shapes.py

#!bin/python

import numpy as np

batch_size = 32
image_height = 256
image_width = 256
num_channels = 3

# You can think of multi-dimensional arrays (a.k.a. ndarrays or tensors) as
# nested list comprehensions.
x = [[[[np.random.randint(255) for _ in range(num_channels)]
                               for _ in range(image_width)]
                               for _ in range(image_height)]
                               for _ in range(batch_size)]
x = np.array(x)
assert x.shape == (batch_size, image_height, image_width, num_channels)

# This might seem trivial but it gives us a systematic way of thinking about
# high-dimensional arrays. There are two things to understand: what one
# particular value looks like, and how these values are arranged together.