trafficSignCnn_v2.py

import tensorflow as tf
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout


# learning transfer from MobileNetV2
# learning transfer using different base network
class TrafficSignNet_v2:
    @staticmethod
    def build(width, height, depth, classes):
        IMG_SHAPE = (width, height, 3)
        chanDim = -1
        base_model = tf.keras.applications.VGG16(input_shape=IMG_SHAPE,
                                                 include_top=False,
                                                 weights='imagenet')

        base_model.trainable = False

        # fine_tune_at = 80 * len(base_model.layers) / 100

        # for layer in base_model.layers[:int(fine_tune_at)]:
        #    layer.trainable = False

        global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
        model = tf.keras.Sequential()

        model.add(base_model)

        model.add(global_average_layer)

        model.add(Dropout(0.3))
        model.add(Dense(classes, kernel_regularizer=tf.keras.regularizers.l2(0.01)))
        model.add(Activation("softmax"))

        return model