用深度神经网络对Iris数据集进行分类的程序--tensorflow

# 引入必要的module
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import urllib

import numpy as np
import tensorflow as tf

# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"

IRIS_TEST = "iris_test.csv"
IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"

def main():
# If the training and test sets aren't stored locally, download them.
if not os.path.exists(IRIS_TRAINING):
raw = urllib.urlopen(IRIS_TRAINING_URL).read()
with open(IRIS_TRAINING, "w") as f:
f.write(raw)

if not os.path.exists(IRIS_TEST):
raw = urllib.urlopen(IRIS_TEST_URL).read()
with open(IRIS_TEST, "w") as f:
f.write(raw)

# 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)

# 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")

# Define the training inputs
def get_train_inputs():
x = tf.constant(training_set.data)
y = tf.constant(training_set.target)
return x, y

# Fit model.
classifier.fit(input_fn=get_train_inputs, steps=2000)

# Define the test inputs
def get_test_inputs():
x = tf.constant(test_set.data)
y = tf.constant(test_set.target)
return x, y

# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=get_test_inputs,steps=1)["accuracy"]
print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

# Classify two new flower samples.
def new_samples():
return np.array(
[[6.4, 3.2, 4.5, 1.5],
[5.8, 3.1, 5.0, 1.7]], dtype=np.float32)

predictions = list(classifier.predict(input_fn=new_samples))

print(
"New Samples, Class Predictions:    {}\n"
.format(predictions))

if __name__ == "__main__":
main()

Test Accuracy: 0.966667
New Samples, Class Predictions:    [1, 1]