So I’m trying to fine tune ResNet50, but i’m new doing that, so I’m trying with a binary classification, but I’m not sure if I’m setting it correctly, because my accuracy at the end is weird, apparently the epochs reach 0,8, but when I’m testing it is very low, here’s my code:

import tensorflow as tf
from sklearn.metrics import roc_auc_score
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.callbacks import EarlyStopping

base_model = tf.keras.applications.ResNet50(
    include_top=False,
    weights='imagenet',
    input_shape=(224, 224, 3),
    classifier_activation='sigmoid',
)

pre_process_fn = tf.keras.applications.resnet50.preprocess_input

x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, activation='sigmoid')(x)

fine_tune_model = tf.keras.Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

fine_tune_model.compile(loss=tf.keras.losses.BinaryCrossentropy(),
                         metrics=[tf.keras.metrics.BinaryAccuracy()],
                         optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001))

new_size = (224, 224)

def preprocess(image, label):
    image = tf.image.resize(image, new_size)
    image = pre_process_fn(image)
    return image, label

train_dataset = train_dataset.map(preprocess)
val_dataset = val_dataset.map(preprocess)
test_dataset = test_dataset.map(preprocess)

BATCH_SIZE = 32
train_dataset = train_dataset.shuffle(1000).batch(BATCH_SIZE)
val_dataset = val_dataset.batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)

history_fine_tune_model = fine_tune_model.fit(train_dataset, epochs=100,
                                               validation_data=val_dataset, callbacks=[early_stopping])

test_loss, test_acc = fine_tune_model.evaluate(test_dataset, verbose=1)
y_pred_prob = fine_tune_model.predict(test_dataset)
auc = roc_auc_score(test_labels, y_pred_prob)

print("AUC:", auc)
print('nTest accuracy:', test_acc)