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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