Created
September 22, 2017 09:27
Star
You must be signed in to star a gist
Brain-State-in-a-Box Network
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import matplotlib.pyplot as plt | |
import numpy as np | |
# Set random seed for reproducibility | |
np.random.seed(1000) | |
nb_patterns = 4 | |
pattern_width = 4 | |
pattern_height = 4 | |
max_iterations = 100 | |
learning_rate = 0.5 | |
# Initialize the patterns | |
X = np.zeros((nb_patterns, pattern_width * pattern_height)) | |
X[0] = [-1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1] | |
X[1] = [-1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1, -1] | |
X[2] = [-1, -1, 1, 1, -1, -1, 1, 1, 1, 1, -1, -1, 1, 1, -1, -1] | |
X[3] = [1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1] | |
# Show the patterns | |
fig, ax = plt.subplots(1, nb_patterns, figsize=(10, 5)) | |
for i in range(nb_patterns): | |
ax[i].matshow(X[i].reshape((pattern_height, pattern_width)), cmap='gray') | |
ax[i].set_xticks([]) | |
ax[i].set_yticks([]) | |
plt.show() | |
# Initialize the weight matrix | |
W = np.random.uniform(-0.1, 0.1, size=(pattern_width * pattern_height, pattern_width * pattern_height)) | |
W = W + W.T | |
# Create a vectorized activation function | |
def activation(x): | |
if x > 1.0: | |
return 1.0 | |
elif x < -1.0: | |
return -1.0 | |
else: | |
return x | |
act = np.vectorize(activation) | |
# Train the network | |
for _ in range(max_iterations): | |
for n in range(nb_patterns): | |
for i in range(pattern_width * pattern_height): | |
for j in range(pattern_width * pattern_height): | |
W[i, j] += learning_rate * X[n, i] * X[n, j] | |
W[j, i] = W[i, j] | |
# Create a corrupted test pattern | |
x_test = np.array([1, -1, 0.7, 1, -0.8, -1, 1, 1, -1, 1, -0.75, -1, 1, 1, 0.9, 1]) | |
# Recover the original patterns | |
A = x_test.copy() | |
for _ in range(max_iterations): | |
for i in range(pattern_width * pattern_height): | |
A[i] = activation(np.dot(W[i], A)) | |
# Show corrupted and recovered patterns | |
fig, ax = plt.subplots(1, 2, figsize=(10, 5)) | |
ax[0].matshow(x_test.reshape(pattern_height, pattern_width), cmap='gray') | |
ax[0].set_title('Corrupted pattern') | |
ax[0].set_xticks([]) | |
ax[0].set_yticks([]) | |
ax[1].matshow(A.reshape(pattern_height, pattern_width), cmap='gray') | |
ax[1].set_title('Recovered pattern') | |
ax[1].set_xticks([]) | |
ax[1].set_yticks([]) | |
plt.show() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment