未腌制的张量流模型无法做出预测

Question

我见过this question 和this one，但没有真正解释发生了什么，也没有为我面临的问题提供解决方案。

下面的代码是我在更大的上下文中尝试做的一个 sn-p。基本上，我正在创建一个包含 tensorflow.keras 模型的对象，我使用从this answer 改编的技巧将其保存到带有泡菜的文件中。我正在处理的实际课程还有其他几个领域和方法，因此我更愿意让它可以腌制并以灵活的方式这样做。请参阅下面的代码，以最小化重现问题。 ReproduceProblem.py:

import pickle
import numpy as np
import tempfile
import tensorflow as tf


def __getstate__(self):
    model_str = ""
    with tempfile.NamedTemporaryFile(suffix=".hdf5", delete=False) as fd:
        tf.keras.models.save_model(self, fd.name, overwrite=True)
        model_str = fd.read()
    d = {"model_str": model_str}
    return d


def __setstate__(self, state):
    with tempfile.NamedTemporaryFile(suffix=".hdf5", delete=False) as fd:
        fd.write(state["model_str"])
        fd.flush()
        model = tf.keras.models.load_model(fd.name)
    self.__dict__ = model.__dict__


class ContainsSequential:
    def __init__(self):
        self.other_field = "potato"
        self.model = tf.keras.models.Sequential()
        self.model.__getstate__ = lambda mdl=self.model: __getstate__(mdl)
        self.model.__setstate__ = __setstate__
        self.model.add(tf.keras.layers.Input(shape=(None, 3)))
        self.model.add(tf.keras.layers.LSTM(3, activation="relu", return_sequences=True))
        self.model.add(tf.keras.layers.Dense(3, activation="linear"))


# Now do the business:
tf.keras.backend.clear_session()
file_name = 'pickle_file.pckl'
instance = ContainsSequential()
instance.model.predict(np.random.rand(3, 1, 3))
print(instance.other_field)
with open(file_name, 'wb') as fid:
    pickle.dump(instance, fid)
with open(file_name, 'rb') as fid:
    restored_instance = pickle.load(fid)
print(restored_instance.other_field)
restored_instance.model.predict(np.random.rand(3, 1, 3))
print('Done')

虽然在instance.model.predict(np.random.rand(3, 1, 3)) 行上没有失败，但在restored_instance.model.predict(np.random.rand(3, 1, 3)) 行上确实失败了，错误消息是：

  File "<path>\ReproduceProblem.py", line 52, in <module>
    restored_instance.model.predict(np.random.rand(3, 1, 3))
  File "<path>\Python\Python39\lib\site-packages\keras\engine\training.py", line 1693, in predict
    if self.distribute_strategy._should_use_with_coordinator:  # pylint: disable=protected-access
  File "<path>\Python\Python39\lib\site-packages\keras\engine\training.py", line 716, in distribute_strategy
    return self._distribution_strategy or tf.distribute.get_strategy()
AttributeError: 'Sequential' object has no attribute '_distribution_strategy'

我对@9​​87654329@ 应该是什么一无所知，但在我的工作流程中，一旦我保存了文件，我就不需要再训练它了，只需用它来进行预测或咨询其他类的属性。我尝试将其设置为 None 并添加更多属性，但没有成功。

Answer 1

像这样重新定义Tensorflow 类的方法是一种危险的方法：

self.model = tf.keras.models.Sequential()
self.model.__getstate__ = lambda mdl=self.model: __getstate__(mdl)
self.model.__setstate__ = __setstate__

我建议避免这种情况，而是重新定义自定义类的 __getstate__ 和 __setstate__ 方法。这是一个工作示例：

import pickle
import numpy as np
import tempfile
import tensorflow as tf


class ContainsSequential:
    def __init__(self):
        self.other_field = "Potato"
        self.model = tf.keras.models.Sequential()
        self.model.add(tf.keras.layers.Input(shape=(None, 3)))
        self.model.add(tf.keras.layers.LSTM(3, activation="relu", return_sequences=True))
        self.model.add(tf.keras.layers.Dense(3, activation="linear"))
        
    def __getstate__(self):
        model_str = ""
        with tempfile.NamedTemporaryFile(suffix=".hdf5", delete=False) as fd:
            tf.keras.models.save_model(self.model, fd.name, overwrite=True)
            model_str = fd.read()
        d = {"model_str": model_str, "other_field": self.other_field}
        return d
    
    def __setstate__(self, state):
        with tempfile.NamedTemporaryFile(suffix=".hdf5", delete=False) as fd:
            fd.write(state["model_str"])
            fd.flush()
            model = tf.keras.models.load_model(fd.name)
        self.model = model
        self.other_field = state["other_field"]

还有一个测试：

tf.keras.backend.clear_session()
file_name = 'pickle_file.pkl'
instance = ContainsSequential()

rnd = np.random.rand(3, 1, 3)
print(1, instance.model.predict(rnd))
with open(file_name, 'wb') as fid:
    pickle.dump(instance, fid)
with open(file_name, 'rb') as fid:
    r_instance = pickle.load(fid)
print(2, r_instance.model.predict(rnd))
print(r_instance.other_field)

Answer 2

您应该使用model.save('path/to/location') 和keras.models.load_model()，而不是使用pickle 序列化/反序列化tensorflow 模型。这是推荐的做法，您可以查看https://www.tensorflow.org/guide/keras/save_and_serialize 的文档。