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收到错误:ufunc'subtract'不包含签名匹配类型为dtype('的循环

[英]Getting error: ufunc 'subtract' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')

I'm getting the error I stated in the title on my machine learning project. 我遇到了我在机器学习项目的标题中指出的错误。 I'm following a guide on the internet . 我正在网上浏览指南 here's the parts that I'm getting the error: 这是我遇到错误的部分:

def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x] - instance2[x]), 2)
    return math.sqrt(distance)

def getNeighbors(trainingSet, testInstance, k):
    distances = []
    length = len(testInstance)-1
    for x in range(len(trainingSet)):
        dist = euclideanDistance(testInstance, trainingSet[x], length)
        distances.append((trainingSet[x], dist))
    distances.sort(key=operator.itemgetter(1))
    neighbors = []
    for x in range(k):
        neighbors.append(distances[x][0])
    return neighbors

neighbors = getNeighbors(training_feature_list, test_feature_list, 3)
print(neighbors)

I've looked around the internet about this question and noticed that many people asked this before but as I understand, the problem emerges from trying to use ufunc on different types of variables. 我在互联网上到处都是这个问题,并注意到之前有很多人问过这个问题,但是据我所知,问题出在试图对不同类型的变量使用ufunc。 But my training_feature_list and test_feature_list are similar. 但是我的training_feature_list和test_feature_list相似。

train set goes like [['5.1' '0.2']['4.9' '0.2']...(30 rows) 火车组像[['5.1''0.2'] ['4.9''0.2'] ...(30行)

test set goes like [['4.8' '0.2']['5.4' '0.4']...(20 rows). 测试集就像[['4.8''0.2'] ['5.4''0.4'] ...(20行)。

I'd be so glad if anyone could briefly explain why this problem emerges (because I probably didn't understand it well) and how to fix it. 如果有人能简要解释为什么出现此问题(因为我可能不太了解)以及如何解决它,我将感到非常高兴。

thanks in advance 提前致谢

If your lists really look like [['5.1' '0.2']['4.9' '0.2']... , then the error is probably caused by the fact that you are trying to subtract one string from another as '5.1' is a string, while 5.1 (which you prbably want) is a floating point number. 如果您的列表确实看起来像[[''5.1''0.2'] ['4.9''0.2'] ...,则该错误可能是由于您试图从另一个字符串中减去一个字符串作为'5.1'而引起的是一个字符串,而5.1(您可能希望使用)是一个浮点数。

If that is not the case than another possible cause for the error (although I would expect a different one) is that you are passing lists instead of numpy arrays, which you should preferably do for calculations, as you can not just subtract one list from another. 如果不是这种情况,则可能是导致错误的另一个可能原因(尽管我希望有另一个原因)是您传递的是列表而不是numpy数组,因此最好进行计算,因为您不能只从中减去一个列表另一个。

UFuncTypeError:ufunc 'clip' 不包含具有签名匹配类型的循环(dtype(' <u32’), dtype(‘<u32’), dtype(‘<u32’)) -> dtype(' <u32’)< div><div id="text_translate"><p> 我使用 Deep Pavlov 框架与 Bert 分类器一起工作,只是因为我需要预测人员的语言是俄语。 基本上,我正在尝试解决多类分类问题。 根据 Deep Pavlov,我们可以轻松地更改配置文件上的一些配置。 我拿了这个配置文件<a href="https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json" rel="nofollow noreferrer">https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/configs/classifiers/rusentiment_convers_bert.json</a>并训练它,结果我花了大约 13 个小时才完成它我的 model 过拟合。</p><p> 我做了一些改变,尤其是这些:</p><pre> "weight_decay_rate": 0.001, "learning_rate_drop_patience": 1, "learning_rate_drop_div": 2.0, "load_before_drop": True, "min_learning_rate": 1e-03, "attention_probs_keep_prob": 0.5, "hidden_keep_prob": 0.5,</pre><p> 另外,我增加了批量大小,之前是 16:</p><pre> "batch_size": 32</pre><p> 并添加了一些指标:</p><pre> "log_loss", "matthews_correlation",</pre><p> 还将validation_patience更改为1并添加了tensorboard func</p><pre> "validation_patience": 1, "tensorboard_log_dir": "logs/",</pre><p> 就是这样。 这些是我对 model 所做的所有更改,当我尝试训练我的 model 时,它给了我以下错误:</p><pre> UFuncTypeError Traceback (most recent call last) /usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py in _wrapfunc(obj, method, *args, **kwds) 60 try: ---&gt; 61 return bound(*args, **kwds) 62 except TypeError: 15 frames UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') During handling of the above exception, another exception occurred: UFuncTypeError Traceback (most recent call last) &lt;__array_function__ internals&gt; in clip(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py in _clip_dep_invoke_with_casting(ufunc, out, casting, *args, **kwargs) 83 # try to deal with broken casting rules 84 try: ---&gt; 85 return ufunc(*args, out=out, **kwargs) 86 except _exceptions._UFuncOutputCastingError as e: 87 # Numpy 1.17.0, 2019-02-24 UFuncTypeError: ufunc 'clip' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32')</pre><p> 起初,我认为它与数据集有关,但是,我没有更改我的数据集,并且在我第一次训练这个 model 时它已经运行。 </p></div></u32’)<></u32’),> - UFuncTypeError: ufunc ‘clip’ did not contain a loop with signature matching types (dtype(‘<U32’), dtype(‘<U32’), dtype(‘<U32’)) -> dtype(‘<U32’)

UFuncTypeError:ufunc 'matmul' 不包含具有签名匹配类型的循环(dtype(' <u32'), dtype('<u32')) -> dtype(' <u32') - streamlit< div><div id="text_translate"><pre> #Linear Regression Model @st.cache(allow_output_mutation=True) def linearRegression(X_train, X_test, y_train, y_test): model = LinearRegression() model.fit(X_train,y_train) score = model.score(X_test, y_test)*100 return score, model</pre><hr><pre> #User input for the model def user_input(): bedrooms = st.slider("Bedrooms: ", 1,15) bathrooms = st.text_input("Bathrooms: ") sqft_living = st.text_input("Square Feet: ") sqft_lot = st.text_input("Lot Size: ") floors = st.text_input("Number Of Floors: ") waterfront = st.text_input("Waterfront? For Yes type '1', For No type '0': ") view = st.slider("View (A higher score will mean a better view): ", 0,4) condition = st.slider("House Condition (A higher score will mean a better condition): ", 1,5) yr_built = st.text_input("Year Built: ") yr_reno = st.text_input("A Renovated Property? For Yes type '1', For No type '0': ") zipcode = st.text_input("Zipcode (5 digit): ") year_sold = st.text_input("Year Sold: ") month_sold = st.slider("Month Sold: ", 1,12) user_input_prediction = np.array([bedrooms,bathrooms,sqft_living, sqft_lot,floors,waterfront,view,condition,yr_built,yr_reno,zipcode,year_sold,month_sold]).reshape(1,-1) return(user_input_prediction)</pre><hr><pre> #Main function if(st.checkbox("Start a Search")): user_input_prediction = user_input() st.write('error1') pred = model.predict(user_input_prediction) st.write('error2') if(st.button("Submit")): st.text("success")</pre><p> 我正在使用 Streamlit 构建一个接受用户输入的 ML model。 在我的主要 function 中,它返回错误UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') and trace返回pred = model.predict(user_input_prediction)主 function 将打印出 error1 但不会打印 error2</p></div></u32')></u32'),> - UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32') - Streamlit

UFuncTypeError: 无法从 dtype(' <u32') to dtype('float32') with casting rule 'same_kind'?< div><div id="text_translate"><p> 我正在尝试创建一个 ML model 来对石头、纸和剪刀的手势图像进行分类。 我不断收到如下错误消息:</p><blockquote><p> UFuncTypeError:无法使用转换规则“same_kind”将 ufunc 'multiply' output 从 dtype('&lt;U32') 转换为 dtype('float32')</p></blockquote><p> 这是我的代码:</p><pre> import tensorflow as to from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow import keras from tensorflow.keras import layers:wget --no-check-certificate \ https.//dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip -O /tmp/rockpaperscissors,zip import zipfile.os local_zip = '/tmp/rockpaperscissors.zip' zip_ref = zipfile,ZipFile(local_zip. 'r') zip_ref.extractall('/tmp') zip_ref.close(),pip install split_folders import split_folders as SF sf,ratio('/tmp/rockpaperscissors/rps-cv-images', output="/tmp/rockpaperscissors/data".seed=1337, ratio=(.8. .2)) root_path = '/tmp/rockpaperscissors/data' train_path = os,path.join(root_path. 'train') validation_path = os,path,join(root_path, 'val') train_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0,2, fill_mode = 'nearest') test_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0.2, fill_mode = 'nearest') train_generator = train_datagen,flow_from_directory( train_path, target_size=(150, 150). batch_size=32, class_mode='categorical') validation_generator = test_datagen,flow_from_directory( validation_path, target_size=(150, 150). batch_size=32. class_mode='categorical') model = keras.Sequential() model,add(layers,Conv2D(32, (5,5), activation='relu', input_shape=(150. 150. 3))) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(64, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(128, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(256, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(512, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model.add(layers.Flatten()) model,add(layers.Dense(512. activation='relu')) model,add(layers.Dense(3. activation='softmax')) model.summary() loss_fn = keras.losses,SparseCategoricalCrossentropy() model,compile(loss=loss_fn. optimizer=RMSprop(), metrics=['accuracy']) model,fit( train_generator, steps_per_epoch=54, epochs=22, validation_data=validation_generator, validation_steps=13, verbose=2)</pre><p> 这是我的代码的链接: <a href="https://colab.research.google.com/drive/1stBPFyuIQTU_2LqDSHLlrLOSSBeuYLNT#scrollTo=r3Q3w-Tm6tnX" rel="nofollow noreferrer">Rock Paper Scissors Classifier</a>谢谢!</p></div></u32')> - UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('<U32') to dtype('float32') with casting rule 'same_kind'?

在 pandas dataframe 中将一列乘以另一列,但得到一个 dtype(' <u32') error< div><div id="text_translate"><p> 我有一个 dataframe ,我想将一列中的每一行乘以另一列(<strong>列 Line Limit 是数据类型 float64</strong> )并返回这些的总和。</p><p> <a href="https://i.stack.imgur.com/2yOSw.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2yOSw.png" alt="在此处输入图像描述"></a></p><pre> def pol_stats(df,refcol,linecol,limitcol): """Summarise account file.""" acc_count = len(pd.unique(df[refcol])) acc_exp = sum((df[linecol] / 100) * limitcol) return (acc_count,acc_exp) pol_stats(df,'Reference','Line','Limit')</pre><p> 但是我收到错误'ufunc'multiply'没有包含签名匹配类型(dtype('&lt;U32'),dtype('&lt;U32'))-&gt; dtype('&lt;U32')'的循环。</p><p> 我尝试在 limitcol 上使用float()或to_numeric()但仍然出现错误。 如果两列都是浮点数据类型,不知道为什么这会是一个问题。</p></div></u32')> - Multiplying one column by another in pandas dataframe but getting a dtype('<U32') error

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Python:Numpy dtype U32 - 简单的 if-else 语句 - Python: Numpy dtype U32 - simple if-else statement UFuncTypeError: 无法从 dtype(' <u32') to dtype('float32') with casting rule 'same_kind'?< div><div id="text_translate"><p> 我正在尝试创建一个 ML model 来对石头、纸和剪刀的手势图像进行分类。 我不断收到如下错误消息:</p><blockquote><p> UFuncTypeError:无法使用转换规则“same_kind”将 ufunc 'multiply' output 从 dtype('&lt;U32') 转换为 dtype('float32')</p></blockquote><p> 这是我的代码:</p><pre> import tensorflow as to from tensorflow.keras.optimizers import RMSprop from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow import keras from tensorflow.keras import layers:wget --no-check-certificate \ https.//dicodingacademy.blob.core.windows.net/picodiploma/ml_pemula_academy/rockpaperscissors.zip -O /tmp/rockpaperscissors,zip import zipfile.os local_zip = '/tmp/rockpaperscissors.zip' zip_ref = zipfile,ZipFile(local_zip. 'r') zip_ref.extractall('/tmp') zip_ref.close(),pip install split_folders import split_folders as SF sf,ratio('/tmp/rockpaperscissors/rps-cv-images', output="/tmp/rockpaperscissors/data".seed=1337, ratio=(.8. .2)) root_path = '/tmp/rockpaperscissors/data' train_path = os,path.join(root_path. 'train') validation_path = os,path,join(root_path, 'val') train_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0,2, fill_mode = 'nearest') test_datagen = ImageDataGenerator( rescale = "none", rotation_range = 30, vertical_flip = True. horizontal_flip = True, zoom_range = 0.1, width_shift_range = 0.1, height_shift_range = 0.1, shear_range = 0.2, fill_mode = 'nearest') train_generator = train_datagen,flow_from_directory( train_path, target_size=(150, 150). batch_size=32, class_mode='categorical') validation_generator = test_datagen,flow_from_directory( validation_path, target_size=(150, 150). batch_size=32. class_mode='categorical') model = keras.Sequential() model,add(layers,Conv2D(32, (5,5), activation='relu', input_shape=(150. 150. 3))) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(64, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(128, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(256, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model,add(layers,Conv2D(512, (3.3). activation='relu')) model,add(layers.MaxPooling2D(2. 2)) model.add(layers.Flatten()) model,add(layers.Dense(512. activation='relu')) model,add(layers.Dense(3. activation='softmax')) model.summary() loss_fn = keras.losses,SparseCategoricalCrossentropy() model,compile(loss=loss_fn. optimizer=RMSprop(), metrics=['accuracy']) model,fit( train_generator, steps_per_epoch=54, epochs=22, validation_data=validation_generator, validation_steps=13, verbose=2)</pre><p> 这是我的代码的链接: <a href="https://colab.research.google.com/drive/1stBPFyuIQTU_2LqDSHLlrLOSSBeuYLNT#scrollTo=r3Q3w-Tm6tnX" rel="nofollow noreferrer">Rock Paper Scissors Classifier</a>谢谢!</p></div></u32')> - UFuncTypeError: Cannot cast ufunc 'multiply' output from dtype('<U32') to dtype('float32') with casting rule 'same_kind'? 在 pandas dataframe 中将一列乘以另一列,但得到一个 dtype(' <u32') error< div><div id="text_translate"><p> 我有一个 dataframe ,我想将一列中的每一行乘以另一列(<strong>列 Line Limit 是数据类型 float64</strong> )并返回这些的总和。</p><p> <a href="https://i.stack.imgur.com/2yOSw.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/2yOSw.png" alt="在此处输入图像描述"></a></p><pre> def pol_stats(df,refcol,linecol,limitcol): """Summarise account file.""" acc_count = len(pd.unique(df[refcol])) acc_exp = sum((df[linecol] / 100) * limitcol) return (acc_count,acc_exp) pol_stats(df,'Reference','Line','Limit')</pre><p> 但是我收到错误'ufunc'multiply'没有包含签名匹配类型(dtype('&lt;U32'),dtype('&lt;U32'))-&gt; dtype('&lt;U32')'的循环。</p><p> 我尝试在 limitcol 上使用float()或to_numeric()但仍然出现错误。 如果两列都是浮点数据类型,不知道为什么这会是一个问题。</p></div></u32')> - Multiplying one column by another in pandas dataframe but getting a dtype('<U32') error ufunc&#39;add&#39;不包含签名匹配类型为dtype(&#39; - ufunc 'add' did not contain a loop with signature matching types dtype('<U23') dtype('<U23') dtype('<U23')
 
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