0.8166

Epoch 96/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7547 – accuracy: 0.7711 – top-5-accuracy: 0.9638 – val_loss: 1.9347 – val_accuracy: 0.5484 – val_top-5-accuracy: 0.8150

Epoch 97/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7603 – accuracy: 0.7692 – top-5-accuracy: 0.9616 – val_loss: 1.8966 – val_accuracy: 0.5522 – val_top-5-accuracy: 0.8144

Epoch 98/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7595 – accuracy: 0.7730 – top-5-accuracy: 0.9610 – val_loss: 1.8728 – val_accuracy: 0.5470 – val_top-5-accuracy: 0.8170

Epoch 99/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7542 – accuracy: 0.7736 – top-5-accuracy: 0.9622 – val_loss: 1.9132 – val_accuracy: 0.5504 – val_top-5-accuracy: 0.8156

Epoch 100/100

176/176 [==============================] – 22s 125ms/step – loss: 0.7410 – accuracy: 0.7787 – top-5-accuracy: 0.9635 – val_loss: 1.9233 – val_accuracy: 0.5428 – val_top-5-accuracy: 0.8120

313/313 [==============================] – 4s 12ms/step – loss: 1.8487 – accuracy: 0.5514 – top-5-accuracy: 0.8186

Test accuracy: 55.14%

Test top 5 accuracy: 81.86%

Sau 100 epochs, mô hình ViT đạt được độ chính xác khoảng 55% và 82% độ chính xác của top 5 trên dữ liệu thử nghiệm. Đây không phải là kết quả cạnh tranh trên bộ dữ liệu CIFAR-100, vì ResNet50V2 được đào tạo từ đầu trên cùng một dữ liệu có thể đạt được độ chính xác 67%.