V2 of the CNN

model_v2.py

inputs = keras.Input(shape=(256, 256, 3))
x = data_augmentation(inputs)
x = layers.Rescaling(1./255)(x)
x = layers.Conv2D(filters=32, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=64, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.Flatten()(x)
x = layers.Dense(512, activation=keras.activations.relu)(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(1, activation=keras.activations.sigmoid)(x)
model = keras.Model(inputs, outputs)

model.compile(optimizer=keras.optimizers.RMSprop(),
              loss=keras.losses.BinaryCrossentropy(),
              metrics=["accuracy"])

callbacks = [
    keras.callbacks.ModelCheckpoint(
        filepath=f"{hotDogDir}hotdog_classifier_v2.keras",
        save_best_only=True,
        monitor="val_loss"
    )
]

history = model.fit(
    train_dataset,
    epochs=50,
    validation_data=validation_dataset,
    callbacks=callbacks
)