tensorflow - Keras 类型错误 : can't pickle _thread. RLock 对象

Question

from keras.layers import Embedding, Dense, Input, Dropout, Reshape
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPool2D
from keras.layers import Concatenate, Lambda
from keras.backend import expand_dims
from keras.models import Model
from keras.initializers import constant, random_uniform, TruncatedNormal


class TextCNN(object):
    def __init__(
      self, sequence_length, num_classes, vocab_size,
      embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

        # input layer
        input_x = Input(shape=(sequence_length, ), dtype='int32')

        # embedding layer
        embedding_layer = Embedding(vocab_size,
                                    embedding_size,
                                    embeddings_initializer=random_uniform(minval=-1.0, maxval=1.0))(input_x)
        embedded_sequences = Lambda(lambda x: expand_dims(embedding_layer, -1))(embedding_layer)

        # Create a convolution + maxpool layer for each filter size
        pooled_outputs = []
        for filter_size in filter_sizes:
            conv = Conv2D(filters=num_filters,
                          kernel_size=[filter_size, embedding_size],
                          strides=1,
                          padding="valid",
                          activation='relu',
                          kernel_initializer=TruncatedNormal(mean=0.0, stddev=0.1),
                          bias_initializer=constant(value=0.1),
                          name=('conv_%d' % filter_size))(embedded_sequences)

            max_pool = MaxPool2D(pool_size=[sequence_length - filter_size + 1, 1],
                                 strides=(1, 1),
                                 padding='valid',
                                 name=('max_pool_%d' % filter_size))(conv)

            pooled_outputs.append(max_pool)

        # combine all the pooled features
        num_filters_total = num_filters * len(filter_sizes)
        h_pool = Concatenate(axis=3)(pooled_outputs)
        h_pool_flat = Reshape([num_filters_total])(h_pool)

        # add dropout
        dropout = Dropout(0.8)(h_pool_flat)

        # output layer
        output = Dense(num_classes,
                       kernel_initializer='glorot_normal',
                       bias_initializer=constant(0.1),
                       activation='softmax',
                       name='scores')(dropout)

        self.model = Model(inputs=input_x, output=output)

# model saver callback
class Saver(Callback):
    def __init__(self, num):
        self.num = num
        self.epoch = 0

    def on_epoch _end(self, epoch, logs={}):
        if self.epoch % self.num == 0:
            name = './model/model.h5'
            self.model.save(name)
        self.epoch += 1


# evaluation callback
class Evaluation(Callback):
    def __init__(self, num):
        self.num = num
        self.epoch = 0

    def on_epoch_end(self, epoch, logs={}):
        if self.epoch % self.num == 0:
            score = model.evaluate(x_train, y_train, verbose=0)
            print('train score:', score[0])
            print('train accuracy:', score[1])
            score = model.evaluate(x_dev, y_dev, verbose=0)
            print('Test score:', score[0])
            print('Test accuracy:', score[1])
        self.epoch += 1


model.fit(x_train, y_train,
          epochs=num_epochs,
          batch_size=batch_size,
          callbacks=[Saver(save_every), Evaluation(evaluate_every)])

Traceback (most recent call last):
  File "D:/Projects/Python Program Design/sentiment-analysis-Keras/train.py", line 107, in <module>
    callbacks=[Saver(save_every), Evaluation(evaluate_every)])
  File "D:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1039, in fit
    validation_steps=validation_steps)
  File "D:\Anaconda3\lib\site-packages\keras\engine\training_arrays.py", line 204, in fit_loop
    callbacks.on_batch_end(batch_index, batch_logs)
  File "D:\Anaconda3\lib\site-packages\keras\callbacks.py", line 115, in on_batch_end
    callback.on_batch_end(batch, logs)
  File "D:/Projects/Python Program Design/sentiment-analysis-Keras/train.py", line 83, in on_batch_end
    self.model.save(name)
  File "D:\Anaconda3\lib\site-packages\keras\engine\network.py", line 1090, in save
    save_model(self, filepath, overwrite, include_optimizer)
  File "D:\Anaconda3\lib\site-packages\keras\engine\saving.py", line 382, in save_model
    _serialize_model(model, f, include_optimizer)
  File "D:\Anaconda3\lib\site-packages\keras\engine\saving.py", line 83, in _serialize_model
    model_config['config'] = model.get_config()
  File "D:\Anaconda3\lib\site-packages\keras\engine\network.py", line 931, in get_config
    return copy.deepcopy(config)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 215, in _deepcopy_list
    append(deepcopy(a, memo))
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 220, in _deepcopy_tuple
    y = [deepcopy(a, memo) for a in x]
  File "D:\Anaconda3\lib\copy.py", line 220, in <listcomp>
    y = [deepcopy(a, memo) for a in x]
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 220, in _deepcopy_tuple
    y = [deepcopy(a, memo) for a in x]
  File "D:\Anaconda3\lib\copy.py", line 220, in <listcomp>
    y = [deepcopy(a, memo) for a in x]
  File "D:\Anaconda3\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "D:\Anaconda3\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "D:\Anaconda3\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "D:\Anaconda3\lib\copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "D:\Anaconda3\lib\copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "D:\Anaconda3\lib\copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "D:\Anaconda3\lib\copy.py", line 169, in deepcopy
    rv = reductor(4)
TypeError: can't pickle _thread.RLock objects

当我尝试使用 model.save 来保存我的模型时，它发生了。我在 StackOverflow 或 GitHub 问题中阅读了一些问题，大多数人认为“引发此异常主要是因为您试图序列化不可序列化的对象。
在上下文中，“不可序列化”对象是 tf.tensor。所以请记住这一点:不要让原始 tf.tensor 在您的模型中徘徊。”但是，我找不到任何“原始 tf.tensor”。
如果您能给我一些帮助，我将不胜感激，谢谢!

Answer 1

可能是由于这一层:

embedded_sequences = Lambda(lambda x: expand_dims(embedding_layer, -1))(embedding_layer)

你应该用

embedded_sequences = Lambda(lambda x: expand_dims(x, -1))(embedding_layer)