如果您想深入了解Floats在内部的工作方式以及原因，则在网上有很多帖子。 或者您可能想看这个

问题是精确的之一。 float32没有足够的精度将小数点放在大约7位数字上。

@mark是的。 展示：

大家 是的，我现在可以看到我缺少的东西！ 再次提供帮助

@wjandrea仍然让我感到困扰的一件事是为什么Numpy继续选择

我正在尝试按照Google Docs of Vertexai的建议运行Gemini Pro：https：//console.cloud.google.com/vertex-ai/publishers/google/google/model-garden/gemini-garden/gemini-pro 导入Vertexai 来自vertexai.preview。

我正在尝试按照Google docs of Vertexai的建议运行Gemini Pro： https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/garden/gemini-pro

import vertexai
from vertexai.preview.generative_models import GenerativeModel, ChatSession

# TODO(developer): Update and un-comment below lines
project_id = "<my_project_id>"
location = "us-central1"
vertexai.init(project=project_id, location=location)

model = GenerativeModel("gemini-1.0-pro")
chat = model.start_chat(response_validation=False)

def get_chat_response(chat: ChatSession, prompt: str):
    response = chat.send_message(prompt)
    return response.text

for prompt in prompt_list:
  print(get_chat_response(chat, prompt))

我仅通过添加 response_validation = false 。

进行了修改。

我以4,5k提示表单 stript_list 以4,5K提示表单运行此代码。

它适用于大约150个提示，然后在以下错误中破裂：

2024-03-11 08:04:47 [error    ] Error in LLM request gender: 400 Request contains an invalid argument.
Traceback (most recent call last):
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/google/api_core/grpc_helpers.py", line 76, in error_remapped_callable
    return callable_(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/grpc/_channel.py", line 1176, in __call__
    return _end_unary_response_blocking(state, call, False, None)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/grpc/_channel.py", line 1005, in _end_unary_response_blocking
    raise _InactiveRpcError(state)  # pytype: disable=not-instantiable
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
grpc._channel._InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
    status = StatusCode.INVALID_ARGUMENT
    details = "Request contains an invalid argument."
    debug_error_string = "UNKNOWN:Error received from peer ipv6:%5B2a00:1450:401b:80e::200a%5D:443 {created_time:"2024-03-11T08:04:47.917745+01:00", grpc_status:3, grpc_message:"Request contains an invalid argument."}"

The above exception was the direct cause of the following exception
...
 File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py", line 727, in send_message
    return self._send_message(
           ^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py", line 814, in _send_message
    response = self._model._generate_content(
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/vertexai/generative_models/_generative_models.py", line 435, in _generate_content
    gapic_response = self._prediction_client.generate_content(request=request)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/google/cloud/aiplatform_v1beta1/services/prediction_service/client.py", line 2080, in generate_content
    response = rpc(
               ^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/google/api_core/gapic_v1/method.py", line 131, in __call__
    return wrapped_func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/l028/.pyenv/versions/playground_env/lib/python3.11/site-packages/google/api_core/grpc_helpers.py", line 78, in error_remapped_callable
    raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.InvalidArgument: 400 Request contains an invalid argument.

1. 如何克服此错误？
2. 这可以是我位于欧盟并使用美国 位置 ？

我已经删除了验证。

我有以下数据来培训模型以检测句子是否有关： 猫或狗 无关关于猫或狗 我运行了以下代码来训练dekisionTreeClalerifier（）模型，然后v ...

我有以下数据训练模型以检测句子是否大约是：

• 猫或狗
• 与猫或狗无关

我运行了以下代码来训练决策者分类器（）模型，然后查看树可视化：

import numpy as np
from numpy.random import seed
import random as rn
import os
import pandas as pd
seed_num = 1
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(seed_num)
rn.seed(seed_num)

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

dummy_train = pd.read_csv('dummy_train.csv')

tree_clf = tree.DecisionTreeClassifier()

X_train = dummy_train["text"]
y_train = dummy_train["label"]

dt_tree_pipe = Pipeline([('vect', CountVectorizer(ngram_range=(1,1),
                                                 binary=True)),
                     ('tfidf', TfidfTransformer(use_idf=False)),
                      ('clf', DecisionTreeClassifier(random_state=seed_num,
                                                 class_weight={0:1, 1:1})),
                   ])

tree_model_fold_1 = dt_tree_pipe.fit(X_train, y_train)

tree.plot_tree(dt_tree_pipe["clf"])

...产生以下树：

第一个节点检查x [7]是否小于或等于0.177。

谢谢本，这真的很有帮助！