检查点keras模型：TypeError：无法pickle _thread.lock对象-Java 学习之路

似乎过去在不同的上下文中发生了错误here，但是我使用ModelCheckpoint回调'm not dumping the model directly -- I'm . 什么可能出错？

信息：

Keras版本2.0.8
Tensorflow版本1.3.0
Python 3.6

重现错误的最小示例：

from keras.layers import Input, Lambda, Dense
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
import tensorflow as tf
import numpy as np

x = Input(shape=(30,3))
low = tf.constant(np.random.rand(30, 3).astype('float32'))
high = tf.constant(1 + np.random.rand(30, 3).astype('float32'))
clipped_out_position = Lambda(lambda x, low, high: tf.clip_by_value(x, low, high),
                                      arguments={'low': low, 'high': high})(x)

model = Model(inputs=x, outputs=[clipped_out_position])
optimizer = Adam(lr=.1)
model.compile(optimizer=optimizer, loss="mean_squared_error")
checkpoint = ModelCheckpoint("debug.hdf", monitor="val_loss", verbose=1, save_best_only=True, mode="min")
training_callbacks = [checkpoint]
model.fit(np.random.rand(100, 30, 3), [np.random.rand(100, 30, 3)], callbacks=training_callbacks, epochs=50, batch_size=10, validation_split=0.33)

错误输出：

Train on 67 samples, validate on 33 samples
Epoch 1/50
10/67 [===>..........................] - ETA: 0s - loss: 0.1627Epoch 00001: val_loss improved from inf to 0.17002, saving model to debug.hdf
Traceback (most recent call last):
  File "debug_multitask_inverter.py", line 19, in <module>
    model.fit(np.random.rand(100, 30, 3), [np.random.rand(100, 30, 3)], callbacks=training_callbacks, epochs=50, batch_size=10, validation_split=0.33)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/engine/training.py", line 1631, in fit

▽
    validation_steps=validation_steps)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/engine/training.py", line 1233, in _fit_loop
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/callbacks.py", line 73, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/callbacks.py", line 414, in on_epoch_end
    self.model.save(filepath, overwrite=True)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/engine/topology.py", line 2556, in save
    save_model(self, filepath, overwrite, include_optimizer)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/models.py", line 107, in save_model
    'config': model.get_config()
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/site-packages/keras/engine/topology.py", line 2397, in get_config
    return copy.deepcopy(config)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 215, in _deepcopy_list
    append(deepcopy(a, memo))
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/om/user/lnj/openmind_env/tensorflow-gpu/lib/python3.6/copy.py", line 169, in deepcopy
    rv = reductor(4)
TypeError: can't pickle _thread.lock objects

1 回答

7
保存 Lambda 图层时，传入的 arguments 也会被保存 . 在这种情况下，它包含两个 tf.Tensor s . 似乎Keras现在不支持在模型配置中序列化 tf.Tensor .

但是，numpy数组可以毫无问题地序列化 . 因此，不是在 arguments 中传递 tf.Tensor ，而是传入numpy数组，并将它们转换为lambda函数中的 tf.Tensor .
```
x = Input(shape=(30,3))
low = np.random.rand(30, 3)
high = 1 + np.random.rand(30, 3)
clipped_out_position = Lambda(lambda x, low, high: tf.clip_by_value(x, tf.constant(low, dtype='float32'), tf.constant(high, dtype='float32')),
                              arguments={'low': low, 'high': high})(x)
```
上面这些行的一个问题是，在尝试加载此模型时，您可能会看到 NameError: name 'tf' is not defined . 那是因为TensorFlow没有导入重建 Lambda 图层的文件（core.py） .

将 tf 更改为 K.tf 可以解决问题 . 您也可以用 K.constant() 替换 tf.constant() ，它会自动将 low 和 high 转换为float32张量 .
```
from keras import backend as K
x = Input(shape=(30,3))
low = np.random.rand(30, 3)
high = 1 + np.random.rand(30, 3)
clipped_out_position = Lambda(lambda x, low, high: K.tf.clip_by_value(x, K.constant(low), K.constant(high)),
                              arguments={'low': low, 'high': high})(x)
```
回复于 2024-04-18T23:41:22+08:00

检查点keras模型：TypeError：无法pickle _thread.lock对象

1 回答

相关问题