先上代码：
from __future__ import absolute_import from __future__ import division from __future__ import print_function # -*- coding: utf-8 -*- """ Created on Tue Nov 14 20:34:00 2017@author: HJL """# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # =============================================================================="""A deep MNIST classifier using convolutional layers.See extensive documentation at https://www.tensorflow.org/get_started/mnist/pros """ # Disable linter warnings to maintain consistency with tutorial. # pylint: disable=invalid-name # pylint: disable=g-bad-import-orderimport argparse import sys #import tempfile import time from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfFLAGS = Nonedef deepnn(x):"""deepnn builds the graph for a deep net for classifying digits.Args:x: an input tensor with the dimensions (N_examples, 784), where 784 is thenumber of pixels in a standard MNIST image.Returns:A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with valuesequal to the logits of classifying the digit into one of 10 classes (thedigits 0-9). keep_prob is a scalar placeholder for the probability ofdropout."""# Reshape to use within a convolutional neural net.# Last dimension is for "features" - there is only one here, since images are# grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.with tf.name_scope('reshape'):x_image = tf.reshape(x, [-1, 28, 28, 1])tf.summary.image('input_image', x_image)# First convolutional layer - maps one grayscale image to 32 feature maps.with tf.name_scope('conv1'):W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)tf.summary.histogram('W_conv1', W_conv1)# Pooling layer - downsamples by 2X.with tf.name_scope('pool1'):h_pool1 = max_pool_2x2(h_conv1)# Second convolutional layer -- maps 32 feature maps to 64.with tf.name_scope('conv2'):W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)# Second pooling layer.with tf.name_scope('pool2'):h_pool2 = max_pool_2x2(h_conv2)# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image# is down to 7x7x64 feature maps -- maps this to 1024 features.with tf.name_scope('fc1'):W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)# Dropout - controls the complexity of the model, prevents co-adaptation of# features.with tf.name_scope('dropout'):keep_prob = tf.placeholder(tf.float32)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# Map the 1024 features to 10 classes, one for each digitwith tf.name_scope('fc2'):W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2return y_conv, keep_probdef conv2d(x, W):"""conv2d returns a 2d convolution layer with full stride."""return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x):"""max_pool_2x2 downsamples a feature map by 2X."""return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME')def weight_variable(shape):"""weight_variable generates a weight variable of a given shape."""initial = tf.truncated_normal(shape, stddev=0.1)return tf.Variable(initial)def bias_variable(shape):"""bias_variable generates a bias variable of a given shape."""initial = tf.constant(0.1, shape=shape)return tf.Variable(initial)def main(_):# Import datamnist = input_data.read_data_sets('./', one_hot=True)# Create the modelx = tf.placeholder(tf.float32, [None, 784])# Define loss and optimizery_ = tf.placeholder(tf.float32, [None, 10])# Build the graph for the deep nety_conv, keep_prob = deepnn(x)with tf.name_scope('loss'):cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv)cross_entropy = tf.reduce_mean(cross_entropy)with tf.name_scope('adam_optimizer'):#train_step = tf.train.AdadeltaOptimizer(1e-4).minimize(cross_entropy)train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)with tf.name_scope('accuracy'):correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))correct_prediction = tf.cast(correct_prediction, tf.float32)accuracy = tf.reduce_mean(correct_prediction)graph_location = "./log/" #tempfile.mkdtemp()print('Saving graph to: %s' % graph_location)train_writer = tf.summary.FileWriter(graph_location)train_writer.add_graph(tf.get_default_graph())#保存默认的图tf.summary.scalar('cross_entropy', cross_entropy)tf.summary.scalar('accuracy', accuracy)merged = tf.summary.merge_all()with tf.Session() as sess:#模型保存 step1saver = tf.train.Saver()checkpoint_dir="./"#返回checkpoint文件中checkpoint的状态ckpt = tf.train.get_checkpoint_state(checkpoint_dir)#print(ckpt)if ckpt and ckpt.model_checkpoint_path:#如果存在以前保存的模型print('Restore the model from checkpoint %s' % ckpt.model_checkpoint_path)# Restores from checkpointsaver.restore(sess, ckpt.model_checkpoint_path)#加载模型start_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])else:#如果不存在之前保存的模型sess.run(tf.global_variables_initializer())#变量初始化start_step = 0print('start training from new state') for i in range(start_step,start_step+20000):batch = mnist.train.next_batch(50)if i % 100 == 0:train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})print('step %d, training accuracy %g' % (i, train_accuracy))#step2 每隔一段时间 保存模型saver.save(sess, './log/my_test_model',global_step=i)summary,_=sess.run([merged, train_step],feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})train_writer.add_summary(summary, i)#time.sleep(0.5)print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))if __name__ == '__main__':#main() parser = argparse.ArgumentParser()parser.add_argument('--data_dir', type=str,default='./data/MNIST/',help='Directory for storing input data')FLAGS, unparsed = parser.parse_known_args()tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
上述代码输出如下：
模型的加载与保存
模型的保存涉及到两个函数：
saver = tf.train.Saver()
和
saver.save(sess, './log/my_test_model',global_step=i)
即，先创建tf.train.Saver 对象，用于后续模型保存与加载，默认保存所有参数。saver.save用于将模型及参数保存到文件中，通过传递一个值给可选参数 global_step ，你可以编号checkpoint 名字。上述代码中每隔100步，将模型保存一次。保存结果如下（默认保存最新的5个模型）：
对于模型的加载，涉及如下函数：
saver = tf.train.Saver()
和
saver.restore(sess, ckpt.model_checkpoint_path)
tf.train.Saver.restore(sess, save_path)
恢复之前保存的变量
这个方法运行构造器为恢复变量所添加的操作。它需要启动图的Session。恢复的变量不需要经过初始化，恢复作为初始化的一种方法。
save_path 参数是之前调用save() 的返回值，或调用 latest_checkpoint() 的返回值。
参数：
- sess: 用于恢复参数的Session
- save_path: 参数之前保存的路径
TensorBoard简单使用
涉及如下几个函数：
train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph())...tf.summary.scalar('cross_entropy', cross_entropy)# tf.summary.scalar('accuracy', accuracy) tf.summary.image('input_image', x_image) tf.summary.histogram('W_conv1', W_conv1) merged = tf.summary.merge_all()... summary,_=sess.run([merged, train_step],feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) train_writer.add_summary(summary, i)
-
Summary：所有需要在TensorBoard上展示的统计结果。 -
tf.name_scope()：为Graph中的Tensor添加层级，TensorBoard会按照代码指定的层级进行展示，初始状态下只绘制最高层级的效果，点击后可展开层级看到下一层的细节。 -
tf.summary.scalar()：添加标量统计结果。 -
tf.summary.histogram()：添加任意shape的Tensor，统计这个Tensor的取值分布。 -
tf.summary.merge_all()：添加一个操作，代表执行所有summary操作，这样可以避免人工执行每一个summary op。 -
tf.summary.FileWrite：用于将Summary写入磁盘，需要制定存储路径logdir，如果传递了Graph对象，则在Graph Visualization会显示Tensor Shape Information。执行summary op后，将返回结果传递给add_summary()方法即可。
最后结果：
Scalar
（对应：
tf.summary.scalar('cross_entropy', cross_entropy) tf.summary.scalar('accuracy', accuracy)
）
对应：
tf.summary.image('input_image', x_image)
对应：
train_writer.add_graph(tf.get_default_graph())
对应：
tf.summary.histogram('W_conv1', W_conv1)
