Modern neural network
This week, I mainly learned several classic modern neural networks: AlexNet, VGG, NiN, geoglenet and ResNet. These networks can be regarded as a process of continuous refinement in the development of neural networks: Starting from the LeNet network, AlexNet is generated by changing the activation function and using DropOu ...
Posted by joey3002 on Sat, 25 Sep 2021 00:04:14 -0700
1. Get to know PyTorch
1. Import pytorch package
2. Create an empty 5x3 tensor
x = torch.empty(5, 3)
3. Create a randomly initialized 5x3 tensor
x = torch.rand(5, 3)
4. Create a 5x3 0 tensor of type long
x = torch.zeros(5, 3, dtype=torch.long)
5. Create tensors directly from the a ...
Posted by jbbadaz on Wed, 22 Sep 2021 22:07:11 -0700
nn.Conv2d -- two dimensional convolution operation
torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
Function: 2D convolution operation is applied to the input signal composed of multiple input planes, which is commonly used in imag ...
Posted by dazraf on Sun, 19 Sep 2021 17:59:02 -0700
Tip 1: Learning Address Point Here Tip 1: Word embeddings: how to transform text into numbers
_Task01 mainly uses RNN framework (note: this paper does not give a detailed explanation of RNN principles), IMDB dataset to build a Baseline model of text affective analysis tasks.
1. Model building process
1.1 Data Preprocessing
Posted by semtex on Wed, 15 Sep 2021 09:34:10 -0700
The best, most efficient and most concise is Plan One.
Step 1: Fixed basic network
# Get the state_dict for the fixed part:
pre_state_dict = torch.load(model_path, map_location=torch.device('cpu')
# Imported (remember strict=False):
print('Load mode ...
Posted by tauchai83 on Tue, 14 Sep 2021 09:46:30 -0700
Generally, the collected image samples are different in size and brightness. In deep learning, we want the sample distribution to be independent and identically distributed, so we need to normalize the samples.Sometimes only a small amount of sample data can be obtained, and it is not easy to obtain a large number of samples. H ...
Posted by The Chancer on Sun, 12 Sep 2021 00:49:40 -0700
1.1 case introduction
In this case, pytoch is used to build a DenseNet network structure for image classification of fashion MNIST dataset. The analysis of this problem can be divided into data preparation, model establishment, training with training set and testing the effect of model with test set.
1.2 environment configurat ...
Posted by OhLordy on Fri, 10 Sep 2021 01:45:07 -0700
Deep learning image classification (XVIII) detailed explanation of Vision Transformer(ViT) network
In the previous section, we talked about the self attention structure in Transformer. In this section, learn the detailed explanation of Vision Transformer(vit). Learning video from Bilibili , refer to blog Detailed explanation of Vision Tran ...
Posted by seaweed on Thu, 09 Sep 2021 21:09:01 -0700
Fundamentals of machine learning
The essence of machine learning: using data to solve problems
Data preprocessing (important in deep learning) - > training phase - > model generation - > prediction phase
We usually choose some data as the test set, such as about 20%.
Sometimes there is an additional verification set of about 20 ...
Posted by hyngvesson on Wed, 08 Sep 2021 00:38:35 -0700
Pytorch Note53 TensorBoard Visualization
A summary of all notes:
Pytorch Note Happy Planet
TensorBoard is a visualization tool for Tensorflow that visualizes the running state of Tensorflow programs through the log files that are output during the running of the Tensorflow program.TensorBoards and TensorFlow programs run in different proc ...
Posted by ossi69 on Tue, 07 Sep 2021 22:00:49 -0700