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09_dataloaders.py

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+import torch
+import torchvision
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+import math
+
+class WineDataset(Dataset):
+    def __init__(self):
+        #data loading
+        xy = np.loadtxt('./data/wine/wine.csv', delimiter=',', dtype=np.float32, skiprows=1)
+        self.x = torch.from_numpy(xy[:,1:])  # n_samples x features
+        self.y = torch.from_numpy(xy[:,[0]]) # n_samples x 1
+        self.n_samples = xy.shape[0]
+
+    def __getitem__(self, idx):
+        return self.x[idx], self.y[idx]
+
+    def __len__(self):
+        return self.n_samples
+
+dataset = WineDataset()
+# first_data = dataset[0]
+# features, labels = first_data
+# print(features, labels)
+batch_size = 4
+dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=2)
+
+# dataiter = iter(dataloader)
+# data = dataiter.next()
+# features, labels = data
+# print(features, labels)
+
+# dummy training loop
+n_epochs = 2
+total_samples = len(dataset)
+n_iter = math.ceil(total_samples/batch_size)
+print(total_samples, n_iter)
+
+for epoch in range(n_epochs):
+    for i, (inputs, labels) in enumerate(dataloader):
+        # forward backward update
+        if (i+1) % 5 == 0:
+            print(f'Epoch {epoch+1}/{n_epochs}, step {i+1}/{n_iter}, inputs {inputs.shape}')
+
+
+# pytorch built in datasets
+# torchvision.datasets.MNIST()
+# fashion-mnist, cifar, coco