Tensorflow模型加载与保存、Tensorboard简单使用

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-order

import argparse
import sys
#import tempfile
import time
from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None

def 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 the
    number of pixels in a standard MNIST image.

  Returns:
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
    equal to the logits of classifying the digit into one of 10 classes (the
    digits 0-9). keep_prob is a scalar placeholder for the probability of
    dropout.
  """
  # 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 digit
  with 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_fc2
  return y_conv, keep_prob

def 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 data
  mnist = input_data.read_data_sets('./', one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  # Build the graph for the deep net
  y_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  step1
    saver = 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 checkpoint
      saver.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 = 0
      print('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)