scikit-learn

How can i create an instance of multi-layer perceptron network to use in bagging classifier?

由 僤鯓⒐⒋嵵緔 提交于 2021-01-01 06:44:00
问题 i am trying to create an instance of multi-layer perceptron network to use in bagging classifier. But i don't understand how to fix them. Here is my code: My task is: 1-To apply bagging classifier (with or without replacement) with eight base classifiers created at the previous step. It would be really great if you show me how can i implement this to my algorithm. I did my search but i couldn't find a way to do that 回答1: To train your BaggingClassifier : from sklearn.datasets import load

Truncate SVD decomposition of Pytorch tensor without transfering to cpu

由 耗尽温柔 提交于 2020-12-31 20:03:53
问题 I'm training a model in Pytorch and I want to use truncated SVD decomposition of input. For calculating SVD I transfer input witch is a Pytorch Cuda Tensor to CPU and using TruncatedSVD from scikit-learn perform truncate, after that, I transfer the result back to GPU. The following is code for my model: class ImgEmb(nn.Module): def __init__(self, input_size, hidden_size): super(ImgEmb, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.drop = nn.Dropout(0.2)

Truncate SVD decomposition of Pytorch tensor without transfering to cpu

由 纵然是瞬间 提交于 2020-12-31 20:00:34
问题 I'm training a model in Pytorch and I want to use truncated SVD decomposition of input. For calculating SVD I transfer input witch is a Pytorch Cuda Tensor to CPU and using TruncatedSVD from scikit-learn perform truncate, after that, I transfer the result back to GPU. The following is code for my model: class ImgEmb(nn.Module): def __init__(self, input_size, hidden_size): super(ImgEmb, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.drop = nn.Dropout(0.2)

独家 | 如何手动优化神经网络模型（附链接）

由 我怕爱的太早我们不能终老 提交于 2020-12-30 16:57:37
翻译：陈丹 校对：车前子 本文 约5400字 ，建议阅读 15 分钟 本文是一个教授如何优化神经网络模型的基础教程，提供了具体的实战代码供读者学习和实践。 标签：神经网络优化 深度学习的神经网络是采用随机梯度下降优化算法对训练数据进行拟合。 利用误差反向传播算法对模型的权值进行更新。优化和权值更新算法的组合是经过仔细挑选的，是目前已知的最有效的拟合神经网络的方法。 然而，也可以使用交替优化算法将神经网络模型拟合到训练数据集。这是一个有用的练习，可以了解更多关于神经网络的是如何运转的，以及应用机器学习时优化的中心性。具有非常规模型结构和不可微分传递函数的神经网络，也可能需要它。 在本教程中，您将了解如何手动优化神经网络模型的权重。 完成本教程后，您将知道： 如何从头开始开发神经网络模型的正向推理通路。 如何优化二值分类感知器模型的权值。 如何利用随机爬山算法优化多层感知器模型的权值。 我们开始吧。 图源土地管理局，权利归其所有 教程概述 本教程分为三个部分：它们是： 优化神经网络 优化感知器模型 优化多层感知器 优化神经网络 深度学习或神经网络是一种灵活的机器学习。 它们是受大脑结构和功能的启发而来的，由节点和层次组成的模型。神经网络模型的工作原理是将给定的输入向量传播到一个或多个层，以产生可用于分类或回归预测建模的数值输出。 通过反复将模型暴露在输入和输出示例中

《概率机器人》PDF习题代码课件+《人工智能一种现代的方法第3版》PDF中英文+《凸优化》PDF习题题解分析

由 允我心安 提交于 2020-12-30 07:04:49
研究机器人时，使机器人能够应对环境、传感器、执行机构、内部模型、近似算法等所带来的不确定性是必须面对的问题。 概率机器人在slam领域被推荐，内容也很充实，对概率机器人学这一新兴领域进行了全面的介绍。概率机器人学依赖统计技术表示信息和进行决策，以容纳当今大多数机器人应用中必然存在的不确定性，是机器人学的一个分支。它依赖统计技术表示信息和制定决策。这样做，可以接纳在当今大多数机器人应用中引起的不确定性。本书主要专注于算法，对于每种算法，均提供了四项内容：伪码示例；完整的数学推导；实验结果；算法优缺点的详细讨论。 《概率机器人》PDF中英文F+代码+习题解答+课件，中文PDF，513页，文字可以复制；英文PDF，668页，文字可以复制；配套习题解答和代码；配套课件。 下载: https://pan.baidu.com/s/1qjJG7E-3KYmn8cXteT6i6Q 提取码: bkyq 《概率机器人》包括了基础知识、定位、地图构建、规划与控制四大部分。共17章，每章的后都提供了练习题和动手实践的项目。致力于用概率的方法明确地表示不确定性，并研究机器人感知和机器人规划与控制的不确定性，以降低机器人系统的不确定性，使机器人能 工作于应用环境中，完成定位、地图构建、规划与控制。 学习人工智能概论时，推荐看看《人工智能:一种现代的方法第三版》，最权威、最经典的人工智能教材

Is there class weight (or alternative way) for GradientBoostingClassifier in Sklearn when dealing with VotingClassifier or Grid search?

由 醉酒当歌 提交于 2020-12-30 06:42:09
问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is

Is there class weight (or alternative way) for GradientBoostingClassifier in Sklearn when dealing with VotingClassifier or Grid search?

由 ぃ、小莉子 提交于 2020-12-30 06:39:38
问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is

Is there class weight (or alternative way) for GradientBoostingClassifier in Sklearn when dealing with VotingClassifier or Grid search?

由 限于喜欢 提交于 2020-12-30 06:39:09
问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is

LabelPropagation - How to avoid division by zero?

由 冷暖自知 提交于 2020-12-29 08:57:57
问题 When using LabelPropagation, I often run into this warning (imho it should be an error because it completely fails the propagation): /usr/local/lib/python3.5/dist-packages/sklearn/semi_supervised/label_propagation.py:279: RuntimeWarning: invalid value encountered in true_divide self.label_distributions_ /= normalizer So after few tries with the RBF kernel, I discovered the paramater gamma has an influence. EDIT: The problem comes from these lines: if self._variant == 'propagation': normalizer

With AWS SageMaker, is it possible to deploy a pre-trained model using the sagemaker SDK?

由 喜夏-厌秋 提交于 2020-12-29 07:37:09
问题 I'm trying to avoid migrating an existing model training process to SageMaker and avoid creating a custom Docker container to host our trained model. My hope was to inject our existing, trained model into the pre-built scikit learn container that AWS provides via the sagemaker-python-sdk. All of the examples that I have found require training the model first which creates the model/model configuration in SageMaker. This is then deployed with the deploy method. Is it possible to provide a
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