# Path: Bunch.py
# class Bunch(object):
#   def __init__(self, **kwds):
#     self.__dict__.update(kwds)
# 
#   def __eq__(self, other):
#     return self.__dict__ == other.__dict__
# 
#   def __str__(self):
#     string = "BUNCH {" + str(self.__dict__)[2:]
#     string = string.replace("': ", ":")
#     string = string.replace(", '", ", ")
#     return string
# 
#   def __repr__(self):
#     return str(self)
# 
#   def to_file_name(self, folder=None, ext=None):
#     res = str(self.__dict__)[2:-1]
#     res = res.replace("'", "")
#     res = res.replace(": ", ".")
#     parts = res.split(', ')
#     res = '_'.join(sorted(parts))
#     
#     if ext is not None:
#       res = '%s.%s' % (res, ext)
#     if folder is not None:
#       res = os.path.join(folder, res)
#     return res


import os
import numpy as np
import tensorflow as tf
import utils as ut
import input as inp
import visualization as vis
import matplotlib.pyplot as plt
import time
import sys
import getch
import model_interpreter as interpreter
import network_utils as nut
import math
from tensorflow.contrib.tensorboard.plugins import projector
from Bunch import Bunch
    self.fetch_datasets()
    self.build_ae_model()

    with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      # nut.print_model_info()
      # nut.list_checkpoint_vars(self.get_latest_checkpoint().replace(EMB_SUFFIX, ''))

      self.saver = tf.train.Saver()
      self._restore_model(sess)
      # nut.print_model_info()

      encoding, decoding = None, None
      for i in range(len(self.train_set)):
        batch = np.expand_dims(self.train_set[i], axis=0)
        enc, dec = sess.run(
          [self.encode, self.decode],
          feed_dict={self.input: batch}
        )

        # print(enc.shape, dec.shape)
        encoding = enc if i == 0 else np.vstack((encoding, enc))
        decoding = dec if i == 0 else np.vstack((decoding, dec))
        print('\r%5d/%d' % (i, len(self.train_set)), end='')
        if i >= max:
          break
      return encoding, decoding

  # @ut.timeit
  def evaluate(self, sess, take):

# Path: Bunch.py
# class Bunch(object):
#   def __init__(self, **kwds):
#     self.__dict__.update(kwds)
# 
#   def __eq__(self, other):
#     return self.__dict__ == other.__dict__
# 
#   def __str__(self):
#     string = "BUNCH {" + str(self.__dict__)[2:]
#     string = string.replace("': ", ":")
#     string = string.replace(", '", ", ")
#     return string
# 
#   def __repr__(self):
#     return str(self)
# 
#   def to_file_name(self, folder=None, ext=None):
#     res = str(self.__dict__)[2:-1]
#     res = res.replace("'", "")
#     res = res.replace(": ", ".")
#     parts = res.split(', ')
#     res = '_'.join(sorted(parts))
#     
#     if ext is not None:
#       res = '%s.%s' % (res, ext)
#     if folder is not None:
#       res = os.path.join(folder, res)
#     return res


# Path: autoencoder.py
import os
import numpy as np
import tensorflow as tf
import utils as ut
import input as inp
import visualization as vis
import matplotlib.pyplot as plt
import time
import sys
import getch
import model_interpreter as interpreter
import network_utils as nut
import math
from tensorflow.contrib.tensorboard.plugins import projector
from Bunch import Bunch

    self.fetch_datasets()
    self.build_ae_model()

    with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      # nut.print_model_info()
      # nut.list_checkpoint_vars(self.get_latest_checkpoint().replace(EMB_SUFFIX, ''))

      self.saver = tf.train.Saver()
      self._restore_model(sess)
      # nut.print_model_info()

      encoding, decoding = None, None
      for i in range(len(self.train_set)):
        batch = np.expand_dims(self.train_set[i], axis=0)
        enc, dec = sess.run(
          [self.encode, self.decode],
          feed_dict={self.input: batch}
        )

        # print(enc.shape, dec.shape)
        encoding = enc if i == 0 else np.vstack((encoding, enc))
        decoding = dec if i == 0 else np.vstack((decoding, dec))
        print('\r%5d/%d' % (i, len(self.train_set)), end='')
        if i >= max:
          break
      return encoding, decoding

  # @ut.timeit
  def evaluate(self, sess, take):

    digest = Bunch(encoded=None, reconstructed=None, source=None,