def train_and_evaluate(data, vector_type):
input_patterns = [data[cat][vector_type] for cat in data.keys()]
# initialize the network
network = HopfieldNetwork(1200)
# train the network
hebbian_training(network, input_patterns)
results = []
for i, cat in enumerate(data.keys()):
cat_data = data[cat]
hyp_vecs = cat_data['hyponym_vectors'].values()
num_vecs = len(hyp_vecs)
correct = 0
mistakes = []
for pattern in hyp_vecs:
output = network.run(pattern)
idx = find_closest(output, input_patterns)
if i == idx: correct += 1
else: mistakes.append(mapping[idx])
mistakes = dict(Counter(mistakes))
results.append({'correct': correct, 'num_vecs': num_vecs,
'mistakes': mistakes, 'category': cat, 'vector_type': vector_type})
return results
])
# first creating the network and then train it with hebbian
network = HopfieldNetwork(35)
hebbian_training(network, input_patterns)
# Create the test patterns by using the training patterns and adding some noise to them
# and use the neural network to denoise them
j_test = j_pattern.flatten()
for i in range(4):
p = randint(0, 34)
j_test[p] *= -1
j_result = network.run(j_test)
j_result.shape = (7, 5)
j_test.shape = (7, 5)
a_test = a_pattern.flatten()
for i in range(4):
p = randint(0, 34)
a_test[p] *= -1
a_result = network.run(a_test)
a_result.shape = (7, 5)
a_test.shape = (7, 5)
s_pattern.flatten()])
# Create the neural network and train it using the training patterns
network = HopfieldNetwork(35)
hebbian_training(network, input_patterns)
# Create the test patterns by using the training patterns and adding some noise to them
# and use the neural network to denoise them
a_test = a_pattern.flatten()
for i in range(2):
p = randint(0, 34)
a_test[p] *= -1
a_result = network.run(a_test)
a_result.shape = (7, 5)
a_test.shape = (7, 5)
u_test = u_pattern.flatten()
for i in range(2):
p = randint(0, 34)
u_test[p] *= -1
u_result = network.run(u_test)
u_result.shape = (7, 5)
u_test.shape = (7, 5)
input_patterns = np.array([a_pattern.flatten(), b_pattern.flatten(), c_pattern.flatten()])
#Create the neural network and train it using the training patterns
network = HopfieldNetwork(35)
hebbian_training(network, input_patterns)
#Create the test patterns by using the training patterns and adding some noise to them
#and use the neural network to denoise them
a_test = a_pattern.flatten()
for i in range(4):
p = randint(0, 34)
a_test[p] *= -1
a_result = network.run(a_test)
a_result.shape = (7, 5)
a_test.shape = (7, 5)
b_test = b_pattern.flatten()
for i in range(4):
p = randint(0, 34)
b_test[p] *= -1
b_result = network.run(b_test)
b_result.shape = (7, 5)
b_test.shape = (7, 5)
