本文整理汇总了Python中sklearn.preprocessing.label.MultiLabelBinarizer.transform方法的典型用法代码示例。如果您正苦于以下问题：Python MultiLabelBinarizer.transform方法的具体用法？Python MultiLabelBinarizer.transform怎么用？Python MultiLabelBinarizer.transform使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.preprocessing.label.MultiLabelBinarizer的用法示例。
在下文中一共展示了MultiLabelBinarizer.transform方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: MultiLabelBinarizer
# 需要导入模块: from sklearn.preprocessing.label import MultiLabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.label.MultiLabelBinarizer import transform [as 别名]
doc2vec = Doc2Vec.load(doc2vec_model_location)
# Convert the categories to one hot encoded categories
labelBinarizer = MultiLabelBinarizer()
labelBinarizer.fit([reuters.categories(fileId) for fileId in reuters.fileids()])
# Convert load the articles with their corresponding categories
train_articles = [{'raw': reuters.raw(fileId), 'categories': reuters.categories(fileId)} for fileId in reuters.fileids() if fileId.startswith('training/')]
test_articles = [{'raw': reuters.raw(fileId), 'categories': reuters.categories(fileId)} for fileId in reuters.fileids() if fileId.startswith('test/')]
shuffle(train_articles)
shuffle(test_articles)
# Convert the articles to document vectors using the doc2vec model
train_data = [doc2vec.infer_vector(word_tokenize(article['raw'])) for article in train_articles]
test_data = [doc2vec.infer_vector(word_tokenize(article['raw'])) for article in test_articles]
train_labels = labelBinarizer.transform([article['categories'] for article in train_articles])
test_labels = labelBinarizer.transform([article['categories'] for article in test_articles])
train_data, test_data, train_labels, test_labels = numpy.asarray(train_data), numpy.asarray(test_data), numpy.asarray(train_labels), numpy.asarray(test_labels)
# Initialize the neural network
model = Sequential()
model.add(Dense(input_dim=doc2vec_dimensions, output_dim=500, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(output_dim=1200, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(output_dim=400, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(output_dim=600, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(output_dim=train_labels.shape[1], activation='sigmoid'))
model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])