How to Reason About Weighted Matrices in Neural Network
By: Tc Wang
Tags: Logistic Regression, Machine Learning

Neural networks often contain repeated patterns of logical regression. Here, logical regression is the formula for making a “decision”.

Hypothesis function

Activation function

The first step in a logical regression is the hypothesis function. This function converts a vector of inputs to a single scalar value (pass/fail, lose/win, temperature value, etc). If you look at the hypothesis function, you notice the variables W and X which represent the weight and input vector respectively. This concept can be expanded and improved upon with the application of matrix multiplication of W and X in a geometric view.

To measure the input X, we tend to consider the X as a column vector with n features. And W is a weighting calculation to map X from n-D to 1-D.

The hypothesis function with the non-transposed W (preferred)

It is worth mentioning that some people might write the hypothesis function with a transposed W as seen below. That is a different notation in which people consider the W as a column vector. For this article, we will prefer the non-transposed notation. And I will explain my rationale later in the article.

The hypothesis function with the transposed W

Intuitively, the transformation of W squashes the space of X from n-D to 1-D. To understand that, let’s revisit the concept of Linear Transform from Linear Algebra.

Step 1. Linear Transform

A linear transformation is a function from one vector space to another that respects the underlying structure of each vector space.

You could imagine plotting the space with grids in 2-D, the grid lines remain parallel and evenly spaced after applying a linear transformation. Following is a demo of skewing a space:

Linear transformations are useful because they preserve the structure of a vector space. So, many qualitative assessments of a vector space still hold!

Step 2. Linear Transform with Square Matrix

The following equation represents a linear transformation, where W is a squared matrix.

Multi-dimensional computations are complicated. So, let’s use 2-D linear transformation, which is a lot simpler and can be plotted geometrically.

The above equation is to transform X from (3, 4) to (6, -3).

To get the idea of a linear transformation, let’s rewrite the equation to the following:

In statement (a), I rewrite the vector (3, 4) as a combination of two scaled unit vectors. We shall call the unit vectors, x-hat and y-hat respectively.

In statement (b), I apply the same transformation to the x-hat and y-hat vectors respectively.

In statement (c), you’ll see x-hat vector becomes (-2, -1); y-hat vector becomes (3, 0).

Each column of W is the new unit vector for the new space!

In the following visual plot, the black grid shows the current coordinates and the light gray grid shows the previous coordinates. Here are the interesting observations:

From the view of old space, the vector looks changed.

However, from the view of the new space, the vector stays the same.

Step 3. Linear Transform with Non-square Matrix

Above, the linear transformation does not alter the space dimension. What if given the transformation matrix is not square?

Let’s use a fat W in 2-D transformation with the same X:

Let’s use a fat W in 2-D transformation with the same X:

The input is a 2D vector and the output is a number! In this example, the space gets squashed! This is in fact the transformation used by the Logistic Regression.

Here is the visual plot:

Now, let’s use a tall W in 2D transformation with the same X as another example:

A 2D vector becomes a 3D vector! In this example, space gets expanded.

Observation

The W matrix can be any size. This transformation either squashes space or expands space depending on the shape of the W matrix. Let’s expand the hypothesis function to fit more than just a Logistic Regression such that it can be used for a Neural Network.

When we think of the W matrix is a linear transformation and each column of the W matrix is the new unit vector (column vector) for the new space, we can conclude:

The number of rows = output dimensions

The number of columns = input dimensions

Usually, we stack the samples in columns in a matrix for a Neural Network, which changes the equation to follow the more generic form:

As you can see, it is like we applied the same W transformation to all the k sample vectors. This means that the column number of X has nothing to do with the W’s dimension.

Side note, this is why I prefer to use the non-transposed W form for the hypothesis function.

Summary

The W is a linear transformation matrix that either squash space or expands space.

In NN, some layer’s W expands the space for exploring more decision trees; some layer’s W squash the space for reaching decisions on the observations.

is a linear transformation matrix that either squash space or expands space. In NN, some layer’s expands the space for exploring more decision trees; some layer’s squash the space for reaching decisions on the observations. The W ’s row number = dimensions of the new space.

’s row number = dimensions of the new space. The W’s column number = dimensions of the old space.

Reference

3Blue1Brown has a course giving the full ideas of the intuition of linear transformation, “The Essence of Linear Algebra” on YouTube.