img_size = (224, 224)

channels = 3

img_shape = (img_size[0], img_size[1], channels)

class_count = len(list(train_gen.class_indices.keys()))

base_model = tf.keras.applications.efficientnet.EfficientNetB7(include_top=False, weights=”imagenet”, input_shape=img_shape, pooling=’max’)

base_model.trainable = False

model = Sequential([
base_model,
BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001),
Dense(128, kernel_regularizer=regularizers.l2(l=0.016), activity_regularizer=regularizers.l1(0.006),
bias_regularizer=regularizers.l1(0.006), activation=’relu’),
Dropout(rate=0.45, seed=123),
Dense(class_count, activation=’softmax’)
])

model.compile(Adamax(learning_rate=0.002), loss=’categorical_crossentropy’, metrics=[‘accuracy’])

model.summary()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[70], line 16
      9 base_model = tf.keras.applications.efficientnet.EfficientNetB7(include_top=False, weights="imagenet", input_shape=img_shape, pooling='max')
     11 base_model.trainable = False
     13 model = Sequential([
     14     base_model,  
     15     BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001), 
---> 16     Dense(128, kernel_regularizer=regularizers.l2(l=0.016), activity_regularizer=regularizers.l1(0.006),
     17           bias_regularizer=regularizers.l1(0.006), activation='relu'), 
     18     Dropout(rate=0.45, seed=123),  
     19     Dense(class_count, activation='softmax')  
     20 ])
     22 model.compile(Adamax(learning_rate=0.002), loss='categorical_crossentropy', metrics=['accuracy'])
     25 model.summary()

TypeError: L2.__init__() got an unexpected keyword argument 'l'