Tensorflow模型加载与保存、Tensorboard简单使用

from __future__ importabsolute_import
from __future__ importdivision
from __future__ importprint_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



importargparse
importsys
#import tempfile
importtime
from tensorflow.examples.tutorials.mnist importinput_data

importtensorflow as tf

FLAGS =None


defdeepnn(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
  returny_conv, keep_prob


defconv2d(x, W):
  """conv2d returns a 2d convolution layer with full stride."""
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


defmax_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')


defweight_variable(shape):
  """weight_variable generates a weight variable of a given shape."""initial = tf.truncated_normal(shape, stddev=0.1)
  returntf.Variable(initial)


defbias_variable(shape):
  """bias_variable generates a bias variable of a given shape."""initial = tf.constant(0.1, shape=shape)
  returntf.Variable(initial)


defmain(_):
  #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)
  

saver = tf.train.Saver()

saver.save(sess, './log/my_test_model',global_step=i)

saver = tf.train.Saver()

saver.restore(sess, ckpt.model_checkpoint_path)

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)

tf.summary.scalar('cross_entropy', cross_entropy)
tf.summary.scalar('accuracy', accuracy)

tf.summary.image('input_image', x_image)

train_writer.add_graph(tf.get_default_graph())

tf.summary.histogram('W_conv1', W_conv1)