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Functions57 in github.com/FengQuanLi/ResnetGPT

↓ 10 callersMethod发送
(self,内容)
运行辅助.py:13
↓ 3 callersMethod__init__
(self, d_model, eps = 1e-6)
Sublayers.py:9
↓ 2 callersMethod__init__
(self, vocab_size, d_model, N, heads, dropout, 最大长度=1024)
ModelA.py:15
↓ 2 callersMethod__init__
(self, vocab_size, d_model)
Embed.py:10
↓ 2 callersFunctioncreate_masks
(src, trg, device)
Batch.py:15
↓ 2 callersFunctionget_key_name
(key)
训练数据截取_A.py:48
↓ 2 callersFunctionget_model
(opt, trg_vocab,model_weights='model_weights')
ModelA.py:71
↓ 2 callersFunction读出引索
(词_数表路径, 数_词表路径)
取训练数据.py:52
↓ 1 callersFunctionattention
(q, k, v, d_k, mask=None, dropout=None)
Sublayers.py:25
↓ 1 callersFunctiongelu
(x)
Sublayers.py:6
↓ 1 callersFunctionget_clones
(module, N)
ModelA.py:9
↓ 1 callersFunctionnopeak_mask
(size, device)
Batch.py:7
↓ 1 callersFunction取图
(窗口名称)
运行辅助.py:16
↓ 1 callersFunction处理方向
()
训练数据截取_A.py:137
↓ 1 callersFunction打印抽样数据
(数_词表,数据, 输出_分)
杂项.py:4
Method__init__
( self, vocab_size_or_config_json_file=12491, n_positions=1024,
config.py:3
Method__init__
( self, d_model=768, n_layers=12, heads=12, dropou
config.py:24
Method__init__
(self, d_model, heads, dropout=0.1)
Layers.py:7
Method__init__
(self,ID)
运行辅助.py:9
Method__init__
(self, trg_vocab, d_model, N, heads, dropout,图向量尺寸=6*6*2048)
ModelA.py:37
Method__init__
(self, trg_vocab, d_model, N, heads, dropout,图向量尺寸=1000)
ModelA.py:54
Method__init__
(self, heads, d_model, dropout = 0.1)
Sublayers.py:42
Method__init__
(self, d_model, d_ff=2048, dropout = 0.1)
Sublayers.py:81
Method__init__
(self,输入_接口, 输出_接口)
Sublayers.py:94
Method__init__
(self, resnet)
resnet_utils.py:6
Method__init__
(self, d_model, max_seq_len=1024, dropout=0.1)
Embed.py:20
Method__init__
(self, num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2., scale_gr
Embed.py:50
Functionbatch_size_fn
Keep augmenting batch and calculate total number of tokens + padding.
Batch.py:52
Methodcreate_batches
(self)
Batch.py:33
Methodforward
(self, x, trg_mask)
Layers.py:21
Methodforward
(self,图向量,操作 ,trg_mask)
ModelA.py:23
Methodforward
(self, 图向量 ,操作, trg_mask)
ModelA.py:46
Methodforward
(self, 图向量 , trg_mask)
ModelA.py:63
Methodforward
(self, x)
Sublayers.py:20
Methodforward
(self, q, k, v, mask=None)
Sublayers.py:56
Methodforward
(self, x)
Sublayers.py:89
Methodforward
(self, x)
Sublayers.py:101
Methodforward
(self, img, att_size=6)
resnet_utils.py:10
Methodforward
(self, x)
Embed.py:15
Methodforward
(self, x)
Embed.py:36
Methodforward
(self, input)
Embed.py:85
Functionget_modelB
(opt, trg_vocab)
ModelA.py:97
Functionnopeak_mask
(size, device)
杂项.py:14
Functionon_press
(key)
训练数据截取_A.py:57
Functionon_release
(key)
训练数据截取_A.py:109
Methodpool
(d, random_shuffler)
Batch.py:35
Methodreset_parameters
(self)
Embed.py:79
Functionstart_listen
()
训练数据截取_A.py:134
Function写出词标号引索
(总词表, 词_数表路径, 数_词表路径)
取训练数据.py:24
Function打印测试数据
(数_词表,数据, 输人_分,标签)
杂项.py:21
Function打印测试数据_A
(数_词表,数据, 输人_分)
杂项.py:46
Function生成测试用numpy数组
(输入表单, 词_数表)
取训练数据.py:123
Function生成测试用numpy数组_A
(输入表单, 词_数表)
取训练数据.py:211
Function生成训练用numpy数组
(输入表单, 词_数表, numpy数组路径)
取训练数据.py:60
Function生成训练用numpy数组_A
(输入表单, 词_数表, numpy数组路径)
取训练数据.py:133
Function读取训练数据
(路径)
取训练数据.py:3
Function读取训练数据_A
(路径)
取训练数据.py:198