Keras TypeError:无法腌制_thread.RLock对象 [英] Keras TypeError: can't pickle _thread.RLock objects
本文介绍了Keras TypeError:无法腌制_thread.RLock对象的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
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保存我的模型时，它发生了.我已经阅读了StackOverflow或GitHub问题中的一些问题，大多数人认为引发此异常的主要原因是，您试图序列化不可序列化的对象. 在上下文中，"unserializable"对象是tf.tensor.因此请记住:不要让原始tf.tensor在模型中徘徊.但是，我找不到任何"raw tf.tensor". 如果您能给我一些帮助，我将不胜感激，谢谢！
When I tried to use model.save to save my model, it happened. I have read some questions in StackOverflow or GitHub issues, most people think "This exception is raised mainly because you're trying to serialize an unserializable object. In the context, the "unserializable" object is the tf.tensor.So remember this: Don't let raw tf.tensors wandering in your model."However, I can't find any "raw tf.tensor". I'll appreciate if you could give me some help, thanks!
推荐答案
可能是由于此层:
embedded_sequences = Lambda(lambda x: expand_dims(embedding_layer, -1))(embedding_layer)
您应该将其替换为
embedded_sequences = Lambda(lambda x: expand_dims(x, -1))(embedding_layer)
这篇关于Keras TypeError:无法腌制_thread.RLock对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
