Tensorflow高层封装Estimator-DNNClassifier Tensorflow高层封装Estimator-DNNCl

直接上代码：

import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #将日志信息输入到屏幕 tf.logging.set_verbosity(tf.logging.INFO)mnist = input_data.read_data_sets("../../datasets/MNIST_data", one_hot=False)# 定义模型的输入。指定的输入都会拼接在一起 feature_columns = [tf.feature_column.numeric_column("image", shape=[784])]# 通过DNNClassifier定义模型。DNNClassifier只能定义多层全连接层神经网络 #hidden_units定义各层隐藏层的的节点个数 estimator = tf.estimator.DNNClassifier(feature_columns=feature_columns, hidden_units=[500], n_classes=10,#输出层节点数 optimizer=tf.train.AdamOptimizer(),#优化函数 model_dir="log")#保存目录

Extracting ../../datasets/MNIST_data/train-images-idx3-ubyte.gz Extracting ../../datasets/MNIST_data/train-labels-idx1-ubyte.gz Extracting ../../datasets/MNIST_data/t10k-images-idx3-ubyte.gz Extracting ../../datasets/MNIST_data/t10k-labels-idx1-ubyte.gz INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_is_chief': True, '_cluster_spec': , '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': 'log', '_save_summary_steps': 100}

train_input_fn = tf.estimator.inputs.numpy_input_fn( x={"image": mnist.train.images},#需要给出所有的输入数据 y=mnist.train.labels.astype(np.int32),对应x的正确标签 num_epochs=None,#数据循环使用轮数，这里不循环使用 batch_size=128,#每个batch的大小 shuffle=True)#是否要对数据进行随机打乱操作estimator.train(input_fn=train_input_fn, steps=10000)#训练模型，DNNClassifier定义的模型默认使用交叉熵作为损失函数

INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Saving checkpoints for 1 into log/model.ckpt. INFO:tensorflow:loss = 304.06, step = 1 INFO:tensorflow:global_step/sec: 37.6473 INFO:tensorflow:loss = 22.9116, step = 101 (2.650 sec) INFO:tensorflow:global_step/sec: 36.5129 INFO:tensorflow:loss = 16.9818, step = 201 (2.738 sec) INFO:tensorflow:global_step/sec: 37.3001 INFO:tensorflow:loss = 18.5241, step = 301 (2.681 sec) INFO:tensorflow:global_step/sec: 37.91 INFO:tensorflow:loss = 22.4564, step = 401 (2.637 sec) INFO:tensorflow:global_step/sec: 36.15 INFO:tensorflow:loss = 24.7065, step = 501 (2.767 sec) INFO:tensorflow:global_step/sec: 35.9403 INFO:tensorflow:loss = 15.0627, step = 601 (2.782 sec) INFO:tensorflow:global_step/sec: 37.5324 INFO:tensorflow:loss = 11.4905, step = 701 (2.664 sec) INFO:tensorflow:global_step/sec: 37.3027 INFO:tensorflow:loss = 6.44032, step = 801 (2.682 sec) INFO:tensorflow:global_step/sec: 36.9734 INFO:tensorflow:loss = 15.6424, step = 901 (2.704 sec) INFO:tensorflow:global_step/sec: 36.7835 INFO:tensorflow:loss = 17.7588, step = 1001 (2.718 sec) INFO:tensorflow:global_step/sec: 36.1868 INFO:tensorflow:loss = 5.8567, step = 1101 (2.764 sec) INFO:tensorflow:global_step/sec: 37.21 INFO:tensorflow:loss = 5.6694, step = 1201 (2.688 sec) INFO:tensorflow:global_step/sec: 36.9753 INFO:tensorflow:loss = 5.92369, step = 1301 (2.704 sec) INFO:tensorflow:global_step/sec: 37.1743 INFO:tensorflow:loss = 4.01127, step = 1401 (2.690 sec) INFO:tensorflow:global_step/sec: 37.0517 INFO:tensorflow:loss = 3.93887, step = 1501 (2.699 sec) INFO:tensorflow:global_step/sec: 35.0115 INFO:tensorflow:loss = 17.9374, step = 1601 (2.859 sec) INFO:tensorflow:global_step/sec: 36.3979 INFO:tensorflow:loss = 12.8719, step = 1701 (2.745 sec) INFO:tensorflow:global_step/sec: 37.053 INFO:tensorflow:loss = 4.94476, step = 1801 (2.698 sec) INFO:tensorflow:global_step/sec: 37.2657 INFO:tensorflow:loss = 8.36541, step = 1901 (2.684 sec) INFO:tensorflow:global_step/sec: 36.1325 INFO:tensorflow:loss = 2.80073, step = 2001 (2.767 sec) INFO:tensorflow:global_step/sec: 35.9877 INFO:tensorflow:loss = 2.12194, step = 2101 (2.779 sec) INFO:tensorflow:global_step/sec: 35.4156 INFO:tensorflow:loss = 5.8448, step = 2201 (2.825 sec) INFO:tensorflow:global_step/sec: 36.4246 INFO:tensorflow:loss = 3.41064, step = 2301 (2.745 sec) INFO:tensorflow:global_step/sec: 37.0978 INFO:tensorflow:loss = 5.94868, step = 2401 (2.696 sec) INFO:tensorflow:global_step/sec: 36.8387 INFO:tensorflow:loss = 3.89859, step = 2501 (2.714 sec) INFO:tensorflow:global_step/sec: 37.4655 INFO:tensorflow:loss = 1.92916, step = 2601 (2.669 sec) INFO:tensorflow:global_step/sec: 36.8676 INFO:tensorflow:loss = 1.62655, step = 2701 (2.715 sec) INFO:tensorflow:global_step/sec: 37.1859 INFO:tensorflow:loss = 1.53982, step = 2801 (2.687 sec) INFO:tensorflow:global_step/sec: 37.2527 INFO:tensorflow:loss = 1.03436, step = 2901 (2.685 sec) INFO:tensorflow:global_step/sec: 36.3516 INFO:tensorflow:loss = 1.27746, step = 3001 (2.750 sec) INFO:tensorflow:global_step/sec: 36.8335 INFO:tensorflow:loss = 1.13165, step = 3101 (2.715 sec) INFO:tensorflow:global_step/sec: 37.1274 INFO:tensorflow:loss = 1.94848, step = 3201 (2.694 sec) INFO:tensorflow:global_step/sec: 37.2034 INFO:tensorflow:loss = 2.70218, step = 3301 (2.687 sec) INFO:tensorflow:global_step/sec: 36.6637 INFO:tensorflow:loss = 1.92926, step = 3401 (2.728 sec) INFO:tensorflow:global_step/sec: 37.3145 INFO:tensorflow:loss = 0.442604, step = 3501 (2.679 sec) INFO:tensorflow:global_step/sec: 36.5398 INFO:tensorflow:loss = 0.900882, step = 3601 (2.737 sec) INFO:tensorflow:global_step/sec: 37.3834 INFO:tensorflow:loss = 4.18892, step = 3701 (2.675 sec) INFO:tensorflow:global_step/sec: 39.2497 INFO:tensorflow:loss = 1.48036, step = 3801 (2.552 sec) INFO:tensorflow:global_step/sec: 38.6457 INFO:tensorflow:loss = 0.884206, step = 3901 (2.583 sec) INFO:tensorflow:global_step/sec: 38.2075 INFO:tensorflow:loss = 0.948791, step = 4001 (2.617 sec) INFO:tensorflow:global_step/sec: 38.4911 INFO:tensorflow:loss = 0.194128, step = 4101 (2.598 sec) INFO:tensorflow:global_step/sec: 39.1245 INFO:tensorflow:loss = 0.927972, step = 4201 (2.556 sec) INFO:tensorflow:global_step/sec: 37.9535 INFO:tensorflow:loss = 0.588909, step = 4301 (2.635 sec) INFO:tensorflow:global_step/sec: 37.7356 INFO:tensorflow:loss = 0.640977, step = 4401 (2.648 sec) INFO:tensorflow:global_step/sec: 38.9475 INFO:tensorflow:loss = 1.55033, step = 4501 (2.569 sec) INFO:tensorflow:global_step/sec: 38.4368 INFO:tensorflow:loss = 0.727761, step = 4601 (2.602 sec) INFO:tensorflow:global_step/sec: 38.6664 INFO:tensorflow:loss = 0.875689, step = 4701 (2.586 sec) INFO:tensorflow:global_step/sec: 38.4306 INFO:tensorflow:loss = 0.840781, step = 4801 (2.602 sec) INFO:tensorflow:global_step/sec: 38.5601 INFO:tensorflow:loss = 0.192555, step = 4901 (2.593 sec) INFO:tensorflow:global_step/sec: 37.9238 INFO:tensorflow:loss = 0.235096, step = 5001 (2.637 sec) INFO:tensorflow:global_step/sec: 38.1754 INFO:tensorflow:loss = 1.71132, step = 5101 (2.621 sec) INFO:tensorflow:global_step/sec: 37.7428 INFO:tensorflow:loss = 0.565316, step = 5201 (2.648 sec) INFO:tensorflow:global_step/sec: 38.406 INFO:tensorflow:loss = 0.435648, step = 5301 (2.604 sec) INFO:tensorflow:global_step/sec: 39.0185 INFO:tensorflow:loss = 0.427775, step = 5401 (2.563 sec) INFO:tensorflow:global_step/sec: 38.5341 INFO:tensorflow:loss = 0.277333, step = 5501 (2.594 sec) INFO:tensorflow:global_step/sec: 38.068 INFO:tensorflow:loss = 0.222045, step = 5601 (2.629 sec) INFO:tensorflow:global_step/sec: 32.132 INFO:tensorflow:loss = 0.717366, step = 5701 (3.113 sec) INFO:tensorflow:global_step/sec: 31.4068 INFO:tensorflow:loss = 1.14066, step = 5801 (3.181 sec) INFO:tensorflow:global_step/sec: 36.2213 INFO:tensorflow:loss = 0.0925966, step = 5901 (2.761 sec) INFO:tensorflow:global_step/sec: 35.4066 INFO:tensorflow:loss = 0.334483, step = 6001 (2.826 sec) INFO:tensorflow:global_step/sec: 38.1624 INFO:tensorflow:loss = 2.09814, step = 6101 (2.619 sec) INFO:tensorflow:global_step/sec: 36.8427 INFO:tensorflow:loss = 0.0559563, step = 6201 (2.723 sec) INFO:tensorflow:global_step/sec: 37.4233 INFO:tensorflow:loss = 0.173522, step = 6301 (2.663 sec) INFO:tensorflow:global_step/sec: 37.2928 INFO:tensorflow:loss = 1.80461, step = 6401 (2.683 sec) INFO:tensorflow:global_step/sec: 37.4197 INFO:tensorflow:loss = 0.500693, step = 6501 (2.673 sec) INFO:tensorflow:global_step/sec: 37.2329 INFO:tensorflow:loss = 0.564081, step = 6601 (2.684 sec) INFO:tensorflow:global_step/sec: 35.6874 INFO:tensorflow:loss = 2.12218, step = 6701 (2.802 sec) INFO:tensorflow:global_step/sec: 37.8897 INFO:tensorflow:loss = 0.913451, step = 6801 (2.639 sec) INFO:tensorflow:global_step/sec: 36.9869 INFO:tensorflow:loss = 0.121778, step = 6901 (2.703 sec) INFO:tensorflow:global_step/sec: 37.2794 INFO:tensorflow:loss = 1.24115, step = 7001 (2.684 sec) INFO:tensorflow:global_step/sec: 36.5617 INFO:tensorflow:loss = 2.33691, step = 7101 (2.734 sec) INFO:tensorflow:global_step/sec: 37.2914 INFO:tensorflow:loss = 0.194615, step = 7201 (2.682 sec) INFO:tensorflow:global_step/sec: 37.4636 INFO:tensorflow:loss = 1.01934, step = 7301 (2.669 sec) INFO:tensorflow:global_step/sec: 36.3987 INFO:tensorflow:loss = 0.0396438, step = 7401 (2.748 sec) INFO:tensorflow:global_step/sec: 37.0134 INFO:tensorflow:loss = 0.318336, step = 7501 (2.701 sec) INFO:tensorflow:global_step/sec: 37.4563 INFO:tensorflow:loss = 0.295077, step = 7601 (2.670 sec) INFO:tensorflow:global_step/sec: 36.1086 INFO:tensorflow:loss = 0.300212, step = 7701 (2.770 sec) INFO:tensorflow:global_step/sec: 37.1408 INFO:tensorflow:loss = 0.0957186, step = 7801 (2.691 sec) INFO:tensorflow:global_step/sec: 37.3442 INFO:tensorflow:loss = 0.207393, step = 7901 (2.678 sec) INFO:tensorflow:global_step/sec: 36.8388 INFO:tensorflow:loss = 0.702379, step = 8001 (2.714 sec) INFO:tensorflow:global_step/sec: 37.0512 INFO:tensorflow:loss = 0.118717, step = 8101 (2.699 sec) INFO:tensorflow:global_step/sec: 37.1012 INFO:tensorflow:loss = 0.414811, step = 8201 (2.698 sec) INFO:tensorflow:global_step/sec: 37.0407 INFO:tensorflow:loss = 0.0207637, step = 8301 (2.698 sec)

INFO:tensorflow:global_step/sec: 37.4401 INFO:tensorflow:loss = 0.915473, step = 8401 (2.671 sec) INFO:tensorflow:global_step/sec: 35.7477 INFO:tensorflow:loss = 0.0145219, step = 8501 (2.797 sec) INFO:tensorflow:global_step/sec: 36.8545 INFO:tensorflow:loss = 0.19546, step = 8601 (2.713 sec) INFO:tensorflow:global_step/sec: 36.4362 INFO:tensorflow:loss = 0.162718, step = 8701 (2.745 sec) INFO:tensorflow:global_step/sec: 38.0979 INFO:tensorflow:loss = 0.110679, step = 8801 (2.625 sec) INFO:tensorflow:global_step/sec: 37.3399 INFO:tensorflow:loss = 0.364681, step = 8901 (2.679 sec) INFO:tensorflow:global_step/sec: 34.5091 INFO:tensorflow:loss = 0.11277, step = 9001 (2.897 sec) INFO:tensorflow:global_step/sec: 36.2612 INFO:tensorflow:loss = 0.0492335, step = 9101 (2.758 sec) INFO:tensorflow:global_step/sec: 36.4491 INFO:tensorflow:loss = 1.55669, step = 9201 (2.743 sec) INFO:tensorflow:global_step/sec: 37.5527 INFO:tensorflow:loss = 3.54684, step = 9301 (2.663 sec) INFO:tensorflow:global_step/sec: 37.1559 INFO:tensorflow:loss = 0.794145, step = 9401 (2.691 sec) INFO:tensorflow:global_step/sec: 37.3445 INFO:tensorflow:loss = 0.211793, step = 9501 (2.679 sec) INFO:tensorflow:global_step/sec: 36.5885 INFO:tensorflow:loss = 0.34873, step = 9601 (2.734 sec) INFO:tensorflow:global_step/sec: 36.6112 INFO:tensorflow:loss = 0.126815, step = 9701 (2.730 sec) INFO:tensorflow:global_step/sec: 37.0562 INFO:tensorflow:loss = 0.021787, step = 9801 (2.699 sec) INFO:tensorflow:global_step/sec: 37.4349 INFO:tensorflow:loss = 0.10613, step = 9901 (2.671 sec) INFO:tensorflow:Saving checkpoints for 10000 into log/model.ckpt. INFO:tensorflow:Loss for final step: 0.0945373.

#测试时的数据输入，与训练时的数据基本一致 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

INFO:tensorflow:Starting evaluation at 2017-12-28-18:43:13 INFO:tensorflow:Restoring parameters from log/model.ckpt-10000 INFO:tensorflow:Finished evaluation at 2017-12-28-18:43:15 INFO:tensorflow:Saving dict for global step 10000: accuracy = 0.9818, average_loss = 0.0919714, global_step = 10000, loss = 11.642Test accuracy: 98.18 % {'average_loss': 0.09197142, 'accuracy': 0.98180002, 'global_step': 10000, 'loss': 11.641952}

【Tensorflow高层封装Estimator-DNNClassifier】