text: import tensorflow as tf
  from keras import backend as K
  
  from utils.nasnet import NASNetMobile
  from utils.data_loader import train_generator, val_generator
  
  sess = tf.Session()
  K.set_session(sess)
  
  image_size = 224
  
  def _float32_feature_list(floats):
      return tf.train.Feature(float_list=tf.train.FloatList(value=floats))
  
  model = NASNetMobile((image_size, image_size, 3), include_top=False, pooling='avg')
  model.summary()
  
  # ''' TRAIN SET '''
  nb_samples = 250000 * 2
  batchsize = 200
  
  with sess.as_default():
      generator = train_generator(batchsize, shuffle=False)
      writer = tf.python_io.TFRecordWriter('weights/nasnet_train.tfrecord')
  
  count = 0
  for _ in range(nb_samples // batchsize):
      x_batch, y_batch = next(generator)
  
      with sess.as_default():
          x_batch = model.predict(x_batch, batchsize, verbose=1)
  
      for i, (x, y) in enumerate(zip(x_batch, y_batch)):
          examples = {
              'features': _float32_feature_list(x.flatten()),
              'scores': _float32_feature_list(y.flatten()),
          }
          features = tf.train.Features(feature=examples)
          example = tf.train.Example(features=features)
          writer.write(example.SerializeToString())
  
      count += batchsize
  
      print("Finished %0.2f percentage storing dataset" % (count  * 100 / float(nb_samples)))
  
  writer.close()
  
  ''' TRAIN SET '''
  nb_samples = 5000
  batchsize = 200
  
  with sess.as_default():
      generator = val_generator(batchsize)
      writer = tf.python_io.TFRecordWriter('weights/nasnet_val.tfrecord')
  
  count = 0
  for _ in range(nb_samples // batchsize):
      x_batch, y_batch = next(generator)
  
      with sess.as_default():
          x_batch = model.predict(x_batch, batchsize, verbose=1)
  
      for i, (x, y) in enumerate(zip(x_batch, y_batch)):
          examples = {
              'features': _float32_feature_list(x.flatten()),
              'scores': _float32_feature_list(y.flatten()),
          }
          features = tf.train.Features(feature=examples)
          example = tf.train.Example(features=features)
          writer.write(example.SerializeToString())
  
      count += batchsize
  
      print("Finished %0.2f percentage storing dataset" % (count  * 100 / float(nb_samples)))
  
  writer.close()
  