The perceptron is a simple algorithm which, given an input vector x of m values (x1, x2, ..., xn) often called input features or simply features, outputs either 1 (yes) or 0 (no). Mathematically, we define a function:
Here, w is a vector of weights, wx is the dot product
, and b is a bias. If you remember elementary geometry, wx + b defines a boundary hyperplane that changes position according to the values assigned to w and b. If x lies above the straight line, then the answer is positive, otherwise it is negative. Very simple algorithm! The perception cannot express a maybe answer. It can answer yes (1) or no (0) if we understand how to define w and b, that is the training process that will be discussed in the following paragraphs.
The initial building block of Keras is a model, and the simplest model is called sequential. A sequential Keras model is a linear pipeline (a stack) of neural networks layers. This code fragment defines a single layer with 12 artificial neurons, and it expects 8 input variables (also known as features):
from keras.models import Sequential model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='random_uniform'))
Each neuron can be initialized with specific weights. Keras provides a few choices, the most common of which are listed as follows:
random_uniform: Weights are initialized to uniformly random small values in (-0.05, 0.05). In other words, any value within the given interval is equally likely to be drawn.random_normal: Weights are initialized according to a Gaussian, with a zero mean and small standard deviation of 0.05. For those of you who are not familiar with a Gaussian, think about a symmetric bell curve shape.zero: All weights are initialized to zero.
A full list is available at https://keras.io/initializations/.
