pytorch framework learning (1) simple construction of the network

PyTorchLearning

Basic requirements & comparison with other frameworks:

Software: IDE with dubg function (such as PyCharm, Eclipse), Jupyter, anaconda, Pytorch Compared
with PyTorch , Tensorflow has more definitions and rules, and it is not very convenient in the debugging process.

framework learning method

Do not stick to specific operations, find and use in the process of actual application

Basic usage

Create a matrix
x = torch.empty(5,3); x =torch.rand(5.3);

It can be seen that the data format output by the
pytorch framework is tensor (tensor) The data format output by the
TensorFlow framework is ndarray

Show matrix size torch.size()
change matrix dimension view(dimension)

Numpy (ndarry) and PyTorch (tensor) interaction

autograd mechanism

Why use a frame? Because programming by yourself is too troublesome, especially when doing backpropagation. When backpropagating
, we need to differentiate the matrix , which is laborious. If the framework helps us finish it, we can concentrate on building the model up

requires_grad=True


Note: If the gradient is not cleared , the gradient will be accumulated!!!
So, when we calculate the gradient, the basic process is:

            **清零->梯度计算->反向传播->清零**

example

Create two arrays x and y:

Build a model:

Formulate hyperparameters & loss function

training model:

Target	annotation	the code
Data format conversion	Convert the input data format to tensor format	torch.from_numpy(x_train)
Gradient reset	Avoid errors in gradient accumulation	.zero_grad()
forward propagation	get predicted data
calculate loss	i.e. the gap between the prediction loss and the label
backpropagation	Find the gradient	loss.backward()
Perform parameter update	Automatically update parameters according to learning rate and gradient	.step()
process display	To prevent the graphics card from exploding	loss.item
predict
model save	After the model is saved, it is a dictionary type (the model is not just a model,	torch.save(model.state_dict(),‘model.pkl’)
model read	There may also be model losses, results, etc. can be put in)	model.load_state_dict(torch.load(“model.pkl”))

At present, the most concise version is finished, and there are many problems that have not been solved (for example, in our code, one batch is all run, and theoretically it should be run one batch at a time), let’s ignore it and learn _later

Now we test the model.
The test is to put the data into the trained network. The obtained data must be converted to the type~

the model is saved