From 2180c68999eb8dc0c7bcec015b2703f5b8b20223 Mon Sep 17 00:00:00 2001
From: zhang <zch921005@126.com>
Date: Wed, 4 May 2022 08:47:54 +0800
Subject: ndarray axis

---
 learn_torch/classification.py | 95 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 learn_torch/classification.py

(limited to 'learn_torch/classification.py')

diff --git a/learn_torch/classification.py b/learn_torch/classification.py
new file mode 100644
index 0000000..555da9f
--- /dev/null
+++ b/learn_torch/classification.py
@@ -0,0 +1,95 @@
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+
+# Get cpu or gpu device for training.
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("Using {} device".format(device))
+
+
+def gen_data():
+    # Download training data from open datasets.
+    training_data = datasets.FashionMNIST(
+        root="data",
+        train=True,
+        download=True,
+        transform=ToTensor(),
+    )
+
+    # Download test data from open datasets.
+    test_data = datasets.FashionMNIST(
+        root="data",
+        train=False,
+        download=True,
+        transform=ToTensor(),
+    )
+    return training_data, test_data
+
+
+# Define model
+class NeuralNetwork(nn.Module):
+    def __init__(self, in_features, out_features):
+        super(NeuralNetwork, self).__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(in_features, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512, out_features)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
+
+
+if __name__ == '__main__':
+    batch_size = 64
+    training_data, test_data = gen_data()
+    # Create data loaders.
+    train_dataloader = DataLoader(training_data, batch_size=batch_size)
+    test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+    for i, (X, y) in enumerate(test_dataloader):
+        print("{}, Shape of X [N, C, H, W]: {}, y: {}".format(i, X.shape, y.shape))
+        break
+
+    model = NeuralNetwork(28*28, 10).to(device)
+    print(model)
+    criterion = nn.CrossEntropyLoss()
+    opt = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+    n_epochs = 10
+
+    for epoch in range(n_epochs):
+        for i, (x_batch, y_batch) in enumerate(train_dataloader):
+            opt.zero_grad()
+            preds = model(x_batch)
+            loss = criterion(preds, y_batch)
+            loss.backward()
+            opt.step()
+            if (i+1) % 100 == 0:
+                print('Train Loss: {}({}/{}): {}'.format(epoch, i, len(train_dataloader), loss.data))
+
+        test(test_dataloader, model, criterion)
-- 
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