init_mobilenets.py

# Create our model with pre-trained MobileNetV2 architecture from imagenet
def create_model(lr=1e-4,decay=1e-4/25, training=False,output_shape=y.shape[1]):
    baseModel = MobileNetV2(weights="imagenet", 
                            include_top=False,
                            input_tensor=Input(shape=(80, 80, 3)))

    headModel = baseModel.output
    headModel = AveragePooling2D(pool_size=(3, 3))(headModel)
    headModel = Flatten(name="flatten")(headModel)
    headModel = Dense(128, activation="relu")(headModel)
    headModel = Dropout(0.5)(headModel)
    headModel = Dense(output_shape, activation="softmax")(headModel)
    
    model = Model(inputs=baseModel.input, outputs=headModel)
    
    if training:
        # define trainable lalyer
        for layer in baseModel.layers:
            layer.trainable = True
            
        # compile model
        optimizer = Adam(lr=lr, decay = decay)
        model.compile(loss="categorical_crossentropy", optimizer=optimizer,metrics=["accuracy"])    
        
    return model

# initilaize initial hyperparameter
INIT_LR = 1e-4
EPOCHS = 30

model = create_model(lr=INIT_LR, decay=INIT_LR/EPOCHS,training=True)