NumPy Array Expand_dims – Mastering Python

Understanding the Need for `expand_dims`

Imagine you have a NumPy array representing a single data point, such as a grayscale image pixel value:

import numpy as np

pixel_value = np.array(100)
print(pixel_value.shape)  # Output: ()  (a 0-dimensional array)

Many machine learning algorithms expect input data to have a specific number of dimensions. For example, a model might anticipate a batch of images, where each image is represented as a 2D array (height x width), and the batch itself forms a third dimension. Simply passing pixel_value won’t work. This is where expand_dims steps in.

Adding Dimensions with `expand_dims`

The expand_dims function takes two arguments:

a: The input array.
axis: The position where you want to insert the new axis. Remember that Python uses zero-based indexing.

Let’s illustrate:

row_vector = np.expand_dims(pixel_value, axis=0)
print(row_vector.shape)  # Output: (1,)

column_vector = np.expand_dims(pixel_value, axis=1)
print(column_vector.shape)  # Output: (1, 1)

Notice how we’ve transformed a 0-dimensional array into 1-dimensional arrays with different orientations.

Expanding Higher-Dimensional Arrays

The power of expand_dims extends beyond simple scalar values. Let’s consider a 2D array:

image = np.array([[1, 2], [3, 4]])
print(image.shape)  # Output: (2, 2)

batched_image = np.expand_dims(image, axis=0)
print(batched_image.shape)  # Output: (1, 2, 2)

Here, we’ve added a new dimension at the beginning, effectively creating a batch of size one. This is a common preprocessing step in deep learning.

Practical Application: Reshaping for Model Input

Many machine learning models require a specific input shape. Let’s say a model expects input of shape (1, 28, 28) (a batch size of 1, 28x28 image). You have an image represented as a (28,28) NumPy array:

single_image = np.random.rand(28, 28)
reshaped_image = np.expand_dims(single_image, axis=0)
print(reshaped_image.shape) #Output: (1, 28, 28)

This example showcases how expand_dims neatly prepares your data for model ingestion. You can similarly use it to add dimensions for channels (e.g., for color images), or any other required dimension.

`expand_dims` vs. `reshape`

While reshape can also alter the shape of an array, expand_dims is specifically designed for adding single-dimensional axes, making it more concise and readable for this particular task. Using reshape for this purpose would require more careful consideration of the array’s dimensions and would be less intuitive.

Using `np.newaxis` for a More Concise Syntax

An alternative to using np.expand_dims is to use np.newaxis within array slicing, providing a slightly more compact notation:

single_image = np.random.rand(28,28)
reshaped_image = single_image[np.newaxis, ...] # adds a new axis at the beginning.  ... represents all other axes.
print(reshaped_image.shape)

This approach offers a more concise way to add dimensions in specific situations. However, np.expand_dims remains more explicit and arguably easier to understand for beginners.

Understanding the Need for expand_dims

Adding Dimensions with expand_dims