def tensorSOM():
import numpy as np
from som import SOM
import tensorflow as tf
sess = tf.InteractiveSession()
map_size = 30
s = SOM(input_shape=(num_features, ),
map_size_n=map_size,
num_expected_iterations=epochs,
session=sess)
sess.run(tf.initialize_all_variables())
#Training inputs
for i in range(10):
print('Epoch: {0}'.format(i))
rnd_ind = np.random.randint(0, len(X))
s.train(X[rnd_ind, :])
print(np.reshape(s.get_weights(), [map_size, map_size, num_features]))
print(np.array(y) * s.get_weights())
data = list()
for i in range(colors.shape[0]):
data.append(torch.FloatTensor(colors[i, :]))
#Train a 20x30 SOM with 100 iterations
n_iter = 100
som = SOM(m, n, 3, n_iter)
for iter_no in range(n_iter):
#Train with each vector one by one
for i in range(len(data)):
som(data[i], iter_no)
#Store a centroid grid for easy retrieval later on
centroid_grid = [[] for i in range(m)]
weights = som.get_weights()
locations = som.get_locations()
for i, loc in enumerate(locations):
centroid_grid[loc[0]].append(weights[i].numpy())
#Get output grid
image_grid = centroid_grid
#Map colours to their closest neurons
mapped = som.map_vects(torch.Tensor(colors))
#Plot
plt.imshow(image_grid)
plt.title('Color SOM')
for i, m in enumerate(mapped):
plt.text(m[1],
