normalize_function,register_jagged_func@register_jagged_func(torch.ops.aten.embedding_dense_backward.default,"self: jt, grad_output: jt, num_weights: any, padding_idx: any, scale_grad_by_freq: any",)defembedding_dense_backward(func,*args,**kwargs):_,new_kwargs=normalize_function(# type: ...
torch.nn.Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=None) 第一个参数 num_embeddings:代表你的embedding有多少个,也可以等价于你期望的词库大小。 第二个参数emb...
TT-Emb supports fused gradient computation and weight updates for better efficiency, where the weights of embedding tables are updated along with backward propagation. If the network is trained with an external optimizer, the gradients will no longer be returned to the optimizer. To enable the fuse...
利用语言模型来训练,分为Forward LM和Backward LM;输入均为一行文本,然后接Embedding层,接着接LM层(LM层是rnn或者lstm实现,可以为单向也可以为双向;Forward LM层输入为一个正向文本序列,预测下一个词;Backward LM输入为反向的文本序列,预测也为下一个词;也就是说这连两个输入序列顺序不一样,但预测结果是一样),...
self.add(Merge([language_forward, language_backward])) self.deep_mlp() self.add(Dense(self._config.output_dim)) self.add(Activation('softmax')) 开发者ID:mateuszmalinowski,项目名称:visual_turing_test-tutorial,代码行数:24,代码来源:model_zoo.py ...
() loss.backward() optimizer.step() train_total_loss += loss.item() sku_encoder_embedding[(i * batch_size) : (i * batch_size + x_input.shape[0])] = encoded.detach().to('cpu').numpy() train_avg_loss = train_total_loss / train_loader_len logger.info(f'epoch: {epoch + 1}...
Input:输入下标大小,输入范围为:>=0,< num_embedding,按照词的index进行编码 output:输出编码后的向量,前面的维度与input的前面的维度相同,维度为embedding_dim 初始化的规则:均值为0,方差为1的正态分布,并且是可学习的 weight (Tensor) – the learnable weights of the module of shape (num_embeddings, embed...
# 需要导入模块: from mxnet.gluon import nn [as 别名]# 或者: from mxnet.gluon.nn importEmbedding[as 别名]def__init__(self, mode, vocab_size, num_embed, num_hidden, num_layers, dropout=0.5, tie_weights=False, **kwargs):super(RNNModel, self).__init__(**kwargs)withself.name_scope...
() weights = None if weight_name: weights = [int(G[u][v][weight_name]) for u,v in edges] labels = nx.get_edge_attributes(G, weight_name) nx.draw_networkx_labels(G, pos, edge_labels=labels) nx.draw_networkx(G, pos, edges=edges, width=weights) else: nodelist1 = [] nodelist...
{ return at::embedding_dense_backward( grad, indices, num_weights, padding_idx, scale_grad_by_freq); } } Tensor embedding_sparse_backward( const Tensor & grad_, const Tensor & indices_, int64_t num_weights, int64_t padding_idx, bool scale_grad_by_freq) { auto indices_arg...