TensorFlow实战框架Chp10--Estimator实现全连接神经网络

• Estimator实现全连接神经网络

# -*- coding: utf-8 -*-
"""
Created on Tue Jul 10 21:54:40 2018

@author: muli
"""

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 将TensorFlow日志信息，输出到屏幕
#tf.logging.set_verbosity(tf.logging.INFO)
# 1. 模型定义
# 加载数据
mnist = input_data.read_data_sets("./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)

# 训练模型
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("\nTest accuracy: %g %%" % (accuracy_score*100))

print(test_results)