我正在尝试访问可以在急切模式或图形模式下运行的函数内的 python 列表，如下所示：import tensorflow as tfimport numpy as npclass SlotGenerator: def __init__(self...

我正在尝试访问可以在急切模式或图形模式下运行的函数内的 python 列表，如下所示：

import tensorflow as tf
import numpy as np

class SlotGenerator:
  def __init__(self):
    pass  # No need for initialization here

  def call(self):
      real_part = tf.random.uniform((1, 1, 30720), dtype=tf.float32)
      imag_part = tf.random.uniform((1, 1, 30720), dtype=tf.float32)
      return tf.complex(real_part, imag_part)

class WaveformGenerator:
  def __init__(self, slot_numbers):
    self.slotgens = [SlotGenerator() for _ in range(len(slot_numbers))]
    
  def gen_single_slot(self, slot_num):
    # Generate random slot_matrix with desired dimensions
    return self.slotgens[slot_num].call()
  
  def gen_slots(self, slot_numbers, batch_size=1, num_ant=1, num_time_samples=30720):
      """Generate slots specified by slot_nos"""
        
      slot_shape = tf.TensorShape([batch_size, num_ant, num_time_samples])

      slots = tf.map_fn(
            lambda slot_num: self.gen_single_slot(slot_num),
            slot_numbers,
            fn_output_signature=tf.TensorSpec(shape=slot_shape, dtype=tf.complex64)
        )
        
      slots = tf.squeeze(slots, axis=[1]) # tf.map_fn adds an extra dimension at the beginning of the tensor
      return tf.concat(slots, axis=0)

  def __call__(self, slot_numbers, eager_mode=False):

    if not eager_mode:
        self.gen_slots = tf.function(self.gen_slots)
    
    slots = self.gen_slots(slot_numbers)

    return slots

# Example usage

slot_numbers = [0,1,2,3,4]  # Example number of slots
slot_numbers = np.array(slot_numbers, dtype=np.int32)
waveform_generator = WaveformGenerator(slot_numbers)

eager_mode = False   
waveform = waveform_generator(slot_numbers, eager_mode=eager_mode)

print(waveform.shape)  # Expected output shape: ([5, 1, 30720])

这在 Eager 模式（设置 eager_mode = True）下运行良好，但在图形模式（设置 eager_mode = False）下失败，并出现以下错误：

TypeError：列表索引必须是整数或切片，而不是 SymbolicTensor

在图形模式下，是否有解决此列表索引问题的方法？或者图形模式是否支持除 Python 列表之外的其他类型，我可以在其中存储类对象？

我需要遍历 json 对象并找到大小与给定值匹配的键对象、键数组。我不明白在哪里设置条件。函数遍历（obj，size）{for（let...

我需要遍历 json 对象并找到大小与给定值匹配的键对象和键数组。

我不明白在哪里设置条件。

function traverse(obj, size) {
    for (let key in obj) {
        if (typeof obj[key] === "object" && obj[key] != null) {
            traverse(obj[key]);
            if (Object.keys(obj[key]).length > size) {
                console.log(key);
            } else if (Array.isArray(obj[key])) {
                if (obj[key].length > size) { 
                    console.log(key);
                }
            } 
        }
    }
}

我尝试重新排列 if 条件，但没有得到任何好的结果

样本测试对象

let objTest = {
    "id": "66100c8e58d0a40e97ce7753",
    "createdBy": "crm1",
    "attributes": {
        "accountsInfoMessages": [{
            "valueOzb": "Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",
            "code": "notCreateAccount",
        }, {
            "valueOzb": "Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",
            "code": "notCreateAccount",
        }, {
            "valueOzb": "Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",
            "code": "notCreateAccount",
        }],
        "selectedCardProduct": null,
        "homePageId": null,
        "branch": null,
        "customerId": "1111",
        "selectForCurrencyTest": "111",
        "selectForFiltersCardType": {
            "values": [{
                "code": "01",
                "valueOzb": "HUMO"
            }, {
                "code": "02",
                "valueOzb": "UzCard",
            }, {
                "code": "03",
                "valueOzb": "Visa"
            }],
        },
        "selectForCurrency": [{
            "valueOzb": "Andor pesetasi",
            "code": "ADP"
        }, {
            "code": "AED",
            "valueOzb": "Birlashgan Arab Amirliklari dirhami"
        }, {
            "valueOzb": "Afg'ani",
            "code": "AFA"
        }, ]
    },
    "selectForCurrency": "Test"
}

我根据自己的理解修正了函数的某些部分。这应该可以正常工作。如果不行，请在评论中告诉我您遇到了什么错误/漏洞。最好在您的问题中添加您正在遍历的对象的实例/简化示例。

编辑：这是经过测试的解决方案的最终草案：

/* Test Data: */
const testObject = {
  owner: 'John Doe',
  prices: [200, 450, -400, 3000, -650, -130, 70, 1300],
  interestRate: 1.2, // %
  pin: 1111,
  nullVal: null,
  objOuter: {
    one: 1,
    two: 2,
    three: 3,
    arr1: [200, 450, -650],
    inner: {
      uno: 1,
      dos: 2,
      tres: 3,
      arr2: [-400, 3000],
      innermost: {
        ich: 1,
        ni: 2,
        san: 3,
        arr3: [-130, 70, 1300],
      },
    },
  },
};

/* Function: */
function traverse(obj, size) {
  for (const [key, val] of Object.entries(obj)) {
    if (!val) continue;
    if (Array.isArray(val) && val.length > size) {
      console.log(key);
      val.forEach(function (el, i) {
        if (typeof el === 'object') traverse(el, size);
      });
    }
    if (typeof val === 'object' && !Array.isArray(val) && Object.keys(val).length > size) {
      console.log(key);
      traverse(val, size);
    }
  }
}
traverse(testObject, 3); // Function call

谢谢。看起来它正在工作。问题仅在于当值是 null 时，在检查数组时 - Object.keys(val) 无法转换 null。我添加了一个检查 null 的语句 if (typeof val === 'object' && val != null && !Array.isArray(val) && Object.keys(val).length > size)

您不需要检查“null”值，因为条件 (typeof val === \'object\') 会处理它。如果 val 是对象类型，即使它为空，其值也不会为 null。就像 console.log({}); 和 console.log([]); 不会打印 null 一样。如果 val 是空对象/数组，则检查其大小/长度的条件将为 false，并且 val 将被忽略。

如果不进行检查，它会在 Object.keys(val) 上抛出“TypeError：无法将未定义或 null 转换为对象”。我已附加一个示例对象。

是的。似乎就是这样。为了正确修复它，我建议在函数的 for-of 循​​环顶部添加此行：if (!val) continue;。我在对答案的最后编辑中添加了此行。

您仅使用一个参数调用 traverse。

if (typeof obj[key] === "object" && obj[key] != null) {
    traverse(obj[key]); // here !
     //... rest of the code
}

它应该是

traverse(obj[key], size);

请提供具有所需结果的对象示例以验证逻辑

您好，我使用 keras 训练了一个 CNN 模型，该模型类似于他们在网站上使用的示例模型，但层数略小，并且在末尾有一个额外的 dropout 层。模型构建函数看起来...

嗨，我用 keras 训练了一个 CNN 模型，类似于他们在网站上使用的示例模型，但层数略小，最后还有一个额外的 dropout 层。模型构建函数看起来有点像这样：

" --------- Model Params ---------"

epochs = 800
image_size = (384, 256)
batch_size = 128
number_of_layers = 4
drop_out = 0.25
num_dropouts = 2
learn_rate = 0.00001
layer_sizes = [64, 128, 256, 512, 728]
class_weight = {0:1, 1:3}
image_rotation = 0.1


def make_model(input_shape, num_classes, layer_num=3, drop_out=0.25, dropouts=1):
    inputs = keras.Input(shape=input_shape)

    # Entry block
    x = layers.Rescaling(1.0 / 255)(inputs)
    x = layers.Conv2D(128, 3, strides=2, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)

    previous_block_activation = x  # Set aside residual
    
    layer_use = []
    for i in range(layer_num): layer_use.append(layer_sizes[i])
        
    for size in layer_use:
        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)

        # Project residual
        residual = layers.Conv2D(size, 1, strides=2, padding="same")(
            previous_block_activation
        )
        x = layers.add([x, residual])  # Add back residual
        previous_block_activation = x  # Set aside next residual

    x = layers.SeparableConv2D(1024, 3, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)

    x = layers.GlobalAveragePooling2D()(x)
    if num_classes == 2:
        units = 1
    else:
        units = num_classes
    

    if dropouts == 1:
        x = layers.Dropout(drop_out)(x)
        # We specify activation=None so as to return logits
        outputs = layers.Dense(units, activation=None)(x)
    elif dropouts == 2:
        x = layers.Dropout(0.2)(x)
        x = layers.Dense(units, activation=None)(x)
        outputs = layers.Dropout(drop_out)(x)

    return keras.Model(inputs, outputs)

model = make_model(input_shape=image_size + (3,), num_classes=2, layer_num=number_of_layers, 
                   drop_out=drop_out, dropouts=num_dropouts)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=learn_rate),
    loss=keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=[keras.metrics.BinaryAccuracy(name="acc")],
  )

model.fit(
    train_ds,
    epochs=epochs,
    callbacks=callbacks,
    validation_data=val_ds,
    class_weight=class_weight
)

该模型训练良好 - 验证准确率达到 97% 左右。使用预测函数时，模型通常会根据我提供的输入数据给出合理的输出。我遇到的问题是，使用该 predict_on_batch 函数时的输出预测并不相同，如果使用相同的输入数据重复，通常会相差 +-0.15。这是什么原因造成的，一旦模型经过训练并用于预测，对于相同的输入数据，预测难道不应该相同吗？

1. 您应该将参数传递给递归调用
2. 您可以添加一个累加器来收集结果

let objTest={id:"66100c8e58d0a40e97ce7753",createdBy:"crm1",attributes:{accountsInfoMessages:[{valueOzb:"Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",code:"notCreateAccount"},{valueOzb:"Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",code:"notCreateAccount"},{valueOzb:"Mijozning holati yoritilgan. Hisob yaratish mumkin emas. Mijoz ma'lumotlarini to'ldiring. Buning uchun \"Kartochkani tanrirlash\" tugmasini bosing.",code:"notCreateAccount"}],selectedCardProduct:null,homePageId:null,branch:null,customerId:"1111",selectForCurrencyTest:"111",selectForFiltersCardType:{values:[{code:"01",valueOzb:"HUMO"},{code:"02",valueOzb:"UzCard"},{code:"03",valueOzb:"Visa"}]},selectForCurrency:[{valueOzb:"Andor pesetasi",code:"ADP"},{code:"AED",valueOzb:"Birlashgan Arab Amirliklari dirhami"},{valueOzb:"Afg'ani",code:"AFA"},]},selectForCurrency:"Test"};

function traverse(obj, size, key = null, result = []) {
  if(Array.isArray(obj)){
    obj.forEach((item,i) => traverse(item, size, i, result));
    obj.length > size && key && result.push(key);
  } else if (obj && typeof obj === "object") {
    const keys = Object.keys(obj);
    keys.forEach(key => traverse(obj[key], size, key, result));
    keys.length > size && key && result.push(key);
  }
  return result;
}

console.log(traverse(objTest, 1));

\'一旦模型经过训练并用于预测，对于相同的输入数据，预测结果难道不应该相同吗？\'。我认为这取决于您的代码，您使用的是自然随机的 Dropout。除此之外，可能还有其他参数需要考虑

谢谢你的回复。该函数返回一个空数组。我无法调试错误在哪里。我已附加一个测试对象。

此代码创建一个客户端类，其中包含变量名称、到达时间和延迟。循环通过读取客户端的到达时间、为其添加延迟并将其设为到达时间，在队列中创建新的客户端。

此代码创建一个客户端类，其中包含变量 name , arrival time 、 和 delay 。循环通过读取客户端的到达时间、为其添加延迟以及使新客户端到达的时间成为队列中的新客户端。将客户端对象附加到队列时，所有客户端对象都是不同的，但是当我尝试读取它们时，它们仅在到达时间上变得相同。为什么会发生这种情况？

import random
import numpy as np
import collections

# a client class for creating a queue using a poisson distribution. The clients are added to a deque
class Client:
    def __init__(self):
        self.name = random.choice(["Juan", "julia", "Daniel", "Rafael", "Lida", "Sandra"])
        self.arrival_time = 0
        self.arrival_time_delay = np.random.poisson(5, size=1)

    def __str__(self) -> str:
        return "Client: {} arrival-time: {} delay {}".format(self.name, self.arrival_time, self.arrival_time_delay)


client_count = 5
queue = collections.deque()

# arrival
time_added = 0
# adding clients to a deque 
for _ in range(client_count):
    client = Client()
    # a new time is the arrival time plus a delay for the next client to appear
    client.arrival_time = time_added
    print(client)
    queue.append(client)
    time_added += client.arrival_time_delay

print(60 * "#")

for c in queue:
    print(c)

#results:
#Client: julia arrival-time: 0 delay [6]
#Client: Sandra arrival-time: [6] delay [3]
#Client: Lida arrival-time: [9] delay [4]
#Client: Juan arrival-time: [13] delay [8]
#Client: Sandra arrival-time: [21] delay [6]
############################################################
#Client: julia arrival-time: 0 delay [6]
#Client: Sandra arrival-time: [27] delay [3]
#Client: Lida arrival-time: [27] delay [4]
#Client: Juan arrival-time: [27] delay [8]
#Client: Sandra arrival-time: [27] delay [6]