# B3 ------------

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Activation, Dropout, BatchNormalization
from tensorflow.keras import regularizers

# Create Model Structure
cnn_img_size = (256, 256)
channels = 3
img_shape = (cnn_img_size[0], cnn_img_size[1], channels)

base_model = tf.keras.applications.efficientnet.EfficientNetB3(include_top= False, weights= "imagenet", input_shape= img_shape, pooling= 'max')

b3_model = Sequential([
    base_model,
    BatchNormalization(axis= -1, momentum= 0.99, epsilon= 0.001),
    Dense(256, kernel_regularizer= regularizers.l2(0.016), activity_regularizer= regularizers.l1(0.006),
                bias_regularizer= regularizers.l1(0.006), activation= 'relu'),
    Dropout(rate= 0.45, seed= 123),
    Dense(4, activation= 'softmax')
])

b3_model.load_weights("efficientnetb3.h5")