调用此即可: class DropPath(nn.Module): def __init__(self, drop_prob=None): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, x): return drop_path(x, self.drop_prob, self.training) 参考资料
AI代码解释 model.patch_embed=backward_hook_wrapper(model.patch_embed)model.pos_drop=backward_hook_wrapper(model.pos_drop)model.patch_drop=backward_hook_wrapper(model.patch_drop)model.norm_pre=backward_hook_wrapper(model.norm_pre)model.blocks=backward_hook_wrapper(model.blocks)model.norm=backward_ho...
self.drop_path = StochasticDepth(drop_p, mode="batch") def forward(self, x: Tensor) -> Tensor: res = x x = self.block(x) x = self.layer_scaler(x) x = self.drop_path(x) x += res return x 好了,现在我们看看最终结果 stage = ConvNexStage(32, 62, depth=1) stage(torch.randn...
qk_norm=False, proj_drop=0., attn_drop=0., init_values=None, drop_path=0., act_layer=None, norm_layer=None, mlp_layer=None ): super().__init__( hidden_size=dim, ffn_hidden_size=int(dim * mlp_ratio), num_attention_heads=num_heads, hidden_dropout=proj_drop, attention_dropout=...
bias=False,qk_norm=False,proj_drop=0.,attn_drop=0.,init_values=None,drop_path=0.,act_layer=None,norm_layer=None,mlp_layer=None):super().__init__(hidden_size=dim,ffn_hidden_size=int(dim * mlp_ratio),num_attention_heads=num_heads,hidden...
def train(batch_size: int=64,num_time_steps: int=1000,num_epochs: int=15,seed: int=-1,ema_decay: float=0.9999,lr=2e-5,checkpoint_path: str=None):set_seed(random.randint(0, 2**32-1)) if seed == -1 else set_seed(...
path: path to save best model """ f_path = checkpoint_path # save checkpoint data to the path given, checkpoint_path torch.save(state, f_path) # if it is a best model, min validation loss if is_best: best_fpath = best_model_path # copy that checkpoint fi...
sys.path.append(".") import d2lzh_pytorch as d2l def dropout(X, drop_prob): X = X.float() assert 0 <= drop_prob <= 1 keep_prob = 1 - drop_prob # 这种情况下把全部元素都丢弃 if keep_prob == 0: return torch.zeros_like(X) ...
%matplotlib inline import torch import torch.nn as nn import numpy as np import sys sys.path.append('..') import d2lzh_pytorch as d2l def dropout(X,drop_prob): X = X.float() assert 0<=drop_prob<=1 keep_prob = 1-drop_prob if keep_prob==0: return torch.torch.zeros_like(X) ...
pytorch 手写droplast pytorch在线编写 文章目录 前言 一、词向量运算 1.数据准备 2.余弦相似度 3.词类类比 二、表情生成器V1 三、表情生成器V2 1.构造嵌入层embedding_layer 2.Dataloader 3.构造LSTM 4.模型训练 5.实验结果 前言 本博客只是记录一下本人在深度学习过程中的学习笔记和编程经验,大部分...