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import pandas as pd import tensorflow as tf   TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv" TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"   # CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species'] # CSV_COLUMN_NAMES = 'label,age,gender,education,consumptionAbility,LBS,house'.split(',') CSV_COLUMN_NAMES = 'label,age,gender,education,consumptionAbility,house'.split(',') # SPECIES = ['Setosa', 'Versicolor', 'Virginica'] # label,age,gender,education,consumptionAbility,LBS,house # label,age,gender,education,consumptionAbility,LBS,house SPECIES = [0, 1] #SPECIES = [1, 0]     def maybe_download():     # train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)     # test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)     #     # return train_path, test_path     # return 'iris_training.csv', 'iris_test.csv'     return 'myu_oriv_tB.csv', 'myu_oriv_rB.csv'     # def load_data(label_name='Species'): def load_data(label_name='label'):     train_path, test_path = maybe_download()       """Parses the csv file in TRAIN_URL and TEST_URL."""       # Create a local copy of the training set.     # train_path = tf.keras.utils.get_file(fname=TRAIN_URL.split('/')[-1],     #                                      origin=TRAIN_URL)     # train_path now holds the pathname: ~/.keras/datasets/iris_training.csv       # Parse the local CSV file.     train = pd.read_csv(filepath_or_buffer=train_path,                         names=CSV_COLUMN_NAMES,  # list of column names                         header=0  # ignore the first row of the CSV file.                         )     # train now holds a pandas DataFrame, which is data structure     # analogous to a table.       # 1. Assign the DataFrame's labels (the right-most column) to train_label.     # 2. Delete (pop) the labels from the DataFrame.     # 3. Assign the remainder of the DataFrame to train_features       #   label_name = y_name     train_features, train_label = train, train.pop(label_name)       # Apply the preceding logic to the test set.     # test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)     test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)     test_features, test_label = test, test.pop(label_name)       # Return four DataFrames.     return (train_features, train_label), (test_features, test_label)     def train_input_fn(features, labels, batch_size):     """An input function for training"""     # Convert the inputs to a Dataset.     dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))       # Shuffle, repeat, and batch the examples.     #  dataset = dataset.shuffle(1000).repeat().batch(batch_size)     dataset = dataset.shuffle(3).repeat().batch(batch_size)       # Return the dataset.     return dataset     def eval_input_fn(features, labels, batch_size):     """An input function for evaluation or prediction"""     features = dict(features)     if labels is None:         # No labels, use only features.         inputs = features     else:         inputs = (features, labels)       # Convert the inputs to a Dataset.     dataset = tf.data.Dataset.from_tensor_slices(inputs)       # Batch the examples     assert batch_size is not None, "batch_size must not be None"     dataset = dataset.batch(batch_size)       # Return the dataset.     return dataset     # The remainder of this file contains a simple example of a csv parser, #     implemented using a the `Dataset` class.   # `tf.parse_csv` sets the types of the outputs to match the examples given in #     the `record_defaults` argument. # CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]] CSV_TYPES = [[0], [0], [0], [0], [0], [0], [0]] CSV_TYPES = [[0], [0], [0], [0], [0], [0]]     def _parse_line(line):     # Decode the line into its fields     fields = tf.decode_csv(line, record_defaults=CSV_TYPES)       # Pack the result into a dictionary     features = dict(zip(CSV_COLUMN_NAMES, fields))       # Separate the label from the features    # label = features.pop('Species')     label = features.pop('label')       return features, label     def csv_input_fn(csv_path, batch_size):     # Create a dataset containing the text lines.     dataset = tf.data.TextLineDataset(csv_path).skip(1)       # Parse each line.     dataset = dataset.map(_parse_line)       # Shuffle, repeat, and batch the examples.     # dataset = dataset.shuffle(1000).repeat().batch(batch_size)     dataset = dataset.shuffle(2).repeat().batch(batch_size)       # Return the dataset.     return dataset

　　

 

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#  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 qq_iris_data_mystudy   parser = argparse.ArgumentParser() parser.add_argument('--batch_size', default=2, type=int, help='batch size') parser.add_argument('--train_steps', default=2, type=int,                     help='number of training steps')   res_f = 'res.txt' with open(res_f, 'w', encoding='utf-8') as fw:     fw.write('')     def main(argv):     args = parser.parse_args(argv[1:])       # Fetch the data     (train_x, train_y), (test_x, test_y) = qq_iris_data_mystudy.load_data()       my_feature_columns, predict_x = [], {}     for key in train_x.keys():         my_feature_columns.append(tf.feature_column.numeric_column(key=key))         #predict_x[key] = [float(i) for i in test_x[key].values]         predict_x[key] = [int(i) for i in test_x[key].values]     expected = [0 for i in predict_x[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=2)       # Train the Model.     classifier.train(         input_fn=lambda: qq_iris_data_mystudy.train_input_fn(train_x, train_y,                                                              args.batch_size),         steps=args.train_steps)       # Evaluate the model.     eval_result = classifier.evaluate(         input_fn=lambda: qq_iris_data_mystudy.eval_input_fn(test_x, test_y,                                                             args.batch_size))       print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))       predictions = classifier.predict(         input_fn=lambda: qq_iris_data_mystudy.eval_input_fn(predict_x,                                                             labels=None,                                                             batch_size=args.batch_size))       template = ('\nmyProgress{}/{}ORI{}||RESULT{}|| Prediction is "{}" ({:.1f}%), expected "{}"')       c, c_all_ = 0, len(expected)     for pred_dict, expec in zip(predictions, expected):         class_id = pred_dict['class_ids'][0]         probability = pred_dict['probabilities'][class_id]         ori = ','.join([str(predict_x[k][c]) for k in predict_x])         print(template.format(c, c_all_, ori, str(pred_dict), qq_iris_data_mystudy.SPECIES[class_id],                               100 * probability, expec))         c += 1             fw_s = '{}---{}\n'.format(ori,pred_dict['probabilities'][1])         with open(res_f, 'a', encoding='utf-8') as fw:             fw.write(fw_s)     if __name__ == '__main__':     tf.logging.set_verbosity(tf.logging.INFO)     tf.app.run(main)