from keras.preprocessing.sequence import pad_sequences from keras.datasets import imdb from torch.utils.data import TensorDataset,DataLoader,RandomSampler,SequentialSampler MAX_WORDS = 10000 # imdb’s vocab_size 即词汇表大小 MAX_LEN = 200 # max length BATCH_SIZE = 256 EMB_SIZE = 128 # embedding...
def collate_fn_padd(batch): ''' Padds batch of variable length note: it converts things ToTensor manually here since the ToTensor transform assume it takes in images rather than arbitrary tensors. ''' ## get sequence lengths lengths = torch.tensor([ t.shape[0] for t in batch ]).to...
df[col] = pad_sequences(df[col], maxlen=seq_max_len, value=0, padding=padding, truncating=truncating).tolist() input_dict = df_to_dict(df)returninput_dict 2. Deep Neural Networks for YouTube Recommendations (YoutubeDNN) YoutubeDNN 是2016年发布的,介绍了 YouTube 推荐系统全方位引入深度...
# padding padding='post'or padding='pre'--- x0=sequence.pad_sequences(x,maxlen=MaxLen,value=0,padding='post') print(x0) # 用 0填充 x1=sequence.pad_sequences(x,maxlen=MaxLen,value=0,padding='pre') print(x1) # 截断 truncating='post' or truncating='pre'--- x = [[1,2,4,5...
我们首先需要使用pad\_sequence填充输入数据，然后使用pack\_padded\_sequence创建PackedSequence并为其提供...
tensors = pad_sequence(tensors) targets = torch.stack(targets) return tensors, targets batch_size = 256 if device == "cuda": num_workers = 1 pin_memory = True else: num_workers = 0 pin_memory = False train_loader = torch.utils.data.DataLoader( ...
nn.ConstantPad2d: 二维常数填充层。对二维张量样本填充常数扩展长度。 4. Non-linear Activations (other) nn.Softmin :将 Softmin 函数应用于 n 维输入 Tensor nn.Softmax :将 Softmax 函数应用于 n 维输入张量 nn.Softmax2d :将 SoftMax 应用于每个空间位置的特征。
nn.utils.rnn.pad_sequence(xs, batch_first=True) return x, edge_index, rel, assoc Example #24Source File: off.py From pytorch_geometric with MIT License 5 votes def write_off(data, path): r"""Writes a :class:`torch_geometric.data.Data` object to an OFF (Object File Format) file...
随机裁剪:transforms.RandomCropclasstorchvision.transforms.RandomCrop(size, padding=None, pad_if_needed=False, fill=0, padding_mode=‘constant’) 功能:依据给定的size随机裁剪 参数: size- (sequenceorint),若为sequence,则为(h,w),若为int,则(size,size) ...
1)torch.nn.ReflectionPad1d它将使用输入边界的反射填充输入张量。 2)torch.nn.ReflactionPad2d它将使用输入边界的反射来填充输入张量。 3)torch.nn.ReplicationPad1它将使用输入边界的复制来填充输入张量。 4)torch.nn.ReplicationPad2d它将使用输入边界的复制来填充输入张量。