場景背景

智能鈦機器學習平台的 Tensorflow 框架為用戶提供了基於 Python API 的 Tensorflow 運行環境，用戶可將編寫好的腳本及依賴文件上傳至框架進行算法訓練。

本文以鳶尾花分類任務為例，向用戶演示，如何利用智能鈦機器學習平台的深度學習框架 TensorFlow 運行自定義代碼，如何通過工作流頁面向自定義代碼傳參，如何查看代碼日誌/報錯信息等。整個工作流運行耗時僅幾十秒，訓練完成後您可進行模型服務部署和在線測試。

數據集介紹

本案例代碼修改自 Tensorflow 官方項目。本案例使用公共的鳶尾花（iris）數據集訓練模型，該數據集包含四個特徵，分別是花萼長度、花萼寬度、花瓣長度、花瓣寬度，我們根據這四個特徵將鳶尾花分成三種物種。

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

整體流程

在智能鈦機器學習平台運行用戶自定義 TensorFlow 代碼，主要包含以下步驟： 1.數據與代碼準備。 2.利用 TensorFlow 框架搭建分類模型。 3.運行自定義代碼及評估效果查看。 4.模型部署及在線測試。工作流示意圖如下：

詳細流程

一、數據與代碼準備

本案例代碼取自 TensorFlow 官方項目，您可通過鏈接下載，或直接拷貝以下代碼。

#  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)