static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day,c#,.net,linq,lambda"/> 时间戳 时间戳 首页 站长 网站Gzip压缩检测 网站死链检测 Whois查询工具 ICP网站备案查询 Meta标签优化分析 网页关键词密度检测 HTTP状态码查询 微信域名检测工具 htaccess转nginx 生成网页Meta标签 生成桌面快捷方式 rem与px转换工具 在线制作ico图标 在线图片免费压缩 在线定时刷新网址 在线调色板 RGB颜色查询 代码对比/归并 JSON工具 Json格式化 Json格式化(上下) Json格式化(左右) Json在线压缩转义 Json生成C#实体类 Json生成Java实体类 Json生成Go结构体 SQL转Java实体类 XML和Json在线互转 Excel/CSV转Json格式 Json转Excel/CSV格式 JSON和GET参数互转 JSON转YAML 格式化转换 HTML格式化/压缩 CSS格式化/压缩 JS格式化/压缩 JS加密/解密 JS代码混合加密 SQL压缩/格式化 PHP代码格式化工具 XML压缩/格式化 Html/JS互转 Html转义工具 Html转C#/JSP Html转PHP代码 Html转ASP/Perl Excel转HTML表格 Html表格生成器 HTML/MarkDown互转 正则表达式测试工具 正则生成代码 Html过滤工具 运行Js/html/css Xpath工具 加解密编码 MD5加密工具 URL网址16进制加密 迅雷旋风URL加解密 Base64加密/解密 Escape加密/解密 对称加密/解密 SHA/SHA256加密 散列/哈希加密大全 摩尔斯电码加解密 密码生成器 UUID在线生成 GUID在线生成 条形码生成器 IP/数字地址转换 图片转Base64 UTF-8转GBK Unicode/ASCII转换 ASCII编码/解码 URL编码/解码 KeyCode键盘按键码 Android按键码 键盘测试工具 网络 IP地址归属地查询 Websocket测试 获取浏览器信息 公共DNS 各地区公共DNS 文本数字 Html在线编辑器 文章自动排版 文章内容采集 简繁字体转换 汉字转为拼音 火星文转换器 文本内容替换 文本内容对比 在线统计字数工具 内容去重工具 文字特效工具 字符串文本压缩工具 驼峰与下划线互转 全角半角转换 英文字母大小写转换 人民币大写转换工具 随机数生成器 Unix时间戳在线转换 常用进制转换工具 RGB颜色在线转换 计算 利率计算器在线 子网掩码计算器 在线科学计算器 对照列表 常用User-Agent Content-Type对照表 Request请求大全 HTTP请求头大全 HTTP状态码 ASCII对照表 HTML特殊字符转义 常见端口大全 Bootstrap字体图标 Android Manifest权限大全 Linux常用命令大全 其他 在线涂鸦画板 在线时钟 世界各地时间 世界各国首都查询 世界各地货币查询 世界各国区号时差查询 世界节日查询 全国少数民族分布查询 中国历史朝代时间查询表 特殊符号大全 历史上的今天 方案 文章 首页 > 解决方案 > 如何使用 Lambda 表达式计算 C# 中的函数，其中包含来自 LinkedList 中多个节点的变量?i static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day c# - 如何使用 Lambda 表达式计算 C# 中的函数，其中包含来自 LinkedList 中多个节点的变量?i static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day 问题描述 static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day2 = new Day() { sunUp = 755, sunDown = 1805 }; Day day3 = new Day() { sunUp = 750, sunDown = 1810 }; Day day4 = new Day() { sunUp = 745, sunDown = 1815 }; Day day5 = new Day() { sunUp = 740, sunDown = 1820 }; Days.AddLast(day1); Days.AddLast(day2); Days.AddLast(day3); Days.AddLast(day4); Days.AddLast(day5); Console.WriteLine("There is an average of {0} minutes of night over the past {1} days.", Calculator(Days), Days.Count); } public static int ToMinutes(int time) { return time / 100 * 60 + time - time / 100 * 100; } class Day { public int sunUp; public int sunDown; } public static int Calculator(LinkedList<Day> Days) { var current = Days.First; int nightDuration = 0; int count = 0; while (current.Next != null) { nightDuration += ToMinutes(current.Value.sunDown) - ToMinutes(current.Next.Value.sunUp); current = current.Next; count++; } return nightDuration/count; } Requirement: Day must be stored in a LinkedList. Is there a clean Lambda Expression equivalent to the Calculator method above? I am having trouble using Lambda's to calculate a function with variables across connected nodes. Thanks for the help! Cheers, Kyle Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] This has stumped me a bit. I have tried several options but cannot get it to work right. What I am doing is using TF2.0 triple loss function with my own data, but it is not working correctly. I used the example from TF for an initial test and that works. function link for reference: https://www.tensorflow.org/addons/tutorials/losses_triplet With one small difference I am on windows so I cannot download and install tensorflow-addons so I grabbed what I needed to make the code work. I am using the same model and compile it the same way, but still have issues. Here is the code: augment = True if augment: train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0, rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.20) # set validation split else: train_datagen = ImageDataGenerator( horizontal_flip=True, rescale=1. / 255, fill_mode='nearest', validation_split=0.20) # set validation split train_generator = train_datagen.flow_from_directory( DATA_PATH, target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='training') # set as training data validation_generator = train_datagen.flow_from_directory( DATA_PATH, # same directory as training data target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='validation') # set as validation data # Build the network model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='valid', activation='relu', input_shape=(28,28, 1))) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu')) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(256, activation=None)) # No activation on final dense layer model.add(tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))) # L2 normalize embeddings # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=triplet_semihard_loss) Its all the same just not the data. I even resize and grayscale the data so it is the same shape as the other input data. Here is the whole error: File "C:\Users\matthew.millar\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] EDIT def triplet_semihard_loss(y_true, y_pred, margin=1.0): labels, embeddings = y_true, y_pred # Reshape label tensor to [batch_size, 1]. lshape = tf.shape(labels) labels = tf.reshape(labels, [lshape[0], 1]) # Build pairwise squared distance matrix. pdist_matrix = pairwise_distance(embeddings, squared=True) # Build pairwise binary adjacency matrix. adjacency = tf.math.equal(labels, tf.transpose(labels)) # Invert so we can select negatives only. adjacency_not = tf.math.logical_not(adjacency) batch_size = tf.size(labels) # Compute the mask. pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1]) mask = tf.math.logical_and( tf.tile(adjacency_not, [batch_size, 1]), tf.math.greater(pdist_matrix_tile, tf.reshape(tf.transpose(pdist_matrix), [-1, 1]))) mask_final = tf.reshape( tf.math.greater( tf.math.reduce_sum( tf.cast(mask, dtype=tf.dtypes.float32), 1, keepdims=True), 0.0), [batch_size, batch_size]) mask_final = tf.transpose(mask_final) adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32) mask = tf.cast(mask, dtype=tf.dtypes.float32) # negatives_outside: smallest D_an where D_an > D_ap. negatives_outside = tf.reshape( _masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size]) negatives_outside = tf.transpose(negatives_outside) # negatives_inside: largest D_an. negatives_inside = tf.tile( _masked_maximum(pdist_matrix, adjacency_not), [1, batch_size]) semi_hard_negatives = tf.where(mask_final, negatives_outside, negatives_inside) loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives) mask_positives = tf.cast( adjacency, dtype=tf.dtypes.float32) - tf.linalg.diag( tf.ones([batch_size])) # In lifted-struct, the authors multiply 0.5 for upper triangular # in semihard, they take all positive pairs except the diagonal. num_positives = tf.math.reduce_sum(mask_positives) triplet_loss = tf.math.truediv( tf.math.reduce_sum( tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),num_positives) return triplet_loss And this is the whole stack trace: Traceback (most recent call last): File "C:/Users/gus/Documents/ImageSimularity/FoodTrainer.py", line 79, in <module> callbacks=callbacks_list) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator steps_name='steps_per_epoch') File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration batch_outs = batch_function(*batch_data) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch class_weight=class_weight, reset_metrics=reset_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch output_loss_metrics=model._output_loss_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch output_loss_metrics=output_loss_metrics)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch training=training)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 166, in _model_loss per_sample_losses = loss_fn.call(targets[i], outs[i]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\losses.py", line 221, in call return self.fn(y_true, y_pred, **self._fn_kwargs) File "C:\Users\gus\Documents\ImageSimularity\Tripleloss.py", line 75, in triplet_semihard_loss labels = tf.reshape(labels, [lshape[0], 1]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 131, in reshape result = gen_array_ops.reshape(tensor, shape, name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8105, in reshape tensor, shape, name=name, ctx=_ctx) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8143, in reshape_eager_fallback ctx=_ctx, name=name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] Process finished with exit code 1 标签： c#.netlinqlambda 解决方案 您使用计算器的伪代码有很多问题（在本例中用于添加） 你有一个有两个整数的类。我相信您的想法是将这两个数字与来自链表的每个其他节点的两个数字相加。但是，您只添加 Pair 中的第一个数字并将其添加到下一个节点的第二个数字......不要认为它会起作用。 array = current.value.int1 + current.Next.value.int2; 应该 array = current.value.int1 + current.value.int2; 工作示例 public static int Calculator(LinkedList<Pair> Pairs) { var current = Pairs.First; int sum = 0; while (current != null) { sum += current.Value.num1 + current.Value.num2; current = current.Next; } return sum; } 主要运行这个， LinkedList<Pair> Pairs = new LinkedList<Pair>(); Pair pair = new Pair() { num1 = 1, num2 = 5 }; Pairs.AddFirst(pair); Pairs.AddLast(pair); Pairs.AddLast(pair); Console.WriteLine( Calculator(Pairs)); 输出 18 可以用来将所有链表相加的Lambda 表达式可以总结为， Console.WriteLine(Pairs.Sum(x => x.num1 + x.num2)); 输出 18 希望能帮助到你 推荐阅读 numpy - 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首页 > 解决方案 > 如何使用 Lambda 表达式计算 C# 中的函数，其中包含来自 LinkedList 中多个节点的变量?i static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day c# - 如何使用 Lambda 表达式计算 C# 中的函数，其中包含来自 LinkedList 中多个节点的变量?i static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day 问题描述 static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day2 = new Day() { sunUp = 755, sunDown = 1805 }; Day day3 = new Day() { sunUp = 750, sunDown = 1810 }; Day day4 = new Day() { sunUp = 745, sunDown = 1815 }; Day day5 = new Day() { sunUp = 740, sunDown = 1820 }; Days.AddLast(day1); Days.AddLast(day2); Days.AddLast(day3); Days.AddLast(day4); Days.AddLast(day5); Console.WriteLine("There is an average of {0} minutes of night over the past {1} days.", Calculator(Days), Days.Count); } public static int ToMinutes(int time) { return time / 100 * 60 + time - time / 100 * 100; } class Day { public int sunUp; public int sunDown; } public static int Calculator(LinkedList<Day> Days) { var current = Days.First; int nightDuration = 0; int count = 0; while (current.Next != null) { nightDuration += ToMinutes(current.Value.sunDown) - ToMinutes(current.Next.Value.sunUp); current = current.Next; count++; } return nightDuration/count; } Requirement: Day must be stored in a LinkedList. Is there a clean Lambda Expression equivalent to the Calculator method above? I am having trouble using Lambda's to calculate a function with variables across connected nodes. Thanks for the help! Cheers, Kyle Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] This has stumped me a bit. I have tried several options but cannot get it to work right. What I am doing is using TF2.0 triple loss function with my own data, but it is not working correctly. I used the example from TF for an initial test and that works. function link for reference: https://www.tensorflow.org/addons/tutorials/losses_triplet With one small difference I am on windows so I cannot download and install tensorflow-addons so I grabbed what I needed to make the code work. I am using the same model and compile it the same way, but still have issues. Here is the code: augment = True if augment: train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0, rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.20) # set validation split else: train_datagen = ImageDataGenerator( horizontal_flip=True, rescale=1. / 255, fill_mode='nearest', validation_split=0.20) # set validation split train_generator = train_datagen.flow_from_directory( DATA_PATH, target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='training') # set as training data validation_generator = train_datagen.flow_from_directory( DATA_PATH, # same directory as training data target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='validation') # set as validation data # Build the network model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='valid', activation='relu', input_shape=(28,28, 1))) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu')) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(256, activation=None)) # No activation on final dense layer model.add(tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))) # L2 normalize embeddings # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=triplet_semihard_loss) Its all the same just not the data. I even resize and grayscale the data so it is the same shape as the other input data. Here is the whole error: File "C:\Users\matthew.millar\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] EDIT def triplet_semihard_loss(y_true, y_pred, margin=1.0): labels, embeddings = y_true, y_pred # Reshape label tensor to [batch_size, 1]. lshape = tf.shape(labels) labels = tf.reshape(labels, [lshape[0], 1]) # Build pairwise squared distance matrix. pdist_matrix = pairwise_distance(embeddings, squared=True) # Build pairwise binary adjacency matrix. adjacency = tf.math.equal(labels, tf.transpose(labels)) # Invert so we can select negatives only. adjacency_not = tf.math.logical_not(adjacency) batch_size = tf.size(labels) # Compute the mask. pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1]) mask = tf.math.logical_and( tf.tile(adjacency_not, [batch_size, 1]), tf.math.greater(pdist_matrix_tile, tf.reshape(tf.transpose(pdist_matrix), [-1, 1]))) mask_final = tf.reshape( tf.math.greater( tf.math.reduce_sum( tf.cast(mask, dtype=tf.dtypes.float32), 1, keepdims=True), 0.0), [batch_size, batch_size]) mask_final = tf.transpose(mask_final) adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32) mask = tf.cast(mask, dtype=tf.dtypes.float32) # negatives_outside: smallest D_an where D_an > D_ap. negatives_outside = tf.reshape( _masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size]) negatives_outside = tf.transpose(negatives_outside) # negatives_inside: largest D_an. negatives_inside = tf.tile( _masked_maximum(pdist_matrix, adjacency_not), [1, batch_size]) semi_hard_negatives = tf.where(mask_final, negatives_outside, negatives_inside) loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives) mask_positives = tf.cast( adjacency, dtype=tf.dtypes.float32) - tf.linalg.diag( tf.ones([batch_size])) # In lifted-struct, the authors multiply 0.5 for upper triangular # in semihard, they take all positive pairs except the diagonal. num_positives = tf.math.reduce_sum(mask_positives) triplet_loss = tf.math.truediv( tf.math.reduce_sum( tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),num_positives) return triplet_loss And this is the whole stack trace: Traceback (most recent call last): File "C:/Users/gus/Documents/ImageSimularity/FoodTrainer.py", line 79, in <module> callbacks=callbacks_list) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator steps_name='steps_per_epoch') File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration batch_outs = batch_function(*batch_data) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch class_weight=class_weight, reset_metrics=reset_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch output_loss_metrics=model._output_loss_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch output_loss_metrics=output_loss_metrics)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch training=training)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 166, in _model_loss per_sample_losses = loss_fn.call(targets[i], outs[i]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\losses.py", line 221, in call return self.fn(y_true, y_pred, **self._fn_kwargs) File "C:\Users\gus\Documents\ImageSimularity\Tripleloss.py", line 75, in triplet_semihard_loss labels = tf.reshape(labels, [lshape[0], 1]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 131, in reshape result = gen_array_ops.reshape(tensor, shape, name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8105, in reshape tensor, shape, name=name, ctx=_ctx) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8143, in reshape_eager_fallback ctx=_ctx, name=name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] Process finished with exit code 1 标签： c#.netlinqlambda 解决方案 您使用计算器的伪代码有很多问题（在本例中用于添加） 你有一个有两个整数的类。我相信您的想法是将这两个数字与来自链表的每个其他节点的两个数字相加。但是，您只添加 Pair 中的第一个数字并将其添加到下一个节点的第二个数字......不要认为它会起作用。 array = current.value.int1 + current.Next.value.int2; 应该 array = current.value.int1 + current.value.int2; 工作示例 public static int Calculator(LinkedList<Pair> Pairs) { var current = Pairs.First; int sum = 0; while (current != null) { sum += current.Value.num1 + current.Value.num2; current = current.Next; } return sum; } 主要运行这个， LinkedList<Pair> Pairs = new LinkedList<Pair>(); Pair pair = new Pair() { num1 = 1, num2 = 5 }; Pairs.AddFirst(pair); Pairs.AddLast(pair); Pairs.AddLast(pair); Console.WriteLine( Calculator(Pairs)); 输出 18 可以用来将所有链表相加的Lambda 表达式可以总结为， Console.WriteLine(Pairs.Sum(x => x.num1 + x.num2)); 输出 18 希望能帮助到你 推荐阅读 numpy - 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static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day
static void Main(string[] args) { LinkedList<Day> Days = new LinkedList<Day>(); Day day1 = new Day() { sunUp = 800, sunDown = 1800 }; Day day2 = new Day() { sunUp = 755, sunDown = 1805 }; Day day3 = new Day() { sunUp = 750, sunDown = 1810 }; Day day4 = new Day() { sunUp = 745, sunDown = 1815 }; Day day5 = new Day() { sunUp = 740, sunDown = 1820 }; Days.AddLast(day1); Days.AddLast(day2); Days.AddLast(day3); Days.AddLast(day4); Days.AddLast(day5); Console.WriteLine("There is an average of {0} minutes of night over the past {1} days.", Calculator(Days), Days.Count); }
public static int ToMinutes(int time) { return time / 100 * 60 + time - time / 100 * 100; }
class Day { public int sunUp; public int sunDown; }
public static int Calculator(LinkedList<Day> Days) { var current = Days.First; int nightDuration = 0; int count = 0; while (current.Next != null) { nightDuration += ToMinutes(current.Value.sunDown) - ToMinutes(current.Next.Value.sunUp); current = current.Next; count++; } return nightDuration/count; }
Requirement: Day must be stored in a LinkedList.
Is there a clean Lambda Expression equivalent to the Calculator method above? I am having trouble using Lambda's to calculate a function with variables across connected nodes.
Thanks for the help!
Cheers, Kyle
This has stumped me a bit. I have tried several options but cannot get it to work right. What I am doing is using TF2.0 triple loss function with my own data, but it is not working correctly. I used the example from TF for an initial test and that works.
function link for reference: https://www.tensorflow.org/addons/tutorials/losses_triplet
With one small difference I am on windows so I cannot download and install tensorflow-addons so I grabbed what I needed to make the code work. I am using the same model and compile it the same way, but still have issues. Here is the code:
augment = True if augment: train_datagen = ImageDataGenerator( rescale=1. / 255, shear_range=0, rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True, fill_mode='nearest', validation_split=0.20) # set validation split else: train_datagen = ImageDataGenerator( horizontal_flip=True, rescale=1. / 255, fill_mode='nearest', validation_split=0.20) # set validation split train_generator = train_datagen.flow_from_directory( DATA_PATH, target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='training') # set as training data validation_generator = train_datagen.flow_from_directory( DATA_PATH, # same directory as training data target_size=(28,28), color_mode='grayscale', batch_size=BATCH_SIZE, class_mode='categorical', subset='validation') # set as validation data # Build the network model = tf.keras.Sequential() model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='valid', activation='relu', input_shape=(28,28, 1))) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='valid', activation='relu')) model.add(tf.keras.layers.MaxPooling2D(pool_size=2)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(256, activation=None)) # No activation on final dense layer model.add(tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(x, axis=1))) # L2 normalize embeddings # Compile the model model.compile(optimizer=tf.keras.optimizers.Adam(0.001), loss=triplet_semihard_loss)
Its all the same just not the data. I even resize and grayscale the data so it is the same shape as the other input data.
Here is the whole error:
File "C:\Users\matthew.millar\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape]
EDIT
def triplet_semihard_loss(y_true, y_pred, margin=1.0): labels, embeddings = y_true, y_pred # Reshape label tensor to [batch_size, 1]. lshape = tf.shape(labels) labels = tf.reshape(labels, [lshape[0], 1]) # Build pairwise squared distance matrix. pdist_matrix = pairwise_distance(embeddings, squared=True) # Build pairwise binary adjacency matrix. adjacency = tf.math.equal(labels, tf.transpose(labels)) # Invert so we can select negatives only. adjacency_not = tf.math.logical_not(adjacency) batch_size = tf.size(labels) # Compute the mask. pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1]) mask = tf.math.logical_and( tf.tile(adjacency_not, [batch_size, 1]), tf.math.greater(pdist_matrix_tile, tf.reshape(tf.transpose(pdist_matrix), [-1, 1]))) mask_final = tf.reshape( tf.math.greater( tf.math.reduce_sum( tf.cast(mask, dtype=tf.dtypes.float32), 1, keepdims=True), 0.0), [batch_size, batch_size]) mask_final = tf.transpose(mask_final) adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32) mask = tf.cast(mask, dtype=tf.dtypes.float32) # negatives_outside: smallest D_an where D_an > D_ap. negatives_outside = tf.reshape( _masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size]) negatives_outside = tf.transpose(negatives_outside) # negatives_inside: largest D_an. negatives_inside = tf.tile( _masked_maximum(pdist_matrix, adjacency_not), [1, batch_size]) semi_hard_negatives = tf.where(mask_final, negatives_outside, negatives_inside) loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives) mask_positives = tf.cast( adjacency, dtype=tf.dtypes.float32) - tf.linalg.diag( tf.ones([batch_size])) # In lifted-struct, the authors multiply 0.5 for upper triangular # in semihard, they take all positive pairs except the diagonal. num_positives = tf.math.reduce_sum(mask_positives) triplet_loss = tf.math.truediv( tf.math.reduce_sum( tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),num_positives) return triplet_loss
And this is the whole stack trace:
Traceback (most recent call last): File "C:/Users/gus/Documents/ImageSimularity/FoodTrainer.py", line 79, in <module> callbacks=callbacks_list) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1297, in fit_generator steps_name='steps_per_epoch') File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_generator.py", line 265, in model_iteration batch_outs = batch_function(*batch_data) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 973, in train_on_batch class_weight=class_weight, reset_metrics=reset_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py", line 264, in train_on_batch output_loss_metrics=model._output_loss_metrics) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 311, in train_on_batch output_loss_metrics=output_loss_metrics)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 252, in _process_single_batch training=training)) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py", line 166, in _model_loss per_sample_losses = loss_fn.call(targets[i], outs[i]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\keras\losses.py", line 221, in call return self.fn(y_true, y_pred, **self._fn_kwargs) File "C:\Users\gus\Documents\ImageSimularity\Tripleloss.py", line 75, in triplet_semihard_loss labels = tf.reshape(labels, [lshape[0], 1]) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\array_ops.py", line 131, in reshape result = gen_array_ops.reshape(tensor, shape, name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8105, in reshape tensor, shape, name=name, ctx=_ctx) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\ops\gen_array_ops.py", line 8143, in reshape_eager_fallback ctx=_ctx, name=name) File "C:\Users\gus\Anaconda3\envs\TF2\lib\site-packages\tensorflow_core\python\eager\execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "<string>", line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape has 32 [Op:Reshape] Process finished with exit code 1
标签： c#.netlinqlambda
您使用计算器的伪代码有很多问题（在本例中用于添加）
你有一个有两个整数的类。我相信您的想法是将这两个数字与来自链表的每个其他节点的两个数字相加。但是，您只添加 Pair 中的第一个数字并将其添加到下一个节点的第二个数字......不要认为它会起作用。
array = current.value.int1 + current.Next.value.int2;
应该
array = current.value.int1 + current.value.int2;
工作示例
public static int Calculator(LinkedList<Pair> Pairs) { var current = Pairs.First; int sum = 0; while (current != null) { sum += current.Value.num1 + current.Value.num2; current = current.Next; } return sum; }
主要运行这个，
LinkedList<Pair> Pairs = new LinkedList<Pair>(); Pair pair = new Pair() { num1 = 1, num2 = 5 }; Pairs.AddFirst(pair); Pairs.AddLast(pair); Pairs.AddLast(pair); Console.WriteLine( Calculator(Pairs));
输出
18
可以用来将所有链表相加的Lambda 表达式可以总结为，
Console.WriteLine(Pairs.Sum(x => x.num1 + x.num2));
希望能帮助到你