mac下tensorflow的helloworld(conda)

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tensorflow
conda

tensorflow conda

基本的

virtualenv tensorenv
source bin/activate

pip install --upgrade tensorflow

python
>>> import tensorflow as tf
>>> hello  = tf.constant('hello TensorFlow!')
>>> sess =  tf.Session()
>>> print sess.run(hello)
hello TensorFlow!

tensorenv/lib/python2.7/site-packages/tensorflow/examples/tutorials/mnist

#########################

安装anaconda

https://www.anaconda.com/download/#macos

安装之后参考
https://blog.csdn.net/lq_547762983/article/details/81003528

conda list

conda create -n tensorenv tensorflow
conda env list

创建名字为tensorenv 的环境并安装tensorflow
安装在/anaconda2/envs 下
/anaconda2/envs/tensorenv


source activate tensorenv

如果需要装其他包继续

conda install -n tensorenv numpy

下载数据：
参考http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_beginners.html

https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/mnist/input_data.py
添加
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

用自己的网，网络限制就想其他办法

反向传播
梯度下降

dropout 解决泛化性不好

无监督学习 https://www.zhihu.com/question/23194489

##################
使用的tf1.0的版本，很多api变了

第一个helloworld

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.constant(5,name="input_a")
b = tf.constant(3,name="input_b")
c = tf.multiply(a,b,name="mul_c")
d = tf.add(a,b,name="add_d")
e = tf.add(c,d,name="add_e")
sess = tf.Session()
sess.run(e)

#writer = tf.train.SummaryWriter('./my_grap',sess.graph)
writer = tf.summary.FileWriter('./my_graph',sess.graph)
writer.close()
sess.close()

tensorboard --logdir="./my_graph"

得到

张量的helloworld
test2.py

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.constant([5,3],name="input_a")
b = tf.reduce_prod(a,name="prod_b")
c = tf.reduce_sum(a,name="sum_c")
d = tf.add(c,b,name="add_d")
sess =  tf.Session()
sess.run(d)

writer = tf.summary.FileWriter('./my_test2',sess.graph)
writer.close()
sess.close()

numpy的helloworld

注意：numpy使用字符串时，不要显示的指定dtype属性

基本概念
tensor -->numpy建立     a = tf.constant(3,name="input_b")
opration ---> b = tf.add(a,3) ;
graph ---> g=tf.Graph() g.as_default()
session --->sess = ts.Session(graph = tf.get_default_graph())
fetches ---->sesion.run(a) ;sesion.run([a,b])
feed_dict ----> sesion.run(b，feed_dict = {a:15})   ---改变参数的值,测试和中间环节改变值调试用，或者是给占位符赋值
placeholder(dtype,shape,name） ---> tf.placeholder(tf.int32,shape=[2])     ---> dtype参数是必须指定的

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

a = tf.add(2,5)
b = tf.multiply(a,3)

sess = tf.Session()
replace_dict = {a:15}

c = sess.run(b,feed_dict=replace_dict)
print(c)

基本机器学习
basic.py

import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def inference(X):
	print("inference")

def loss(X,Y):
	print("loss")
	loss_v = tf.constant("myloss",name="loss_value")
	return loss_v

def inputs():
	print("input")
	return 1,2

def train(total_loss):
	print("train")
	hello = tf.constant("ha",name="train_value")
	return hello

def evaluate(sess,X,Y):
	print("evaluate")


with tf.Session() as sess:
#	tf.initialize_all_variables().run()
	tf.global_variables_initializer().run()
	X,Y = inputs()

	total_loss = loss(X,Y)
	train_op = train(total_loss)
	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess,coord=coord)
	training_steps = 100
	for step in range(training_steps):
		sess.run([train_op])
		if step % 10 ==0:
			print("loss:",sess.run([total_loss]))
	evaluate(sess,X,Y)

	coord.request_stop()
	coord.join(threads)
	sess.close()

检查点，针对变量的
注意：变量不能放在定义的方法里
saver要放在初始化变量之后
check.py

import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def inference(X):
	print("inference")

def loss(X,Y):
	print("loss")
	return X

def inputs(X):
	print("input")
	Y = tf.constant("myY",name="Y_value")
	return X,Y

def train(total_loss):
	print("train")
	hello = tf.constant("ha",name="train_value")
	return hello

def evaluate(sess,X,Y):
	print("evaluate")
with tf.Session() as sess:
	#tf.global_variables_initializer().run()
	thisX = tf.Variable("thisX",name="thisX_value")
	sess.run(tf.global_variables_initializer())
	saver = tf.train.Saver()
	X,Y = inputs(thisX)

	total_loss = loss(X,Y)
	train_op = train(total_loss)
	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess,coord=coord)
	training_steps = 100
	for step in range(training_steps):
		sess.run([train_op])
		if step % 10 ==0:
			print("loss:",sess.run([total_loss]))
			saver.save(sess,'./work/my-model',global_step=step)
	evaluate(sess,X,Y)

	coord.request_stop()
	coord.join(threads)
	saver.save(sess,'./work/my-model',global_step=training_steps)
	sess.close()

恢复检查点
restore.py

import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

def inference(X):
	print("inference")

def loss(X,Y):
	print("loss")
	return X

def inputs(X):
	print("input")
	Y = tf.constant("myY",name="Y_value")
	return X,Y

def train(total_loss):
	print("train")
	hello = tf.constant("ha",name="train_value")
	return hello

def evaluate(sess,X,Y):
	print("evaluate")
with tf.Session() as sess:
	#tf.global_variables_initializer().run()
	thisX = tf.Variable("thisX",name="thisX_value")
	sess.run(tf.global_variables_initializer())
	saver = tf.train.Saver()
	X,Y = inputs(thisX)

	total_loss = loss(X,Y)
	train_op = train(total_loss)
	coord = tf.train.Coordinator()
	threads = tf.train.start_queue_runners(sess=sess,coord=coord)
	training_steps = 1000

	initial_step = 0
	ckpt = tf.train.get_checkpoint_state(os.path.dirname("./work/my-model"))
	print("debug---->0 "+str(ckpt))
	if ckpt and ckpt.model_checkpoint_path:
		print("debug---->1")
		saver.restore(sess,ckpt.model_checkpoint_path)
		initial_step =  int(ckpt.model_checkpoint_path.rsplit('-',1)[1])
		print("debug---->2.1 " + str(initial_step))
		print("debug---->2.2 " + str(initial_step))
		for step in range( initial_step,training_steps):
			print("debug---->3")
			sess.run([train_op])
			if step % 10 ==0:
				print("loss:",sess.run([total_loss]))
				saver.save(sess,'./work/my-model',global_step=step)
	evaluate(sess,X,Y)

	coord.request_stop()
	coord.join(threads)
	saver.save(sess,'./work/my-model',global_step=training_steps)
	sess.close()

关于saver

https://www.tensorflow.org/programmers_guide/saved_model?hl=zh-cn