Model is created like below:
    IMG_SHAPE=(224,224,3)
    class_names=["apple_braeburn", "apple_golden_delicious",  "apple_topaz",  "peach",  "pear"]
    
    data_augmentation = tf.keras.Sequential([
      tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
      tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
    ])
    
    preprocess_input = tf.keras.applications.vgg16.preprocess_input
    
    inputs = tf.keras.Input(shape=(IMG_SHAPE))#入力Tensor
    inputs=data_augmentation(inputs)
    inputs = preprocess_input(inputs)

    base_model = tf.keras.applications.VGG16(input_shape=IMG_SHAPE,
                                           include_top=False,
                                           weights='imagenet',
                                           classes = len(class_names), input_tensor=inputs)
    base_model.trainable = False

    last_layer=base_model.layers[-1]
    last_layer_outputshape=last_layer.output_shape
    dense_unit_to_set=last_layer_outputshape[-1]

    x2=base_model.output
    x2 = tf.keras.layers.GlobalAveragePooling2D()(x2)
    x2 = tf.keras.layers.Dropout(0.2)(x2)
    x2=tf.keras.layers.Dense(dense_unit_to_set,activation='relu')(x2)

    prediction_layer = tf.keras.layers.Dense(len(class_names),activation="softmax")
    x2 = prediction_layer(x2)

    model = tf.keras.Model(inputs=base_model.input, outputs=x2)
    

    base_learning_rate = 0.0001
    batch_size= BATCH_SIZE
    momentum_usage=0.99
    initial_epochs = 50

    sgd=tf.keras.optimizers.SGD(learning_rate=base_learning_rate,momentum=momentum_usage)

   model2.compile(optimizer=sgd,loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),metrics=['accuracy'])