python3 tensorflow

#加载包
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np


# 数据集名称，数据集要放在你的工作目录下
IRIS_TRAINING = "d:/mhuanjing.csv"
IRIS_TEST = "d:/mhuanjing.csv"

# 数据集读取，训练集和测试集
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)

# 特征
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=18)]

# 构建DNN网络，3层，每层分别为10,20,10个节点
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,hidden_units=[100, 200, 100],n_classes=7,model_dir="c:/tmp/")

# 拟合模型，迭代2000步
classifier.fit(x=training_set.data,y=training_set.target,steps=2000)

# 计算精度
accuracy_score = classifier.evaluate(x=test_set.data,y=test_set.target)["accuracy"]

print('Accuracy: {0:f}'.format(accuracy_score))

# 预测新样本的类别
# new_samples = np.array([[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7],[6.4,2.8,5.6,2.2],[5.9,3,4.2,1.5]], dtype=float)
# y = list(classifier.predict(new_samples, as_iterable=True))
# print(y)
# print(type(y))
# print('Predictions: {}'.format(str(y)))