TypeError：无法将稀疏张量类型的对象转换为张量答案

【问题标题】：TypeError: Failed to convert object of type Sparsetensor to TensorTypeError：无法将稀疏张量类型的对象转换为张量
【发布时间】：2021-01-05 02:32:17
【问题描述】：

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。如何解决？

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

【问题讨论】：

标签： python keras

【解决方案1】：

我也收到了错误信息

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。

在矢量化训练和验证文本上调用 todense() 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，它似乎也可以工作：

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

【讨论】：

【解决方案2】：

您可以找到类似的未解决问题here。

建议的解决方案是使用 Tensorflow 2.1.0 版和 Keras 2.3.1 版。

【讨论】：