对于背景，我指的是用于情感分类的Hierarchical Attention Network。
对于代码：我的完整代码发布在下面，但它只是作者在上面的链接上发布的原始代码的简单修订。我在下面解释我的更改。对于训练数据：here对于单词嵌入：这是手套嵌入here密钥配置：Keras 2.0.9，Scikit-Learn 0.19.1，Theano 0.9.0
在上面的链接中发布的原始代码采用3D形状输入，即（评论，句子，单词）。注意机制适用于句子，也适用于单词。因此，它有两个注意组件，您可以在网页的第四个代码块中看到。
我想将其更改为仅采用2D形状输入的一个。我是这样做的
但是，当调用'model.fit'时，代码会生成错误。我在下面发布了完整的代码和错误。
码：
import numpy as np
import pandas as pd
import re
from bs4 import BeautifulSoup
import os
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
from keras.utils import plot_model
from keras.utils.np_utils import to_categorical
from keras.layers import Dense, Input
from keras.layers import Embedding, GRU, Bidirectional, TimeDistributed
from keras.models import Model
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers
MAX_SENT_LENGTH = 100
MAX_NB_WORDS = 20000
EMBEDDING_DIM = 100
VALIDATION_SPLIT = 0.2
def clean_str(string):
"""
Tokenization/string cleaning for dataset
Every dataset is lower cased except
"""
string = re.sub(r"\\", "", string)
string = re.sub(r"\'", "", string)
string = re.sub(r"\"", "", string)
return string.strip().lower()
#replace this to your own file path
data_train = pd.read_csv('/home/zz/Work/wop/data/sentiment/labeledTrainData_small.tsv', sep='\t')
print(data_train.shape)
labels = []
texts = []
for idx in range(data_train.review.shape[0]):
text = BeautifulSoup(data_train.review[idx])
text = clean_str(text.get_text().encode('ascii', 'ignore').decode('ascii'))
texts.append(text)
labels.append(data_train.sentiment[idx])
tokenizer = Tokenizer(nb_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(texts)
##################################
# Change 1. The input shape is now 2D (sentence, words) instead of 3D
##################################
data = np.zeros((len(texts), MAX_SENT_LENGTH), dtype='int32')
for i, content in enumerate(texts):
wordTokens = text_to_word_sequence(content)
k = 0
for _, word in enumerate(wordTokens):
if k < MAX_SENT_LENGTH and tokenizer.word_index[word] < MAX_NB_WORDS:
data[i, k] = tokenizer.word_index[word]
k = k + 1
##################################
word_index = tokenizer.word_index
print('Total %s unique tokens.' % len(word_index))
labels = to_categorical(np.asarray(labels))
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', labels.shape)
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
x_train = data[:-nb_validation_samples]
y_train = labels[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels[-nb_validation_samples:]
print('Number of positive and negative reviews in traing and validation set')
print(y_train.sum(axis=0))
print(y_val.sum(axis=0))
#replace with your own embedding file path
GLOVE_DIR = "/home/zz/Work/data/glove.6B"
embeddings_index = {}
f = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'))
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Total %s word vectors.' % len(embeddings_index))
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
# building Hierachical Attention network
embedding_matrix = np.random.random((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
embedding_layer = Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SENT_LENGTH,
trainable=True,
mask_zero=True)
class AttLayer(Layer):
def __init__(self, attention_dim,**kwargs):
self.init = initializers.get('normal')
self.supports_masking = True
self.attention_dim = attention_dim
super(AttLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
self.b = K.variable(self.init((self.attention_dim,)))
self.u = K.variable(self.init((self.attention_dim, 1)))
self.trainable_weights = [self.W, self.b, self.u]
super(AttLayer, self).build(input_shape)
def compute_mask(self, inputs, mask=None):
return None
def call(self, x, mask=None):
# size of x :[batch_size, sel_len, attention_dim]
# size of u :[batch_size, attention_dim]
# uit = tanh(xW+b)
uit = K.tile(K.expand_dims(self.W, axis=0), (K.shape(x)[0], 1, 1))
uit = tf.matmul(x, uit)
uit = K.tanh(K.bias_add(uit, self.b))
ait = K.dot(uit, self.u)
ait = K.squeeze(ait, -1)
ait = K.exp(ait)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
ait *= K.cast(mask, K.floatx())
ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())
ait = K.expand_dims(ait)
weighted_input = x * ait
output = K.sum(weighted_input, axis=1)
return output
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1])
#################################################
# Change 2. The model contains only one attention block now
#################################################
sentence_input = Input(shape=(MAX_SENT_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sentence_input)
l_lstm = Bidirectional(GRU(100, return_sequences=True))(embedded_sequences)
l_dense = TimeDistributed(Dense(200))(l_lstm)
l_att = AttLayer(100)(l_dense)
############################################
preds = Dense(2, activation='softmax')(l_att)
model = Model(sentence_input, preds)
#### clone the model #### Line X
model_copy = clone_model(model)
plot_model(model, to_file="model.png")
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print("model fitting - Hierachical attention network")
model.fit(x_train, y_train, validation_data=(x_val, y_val),
nb_epoch=10, batch_size=50,verbose=2)
错误：代码的最后一行生成错误跟踪：
Epoch 1/10
Traceback (most recent call last):
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py", line 884, in __call__
self.fn() if output_subset is None else\
ValueError: Input dimension mis-match. (input[0].shape[1] = 50, input[1].shape[1] = 100)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/zz/Work/wop/code/python/src/3rdparty/han/textClassfierHATT2D.py", line 187, in <module>
nb_epoch=10, batch_size=50,verbose=2)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1631, in fit
validation_steps=validation_steps)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1213, in _fit_loop
outs = f(ins_batch)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/keras/backend/theano_backend.py", line 1223, in __call__
return self.function(*inputs)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py", line 898, in __call__
storage_map=getattr(self.fn, 'storage_map', None))
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/theano/gof/link.py", line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/six.py", line 692, in reraise
raise value.with_traceback(tb)
File "/home/zz/Programs/anaconda3/lib/python3.6/site-packages/theano/compile/function_module.py", line 884, in __call__
self.fn() if output_subset is None else\
ValueError: Input dimension mis-match. (input[0].shape[1] = 50, input[1].shape[1] = 100)
Apply node that caused the error: Elemwise{mul,no_inplace}(InplaceDimShuffle{x,0}.0, Elemwise{Cast{float32}}.0)
Toposort index: 459
Inputs types: [TensorType(float32, row), TensorType(float32, matrix)]
Inputs shapes: [(1, 50), (50, 100)]
Inputs strides: [(200, 4), (400, 4)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[Sum{axis=[1], acc_dtype=float64}(Elemwise{mul,no_inplace}.0)]]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
我真的很感激这方面的一些建议，非常感谢！
在参考教程中，它选择使用theano而不是tensorflow，因为dot中tensorflow的行为与numpy中的行为不同。但我不熟悉theano所以我很难通过使用theano后端使其正常工作。我宁愿使用一系列操作来模仿dot中numpy的行为。之后我将K.dot改为一系列操作。
import tensorflow as tf
import numpy as np
import pandas as pd
import re
from bs4 import BeautifulSoup
import os
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
from keras.utils import plot_model
from keras.utils.np_utils import to_categorical
from keras.layers import Dense, Input
from keras.layers import Embedding, GRU, Bidirectional, TimeDistributed, Lambda
from keras.models import Model
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers
class AttLayer(Layer):
def __init__(self, attention_dim, **kwargs):
self.init = initializers.get('normal')
self.supports_masking = True
self.attention_dim = attention_dim
super(AttLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
self.b = K.variable(self.init((self.attention_dim,)))
self.u = K.variable(self.init((self.attention_dim, 1)))
self.trainable_weights = [self.W, self.b, self.u]
super(AttLayer, self).build(input_shape)
def compute_mask(self, inputs, mask=None):
return None
def call(self, x, mask=None):
# size of x :[batch_size, sel_len, attention_dim]
# size of u :[batch_size, attention_dim]
# uit = tanh(xW+b)
uit = K.tile(K.expand_dims(self.W, axis=0), (K.shape(x)[0], 1, 1))
uit = tf.matmul(x, uit)
uit = K.tanh(K.bias_add(uit, self.b))
ait = K.dot(uit, self.u)
ait = K.squeeze(ait, -1)
ait = K.exp(ait)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
ait *= K.cast(mask, K.floatx())
ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())
ait = K.expand_dims(ait)
weighted_input = x * ait
output = K.sum(weighted_input, axis=1)
return output
def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1])
# https://github.com/keras-team/keras/issues/5401
# solve the problem of keras.models.clone_model
# and model.save_weights, model.load_weights
def get_config(self):
config = {'attention_dim': self.attention_dim}
base_config = super(AttLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
此外compute_mask现在返回None，因为在sel_len的输出中没有AttLayer轴。
以下是验证两个操作是等效的脚本：
B = 8
S = 100
E = 200
A = 50
X = np.random.randn(B, S, E)
W = np.random.randn(E, A)
np_result = np.dot(X, W) #shape correct
X_ph = tf.placeholder(tf.float64)
W_ph = tf.placeholder(tf.float64)
tf_dot = tf.matmul(X_ph,
tf.tile(
tf.expand_dims(W_ph, axis=0),
(K.shape(X_ph)[0], 1, 1)))
with tf.Session() as sess:
tf_result = sess.run(tf_dot,
feed_dict = {X_ph:X, W_ph:W})
print(np.allclose(np_result, tf_result)) #True
训练历史（我将batch_size设为8）：
Train on 20000 samples, validate on 5000 samples
Epoch 1/10
20000/20000 [==============================] - 1247s 62ms/step - loss: 0.4203 - acc: 0.8044 - val_loss: 0.3520 - val_acc: 0.8468
Epoch 2/10
20000/20000 [==============================] - 985s 49ms/step - loss: 0.2344 - acc: 0.9070 - val_loss: 0.3411 - val_acc: 0.8586
Epoch 3/10
20000/20000 [==============================] - 996s 50ms/step - loss: 0.0982 - acc: 0.9628 - val_loss: 0.4474 - val_acc: 0.8512
Epoch 4/10
20000/20000 [==============================] - 966s 48ms/step - loss: 0.0285 - acc: 0.9904 - val_loss: 0.7837 - val_acc: 0.8408
Epoch 5/10
20000/20000 [==============================] - 912s 46ms/step - loss: 0.0179 - acc: 0.9936 - val_loss: 1.0177 - val_acc: 0.8440
Epoch 6/10
20000/20000 [==============================] - 910s 45ms/step - loss: 0.0105 - acc: 0.9963 - val_loss: 1.0635 - val_acc: 0.8418
Epoch 7/10
20000/20000 [==============================] - 909s 45ms/step - loss: 0.0101 - acc: 0.9964 - val_loss: 1.0966 - val_acc: 0.8372
Epoch 8/10
20000/20000 [==============================] - 909s 45ms/step - loss: 0.0057 - acc: 0.9981 - val_loss: 1.2678 - val_acc: 0.8392
Epoch 9/10
20000/20000 [==============================] - 910s 46ms/step - loss: 0.0077 - acc: 0.9974 - val_loss: 1.2166 - val_acc: 0.8258
Epoch 10/10
20000/20000 [==============================] - 910s 46ms/step - loss: 0.0056 - acc: 0.9985 - val_loss: 1.4640 - val_acc: 0.8204