Deep Learning Intuitions

TLDR

Stacking n-layers RELUS in a feed forward neural network is functionally equivalent to a set of nested inequalities.

RELU intuition

In think this is something I came up with during first going over Jeffery Hinton’s Deep learning Course on Coursea. Would be nice to think if other DL blocks have a simple conceptual form. You may have heard of linear programming - even learned about it in high school. Linear programming is a basically optimizing under linear constraints.

RELU feed forward networks are very similar to constraints used in linear programming. let’s consider how and why.

relu = lambda x: x * gradient if x> bias else 0 #relu
x = np.random.randn(3, 1) #random initialization
out = relu(Weights * x +bias) 

\left{ 0 & x < b \\ x & x >= b so for each input in x we only see values in (x>bias) if we set biases = 0 which we can if we normalize and center our data during preprocessing then in the out put we only see values of x>0

For a 2 layer network

relu = lambda x: x * gradient if x> bias else 0 #relu
x = np.random.randn(3, 1) 
activation_1 = relu((W * x)+b)
out = relu(np.dot( W,relu(np.dot(W_1,x)+b_1)+b_2)

Forward-pass of a 3-layer neural network:

f = lambda x: 1.0/(1.0 + np.exp(-x)) # activation function (use sigmoid) 
x = np.random.randn(3, 1) # random input vector of three numbers (3x1) 
h1 = f(np.dot(W1, x) + b1) # calculate first hidden layer activations (4x1) 
h2 = f(np.dot(W2, h1) + b2) # calculate second hidden layer activations 
(4x1) out = np.dot(W3, h2) + b3 # output neuron (1x1)

the functional form of a DNN with one RELU layer looks like:

y = ax+b

A fully connected layers of RELUs with zero biases is just a set of inequalities

e.g.

x>a \text{ or } x \in (a,\infty) \\ x<b \\ y>c \\ y<c

based on the parameters. A second layer of RELU has second order inequalities e.g. 

x < b \text{ and } x > a \text{ or x } \in (a,b)

x > a \text{ and } y > a \text{ or } (x,y) \in X