8wDlpd.png
首页
8wDFp9.png
分类
8wDEOx.png
8wDMfH.png
消息
8wDKte.png
我的
  2. tensorflow
  3. 解决具有不同数据类型分类特征的 Keras 函数模型的类型转换错误

解决具有不同数据类型分类特征的 Keras 函数模型的类型转换错误

casecha 2月前
26 0

我正在创建一个模型，该模型导入分类字符串和数字数据以及连续特征的 csv，以预测学生是否会毕业。IndexAgeHousehold_SizeGender

我正在创建一个模型，该模型导入分类字符串和数字数据以及连续特征的 csv 来预测学生是否会毕业。

指数 年龄 房子大小 性别 婚姻状况 职业 收入水平 已毕业
0 三十二 4 女性 已婚 护士 60000 1
1 四十五 2 男性 离婚 老师 80000 1
2 二十八 1 女性 单身的 软件工程师 120000 1
3 62 2 男性 已婚 已退休 20000 1
4 三十五 3 女性 已婚 律师 160000 1

图层创建函数定义 

def get_normalization_layer(name, dataset):
  # Create a Normalization layer for the feature.
  norm=name+'_norm'
  normalizer = layers.Normalization(axis=None, name=norm)

  # Prepare a Dataset that only yields the feature.
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the statistics of the data.
  normalizer.adapt(feature_ds)

  return normalizer

def get_category_encoding_layer(name, dataset, dtype, max_tokens=None):
  # Create a layer that turns strings into integer indices.
  ind=name+'_index'
  if dtype == 'string':
    index = layers.StringLookup(max_tokens=max_tokens, name=ind)
  # Otherwise, create a layer that turns integer values into integer indices.
  else:
    index = layers.IntegerLookup(max_tokens=max_tokens, name=ind)

  # Prepare a `tf.data.Dataset` that only yields the feature.
  feature_ds = dataset.map(lambda x, y: x[name])

  # Learn the set of possible values and assign them a fixed integer index.
  index.adapt(feature_ds)

  # Encode the integer indices.
  enc=name+'_encoding'
  encoder = layers.CategoryEncoding(num_tokens=index.vocabulary_size(), name=enc)

  # Apply multi-hot encoding to the indices. The lambda function captures the
  # layer, so you can use them, or include them in the Keras Functional model later.
  return lambda feature: encoder(index(feature))

模型创建 

all_inputs = []
encoded_features = []

categorical_str_cols = ['Gender', 'Marital_Status', 'Occupation']

for header in categorical_str_cols:
  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='string')

  encoding_layer = get_category_encoding_layer(name=header,
                                               dataset=train_ds,
                                               dtype='string')
  encoded_categorical_str_col = encoding_layer(categorical_col)
  all_inputs.append(categorical_col)
  encoded_features.append(encoded_categorical_str_col)

categorical_int_cols = ['Age', 'Household_Size']

for header in categorical_int_cols:
  categorical_col = tf.keras.Input(shape=(1,), name=header, dtype='int64')
  encoding_layer = get_category_encoding_layer(name=header,
                                               dataset=train_ds,
                                               dtype='int64')
  encoded_categorical_int_col = encoding_layer(categorical_col)
  all_inputs.append(categorical_col)
  encoded_features.append(encoded_categorical_int_col)

# Numerical features.
for header in ['Income_Level']:
  numeric_col = tf.keras.Input(shape=(1,), name=header)
  normalization_layer = get_normalization_layer(header, train_ds)
  encoded_numeric_col = normalization_layer(numeric_col)
  all_inputs.append(numeric_col)
  encoded_features.append(encoded_numeric_col)

all_features = tf.keras.layers.concatenate(encoded_features)
x = tf.keras.layers.Dense(32, activation="relu")(all_features)
x = tf.keras.layers.Dropout(0.5)(x)
output = tf.keras.layers.Dense(1)(x)

model = tf.keras.Model(all_inputs, output)

model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=["accuracy"])

模型图像

由于声誉太低，无法发布图片链接： 来自 plot_model 的模型图像

当我在 tf.dataset 上运行 model.fit 时，收到错误： 

---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
Cell In[539], line 1
----> 1 model.fit(train_ds, epochs=10, validation_data=val_ds)

File ~\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File ~\anaconda3\Lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     51 try:
     52   ctx.ensure_initialized()
---> 53   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     54                                       inputs, attrs, num_outputs)
     55 except core._NotOkStatusException as e:
     56   if name is not None:

UnimplementedError: Graph execution error:

Detected at node functional_27_1/Cast defined at (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main

  File "<frozen runpy>", line 88, in _run_code

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel_launcher.py", line 17, in <module>

  File "C:\Users\-\anaconda3\Lib\site-packages\traitlets\config\application.py", line 1075, in launch_instance

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\kernelapp.py", line 701, in start

  File "C:\Users\-\anaconda3\Lib\site-packages\tornado\platform\asyncio.py", line 205, in start

  File "C:\Users\-\anaconda3\Lib\asyncio\windows_events.py", line 322, in run_forever

  File "C:\Users\-\anaconda3\Lib\asyncio\base_events.py", line 641, in run_forever

  File "C:\Users\-\anaconda3\Lib\asyncio\base_events.py", line 1987, in _run_once

  File "C:\Users\-\anaconda3\Lib\asyncio\events.py", line 88, in _run

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\kernelbase.py", line 534, in dispatch_queue

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\kernelbase.py", line 523, in process_one

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\kernelbase.py", line 429, in dispatch_shell

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\kernelbase.py", line 767, in execute_request

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\ipkernel.py", line 429, in do_execute

  File "C:\Users\-\anaconda3\Lib\site-packages\ipykernel\zmqshell.py", line 549, in run_cell

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\interactiveshell.py", line 3075, in run_cell

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\interactiveshell.py", line 3130, in _run_cell

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\interactiveshell.py", line 3334, in run_cell_async

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\interactiveshell.py", line 3517, in run_ast_nodes

  File "C:\Users\-\anaconda3\Lib\site-packages\IPython\core\interactiveshell.py", line 3577, in run_code

  File "C:\Users\-\AppData\Local\Temp\ipykernel_5448\2809293729.py", line 1, in <module>

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py", line 117, in error_handler

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 318, in fit

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 121, in one_step_on_iterator

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 108, in one_step_on_data

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 51, in train_step

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py", line 117, in error_handler

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\layers\layer.py", line 882, in __call__

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py", line 117, in error_handler

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\ops\operation.py", line 46, in __call__

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py", line 156, in error_handler

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\models\functional.py", line 167, in call

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\models\functional.py", line 258, in _standardize_inputs

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\models\functional.py", line 218, in _convert_inputs_to_tensors

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\ops\core.py", line 743, in convert_to_tensor

  File "C:\Users\-\anaconda3\Lib\site-packages\keras\src\backend\tensorflow\core.py", line 120, in convert_to_tensor

Cast int64 to string is not supported
     [[{{node functional_27_1/Cast}}]] [Op:__inference_one_step_on_iterator_97454]

示例 tf.dataset 批次大小 3 

{'Age': <tf.Tensor: shape=(3, 1), dtype=int64, numpy=
array([[23],
       [29],
       [31]], dtype=int64)>, 'Household_Size': <tf.Tensor: shape=(3, 1), dtype=int64, numpy=
array([[3],
       [3],
       [3]], dtype=int64)>, 'Gender': <tf.Tensor: shape=(3, 1), dtype=string, numpy=
array([[b'Female'],
       [b'Female'],
       [b'Female']], dtype=object)>, 'Marital_Status': <tf.Tensor: shape=(3, 1), dtype=string, numpy=
array([[b'Single'],
       [b'Single'],
       [b'Single']], dtype=object)>, 'Occupation': <tf.Tensor: shape=(3, 1), dtype=string, numpy=
array([[b'Server'],
       [b'Nurse'],
       [b'Retail Associate']], dtype=object)>, 'Income_Level': <tf.Tensor: shape=(3, 1), dtype=int64, numpy=
array([[30000],
       [50000],
       [30000]], dtype=int64)>}
tf.Tensor([1 1 1], shape=(3,), dtype=int64)

但是，如果我排除字符串分类层和数据，或者仅使用字符串层/数据，模型将成功运行其训练。我应该如何创建层，以便我可以在同一模型中同时拥有这两种数据类型？

帖子版权声明 1、本帖标题：解决具有不同数据类型分类特征的 Keras 函数模型的类型转换错误
    本站网址：http://xjnalaquan.com/
2、本网站的资源部分来源于网络，如有侵权，请联系站长进行删除处理。
3、会员发帖仅代表会员个人观点，并不代表本站赞同其观点和对其真实性负责。
4、本站一律禁止以任何方式发布或转载任何违法的相关信息，访客发现请向站长举报
5、站长邮箱：yeweds@126.com 除非注明，本帖由casecha在本站《tensorflow》版块原创发布， 转载请注明出处！
收藏的用户（0） X
正在加载信息~
最新回复 (0)
  • 引用 2楼

    我有一个建筑物放置项目，它不适用于 3D 房屋对象。所有对象都具有刚体和盒子碰撞器组件，而只有房屋预制对象才有子对象。我的 s...

    我有一个建筑物放置项目，它不适用于 3D 房屋对象。所有对象都有刚体和盒子碰撞器组件，而只有房屋预制对象才有子对象。我的脚本如下：

    BuildingManager 脚本： 

    public class BuildingManager : MonoBehaviour
{
      private GameObject pendingObject;
      [SerializeField] private Material[] materials;
      public bool canPlace;

void Update()
{
     if (pendingObject != null)
     {
         UpDateMaterials();
          
      pendingObject.transform.position = pos;

         if (Input.GetMouseButtonDown(0) && canPlace)
         {
             PlaceObject();
         }
     }
}

public void PlaceObject()
{
     pendingObject.GetComponent<MeshRenderer>().material = materials[2];
     pendingObject = null;
}

public void UpDateMaterials()
{
     if (canPlace)
     {
         pendingObject.GetComponent<MeshRenderer>().material = materials[0];
     
     }
     if (!canPlace)
     {
         pendingObject.GetComponent<MeshRenderer>().material = materials[1];
     
     }
}
}

    CheckPlacement 脚本： 

    public class CheckPlacement : MonoBehaviour
{
    BuildingManager buildingManager;

    void Start()
    {
        buildingManager = GameObject.Find("BuildingManager").GetComponent<BuildingManager>();
    }

   private void OnTriggerEnter(Collider other)
    {
        if (other.gameObject.CompareTag("Building"))
        {
            Debug.Log("Can't Place Building");
            buildingManager.canPlace = false;
        }
    }

    private void OnTriggerExit(Collider other)
    {
        if (other.gameObject.CompareTag("Building"))
        {
            Debug.Log("Can Place Building");
             buildingManager.canPlace = true;
       }
    }
}

    这些脚本可以很好地与 Unity 的默认 3D 对象（如 3D 胶囊、球体、盒子对象）配合使用。例如，我尝试将胶囊对象与已经在地面（平面）上的房屋对象重叠。胶囊对象在与房屋对象碰撞时变为透明红色，当我将其拖到地面时，它变为透明绿色。

    欢迎提出任何建议...

  • 引用 3楼

    由于房屋对象有多个子对象，因此您需要更新所有网格渲染器。为此，请使用 GetComponentsInChildren 获取所有网格渲染器，并在更改材质时循环遍历这些渲染器。如果对象没有变化，您可以缓存网格渲染器以避免多次调用 GetComponents。 

    private MeshRenderer[] meshRenderers;

private void Awake()
{
    meshRenderers = pendingObject.GetComponentsInChildren<MeshRenderer>();
}
...

private void UpdateMaterials()
{
    if (canPlace)
    {
        UpdateMeshRenderersMaterial(materials[0]);
    }
    else
    {
        UpdateMeshRenderersMaterial(materials[1]);
    }
}
private void PlaceObject()
{
    UpdateMeshRenderersMaterial(materials[2]);
}
private void UpdateMeshRenderersMaterial(Material material)
{
    foreach (var meshRenderer in meshRenderers)
    {
        meshRenderer.material = material;
    }
}

    我不会每帧都更新材质（即使放置状态没有改变！）， canPlace 而是将其设为私有，只允许其他组件通过函数进行更改。这样，您就可以响应对象放置状态的变化，从而避免不必要地更改材质。我添加了一个强制更新参数，默认为 false，当该参数为 true 时，将强制更新材质。 

    private bool canPlace;

public void UpdatePlacementStatus(bool canPlace, bool forceUpdate = false)
{
    if (forceUpdate || this.canPlace != canPlace)
    {
        this.canPlace = canPlace;

        UpdateMaterials();
    }
}

    来自其他组件： 

    Debug.Log("Can't Place Building");
buildingManager.UpdatePlacementStatus(false);
返回
作者最近主题：