DNNClassifier 深度神经网络 分类器

An Example of a DNNClassifier for the Iris dataset.

models/premade_estimator.py at master · tensorflow/models · GitHub https://github.com/tensorflow/models/blob/master/samples/core/get_started/premade_estimator.py

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']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']

def maybe_download():
    # train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-], TRAIN_URL)
    # test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-], TEST_URL)
    #
    # return train_path, test_path
    return 'iris_training.csv', 'iris_test.csv'

def load_data(y_name='Species'):
    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
    train_path, test_path = maybe_download()

    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=)
    train_x, train_y = train, train.pop(y_name)

    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=)
    test_x, test_y = test, test.pop(y_name)

    return (train_x, train_y), (test_x, test_y)

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().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], []]

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')

    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()

    # Parse each line.
    dataset = dataset.map(_parse_line)

    # Shuffle, repeat, and batch the examples.
    dataset = dataset.shuffle().repeat().batch(batch_size)

    # Return the dataset.
    return dataset

#  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')

def main(argv):
    args = parser.parse_args(argv[1:])

    # Fetch the data
    (train_x, train_y), (test_x, test_y) = iris_data.load_data()

    # 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))

    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)

C:\Users\Public\py36\python.exe C:/Users/sas/PycharmProjects/py_win_to_unix/sci/iris/premade_estimator.py
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: D:\MYTMPH~1\tmpsp673n0v
INFO:tensorflow:Using config: {'_model_dir': 'D:\\MYTMPH~1\\tmpsp673n0v', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001A3C68216D8>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
2018-04-27 19:57:52.516828: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 1 into D:\MYTMPH~1\tmpsp673n0v\model.ckpt.
INFO:tensorflow:loss = 276.79517, step = 1
INFO:tensorflow:global_step/sec: 631.226
INFO:tensorflow:loss = 33.67822, step = 101 (0.158 sec)
INFO:tensorflow:global_step/sec: 923.465
INFO:tensorflow:loss = 17.75303, step = 201 (0.107 sec)
INFO:tensorflow:global_step/sec: 1072.41
INFO:tensorflow:loss = 10.760817, step = 301 (0.094 sec)
INFO:tensorflow:global_step/sec: 1262.46
INFO:tensorflow:loss = 10.723449, step = 401 (0.079 sec)
INFO:tensorflow:global_step/sec: 852.425
INFO:tensorflow:loss = 7.739768, step = 501 (0.117 sec)
INFO:tensorflow:global_step/sec: 1017.69
INFO:tensorflow:loss = 6.8775907, step = 601 (0.098 sec)
INFO:tensorflow:global_step/sec: 1216.27
INFO:tensorflow:loss = 8.007765, step = 701 (0.082 sec)
INFO:tensorflow:global_step/sec: 898.502
INFO:tensorflow:loss = 4.028232, step = 801 (0.111 sec)
INFO:tensorflow:global_step/sec: 1108.16
INFO:tensorflow:loss = 4.0325384, step = 901 (0.090 sec)
INFO:tensorflow:Saving checkpoints for 1000 into D:\MYTMPH~1\tmpsp673n0v\model.ckpt.
INFO:tensorflow:Loss for final step: 7.3920045.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2018-04-27-11:57:54
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from D:\MYTMPH~1\tmpsp673n0v\model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2018-04-27-11:57:54
INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.96666664, average_loss = 0.060932837, global_step = 1000, loss = 1.8279852

Test set accuracy: 0.967

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from D:\MYTMPH~1\tmpsp673n0v\model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.

Prediction is "Setosa" (100.0%), expected "Setosa"

Prediction is "Versicolor" (98.8%), expected "Versicolor"

Prediction is "Virginica" (97.5%), expected "Virginica"

Process finished with exit code 0

　　

120,4,setosa,versicolor,virginica
6.4,2.8,5.6,2.2,2
5.0,2.3,3.3,1.0,1
4.9,2.5,4.5,1.7,2
4.9,3.1,1.5,0.1,0
5.7,3.8,1.7,0.3,0
4.4,3.2,1.3,0.2,0
5.4,3.4,1.5,0.4,0
6.9,3.1,5.1,2.3,2
6.7,3.1,4.4,1.4,1
5.1,3.7,1.5,0.4,0
5.2,2.7,3.9,1.4,1
6.9,3.1,4.9,1.5,1
5.8,4.0,1.2,0.2,0
5.4,3.9,1.7,0.4,0
7.7,3.8,6.7,2.2,2
6.3,3.3,4.7,1.6,1
6.8,3.2,5.9,2.3,2
7.6,3.0,6.6,2.1,2
6.4,3.2,5.3,2.3,2
5.7,4.4,1.5,0.4,0
6.7,3.3,5.7,2.1,2
6.4,2.8,5.6,2.1,2
5.4,3.9,1.3,0.4,0
6.1,2.6,5.6,1.4,2
7.2,3.0,5.8,1.6,2
5.2,3.5,1.5,0.2,0
5.8,2.6,4.0,1.2,1
5.9,3.0,5.1,1.8,2
5.4,3.0,4.5,1.5,1
6.7,3.0,5.0,1.7,1
6.3,2.3,4.4,1.3,1
5.1,2.5,3.0,1.1,1
6.4,3.2,4.5,1.5,1
6.8,3.0,5.5,2.1,2
6.2,2.8,4.8,1.8,2
6.9,3.2,5.7,2.3,2
6.5,3.2,5.1,2.0,2
5.8,2.8,5.1,2.4,2
5.1,3.8,1.5,0.3,0
4.8,3.0,1.4,0.3,0
7.9,3.8,6.4,2.0,2
5.8,2.7,5.1,1.9,2
6.7,3.0,5.2,2.3,2
5.1,3.8,1.9,0.4,0
4.7,3.2,1.6,0.2,0
6.0,2.2,5.0,1.5,2
4.8,3.4,1.6,0.2,0
7.7,2.6,6.9,2.3,2
4.6,3.6,1.0,0.2,0
7.2,3.2,6.0,1.8,2
5.0,3.3,1.4,0.2,0
6.6,3.0,4.4,1.4,1
6.1,2.8,4.0,1.3,1
5.0,3.2,1.2,0.2,0
7.0,3.2,4.7,1.4,1
6.0,3.0,4.8,1.8,2
7.4,2.8,6.1,1.9,2
5.8,2.7,5.1,1.9,2
6.2,3.4,5.4,2.3,2
5.0,2.0,3.5,1.0,1
5.6,2.5,3.9,1.1,1
6.7,3.1,5.6,2.4,2
6.3,2.5,5.0,1.9,2
6.4,3.1,5.5,1.8,2
6.2,2.2,4.5,1.5,1
7.3,2.9,6.3,1.8,2
4.4,3.0,1.3,0.2,0
7.2,3.6,6.1,2.5,2
6.5,3.0,5.5,1.8,2
5.0,3.4,1.5,0.2,0
4.7,3.2,1.3,0.2,0
6.6,2.9,4.6,1.3,1
5.5,3.5,1.3,0.2,0
7.7,3.0,6.1,2.3,2
6.1,3.0,4.9,1.8,2
4.9,3.1,1.5,0.1,0
5.5,2.4,3.8,1.1,1
5.7,2.9,4.2,1.3,1
6.0,2.9,4.5,1.5,1
6.4,2.7,5.3,1.9,2
5.4,3.7,1.5,0.2,0
6.1,2.9,4.7,1.4,1
6.5,2.8,4.6,1.5,1
5.6,2.7,4.2,1.3,1
6.3,3.4,5.6,2.4,2
4.9,3.1,1.5,0.1,0
6.8,2.8,4.8,1.4,1
5.7,2.8,4.5,1.3,1
6.0,2.7,5.1,1.6,1
5.0,3.5,1.3,0.3,0
6.5,3.0,5.2,2.0,2
6.1,2.8,4.7,1.2,1
5.1,3.5,1.4,0.3,0
4.6,3.1,1.5,0.2,0
6.5,3.0,5.8,2.2,2
4.6,3.4,1.4,0.3,0
4.6,3.2,1.4,0.2,0
7.7,2.8,6.7,2.0,2
5.9,3.2,4.8,1.8,1
5.1,3.8,1.6,0.2,0
4.9,3.0,1.4,0.2,0
4.9,2.4,3.3,1.0,1
4.5,2.3,1.3,0.3,0
5.8,2.7,4.1,1.0,1
5.0,3.4,1.6,0.4,0
5.2,3.4,1.4,0.2,0
5.3,3.7,1.5,0.2,0
5.0,3.6,1.4,0.2,0
5.6,2.9,3.6,1.3,1
4.8,3.1,1.6,0.2,0
6.3,2.7,4.9,1.8,2
5.7,2.8,4.1,1.3,1
5.0,3.0,1.6,0.2,0
6.3,3.3,6.0,2.5,2
5.0,3.5,1.6,0.6,0
5.5,2.6,4.4,1.2,1
5.7,3.0,4.2,1.2,1
4.4,2.9,1.4,0.2,0
4.8,3.0,1.4,0.1,0
5.5,2.4,3.7,1.0,1

　　

30,4,setosa,versicolor,virginica
5.9,3.0,4.2,1.5,1
6.9,3.1,5.4,2.1,2
5.1,3.3,1.7,0.5,0
6.0,3.4,4.5,1.6,1
5.5,2.5,4.0,1.3,1
6.2,2.9,4.3,1.3,1
5.5,4.2,1.4,0.2,0
6.3,2.8,5.1,1.5,2
5.6,3.0,4.1,1.3,1
6.7,2.5,5.8,1.8,2
7.1,3.0,5.9,2.1,2
4.3,3.0,1.1,0.1,0
5.6,2.8,4.9,2.0,2
5.5,2.3,4.0,1.3,1
6.0,2.2,4.0,1.0,1
5.1,3.5,1.4,0.2,0
5.7,2.6,3.5,1.0,1
4.8,3.4,1.9,0.2,0
5.1,3.4,1.5,0.2,0
5.7,2.5,5.0,2.0,2
5.4,3.4,1.7,0.2,0
5.6,3.0,4.5,1.5,1
6.3,2.9,5.6,1.8,2
6.3,2.5,4.9,1.5,1
5.8,2.7,3.9,1.2,1
6.1,3.0,4.6,1.4,1
5.2,4.1,1.5,0.1,0
6.7,3.1,4.7,1.5,1
6.7,3.3,5.7,2.5,2
6.4,2.9,4.3,1.3,1

　　

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']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']

def maybe_download():
    # train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-], TRAIN_URL)
    # test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-], TEST_URL)
    #
    # return train_path, test_path
    return 'iris_training.csv', 'iris_test.csv'

def load_data(label_name='Species'):
    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('/')[-],
    #                                      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=  # ignore the first row of the CSV file.
                        )
    # train now holds a pandas DataFrame, which is data structure
    # analogous to a table.

    # . Assign the DataFrame's labels (the right-most column) to train_label.
    # . Delete (pop) the labels from the DataFrame.
    # . 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('/')[-], TEST_URL)
    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=)
    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().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], []]

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')

    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()

    # Parse each line.
    dataset = dataset.map(_parse_line)

    # Shuffle, repeat, and batch the examples.
    dataset = dataset.shuffle().repeat().batch(batch_size)

    # Return the dataset.
    return dataset

#  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_mystudy

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')

(train_x, train_y), (test_x, test_y) = iris_data_mystudy.load_data()

import os

def main(argv):
    args = parser.parse_args(argv[1:])

    # Fetch the data
    (train_x, train_y), (test_x, test_y) = 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]
    expected = ['' 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=3)

    # Train the Model.
    classifier.train(
        input_fn=lambda: 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: 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: 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), iris_data_mystudy.SPECIES[class_id],
                              100 * probability, expec))
        c += 1

if __name__ == '__main__':
    tf.logging.set_verbosity(tf.logging.INFO)
    tf.app.run(main)

　　

C:\Users\Public\py36\python.exe C:/Users/sas/PycharmProjects/py_win_to_unix/sci/iris/premade_estimator_mywholedata.py
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: D:\MYTMPH~\tmpx25o9607
INFO:tensorflow:Using config: {'_model_dir': 'D:\\MYTMPH~1\\tmpx25o9607', '_tf_random_seed': None, '_save_summary_steps': , '_save_checkpoints_steps': None, '_save_checkpoints_secs': , '_session_config': None, '_keep_checkpoint_max': , '_keep_checkpoint_every_n_hours': , '_log_step_count_steps': , '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000002765C0B2A20>, '_task_type': 'worker', '_task_id': , '_global_id_in_cluster': , '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': , '_num_worker_replicas': }
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
-- ::00.872812: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for  into D:\MYTMPH~\tmpx25o9607\model.ckpt.
INFO:tensorflow:loss = 234.66115, step =
INFO:tensorflow:global_step/sec: 660.215
INFO:tensorflow:loss = 17.675238, step =  (0.151 sec)
INFO:tensorflow:global_step/sec: 942.801
INFO:tensorflow:loss = 11.180588, step =  (0.106 sec)
INFO:tensorflow:global_step/sec: 1299.09
INFO:tensorflow:loss = 7.819012, step =  (0.076 sec)
INFO:tensorflow:global_step/sec: 1279.31
INFO:tensorflow:loss = 8.395781, step =  (0.079 sec)
INFO:tensorflow:global_step/sec: 1120.52
INFO:tensorflow:loss = 12.372395, step =  (0.089 sec)
INFO:tensorflow:global_step/sec: 1178.67
INFO:tensorflow:loss = 7.282875, step =  (0.084 sec)
INFO:tensorflow:global_step/sec: 1218.92
INFO:tensorflow:loss = 8.7485, step =  (0.082 sec)
INFO:tensorflow:global_step/sec: 968.145
INFO:tensorflow:loss = 3.7724056, step =  (0.104 sec)
INFO:tensorflow:global_step/sec: 934.229
INFO:tensorflow:loss = 3.3475294, step =  (0.107 sec)
INFO:tensorflow:Saving checkpoints for  into D:\MYTMPH~\tmpx25o9607\model.ckpt.
INFO:tensorflow:Loss for final step: 5.2043657.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at ---::
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from D:\MYTMPH~\tmpx25o9607\model.ckpt-
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at ---::
INFO:tensorflow:Saving dict for global step : accuracy = 1.0, average_loss = 0.04594822, global_step = , loss = 1.3784466

Test set accuracy: 1.000

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from D:\MYTMPH~\tmpx25o9607\model.ckpt-
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.

myProgress0/30ORI5.,3.0,4.2,1.5||RESULT{'logits': array([-4.073111 ,  3.3400419, -3.450334 ], dtype=float32), 'probabilities': array([6.0222525e-04, 9.9827516e-01, 1.1226065e-03], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.8%), expected ""

myProgress1/30ORI6.,3.1,5.4,2.1||RESULT{'logits': array([-8.557374 ,  0.5901505,  3.692759 ], dtype=float32), 'probabilities': array([4.5787260e-06, 4.2999577e-02, 9.5699579e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (95.7%), expected ""

myProgress2/30ORI5.,3.3,1.7,0.5||RESULT{'logits': array([ 15.67865 ,   9.518664, -17.122147], dtype=float32), 'probabilities': array([9.9789220e-01, 2.1078316e-03, 5.6738612e-15], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (99.8%), expected ""

myProgress3/30ORI6.,3.4,4.5,1.6||RESULT{'logits': array([-4.488565 ,  2.8848784, -2.4938211], dtype=float32), 'probabilities': array([6.244299e-04, 9.947857e-01, 4.589761e-03], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.5%), expected ""

myProgress4/30ORI5.,2.5,4.0,1.3||RESULT{'logits': array([-4.125968 ,  2.9445832, -2.7388015], dtype=float32), 'probabilities': array([8.4616721e-04, 9.9576628e-01, 3.3876204e-03], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.6%), expected ""

myProgress5/30ORI6.,2.9,4.3,1.3||RESULT{'logits': array([-3.5961967,  4.0570755, -4.9506564], dtype=float32), 'probabilities': array([4.7420594e-04, 9.9940348e-01, 1.2238618e-04], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.9%), expected ""

myProgress6/30ORI5.,4.2,1.4,0.2||RESULT{'logits': array([ 21.595142,  11.861579, -21.650354], dtype=float32), 'probabilities': array([9.9994075e-01, 5.9257236e-05, 1.6545992e-19], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress7/30ORI6.,2.8,5.1,1.5||RESULT{'logits': array([-6.8899775,  1.2537876,  1.5890163], dtype=float32), 'probabilities': array([1.2113204e-04, 4.1691846e-01, 5.8296043e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (58.3%), expected ""

myProgress8/30ORI5.,3.0,4.1,1.3||RESULT{'logits': array([-3.3489664,  3.5279539, -4.189754 ], dtype=float32), 'probabilities': array([1.0297953e-03, 9.9852604e-01, 4.4422352e-04], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.9%), expected ""

myProgress9/30ORI6.,2.5,5.8,1.8||RESULT{'logits': array([-9.557738 , -0.5458323,  6.196618 ], dtype=float32), 'probabilities': array([1.4370033e-07, 1.1783625e-03, 9.9882144e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.9%), expected ""

myProgress10/30ORI7.,3.0,5.9,2.1||RESULT{'logits': array([-9.772497  , -0.28590763,  5.876704  ], dtype=float32), 'probabilities': array([1.5948658e-07, 2.1023140e-03, 9.9789751e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.8%), expected ""

myProgress11/30ORI4.,3.0,1.1,0.1||RESULT{'logits': array([ 17.55983 ,   9.681561, -17.754019], dtype=float32), 'probabilities': array([9.9962127e-01, 3.7874514e-04, 4.6049518e-16], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress12/30ORI5.,2.8,4.9,2.0||RESULT{'logits': array([-7.803207 , -0.3124646,  4.896084 ], dtype=float32), 'probabilities': array([3.0366703e-06, 5.4398365e-03, 9.9455714e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.5%), expected ""

myProgress13/30ORI5.,2.3,4.0,1.3||RESULT{'logits': array([-4.5208964,  2.6824176, -2.0642245], dtype=float32), 'probabilities': array([7.3716807e-04, 9.9066305e-01, 8.5997432e-03], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.1%), expected ""

myProgress14/30ORI6.,2.2,4.0,1.0||RESULT{'logits': array([-3.103953 ,  4.2947545, -5.656597 ], dtype=float32), 'probabilities': array([6.1163987e-04, 9.9934071e-01, 4.7631765e-05], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.9%), expected ""

myProgress15/30ORI5.,3.5,1.4,0.2||RESULT{'logits': array([ 19.246971,  10.753842, -19.625887], dtype=float32), 'probabilities': array([9.9979514e-01, 2.0482930e-04, 1.3111250e-17], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress16/30ORI5.,2.6,3.5,1.0||RESULT{'logits': array([ 0.12415126,  5.1074505 , -7.748658  ], dtype=float32), 'probabilities': array([6.8047806e-03, 9.9319261e-01, 2.5923666e-06], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.3%), expected ""

myProgress17/30ORI4.,3.4,1.9,0.2||RESULT{'logits': array([ 14.914921,   9.332862, -16.685436], dtype=float32), 'probabilities': array([9.9624938e-01, 3.7506856e-03, 1.8815136e-14], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (99.6%), expected ""

myProgress18/30ORI5.,3.4,1.5,0.2||RESULT{'logits': array([ 18.556929,  10.536166, -19.18138 ], dtype=float32), 'probabilities': array([9.996716e-01, 3.284615e-04, 4.076791e-17], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress19/30ORI5.,2.5,5.0,2.0||RESULT{'logits': array([-8.281928 , -0.5296105,  5.5087314], dtype=float32), 'probabilities': array([1.0227221e-06, 2.3798312e-03, 9.9761909e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.8%), expected ""

myProgress20/30ORI5.,3.4,1.7,0.2||RESULT{'logits': array([ 18.629036,  10.756583, -19.529491], dtype=float32), 'probabilities': array([9.9961901e-01, 3.8095328e-04, 2.6779140e-17], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress21/30ORI5.,3.0,4.5,1.5||RESULT{'logits': array([-5.327266  ,  1.7238306 , -0.07224458], dtype=float32), 'probabilities': array([7.4258365e-04, 8.5703361e-01, 1.4222382e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (85.7%), expected ""

myProgress22/30ORI6.,2.9,5.6,1.8||RESULT{'logits': array([-8.589258 , -0.3179294,  5.2680035], dtype=float32), 'probabilities': array([9.5552411e-07, 3.7362350e-03, 9.9626285e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.6%), expected ""

myProgress23/30ORI6.,2.5,4.9,1.5||RESULT{'logits': array([-6.850107 ,  1.4749087,  1.2317538], dtype=float32), 'probabilities': array([1.3583169e-04, 5.6041485e-01, 4.3944934e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (56.0%), expected ""

myProgress24/30ORI5.,2.7,3.9,1.2||RESULT{'logits': array([-2.8687124,  4.1638584, -5.565254 ], dtype=float32), 'probabilities': array([8.818289e-04, 9.990588e-01, 5.946907e-05], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.9%), expected ""

myProgress25/30ORI6.,3.0,4.6,1.4||RESULT{'logits': array([-4.7632866,  2.8746686, -2.311274 ], dtype=float32), 'probabilities': array([4.7890263e-04, 9.9396026e-01, 5.5608703e-03], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.4%), expected ""

myProgress26/30ORI5.,4.1,1.5,0.1||RESULT{'logits': array([ 20.011753,  11.262881, -20.466146], dtype=float32), 'probabilities': array([9.9984133e-01, 1.5861503e-04, 2.6339257e-18], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Setosa" (100.0%), expected ""

myProgress27/30ORI6.,3.1,4.7,1.5||RESULT{'logits': array([-4.609805 ,  3.8163486, -3.9574132], dtype=float32), 'probabilities': array([2.1892253e-04, 9.9936074e-01, 4.2035911e-04], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (99.9%), expected ""

myProgress28/30ORI6.,3.3,5.7,2.5||RESULT{'logits': array([-9.505449 , -0.5826268,  6.30414  ], dtype=float32), 'probabilities': array([1.3600010e-07, 1.0201682e-03, 9.9897963e-01], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Virginica" (99.9%), expected ""

myProgress29/30ORI6.,2.9,4.3,1.3||RESULT{'logits': array([-3.4441397,  4.3723693, -5.5904927], dtype=float32), 'probabilities': array([4.0284474e-04, 9.9955004e-01, 4.7096488e-05], dtype=float32), 'class_ids': array([], dtype=int64), 'classes': array([b''], dtype=object)}|| Prediction is "Versicolor" (100.0%), expected ""

Process finished with exit code