tf.estimator 是Tensorflow的高级API, 可快速训练和评估各种传统机器学习模型。
看下面一段代码, 使用神经网络应用到Iris数据集上。
import os from six.moves.urllib.request import urlopen 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 = urlopen(IRIS_TRAINING_URL).read() with open(IRIS_TRAINING, "wb") as f: f.write(raw) if not os.path.exists(IRIS_TEST): raw = urlopen(IRIS_TEST_URL).read() with open(IRIS_TEST, "wb") 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.feature_column.numeric_column("x", shape=[4])] # Build 3 layer DNN with 10, 20, 10 units respectively. classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns, hidden_units=[10, 20, 10], n_classes=3, model_dir="/tmp/iris_model") # Define the training inputs train_input_fn = tf.estimator.inputs.numpy_input_fn( x={"x": np.array(training_set.data)}, y=np.array(training_set.target), num_epochs=None, shuffle=True) # Train model. classifier.train(input_fn=train_input_fn, steps=2000) # Define the test inputs test_input_fn = tf.estimator.inputs.numpy_input_fn( x={"x": np.array(test_set.data)}, y=np.array(test_set.target), num_epochs=1, shuffle=False) # Evaluate accuracy. accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"] print("\nTest Accuracy: {0:f}\n".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=np.float32) predict_input_fn = tf.estimator.inputs.numpy_input_fn( x={"x": new_samples}, num_epochs=1, shuffle=False) predictions = list(classifier.predict(input_fn=predict_input_fn)) predicted_classes = [p["classes"] for p in predictions] print( "New Samples, Class Predictions: {}\n" .format(predicted_classes)) if __name__ == "__main__": main()