我正在尝试使用 sigmoid 来连接具有不同嵌入矩阵的两个模型的输出。但我不断在连接线上收到错误。我已经尝试了类似问题的其他建议，但它一直给出相同的错误。我觉得我失去了一些东西，但我找不到它。请帮忙解释一下。谢谢
############################ MODEL 1 ######################################
input_tensor=Input(shape=(35,))
input_layer= Embedding(vocab_size, 300, input_length=35, weights=[embedding_matrix],trainable=True)(input_tensor)
conv_blocks = []
filter_sizes = (2,3,4)
for fx in filter_sizes:
conv_layer= Conv1D(100, kernel_size=fx, activation='relu', data_format='channels_first')(input_layer) #filters=100, kernel_size=3
maxpool_layer = MaxPooling1D(pool_size=4)(conv_layer)
flat_layer= Flatten()(maxpool_layer)
conv_blocks.append(flat_layer)
conc_layer=concatenate(conv_blocks, axis=1)
graph = Model(inputs=input_tensor, outputs=conc_layer)
model = Sequential()
model.add(graph)
model.add(Dropout(0.2))
############################ MODEL 2 ######################################
input_tensor_1=Input(shape=(35,))
input_layer_1= Embedding(vocab_size, 300, input_length=35, weights=[embedding_matrix_1],trainable=True)(input_tensor_1)
conv_blocks_1 = []
filter_sizes_1 = (2,3,4)
for fx in filter_sizes_1:
conv_layer_1= Conv1D(100, kernel_size=fx, activation='relu', data_format='channels_first')(input_layer_1) #filters=100, kernel_size=3
maxpool_layer_1 = MaxPooling1D(pool_size=4)(conv_layer_1)
flat_layer_1= Flatten()(maxpool_layer_1)
conv_blocks_1.append(flat_layer_1) …这是什么意思9.06 GiB reserved in total by PyTorch。
如果我7.80 GiB total capacity对同一个脚本使用较小尺寸的 GPU ，它6.20 GiB reserved in total by PyTorch
会显示 Pytorch 中的预留如何工作以及为什么预留内存会根据 GPU 尺寸而变化？
为了解决错误消息，RuntimeError: CUDA out of memory. Tried to allocate 2.86 GiB (GPU 0; 10.92 GiB total capacity; 9.02 GiB already allocated; 1.29 GiB free; 9.06 GiB reserved in total by PyTorch)我尝试将批量大小从 10 减少到 5 到 3。我尝试使用del x_train1. 我也试过使用torch.cuda.empty_cache(). with torch.no_grad()在应用预x_train1 = bert_model(train_indices)[2]训练模型以及训练和验证新模型时，我也使用过。但它们都不起作用。
这是跟踪： …
是否可以将从 SentenceTransformer 派生的句子表示解码回句子？
请参阅文档中的示例
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
#Our sentences we like to encode
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
#Sentences are encoded by calling model.encode()
embeddings = model.encode(sentences)
我想解码表示以获得句子
embedding = [[-1.76214352e-01 1.20600984e-01 -2.93624014e-01 -2.29858071e-01
-8.22928399e-02 2.37709314e-01 ... 3.39985073e-0]]
sentence = model.decode(embedding)
print(sentence)
'This framework generates embeddings for each input sentence'