我正在使用keras来运行bi-LSTM来在python中进行情绪分析，但是，python给了我错误信息：'NoneType'对象没有属性'update'。我在网站上搜索但是这个问题仍然无法解决。这是我的代码：
import numpy as np
import pickle as pk
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
import os
os.environ['KERAS_BACKEND'] = 'theano'
from keras.layers import Dense, Input
from keras.layers import Embedding, LSTM, Bidirectional
from keras.models import Model
from keras import regularizers
def get_idx_from_sent(sent, word_idx_map, max_l=1187, filter_h=3):
"""
Transforms sentence into a list of indices. Pad with zeroes.
"""
x = []
pad = filter_h - 1
for i in range(pad):
x.append(0)
words = sent.split()
for word in words:
if word in word_idx_map:
x.append(word_idx_map[word])
while len(x) < max_l + 2 * pad:
x.append(0)
return x
def make_idx_data_cv(revs, word_idx_map, max_l=1187, k=300, filter_h=3):
"""
Transforms sentences into a 2-d matrix.
"""
data = []
for rev in revs:
sent = get_idx_from_sent(rev["text"], word_idx_map, max_l, filter_h)
sent.append(rev["y"])
data.append(sent)
x = np.array(data, dtype="int")[:,:-1]
data_y= np.array(data, dtype="int")[:,-1]
return x, data_y
#load data
x = pk.load(open("mr.p", "rb"))
revs, W, W2, word_idx_map, vocab = x[0], x[1], x[2], x[3], x[4]
X,Y = make_idx_data_cv(revs, word_idx_map, max_l=1187, k=300,filter_h=3)
#Keras layers
embedding_layer = Embedding(len(vocab) + 1,
300,
weights=[W],
input_length=1191,
trainable=True)
sequence_input = Input(shape=(1191,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
l_lstm1 = Bidirectional(LSTM(100,return_sequences=True,kernel_regularizer=regularizers.l2(0.001),recurrent_regularizer=regularizers.l2(0.001),
dropout=0.4,recurrent_dropout=0.4))(embedded_sequences)
l_lstm2 = Bidirectional(LSTM(100,return_sequences=True,kernel_regularizer=regularizers.l2(0.001),recurrent_regularizer=regularizers.l2(0.001),
dropout=0.4,recurrent_dropout=0.4))(l_lstm1)
l_lstm3 = Bidirectional(LSTM(100,kernel_regularizer=regularizers.l2(0.001),recurrent_regularizer=regularizers.l2(0.001),
dropout=0.4,recurrent_dropout=0.4))(l_lstm2)
preds = Dense(1, activation='sigmoid',kernel_regularizer=regularizers.l2(0.001),activity_regularizer=regularizers.l1(0.001))\
(l_lstm3)
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['acc'])
print("model fitting - Bidirectional LSTM")
model.summary()
#estimate the accuracy
estimator = KerasClassifier(build_fn=model, epochs=50, batch_size=64, verbose=2)
kfold = KFold(n_splits=10, shuffle=True, random_state=7)
results = cross_val_score(estimator, X, Y, cv=kfold)
但是，它停留在最后一行，这是错误消息：
Traceback (most recent call last):
File "C:/Users/ruowe/PycharmProjects/resnet/lstm.py", line 105, in <module>
results = cross_val_score(estimator, X, Y, cv=kfold)
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 342, in cross_val_score
pre_dispatch=pre_dispatch)
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 206, in cross_validate
for train, test in cv.split(X, y, groups))
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 779, in __call__
while self.dispatch_one_batch(iterator):
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 620, in dispatch_one_batch
tasks = BatchedCalls(itertools.islice(iterator, batch_size))
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py", line 127, in __init__
self.items = list(iterator_slice)
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 206, in <genexpr>
for train, test in cv.split(X, y, groups))
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "C:\Users\ruowe\Anaconda3\lib\site-packages\sklearn\base.py", line 53, in clone
return copy.deepcopy(estimator)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 182, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 298, in _reconstruct
state = deepcopy(state, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 244, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 155, in deepcopy
y = copier(x, memo)
...
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 244, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 182, in deepcopy
y = _reconstruct(x, rv, 1, memo)
File "C:\Users\ruowe\Anaconda3\lib\copy.py", line 307, in _reconstruct
y.__dict__.update(state)
AttributeError: 'NoneType' object has no attribute 'update'
有没有人遇到过同样的问题？请告诉我接下来该怎么做。
Per the docs，build_fn应该返回一个模型。它不应该是一个模型。
build_fn应该构建，编译并返回一个Keras模型，然后将其用于拟合/预测。以下三个值之一可以传递给build_fn
当你调用Model时，__call__实例不会返回自己或新模型。
我相信你的意图是这样做：
def get_model():
model = Model(sequence_input, preds)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['acc'])
print("model fitting - Bidirectional LSTM")
model.summary()
return model
#estimate the accuracy
estimator = KerasClassifier(build_fn=get_model, epochs=50, batch_size=64, verbose=2)