Introduction to Deep Learning with PyTorch

1、Introduction to PyTorch, a Deep Learning Library

python 复制代码
import torch

# supports:
## image data with torchvision
## audio data with torchaudio
## text data with torchtext

1.2、Tensors: the building blocks of networks in PyTorch

1.2.1、Load from list

python 复制代码
import torch

lst = [[1,2,3], [4,5,6]]
tensor = torch.tensor(lst)

1.2.2、Load from NumPy array

python 复制代码
np_array = np.array(array)
np_tensor = torch.from_numpy(np_array)

1.3、Creating our first neural network

1.3.1、A basic, two-layer network with no hidden layers

python 复制代码
import torch.nn as nn

# Create input_tensor with three features
input_tensor = torch.tensor([0.3471, 0.4547, -0.2356])

# Define our first linear layer
linear_layer = nn.Linear(in_features=3, out_features=2

# Pass input through linear layer
output = linear_layer(input_tensor)



# Show the output
print(output)

# Each linear layer has a .weight and .bias property
linear_layer.weight
linear_layer.bias
  • Networks with only linear layers are called fully connected networks.

1.3.2、Stacking layers with nn.Sequential()

python 复制代码
# Create network with three linear layers
model = nn.Sequential(
    nn.Linear(10,18),
    nn.Linear(18,20),
    nn.Linear(20, 5),
)

1.4、Discovering activation functions

  • Activation functions add non-linearity to the network.

  • A model can learn more complex relationships with non-linearity.

  • Two-class classification: Sigmoid function demo:

    python 复制代码
    import torch
    import torch.nn as nn
    
    input_tensor = torch.tensor([[6.0]])
    sigmoid = nn.Sigmoid()
    output = sigmoid(input_tensor)
    
    # tensor([[0.9975]])
  • Application for Sigmoid function:

    python 复制代码
    model = nn.Sequential(
        nn.Linear(6,4),
        nn.Linear(4,1),
        nn.Sigmoid()
    )
  • Multi-class classification: Softmax demo:

    python 复制代码
    import torch
    import torch.nn as nn
    
    input_tensor = torch.tensor([[4.3, 6.1, 2.3]])
    
    # dim=-1 indicates softmax is applied to the input tensor's last dimension
    # nn.Softmax() can be used as last step in nn.Sequential()
    probabilities = nn.Softmax(dim=-1)
    output_tensor = probabilities(input_tensor)
    
    print(output_tensor)
    
    # tensor([[0.1392, 0.8420, 0.0188]])

2、Training Our First Neural Network with PyTorch

2.1、Running a forward pass

2.1.1、Forward pass

  • Input data is passed forward or propagated through a network.
  • Coputations performed at each layer.
  • Outputs of each layer passed to each subsequent layer.
  • Output of final layer: "prediction".
  • Used for both training and prediction.
  • Some possible outputs:

2.1.2、Backward pass

2.1.3、Binary classification: forward pass

2.1.4、Multi-class classification: forward pass

2.1.5、Regression: forward pass

2.2、Using loss functions to assess model predictions

2.2.1、Why we need a loss function?

  • Give feedback to model during training.
  • Take in model prediction and ground truth .
  • Output a float.

2.2.2、One-hot encoding concepts

python 复制代码
import torch.nn.functional as F

F.one_hot(torch.tensor(0), num_classes = 3)
# tensor([1,0,0]) --- first class

F.one_hot(torch.tensor(1), num_classes = 3)
# tensor([0,1,0]) --- second class

F.one_hot(torch.tensor(2), num_classes = 3)
# tensor([0,0,1]) --- third class

2.2.3、Cross entropy loss in PyTorch

python 复制代码
from torch.nn import CrossEntropyLoss

scores = tensor([[-0.1211, 0.1059]])
one_hot_target = tensor([[1, 0]])

criterion = CrossEntropyLoss()
criterion(scores.double(), one_hot_target.double())
# tensor(0.8131, dtype=torch.float64)

2.2.4、Bringing it all together

2.3、Using derivatives to update model parameters

2.3.1、Minimizing the loss

  • High loss: model prediction is wrong
  • Low loss: model prediction is correct

2.3.2、Connecting derivatives and model training

2.3.3、Backpropagation concepts

2.3.4、Gradient descent

2.4、Writing our first training loop

2.4.1、Training a neural network

2.4.2、Mean Squared Error (MSE) Loss

2.4.3、Before the training loop

2.4.4、The training loop

3、Neural Network Architecture and Hyperparameters

3.1、Discovering activation functions between layers

3.1.1、Limitations of the sigmoid and softmax function

3.1.2、Introducing ReLU

3.1.3、Introducing Leaky ReLU

3.2、A deeper dive into neural network architecture

3.2.1、Counting the number of parameters

3.3、Learning rate and momentum

3.4、Layer initialization and transfer learning

3.4.1、Layer initialization

3.4.2、Transfer learning and fine tuning

4、Evaluating and Improving Models

4.1、A deeper dive into loading data

4.1.1、Recalling TensorDataset

4.1.2、Recalling DataLoader

4.2、Evaluating model performance

4.2.1、Model evaluation metrics

4.2.2、Calculating training loss

4.2.3、Calculating validation loss

4.2.4、Calculating accuracy with torchmetrics

4.3、Fighting overfitting

4.4、Improving model performance

  • Overfit the training set
  • Reduce overfitting
  • Fine-tune the hyperparameters
相关推荐
聚客AI14 分钟前
Embedding进化论:从Word2Vec到OpenAI三代模型技术跃迁
人工智能·llm·掘金·日新计划
weixin_3875456433 分钟前
深入解析 AI Gateway:新一代智能流量控制中枢
人工智能·gateway
聽雨2371 小时前
03每日简报20250705
人工智能·社交电子·娱乐·传媒·媒体
二川bro1 小时前
飞算智造JavaAI:智能编程革命——AI重构Java开发新范式
java·人工智能·重构
acstdm1 小时前
DAY 48 CBAM注意力
人工智能·深度学习·机器学习
澪-sl1 小时前
基于CNN的人脸关键点检测
人工智能·深度学习·神经网络·计算机视觉·cnn·视觉检测·卷积神经网络
羊小猪~~2 小时前
数据库学习笔记(十七)--触发器的使用
数据库·人工智能·后端·sql·深度学习·mysql·考研
摸爬滚打李上进2 小时前
重生学AI第十六集:线性层nn.Linear
人工智能·pytorch·python·神经网络·机器学习
HuashuiMu花水木2 小时前
PyTorch笔记1----------Tensor(张量):基本概念、创建、属性、算数运算
人工智能·pytorch·笔记
lishaoan772 小时前
使用tensorflow的线性回归的例子(四)
人工智能·tensorflow·线性回归