def run_hf_demo_alphabet(letters,
initialization_noise_level=0.2,
random_seed=None):
"""
Simple demo
Args:
letters:
initialization_noise_level:
random_seed:
Returns:
"""
# fixed size 10 for the alphabet.
pattern_size = 10
# pick some letters we want to store in the network
if letters is None:
letters = ['A', 'B', 'C', 'R', 'S', 'X', 'Y', 'Z']
reference_pattern = 0
# instantiate a hofpfield network
hopfield_net = network.HopfieldNetwork(pattern_size**2)
# for the demo, use a seed to get a reproducible pattern
np.random.seed(random_seed)
# load the dictionary
abc_dict = pattern_tools.load_alphabet()
# for each key in letters, append the pattern to the list
pattern_list = [abc_dict[key] for key in letters]
hfplot.plot_pattern_list(pattern_list)
hopfield_net.store_patterns(pattern_list)
hopfield_net.set_state_from_pattern(
pattern_tools.get_noisy_copy(abc_dict[letters[reference_pattern]],
initialization_noise_level))
states = hopfield_net.run_with_monitoring(6)
state_patterns = pattern_tools.reshape_patterns(states,
pattern_list[0].shape)
hfplot.plot_state_sequence_and_overlap(state_patterns, pattern_list,
reference_pattern)
plt.show()
def run_hf_demo_alphabet(letters, initialization_noise_level=0.2, random_seed=None):
"""
Simple demo
Args:
letters:
initialization_noise_level:
random_seed:
Returns:
"""
# fixed size 10 for the alphabet.
pattern_size = 10
# pick some letters we want to store in the network
if letters is None:
letters = ['A', 'B', 'C', 'R', 'S', 'X', 'Y', 'Z']
reference_pattern = 0
# instantiate a hofpfield network
hopfield_net = network.HopfieldNetwork(pattern_size**2)
# for the demo, use a seed to get a reproducible pattern
np.random.seed(random_seed)
# load the dictionary
abc_dict = pattern_tools.load_alphabet()
# for each key in letters, append the pattern to the list
pattern_list = [abc_dict[key] for key in letters]
hfplot.plot_pattern_list(pattern_list)
hopfield_net.store_patterns(pattern_list)
hopfield_net.set_state_from_pattern(
pattern_tools.get_noisy_copy(abc_dict[letters[reference_pattern]], initialization_noise_level))
states = hopfield_net.run_with_monitoring(6)
state_patterns = pattern_tools.reshape_patterns(states, pattern_list[0].shape)
hfplot.plot_state_sequence_and_overlap(state_patterns, pattern_list, reference_pattern)
plt.show()
def main( plot = True ):
# set a seed to reproduce the same noise in the next run
# numpy.random.seed(123)
# access the first element and get it's size (they are all of same size)
pattern_shape = abc_dictionary['A'].shape
# create an instance of the class HopfieldNetwork
hopfield_net = network.HopfieldNetwork(
nr_neurons = pattern_shape[0]*pattern_shape[1]
)
pattern_list = [abc_dictionary[key] for key in letter_list ]
if plot:
plt.figure( figsize=(12, 6) )
for i, a in enumerate(pattern_list):
ax = plt.subplot( nRows, maxCols, i+1)
ax.imshow( a )
ax.axis('off')
# store the patterns
hopfield_net.store_patterns(pattern_list)
print( "[INFO ] Saved patterns into hopfield network." )
# create a noisy version of a pattern and use that to initialize the network
l = random.choice( letter_list )
noise_level = 0.5
if add_extra:
noise_level = 0.1
print( '[INFO] More patterns than net can handle. Catastrophic'
' forgetting going to happen.'
)
noisy_init_state = add_noise( l, noise_level= noise_level)
hopfield_net.set_state_from_pattern(noisy_init_state)
# from this initial state, let the network dynamics evolve.
states = hopfield_net.run_with_monitoring(nr_steps=4)
# each network state is a vector. reshape it to the same shape used to create the patterns.
states_as_patterns = pattern_tools.reshape_patterns(states, pattern_list[0].shape)
if plot:
for i, pat in enumerate( states_as_patterns ):
overlap_list = pattern_tools.compute_overlap_list(pat, pattern_list)
ax = plt.subplot( nRows, maxCols, maxCols+i+1 )
ax.imshow( pat )
ax.axis('off')
ax1 = plt.subplot( nRows, maxCols, 2*maxCols+i+1)
ax1.bar( range(len(overlap_list)), overlap_list )
# ax1.set_ylim( -1, 1 )
ax1.set_xticks( range(len(overlap_list)) )
ax1.set_xticklabels( letter_list )
plt.tight_layout()
outfile = 'figures/final_%s.png' % l
plt.savefig( outfile )
print( '|| Saved to %s' % outfile )
plt.show()
plt.close()
return pattern_list, noisy_init_state, states_as_patterns
def main(plot=True):
# set a seed to reproduce the same noise in the next run
# numpy.random.seed(123)
# access the first element and get it's size (they are all of same size)
pattern_shape = abc_dictionary['A'].shape
# create an instance of the class HopfieldNetwork
hopfield_net = network.HopfieldNetwork(nr_neurons=pattern_shape[0] *
pattern_shape[1])
pattern_list = [abc_dictionary[key] for key in letter_list]
if plot:
plt.figure(figsize=(12, 6))
for i, k in enumerate(letter_list):
a = abc_dictionary[k]
ax = plt.subplot(nRows, maxCols, i + 1)
ax.imshow(a)
ax.axis('off')
save_letter(a, '%s.txt' % k)
# store the patterns
hopfield_net.store_patterns(pattern_list)
print("[INFO ] Saved patterns into hopfield network.")
# create a noisy version of a pattern and use that to initialize the network
l = random.choice(letter_list)
noise_level = 0.5
if add_extra:
noise_level = 0.1
print('[INFO] More patterns than net can handle. Catastrophic'
' forgetting going to happen.')
noisy_init_state = add_noise(l, noise_level=noise_level)
hopfield_net.set_state_from_pattern(noisy_init_state)
# from this initial state, let the network dynamics evolve.
states = hopfield_net.run_with_monitoring(nr_steps=4)
# each network state is a vector. reshape it to the same shape used to create the patterns.
states_as_patterns = pattern_tools.reshape_patterns(
states, pattern_list[0].shape)
if plot:
for i, pat in enumerate(states_as_patterns):
overlap_list = pattern_tools.compute_overlap_list(
pat, pattern_list)
ax = plt.subplot(nRows, maxCols, maxCols + i + 1)
save_letter(pat, '%s%s.txt' % (l, i))
ax.imshow(pat)
ax.axis('off')
ax1 = plt.subplot(nRows, maxCols, 2 * maxCols + i + 1)
ax1.bar(range(len(overlap_list)), overlap_list)
# ax1.set_ylim( -1, 1 )
ax1.set_xticks(range(len(overlap_list)))
ax1.set_xticklabels(letter_list)
plt.tight_layout()
outfile = 'figures/final_%s.png' % l
plt.savefig(outfile)
print('|| Saved to %s' % outfile)
plt.show()
plt.close()
return pattern_list, noisy_init_state, states_as_patterns