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Contest100000572 – 《算法笔记》2.8小节——CC++快速入门->结构体(struct)的使用

*********************************************************/ Main.c: In function ‘main’: Main.c:8:12: warning...: missing braces around initializer [-Wmissing-braces] }leader[3]={"Li",0,"Zhang",0,"Fun",0};...^ Main.c:8:12: note: (near initialization for ‘leader’) Main.c:13:3: warning: ‘gets’ is deprecated [-...: ignoring return value of:警告：忽略了函数的返回值，可能是函数用错或者没有考虑到返回值异常的情况 : In function ‘main’:Main.c:8:12: warning...: missing braces around initializer [-Wmissing-braces] }leader[3]={"Li",0,"Zhang",0,"Fun",0};

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Effective Modern C++翻译(3)-条款2：明白auto类型推导

std::initializer_list模板的类型，而模板类型推导面对大括号的初始化式(braced initializer)时，代码将不会通过（这是由于完美转发perfect forwarding的结果...in mind if you embrace the philosophy of uniform initialization of enclosing initializing values in braces...int>类型陷阱的主要原因是一些程序员只有当必要的时候，才使用大括号的初始化式进行初始化）（This pitfall is one of the reasons some developers put braces...around their initializers only when they have to....is inside braces），只有这种情况下，auto下被推导为std::initializer_list,而模板会失败。

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Java开发手册暨Code Style XML

--IDEA使用了对应的配置：Java:SPACE_WITHIN_ARRAY_INITIALIZER_BRACES，但感觉不太好，IDEA默认不插入，Eclipse也使用不插入--> INITIALIZER_BRACES--> braces_in_array_initializer...--Idea -> Wrapping And Braces -> try statement -> catch.......--org.eclipse.jdt.core.formatter.blank_lines_between_type_declarations=Java:BLANK_LINES_AROUND_CLASS-...--org.eclipse.jdt.core.formatter.blank_lines_before_method=Java:BLANK_LINES_AROUND_METHOD--> <

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斯坦福tensorflow教程-实例代码简单代码关于占位符 placeholder与feed_dictvariable 变量

Modified time: 2018-05-13 11:41:55 import os # 在tensorflow的log日志等级如下： # - 0：显示所有日志（默认等级） # - 1：显示info、warning...和error日志 # - 2：显示warning和error信息 # - 3：显示error日志信息 # 保持默认日志等级时候，tensorflow执行会出现类似以下警告： os.environ['TF_CPP_MIN_LOG_LEVEL...import numpy as np import tensorflow as tf # Example 1: creating variables s = tf.Variable(2, name='scalar...big_matrix') V = tf.Variable(tf.truncated_normal([784, 10]), name='normal_matrix') s = tf.get_variable('scalar...# >> 100 # create a variable whose original value is 2 a = tf.get_variable('scalar

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Kotlin 编码规约

layout Generally, the contents of a class is sorted in the following order: Property declarations and initializer...Horizontal whitespace Put spaces around binary operators (a + b)....Exception: don't put spaces around the "range to" operator (0..i)...., if the initializer is long, add a line break after the equals signand indent the initializer by four...Lambda formatting In lambda expressions, spaces should be used around the curly braces, as well as around

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Angular学习（03）--lint检查规范和WebStorm小技巧

对于空格，我没有改掉默认格式化时空格风格，只是增加了几种场景也需要自动进行空格处理，分别是： Within -> ES6 import/export braces 导入语句 {} 距离内容之间增加一个空格...Within -> Object literal braces 勾选 Within -> Object literal type braces 勾选这两个是对象内部的空格处理，默认也是没有的，如： ?...'if()' statement -> Force braces 设置为 always 这个是设置，即使 if 代码块内只有简单的一行代码，也要自动为其加上大括号处理，默认是不做处理。 ?...Array initializer 设置为 Chop down if long Array initializer -> Align when multiline 勾选 Array initializer...-> New line after '[' 勾选 Array initializer -> Place ']' on new line 勾选这个是用来设置数组的处理，以上配置的意思是，当数组过长时，

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格式设置规则

dotnet_diagnostic.IDE0055.severity = 严重性值必须是 warning 或 error 才能在生成时强制执行。...csharp_indent_block_contents = false static void Hello() { Console.WriteLine("Hello"); } csharp_indent_braces...Property 值选项名称 csharp_indent_braces 适用的语言 C# 选项值 true - false - 代码示例： // csharp_indent_braces = true...static void Hello() { Console.WriteLine("Hello"); } // csharp_indent_braces = false static...代码示例： // csharp_space_around_binary_operators = before_and_after return x * (x - y); // csharp_space_around_binary_operators

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代码样式规则选项

silent dotnet_style_readonly_field = true:suggestion # Expression-level preferences dotnet_style_object_initializer...= true:suggestion dotnet_style_collection_initializer = true:suggestion dotnet_style_explicit_tuple_names...sealed,override,readonly,unsafe,volatile,async:suggestion # Expression-level preferences csharp_prefer_braces...csharp_space_before_colon_in_inheritance_clause = true csharp_space_after_colon_in_inheritance_clause = true csharp_space_around_binary_operators

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Tensorflow | 函数名字的更新

tf.GLOBAL_VARIABLES tf.all_variables ——> tf.global_variables tf.initialize_all_variables ——> tf.global_variables_initializer...tf.initialize_local_variables ——> tf.local_variables_initializer tf.initialize_variables ——> tf.variables_initializer...——>tf.summary.scalar tf.histogram_summary ——> tf.summary.histogram tf.image_summary ——>tf.summary.image...tf.merge_all_summaries ——> tf.summary.merge_all tf.merge_summary ——> tf.summary.merge tf.scalar_summary...——> tf.summary.scalar tf.train.SummaryWriter ——> tf.summary.FileWriter 数值计算函数的更新 tf.sub ——> tf.subtract

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log4j conversion pattern各格式含义

WARNING Generating the caller class information is slow....The date conversion specifier may be followed by a date format specifier enclosed between braces....WARNING Generating caller location information is extremely slow and should be avoided unless execution...WARNING Generating caller location information is extremely slow and should be avoided unless execution...The X conversion character must be followed by the key for the map placed between braces, as in %X{clientNumber

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tf.contrib.summary

("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate a record #...("loss", my_loss) # In this case every call to tf.contrib.summary.scalar will generate an op, # note...with tf.Session(...) as sess: tf.global_variables_initializer().run() tf.contrib.summary.initialize...(...): Writes a scalar summary if possible. should_record_summaries(...): Returns boolean Tensor which...is true if summaries should be recorded. summary_writer_initializer_op(...): Graph-mode only.

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深度学习（2）——tensorflow可视化TensorFlow变量作用域TensorFlow可视化示例

log_device_placement=True, allow_soft_placement=True)) as sess: # # 初始化 # tf.global_variables_initializer...().run() # # 执行结果 # print(sess.run(r)) 2变量作用域下定义多个变量 # 方式二 def my_func(x): # initializer：...=tf.random_normal_initializer())[0] b = tf.get_variable(name='b', shape=[1], initializer=tf.random_normal_initializer...1 生成时间文件 TensorBoard通过读取TensorFlow的事件文件来运行，TensorFlow的事件文件包括了在TensorFlow运行中涉及到的主要数据，比如：scalar、image...() tf.summary.scalar("sum",sum) tf.summary.scalar("i",i) with tf.Session(config=tf.ConfigProto(log_device_placement

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Tensorflow|通过Variable及assign()感悟一点设计之道

01 Variable a = tf.Variable(2, name="scalar") # create variable a with scalar value b = tf.Variable(...最简单直接的初始化所有变量的方法： init = tf.global_variables_initializer() with tf.Session() as sess: sess.run...(init) 初始化指定变量： #初始化变量a和b init_ab = tf.variables_initializer([a, b], name="init_ab") with tf.Session...object W = tf.Variable(tf.truncated_normal([700, 10])) with tf.Session() as sess: sess.run(W.initializer...事实上， initializer op 是一个特殊的assign op.

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04.卷积神经网络 W1.卷积神经网络（作业：手动TensorFlow 实现卷积神经网络）

of shape (m, n_H, n_W, n_C) representing a batch of m images pad -- integer, amount of padding around...Cast b to a float() so that Z results in a scalar value....Arguments: n_H0 -- scalar, height of an input image n_W0 -- scalar, width of an input image...n_C0 -- scalar, number of channels of the input n_y -- scalar, number of classes Returns...dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer(seed=0)) ### END CODE HERE ###

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Convolutional Neural Networks: Application

Arguments: n_H0 -- scalar, height of an input image n_W0 -- scalar, width of an input image...n_C0 -- scalar, number of channels of the input n_y -- scalar, number of classes Returns...n_y]) ### END CODE HERE ### return X, Y Initialize parameters 使用tf.contrib.layers.xavier_initializer...= tf.contrib.layers.xavier_initializer(seed = 0)) W2 = tf.get_variable("W2", [2, 2, 8, 16], initializer...= tf.contrib.layers.xavier_initializer(seed = 0)) ### END CODE HERE ### parameters = {"W1":

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图像识别（三）cifar10.py

activations', x) #tf.summary.histogram(tags, values, collections=None, name=None) 用来显示直方图信息 tf.summary.scalar...(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) #tf.summary.scalar(tags, values, collections=...tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar...for l in losses + [total_loss]: # 将每个loss命名为raw,并将损失的移动平均命名为初始=始损失 tf.summary.scalar(l.op.name...+ ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) return loss_averages_op

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在 Visual Studio 中使用 EditorConfig 统一代码风格（含原生与插件）

dotnet_sort_system_directives_first = true dotnet_style_coalesce_expression = true:suggestion dotnet_style_collection_initializer...dotnet_style_explicit_tuple_names = true:suggestion dotnet_style_null_propagation = true:suggestion dotnet_style_object_initializer...extern,new,virtual,abstract,sealed,override,readonly,unsafe,volatile,async:suggestion csharp_prefer_braces...csharp_space_after_colon_in_inheritance_clause = true csharp_space_after_keywords_in_control_flow_statements = true csharp_space_around_binary_operators...statements to be on a new line after the closing brace csharp_new_line_before_finally = true #require braces

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TF入门02-TensorFlow Ops

[1, 1], [1, 1]] # 创建任意维度，内部值都相同的常量 tf.fill(dims, value, name=None) # create a tensor filled with a scalar...# 实数 t_0 = 19 # Treated as a 0-d tensor, or "scalar" tf.zeros_like(t_0) # ==> 0 tf.ones_like(t_0) # =...tf.Variable()和tf.get_variable()，使用方法如下： # create variables with tf.Variable s = tf.Variable(2, name="scalar...with tf.get_variable s = tf.get_variable("scalar", initializer=tf.constant(2)) m = tf.get_variable(...a = tf.get_variable('scalar', initializer=tf.constant(2)) a_times_two = a.assign(a * 2) with tf.Session

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Convolution_model_Application_v1a

Arguments: n_H0 -- scalar, height of an input image n_W0 -- scalar, width of an input image...n_C0 -- scalar, number of channels of the input n_y -- scalar, number of classes Returns...("W1", [4,4,3,8], initializer=tf.contrib.layers.xavier_initializer(seed = 0)) # W2 = tf.get_variable...("W2", [2,2,8,16], initializer=tf.contrib.layers.xavier_initializer(seed = 0)) ### END CODE HERE...If you wish, feel free to play around with this dataset further.

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TensorFlow从0到1 - 17 - Step By Step上手TensorBoard

accuracy_scalar = tf.summary.scalar('accuracy', accuracy) loss_scalar = tf.summary.scalar('loss', loss...(784.0)) '''W_2 = tf.get_variable( name="W_2", regularizer=regularizer, initializer...(100.0)) '''W_3 = tf.get_variable( name="W_3", regularizer=regularizer, initializer...tf.train.GradientDescentOptimizer(0.5).minimize(loss) sess = tf.InteractiveSession() tf.global_variables_initializer...= tf.summary.scalar('accuracy', accuracy) loss_scalar = tf.summary.scalar('loss', loss) merged

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Contest100000572 – 《算法笔记》2.8小节——CC++快速入门->结构体(struct)的使用

Effective Modern C++翻译(3)-条款2：明白auto类型推导

Java开发手册暨Code Style XML

斯坦福tensorflow教程-实例代码简单代码关于占位符 placeholder与feed_dictvariable 变量

Kotlin 编码规约

Angular学习（03）--lint检查规范和WebStorm小技巧

格式设置规则

代码样式规则选项

Tensorflow | 函数名字的更新

log4j conversion pattern各格式含义

tf.contrib.summary

深度学习（2）——tensorflow可视化TensorFlow变量作用域TensorFlow可视化示例

Tensorflow|通过Variable及assign()感悟一点设计之道

04.卷积神经网络 W1.卷积神经网络（作业：手动TensorFlow 实现卷积神经网络）

Convolutional Neural Networks: Application

图像识别（三）cifar10.py

在 Visual Studio 中使用 EditorConfig 统一代码风格（含原生与插件）

TF入门02-TensorFlow Ops

Convolution_model_Application_v1a

TensorFlow从0到1 - 17 - Step By Step上手TensorBoard

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