emuccino

from tensorflow.keras.callbacks import EarlyStopping

early_stop = EarlyStopping(
    monitor='val_loss', min_delta=0, patience=5, verbose=0, mode='auto',
    baseline=None, restore_best_weights=True)

model.fit(x_train, {device:y_train for device in device_names},
          batch_size=64,
          epochs=1000,
          verbose=1,

emuccino

import numpy as np
from scipy.special import softmax

def get_results(confidence_threshold):
    print('confidence threshold:',confidence_threshold)
    
    predictions = []
    confidence = []
    exit_level = []
    

emuccino

import matplotlib.pyplot as plt

def show_image_compression(samples):
    #get encoded samples offloaded to edge
    _, offload_end = device_models['end'].predict(samples)
    #get encoded samples offloaded to cloud
    _, offload_edge = device_models['edge'].predict(offload_end)
    #show sample represenations at each device level
    for i in range(len(samples)):
        print(i)

emuccino