先试试这个
python 38 的情况
pip install tensorflow==2.6.2 Keras==2.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
pip install tensorflow==2.6.2 Keras==2.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
pip install protobuf==3.20.1 -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
pip install torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==4.13.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install --upgrade nni --ignore-installed -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install gensim==4.2.0 -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
pip install numpy==1.20.3 -i https://pypi.tuna.tsinghua.edu.cn/simple
#model = Word2Vec(LineSentence(open('word2vec_txt.txt', 'r', encoding='utf-8')), sg=0, vector_size=64, window=8,min_count=2, workers=4)
# 模型保存
# model.save('word2vec.model')
# 通过模型加载词向量(recommend)
model_vec = gensim.models.Word2Vec.load('word2vec.model')
dic = model_vec.wv.index_to_key
# print(dic)
print(len(dic))
print(model_vec.wv['痔疮'])
print(model_vec.wv.most_similar('痔疮', topn=2))
print(query_list[:10])
不仅仅是rmsprop优化器，adam也是一样。
调用adam优化器
使用
optimizer =adam_v2.Adam(learning_rate=1e-4)
而不是
optimizer = Adam(lr=1e-4)
调用rmsprop优化器
使用
optimizer =rmsprop_v2.rmsprop(learning_rate=1e-4)
而不是
optimizer = rmsprop(lr=1e-4) 或 optimizer = RMSprop(lr=1e-4)
