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SET(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -O2")
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2")

cmake_minimum_required( VERSION 3.8 )
project(hello)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_FLAGS  "-g")
set(CMAKE_CXX_FLAGS  "-Wall")

add_executable (go src/go.cpp)
add_executable(main src/main.cpp)

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tf.Variable 和 tf.get_variable 区别

使用tf.Variable时，如果检测到命名冲突，系统会自己处理。使用tf.get_variable()时，系统不会处理冲突，而会报错
所以还是用tf.get_variable()好

name_scope 和 variable_scope

get_variable只看variable_scope, variable都看

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In [47]: with tf.name_scope('nsc1'):
    ...:     v1 = tf.Variable([1], name="v1")
    ...:     with tf.variable_scope("vsc1"):
    ...:         v2 = tf.Variable([1], name="v2")
    ...:         v3 = tf.get_variable(name="v3", shape=[])
    ...:

In [48]: print v1.name
nsc1/v1:0

In [49]: print v2.name
nsc1/vsc1/v2:0

In [50]: print v3.name
vsc1/v3:0

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数字理解

输出结果的shape计算：
‘SAME’ 类型的padding,其输出的height和width计算如下：
out_height = ceil(float(in_height) / float(strides[1])) ceil：向上取整
out_width = ceil(float(in_width) / float(strides[2]))

‘VALID’类型的padding, 其输出的height和width计算如下：
out_height = ceil(float(in_height – filter_height + 1) / float(strides[1]))
out_width = ceil(float(in_width – filter_width + 1) / float(strides[2]))

图片理解

具体计算理解

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x =
0 1 2 3
1 2 3 4
2 3 4 5
3 4 5 6

w = 
0 1 2
1 2 3
2 3 4
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def testConv2DSameEven(self):
   n, n2 = 4, 2

   # Input image.
   x = create_test_input(1, n, n, 1)

   # Convolution kernel.
   w = create_test_input(1, 3, 3, 1)
   w = array_ops.reshape(w, [3, 3, 1, 1])

   variable_scope.get_variable('Conv/weights', initializer=w)
   variable_scope.get_variable('Conv/biases', initializer=array_ops.zeros([1]))
   variable_scope.get_variable_scope().reuse_variables()

   y1 = layers.conv2d(x, 1, [3, 3], stride=1, scope='Conv')
   y1_expected = math_ops.to_float([[14, 28, 43, 26], [28, 48, 66, 37],
                                    [43, 66, 84, 46], [26, 37, 46, 22]])
   y1_expected = array_ops.reshape(y1_expected, [1, n, n, 1])

   y2 = resnet_utils.subsample(y1, 2)
   y2_expected = math_ops.to_float([[14, 43], [43, 84]])
   y2_expected = array_ops.reshape(y2_expected, [1, n2, n2, 1])

   y3 = resnet_utils.conv2d_same(x, 1, 3, stride=2, scope='Conv')
   y3_expected = y2_expected

   y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
   y4_expected = math_ops.to_float([[48, 37], [37, 22]])
   y4_expected = array_ops.reshape(y4_expected, [1, n2, n2, 1])

   with self.test_session() as sess:
     sess.run(variables.global_variables_initializer())
     self.assertAllClose(y1.eval(), y1_expected.eval())
     self.assertAllClose(y2.eval(), y2_expected.eval())
     self.assertAllClose(y3.eval(), y3_expected.eval())
     self.assertAllClose(y4.eval(), y4_expected.eval())

总结

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1. net = conv2d_same(inputs, num_outputs, 3, stride=stride)
2. net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')
   net = subsample(net, factor=stride)
3. net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')
1和2是等价的，当inputs的高或宽是偶数时3与1,2不一样。

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subsample

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def subsample(inputs, factor, scope=None):
  """Subsamples the input along the spatial dimensions.
  Args:
    inputs: A `Tensor` of size [batch, height_in, width_in, channels].
    factor: The subsampling factor.
    scope: Optional variable_scope.
  Returns:
    output: A `Tensor` of size [batch, height_out, width_out, channels] with the
      input, either intact (if factor == 1) or subsampled (if factor > 1).
  """
  if factor == 1:
    return inputs
  else:
    return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)

bottleneck

开始没理解resnet_utils.subsample和slim.conv2d的区别，后来发现其实
问题在于depth, 如何depth已经搞定了，那么就直接subsample就好了，如果
sample没搞定就得conv. 从这里可以看出一个问题desample的效果会好于conv2d.

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def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None):
 with tf.variable_scope(scope, 'bottleneck_v2', [inputs]) as sc:
    depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    preact = slim.batch_norm(inputs, activation_fn=tf.nn.relu, scope='preact')
    if depth == depth_in:
      shortcut = resnet_utils.subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(preact, depth, [1, 1], stride=stride,
                             normalizer_fn=None, activation_fn=None,
                             scope='shortcut')

    residual = slim.conv2d(preact, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = resnet_utils.conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           normalizer_fn=None, activation_fn=None,
                           scope='conv3')

    output = shortcut + residual

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.name,
                                            output)

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config

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tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
sess = tf.Session(config=tfconfig)

声明placeholder

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self._image = tf.placeholder(tf.float32, shape=[1, None, None, 3])

数字转换

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tf.to_int32()
tf.ceil()
np.float32()

数组操作

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tf.transpose()
tf.reshape()

py_func

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py_func(
    func,
    inp,
    Tout,
    stateful=True,
    name=None
)

时间

loss 常用函数

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cross_entropy = -tf.reduce_sum(true_y*tf.log(out_y))
correct_prediction = tf.equal(tf.argmax(out_y,1), tf.argmax(true_y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

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conda env export > environment.yml
conda env create -f environment.yml

conda list --explicit > spec-file.txt
conda create --name myenv --file spec-file.txt
conda install --name myenv --file spec-file.txt

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sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates
sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D
echo "deb https://apt.dockerproject.org/repo ubuntu-xenial main" | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt-get install linux-image-extra-$(uname -r) linux-image-extra-virtual
sudo apt-get install -y docker-engine
sudo service docker start
sudo docker run hello-world
sudo usermod -aG docker $(whoami)