bpnn_mnist.py

import numpy as np
import matplotlib.pyplot as plt
from load_binary_files import training_ims, training_labels
from init_functions import p_matrix, w_connectivity
from corrup import distort_binnary
    
# Select quantity of data to use
N_data_total = len(training_labels)
percentage = 0.05
N_to_use = int(percentage * N_data_total)

# Decide how much data to use
X = training_ims[0:N_to_use]
Y = training_labels[0:N_to_use]

# First we need to calculate the probabilities w and beta
p_vector = np.mean(X, axis=0)
p_matrix = p_matrix(X)

beta = p_vector
beta[beta == 0] = 1.0 / N_to_use ** 2
w = w_connectivity(p_vector, p_matrix, N_to_use)

# Now we create a distorted pattern
 
# Now we need to update the pattern
label = 2
percentage = 0.1
dis_pattern = distort_binnary(label, percentage, X)

log_beta = np.log(beta)
G = 1.1

iterations = 1000

for t in range(iterations):
    s = log_beta + np.log(np.dot(w, o))
    o = np.exp(G * s) / np.sum(np.exp(G * s))


plt.subplot(1, 3, 1)
plt.imshow(X[pattern].reshape((28, 28)))
plt.title('Original')

plt.subplot(1, 3, 2)
plt.imshow(o.reshape((28, 28)))
plt.title('O')

plt.subplot(1, 3, 3)
plt.imshow(s.reshape((28, 28)))
plt.title('S')

plt.show()