Python 将字符串和数组数据从CSV文件转换为TFR记录时出错
我按照这些示例将csv文件转换为tfrecords 这是我尝试的代码Python 将字符串和数组数据从CSV文件转换为TFR记录时出错,python,pandas,tensorflow,tfrecord,Python,Pandas,Tensorflow,Tfrecord,我按照这些示例将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]
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
但是我得到了这些错误
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
此外,由于我有一个数组,似乎必须使用example.SerializeToString()
,但不确定如何执行该操作
下面是复制错误的完整代码,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())
试一试
这将有助于您的第6行错误,同样的更改也适用于第7行
把你的号码登记进去
question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
我想应该是0,1,2和3
在更正到正确的顺序时,仍然会出现错误。
所以,加上
它说这是一根线。如果它真的是一个字符串,那么您需要为
float_list.value.append
到
如果您有一个数组,那么您需要使用
tf.serialize_tensor
下面是tf.serialize_张量的一个简单示例
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?'>
您需要将其保存为字节。试试看
这将有助于您的第6行错误,同样的更改也适用于第7行
把你的号码登记进去
question, answer, question_bert, answer_bert = row[0], row[1] , row[1], row[2]
我想应该是0,1,2和3
在更正到正确的顺序时,仍然会出现错误。
所以,加上
它说这是一根线。如果它真的是一个字符串,那么您需要为
float_list.value.append
到
如果您有一个数组,那么您需要使用
tf.serialize_tensor
下面是tf.serialize_张量的一个简单示例
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?'>
您需要将其保存为字节