Pytorch中定義神經網絡
深度學習使用人工神經網絡(模型),它是由許多層相互連接的單元組成的計算系統。通過將數據傳遞給這些相互連接的單元,神經網絡能夠學習如何近似計算將輸入轉換成輸出。在PyTorch中,神經網絡能夠使用torch.nn包來構建。
介紹
Pytorch提供了優雅設計的模塊和類,包括torch.nn,幫助我們創建和訓練神經網絡。一個nn.Module 包括layers,和forward(input)方法,然後返回一個輸出。
步驟
1. 導入包
2. 定義和初始化神經網絡
3. 指定數據如何貫穿模型
4. 測試
1. Import necessary libraries for loading our data
import torch
import torch.nn as nn
import torch.nn.functional as F
2. Define and intialize the neural network
我們的網絡可以識別圖像。我們將使用PyTorch內置的一個叫做卷積的過程。卷積是將圖像中的每個元素與它的局部鄰居相加,由一個核函數或一個小的martrix加權,幫助我們從輸入圖像中提取某些特徵(如邊緣檢測、銳度、模糊度等)。
class Net(nn.Module):
def __init__(self):
super(Net,self).__init__()
# First 2D convolutional layer, taking in 1 input channel (image),
# outputting 32 convolutional features, with a square kernel size of 3
self.conv1 = nn.Conv2d(1, 32, 3, 1)
# Second 2D convolutional layer, taking in the 32 input layers,
# outputting 64 convolutional features, with a square kernel size of 3
self.conv2 = nn.Conv2d(32, 64 ,3 , 1)
# Designed to ensure that adjacent pixels are either all 0s or all active
# with an input probability
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
#First fully connected layer
self.fc1 = nn.Linear(9216, 128)
#Second fully connected layer that outputs our labels
self.fc2 = nn.Linear(128, 10)
my_nn = Net()
print(my_nn)
3. Specify how data will pass through your model
當您使用PyTorch構建模型時,您只需定義forward函數,它將數據傳遞到計算圖(即我們的神經網絡)。這代表了我們的前饋(feed-forward)算法。
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
# x represents our data
def forward(self, x):
# Pass data through conv1
x = self.conv1(x)
# Use the rectified-linear activation function over x
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
# Run max pooling over x
x = F.max_pool2d(x,2)
# Pass data through dropout1
x = self.dropout1(x)
# Flatten x with start_dim=1
x = torch.flatten(x, 1)
# Pass data through fc1
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
# Apply softmax to x
output = F.log_softmax(x, dim=1)
return output
4. [Optional] Pass data through your model to test
爲了確保我們收到我們想要的輸出,讓我們通過傳遞一些隨機數據來測試我們的模型。
# Equates to one random 28x28 image
random_data = torch.rand((1,1,28,28))
my_nn = Net()
result = my_nn(random_data)
print(result)