- 官网：https://www.tensorflow.org/
- TensorFlow是Google开发的一款神经网络的Python外部的结构包, 也是一个采用数据流图来进行数值计算的开源软件库.
- 先绘制计算结构图, 也可以称是一系列可人机交互的计算操作, 然后把编辑好的Python文件 转换成 更高效的C++, 并在后端进行计算.
- 擅长的任务就是训练深度神经网络
- 快速的入门神经网络,大大降低了深度学习（也就是深度神经网络）的开发成本和开发难度
- TensorFlow 的开源性, 让所有人都能使用并且维护
- 暂不支持Windows下安装TensorFlow,可以在虚拟机里使用或者安装Docker安装
- 这里在CentOS6.5下进行安装
- 安装Python2.7，默认CentOS中安装的是Python2.6
- 先安装zlib的依赖，下面安装easy_install时会用到
yum install zlib
yum install zlib-devel
- 在安装openssl的依赖，下面安装pip时会用到
yum install openssl
yum install openssl-devel
- 下载安装包，我传到
github上的安装包，https协议后面加上--no-check-certificate，：
wget https://raw.githubusercontent.com/lawlite19/LinuxSoftware/master/python/Python-2.7.12.tgz --no-check-certificate
- 解压缩：
tar -zxvf xxx - 进入，配置：
./configure --prefix=/usr/local/python2.7 - 编译并安装：
make && make install - 创建链接来使系统默认python变为python2.7,
ln -fs /usr/local/python2.7/bin/python2.7 /usr/bin/python - 修改一下yum，因为yum的执行文件还是需要原来的python2.6,
vim /usr/bin/yum
#!/usr/bin/python
修改为系统原有的python版本地址
#!/usr/bin/python2.6
-
安装easy_install
-
下载：
wget https://raw.githubusercontent.com/lawlite19/LinuxSoftware/blob/master/python/setuptools-26.1.1.tar.gz --no-check-certificate -
解压缩：
tar -zxvf xxx -
python setup.py build#注意这里python是新的python2.7 -
python setup.py install -
到
/usr/local/python2.7/bin目录下查看就会看到easy_install了 -
创建一个软连接：
ln -s /usr/local/python2.7/bin/easy_install /usr/local/bin/easy_install -
就可以使用
easy_install 包名进行安装 -
安装pip
-
下载:
-
解压缩：
tar -zxvf xxx -
安装：
python setup.py install -
到
/usr/local/python2.7/bin目录下查看就会看到pip了 -
同样创建软连接：
ln -s /usr/local/python2.7/bin/pip /usr/local/bin/pip -
就可以使用
pip install 包名进行安装包了 -
安装wingIDE
-
默认安装到
/usr/local/lib下，进入，执行./wing命令即可执行 -
创建软连接：
ln -s /usr/local/lib/wingide5.1/wing /usr/local/bin/wing -
破解：
-
[另]安装VMwareTools，可以在windows和Linux之间复制粘贴
-
启动CentOS
-
选择VMware中的虚拟机-->安装VMware Tools
-
会自动弹出VMware Tools的文件夹
-
拷贝一份到root目录下
cp VMwareTools-9.9.3-2759765.tar.gz /root -
解压缩
tar -zxvf VMwareTools-9.9.3-2759765.tar.gz -
进入目录执行，
vmware-install.pl，一路回车下去即可 -
重启CentOS即可
-
安装numpy
-
直接安装没有出错
-
安装scipy
-
安装依赖：
yum install bzip2-devel pcre-devel ncurses-devel readline-devel tk-devel gcc-c++ lapack-devel -
安装即可：
pip install scipy -
安装matplotlib
-
安装依赖：
yum install libpng-devel -
安装即可：
pip install matplotlib -
运行可能有以下的错误：
ImportError: No module named _tkinter
安装：tcl8.5.9-src.tar.gz
-
进入安装即可,
./confgiure make make install安装：tk8.5.9-src.tar.gz -
进入安装即可。
-
[注意]要重新安装一下Pyhton2.7才能链接到
tkinter -
安装scikit-learn
-
直接安装没有出错，但是缺少包
bz2 -
将系统中
python2.6的bz2复制到python2.7对应文件夹下
cp /usr/lib/python2.6/lib-dynload/bz2.so /usr/local/python2.7/lib/python2.7/lib-dynload
- 安装TensorFlow
- 官网点击
- 选择对应的版本
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc0-cp27-none-linux_x86_64.whl
# Mac OS X, CPU only, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc0-py2-none-any.whl
# Mac OS X, GPU enabled, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc0-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
# Mac OS X, CPU only, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc0-py3-none-any.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc0-py3-none-any.whl
- 对应
python版本
# Python 2
$ sudo pip install --upgrade $TF_BINARY_URL
# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL
- 可能缺少依赖
glibc,看对应提示的版本， - 还有可能报错
ImportError: /usr/lib64/libstdc++.so.6: version `GLIBCXX_3.4.19' not found (required by /usr/local/python2.7/lib/python2.7/site-packages/tensorflow/python/_pywrap_tensorflow.so)
- 安装对应版本的glibc
- 查看现有版本的glibc,
strings /lib64/libc.so.6 |grep GLIBC - 下载对应版本：
wget http://ftp.gnu.org/gnu/glibc/glibc-2.17.tar.gz - 解压缩：
tar -zxvf glibc-2.17 - 进入文件夹创建
build文件夹cd glibc-2.17 && mkdir build - 配置：
../configure \
--prefix=/usr \
--disable-profile \
--enable-add-ons \
--enable-kernel=2.6.25 \
--libexecdir=/usr/lib/glibc
-
编译安装：
make && make install -
可以再用命令：
strings /lib64/libc.so.6 |grep GLIBC查看 -
添加GLIBCXX_3.4.19的支持
-
复制到
/usr/lib64文件夹下：cp libstdc++.so.6.0.20 /usr/lib64/ -
添加执行权限：
chmod +x /usr/lib64/libstdc++.so.6.0.20 -
删除原来的：
rm -rf /usr/lib64/libstdc++.so.6 -
创建软连接：
ln -s /usr/lib64/libstdc++.so.6.0.20 /usr/lib64/libstdc++.so.6 -
可以查看是否有个版本：
strings /usr/lib64/libstdc++.so.6 | grep GLIBCXX -
运行还可能报错编码的问题，这里安装
0.10.0版本:pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl -
安装
pandas -
pip install pandas没有问题
- Tensorflow 首先要定义神经网络的结构,然后再把数据放入结构当中去运算和 training
- TensorFlow是采用数据流图（data flow graphs）来计算
- 首先我们得创建一个数据流流图
- 然后再将我们的数据（数据以张量(tensor)的形式存在）放在数据流图中计算
- 张量（tensor):
- 张量有多种. 零阶张量为 纯量或标量 (scalar) 也就是一个数值. 比如 1
- 一阶张量为 向量 (vector), 比如 一维的 [1, 2, 3]
- 二阶张量为 矩阵 (matrix), 比如 二维的 [[1, 2, 3],[4, 5, 6],[7, 8, 9]]
- 以此类推, 还有 三阶 三维的 …
- 求
y=1*x+3中的权重1和偏置3 - 定义这个函数
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*1.0+3.0
- 创建TensorFlow结构
Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0)) # 创建变量Weight是，范围是 -1.0~1.0
biases = tf.Variable(tf.zeros([1])) # 创建偏置，初始值为0
y = Weights*x_data+biases # 定义方程
loss = tf.reduce_mean(tf.square(y-y_data)) # 定义损失，为真实值减去我们每一步计算的值
optimizer = tf.train.GradientDescentOptimizer(0.5) # 0.5 是学习率
train = optimizer.minimize(loss) # 使用梯度下降优化
init = tf.initialize_all_variables() # 初始化所有变量
- 定义
Session
sess = tf.Session()
sess.run(init)
- 输出结果
for i in range(201):
sess.run(train)
if i%20 == 0:
print i,sess.run(Weights),sess.run(biases)
结果为：
0 [ 1.60895896] [ 3.67376709]
20 [ 1.04673827] [ 2.97489643]
40 [ 1.011392] [ 2.99388123]
60 [ 1.00277638] [ 2.99850869]
80 [ 1.00067675] [ 2.99963641]
100 [ 1.00016499] [ 2.99991131]
120 [ 1.00004005] [ 2.99997854]
140 [ 1.00000978] [ 2.99999475]
160 [ 1.0000025] [ 2.99999857]
180 [ 1.00000119] [ 2.99999928]
200 [ 1.00000119] [ 2.99999928]
- 运行
session.run()可以获得你要得知的运算结果, 或者是你所要运算的部分 - 定义常量矩阵：
tf.constant([[3,3]]) - 矩阵乘法 ：
tf.matmul(matrix1,matrix2) - 运行Session的两种方法：
- 手动关闭
sess = tf.Session()
print sess.run(product)
sess.close()
- 使用
with，执行完会自动关闭
with tf.Session() as sess:
print sess.run(product)
- 定义变量：
tf.Variable() - 初始化所有变量：
init = tf.initialize_all_variables() - 需要再在 sess 里,
sess.run(init), 激活变量 - 输出时，一定要把 sess 的指针指向变量再进行
print才能得到想要的结果
- 首先定义
Placeholder，然后在Session.run()的时候输入值 placeholder与feed_dict={}是绑定在一起出现的
input1 = tf.placeholder(tf.float32) #在 Tensorflow 中需要定义 placeholder 的 type ，一般为 float32 形式
input2 = tf.placeholder(tf.float32)
output = tf.mul(input1,input2) # 乘法运算
with tf.Session() as sess:
print sess.run(output,feed_dict={input1:7.,input2:2.}) # placeholder 与 feed_dict={} 是绑定在一起出现的
'''参数：输入数据，前一层size，当前层size，激活函数'''
def add_layer(inputs,in_size,out_size,activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size,out_size])) #随机初始化权重
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1) # 初始化偏置，+0.1
Ws_plus_b = tf.matmul(inputs,Weights) + biases # 未使用激活函数的值
if activation_function is None:
outputs = Ws_plus_b
else:
outputs = activation_function(Ws_plus_b) # 使用激活函数激活
return outputs
- 定义二次函数
x_data = np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)
y_data = np.square(x_data)-0.5+noise
- 定义
Placeholder,用于后期输入数据
xs = tf.placeholder(tf.float32,[None,1]) # None代表无论输入有多少都可以,只有一个特征，所以这里是1
ys = tf.placeholder(tf.float32,[None,1])
- 定义神经层
layer
layer1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) # 第一层，输入层为1，隐含层为10个神经元，Tensorflow 自带的激励函数tf.nn.relu
- 定义输出层
prediction = add_layer(layer1, 10, 1) # 利用上一层作为输入
- 计算
loss损失
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) # 对二者差的平方求和再取平均
- 梯度下降最小化损失
train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
- 初始化所有变量
init = tf.initialize_all_variables()
- 定义Session
sess = tf.Session()
sess.run(init)
- 输出
for i in range(1000):
sess.run(train,feed_dict={xs:x_data,ys:y_data})
if i%50==0:
print sess.run(loss,feed_dict={xs:x_data,ys:y_data})
结果：
0.45402
0.0145364
0.00721318
0.0064215
0.00614493
0.00599307
0.00587578
0.00577039
0.00567172
0.00558008
0.00549546
0.00541595
0.00534059
0.00526139
0.00518873
0.00511403
0.00504063
0.0049613
0.0048874
0.004819
- 显示数据
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x_data,y_data)
plt.ion() # 绘画之后不暂停
plt.show()
- 动态绘画
try:
ax.lines.remove(lines[0]) # 每次绘画需要移除上次绘画的结果，放在try catch里因为第一次执行没有，所以直接pass
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
# plot the prediction
lines = ax.plot(x_data, prediction_value, 'r-', lw=3) # 绘画
plt.pause(0.1) # 停0.1s
- 输入
input
with tf.name_scope('input'):
xs = tf.placeholder(tf.float32,[None,1],name='x_in') #
ys = tf.placeholder(tf.float32,[None,1],name='y_in')
layer层
def add_layer(inputs,in_size,out_size,activation_function=None):
with tf.name_scope('layer'):
with tf.name_scope('Weights'):
Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='b')
with tf.name_scope('Ws_plus_b'):
Ws_plus_b = tf.matmul(inputs,Weights) + biases
if activation_function is None: outputs = Ws_plus_b
else:
outputs = activation_function(Ws_plus_b)
return outputs
loss和train
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
with tf.name_scope('train'):
train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
- 写入文件中
writer = tf.train.SummaryWriter("logs/", sess.graph)
- 浏览器中查看（chrome浏览器）
- 在终端输入：
tensorboard --logdir='logs/'，它会给出访问地址 - 浏览器中查看即可。
tensorboard命令在安装python目录的bin目录下，可以创建一个软连接
- 可视化Weights权重和biases偏置
- 每一层起个名字
layer_name = 'layer%s'%n_layer
- tf.histogram_summary(name,value)
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
layer_name = 'layer%s'%n_layer
with tf.name_scope(layer_name):
with tf.name_scope('Weights'):
Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
tf.histogram_summary(layer_name+'/weights', Weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='b')
tf.histogram_summary(layer_name+'/biases',biases)
with tf.name_scope('Ws_plus_b'):
Ws_plus_b = tf.matmul(inputs,Weights) + biases
if activation_function is None:
outputs = Ws_plus_b
else:
outputs = activation_function(Ws_plus_b)
tf.histogram_summary(layer_name+'/outputs',outputs)
return outputs
- merge所有的summary
merged =tf.merge_all_summaries()
- 写入文件中
writer = tf.train.SummaryWriter("logs/", sess.graph)
- 训练1000次，每50步显示一次：
for i in range(1000):
sess.run(train,feed_dict={xs:x_data,ys:y_data})
if i%50==0:
summary = sess.run(merged, feed_dict={xs: x_data, ys:y_data})
writer.add_summary(summary, i)
-
同样适用
tensorboard查看
-
可视化损失函数（代价函数）
-
添加：
tf.scalar_summary('loss',loss)
- 全部代码：
https://github.com/lawlite19/MachineLearning_TensorFlow/blob/master/Mnist_02/mnist.py - 自己的数据集，没有使用tensorflow中mnist数据集，
- 之前在机器学习中用Python实现过，地址：
https://github.com/lawlite19/MachineLearning_Python,这里使用tensorflow实现 - 神经网络只有两层
- 添加一层
'''添加一层神经网络'''
def add_layer(inputs,in_size,out_size,activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size,out_size])) # 权重，in*out
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
Ws_plus_b = tf.matmul(inputs,Weights) + biases # 计算权重和偏置之后的值
if activation_function is None:
outputs = Ws_plus_b
else:
outputs = activation_function(Ws_plus_b) # 调用激励函数运算
return outputs
- 运行函数
'''运行函数'''
def NeuralNetwork():
data_digits = spio.loadmat('data_digits.mat')
X = data_digits['X']
y = data_digits['y']
m,n = X.shape
class_y = np.zeros((m,10)) # y是0,1,2,3...9,需要映射0/1形式
for i in range(10):
class_y[:,i] = np.float32(y==i).reshape(1,-1)
xs = tf.placeholder(tf.float32, shape=[None,400]) # 像素是20x20=400，所以有400个feature
ys = tf.placeholder(tf.float32, shape=[None,10]) # 输出有10个
prediction = add_layer(xs, 400, 10, activation_function=tf.nn.softmax) # 两层神经网络，400x10
#prediction = add_layer(layer1, 25, 10, activation_function=tf.nn.softmax)
#loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
loss = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) # 定义损失函数（代价函数），
train = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss) # 使用梯度下降最小化损失
init = tf.initialize_all_variables() # 初始化所有变量
sess = tf.Session() # 创建Session
sess.run(init)
for i in range(4000): # 迭代训练4000次
sess.run(train, feed_dict={xs:X,ys:class_y}) # 训练train，填入数据
if i%50==0: # 每50次输出当前的准确度
print(compute_accuracy(xs,ys,X,class_y,sess,prediction))
- 计算准确度
'''计算预测准确度'''
def compute_accuracy(xs,ys,X,y,sess,prediction):
y_pre = sess.run(prediction,feed_dict={xs:X})
correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(y,1)) #tf.argmax 给出某个tensor对象在某一维上的其数据最大值所在的索引值,即为对应的数字，tf.equal 来检测我们的预测是否真实标签匹配
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 平均值即为准确度
result = sess.run(accuracy,feed_dict={xs:X,ys:y})
return result
- 输出每一次预测的结果准确度
- 全部代码：
https://github.com/lawlite19/MachineLearning_TensorFlow/blob/master/Mnist_02/mnist.py - 采用TensorFlow中的mnist数据集（可以取网站下载它的数据集，http://yann.lecun.com/exdb/mnist/）
- 实现代码与上面类似，它有专门的测试集
- 随机梯度下降
SGD,每次选出100个数据进行训练
for i in range(2000):
batch_xs, batch_ys = minist.train.next_batch(100)
sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
if i%50==0:
print(compute_accuracy(xs,ys,minist.test.images, minist.test.labels,sess,prediction))
- 输出每一次预测的结果准确度
- 关于卷积神经网络CNN可以查看我的博客：http://blog.csdn.net/u013082989/article/details/53673602
- 或者github：https://github.com/lawlite19/DeepLearning_Python
- 全部代码：
https://github.com/lawlite19/MachineLearning_TensorFlow/blob/master/Mnist_03_CNN/mnist_cnn.py - 采用TensorFlow中的mnist数据集（可以取网站下载它的数据集，http://yann.lecun.com/exdb/mnist/）
- 权重和偏置初始化函数
- 权重使用的
truncated_normal进行初始化,stddev标准差定义为0.1 - 偏置初始化为常量0.1
'''权重初始化函数'''
def weight_variable(shape):
inital = tf.truncated_normal(shape, stddev=0.1) # 使用truncated_normal进行初始化
return tf.Variable(inital)
'''偏置初始化函数'''
def bias_variable(shape):
inital = tf.constant(0.1,shape=shape) # 偏置定义为常量
return tf.Variable(inital)
- 卷积函数
strides[0]和strides[3]的两个1是默认值，中间两个1代表padding时在x方向运动1步，y方向运动1步padding='SAME'代表经过卷积之后的输出图像和原图像大小一样
'''卷积函数'''
def conv2d(x,W):#x是图片的所有参数，W是此卷积层的权重
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#strides[0]和strides[3]的两个1是默认值，中间两个1代表padding时在x方向运动1步，y方向运动1步
- 池化函数
ksize指定池化核函数的大小- 根据池化核函数的大小定义
strides的大小
'''池化函数'''
def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],
strides=[1,2,2,1], padding='SAME')#池化的核函数大小为2x2，因此ksize=[1,2,2,1]，步长为2，因此strides=[1,2,2,1]
- 加载
mnist数据和定义placeholder - 输入数据
x_image最后一个1代表channel的数量,若是RGB3个颜色通道就定义为3 keep_prob用于dropout防止过拟合
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # 下载数据
xs = tf.placeholder(tf.float32,[None,784]) # 输入图片的大小，28x28=784
ys = tf.placeholder(tf.float32,[None,10]) # 输出0-9共10个数字
keep_prob = tf.placeholder(tf.float32) # 用于接收dropout操作的值，dropout为了防止过拟合
x_image = tf.reshape(xs,[-1,28,28,1]) #-1代表先不考虑输入的图片例子多少这个维度，后面的1是channel的数量，因为我们输入的图片是黑白的，因此channel是1，例如如果是RGB图像，那么channel就是3
- 第一层卷积和池化
- 使用ReLu激活函数
'''第一层卷积，池化'''
W_conv1 = weight_variable([5,5,1,32]) # 卷积核定义为5x5,1是输入的通道数目，32是输出的通道数目
b_conv1 = bias_variable([32]) # 每个输出通道对应一个偏置
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) # 卷积运算，并使用ReLu激活函数激活
h_pool1 = max_pool_2x2(h_conv1) # pooling操作
- 第二层卷积和池化
'''第二层卷积，池化'''
W_conv2 = weight_variable([5,5,32,64]) # 卷积核还是5x5,32个输入通道，64个输出通道
b_conv2 = bias_variable([64]) # 与输出通道一致
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
- 全连接第一层
'''全连接层'''
h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64]) # 将最后操作的数据展开
W_fc1 = weight_variable([7*7*64,1024]) # 下面就是定义一般神经网络的操作了，继续扩大为1024
b_fc1 = bias_variable([1024]) # 对应的偏置
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1) # 运算、激活（这里不是卷积运算了，就是对应相乘）
dropout防止过拟合
'''dropout'''
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob) # dropout操作
- 最后一层全连接预测,使用梯度下降优化交叉熵损失函数
- 使用softmax分类器分类
'''最后一层全连接'''
W_fc2 = weight_variable([1024,10]) # 最后一层权重初始化
b_fc2 = bias_variable([10]) # 对应偏置
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2) # 使用softmax分类器
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1])) # 交叉熵损失函数来定义cost function
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy) # 调用梯度下降
- 定义Session，使用
SGD训练
'''下面就是tf的一般操作，定义Session，初始化所有变量，placeholder传入值训练'''
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100) # 使用SGD，每次选取100个数据训练
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) # dropout值定义为0.5
if i % 50 == 0:
print compute_accuracy(xs,ys,mnist.test.images, mnist.test.labels,keep_prob,sess,prediction) # 每50次输出一下准确度
- 计算准确度函数
- 和上面的两个计算准确度的函数一致，就是多了个dropout的参数
keep_prob
- 和上面的两个计算准确度的函数一致，就是多了个dropout的参数
'''计算准确度函数'''
def compute_accuracy(xs,ys,X,y,keep_prob,sess,prediction):
y_pre = sess.run(prediction,feed_dict={xs:X,keep_prob:1.0}) # 预测，这里的keep_prob是dropout时用的，防止过拟合
correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(y,1)) #tf.argmax 给出某个tensor对象在某一维上的其数据最大值所在的索引值,即为对应的数字，tf.equal 来检测我们的预测是否真实标签匹配
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # 平均值即为准确度
result = sess.run(accuracy,feed_dict={xs:X,ys:y,keep_prob:1.0})
return result
- 测试集上准确度
- 使用
top命令查看占用的CPU和内存，还是很消耗CPU和内存的，所以上面只输出了四次我就终止了
- 由于我在虚拟机里运行的
TensorFlow程序，分配了5G的内存，若是内存不够会报一个错误。
- 定义要保存的数据
W = tf.Variable(initial_value=[[1,2,3],[3,4,5]],
name='weights', dtype=tf.float32) # 注意需要指定name和dtype
b = tf.Variable(initial_value=[1,2,3],
name='biases', dtype=tf.float32)
init = tf.initialize_all_variables()
- 保存
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
save_path = saver.save(sess, 'my_network/save_net.ckpt') # 保存目录，注意要在当前项目下建立my_network的目录
print ('保存到 :',save_path)
- 定义数据
W = tf.Variable(np.arange(6).reshape((2,3)),
name='weights', dtype=tf.float32) # 注意与之前保存的一致
b = tf.Variable(np.arange((3)),
name='biases', dtype=tf.float32)
restore提取
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess,'my_network/save_net.ckpt')
print('weights:',sess.run(W)) # 输出一下结果
print('biases:',sess.run(b))
- 使用
MNIST数据集
'''Load MNIST data and print some information'''
data = input_data.read_data_sets("MNIST_data", one_hot = True)
print("Size of:")
print("\t training-set:\t\t{}".format(len(data.train.labels)))
print("\t test-set:\t\t\t{}".format(len(data.test.labels)))
print("\t validation-set:\t{}".format(len(data.validation.labels)))
print(data.test.labels[0:5])
data.test.cls = np.array([label.argmax() for label in data.test.labels]) # get the actual value
print(data.test.cls[0:5])- 实现函数
'''define a funciton to plot 9 images'''
def plot_images(images, cls_true, cls_pred = None):
'''
@parameter images: the images info
@parameter cls_true: the true value of image
@parameter cls_pred: the prediction value, default is None
'''
assert len(images) == len(cls_true) == 9 # only show 9 images
fig, axes = plt.subplots(nrows=3, ncols=3)
for i, ax in enumerate(axes.flat):
ax.imshow(images[i].reshape(img_shape), cmap="binary") # binary means black_white image
# show the true and pred values
if cls_pred is None:
xlabel = "True: {0}".format(cls_true[i])
else:
xlabel = "True: {0},Pred: {1}".format(cls_true[i],cls_pred[i])
ax.set_xlabel(xlabel)
ax.set_xticks([]) # remove the ticks
ax.set_yticks([])
plt.show()- 选择测试集中的9张图显示
'''show 9 images'''
images = data.test.images[0:9]
cls_true = data.test.cls[0:9]
plot_images(images, cls_true)
- 定义
placeholder
'''define the placeholder'''
X = tf.placeholder(tf.float32, [None, img_size_flat]) # None means the arbitrary number of labels, the features size is img_size_flat
y_true = tf.placeholder(tf.float32, [None, num_classes]) # output size is num_classes
y_true_cls = tf.placeholder(tf.int64, [None])- 定义
weights和biases
'''define weights and biases'''
weights = tf.Variable(tf.zeros([img_size_flat, num_classes])) # img_size_flat*num_classes
biases = tf.Variable(tf.zeros([num_classes]))- 定义模型
'''define the model'''
logits = tf.matmul(X,weights) + biases
y_pred = tf.nn.softmax(logits)
y_pred_cls = tf.argmax(y_pred, dimension=1)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true,
logits=logits)
cost = tf.reduce_mean(cross_entropy)
'''define the optimizer'''
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(cost)
- 定义求准确度
'''define the accuracy'''
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))- 定义
session
'''run the datagraph and use batch gradient descent'''
session = tf.Session()
session.run(tf.global_variables_initializer())
batch_size = 100'''define a function to run the optimizer'''
def optimize(num_iterations):
'''
@parameter num_iterations: the traning times
'''
for i in range(num_iterations):
x_batch, y_true_batch = data.train.next_batch(batch_size)
feed_dict_train = {X: x_batch,y_true: y_true_batch}
session.run(optimizer, feed_dict=feed_dict_train)
- 代码
feed_dict_test = {X: data.test.images,
y_true: data.test.labels,
y_true_cls: data.test.cls}
'''define a function to print the accuracy'''
def print_accuracy():
acc = session.run(accuracy, feed_dict=feed_dict_test)
print("Accuracy on test-set:{0:.1%}".format(acc))- 输出：
Accuracy on test-set:89.4%
- 代码
'''define a function to plot the error prediciton'''
def plot_example_errors():
correct, cls_pred = session.run([correct_prediction, y_pred_cls], feed_dict=feed_dict_test)
incorrect = (correct == False)
images = data.test.images[incorrect] # get the prediction error images
cls_pred = cls_pred[incorrect] # get prediction value
cls_true = data.test.cls[incorrect] # get true value
plot_images(images[0:9], cls_true[0:9], cls_pred[0:9])- 输出：
- 代码
'''define a fucntion to plot weights'''
def plot_weights():
w = session.run(weights)
w_min = np.min(w)
w_max = np.max(w)
fig, axes = plt.subplots(3, 4)
fig.subplots_adjust(0.3, 0.3)
for i, ax in enumerate(axes.flat):
if i<10:
image = w[:,i].reshape(img_shape)
ax.set_xlabel("Weights: {0}".format(i))
ax.imshow(image, vmin=w_min,vmax=w_max,cmap="seismic")
ax.set_xticks([])
ax.set_yticks([])
plt.show()- 输出：
- 代码：
'''define a function to printand plot the confusion matrix using scikit-learn.'''
def print_confusion_martix():
cls_true = data.test.cls # test set actual value
cls_pred = session.run(y_pred_cls, feed_dict=feed_dict_test) # test set predict value
cm = confusion_matrix(y_true=cls_true,y_pred=cls_pred) # use sklearn confusion_matrix
print(cm)
plt.imshow(cm, interpolation='nearest',cmap=plt.cm.Blues) # Plot the confusion matrix as an image.
plt.tight_layout()
plt.colorbar()
tick_marks = np.arange(num_classes)
tick_marks = np.arange(num_classes)
plt.xticks(tick_marks, range(num_classes))
plt.yticks(tick_marks, range(num_classes))
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()- 输出：
- 使用
MNIST数据集 - 加载数据，绘制9张图等函数与上面一致，
readme中不再写出
'''define cnn description'''
filter_size1 = 5 # the first conv filter size is 5x5
num_filters1 = 32 # there are 32 filters
filter_size2 = 5 # the second conv filter size
num_filters2 = 64 # there are 64 filters
fc_size = 1024 # fully-connected layer'''define a function to intialize weights'''
def initialize_weights(shape):
'''
@param shape：the shape of weights
'''
return tf.Variable(tf.truncated_normal(shape=shape, stddev=0.1))
'''define a function to intialize biases'''
def initialize_biases(length):
'''
@param length: the length of biases, which is a vector
'''
return tf.Variable(tf.constant(0.1,shape=[length]))'''define a function to do conv and pooling if used'''
def conv_layer(input,
num_input_channels,
filter_size,
num_output_filters,
use_pooling=True):
'''
@param input: the input of previous layer's output
@param num_input_channels: input channels
@param filter_size: the weights filter size
@param num_output_filters: the output number channels
@param use_pooling: if use pooling operation
'''
shape = [filter_size, filter_size, num_input_channels, num_output_filters]
weights = initialize_weights(shape=shape)
biases = initialize_biases(length=num_output_filters) # one for each filter
layer = tf.nn.conv2d(input=input, filter=weights, strides=[1,1,1,1], padding='SAME')
layer += biases
if use_pooling:
layer = tf.nn.max_pool(value=layer,
ksize=[1,2,2,1],
strides=[1,2,2,1],
padding="SAME") # the kernel function size is 2x2,so the ksize=[1,2,2,1]
layer = tf.nn.relu(layer)
return layer, weights'''define a function to flat conv layer'''
def flatten_layer(layer):
'''
@param layer: the conv layer
'''
layer_shape = layer.get_shape() # get the shape of the layer(layer_shape == [num_images, img_height, img_width, num_channels])
num_features = layer_shape[1:4].num_elements() # [1:4] means the last three demension, namely the flatten size
layer_flat = tf.reshape(layer, [-1, num_features]) # reshape to flat,-1 means don't care about the number of images
return layer_flat, num_features'''define a function to do fully-connected'''
def fc_layer(input, num_inputs, num_outputs, use_relu=True):
'''
@param input: the input
@param num_inputs: the input size
@param num_outputs: the output size
@param use_relu: if use relu activation function
'''
weights = initialize_weights(shape=[num_inputs, num_outputs])
biases = initialize_biases(num_outputs)
layer = tf.matmul(input, weights) + biases
if use_relu:
layer = tf.nn.relu(layer)
return layer- 定义
placeholder
'''define the placeholder'''
X = tf.placeholder(tf.float32, shape=[None, img_flat_size], name="X")
X_image = tf.reshape(X, shape=[-1, img_size, img_size, num_channels]) # reshape to the image shape
y_true = tf.placeholder(tf.float32, [None, num_classes], name="y_true")
y_true_cls = tf.argmax(y_true, axis=1)
keep_prob = tf.placeholder(tf.float32) # drop out placeholder
- 定义卷积、dropout、和全连接
'''define the cnn model'''
layer_conv1, weights_conv1 = conv_layer(input=X_image, num_input_channels=num_channels,
filter_size=filter_size1,
num_output_filters=num_filters1,
use_pooling=True)
print("conv1:",layer_conv1)
layer_conv2, weights_conv2 = conv_layer(input=layer_conv1, num_input_channels=num_filters1,
filter_size=filter_size2,
num_output_filters=num_filters2,
use_pooling=True)
print("conv2:",layer_conv2)
layer_flat, num_features = flatten_layer(layer_conv2) # the num_feature is 7x7x36=1764
print("flatten layer:", layer_flat)
layer_fc1 = fc_layer(layer_flat, num_features, fc_size, use_relu=True)
print("fully-connected layer1:", layer_fc1)
layer_drop_out = tf.nn.dropout(layer_fc1, keep_prob) # dropout operation
layer_fc2 = fc_layer(layer_drop_out, fc_size, num_classes,use_relu=False)
print("fully-connected layer2:", layer_fc2)
y_pred = tf.nn.softmax(layer_fc2)
y_pred_cls = tf.argmax(y_pred, axis=1)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true,
logits=layer_fc2)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(cost) # use AdamOptimizer优化
- 定义求准确度
'''define accuracy'''
correct_prediction = tf.equal(y_true_cls, y_pred_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,dtype=tf.float32))- 代码：
'''define a function to run train the model with bgd'''
total_iterations = 0 # record the total iterations
def optimize(num_iterations):
'''
@param num_iterations: the total interations of train batch_size operation
'''
global total_iterations
start_time = time.time()
for i in range(total_iterations,total_iterations + num_iterations):
x_batch, y_batch = data.train.next_batch(batch_size)
feed_dict = {X: x_batch, y_true: y_batch, keep_prob: 0.5}
session.run(optimizer, feed_dict=feed_dict)
if i % 10 == 0:
acc = session.run(accuracy, feed_dict=feed_dict)
msg = "Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}" # {:>6}means the fixed width,{1:>6.1%}means the fixed width is 6 and keep 1 decimal place
print(msg.format(i + 1, acc))
total_iterations += num_iterations
end_time = time.time()
time_dif = end_time-start_time
print("time usage:"+str(timedelta(seconds=int(round(time_dif)))))- 输出：
Optimization Iteration: 651, Training Accuracy: 99.0%
Optimization Iteration: 661, Training Accuracy: 99.0%
Optimization Iteration: 671, Training Accuracy: 99.0%
Optimization Iteration: 681, Training Accuracy: 99.0%
Optimization Iteration: 691, Training Accuracy: 99.0%
Optimization Iteration: 701, Training Accuracy: 99.0%
Optimization Iteration: 711, Training Accuracy: 99.0%
Optimization Iteration: 721, Training Accuracy: 99.0%
Optimization Iteration: 731, Training Accuracy: 99.0%
Optimization Iteration: 741, Training Accuracy: 100.0%
Optimization Iteration: 751, Training Accuracy: 99.0%
Optimization Iteration: 761, Training Accuracy: 99.0%
Optimization Iteration: 771, Training Accuracy: 97.0%
Optimization Iteration: 781, Training Accuracy: 96.0%
Optimization Iteration: 791, Training Accuracy: 98.0%
Optimization Iteration: 801, Training Accuracy: 100.0%
Optimization Iteration: 811, Training Accuracy: 100.0%
Optimization Iteration: 821, Training Accuracy: 97.0%
Optimization Iteration: 831, Training Accuracy: 98.0%
Optimization Iteration: 841, Training Accuracy: 99.0%
Optimization Iteration: 851, Training Accuracy: 99.0%
Optimization Iteration: 861, Training Accuracy: 99.0%
Optimization Iteration: 871, Training Accuracy: 96.0%
Optimization Iteration: 881, Training Accuracy: 99.0%
Optimization Iteration: 891, Training Accuracy: 99.0%
Optimization Iteration: 901, Training Accuracy: 98.0%
Optimization Iteration: 911, Training Accuracy: 99.0%
Optimization Iteration: 921, Training Accuracy: 99.0%
Optimization Iteration: 931, Training Accuracy: 99.0%
Optimization Iteration: 941, Training Accuracy: 98.0%
Optimization Iteration: 951, Training Accuracy: 100.0%
Optimization Iteration: 961, Training Accuracy: 99.0%
Optimization Iteration: 971, Training Accuracy: 98.0%
Optimization Iteration: 981, Training Accuracy: 99.0%
Optimization Iteration: 991, Training Accuracy: 100.0%
time usage:0:07:07batch_size_test = 256
def print_test_accuracy(print_error=False,print_confusion_matrix=False):
'''
@param print_error: whether plot the error images
@param print_confusion_matrix: whether plot the confusion_matrix
'''
num_test = len(data.test.images)
cls_pred = np.zeros(shape=num_test, dtype=np.int) # declare the cls_pred
i = 0
#predict the test set using batch_size
while i < num_test:
j = min(i + batch_size_test, num_test)
images = data.test.images[i:j,:]
labels = data.test.labels[i:j,:]
feed_dict = {X:images,y_true:labels,keep_prob:0.5}
cls_pred[i:j] = session.run(y_pred_cls,feed_dict=feed_dict)
i = j
cls_true = data.test.cls
correct = (cls_true == cls_pred)
correct_sum = correct.sum() # correct predictions
acc = float(correct_sum)/num_test
msg = "Accuracy on Test-Set: {0:.1%} ({1} / {2})"
print(msg.format(acc, correct_sum, num_test))
if print_error:
plot_error_pred(cls_pred,correct)
if print_confusion_matrix:
plot_confusin_martrix(cls_pred)- 代码：
'''define a function to plot conv weights'''
def plot_conv_weights(weights,input_channel=0):
'''
@param weights: the conv filter weights, for example: the weights_conv1 and weights_conv2, which are 4 dimension [filter_size, filter_size, num_input_channels, num_output_filters]
@param input_channel: the input_channels
'''
w = session.run(weights)
w_min = np.min(w)
w_max = np.max(w)
num_filters = w.shape[3] # get the number of filters
num_grids = math.ceil(math.sqrt(num_filters))
fig, axes = plt.subplots(num_grids, num_grids)
for i, ax in enumerate(axes.flat):
if i < num_filters:
img = w[:,:,input_channel,i] # the ith weight
ax.imshow(img,vmin=w_min,vmax=w_max,interpolation="nearest",cmap='seismic')
ax.set_xticks([])
ax.set_yticks([])
plt.show()- 输出：
- 第一层：
- 第二层：
- 第一层：
- 代码：
'''define a function to plot conv output layer'''
def plot_conv_layer(layer, image):
'''
@param layer: the conv layer, which is also a image after conv
@param image: the image info
'''
feed_dict = {X:[image]}
values = session.run(layer, feed_dict=feed_dict)
num_filters = values.shape[3] # get the number of filters
num_grids = math.ceil(math.sqrt(num_filters))
fig, axes = plt.subplots(num_grids,num_grids)
for i, ax in enumerate(axes.flat):
if i < num_filters:
img = values[0,:,:,i]
ax.imshow(img, interpolation="nearest",cmap="binary")
ax.set_xticks([])
ax.set_yticks([])
plt.show()- 输出：
- 第一层：
- 第二层：
- 第一层：
- 使用
MNIST数据集 - 加载数据，绘制9张图等函数与九一致，
readme中不再写出
- 定义
placeholder,与之前的一致
'''declare the placeholder'''
X = tf.placeholder(tf.float32, [None, img_flat_size], name="X")
X_img = tf.reshape(X, shape=[-1,img_size,img_size, num_channels])
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name="y_true")
y_true_cls = tf.argmax(y_true,1)- 使用
prettytensor实现CNN模型
'''define the cnn model with prettytensor'''
x_pretty = pt.wrap(X_img)
with pt.defaults_scope(): # or pt.defaults_scope(activation_fn=tf.nn.relu) if just use one activation function
y_pred, loss = x_pretty.\
conv2d(kernel=5, depth=16, activation_fn=tf.nn.relu, name="conv_layer1").\
max_pool(kernel=2, stride=2).\
conv2d(kernel=5, depth=36, activation_fn=tf.nn.relu, name="conv_layer2").\
max_pool(kernel=2, stride=2).\
flatten().\
fully_connected(size=128, activation_fn=tf.nn.relu, name="fc_layer1").\
softmax_classifier(num_classes=num_classes, labels=y_true)- 获取卷积核的权重(后续可视化)
'''define a function to get weights'''
def get_weights_variable(layer_name):
with tf.variable_scope(layer_name, reuse=True):
variable = tf.get_variable("weights")
return variable
conv1_weights = get_weights_variable("conv_layer1")
conv2_weights = get_weights_variable("conv_layer2")- 定义
optimizer训练，和之前的一样了
'''define optimizer to train'''
optimizer = tf.train.AdamOptimizer().minimize(loss)
y_pred_cls = tf.argmax(y_pred,1)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
session = tf.Session()
session.run(tf.global_variables_initializer())