input一般是[batch size, maximum sequence length, embedding dimension]也就是 batch_major的格式[b,t,d] *tensorflow实际上会把所有input自己调整成[t,b,d]也就是time_major的格式 假设你设置的batch size是20个sequence/batch,sequence中每个词语的word embedding的维度是128*1,本轮你的20个sequence里面最长的...
- `word_embeds` 是一个嵌入层,它会将单词索引转换为嵌入向量。 - `embedded_sentences` 是填充后的句子经过嵌入层转换后的输出,形状为 `(batch_size, seq_len, embed_dim)`。 - `lstm` 是一个双向 LSTM 层,设置了 `batch_first=True`,因此它接受形状为 `(batch_size, seq_len, input_size)` 的输入。
randn(batch, sentence_length, embedding_dim) layer_norm = torch.nn.LayerNorm(embedding_dim) #在embedding_dim维度上做归一化 output = layer_norm(embedding) print(output.shape) # Image Example N, C, H, W = 20, 5, 10, 10 input = torch.randn(N, C, H, W) layer_norm = torch.nn....
(batch_size, seq_size, dim) layer_norm = torch.nn.LayerNorm(dim, elementwise_affine = False) print("y: ", layer_norm(embedding)) layer_norm_2 = torch.nn.LayerNorm([seq_size,dim], elementwise_affine = False) F=np.array(embedding[1].flatten()) MF=np.mean(F) VF=np.std(F) ...
batch_size=FLAGS.batch_size, seq_length=seq_max_len, emmbedding_size=FLAGS.embedding_dim, word2vec_vocab=word2vec_vocab, word2vec_vec=word2vec_vec, is_shuffle=False) total_loss =0total_acc =0index =0total_correct_predictions =0total_dev_data =0forbatchindev_baches:if(len(batch[0])...
() == 'cpu': encode_kwargs['batch_size'] = 2 else: batch_size_mapping = { 'instructor-xl': 2, 'instructor-large': 3, ('jina-embedding-l', 'bge-large', 'gte-large', 'roberta-large'): 4, 'jina-embedding-s': 9, ('bge-small', 'gte-small'): 10, ('MiniLM',): 20, ...
seq_len_ph = tf.placeholder(tf.int32, [None], name='seq_len_ph') keep_prob_ph = tf.placeholder(tf.float32, name='keep_prob_ph')# Embedding layerwithtf.name_scope('Embedding_layer'): embeddings_var = tf.Variable(tf.random_uniform([vocabulary_size, EMBEDDING_DIM],-1.0,1.0), traina...
GetModelIOTensorDim UnLoadModel SetModelPriority Cancel 模型编译类 BuildModel ReadBinaryProto(const string path) ReadBinaryProto(void* data, uint32_t size) InputMemBufferCreate(void* data, uint32_t size) InputMemBufferCreate(const string path) OutputMemBufferCreate MemBufferDestroy ...
embedding_dim ).to(device) prompt = "USER: {}\n ASSISTANT:".format(prompt) prompt_ids = self.tokenizer.encode(prompt) prompt_length = len(prompt_ids) prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(device) if hasattr(self.llm.model, "embed_tokens"): inputs_embeds = ...
clone_scatter_output_in_embedding ... True consumed_train_samples ... 0 consumed_valid_samples ... 0 context_parallel_size ... 1 create_attention_mask_in_dataloader ... False data_cache_path ... None data_parallel_random_init ... False data_parallel_size ......