TensorFlow-相关 API（学习笔记 ）

1.tf.nn.conv2d

conv2d(
    input,
    filter,
    strides,
    padding,
    use_cudnn_on_gpu=True,
    data_format='NHWC',
    name=None
)
参数名必选类型说明
input 是 tensor 是一个 4 维的 tensor，即 [ batch, in_height, in_width, in_channels ]（若 input 是图像，[ 训练时一个 batch 的图片数量, 图片高度, 图片宽度, 图像通道数 ]）
filter 是 tensor 是一个 4 维的 tensor，即 [ filter_height, filter_width, in_channels, out_channels ]（若 input 是图像，[ 卷积核的高度，卷积核的宽度，图像通道数，卷积核个数 ]）,filter 的 in_channels 必须和 input 的 in_channels 相等
strides 是 列表 长度为 4 的 list，卷积时候在 input 上每一维的步长，一般 strides[0] = strides[3] = 1
padding 是 string 只能为 " VALID "，" SAME " 中之一，这个值决定了不同的卷积方式。VALID 丢弃方式；SAME：补全方式
use_cudnn_on_gpu 否 bool 是否使用 cudnn 加速，默认为 true
data_format 否 string 只能是 " NHWC ", " NCHW "，默认 " NHWC "
name 否 string 运算名称

 

创建conv2d.py

import tensorflow as tf

a = tf.constant([1,1,1,0,0,0,1,1,1,0,0,0,1,1,1,0,0,1,1,0,0,1,1,0,0],dtype=tf.float32,shape=[1,5,5,1])
b = tf.constant([1,0,1,0,1,0,1,0,1],dtype=tf.float32,shape=[3,3,1,1])
c = tf.nn.conv2d(a,b,strides=[1, 2, 2, 1],padding='VALID')
d = tf.nn.conv2d(a,b,strides=[1, 2, 2, 1],padding='SAME')
with tf.Session() as sess:
    print ("c shape:")
    print (c.shape)
    print ("c value:")
    print (sess.run(c))
    print ("d shape:")
    print (d.shape)
    print ("d value:")
    print (sess.run(d))

 执行结果：

c shape:
(1, 2, 2, 1)
c value:
[[[[ 4.]
   [ 4.]]

  [[ 2.]
   [ 4.]]]]
d shape:
(1, 3, 3, 1)
d value:
[[[[ 2.]
   [ 3.]
   [ 1.]]

  [[ 1.]
   [ 4.]
   [ 3.]]

  [[ 0.]
   [ 2.]
   [ 1.]]]]

 2.tf.nn.relu

relu(
    features,
    name=None
)
参数名必选类型说明
features 是 tensor 是以下类型float32, float64, int32, int64, uint8, int16, int8, uint16, half
name 否 string 运算名称

 

创建源文件 relu.py

import tensorflow as tf

a = tf.constant([1,-2,0,4,-5,6])
b = tf.nn.relu(a)
with tf.Session() as sess:
    print (sess.run(b))

  执行结果：

[1 0 0 4 0 6]

 3.tf.nn.max_pool

max_pool(
    value,
    ksize,
    strides,
    padding,
    data_format='NHWC',
    name=None
)
参数名必选类型说明
value 是 tensor 4 维的张量，即 [ batch, height, width, channels ]，数据类型为 tf.float32
ksize 是 列表 池化窗口的大小，长度为 4 的 list，一般是 [1, height, width, 1]，因为不在 batch 和 channels 上做池化，所以第一个和最后一个维度为 1
strides 是 列表 池化窗口在每一个维度上的步长，一般 strides[0] = strides[3] = 1
padding 是 string 只能为 " VALID "，" SAME " 中之一，这个值决定了不同的池化方式。VALID 丢弃方式；SAME：补全方式
data_format 否 string 只能是 " NHWC ", " NCHW "，默认" NHWC "
name 否 string 运算名称

 

 创建源文件 max_pool.py

import tensorflow as tf

a = tf.constant([1,3,2,1,2,9,1,1,1,3,2,3,5,6,1,2],dtype=tf.float32,shape=[1,4,4,1])
b = tf.nn.max_pool(a,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='VALID')
c = tf.nn.max_pool(a,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME')
with tf.Session() as sess:
    print ("b shape:")
    print (b.shape)
    print ("b value:")
    print (sess.run(b))
    print ("c shape:")
    print (c.shape)
    print ("c value:")
    print (sess.run(c))

执行结果：

b shape:
(1, 2, 2, 1)
b value:
[[[[ 9.]
   [ 2.]]

  [[ 6.]
   [ 3.]]]]
c shape:
(1, 2, 2, 1)
c value:
[[[[ 9.]
   [ 2.]]

  [[ 6.]
   [ 3.]]]]

 4.tf.nn.dropout

dropout(
    x,
    keep_prob,
    noise_shape=None,
    seed=None,
    name=None
) 
参数名必选类型说明
x 是 tensor 输出元素是 x 中的元素以 keep_prob 概率除以 keep_prob，否则为 0
keep_prob 是 scalar Tensor dropout 的概率，一般是占位符
noise_shape 否 tensor 默认情况下，每个元素是否 dropout 是相互独立。如果指定 noise_shape，若 noise_shape[i] == shape(x)[i]，该维度的元素是否 dropout 是相互独立，若 noise_shape[i] != shape(x)[i] 该维度元素是否 dropout 不相互独立，要么一起 dropout 要么一起保留
seed 否 数值 如果指定该值，每次 dropout 结果相同
name 否 string 运算名称

 

 创建源文件 dropout.py

import tensorflow as tf

a = tf.constant([1,2,3,4,5,6],shape=[2,3],dtype=tf.float32)
b = tf.placeholder(tf.float32)
c = tf.nn.dropout(a,b,[2,1],1)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print (sess.run(c,feed_dict={b:0.75}))

 

  执行结果：

 

[[ 0.          0.          0.        ]
 [ 5.33333349  6.66666651  8.        ]]

5. tf.nn.sigmoid_cross_entropy_with_logits

sigmoid_cross_entropy_with_logits(
    _sentinel=None,
    labels=None,
    logits=None,
    name=None
)

 

参数名必选类型说明
_sentinel 否 None 没有使用的参数
labels 否 Tensor type, shape 与 logits相同
logits 否 Tensor type 是 float32 或者 float64
name 否 string 运算名称

  创建源文件 sigmoid_cross_entropy_with_logits.py

 

import tensorflow as tf
x = tf.constant([1,2,3,4,5,6,7],dtype=tf.float64)
y = tf.constant([1,1,1,0,0,1,0],dtype=tf.float64)
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels = y,logits = x)
with tf.Session() as sess:
    print (sess.run(loss))

 

  执行结果：

 

[  3.13261688e-01   1.26928011e-01   4.85873516e-02   4.01814993e+00
   5.00671535e+00   2.47568514e-03   7.00091147e+00]

 6.tf.truncated_normal

truncated_normal(
    shape,
    mean=0.0,
    stddev=1.0,
    dtype=tf.float32,
    seed=None,
    name=None
)

 

参数名必选类型说明
shape 是 1 维整形张量或 array 输出张量的维度
mean 否 0 维张量或数值 均值
stddev 否 0 维张量或数值 标准差
dtype 否 dtype 输出类型
seed 否 数值 随机种子，若 seed 赋值，每次产生相同随机数
name 否 string 运算名称

  创建源文件 truncated_normal.py

 

import tensorflow as tf
initial = tf.truncated_normal(shape=[3,3], mean=0, stddev=1)
print(tf.Session().run(initial))

 

  执行结果：

 

[[ 0.18815269 -0.4689253   0.63908994]
 [ 0.01734953 -0.46975166 -0.25023392]
 [ 1.12803638 -1.84143591  0.15422213]]

7.tf.constant

constant(
    value,
    dtype=None,
    shape=None,
    name='Const',
    verify_shape=False
)

 

参数名必选类型说明
value 是 常量数值或者 list 输出张量的值
dtype 否 dtype 输出张量元素类型
shape 否 1 维整形张量或 array 输出张量的维度
name 否 string 张量名称
verify_shape 否 Boolean 检测 shape 是否和 value 的 shape 一致，若为 Fasle，不一致时，会用最后一个元素将 shape 补全

  创建源文件 constant.py

 

#!/usr/bin/python

import tensorflow as tf
import numpy as np
a = tf.constant([1,2,3,4,5,6],shape=[2,3])
b = tf.constant(-1,shape=[3,2])
c = tf.matmul(a,b)

e = tf.constant(np.arange(1,13,dtype=np.int32),shape=[2,2,3])
f = tf.constant(np.arange(13,25,dtype=np.int32),shape=[2,3,2])
g = tf.matmul(e,f)
with tf.Session() as sess:
    print (sess.run(a))
    print ("##################################")
    print (sess.run(b))
    print ("##################################")
    print (sess.run(c))
    print ("##################################")
    print (sess.run(e))
    print ("##################################")
    print (sess.run(f))
    print ("##################################")
    print (sess.run(g))

 

  执行结果：

 

[[1 2 3]
 [4 5 6]]
##################################
[[-1 -1]
 [-1 -1]
 [-1 -1]]
##################################
[[ -6  -6]
 [-15 -15]]
##################################
[[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]
##################################
[[[13 14]
  [15 16]
  [17 18]]

 [[19 20]
  [21 22]
  [23 24]]]
##################################
[[[ 94 100]
  [229 244]]

 [[508 532]
  [697 730]]]

 8.tf.placeholder

placeholder(
    dtype,
    shape=None,
    name=None
)

 

参数名必选类型说明
dtype 是 dtype 占位符数据类型
shape 否 1 维整形张量或 array 占位符维度
name 否 string 占位符名称

  创建源文件 placeholder.py

 

#!/usr/bin/python

import tensorflow as tf
import numpy as np

x = tf.placeholder(tf.float32,[None,3])
y = tf.matmul(x,x)
with tf.Session() as sess:
    rand_array = np.random.rand(3,3)
    print(sess.run(y,feed_dict={x:rand_array}))

 

  执行结果：

[[ 0.64431196  0.68349576  0.57412398]
 [ 0.84553117  1.64796805  0.7788316 ]
 [ 0.84342241  0.8947317   0.8024016 ]]

 9.tf.nn.bias_add 将偏差项 bias 加到 value 上面，可以看做是 tf.add 的一个特例，其中 bias 必须是一维的，并且维度和 value 的最后一维相同，数据类型必须和 value 相同。

bias_add(
    value,
    bias,
    data_format=None,
    name=None
)

 

参数名必选类型说明
value 是 张量 数据类型为 float, double, int64, int32, uint8, int16, int8, complex64, or complex128
bias 是 1 维张量 维度必须和 value 最后一维维度相等
data_format 否 string 数据格式，支持 ' NHWC ' 和 ' NCHW '
name 否 string 运算名称

  创建源文件 bias_add.py

 

#!/usr/bin/python

import tensorflow as tf
import numpy as np

a = tf.constant([[1.0, 2.0],[1.0, 2.0],[1.0, 2.0]])
b = tf.constant([2.0,1.0])
c = tf.constant([1.0])
sess = tf.Session()
print (sess.run(tf.nn.bias_add(a, b)))
#print (sess.run(tf.nn.bias_add(a,c))) error
print ("##################################")
print (sess.run(tf.add(a, b)))
print ("##################################")
print (sess.run(tf.add(a, c)))

 

  执行结果：

 

[[ 3.  3.]
 [ 3.  3.]
 [ 3.  3.]]
##################################
[[ 3.  3.]
 [ 3.  3.]
 [ 3.  3.]]
##################################
[[ 2.  3.]
 [ 2.  3.]
 [ 2.  3.]]

 10.tf.reduce_mean 

reduce_mean(
    input_tensor,
    axis=None,
    keep_dims=False,
    name=None,
    reduction_indices=None
)

 

参数名必选类型说明
input_tensor 是 张量 输入待求平均值的张量
axis 否 None、0、1 None：全局求平均值；0：求每一列平均值；1：求每一行平均值
keep_dims 否 Boolean 保留原来的维度(例如不会从二维矩阵降为一维向量)
name 否 string 运算名称
reduction_indices 否 None 和 axis 等价，被弃用

  创建源文件 reduce_mean.py

 

#!/usr/bin/python

import tensorflow as tf
import numpy as np

initial = [[1.,1.],[2.,2.]]
x = tf.Variable(initial,dtype=tf.float32)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print(sess.run(tf.reduce_mean(x)))
    print(sess.run(tf.reduce_mean(x,0))) #Column
    print(sess.run(tf.reduce_mean(x,1))) #row

 

  执行结果：

 

1.5
[ 1.5  1.5]
[ 1.  2.]

 11.tf.squared_difference 计算张量 x、y 对应元素差平方

squared_difference(
    x,
    y,
    name=None
)

 

参数名必选类型说明
x 是 张量 是 half, float32, float64, int32, int64, complex64, complex128 其中一种类型
y 是 张量 是 half, float32, float64, int32, int64, complex64, complex128 其中一种类型
name 否 string 运算名称

  创建源文件 squared_difference.py

 

#!/usr/bin/python

import tensorflow as tf
import numpy as np

initial_x = [[1.,1.],[2.,2.]]
x = tf.Variable(initial_x,dtype=tf.float32)
initial_y = [[3.,3.],[4.,4.]]
y = tf.Variable(initial_y,dtype=tf.float32)
diff = tf.squared_difference(x,y)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print(sess.run(diff))

 

  执行结果：

[[ 4.  4.]
 [ 4.  4.]]

 12.tf.square 计算张量对应元素平方

square(
    x,
    name=None
)

 

参数名必选类型说明
x 是 张量 是 half, float32, float64, int32, int64, complex64, complex128 其中一种类型
name 否 string 运算名称

  创建源文件 square.py

 

#!/usr/bin/python
import tensorflow as tf
import numpy as np

initial_x = [[1.,1.],[2.,2.]]
x = tf.Variable(initial_x,dtype=tf.float32)
x2 = tf.square(x)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print(sess.run(x2))

 

  执行结果：

[[ 1.  1.]
 [ 4.  4.]]

 13.tf.Variable 维护图在执行过程中的状态信息，例如神经网络权重值的变化。

 

__init__(
    initial_value=None,
    trainable=True,
    collections=None,
    validate_shape=True,
    caching_device=None,
    name=None,
    variable_def=None,
    dtype=None,
    expected_shape=None,
    import_scope=None
)

 

参数名类型说明
initial_value 张量 Variable 类的初始值，这个变量必须指定 shape 信息，否则后面 validate_shape 需设为 False
trainable Boolean 是否把变量添加到 collection GraphKeys.TRAINABLE_VARIABLES 中（collection 是一种全局存储，不受变量名生存空间影响，一处保存，到处可取）
collections Graph collections 全局存储，默认是 GraphKeys.GLOBAL_VARIABLES
validate_shape Boolean 是否允许被未知维度的 initial_value 初始化
caching_device string 指明哪个 device 用来缓存变量
name string 变量名
dtype dtype 如果被设置，初始化的值就会按照这个类型初始化
expected_shape TensorShape 要是设置了，那么初始的值会是这种维度

  创建源文件 Variable.py

 

#!/usr/bin/python

import tensorflow as tf
initial = tf.truncated_normal(shape=[10,10],mean=0,stddev=1)
W=tf.Variable(initial)
list = [[1.,1.],[2.,2.]]
X = tf.Variable(list,dtype=tf.float32)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    print ("##################(1)################")
    print (sess.run(W))
    print ("##################(2)################")
    print (sess.run(W[:2,:2]))
    op = W[:2,:2].assign(22.*tf.ones((2,2)))
    print ("###################(3)###############")
    print (sess.run(op))
    print ("###################(4)###############")
    print (W.eval(sess)) #computes and returns the value of this variable
    print ("####################(5)##############")
    print (W.eval())  #Usage with the default session
    print ("#####################(6)#############")
    print (W.dtype)
    print (sess.run(W.initial_value))
    print (sess.run(W.op))
    print (W.shape)
    print ("###################(7)###############")
    print (sess.run(X))

 

  执行结果：

##################(1)################
[[-0.14252207  0.43376675  0.75065768  0.89276749  1.16391671  0.39532429
  -0.56278807 -0.49753642  0.23130737 -0.51338279]
 [-0.43028545 -1.24873769 -0.73239309  0.434468   -0.97399759  0.13766721
  -0.6361087  -0.82712436  1.71831048 -0.44968474]
 [-0.96064204 -0.83682173  0.26545268  0.22578485  0.65014762 -0.30830157
  -1.57317054 -0.35661098  1.40849245 -0.37030414]
 [-0.37272176  0.73461288  0.39292559 -1.40008056 -0.37535539  0.24140523
   1.6811192  -0.48886588  1.15467834  0.61565816]
 [-0.39579329 -0.23154807 -1.01895738 -0.95105737  1.24795806 -0.03846256
  -1.71738017 -0.80132687  0.53553152 -0.06413679]
 [-0.97320521 -0.24279226  1.36213648  1.56002438 -1.11646473 -0.35991025
   0.91412318  0.97508883 -1.16207206 -0.68734062]
 [ 0.49044254 -1.87386227 -0.70803815 -0.6591838   0.08034691 -1.24559033
  -0.29389012 -0.2189652  -1.08279467 -0.0175346 ]
 [-0.5608176   1.08259249  1.66278481 -0.33977437  0.42875817  0.55927169
   0.76387608  0.37792665  0.85006535  1.05124724]
 [ 1.75331545 -0.6333124  -0.10046791 -0.1780251  -1.31002116  1.90098214
   0.84569824 -1.42502522 -0.67300171  0.68910873]
 [-1.7385     -0.9806214  -0.32636395 -0.50020444 -0.53104508 -0.33903483
  -0.35751811 -0.03737256 -1.26822579 -1.38264406]]
##################(2)################
[[-0.14252207  0.43376675]
 [-0.43028545 -1.24873769]]
###################(3)###############
[[  2.20000000e+01   2.20000000e+01   7.50657678e-01   8.92767489e-01
    1.16391671e+00   3.95324290e-01  -5.62788069e-01  -4.97536421e-01
    2.31307372e-01  -5.13382792e-01]
 [  2.20000000e+01   2.20000000e+01  -7.32393086e-01   4.34468001e-01
   -9.73997593e-01   1.37667209e-01  -6.36108696e-01  -8.27124357e-01
    1.71831048e+00  -4.49684739e-01]
 [ -9.60642040e-01  -8.36821735e-01   2.65452683e-01   2.25784853e-01
    6.50147617e-01  -3.08301568e-01  -1.57317054e+00  -3.56610984e-01
    1.40849245e+00  -3.70304137e-01]
 [ -3.72721761e-01   7.34612882e-01   3.92925590e-01  -1.40008056e+00
   -3.75355393e-01   2.41405234e-01   1.68111920e+00  -4.88865882e-01
    1.15467834e+00   6.15658164e-01]
 [ -3.95793289e-01  -2.31548071e-01  -1.01895738e+00  -9.51057374e-01
    1.24795806e+00  -3.84625569e-02  -1.71738017e+00  -8.01326871e-01
    5.35531521e-01  -6.41367882e-02]
 [ -9.73205209e-01  -2.42792264e-01   1.36213648e+00   1.56002438e+00
   -1.11646473e+00  -3.59910250e-01   9.14123178e-01   9.75088835e-01
   -1.16207206e+00  -6.87340617e-01]
 [  4.90442544e-01  -1.87386227e+00  -7.08038151e-01  -6.59183800e-01
    8.03469121e-02  -1.24559033e+00  -2.93890119e-01  -2.18965203e-01
   -1.08279467e+00  -1.75346043e-02]
 [ -5.60817599e-01   1.08259249e+00   1.66278481e+00  -3.39774370e-01
    4.28758174e-01   5.59271693e-01   7.63876081e-01   3.77926648e-01
    8.50065351e-01   1.05124724e+00]
 [  1.75331545e+00  -6.33312404e-01  -1.00467913e-01  -1.78025097e-01
   -1.31002116e+00   1.90098214e+00   8.45698237e-01  -1.42502522e+00
   -6.73001707e-01   6.89108729e-01]
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   -5.31045079e-01  -3.39034826e-01  -3.57518107e-01  -3.73725556e-02
   -1.26822579e+00  -1.38264406e+00]]
###################(4)###############
[[  2.20000000e+01   2.20000000e+01   7.50657678e-01   8.92767489e-01
    1.16391671e+00   3.95324290e-01  -5.62788069e-01  -4.97536421e-01
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    8.50065351e-01   1.05124724e+00]
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   -1.26822579e+00  -1.38264406e+00]]
####################(5)##############
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    8.50065351e-01   1.05124724e+00]
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   -1.26822579e+00  -1.38264406e+00]]
#####################(6)#############
<dtype: 'float32_ref'>
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None
(10, 10)
###################(7)###############
[[ 1.  1.]
 [ 2.  2.]]

 

posted @ 2018-01-05 00:14  舆  阅读(869)  评论(0)  编辑  收藏  举报