在將結果特征向量傳遞給 LDA 模型之前，我正在嘗試使用來自 TensorFlow 集線器的預訓練模型而不是頻率向量化技術進行詞嵌入。
我按照 TensorFlow 模型的步驟進行操作，但是在將結果特征向量傳遞給 LDA 模型時出現此錯誤：
Negative values in data passed to LatentDirichletAllocation.fit
到目前為止，這是我已經實作的：
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow_hub as hub
from sklearn.decomposition import LatentDirichletAllocation
embed = hub.load("https://tfhub.dev/google/tf2-preview/nnlm-en-dim50-with-normalization/1")
embeddings = embed(["cat is on the mat", "dog is in the fog"])
lda_model = LatentDirichletAllocation(n_components=2, max_iter=50)
lda = lda_model.fit_transform(embeddings)
我意識到會print(embeddings)列印一些負值，如下所示：
tf.Tensor(
[[ 0.16589954 0.0254965 0.1574857 0.17688066 0.02911299 -0.03092718
0.19445257 -0.05709129 -0.08631689 -0.04391516 0.13032274 0.10905275
-0.08515751 0.01056632 -0.17220995 -0.17925954 0.19556305 0.0802278
-0.03247919 -0.49176937 -0.07767699 -0.03160921 -0.13952136 0.05959712
0.06858718 0.22386682 -0.16653948 0.19412343 -0.05491862 0.10997339
-0.15811177 -0.02576607 -0.07910853 -0.258499 -0.04206644 -0.20052543
0.1705603 -0.15314153 0.0039225 -0.28694248 0.02468278 0.11069503
0.03733957 0.01433943 -0.11048374 0.11931834 -0.11552787 -0.11110869
0.02384969 -0.07074881]
但是，有沒有辦法解決這個問題？
uj5u.com熱心網友回復：
由于 的fit函式LatentDirichletAllocation不允許負陣列，我建議你在embeddings.
這是代碼片段：
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow_hub as hub
from tensorflow.math import softplus
from sklearn.decomposition import LatentDirichletAllocation
embed = hub.load("https://tfhub.dev/google/tf2-preview/nnlm-en-dim50-with-normalization/1")
embeddings = softplus(embed(["cat is on the mat", "dog is in the fog"]))
lda_model = LatentDirichletAllocation(n_components=2, max_iter=50)
lda = lda_model.fit_transform(embeddings)
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標籤：张量流 scikit-学习 低密度脂蛋白 词嵌入 主题建模