Question

我正在按照以下示例将我的csv文件转换为tfrecords。

这是我尝试的代码

csv = pd.read_csv("ehealth.csv").values
with tf.python_io.TFRecordWriter("ehealth.tfrecords") as writer:
    for row in csv:
        question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
        example = tf.train.Example()
        example.features.feature["question"].bytes_list.value.extend(question.encode("utf8"))
        example.features.feature["answer"].bytes_list.value.extend(answer.encode("utf8"))
        example.features.feature["question_bert"].float_list.value.extend(question_bert)
        example.features.feature["answer_bert"].float_list.value.append(answer_bert)
        writer.write(example.SerializeToString())

这是我的错误

TypeError                                 Traceback (most recent call last) <ipython-input-36-0a8c5e073d84> in <module>()
      4         question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
      5         example = tf.train.Example()
----> 6         example.features.feature["question"].bytes_list.value.extend(question.encode("utf8"))
      7         example.features.feature["answer"].bytes_list.value.extend(answer.encode("utf8"))
      8         example.features.feature["question_bert"].float_list.value.extend(question_bert)

TypeError: 104 has type int, but expected one of: bytes

编码字符串时似乎出现了问题。我对这两行进行了评论，以确保其他所有功能均正常运行

csv = pd.read_csv("ehealth.csv").values
with tf.python_io.TFRecordWriter("ehealth.tfrecords") as writer:
    for row in csv:
        question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
        example = tf.train.Example()
#         example.features.feature["question"].bytes_list.value.extend(question)
#         example.features.feature["answer"].bytes_list.value.extend(answer)
        example.features.feature["question_bert"].float_list.value.extend(question_bert)
        example.features.feature["answer_bert"].float_list.value.append(answer_bert)
        writer.write(example.SerializeToString())

但是我得到这些错误

TypeError                                 Traceback (most recent call last) <ipython-input-13-565b43316ef5> in <module>()
      6 #         example.features.feature["question"].bytes_list.value.extend(question)
      7 #         example.features.feature["answer"].bytes_list.value.extend(answer)
----> 8         example.features.feature["question_bert"].float_list.value.extend(question_bert)
      9         example.features.feature["answer_bert"].float_list.value.append(answer_bert)
     10         writer.write(example.SerializeToString())

TypeError: 's' has type str, but expected one of: int, long, float

事实证明，问题是熊猫将我的数组解释为字符串而不是数组

type( csv[0][2])

->str

此外，由于我有一个数组，看来我必须使用example.SerializeToString()，但不确定如何执行此操作。

下面是重现错误的完整代码，包括从Google驱动器下载csv文件的代码。

import pandas as pd
import numpy as np
import requests
import tensorflow as tf

def download_file_from_google_drive(id, destination):
    URL = "https://docs.google.com/uc?export=download"

    session = requests.Session()

    response = session.get(URL, params = { 'id' : id }, stream = True)
    token = get_confirm_token(response)

    if token:
        params = { 'id' : id, 'confirm' : token }
        response = session.get(URL, params = params, stream = True)

    save_response_content(response, destination)    

def get_confirm_token(response):
    for key, value in response.cookies.items():
        if key.startswith('download_warning'):
            return value

    return None

def save_response_content(response, destination):
    CHUNK_SIZE = 32768

    with open(destination, "wb") as f:
        for chunk in response.iter_content(CHUNK_SIZE):
            if chunk: # filter out keep-alive new chunks
                f.write(chunk)

# download_file_from_google_drive('1rMjqKkMnt6_vROrGmlTGStNGmwPO4YFX', 'model.zip') #

file_id = '1anbEwfViu9Rzu7tWKgPb_We1EwbA4x1-'
destination = 'ehealth.csv'
download_file_from_google_drive(file_id, destination)

healthdata=pd.read_csv('ehealth.csv')
healthdata.head()

csv = pd.read_csv("ehealth.csv").values
with tf.python_io.TFRecordWriter("ehealth.tfrecords") as writer:
    for row in csv:
        question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
        example = tf.train.Example()
        example.features.feature["question"].bytes_list.value.extend(question)
        example.features.feature["answer"].bytes_list.value.extend(answer)
        example.features.feature["question_bert"].float_list.value.extend(question_bert)
        example.features.feature["answer_bert"].float_list.value.append(answer_bert)
        writer.write(example.SerializeToString())


csv = pd.read_csv("ehealth.csv").values
with tf.python_io.TFRecordWriter("ehealth.tfrecords") as writer:
    for row in csv:
        question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
        example = tf.train.Example()
#         example.features.feature["question"].bytes_list.value.extend(question)
#         example.features.feature["answer"].bytes_list.value.extend(answer)
        example.features.feature["question_bert"].float_list.value.extend(question_bert)
        example.features.feature["answer_bert"].float_list.value.append(answer_bert)
        writer.write(example.SerializeToString())

Answer 1

尝试

example.features.feature["question"].bytes_list.value.extend([bytes(question, 'utf-8')])

这将帮助您解决第6行的错误，对第7行应用相同的更改。

然后检查您的号码

question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]

我认为应该是0、1、2和3。

在更正正确顺序时，您仍然会收到错误消息。因此，添加

print(type(question_bert))

它说这是一个字符串。如果它确实是一个字符串，则需要更改为

float_list.value.append

到

bytes_list.value.extend

如果有数组，则需要使用

tf.serialize_tensor

这是tf.serialize_tensor的简单示例

a = np.array([[1.0, 2, 46], [0, 0, 1]])
b=tf.serialize_tensor(a)
b

输出为

<tf.Tensor: id=25, shape=(), dtype=string, numpy=b'\x08\x02\x12\x08\x12\x02\x08\x02\x12\x02\x08\x03"0\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00G@\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf0?'>

您需要将其另存为字节。

将字符串和数组数据从CSV文件转换为tfrecords时出错

1 个答案: