Python 如何使用KERA确定类别？
我正在研究一种使用Keras对邮件进行分类的方法。我阅读已经分类的邮件，标记它们以创建一个链接到文件夹的字典 所以我用熊猫创建了一个数据框：Python 如何使用KERA确定类别？,python,machine-learning,keras,nlp,classification,Python,Machine Learning,Keras,Nlp,Classification,我正在研究一种使用Keras对邮件进行分类的方法。我阅读已经分类的邮件，标记它们以创建一个链接到文件夹的字典 所以我用熊猫创建了一个数据框： data = pd.DataFrame(list(zip(lst, lst2)), columns=['text', 'folder']) 文本列是电子邮件中存在的所有单词所在的位置，文件夹列是电子邮件所属的类（路径） 正因为如此，我创建了我的模型，该模型给出了这些结果： 3018/3018[===============================
data = pd.DataFrame(list(zip(lst, lst2)), columns=['text', 'folder'])
model.predict\u classes（numpy.array）#lst contains all the words in the mail
#lst2 the class/path of lst
data = pd.DataFrame(list(zip(lst, lst2)), columns=['text', 'folder'])
train_size = int(len(data) * .8)
train_posts = data['text'][:train_size]
train_tags = data['folder'][:train_size]
test_posts = data['text'][train_size:]
test_tags = data['folder'][train_size:]
num_labels = 200 #The numbers of total classes
#the way I tokenize and encode my data
tokenizer = Tokenizer(num_words=len(lst))
tokenizer.fit_on_texts(pd.concat([train_posts, test_posts], axis = 1))
x_train = tokenizer.texts_to_matrix(train_posts, mode=TOKENISER_MODE)
x_test = tokenizer.texts_to_matrix(test_posts, mode=TOKENISER_MODE)
encoder = preprocessing.LabelBinarizer()
encoder.fit(train_tags)
y_train = encoder.transform(train_tags)
y_test = encoder.transform(test_tags)
#my model, vocab_size = len(lst) = number of the words present in the mails
model = Sequential()
model.add(Dense(16, input_shape=(vocab_size,)))
model.add(Activation('elu'))
model.add(Dropout(0.2))
model.add(Dense(32))
model.add(Activation('elu'))
model.add(Dropout(0.2))
model.add(Dense(16))
model.add(Activation('elu'))
model.add(Dropout(0.2))
model.add(Dense(num_labels))
model.add(Activation('sigmoid'))
model.summary()
#compile training and evaluate
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=batch_size, epochs=100, verbose=1, validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])
#read the random file
sentences = read_files("mail.eml")
sentences = ' '.join(sentences)
sentences = sentences.lower()
salut = unidecode.unidecode(sentences)
#predict
pred = model.predict_classes(salut, batch_size=batch_size, verbose=1)
print(pred)
predread\u filesmodel.predict\u classes（）pred对于初学者，假设您处于多类别设置中（每个样本只能属于一个类别），而不是
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# this gives probability values, an array of shape (n_samples, n_labels)
preds_prob = model.predict(salut)
# this gives the name of classes with prob > 0.5
preds_cls = encoder.inverse_transform(preds_prob, 0.5)