InvalidArgumentError 使用 keras 进行情感分析

Question

我使用 Keras 构建了一个情感分析器作为二元分类问题。我正在使用 GRU 使用 Imdb 数据集。我的代码是：

# coding=utf-8
# ==========
#   MODEL
# ==========

# imports
from __future__ import print_function
from timeit import default_timer as timer
from datetime import timedelta
from keras.models import Sequential
from keras.preprocessing import sequence
from keras import regularizers
from keras.layers import Dense, Embedding
from keras.layers import GRU, LeakyReLU, Bidirectional
from keras.datasets import imdb

#start a timer
start = timer()

# Hyperparameters
Model_Name = 'my_model.h5'
vocab_size = 5000
maxlen = 1000
batch_size = 512
hidden_layer_size = 2
test_split = 0.3
dropout = 0.1
num_epochs = 1
alpha = 0.2
validation_split = 0.25
l1 = 0.01
l2 = 0.01

# Dataset loading
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(path="imdb.npz",
                                                      maxlen=maxlen)

print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')

# Data preprocessing
# Sequence padding
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)

# Network building
print('Build model...')
model = Sequential()
model.add(Embedding(vocab_size, hidden_layer_size))
model.add(Bidirectional(GRU(hidden_layer_size, kernel_initializer='uniform', kernel_regularizer=regularizers.l1_l2(l1=l1,l2=l2), dropout=dropout, recurrent_dropout=dropout,return_sequences=True)))
model.add(LeakyReLU())
model.add(Bidirectional(GRU(hidden_layer_size, kernel_initializer='uniform', dropout=dropout, kernel_regularizer=regularizers.l1_l2(l1=l1,l2=l2), recurrent_dropout=dropout)))
model.add(LeakyReLU())
model.add(Dense(1, activation='softmax', kernel_initializer='uniform', kernel_regularizer=regularizers.l1_l2(l1=l1,l2=l2)))

# Compile my model
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

print('Train...')

# Fit the model
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=num_epochs, validation_split=validation_split)
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)

# Create a summary, a plot and print the scores of the model
model.summary()

print('Test score:', score)
print('Test accuracy:', acc)

# Save model architecture, weights, training configuration (loss,optimizer),
# and also the state of the optimizer, so you can resume where you stopped
model.save(Model_Name)
end = timer()
print('Running time:  ' + str(timedelta(seconds=(end - start))) + '  in Hours:Minutes:Seconds')

我不断收到一条我不完全理解的错误消息：

InvalidArgumentError (see above for traceback): indices[502,665] = 5476 is not in [0, 5000)
[[Node: embedding_1/Gather = Gather[Tindices=DT_INT32, Tparams=DT_FLOAT, validate_indices=true, _device=/job:localhost/replica:0/task:0/device:CPU:0](embedding_1/embeddings/read, embedding_1/Cast)]]

任何人都可以帮助我了解导致此错误的原因以及解决方法吗？

Answer 1

错误提示不存在单词索引。那是因为你只限制了 Emedding 特征的数量（即有一个索引为 5476 的词不在 [0, 5000) 范围内，其中 5000 指的是你设置的 vocab_size）。要解决此问题，您还需要将 vocab_size 作为 load_data 函数的 num_words 参数传递，如下所示：

... = imdb.load_data(num_words=vocab_size, ...)

通过这种方式，您可以将单词限制为最常见的单词（即数据集中出现频率最高的前 vocab_size 个单词），其索引范围为 [0, vocab_size).

InvalidArgumentError 使用 keras 进行情感分析

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