February 2018
Intermediate to advanced
262 pages
6h 59m
English
Content preview from Deep Learning with PyTorch
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3-D tensors
When we add multiple matrices together, we get a 3-D tensor. 3-D tensors are used to represent data-like images. Images can be represented as numbers in a matrix, which are stacked together. An example of an image shape is 224, 224, 3, where the first index represents height, the second represents width, and the third represents a channel (RGB). Let's see how a computer sees a panda, using the next code snippet:
from PIL import Image# Read a panda image from disk using a library called PIL and convert it to numpy arraypanda = np.array(Image.open('panda.jpg').resize((224,224)))panda_tensor = torch.from_numpy(panda)panda_tensor.size()Output - torch.Size([224, 224, 3])#Display pandaplt.imshow(panda)
Since displaying the tensor of ...
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