我正在为 imdb 情感分析数据集构建文本分类模型。我下载了数据集并按照此处给出的教程进行操作 - https://developers.google.com/machine-learning/guides/text-classification/step-4
我得到的错误是
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> to Tensor. Contents: SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(None, 2), dtype=int64), values=Tensor("DeserializeSparse:1", shape=(None,), dtype=float32), dense_shape=Tensor("stack:0", shape=(2,), dtype=int64)). Consider casting elements to a supported type.
x_train 和 x_val 的类型是 scipy.sparse.csr.csr_matrix。这在传递给顺序模型时会出错。怎么解决?import tensorflow as tf
import numpy as np
from tensorflow.python.keras.preprocessing import sequence
from tensorflow.python.keras.preprocessing import text
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
# Vectorization parameters
# Range (inclusive) of n-gram sizes for tokenizing text.
NGRAM_RANGE = (1, 2)
# Limit on the number of features. We use the top 20K features.
TOP_K = 20000
# Whether text should be split into word or character n-grams.
# One of 'word', 'char'.
TOKEN_MODE = 'word'
# Minimum document/corpus frequency below which a token will be discarded.
MIN_DOCUMENT_FREQUENCY = 2
# Limit on the length of text sequences. Sequences longer than this
# will be truncated.
MAX_SEQUENCE_LENGTH = 500
def ngram_vectorize(train_texts, train_labels, val_texts):
"""Vectorizes texts as ngram vectors.
1 text = 1 tf-idf vector the length of vocabulary of uni-grams + bi-grams.
# Arguments
train_texts: list, training text strings.
train_labels: np.ndarray, training labels.
val_texts: list, validation text strings.
# Returns
x_train, x_val: vectorized training and validation texts
"""
# Create keyword arguments to pass to the 'tf-idf' vectorizer.
kwargs = {
'ngram_range': NGRAM_RANGE, # Use 1-grams + 2-grams.
'dtype': 'int32',
'strip_accents': 'unicode',
'decode_error': 'replace',
'analyzer': TOKEN_MODE, # Split text into word tokens.
'min_df': MIN_DOCUMENT_FREQUENCY,
}
vectorizer = TfidfVectorizer(**kwargs)
# Learn vocabulary from training texts and vectorize training texts.
x_train = vectorizer.fit_transform(train_texts)
# Vectorize validation texts.
x_val = vectorizer.transform(val_texts)
# Select top 'k' of the vectorized features.
selector = SelectKBest(f_classif, k=min(TOP_K, x_train.shape[1]))
selector.fit(x_train, train_labels)
x_train = selector.transform(x_train)
x_val = selector.transform(x_val)
x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
return x_train, x_val
最佳答案
我也收到错误信息
TypeError: Failed to convert object of type <class 'tensorflow.python.framework.sparse_tensor.SparseTensor'> [...]
当我基于 Google Machine Learning Guide for text classification 构建模型时.调用
todense()
矢量化训练和验证文本对我有用:x_train = vectorizer.fit_transform(train_texts).todense()
x_val = vectorizer.transform(val_texts).todense()
(虽然看起来很慢,但我不得不限制训练样本。)编辑:
当我删除这一行(而不是添加
.todense()
)时,它似乎也有效:model.add(Dropout(rate=dropout_rate, input_shape=x_train.shape[1:]))
有关更多详细信息,请参阅此讨论:https://github.com/tensorflow/tensorflow/issues/47931
关于python - 类型错误 : Failed to convert object of type Sparsetensor to Tensor,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/63950888/