Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors ● torch.nn: Models, Loss Functions ● torch.optim: Optimization ● Save/load models Prerequisites __getitem__(4) 0 1 2 3 4 Tensors ● High-dimensional matrices (arrays) 1-D tensor e.g. audio 2-D tensor e.g. black&white images 3-D tensor e.g. RGB images Tensors – Shape of Tensors ● Check with .shape() dim in PyTorch == axis in NumPy dim 0 dim 0 dim 1 dim 0 dim 1 dim 2 5 5 3 5 4 3 Tensors – Creating Tensors ● Directly from data (list or numpy.ndarray) x = torch.tensor([[1, -1], [-1, 1]]) x

黄海广 副教授 2 本章目录 01 Tensors张量 02 Autograd自动求导 03 神经网络 04 训练一个分类器 3 1.Tensors张量 01 Tensors张量 02 Autograd自动求导 03 神经网络 04 训练一个分类器 4 1.Tensors张量的概念 Tensor实际上就是一个多维数组（multidimensional 创建与另一个张量具有相同大小的张量，请使用 torch.*_like  如torch.rand_like()  创建与其他张量具有相似类型但大小不同的张量，请使 用tensor.new_*创建操作。 1.Tensors张量的概念 6  查看张量的属性  查看Tensor类型  tensor1 = torch.randn(2,3) #形状为(2,3)一组从标准正态分布 中随机抽取的数据  tensor1  tensor1.ndim #查看维度  查看Tensor是否存储在GPU上  tensor1.is_cuda  查看Tensor的梯度  tensor1.grad 1.Tensors张量的概念 7  Tensor在CPU和GPU之间转换，以及numpy之间的转换  CPU tensor转GPU tensor  cpu_tensor.cuda()  GPU tensor

the basic neural network operation is as follows: f(X; W, b) = σ(XW + b) Here, X, W and b are tensors (mathematical term for an n-dimensional matrix) to denote inputs, weights and the bias respectively etc. A neural network model learns W and b tensors which are stored with the model. Hence, they contribute significantly to its size. Of the two tensors, W naturally dominates the size owing to its higher weights to reduce a model’s size: 3The terms tensor and matrix are used interchangeably in the text. Tensors are N-dimensional matrices. The shape of a tensor denotes the size of each of its dimensions, and

input_tensors, out_ndims) -> out_shape_tensors ○ Data dependent (op_attrs, input_data, out_ndims) -> out_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors© 2019 input_tensors, out_ndims) -> out_shape_tensors ○ Data dependent (op_attrs, input_data, out_ndims) -> out_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors ● Why inputs[j][i] return out Shape function example Use hybrid script to write shape function Input shape tensors Type checking Data independent© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved

shape. >>> k.int_shape(data) (1, 3, 3) dot It is used to multiply two tensors. Consider a and b are two tensors and c will be the outcome of multiply of ab. Assume a shape is (4,2) and b shape the input tensors of the model as list. >>> inputs = model.inputs >>> inputs []  model.outputs: Returns all the output tensors of the model is_categorical_crossentropy All above loss function accepts two arguments:  y_true - true labels as tensors  y_pred - prediction with same shape as y_true Import the losses module before using loss function

messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) # Directly use generate() and tokenizer.decode() to get the output. # Use `max_new_tokens` add_generation_prompt=True (续下页) 6 Chapter 1. 文档 Qwen (接上页) ) model_inputs = tokenizer([text], return_tensors="pt").to(device) # Directly use generate() and tokenizer.decode() to get the output. # Use `max_new_tokens` messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids

number of LSTM cells based on the sentence’s length. PyTorch • Fundamental Concepts of PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like examples Train Model •Train weights Evaluate Model •Visualise Tensor • Tensor? • PyTorch Tensors are just like numpy arrays, but they can run on GPU. • Examples: And more operations like: Indexing of operations for autograd • t.grad_fn Loading Data, Devices and CUDA • Numpy arrays to PyTorch tensors • torch.from_numpy(x_train) • Returns a cpu tensor! • PyTorch tensor to numpy • t.numpy() • Using

compile(self, optimizer, loss, metrics=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None) 用于配置训练模型。 参数 • optimizer: 字符串（优化器名）或者优化器对象。详见 optimizers。 • loss: 字符串（目标函数名）或目标函数。详见 l class_weight 评估和加权的度 量标准列表。 • target_tensors: 默认情况下，Keras 将为模型的目标创建一个占位符，在训练过程中将使用 目标数据。相反，如果你想使用自己的目标张量（反过来说，Keras 在训练期间不会载入 这些目标张量的外部 Numpy 数据），您可以通过 target_tensors 参数指定它们。它应该 是单个张量（对于单输出 Sequential loss, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None) 用于配置训练模型。 参数 • optimizer: 字符串（优化器名）或者优化器对象。详见 optimizers。 • loss: 字符串（目标函数名）或目标函数。详见 l

classification algorithm 1. Load model and labels 2. Build interpreter 3. Allocate input and output tensors 4. Read image 5. Resize image 6. Copy resized image to input tensor 7. Run inference 8. Extract std::unique_ptr interpreter; 11 builder(&interpreter); 20 / 503. Allocate input and output tensors 1 // defined and properly initialized elsewhere: 2 // std::unique_ptr interpreter;

wavelength, position vector, ...) • Quantities may have different character – scalars – vectors – tensors CppCon 2024: Improving our safety with a quantities and units library Introducing quantity_spec wavelength, position vector, ...) • Quantities may have different character – scalars – vectors – tensors • Quantities may be defined as non-negative CppCon 2024: Improving our safety with a quantities