Keras:使用不受支持的形状急切地执行tf.constant

Question

我正试图在谷歌计算引擎中运行以下代码：

import itertools
import math
import pandas as pd
import os
import numpy as np
ner_df = pd.read_csv('ner_dataset.csv', encoding = 'ISO-8859-1')
sentences_words = []
sentences_tags = []
curr_sent_num = -1
current_sentence_words = []
current_sentence_tags = []
for sent_num, word, tag in ner_df[['Sentence #', 'Word', 'Tag']].values:   
if isinstance(sent_num, str) and 'Sentence: ' in sent_num:
curr_sent_num = int(sent_num.split(':')[1].strip())
if current_sentence_words and current_sentence_tags:
sentences_words.append(current_sentence_words)
sentences_tags.append(current_sentence_tags)
current_sentence_words = []
current_sentence_tags = []
current_sentence_words.append(word)
current_sentence_tags.append(tag)
len(sentences_words), len(sentences_tags)
train_size = int(len(sentences_words) * 0.8)
train_sentences_words = sentences_words[:train_size]
train_sentences_tags = sentences_tags[:train_size]
test_sentences_words = sentences_words[train_size:]
test_sentences_tags = sentences_tags[train_size:]
print('Train:', len(train_sentences_words), len(train_sentences_tags))
print('Test:', len(test_sentences_words), len(test_sentences_tags)) 
vocab = set(itertools.chain(*[[w for w in s] for s in train_sentences_words])) 
tags = set(itertools.chain(*[[w for w in s] for s in train_sentences_tags]))
sentenecs_lens = map(len, train_sentences_words)
print(len(vocab), len(tags), len(list(sentenecs_lens)))
MAX_LEN = 75#max(sentenecs_lens)
VOCAB_SIZE = len(vocab)
print('VOCAB_SIZE:', VOCAB_SIZE)
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
words_tokenizer = Tokenizer(num_words=VOCAB_SIZE, filters=[], oov_token='__UNKNOWN__')
words_tokenizer.fit_on_texts(map(lambda s: ' '.join(s), train_sentences_words))
word_index = words_tokenizer.word_index
word_index['__PADDING__'] = 0
index_word = {i:w for w, i in word_index.items()}
print('Unique tokens:', len(word_index)) 
train_sequences = words_tokenizer.texts_to_sequences(map(lambda s: ' '.join(s), train_sentences_words))
test_sequences = words_tokenizer.texts_to_sequences(map(lambda s: ' '.join(s), test_sentences_words))
train_sequences_padded = pad_sequences(train_sequences, maxlen=MAX_LEN)
test_sequences_padded = pad_sequences(test_sequences, maxlen=MAX_LEN)
print(train_sequences_padded.shape, test_sequences_padded.shape) 
tags_tokenizer = Tokenizer(num_words=len(tags), filters='', oov_token='__UNKNOWN__', lower=False)
tags_tokenizer.fit_on_texts(map(lambda s: ' '.join(s), train_sentences_tags))
tag_index = tags_tokenizer.word_index
tag_index['__PADDING__'] = 0
index_tag = {i:w for w, i in tag_index.items()}
index_tag_wo_padding = dict(index_tag)
index_tag_wo_padding[tag_index['__PADDING__']] = '0'
print('Unique tags:', len(tag_index)) 
train_tags = tags_tokenizer.texts_to_sequences(map(lambda s: ' '.join(s), train_sentences_tags))
test_tags = tags_tokenizer.texts_to_sequences(map(lambda s: ' '.join(s), test_sentences_tags))
train_tags_padded = pad_sequences(train_tags, maxlen=MAX_LEN)
test_tags_padded = pad_sequences(test_tags, maxlen=MAX_LEN)
train_tags_padded = np.expand_dims(train_tags_padded, -1)
test_tags_padded = np.expand_dims(test_tags_padded, -1)
print(train_tags_padded.shape, test_tags_padded.shape) 
for w, t in zip(train_sequences_padded[123], train_tags_padded[123]):
print(index_word[w], index_tag[t[0]]) 
from keras.layers import Dense, Input, LSTM, Embedding, Bidirectional, Dropout
from keras.models import Model
from keras.initializers import Constant
embeddings = {}
with open('glove.6B.50d.txt') as f:
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings[word] = coefs
print('# vectors:',  len(embeddings))
# prepare embedding matrix
num_words = min(VOCAB_SIZE, len(word_index) + 1)
embedding_matrix = np.zeros((num_words, 50))
for word, i in word_index.items():
if i >= VOCAB_SIZE:
continue
embedding_vector = embeddings.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
pretrained_embedding_layer = Embedding(VOCAB_SIZE, 50, embeddings_initializer=Constant(embedding_matrix), input_length=MAX_LEN, trainable=False)
sequence_input = Input(shape=(MAX_LEN,), dtype='int32')
embedded_sequences = pretrained_embedding_layer(sequence_input)
...

我在最后一条语句中得到以下错误：

TypeError：使用不支持的形状急切地执行tf.constant(值有1441550个元素，形状为(31815，50(，有1590750个元素(。

然而，我可以在Colab中毫无问题地运行代码。如何解决此问题？

Answer 1

由于嵌入层的input_dim(也称为词汇表大小(与传递给embeddings_initializer的嵌入矩阵中的字数不匹配，因此发生错误。换句话说，要解决这个问题，您需要确保embedding_matrix.shape[0] == input_dim。

Answer 2

看起来错误是由嵌入层引起的。可能keras的Constant初始值设定项不适用于tensorflow的急切执行。如果要使用trainable=False，我建议使用tf.nn.embedding_lookup，它类似于keras的嵌入式API。