def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
for k in doc_keys:
X_docs.append(vecnorm(doc2vec(docs[k], n_vocab), 'logmax1', 0))
del docs[k]
X_docs = np.r_[X_docs]
if args.noise == 'gs':
X_docs_noisy = add_gaussian_noise(X_docs, 0.1)
elif args.noise == 'sp':
X_docs_noisy = add_salt_pepper_noise(X_docs, 0.1)
pass
elif args.noise == 'mn':
X_docs_noisy = add_masking_noise(X_docs, 0.01)
else:
pass
n_samples = X_docs.shape[0]
np.random.seed(0)
val_idx = np.random.choice(range(n_samples), args.n_val, replace=False)
train_idx = list(set(range(n_samples)) - set(val_idx))
X_train = X_docs[train_idx]
X_val = X_docs[val_idx]
del X_docs
if args.noise:
# X_train_noisy = X_docs_noisy[:-n_val]
# X_val_noisy = X_docs_noisy[-n_val:]
X_train_noisy = X_docs_noisy[train_idx]
X_val_noisy = X_docs_noisy[val_idx]
print 'added %s noise' % args.noise
else:
X_train_noisy = X_train
X_val_noisy = X_val
start = timeit.default_timer()
ae = AutoEncoder(n_vocab,
args.n_dim,
comp_topk=args.comp_topk,
ctype=args.ctype,
save_model=args.save_model)
ae.fit([X_train_noisy, X_train], [X_val_noisy, X_val], nb_epoch=args.n_epoch, \
batch_size=args.batch_size, contractive=args.contractive)
print 'runtime: %ss' % (timeit.default_timer() - start)
if args.output:
train_doc_codes = ae.encoder.predict(X_train)
val_doc_codes = ae.encoder.predict(X_val)
doc_keys = np.array(doc_keys)
dump_json(
dict(zip(doc_keys[train_idx].tolist(), train_doc_codes.tolist())),
args.output + '.train')
dump_json(
dict(zip(doc_keys[val_idx].tolist(), val_doc_codes.tolist())),
args.output + '.val')
print 'Saved doc codes file to %s and %s' % (args.output + '.train',
args.output + '.val')
def train(args):
corpus = load_corpus(args.input)
n_vocab, docs = len(corpus['vocab']), corpus['docs']
corpus.clear()
# vocab = corpus['vocab']
corpus.clear() # save memory
doc_keys = docs.keys()
X_docs = []
for k in doc_keys:
X_docs.append(vecnorm(doc2vec(docs[k], n_vocab), 'logmax1', 0))
del docs[k]
X_docs = np.r_[X_docs]
if args.noise == 'gs':
X_docs_noisy = add_gaussian_noise(X_docs, 0.1)
elif args.noise == 'sp':
X_docs_noisy = add_salt_pepper_noise(X_docs, 0.1)
pass
elif args.noise == 'mn':
X_docs_noisy = add_masking_noise(X_docs, 0.01)
else:
pass
n_samples = X_docs.shape[0]
np.random.seed(0)
val_idx = np.random.choice(range(n_samples), args.n_val, replace=False)
train_idx = list(set(range(n_samples)) - set(val_idx))
X_train = X_docs[train_idx]
X_val = X_docs[val_idx]
del X_docs
if args.noise:
# X_train_noisy = X_docs_noisy[:-n_val]
# X_val_noisy = X_docs_noisy[-n_val:]
X_train_noisy = X_docs_noisy[train_idx]
X_val_noisy = X_docs_noisy[val_idx]
print 'added %s noise' % args.noise
else:
X_train_noisy = X_train
X_val_noisy = X_val
start = timeit.default_timer()
ae = AutoEncoder(n_vocab,
args.n_dim,
comp_topk=args.comp_topk,
ctype=args.ctype,
save_model=args.save_model)
ae.fit([X_train_noisy, X_train], [X_val_noisy, X_val], nb_epoch=args.n_epoch, \
batch_size=args.batch_size, contractive=args.contractive)
print 'runtime: %ss' % (timeit.default_timer() - start)
if args.output:
train_doc_codes = ae.encoder.predict(X_train)
val_doc_codes = ae.encoder.predict(X_val)
doc_keys = np.array(doc_keys)
dump_json(
dict(zip(doc_keys[train_idx].tolist(), train_doc_codes.tolist())),
args.output + '.train')
dump_json(
dict(zip(doc_keys[val_idx].tolist(), val_doc_codes.tolist())),
args.output + '.val')
print 'Saved doc codes file to %s and %s' % (args.output + '.train',
args.output + '.val')
def unitmatrix(matrix, norm='l2', axis=1):
if norm == 'l1':
maxtrixlen = np.sum(np.abs(matrix), axis=axis)
if norm == 'l2':
maxtrixlen = np.linalg.norm(matrix, axis=axis)
if np.any(maxtrixlen <= 0):
return matrix
else:
maxtrixlen = maxtrixlen.reshape(
1, len(maxtrixlen)) if axis == 0 else maxtrixlen.reshape(
len(maxtrixlen), 1)
return matrix / maxtrixlen
def calc_pairwise_dev(weights):
# the average squared deviation from 0 (90 degree)
weights = unitmatrix(weights, axis=0) # normalize
n = weights.shape[1]
score = 0.
for i in range(n):
for j in range(i + 1, n):
score += (weights[:, i].dot(weights[:, j]))**2
return np.sqrt(2. * score / n / (n - 1))
from keras.models import load_model
