我们将详细介绍Transformer的核心原理，包括自注意力机制、多头注意力和位置编码等关键概念，并提供在TensorFlow中实现这些组件的具体步骤和代码示例。通过对Transformer模型的深入分析，将帮助读者理解其各个组成部分的功能和相互作用。此外，我们还将讨论TensorFlow与其他深度学习框架（如PyTorch）的比较，强调TensorFlow在生产部署和大规模应用中的优势。
pythonnlpmachine-learningnatural-language-processingdeep-learningtensorflowpytorchtransformerspeech-recognitionseq2seqflaxpretrained-modelslanguage-modelsnlp-librarylanguage-modelhacktoberfestbertjaxpytorch-transformersmodel-hub UpdatedJun 4, 2025 Python labmlai/annotated_deep_learning_paper_implementations ...
学习如何从基础的Transformer发展到更复杂的模型,如BERT(Bidirectional Encoder Representations from Transformers)和GPT(Generative Pre-trained Transformer)。 建议你可以参考刚从openai离职的andrej karpathy的《Let’s build GPT: from scratch, in code, spelled out》视频,并且基于其colab的代码运行尝试创建一个GPT。
This is the Transformer architecture fromAttention Is All You Need, applied to timeseries instead of natural language. This example requires TensorFlow 2.4 or higher. Load the dataset We are going to use the same dataset and preprocessing as theTimeSeries Classification from Scratchexample. library(...
import tensorflow as tf from vit_tensorflow import ViT v = ViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 16, mlp_dim = 2048, dropout = 0.1, emb_dropout = 0.1 ) img = tf.random.normal([1, 256, 256, 3]) preds = v(img) #...
和DynamicViT利用pretrained weights和plain ViT不一样,我们直接从hierarchical Vision Transformer入手并且train from scratch,如PVT,用几层FC对前两个stage中每个block的input tokens做一个dynamic的选取,使得前期MSA只需要处理1/4, 1/8 scale下选取的部分token。这个思路听起来感觉没什么问题,可视化效果也确实不错,如...
任何Transformer架构的基本操作就是self-attention。 我们将在后面解释“self-attention”这个名称的来源,现在不需要纠结于此。 Self-attention是一个序列到序列的操作:一组向量输入,一组向量输出。让我们用 表示输入向量,对应的输出向量 。所有的向量都有相同的维度k。
import tensorflow as tf from data_load import load_vocab from modules import get_token_embeddings, ff, positional_encoding, multihead_attention, label_smoothing, noam_scheme from utils import convert_idx_to_token_tensor from tqdm import tqdm import logging logging.basicConfig(level=logging.INFO) 简...
import tensorflow as tf from data_load import load_vocab from modules import get_token_embeddings, ff, positional_encoding, multihead_attention, label_smoothing, noam_scheme from utils import convert_idx_to_token_tensor from tqdm import tqdm import logging logging.basicConfig(level=logging.INFO) 简...
You can learn more about how to implement a Transformer from scratch in our separate tutorial. Their introduction has spurred a significant surge in the field, often referred to as Transformer AI. This revolutionary model laid the groundwork for subsequent breakthroughs in the realm of large ...