供个人学习记录，来源于：
https://github.com/machinelearningmindset/TensorFlow-Course#why-use-tensorflow
from __future__ import print_function
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
import os
######################################
######### Necessary Flags ############
######################################
num_classes = 10
batch_size = 128
num_epochs = 10
##########################################
######## Learning rate flags #############
##########################################
initial_learning_rate = 0.001 #学习率
learning_rate_decay_factor = 0.95 #衰减率
num_epochs_per_decay = 1 #使用次数
#########################################
########## status flags #################
#########################################
is_training = False
fine_tuning = False
online_test = True
allow_soft_placement = True
log_device_placement = False
##########################################
####### Load and Organize Data ###########
##########################################
'''
In this part the input must be prepared.
1 - The MNIST data will be downloaded.
2 - The images and labels for both training and testing will be extracted.
3 - The prepared data format(?,784) is different by the appropriate image shape(?,28,28,1) which needs
to be fed to the CNN architecture. So it needs to be reshaped.
'''
# Download and get MNIST dataset(available in tensorflow.contrib.learn.python.learn.datasets.mnist)
# It checks and download MNIST if it's not already downloaded then extract it.
# The 'reshape' is True by default to extract feature vectors but we set it to false to we get the original images.
mnist = input_data.read_data_sets("MNIST_data/", reshape=True, one_hot=True)
train_data = mnist.train.images
train_label = mnist.train.labels
test_data = mnist.test.images
test_label = mnist.test.labels
# # The 'input.provide_data' is provided to organize any custom dataset which has specific characteristics.
# data = input.provide_data(mnist)
# Dimentionality of train
dimensionality_train = train_data.shape
# Dimensions
num_train_samples = dimensionality_train[0] #训练数据量
num_features = dimensionality_train[1] #训练图片大小
#######################################
########## Defining Graph ############
#######################################
graph = tf.Graph() #实例化一个类
with graph.as_default(): #创建一张默认图
###################################
########### Parameters ############
###################################
# global step
global_step = tf.Variable(0, name="global_step", trainable=False) #用于衰减的全局步骤
# learning rate policy
decay_steps = int(num_train_samples / batch_size * #衰减速度，每隔**衰减一次
num_epochs_per_decay)
learning_rate = tf.train.exponential_decay(initial_learning_rate, #指数梯度下降法
global_step,
decay_steps,
learning_rate_decay_factor,
staircase=True,
name='exponential_decay_learning_rate')
###############################################
########### Defining place holders ############
###############################################
image_place = tf.placeholder(tf.float32, shape=([None, num_features]), name='image')
label_place = tf.placeholder(tf.float32, shape=([None, num_classes]), name='gt')
dropout_param = tf.placeholder(tf.float32)
##################################################
########### Model + Loss + Accuracy ##############
##################################################
# MODEL(MPL with two hidden layer)
# LAYER-1
net = tf.contrib.layers.fully_connected(inputs=image_place, num_outputs=250, scope='fc-1') #定义全连接层
# LAYER-2
net = tf.contrib.layers.fully_connected(inputs=net, num_outputs=250, scope='fc-2') #第二个全连接层
# SOFTMAX
logits_pre_softmax = tf.contrib.layers.fully_connected(inputs=net, num_outputs=num_classes, scope='fc-3') #softmax层
# Define loss
softmax_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits_pre_softmax, labels=label_place))
# Accuracy
accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(logits_pre_softmax, 1), tf.argmax(label_place, 1)), tf.float32))
#############################################
########### training operation ##############
#############################################
# Define optimizer by its default values
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# 'train_op' is a operation that is run for gradient update on parameters.
# Each execution of 'train_op' is a training step.
# By passing 'global_step' to the optimizer, each time that the 'train_op' is run, Tensorflow
# update the 'global_step' and increment it by one!
# gradient update.
with tf.name_scope('train_scope'):
grads = optimizer.compute_gradients(softmax_loss) #计算损失函数对变量的梯度
train_op = optimizer.apply_gradients(grads, global_step=global_step) #变量梯度更新
###############################################
############ Define Sammaries #################
###############################################
# Summaries for loss and accuracy
tf.summary.scalar("loss", softmax_loss, collections=['train', 'test'])
tf.summary.scalar("accuracy", accuracy, collections=['train', 'test'])
tf.summary.scalar("global_step", global_step, collections=['train'])
tf.summary.scalar("learning_rate", learning_rate, collections=['train'])
# Merge all summaries together.
summary_train_op = tf.summary.merge_all('train')
summary_test_op = tf.summary.merge_all('test')
############################################
############ Run the Session ###############
############################################
session_conf = tf.ConfigProto( #session参数配置
allow_soft_placement=allow_soft_placement,
log_device_placement=log_device_placement)
sess = tf.Session(graph=graph, config=session_conf)
with sess.as_default(): #创建一个默认回话
# Initialize all variables
sess.run(tf.global_variables_initializer())
###################################################################
########## Run the training and loop over the batches #############
###################################################################
for epoch in range(num_epochs):
total_batch_training = int(train_data.shape[0] / batch_size)
# go through the batches
for batch_num in range(total_batch_training):
#################################################
########## Get the training batches #############
#################################################
start_idx = batch_num * batch_size
end_idx = (batch_num + 1) * batch_size
# Fit training using batch data
train_batch_data, train_batch_label = train_data[start_idx:end_idx], train_label[
start_idx:end_idx]
########################################
########## Run the session #############
########################################
# Run optimization op (backprop) and Calculate batch loss and accuracy
# When the tensor tensors['global_step'] is evaluated, it will be incremented by one.
batch_loss, _, training_step = sess.run(
[softmax_loss, train_op, global_step],
feed_dict={image_place: train_batch_data,
label_place: train_batch_label,
dropout_param: 0.5})
#################################################
########## Plot the progressive bar #############
#################################################
print("Epoch #" + str(epoch + 1) + ", Train Loss=" + \
"{:.3f}".format(batch_loss))
#####################################################
########## Evaluation on the test data #############
#####################################################
if online_test:
# WARNING: In this evaluation the whole test data is fed. In case the test data is huge this implementation
# may lead to memory error. In presense of large testing samples, batch evaluation on testing is
# recommended as in the training phase.
test_accuracy_epoch, test_summaries = sess.run(
[accuracy, summary_test_op],
feed_dict={image_place: test_data,
label_place: test_label,
dropout_param: 1.})
print("Test Accuracy= " + \
"{:.4f}".format(test_accuracy_epoch))
###########################################################
########## Write the summaries for test phase #############
###########################################################
# Returning the value of global_step if necessary
current_step = tf.train.global_step(sess, global_step)
# Evaluation of the model
total_test_accuracy = sess.run(accuracy, feed_dict={
image_place: test_data,
label_place: test_label,
dropout_param: 1.})
print("Final Test Accuracy is %.2f" % total_test_accuracy)