问神经网络中的多变量输出|为什么凯拉斯会产生负binary_cross_entropy？

EN

import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split

#Load the datasets
chunk_train = pd.read_csv('/Users/romeoleon/Downloads/train.csv',chunksize=10**6)
data_train = pd.concat(chunk_train)

#Map the values for sex columns
data_train.sex = data_train.sex.map({'M':0,'F':1})

#Remove the rows with missing data
print('Missing rows represent {} percent of the dataframe'.format(data_train['keywords'].isna().sum()/len(data_train.keywords)*100))

#Drop the missing values
data_train.dropna(inplace=True)

#Plot the distribution of numerical variables
sns.histplot(data_train.age,bins=85)
plt.show()
sns.countplot(x='sex',data=data_train)
plt.show()

#Prepare the data to feed it to the NN
import numpy as np
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

x_train, x_test, y_train, y_test = train_test_split(data_train['keywords'],data_train[["age","sex"]],test_size=0.2)

#Choose parameters
vocab_size = 1000
maxlen = 300
batch_size = 32
embedding_dims = 100
hidden_dims = 5
filters = 250
kernel_size = 3
epochs = 10

#Tokenize the words
tokenizer = Tokenizer(num_words=vocab_size)

tokenizer.fit_on_texts(x_train)

X_train = tokenizer.texts_to_matrix(x_train)
X_test = tokenizer.texts_to_matrix(x_test)

#Pad sequencing : Ensure all sequences have the same length
from tensorflow.keras.preprocessing import sequence
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, MaxPooling1D
from tensorflow.keras.layers import Embedding, LSTM
from tensorflow.keras.layers import Conv1D, Flatten

#Create the model
model = Sequential()
model.add(Embedding(vocab_size, embedding_dims, input_length=maxlen, trainable=True))

model.add(Dropout(0.5))
model.add(Conv1D(filters,
                 kernel_size,
                 padding='valid',
                 activation='relu'))
#model.add(MaxPooling1D(pool_size=4))
model.add(Flatten())
model.add(Dense(hidden_dims, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='sigmoid'))

# Compile neural model
model.compile(loss='binary_crossentropy', # Cross-entropy
                optimizer='adam', # Root Mean Square Propagation
                metrics=['accuracy']) # Accuracy performance metric

model.summary()

#Fit the model
model.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=1,
          validation_data=(X_test, y_test), verbose=1)