"from transformer_lens.utils import gelu_new, tokenize_and_concatenate\n", "import torch as t\n", "from torch import Tensor\n", "import torch.nn as nn\n", "import numpy as np\n", "import math\n", "from tqdm.notebook import tqdm\n", ...
TRANSFORMERS FROM SCRATCH Seq2seq pay Attention to Self Attention: Part 2
总共实现了这几个层: numpy实现vision transformer图像输入的patch - 知乎 (zhihu.com) numpy实现vision transformer的position embedding - 知乎 (zhihu.com) numpy实现multi-attention层的前向传播和反向传播 -…
下面代码块中的第一行是将Mountain at Dusk⁴的数据类型从NumPy数组更改为Torch张量。我们还必须对张量进行unsqueeze⁶操作,以创建一个通道维度和一个批处理大小维度。与上面一样,我们只有一个通道。由于只有一个图像,批处理大小为1。 x = torch.from_numpy(mountains).uns...
importnumpyasnpimportmatplotlib.pyplotaspltimportseabornassnsimportmathdefget_positional_encoding(max_seq_len,embed_dim):# 初始化一个positional encoding# embed_dim: 字嵌入的维度# max_seq_len: 最大的序列长度positional_encoding=np.array([[pos/np.power(10000,2*i/embed_dim)foriinrange(embed_dim)...
When you train RWKV from scratch, try my initialization for best performance. Check generate_init_weight() of src/model.py: emb.weight => nn.init.uniform_(a=-1e-4, b=1e-4) (Note ln0 of block0 is the layernorm for emb.weight) head.weight => nn.init.orthogonal_(gain=0.5*sqrt...
下面代码块中的第一行是将Mountain at Dusk⁴的数据类型从NumPy数组更改为Torch张量。我们还必须对张量进行unsqueeze⁶操作,以创建一个通道维度和一个批处理大小维度。与上面一样,我们只有一个通道。由于只有一个图像,批处理大小为1。 x = torch.from_numpy(mountains).unsqueeze(0).unsqueeze(0).to(torch.floa...
The proposal is implemented by NumPy [47] and the Vitis AI Library [48] which is a collection of high-level libraries and APIs designed for efficient AI inference. About the model partitioning task. The patch embedding layer of both ViT-B and Swim-T is partitioned as the edge-side model...
# the model trained from scratch ohist = [] shist = [] ohist = [h.cpu().numpy() for h in hist] shist = [h.cpu().numpy() for h in scratch_hist] plt.title("Validation Accuracy vs. Number of Training Epochs") plt.xlabel("Training Epochs") ...
# 导入需要的库importosimporttimeimportmathimportjsonimportjoblibimportrandomimportargparseimportnumpyasnpimporttensorflowastf # 导入一些工具函数和模块 from tqdmimporttqdm # 进度条 from functoolsimportpartial # 高阶函数工具 from sklearn.utilsimportshuffle # 打乱数据 ...