pdfcoffee

Unsupervised Learning
self._neighbourhood = []
self._topography = []
self._num_iterations = int(num_iterations)
self._learned = False
self.dim = dim
self.eta = float(eta)
if sigma is None:
sigma = max(m,n)/2.0 # Constant radius
else:
sigma = float(sigma)
self.sigma = sigma
print('Network created with dimensions',m,n)
# Weight Matrix and the topography of neurons
self._W = tf.random.normal([m*n, dim], seed = 0)
self._topography = np.array(list(self._neuron_location(m, n)))
The most important function of the class is the train() function, where we use the
Kohonen algorithm as discussed before to find the winner units and then update the
weights based on the neighborhood function:
def training(self,x, i):
m = self._m
n= self._n
# Finding the Winner and its location
d = tf.sqrt(tf.reduce_sum(tf.pow(self._W - tf.stack([x for i in
range(m*n)]),2),1))
self.WTU_idx = tf.argmin(d,0)
slice_start = tf.pad(tf.reshape(self.WTU_idx, [1]),np.
array([[0,1]]))
self.WTU_loc = tf.reshape(tf.slice(self._topography, slice_
start,[1,2]), [2])
# Change learning rate and radius as a function of iterations
learning_rate = 1 - i/self._num_iterations
_eta_new = self.eta * learning_rate
_sigma_new = self.sigma * learning_rate
[ 388 ]