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Editor to share with you how to achieve handwritten digital picture recognition in pytorch. I hope you will get something after reading this article. Let's discuss it together.

The details are as follows

Dataset: MNIST dataset, which will be downloaded automatically in the code, not manually. The data set is very small, do not need GPU equipment, you can well understand the charm of pytorch.

Model + training + Forecast Program:

Import torchfrom torch import nnfrom torch.nn import functional as Ffrom torch import optimimport torchvisionfrom matplotlib import pyplot as pltfrom utils import plot_image, plot_curve, one_hot# step1 load datasetbatch_size = 512train_loader = torch.utils.data.DataLoader (torchvision.datasets.MNIST ('mnist_data', train=True, download=True, transform=torchvision.transforms.Compose ([torchvision.transforms.ToTensor ()) Torchvision.transforms.Normalize ((0.1307,), (0.3081,)]), batch_size=batch_size Shuffle=True) test_loader = torch.utils.data.DataLoader (torchvision.datasets.MNIST ('mnist_data/', train=False, download=True, transform=torchvision.transforms.Compose ([torchvision.transforms.ToTensor (), torchvision.transforms.Normalize ((0.1307,)) (0.3081,)]), batch_size=batch_size, shuffle=False) x, y = next (iter (train_loader)) print (x.shape, y.shape, x.min (), x.max ()) plot_image (x, y) "image_sample") class Net (nn.Module): def _ _ init__ (self): super (Net, self). _ _ init__ () self.fc1 = nn.Linear (28,28,256) self.fc2 = nn.Linear (256,64) self.fc3 = nn.Linear (64,10) def forward (self, x): # x: [b, 1,28 28] # H2 = relu (xw1 + b1) x = F.relu (self.fc1 (x)) # h3 = relu (h2w2 + b2) x = F.relu (self.fc2 (x)) # h4 = h3w3 + b3 x = self.fc3 (x) return xnet = Net () optimizer = optim.SGD (net.parameters (), lr=0.01 Momentum=0.9) train_loss = [] for epoch in range (3): for batch_idx, (x, y) in enumerate (train_loader): # the image loaded is a four-dimensional tensor X: [B, 1,28,28], y: [512] # but the input of our network is an one-dimensional vector (that is, two-dimensional tensor) So to do the flattening operation x = x.view (x.size (0), 28: 28) # [b, 10] out = net (x) y_onehot = one_hot (y) # loss = mse (out, y_onehot) loss = F.mse_loss (out Y_onehot) optimizer.zero_grad () loss.backward () # w' = w-lr*grad optimizer.step () train_loss.append (loss.item ()) if batch_idx% 10 = 0: print (epoch, batch_idx, loss.item () plot_curve (train_loss) # we get optimal [W1, b1, w2, b2, w3, b3] total_correct = 0for x Y in test_loader: X = x.view (x.size (0), 28,28) out = net (x) # out: [b, 10] pred = out.argmax (dim=1) correct = pred.eq (y). Sum (). Float (). Item () total_correct + = correcttotal_num = len (test_loader.dataset) acc = total_correct/total_numprint ("acc:", acc) x Y = next (iter (test_loader)) out = net (x.view (x.size (0), 28,28)) pred = out.argmax (dim=1) plot_image (x, pred, "test")

The function called in the main program (named utils):

Import torchfrom matplotlib import pyplot as pltdef plot_curve (data): fig = plt.figure () plt.plot (range (len (data)), data, color='blue') plt.legend (['value'], loc='upper right') plt.xlabel (' step') plt.ylabel ('value') plt.show () def plot_image (img, label, name): fig = plt.figure () for i in range (6): plt.subplot (2) 3, I + 1) plt.tight_layout () plt.imshow (IMG [I] [0] * 0.3081 / 0.1307, cmap='gray', interpolation='none') plt.title ("{}: {}" .format (name, item [)) plt.xticks ([]) plt.yticks ([]) plt.show () def one_hot (label) Depth=10): out = torch.zeros (label.size (0), depth) idx = torch.LongTensor (label). View (- 1,1) out.scatter_ (dim=1, index=idx, value=1) return out has finished reading this article I believe you have a certain understanding of "how to achieve handwritten digital picture recognition in pytorch". If you want to know more about it, you are welcome to follow the industry information channel. Thank you for reading!

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