Deep-Learning-with-PyTorch

A dataset of tiny images
167
ACTUAL
DATA
DATASET Q: HOW MANY ELEMENTS ?
HUMAN DOG HUMAN
DOG
HUMAN
4
5
Q: MAY I GET ITEM 4 ?
4
“HUMAN”
Figure 7.2 Concept of a PyTorch Dataset object: it doesn’t necessarily hold the data, but it
provides uniform access to it through __len__ and __getitem__.
Similarly, since the dataset is equipped with the __getitem__ method, we can use the
standard subscript for indexing tuples and lists to access individual items. Here, we get
a PIL (Python Imaging Library, the PIL package) image with our desired output—an
integer with the value 1, corresponding to “automobile”:
# In[6]:
img, label = cifar10[99]
img, label, class_names[label]
# Out[6]:
(<PIL.Image.Image image mode=RGB size=32x32 at 0x7FB383657390>,
1,
'automobile')
So, the sample in the data.CIFAR10 dataset is an instance of an RGB PIL image. We
can plot it right away:
# In[7]:
plt.imshow(img)
plt.show()
This produces the output shown in figure 7.3. It’s a red car! 3
3
It doesn’t translate well to print; you’ll have to take our word for it, or check it out in the eBook or the Jupyter
Notebook.