华为云AI开发平台ModelArts多层感知机分类_云淘科技

概述

“多层感知机分类”节点可用于建立一个基于前馈人工神经网络的分类模型。

前馈人工神经网络采用一种单向多层结构。其中每一层包含若干个神经元,同一层的神经元之间没有互相连接,层间信息的传送只沿一个方向进行。其中第一层称为输入层。最后一层为输出层,中间为隐层。K+1层前馈神经网络矩阵形式如下表示,其中X为特征集,w为权重值,b为偏置量,y为预测值。

中间层的节点使用sigmod函数:

输出层的节点使用softmax函数:

输出层中的节点个数对应类别数量。

输入

参数

子参数

参数说明

inputs

dataframe

inputs为字典类型,dataframe为pyspark中的DataFrame类型对象

输出

spark pipeline类型的模型

参数说明

参数

子参数

参数说明

b_use_default_encoder

是否使用默认编码,默认为True

input_features_str

输入的列名以逗号分隔组成的字符串,例如:

“column_a”

“column_a,column_b”

label_col

目标列

classifier_label_index_col

目标列经过标签编码后的新的列名,默认为”label_index”

classifier_feature_vector_col

算子输入的特征向量列的列名,默认为”model_features”

prediction_col

算子输出的预测label的列名,默认为”prediction”

prediction_index_col

算子输出的预测label对应的标签列,默认为”prediction_index”

max_iter

最大迭代次数,默认为100

tol

收敛阈值,默认为1e-6

seed

随机数种子,默认为0

layers_str

层的个数用逗号分隔组成的字符串,例如:

“2,3,4”

“3”

step_size

步长,默认为0.03

solver

用来优化的处理算法,支持l-bfgs、gd,默认为”l-bfgs”

initial_weights_str

初始化权重用逗号分隔组成的字符串,例如:

“0.01”

“0.01,0.02,0.04”

样例

inputs = {
    "dataframe": None  # @input {"label":"dataframe","type":"DataFrame"}
}
params = {
    "inputs": inputs,
    "b_output_action": True,
    "b_use_default_encoder": True,  # @param {"label": "b_use_default_encoder", "type": "boolean", "required": "true", "helpTip": ""}
    "input_features_str": "",  # @param {"label": "input_features_str", "type": "string", "required": "false", "helpTip": ""}
    "outer_pipeline_stages": None,
    "label_col": "",  # @param {"label": "label_col", "type": "string", "required": "true", "helpTip": ""}
    "classifier_label_index_col": "label_index",  # @param {"label": "classifier_label_index_col", "type": "string", "required": "true", "helpTip": ""}
    "classifier_feature_vector_col": "model_features",  # @param {"label": "classifier_feature_vector_col", "type": "string", "required": "true", "helpTip": ""}
    "prediction_col": "prediction",  # @param {"label": "prediction_col", "type": "string", "required": "true", "helpTip": ""}
    "prediction_index_col": "prediction_index",  # @param {"label": "prediction_index_col", "type": "string", "required": "true", "helpTip": ""}
    "max_iter": 100,  # @param {"label": "max_iter", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
    "tol": 1e-6,  # @param {"label": "tol", "type": "number", "required": "true", "range": "(0,none)", "helpTip": ""}
    "seed": 0,  # @param {"label": "seed", "type": "integer", "required": "false", "range": "[0,2147483647]", "helpTip": ""}
    "layers_str": "",  # @param {"label": "layers_str", "type": "string", "required": "false", "helpTip": ""}
    "block_size": 128,
    "step_size": 0.03,  # @param {"label": "step_size", "type": "number", "required": "true", "range": "(0,none)", "helpTip": ""}
    "solver": "l-bfgs",  # @param {"label": "solver", "type": "enum", "required": "true", "options": "gd,l-bfgs", "helpTip": ""}
    "initial_weights_str": ""  # @param {"label": "initial_weights_str", "type": "string", "required": "false", "helpTip": ""}
}
multilayer_perception_classifier____id___ = MLSMultilayerPerceptronClassifier(**params)
multilayer_perception_classifier____id___.run()
# @output {"label":"pipeline_model","name":"multilayer_perception_classifier____id___.get_outputs()['output_port_1']","type":"PipelineModel"}

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