场景背景

智能钛机器学习平台的 Tensorflow 框架为用户提供了基于 Python API 的 Tensorflow 运行环境，用户可将编写好的脚本及依赖文件上传至框架进行算法训练。

本文以鸢尾花分类任务为例，向用户演示，如何利用智能钛机器学习平台的深度学习框架 TensorFlow 运行自定义代码，如何通过工作流页面向自定义代码传参，如何查看代码日志/报错信息等。整个工作流运行耗时仅几十秒，训练完成后您可进行模型服务部署和在线测试。

数据集介绍

本案例代码修改自 Tensorflow 官方项目。本案例使用公共的鸢尾花（iris）数据集训练模型，该数据集包含四个特征，分别是花萼长度、花萼宽度、花瓣长度、花瓣宽度，我们根据这四个特征将鸢尾花分成三种物种。

CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']  SPECIES = ['Setosa', 'Versicolor', 'Virginica']

整体流程

在智能钛机器学习平台运行用户自定义 TensorFlow 代码，主要包含以下步骤： 1.数据与代码准备。 2.利用 TensorFlow 框架搭建分类模型。 3.运行自定义代码及评估效果查看。 4.模型部署及在线测试。工作流示意图如下：

详细流程

一、数据与代码准备

本案例代码取自 TensorFlow 官方项目，您可通过链接下载，或直接拷贝以下代码。

#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.  #  #  Licensed under the Apache License, Version 2.0 (the "License");  #  you may not use this file except in compliance with the License.  #  You may obtain a copy of the License at  #  #   http://www.apache.org/licenses/LICENSE-2.0  #  #  Unless required by applicable law or agreed to in writing, software  #  distributed under the License is distributed on an "AS IS" BASIS,  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  #  See the License for the specific language governing permissions and  #  limitations under the License.  """An Example of a DNNClassifier for the Iris dataset."""  from __future__ import absolute_import  from __future__ import division  from __future__ import print_function    import argparse    import tensorflow as tf    import iris_data    parser = argparse.ArgumentParser()    parser.add_argument('--batch_size', default=100, type=int,                      help='batch size')  parser.add_argument('--train_steps', default=1000, type=int,                      help='number of training steps')  parser.add_argument('--train_path', type=str,                      help='path to the train data file.')  parser.add_argument('--test_path', type=str,                      help='path to the test data file.')  parser.add_argument('--export_dir', type=str,                      help='path to export the train model (.pb file)')      def main(argv):      args = parser.parse_args(argv[1:])        # Fetch the data      (train_x, train_y), (test_x, test_y) = iris_data.load_data(args.train_path,                                                                 args.test_path)        # Feature columns describe how to use the input.      my_feature_columns = []      for key in train_x.keys():          my_feature_columns.append(tf.feature_column.numeric_column(key=key))        # Build 2 hidden layer DNN with 10, 10 units respectively.      classifier = tf.estimator.DNNClassifier(          feature_columns=my_feature_columns,          # Two hidden layers of 10 nodes each.          hidden_units=[10, 10],          # The model must choose between 3 classes.          n_classes=3,      )        # Train the Model.      classifier.train(          input_fn=lambda: iris_data.train_input_fn(train_x, train_y,                                                    args.batch_size),          steps=args.train_steps)        # Evaluate the model.      eval_result = classifier.evaluate(          input_fn=lambda: iris_data.eval_input_fn(test_x, test_y,                                                   args.batch_size))        def serving_input_receiver_fn():          input_placeholder = tf.placeholder(shape=[4], dtype=tf.string)          sepal_length_placeholder = tf.strings.to_number(input_placeholder[0:1])          sepal_width_placeholder = tf.strings.to_number(input_placeholder[1:2])          petal_length_placeholder = tf.strings.to_number(input_placeholder[2:3])          petal_width_placeholder = tf.strings.to_number(input_placeholder[3:])            # How the input data is fed into model_fn.          features = {              "SepalLength": sepal_length_placeholder,              "SepalWidth": sepal_width_placeholder,              "PetalLength": petal_length_placeholder,              "PetalWidth": petal_width_placeholder          }            return tf.estimator.export.ServingInputReceiver(features,                                                          input_placeholder)        if not tf.gfile.Exists(args.export_dir):          tf.gfile.MakeDirs(args.export_dir)        classifier.export_saved_model(          export_dir_base=args.export_dir,          serving_input_receiver_fn=serving_input_receiver_fn)        print('nTest set accuracy: {accuracy:0.3f}n'.format(**eval_result))        # Generate predictions from the model      expected = ['Setosa', 'Versicolor', 'Virginica']      predict_x = {          'SepalLength': [5.1, 5.9, 6.9],          'SepalWidth': [3.3, 3.0, 3.1],          'PetalLength': [1.7, 4.2, 5.4],          'PetalWidth': [0.5, 1.5, 2.1],      }        predictions = classifier.predict(          input_fn=lambda: iris_data.eval_input_fn(predict_x,                                                   labels=None,                                                   batch_size=args.batch_size))        template = 'nPrediction is "{}" ({:.1f}%), expected "{}"'        for pred_dict, expec in zip(predictions, expected):          class_id = pred_dict['class_ids'][0]          probability = pred_dict['probabilities'][class_id]            print(template.format(iris_data.SPECIES[class_id],                                100 * probability, expec))      if __name__ == '__main__':      tf.logging.set_verbosity(tf.logging.INFO)      tf.app.run(main)