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！
答案 0 :(得分：0)
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)