tf_glove.py 文件源码

def generate_tsne(self, path="glove/model/model", size=(100, 100), word_count=1000, embeddings=None):
        if embeddings is None:
            embeddings = self.embeddings
        from sklearn.manifold import TSNE
        tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
        low_dim_embs = tsne.fit_transform(numpy.asarray(list(embeddings.values())))
        labels = self.words[:word_count]
        return _plot_with_labels(low_dim_embs, labels, path, size)