Knowledge

Model

• Tensorflowのモデルはmodel-ckpt.meta, model-ckpt.data-0000-of-00001, model-ckpt.index, checkpointの4つの変数を持つ^*1
• ウェブサイトなどでデプロイするときに一つにまとめた.pbファイルにする
• kerasで保存する対象とそのコード。くわしくは下記サイトを参照^*2

References

• *1:Freeze Tensorflow models and serve on web
• *2:Keras FAQ: Kerasに関するよくある質問

TF2.0

Model save and load

• .jsonと.h(.hdf5)で読み込み.pbで保存

  import logging
  import tensorflow as tf
  from tensorflow.compat.v1 import graph_util
  from tensorflow.python.keras import backend as K
  from tensorflow import keras
  
  # necessary !!!
  tf.compat.v1.disable_eager_execution()
  
  # read the model
  json_string = open('./model/octClsf_1912280210_model.json').read()
  model = tf.keras.models.model_from_json(json_string)
  model.summary()
  # read the weights
  model.load_weights('./model/octClsf_1912280210_weights.hdf5')
  # save pb
  with K.get_session() as sess:
  output_names = [out.op.name for out in model.outputs]
  input_graph_def = sess.graph.as_graph_def()
  for node in input_graph_def.node:
      node.device = ""
  graph = graph_util.remove_training_nodes(input_graph_def)
  graph_frozen = graph_util.convert_variables_to_constants(sess, graph, output_names)
  tf.io.write_graph(graph_frozen, './model', 'model.pb')
  logging.info("save pb successfully！")
  

TF1.0

Model save and load

• .jsonと.h(.hdf5)で読み込み.pbで保存。読み込みできないのでウソの可能性がある^*1

  import tensorflow as tf
  from keras import backend as K
  from keras.models import model_from_json
  
  # This line must be executed before loading Keras model.
  K.set_learning_phase(0)
  from keras.models import load_model
  
  # model = load_model('./model/keras_model.h5')
  # read the model
  # json_string = open('./model/octClsf_1912280210_model.json').read()
  # model = keras.model.model_from_json(json_string)
  # model = model_from_json(json_string)
  
  # read the weights
  # model.load_weights('./model/octClsf_1912280210_weights.hdf5')
  #model.load_model('./model/octClsf_1912280210_weights.hdf5')
  # model = load_model('./model/octClsf_1912280210_weights.hdf5')
  model = load_model('./model/unet_3class.hdf5')
  # model.summary()
  def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
  """
  Freezes the state of a session into a pruned computation graph.
  Creates a new computation graph where variable nodes are replaced by
  constants taking their current value in the session. The new graph will be
  pruned so subgraphs that are not necessary to compute the requested
  outputs are removed.
  @param session The TensorFlow session to be frozen.
  @param keep_var_names A list of variable names that should not be frozen,
                        or None to freeze all the variables in the graph.
  @param output_names Names of the relevant graph outputs.
  @param clear_devices Remove the device directives from the graph for better portability.
  @return The frozen graph definition.
  """
  from tensorflow.python.framework.graph_util import convert_variables_to_constants
  graph = session.graph
  with graph.as_default():
      freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
      output_names = output_names or []
      output_names += [v.op.name for v in tf.global_variables()]
      # Graph -> GraphDef ProtoBuf
      input_graph_def = graph.as_graph_def()
      if clear_devices:
          for node in input_graph_def.node:
              node.device = ""
      frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                    output_names, freeze_var_names)
      return frozen_graph
  
  
  frozen_graph = freeze_session(K.get_session(),
                            output_names=[out.op.name for out in model.outputs])
  tf.train.write_graph(frozen_graph, "model", "tf1_model.pb", as_text=False)
  logging.info("save pb successfully！")
  

References

• *1:kerasモデルh5ファイルをテンソルフロー保存モデル（pb）に変換する方法
• *2:How to freeze a graph in Tensorflow
• *3:How to restore Tensorflow model from .pb file in python?
• *4:How to convert trained Keras model to a single TensorFlow .pb file and make prediction

Keras

Model save and load

• 重みからmodel読み込むとエラーになる。JSON→HDF5の順なら問題ない

  model.save_weights('model.hdf5')
  

• load_modelでパスを指定して読み込むことでエラーを解消^*1

  from keras.models import load_model
  model = load_model('model.h5')
  

• カスタムされたモデルは引数をつけないとロードできない^*2

References

• *1:Save and load weights in keras
• *2:kerasのモデルのload_modelでエラー
• *3:[TF]KerasでModelとParameterをLoad/Saveする方法
• *4:Kerasで可視化いろいろ
  
  • よさそうなのでやってみる