for j=1:num_layers lamdpor(j) = lamds(j) .* (1 - phi(j)) + phi(j) .* lamdaf(j); dtt=dz*qg/lamdpor(j); %竖向网格温差 if(j==1) j00=2; j01=nz/4+1; elseif(j==2) j00=nz/4+2; j01=2*nz/4+1; elseif(j==3) j00=2*nz/4+2; j01=3*nz/4+1; else j00=3*nz/4+2; j01=nz+1; end for i=j00:j01 Tc=Tc+dtt; if(i<=nj) T0f1(i)=Tc; T0f2(i)=Tc; end end end优化代码

serverless-puppeteer-layers：无服务器框架+ AWS Lambda层+ Puppeteer =:red_heart_selector:

使用AWS Lambda层的无服务器伪造... address=https://google.com 或直接将此ARN应用于您的函数： arn:aws:lambda:us-east-1:085108115628:layer:chrome:6自行捆绑最新的Chromium 先决条件： brotli./download-latest.sh

04_knn_layers9k5_k近邻_K._wayck1_

使用k近邻算法，基于吴恩达的课程进行整理。使用多个例子。

word2vec.rar_VEC-361_layers5cb_vec361_word2vec_word2vec 中文

word2vec的实现源码，并加上中文注解！

qg=0.075; ha=15; Ta=15; nz=40; phi=0.1; dz=50; num_layers=4; nj=41; lamdaf=[0,0,0.618,0]; lamds=[1,2,3,4]; Tc=qg/ha+Ta; %地表温度 T0f1(1)=Tc; T0f2(1)=Tc; j0 = [2, nz/4+2, 2nz/4+2, 3nz/4+2, nz+1]; % 计算j00和j01的起始值 j00 = j0(1:end-1); j01 = j0(2:end) - 1; lamdpor = lamds .* (1 - phi) + phi .* lamdaf; % 计算lamdpor dzqg = dz * qg; % 计算dz*qg Tc = cumsum(dzqg ./ lamdpor) + Ta; % 计算温度，加上Ta for j = 1:num_layers T0f1(j00(j):min(j01(j),nj)) = Tc(j00(j):min(j01(j),nj)); % 将温度赋值给T0f1 T0f2(j00(j):min(j01(j),nj)) = Tc(j00(j):min(j01(j),nj)); % 将温度赋值给T0f2 end超出维度

这段代码可能出现超出维度的问题是因为在第12...T0f1(j00(j):min(j01(j),nj)) = Tc(j00(j):min(j01(j),nj)); T0f2(j00(j):min(j01(j),nj)) = Tc(j00(j):min(j01(j),nj)); 这样可以保证不会超出数组 Tc 的维度。

class GraphSAGE(nn.Module): def init(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).init() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下，让它适用于异质图

for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self....

layers = [] num_levels = len(num_channels) for i in range(num_levels): dilation_size = 2 ** i in_channels = num_inputs if i == 0 else num_channels[i-1] out_channels = num_channels[i] layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size, padding=(kernel_size-1) * dilation_size, dropout=dropout)] self.network = nn.Sequential(*layers)每句话什么意思

3. for i in range(num_levels): 这一行代码开始循环构建多个TemporalBlock层，i为循环变量，从0到num_levels-1。 4. dilation_size = 2 ** i 这一行代码计算当前TemporalBlock层的空洞卷积的空洞大小，每个...

解释这段代码： def init(self, input_size, hidden_size=1, output_size=4, num_layers=1): super().init() self.lstm = nn.LSTM(input_size, hidden_size, num_layers) self.forwardCalculation = nn.Linear(hidden_size, output_size)

默认情况下，hidden_size 是 1，output_size 是 4，num_layers 是 1。在构造函数中，首先调用了 super().__init__()，这是为了继承并初始化父类的构造函数。然后，定义了一个 LSTM 层 self.lstm，使用了...

解释： num_features = num_init_features self.layer1 = _DenseBlock(num_layers=blocks[0], num_input_features=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[0] * growth_rate

在这段代码中，首先将 num_features 设为初始特征数量 num_init_features，然后定义一个 DenseBlock，其中 num_layers=blocks[0] 表示该 DenseBlock 中有 blocks[0] 个卷积层，num_input_features=num_features 表示...

import random filename = 'supercu.lmp' file_object = open(filename,'r') lines = file_object.readlines() num_layers = 150 num_atom_a_layer = 20000 idx_gradient = 0.1 num_random = [] for idx_layer in range(1,num_layers+1): num_cu_float = pow(idx_layer/num_layers,idx_gradient)num_atom_a_layer num_cu = int(num_cu_float) list_random = random.sample(range((idx_layer-1)num_atom_a_layer,idx_layer*num_atom_a_layer),num_cu) num_random = num_random + list_random num_random.sort() for index in range (len(lines)): strT = lines[index] strL = strT.split() if int(strL[0]) in num_random: strT = strT[:14]+'2'+strT[15:] lines[index] = strT file_object.close strTT = "".join(lines) file_object = open(filename,'w') file_object.write(strTT) file_object.close

idx_layer_ratio = [idx_layer/num_layers for idx_layer in range(1,num_layers+1)] num_cu_float_list = [pow(idx, idx_gradient)*num_atom_a_layer for idx in idx_layer_ratio] 3. 可以使用列表推导式代替...

def flops(self): flops = 0 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() # flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) # flops += self.num_features * self.num_classes return flops

这是一个类中的方法，用于计算模型的 FLOPs（floating-point operations，即浮点运算量），可以用于评估模型的计算复杂度。具体实现中，首先计算输入图像的 Patch Embedding 的 FLOPs，然后循环遍历所有的 ...

我需要你帮我优化代码，尽可能的来降低运算量，减少for循环的使用，多采用其他库，代码如下： import random filename = 'supercu.lmp' file_object = open(filename,'r') lines = file_object.readlines() num_layers = 150 num_atom_a_layer = 20000 idx_gradient = 0.1 num_random = [] for idx_layer in range(1,num_layers+1): num_cu_float = pow(idx_layer/num_layers,idx_gradient)num_atom_a_layer num_cu = int(num_cu_float) list_random = random.sample(range((idx_layer-1)num_atom_a_layer,idx_layer*num_atom_a_layer),num_cu) num_random = num_random + list_random num_random.sort() for index in range (len(lines)): strT = lines[index] strL = strT.split() if int(strL[0]) in num_random: strT = strT[:14]+'2'+strT[15:] lines[index] = strT file_object.close strTT = "".join(lines) file_object = open(filename,'w') file_object.write(strTT) file_object.close

for idx_layer in range(1,num_layers+1): num_cu_float = pow(idx_layer/num_layers,idx_gradient)*num_atom_a_layer num_cu = int(num_cu_float) list_random = random.sample(range((idx_layer-1)*num_atom_a_...

帮我优化这个代码，必须要降低运算量，多采用dataframe或者其他库 import random filename = 'supercu.lmp' file_object = open(filename,'r') lines = file_object.readlines() num_layers = 150 num_atom_a_layer = 20000 idx_gradient = 0.1 num_random = [] for idx_layer in range(1,num_layers+1): num_cu_float = pow(idx_layer/num_layers,idx_gradient)num_atom_a_layer num_cu = int(num_cu_float) list_random = random.sample(range((idx_layer-1)num_atom_a_layer,idx_layer*num_atom_a_layer),num_cu) num_random = num_random + list_random num_random.sort() for index in range (len(lines)): strT = lines[index] strL = strT.split() if int(strL[0]) in num_random: strT = strT[:14]+'2'+strT[15:] lines[index] = strT file_object.close strTT = "".join(lines) file_object = open(filename,'w') file_object.write(strTT) file_object.close

for idx_layer in range(1, num_layers+1): num_cu_float = pow(idx_layer/num_layers, idx_gradient)*num_atom_a_layer num_cu = int(num_cu_float) list_random = random.sample(range((idx_layer-1)*num_atom_...

这段代码中加一个test loss功能 class LSTM(nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().init() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if name == 'main': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")

if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次") 在这个代码中，我们首先加载了测试...

逐行解释代码： def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

- weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda()：创建一个大小为(self.num_layers, self.len_seq)的张量，每个元素初始化为(1.0 / self.len_seq)，并将其移动到GPU上...

解释class GraphMLPEncoder(FairseqEncoder): def init(self, args): super().init(dictionary=None) self.max_nodes = args.max_nodes self.emb_dim = args.encoder_embed_dim self.num_layer = args.encoder_layers self.num_classes = args.num_classes self.atom_encoder = GraphNodeFeature( num_heads=1, num_atoms=512*9, num_in_degree=512, num_out_degree=512, hidden_dim=self.emb_dim, n_layers=self.num_layer, ) self.linear = torch.nn.ModuleList() self.batch_norms = torch.nn.ModuleList() for layer in range(self.num_layer): self.linear.append(torch.nn.Linear(self.emb_dim, self.emb_dim)) self.batch_norms.append(torch.nn.BatchNorm1d(self.emb_dim)) self.graph_pred_linear = torch.nn.Linear(self.emb_dim, self.num_classes)

它具有一些参数，如头数（num_heads）、原子数（num_atoms）、入度数（num_in_degree）、出度数（num_out_degree）、隐藏维度（hidden_dim）和层数（n_layers）。然后，它创建了两个列表：linear和batch_norms。...

这段代码哪里错了 class my_BiGRU(torch.nn.Module): def init(self, input_size, hidden_size, num_layers, output_size, gru_dropout): super(my_BiGRU, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.gru_dropout = gru_dropout self.gru = torch.nn.GRU(input_size, hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True) self.fc = torch.nn.Linear(hidden_size * 2, output_size) def _setup_BiGRU_layers(self): self.BiGRU_layers = my_BiGRU(input_size=self.args.capsule_out_dim, output_size=self.args.apsule_out_features, hidden_size=self.args.BiGRU_hiddensize, num_layers=self.args.BiGRU_layers, gru_dropout=self.args.BiGRU_dropout, )

1.函数 _setup_BiGRU_layers 中的代码应该是在类的构造函数 __init__ 中进行初始化，而不是在另外一个函数中进行初始化。 2.在调用 my_BiGRU 类时，参数名称有误，应该将 apsule_out_features 改为 ...

关于keras.layers.Conv1D的kernel_size参数使用介绍

主要介绍了关于keras.layers.Conv1D的kernel_size参数使用介绍，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

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word2vec.rar_VEC-361_layers5cb_vec361_word2vec_word2vec 中文

解释这段代码： def __init__(self, input_size, hidden_size=1, output_size=4, num_layers=1): super().__init__() self.lstm = nn.LSTM(input_size, hidden_size, num_layers) self.forwardCalculation = nn.Linear(hidden_size, output_size)

解释： num_features = num_init_features self.layer1 = _DenseBlock(num_layers=blocks[0], num_input_features=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate) num_features = num_features + blocks[0] * growth_rate

def flops(self): flops = 0 flops += self.patch_embed.flops() for i, layer in enumerate(self.layers): flops += layer.flops() # flops += self.num_features * self.patches_resolution[0] * self.patches_resolution[1] // (2 ** self.num_layers) # flops += self.num_features * self.num_classes return flops

关于keras.layers.Conv1D的kernel_size参数使用介绍

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解释这段代码： def init(self, input_size, hidden_size=1, output_size=4, num_layers=1): super().init() self.lstm = nn.LSTM(input_size, hidden_size, num_layers) self.forwardCalculation = nn.Linear(hidden_size, output_size)