Question

我有一个包含4000行和两列的数据集。第一列包含一些句子，第二列包含一些数字。有大约4000个句子，它们由大约100个不同的数字分类。例如：

Sentences                                         Codes

Google headquarters is in California              87390
Steve Jobs was a great man                        70214
Steve Jobs has done great technology innovations  70214
Google pixel is a very nice phone                 87390
Microsoft is another great giant in technology    67012
Bill Gates founded Microsoft                      67012

类似地，总共有4000行包含这些句子，并且这些行被分类为100个这样的代码

我尝试了下面的代码，但是当我预测时，它为所有预测相同的值。在其他词中y_pred给出了一个相同值的数组。

我可以知道代码在哪里出错

import pandas as pd
import numpy as np

xl = pd.ExcelFile("dataSet.xlsx")
df = xl.parse('Sheet1') 

#df = df.sample(frac=1).reset_index(drop=True)# shuffling the dataframe


df = df.sample(frac=1).reset_index(drop=True)# shuffling the dataframe
X = df.iloc[:, 0].values
Y = df.iloc[:, 1].values

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import pickle 

count_vect = CountVectorizer()
X = count_vect.fit_transform(X)

tfidf_transformer = TfidfTransformer()
X = tfidf_transformer.fit_transform(X)

X = X.toarray()

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)
y = Y.reshape(-1, 1)  # Because Y has only one column

onehotencoder = OneHotEncoder(categories='auto')
Y = onehotencoder.fit_transform(y).toarray()


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

inputDataLength = len(X_test[0])
outputDataLength = len(Y[0])

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.layers import Dropout

# fitting the model
embedding_vector_length = 100
model = Sequential()
model.add(Embedding(outputDataLength,embedding_vector_length, input_length=inputDataLength))
model.add(Dropout(0.2))
model.add(LSTM(outputDataLength))
model.add(Dense(outputDataLength, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=20)
y_pred = model.predict(X_test)
invorg = model.inverse_transform(y_test)
y_test = labelencoder_Y.inverse_transform(invorg)
inv = onehotencoder.inverse_transform(y_pred)
y_pred = labelencoder_Y.inverse_transform(inv)

Answer 1

即使您有binary_crossentropy个类，您仍在使用100。这不是正确的事情。您必须使用categorical_crossentropy来完成此任务。

像这样编译模型，

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

此外，您正在使用模型进行预测并转换为此类标签，

y_pred = model.predict(X_test)
inv = onehotencoder.inverse_transform(y_pred)
y_pred = labelencoder_Y.inverse_transform(inv)

由于使用softmax激活了模型以获取类标签，因此必须找到预测的argmax。

例如，如果预测为[0.2, 0.3, 0.0005, 0.99]，则必须采用argmax，这将为您提供输出3。可能性很高的班级。

所以您必须像这样修改预测代码，

y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred = labelencoder_Y.inverse_transform(y_pred)

invorg = np.argmax(y_test, axis=1)
invorg = labelencoder_Y.inverse_transform(invorg)

现在，您将在invorg中获得实际的类别标签，并在y_pred中获得预测的类别标签

使用LSTM训练的模型对所有模型仅预测相同的值

1 个答案: