隐私计算FATE-多分类数据分析算法测试

"model" ] } } }}3.2. 作准备conf文件

创建者文件 homo_nn_multi_label_conf.json 主旨如下 ：

{ "dsl_version": 2, "initiator": { "role": "guest", "party_id": 9999 }, "role": { "arbiter": [ 10000 ], "host": [ 10000 ], "guest": [ 9999 ] }, "component_parameters": { "common": { "data_transform_0": { "with_label": true }, "homo_nn_0": { "encode_label": true, "max_iter": 15, "batch_size": -1, "early_stop": { "early_stop": "diff", "eps": 0.0001 }, "optimizer": { "learning_rate": 0.05, "decay": 0.0, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false, "optimizer": "Adam" }, "loss": "categorical_crossentropy", "metrics": [ "accuracy" ], "nn_define": { "class_name": "Sequential", "config": { "name": "sequential", "layers": [ { "class_name": "Dense", "config": { "name": "dense", "trainable": true, "batch_input_shape": [ null, 18 ], "dtype": "float32", "units": 5, "activation": "relu", "use_bias": true, "kernel_initializer": { "class_name": "GlorotUniform", "config": { "seed": null, "dtype": "float32" } }, "bias_initializer": { "class_name": "Zeros", "config": { "dtype": "float32" } }, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null } }, { "class_name": "Dense", "config": { "name": "dense_1", "trainable": true, "dtype": "float32", "units": 4, "activation": "sigmoid", "use_bias": true, "kernel_initializer": { "class_name": "GlorotUniform", "config": { "seed": null, "dtype": "float32" } }, "bias_initializer": { "class_name": "Zeros", "config": { "dtype": "float32" } }, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null } } ] }, "keras_version": "2.2.4-tf", "backend": "tensorflow" }, "config_type": "keras" } }, "role": { "host": { "0": { "reader_0": { "table": { "name": "muti_breast_homo_host", "namespace": "experiment" } } } }, "guest": { "0": { "reader_0": { "table": { "name": "muti_breast_homo_guest", "namespace": "experiment" } } } } } }}

忽略 reader_0 模组的详见名和取名室内空间才可与浏览信息时配有的明确。

3.3. 提交使命

执行表列出下达：

flow job submit -d homo_nn_dsl.json -c homo_nn_multi_label_conf.json

执行成功后，查询 dashboard 显示：

四、作准备分析信息

与前面锻炼的信息codice_一样，但是主旨不一样，y 值全为 0

4.1. guest侧

浏览到 Fate 中的，详见名为 predict_muti_breast_homo_guest 取名室内空间为 experiment

4.2. host侧

浏览到 Fate 中的，详见名为 predict_muti_breast_homo_host 取名室内空间为 experiment

五、作准备分析配有

本文只描绘出极其重要其余部分，关于参考的分析迭代，特地查询评论《隐私权算出FATE-离直通分析》

创建者文件 homo_nn_multi_label_predict.json 主旨如下 ：

{ "dsl_version": 2, "initiator": { "role": "guest", "party_id": 9999 }, "role": { "arbiter": [ 10000 ], "host": [ 10000 ], "guest": [ 9999 ] }, "job_parameters": { "common": { "model_id": "arbiter-10000#guest-9999#host-10000#model", "model_version": "202207061504081543620", "job_type": "predict" } }, "component_parameters": { "role": { "guest": { "0": { "reader_0": { "table": { "name": "predict_muti_breast_homo_guest", "namespace": "experiment" } } } }, "host": { "0": { "reader_0": { "table": { "name": "predict_muti_breast_homo_host", "namespace": "experiment" } } } } } }}

忽略表列出都只：

model_id 和 model_version 才可修改为数学模型部署后的版本号。

reader_0 模组的详见名和取名室内空间才可与浏览信息时配有的明确。

六、执行分析使命

执行表列出下达：

flow job submit -c homo_nn_multi_label_predict.json

执行成功后，查询 homo_nn_0 模组的信息反向：

可以看到算法反向的分析结果。

。

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