Here, we will look at an example of how to build an RNN to identify handwritten numbers from the MNIST database:
import torchfrom torch import nnimport torchvision.datasets as dsetsimport torchvision.transforms as transformsimport matplotlib.pyplot as plt# torch.manual_seed(1) # reproducible# Hyper ParametersEPOCH = 1 # train the training data n times, to save time, we just train 1 epochBATCH_SIZE = 64TIME_STEP = 28 # rnn time step / image heightINPUT_SIZE = 28 # rnn input size / image widthLR = 0.01 # learning rateDOWNLOAD_MNIST = True # set to True if haven't download the data# Mnist digital datasettrain_data = dsets.MNIST( root='./mnist/', train=True, # this is training data transform=transforms.ToTensor(), # Converts ...
