吴裕雄--天生自然TensorFlow高层封装：Estimator-DNNClassifier

# 1. 模型定义。
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tf.logging.set_verbosity(tf.logging.INFO)

mnist = input_data.read_data_sets("F:\TensorFlowGoogle\201806-github\datasets\MNIST_data", one_hot=False)

# 定义模型的输入。
feature_columns = [tf.feature_column.numeric_column("image", shape=[784])]

# 通过DNNClassifier定义模型。
estimator = tf.estimator.DNNClassifier(feature_columns=feature_columns,hidden_units=[500],n_classes=10,optimizer=tf.train.AdamOptimizer(),model_dir="log")

# 2. 训练模型。
train_input_fn = tf.estimator.inputs.numpy_input_fn(x={"image": mnist.train.images},y=mnist.train.labels.astype(np.int32),num_epochs=None,batch_size=128,shuffle=True)

estimator.train(input_fn=train_input_fn, steps=10000)

# 3. 测试模型。
test_input_fn = tf.estimator.inputs.numpy_input_fn(x={"image": mnist.test.images},y=mnist.test.labels.astype(np.int32),num_epochs=1,batch_size=128,shuffle=False)

test_results = estimator.evaluate(input_fn=test_input_fn)
accuracy_score = test_results["accuracy"]
print("
Test accuracy: %g %%" % (accuracy_score*100))

print(test_results)