Introduction to Deep Learning with PyTorch

news2024/9/21 20:41:30

1、Introduction to PyTorch, a Deep Learning Library

1.1、Importing PyTorch and related packages

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

import torch

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

1.2.2、Load from NumPy array

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

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()

# 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:

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:

model = nn.Sequential(
    nn.Linear(6,4),
    nn.Linear(4,1),
    nn.Sigmoid()
)

Multi-class classification: Softmax demo:

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 $\hat{y}$ and ground truth ${y}$ .
Output a float.

2.2.2、One-hot encoding concepts

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

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)