用DNN对Iris数据分类的代码--tensorflow--logging/monitoring/earlystopping/visualizing

本博客是对 用深度神经网络对Iris数据集进行分类的程序–tensorflow
里面的代码进行修改,使其可以记录训练日志,监控训练指标,设置early stopping, 并在TensorBoard中进行可视化.

注意和原程序进行对比,看看增加了哪些code

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import numpy as np
import tensorflow as tf

tf.logging.set_verbosity(tf.logging.INFO)

# Data sets
IRIS_TRAINING = os.path.join(os.path.dirname(__file__), "iris_training.csv")
IRIS_TEST = os.path.join(os.path.dirname(__file__), "iris_test.csv")

def main(unused_argv):
    # Load datasets.
    training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32)
    test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
        filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32)

    validation_metrics = {
    "accuracy":
        tf.contrib.learn.MetricSpec(
            metric_fn=tf.contrib.metrics.streaming_accuracy,
            prediction_key=tf.contrib.learn.PredictionKey.
            CLASSES),
    "precision":
        tf.contrib.learn.MetricSpec(
            metric_fn=tf.contrib.metrics.streaming_precision,
            prediction_key=tf.contrib.learn.PredictionKey.
            CLASSES),
    "recall":
        tf.contrib.learn.MetricSpec(
            metric_fn=tf.contrib.metrics.streaming_recall,
            prediction_key=tf.contrib.learn.PredictionKey.
            CLASSES)
    }

    validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(
    test_set.data,
    test_set.target,
    every_n_steps=50,
    metrics=validation_metrics,
    early_stopping_metric="loss",
    early_stopping_metric_minimize=True,
    early_stopping_rounds=200)

    # Specify that all features have real-value data
    feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

    # Build 3 layer DNN with 10, 20, 10 units respectively.
    classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                                hidden_units=[10, 20, 10],
                                                n_classes=3,
                                                model_dir="/tmp/iris_model",
                                                config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))

    # Fit model.
    classifier.fit(x=training_set.data,
                   y=training_set.target,
                   steps=2000,
                   monitors=[validation_monitor])

    # Evaluate accuracy.
    accuracy_score = classifier.evaluate(x=test_set.data,
                                         y=test_set.target)["accuracy"]
    print('Accuracy: {0:f}'.format(accuracy_score))

    # Classify two new flower samples.
    new_samples = np.array(
        [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
    y = list(classifier.predict(new_samples, as_iterable=True))
    print('Predictions: {}'.format(str(y)))

if __name__ == "__main__":
  tf.app.run()

在命令行输入

$ tensorboard --logdir=/tmp/iris_model/

可以看到loss/accuracy/recall/precision/dnn/global_step等指标的可视化结果