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)
