tf35：tf.estimator

tf35：tf.estimator

MachineLP的Github（欢迎follow）：

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 urlopenimport numpy as np
import tensorflow as tf# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = ".csv"IRIS_TEST = "iris_test.csv"
IRIS_TEST_URL = ".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 datafeature_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 inputstrain_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 inputstest_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()