2.1Pytorch Basic Exercise
transpose()Only operate two dimensions at a time
Function Returns the input matrixinputThe transposition. Exchange dimensiondim0anddim1
- NPut (Tensor) – Enter a tensive, required
- DIM0 (int) – Transposed first dimension, default 0, optional
- DIM1 (int) – Secondary, default 1, optional
One is a uniform distribution, one is a standard normal distribution.
2.2 Spiral Data Classification
Linux systemwgetis a tool for downloading a file
(1) Tensor and Numpy are matrices, the difference is that the former can run on the GPU, which can only be on the CPU;
(2) Tensor and Numpy are very convenient to transform each other, and the type is also compatible.
device=torch.device("cpu")Representative Use CPU, and
device=torch.device("cuda")The represented GPU is used.
When we specify the device, we need to load the model to the corresponding device, and it needs to be used.
model=model.to(device)Load the model into the corresponding device.
Some of the neural network structure is partially improved, and a hidden layer is added between the input and output layers, and the RELU activation function has been added, which constitutes a simple 3-layer neural network, input-hidden layer-output, but this is enough to achieve Very good nonlinearity.