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Chapter 10
# Calculating Neighbourhood function
distance_square = tf.reduce_sum(tf.pow(tf.subtract(
self._topography, tf.stack([self.WTU_loc for i in range(m *
n)])), 2), 1)
neighbourhood_func = tf.exp(tf.negative(tf.math.divide(tf.cast(
distance_square, "float32"), tf.pow(_sigma_new, 2))))
# multiply learning rate with neighbourhood func
eta_into_Gamma = tf.multiply(_eta_new, neighbourhood_func)
# Shape it so that it can be multiplied to calculate dW
weight_multiplier = tf.stack([tf.tile(tf.slice(
eta_into_Gamma, np.array([i]), np.array([1])), [self.dim])
for i in range(m * n)])
delta_W = tf.multiply(weight_multiplier,
tf.subtract(tf.stack([x for i in range(m * n)]),self._W))
new_W = self._W + delta_W
self._W = new_W
The fit() function is a helper function that calls the train() function and stores the
centroid grid for easy retrieval:
def fit(self, X):
"""
Function to carry out training
"""
for i in range(self._num_iterations):
for x in X:
self.training(x,i)
# Store a centroid grid for easy retrieval
centroid_grid = [[] for i in range(self._m)]
self._Wts = list(self._W)
self._locations = list(self._topography)
for i, loc in enumerate(self._locations):
centroid_grid[loc[0]].append(self._Wts[i])
self._centroid_grid = centroid_grid
self._learned = True
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