用深度神经网络对Iris数据集进行分类的程序--tensorflow

先确保你已经安装了tensorflow…

# 引入必要的modulefrom __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osimport urllibimport numpy as npimport tensorflow as tf# Data setsIRIS_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()

运行结果:

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