TensorFlow版本2发布后，使用TensorFlow变得更简单和方便，但看网上的很多代码是使用的TensorFlow1进行完成的，每次遇到不懂的函数去查，理解记忆的一般，感觉还是有点不清楚
所以又快速看了一下TensorFlow1常用的方法，感觉似乎比之前清楚了一下。整理记录下方便阅读代码
这个TensorFlow教程挺好的 https://www.w3cschool.cn/tensorflow_python/
课程的话个人感觉莫凡系列的挺好懂
TensorFlow 的特点：
- 使用图 (graph) 来表示计算任务.
- 在被称之为
会话 (Session)的上下文 (context) 中执行图. - 使用 tensor 表示数据.
- 通过
变量 (Variable)维护状态. - 使用 feed 和 fetch 可以为任意的操作(arbitrary operation) 赋值或者从其中获取数据
TensorFlow基本使用之创建图 ，在会话中使用图
-
import tensorflow as tf
-
-
#
-
# 创建一个常量，产生
1*
2的矩阵
-
m1 = tf.constant(
[[2, 2]])
-
m2 = tf.constant(
[[3],
-
[3]])
-
product = tf.matmul(m1, m2)
-
-
print(product) # wrong! no result
-
-
# 在一个会话中启动图
-
# 调用 sess 的
'run()' 方法来执行矩阵乘法 op, 传入
'product' 作为该方法的参数.
-
-
# method1 use session
-
sess = tf.Session()
-
result = sess.run(product)
-
# 上面提到,
'product' 代表了矩阵乘法 op 的输出, 传入它是向方法表明, 我们希望取回矩阵乘法 op 的输出.
-
print(result) # 返回值
'result' 是一个 numpy `ndarray` 对象
-
sess.
close()
-
-
# method2 use session
-
with tf.Session() as sess:
-
result_ = sess.run(product)
-
print(result_)
使用cpu，GPU
如果机器上有超过一个可用的 GPU, 除第一个外的其它 GPU 默认是不参与计算的. 为了让 TensorFlow 使用这些 GPU, 你必须将 op 明确指派给它们执行. with...Device 语句用来指派特定的 CPU 或 GPU 执行操作:
-
with tf.Session() as sess:
-
with tf.device(
"/gpu:1"):
-
matrix1 = tf.constant(
[[3., 3.]])
-
matrix2 = tf.constant(
[[2.],[2.]])
-
product = tf.matmul(matrix1, matrix2)
-
...
设备用字符串进行标识. 目前支持的设备包括:
"/cpu:0": 机器的 CPU."/gpu:0": 机器的第一个 GPU, 如果有的话."/gpu:1": 机器的第二个 GPU, 以此类推.
变量or feed
-
import tensorflow as tf
-
-
input1 = tf.placeholder(dtype=tf.float32)
-
input2 = tf.placeholder(dtype=tf.float32)
-
output = tf.multiply(input1, input2)
-
-
with tf.Session()
as sess:
-
result=sess.run([
output], feed_dict={input1:[
7.], input2:[
2.]})
-
print(
result)
-
-
-
-
import tensorflow
as tf
-
# 创建一个变量
-
var = tf.Variable(
0)
# our first variable in the "global_variable" set
-
-
add_operation = tf.add(
var,
1)
-
update_operation = tf.assign(
var, add_operation)
-
-
with tf.Session()
as sess:
-
# 必须初始化 once define variables, you have to initialize them by doing this
-
sess.run(tf.global_variables_initializer())
-
for _
in
range(
3):
-
sess.run(update_operation)
-
print(sess.run(
var))
激活函数
-
import tensorflow
as tf
-
import numpy
as np
-
import matplotlib.pyplot
as plt
-
-
# fake data
-
x = np.linspace(-
5,
5,
200) # x data, shape=(
100,
1)
-
-
# following are popular activation functions
-
y_relu = tf.nn.relu(x)
-
y_sigmoid = tf.nn.sigmoid(x)
-
y_tanh = tf.nn.tanh(x)
-
y_softplus = tf.nn.softplus(x)
-
# y_softmax = tf.nn.softmax(x) softmax
is a special kind
of activation
function, it is about probability
-
-
sess =
tf.
Session
()
-
y_relu,
y_sigmoid,
y_tanh,
y_softplus =
sess.
run
([y_relu, y_sigmoid, y_tanh, y_softplus])
-
-
#
plt
to
visualize
these
activation
function
-
plt.
figure
(1, figsize=(8, 6))
-
plt.
subplot
(221)
-
plt.
plot
(x, y_relu, c='red', label='relu')
-
plt.
ylim
((-1, 5))
-
plt.
legend
(loc='best')
-
-
plt.
subplot
(222)
-
plt.
plot
(x, y_sigmoid, c='red', label='sigmoid')
-
plt.
ylim
((-0.2, 1.2))
-
plt.
legend
(loc='best')
-
-
plt.
subplot
(223)
-
plt.
plot
(x, y_tanh, c='red', label='tanh')
-
plt.
ylim
((-1.2, 1.2))
-
plt.
legend
(loc='best')
-
-
plt.
subplot
(224)
-
plt.
plot
(x, y_softplus, c='red', label='softplus')
-
plt.
ylim
((-0.2, 6))
-
plt.
legend
(loc='best')
-
-
plt.
show
()
简单神经网络构造
-
import tensorflow as tf
-
import matplotlib.pyplot as plt
-
import numpy as np
-
-
tf.set_random_seed(
1)
-
np.random.seed(
1)
-
-
# fake data
-
x = np.linspace(-
1,
1,
100)[:, np.newaxis] # shape (
100,
1)
-
noise = np.random.normal(
0,
0.
1, size=x.shape)
-
y = np.power(x,
2) + noise # shape (
100,
1) + some noise
-
-
# plot data
-
plt.scatter(x, y)
-
plt.show()
-
-
tf_x = tf.placeholder(tf.float
32, x.shape) # input x
-
tf_y = tf.placeholder(tf.float
32, y.shape) # input y
-
-
# neural network layers
-
l1 = tf.layers.dense(tf_x,
10, tf.nn.relu) # hidden layer
-
output = tf.layers.dense(l
1,
1) # output layer
-
-
loss = tf.losses.mean_squared_error(tf_y, output) # compute cost
-
optimizer = tf.train.GradientDescentOptimizer(learning_rate=
0.
5)
-
train_op = optimizer.minimize(loss)
-
-
sess = tf.Session() # control training and others
-
sess.run(tf.global_variables_initializer()) # initialize var in graph
-
-
plt.ion() # something about plotting 打开交互模式
-
-
for step in range(
100):
-
# train and net output
-
_, l, pred = sess.run([train_op, loss, output], {tf_x: x, tf_y: y})
-
if step %
5 ==
0:
-
# plot and show learning process
-
plt.cla()#清除活动轴
-
plt.scatter(x, y)
-
plt.plot(x, pred, 'r-', lw=
5)
-
plt.text(
0.
5,
0, 'Loss=%.
4f' % l, fontdict={'size':
20, 'color': 'red'})
-
plt.pause(
0.
1)
-
-
plt.ioff()#关闭交互模式用于阻塞程序，不让图片关闭
-
plt.show()
优化器
-
import tensorflow
as tf
-
import matplotlib.pyplot
as plt
-
import numpy
as np
-
-
tf.set_random_seed(
1)
-
np.random.seed(
1)
-
-
LR =
0.01
-
BATCH_SIZE =
32
-
-
# fake data
-
x = np.linspace(
-1,
1,
100)[:, np.newaxis] # shape (
100,
1)
-
noise = np.random.normal(
0,
0.1, size=x.shape)
-
y = np.power(x,
2) + noise # shape (
100,
1) + some noise
-
-
# plot dataset
-
plt.scatter(x, y)
-
plt.show()
-
-
# default network
-
class Net:
-
def __init__(self, opt, **kwargs):
-
self.x = tf.placeholder(tf.float32, [None, 1])
-
self.y = tf.placeholder(tf.float32, [None, 1])
-
l = tf.layers.dense(self.x, 20, tf.nn.relu)
-
out = tf.layers.dense(l, 1)
-
self.loss = tf.losses.mean_squared_error(self.y, out)
-
self.train = opt(LR, **kwargs).minimize(self.loss)
-
-
# different nets
-
net_SGD = Net(tf.train.GradientDescentOptimizer)
-
net_Momentum = Net(tf.train.MomentumOptimizer, momentum=0.9)
-
net_RMSprop = Net(tf.train.RMSPropOptimizer)
-
net_Adam = Net(tf.train.AdamOptimizer)
-
nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
-
-
sess = tf.Session()
-
sess.run(tf.global_variables_initializer())
-
-
losses_his = [[], [], [], []] # record loss
-
-
# training
-
for step in range(300): # for each training step
-
index = np.random.randint(0, x.shape[0], BATCH_SIZE)
-
b_x = x[index]
-
b_y = y[index]
-
-
for net, l_his in zip(nets, losses_his):
-
_, l = sess.run([net.train, net.loss], {net.x: b_x, net.y: b_y})
-
l_his.append(l) # loss recoder
-
-
# plot loss history
-
labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
-
for i, l_his in enumerate(losses_his):
-
plt.plot(l_his, label=labels[i])
-
plt.legend(loc='best')
-
plt.xlabel('Steps')
-
plt.ylabel('Loss')
-
plt.ylim((0, 0.2))
-
plt.show()
保存模型与加载模型
-
"""
-
Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
-
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
-
-
Dependencies:
-
tensorflow: 1.1.0
-
matplotlib
-
numpy
-
"""
-
import tensorflow
as tf
-
import matplotlib.pyplot
as plt
-
import numpy
as np
-
-
tf.set_random_seed(
1)
-
np.random.seed(
1)
-
-
# fake data
-
x = np.linspace(
-1,
1,
100)[:, np.newaxis]
# shape (100, 1)
-
noise = np.random.normal(
0,
0.1, size=x.shape)
-
y = np.power(x,
2) + noise
# shape (100, 1) + some noise
-
-
-
def save():
-
print(
'This is save')
-
# build neural network
-
tf_x = tf.placeholder(tf.float32, x.shape)
# input x
-
tf_y = tf.placeholder(tf.float32, y.shape)
# input y
-
l = tf.layers.dense(tf_x,
10, tf.nn.relu)
# hidden layer
-
o = tf.layers.dense(l,
1)
# output layer
-
loss = tf.losses.mean_squared_error(tf_y, o)
# compute cost
-
train_op = tf.train.GradientDescentOptimizer(learning_rate=
0.5).minimize(loss)
-
-
sess = tf.Session()
-
sess.run(tf.global_variables_initializer())
# initialize var in graph
-
-
saver = tf.train.Saver()
# define a saver for saving and restoring
-
-
for step
in range(
100):
# train
-
sess.run(train_op, {tf_x: x, tf_y: y})
-
-
saver.save(sess,
'./params', write_meta_graph=
False)
# meta_graph is not recommended
-
-
# plotting
-
pred, l = sess.run([o, loss], {tf_x: x, tf_y: y})
-
plt.figure(
1, figsize=(
10,
5))
-
plt.subplot(
121)
-
plt.scatter(x, y)
-
plt.plot(x, pred,
'r-', lw=
5)
-
plt.text(
-1,
1.2,
'Save Loss=%.4f' % l, fontdict={
'size':
15,
'color':
'red'})
-
-
-
def reload():
-
print(
'This is reload')
-
# build entire net again and restore
-
tf_x = tf.placeholder(tf.float32, x.shape)
# input x
-
tf_y = tf.placeholder(tf.float32, y.shape)
# input y
-
l_ = tf.layers.dense(tf_x,
10, tf.nn.relu)
# hidden layer
-
o_ = tf.layers.dense(l_,
1)
# output layer
-
loss_ = tf.losses.mean_squared_error(tf_y, o_)
# compute cost
-
-
sess = tf.Session()
-
# don't need to initialize variables, just restoring trained variables
-
saver = tf.train.Saver()
# define a saver for saving and restoring
-
saver.restore(sess,
'./params')
-
-
# plotting
-
pred, l = sess.run([o_, loss_], {tf_x: x, tf_y: y})
-
plt.subplot(
122)
-
plt.scatter(x, y)
-
plt.plot(x, pred,
'r-', lw=
5)
-
plt.text(
-1,
1.2,
'Reload Loss=%.4f' % l, fontdict={
'size':
15,
'color':
'red'})
-
plt.show()
-
-
-
save()
-
-
# destroy previous net
-
tf.reset_default_graph()
-
-
reload()
tensorboard可视化
参考文档：TensorBoard:图表可视化 - TensorFlow官方文档中文版 (pythontab.com)
-
1.创建writer，写日志文件
-
writer=tf.summary.FileWriter(
'/path/to/logs', tf.get_default_graph())
-
-
2.保存日志文件
-
writer.close()
-
-
3.运行可视化命令，启动服务
-
tensorboard –logdir
/path/to/logs
-
-
4.打开可视化界面
-
通过浏览器打开服务器访问端口
http:
//xxx.xxx.xxx.xxx:6006
TensorBoard的使用流程
- 添加记录节点：
tf.summary.scalar/image/histogram()等 - 汇总记录节点：
merged = tf.summary.merge_all() - 运行汇总节点：
summary = sess.run(merged)，得到汇总结果 - 日志书写器实例化：
summary_writer = tf.summary.FileWriter(logdir, graph=sess.graph)，实例化的同时传入 graph 将当前计算图写入日志 - 调用日志书写器实例对象
summary_writer的add_summary(summary, global_step=i)方法将所有汇总日志写入文件 - 调用日志书写器实例对象
summary_writer的close()方法写入内存，否则它每隔120s写入一次
-
"""
-
Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
-
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
-
-
Dependencies:
-
tensorflow: 1.1.0
-
numpy
-
"""
-
import tensorflow
as tf
-
import numpy
as np
-
-
tf.set_random_seed(
1)
-
np.random.seed(
1)
-
-
# fake data
-
x = np.linspace(
-1,
1,
100)[:, np.newaxis]
# shape (100, 1)
-
noise = np.random.normal(
0,
0.1, size=x.shape)
-
y = np.power(x,
2) + noise
# shape (100, 1) + some noise
-
-
with tf.variable_scope(
'Inputs'):
#用tf.variable_scope命名Inputs（名称），x,y属于Inputs层级下的节点
-
tf_x = tf.placeholder(tf.float32, x.shape, name=
'x')
-
tf_y = tf.placeholder(tf.float32, y.shape, name=
'y')
-
-
with tf.variable_scope(
'Net'):
-
l1 = tf.layers.dense(tf_x,
10, tf.nn.relu, name=
'hidden_layer')
-
output = tf.layers.dense(l1,
1, name=
'output_layer')
-
-
# add to histogram summary
-
tf.summary.histogram(
'h_out', l1)
-
tf.summary.histogram(
'pred', output)
-
-
loss = tf.losses.mean_squared_error(tf_y, output, scope=
'loss')
-
train_op = tf.train.GradientDescentOptimizer(learning_rate=
0.5).minimize(loss)
-
tf.summary.scalar(
'loss', loss)
# add loss to scalar summary
-
-
sess = tf.Session()
-
sess.run(tf.global_variables_initializer())
-
-
writer = tf.summary.FileWriter(
'./log', sess.graph)
# write to file
-
merge_op = tf.summary.merge_all()
# operation to merge all summary
-
-
for step
in range(
100):
-
# train and net output
-
_, result = sess.run([train_op, merge_op], {tf_x: x, tf_y: y})
-
writer.add_summary(result, step)
-
-
# Lastly, in your terminal or CMD, type this :
-
# $ tensorboard --logdir path/to/log
-
# open you google chrome, type the link shown on your terminal or CMD. (something like this: http://localhost:6006)
注意：节点域：tf2使用tf.name_scope('XX'):使可视化更简洁
启动TensorBoard
输入下面的指令来启动TensorBoard
tensorboard --logdir=/path/to/log-directory
这里的参数 logdir 指向 SummaryWriter 序列化数据的存储路径。如果logdir目录的子目录中包含另一次运行时的数据，那么 TensorBoard 会展示所有运行的数据。一旦 TensorBoard 开始运行，你可以通过在浏览器中输入 localhost:6006 来查看 TensorBoard。
梯度下降
-
"""
-
Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
-
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
-
-
Dependencies:
-
tensorflow: 1.1.0
-
matplotlib
-
numpy
-
"""
-
import tensorflow
as tf
-
import numpy
as np
-
import matplotlib.pyplot
as plt
-
from mpl_toolkits.mplot3d
import Axes3D
-
-
LR =
0.1
-
REAL_PARAMS = [
1.2,
2.5]
-
INIT_PARAMS = [[
5,
4],
-
[
5,
1],
-
[
2,
4.5]][
2]
-
-
x = np.linspace(
-1,
1,
200, dtype=np.float32)
# x data
-
-
# Test (1): Visualize a simple linear function with two parameters,
-
# you can change LR to 1 to see the different pattern in gradient descent.
-
-
# y_fun = lambda a, b: a * x + b
-
# tf_y_fun = lambda a, b: a * x + b
-
-
-
# Test (2): Using Tensorflow as a calibrating tool for empirical formula like following.
-
-
# y_fun = lambda a, b: a * x**3 + b * x**2
-
# tf_y_fun = lambda a, b: a * x**3 + b * x**2
-
-
-
# Test (3): Most simplest two parameters and two layers Neural Net, and their local & global minimum,
-
# you can try different INIT_PARAMS set to visualize the gradient descent.
-
-
y_fun =
lambda a, b: np.sin(b*np.cos(a*x))
-
tf_y_fun =
lambda a, b: tf.sin(b*tf.cos(a*x))
-
-
noise = np.random.randn(
200)/
10
-
y = y_fun(*REAL_PARAMS) + noise
# target
-
-
# tensorflow graph
-
a, b = [tf.Variable(initial_value=p, dtype=tf.float32)
for p
in INIT_PARAMS]
-
pred = tf_y_fun(a, b)
-
mse = tf.reduce_mean(tf.square(y-pred))
-
train_op = tf.train.GradientDescentOptimizer(LR).minimize(mse)
-
-
a_list, b_list, cost_list = [], [], []
-
with tf.Session()
as sess:
-
sess.run(tf.global_variables_initializer())
-
for t
in range(
400):
-
a_, b_, mse_ = sess.run([a, b, mse])
-
a_list.append(a_); b_list.append(b_); cost_list.append(mse_)
# record parameter changes
-
result, _ = sess.run([pred, train_op])
# training
-
-
-
# visualization codes:
-
print(
'a=', a_,
'b=', b_)
-
plt.figure(
1)
-
plt.scatter(x, y, c=
'b')
# plot data
-
plt.plot(x, result,
'r-', lw=
2)
# plot line fitting
-
# 3D cost figure
-
fig = plt.figure(
2); ax = Axes3D(fig)
-
a3D, b3D = np.meshgrid(np.linspace(
-2,
7,
30), np.linspace(
-2,
7,
30))
# parameter space
-
cost3D = np.array([np.mean(np.square(y_fun(a_, b_) - y))
for a_, b_
in zip(a3D.flatten(), b3D.flatten())]).reshape(a3D.shape)
-
ax.plot_surface(a3D, b3D, cost3D, rstride=
1, cstride=
1, cmap=plt.get_cmap(
'rainbow'), alpha=
0.5)
-
ax.scatter(a_list[
0], b_list[
0], zs=cost_list[
0], s=
300, c=
'r')
# initial parameter place
-
ax.set_xlabel(
'a'); ax.set_ylabel(
'b')
-
ax.plot(a_list, b_list, zs=cost_list, zdir=
'z', c=
'r', lw=
3)
# plot 3D gradient descent
-
plt.show()
转载：https://blog.csdn.net/sereasuesue/article/details/116534048
查看评论