DISCR_HIDDEN_DIM = args.Ddim
HALF_BATCH_SIZE = 128
MODEL_FILENAME = 'model.pkl'
rng = check_random_state(0)
lang1 = args.lang1
lang2 = args.lang2
dataDir = 'data/' + args.config + '/'
print >> sys.stderr, 'Loading', lang1, 'embeddings...'
we1 = WordEmbeddings()
we1.load_from_word2vec(dataDir, lang1)
we1.downsample_frequent_words()
we1.vectors = normalize(we1.vectors).astype(theano.config.floatX)
we_batches1 = we1.sample_batches(batch_size=HALF_BATCH_SIZE, random_state=rng)
print >> sys.stderr, 'Loading', lang2, 'embeddings...'
we2 = WordEmbeddings()
we2.load_from_word2vec(dataDir, lang2)
we2.downsample_frequent_words()
we2.vectors = normalize(we2.vectors).astype(theano.config.floatX)
we_batches2 = we2.sample_batches(batch_size=HALF_BATCH_SIZE, random_state=rng)
assert we1.embedding_dim == we2.embedding_dim
d = we1.embedding_dim
discriminator = Discriminator(d, DISCR_NUM_HIDDEN_LAYERS, DISCR_HIDDEN_DIM,
args.input_noise_param, args.hidden_noise_param)
else:
accumulators[:] = ACCUMULATOR_EXPAVG * np.array([accuracy_val, loss_val, alt_accuracy_val, alt_loss_val, gen_loss_val, recon_gen_loss_val, adv_gen_loss_val, cos_gen_loss_val, float(skip_generator), float(skip_discriminator), preout_grad_norm_val]) + (1.0 - ACCUMULATOR_EXPAVG) * accumulators
if batch_id % print_every_n == 0:
print >> sys.stderr, 'batch: %s, acc: %s, loss: %s, alt acc: %s, alt loss: %s, gloss: %s, grloss: %s, galoss: %s, gcloss: %s, gskip: %s, dskip: %s, gn: %s' % tuple([batch_id] + accumulators.tolist())
def save_model():
params_vals = lasagne.layers.get_all_param_values([discriminator_0.l_out, discriminator_1.l_out, gen_l_out])
cPickle.dump(params_vals, open(MODEL_FILENAME, 'wb'), protocol=cPickle.HIGHEST_PROTOCOL)
print >> sys.stderr, 'Loading Italian embeddings...'
we_it = WordEmbeddings()
we_it.load_from_word2vec('./it')
we_it.downsample_frequent_words()
skn_it = StandardScaler()
we_it.vectors = skn_it.fit_transform(we_it.vectors).astype(theano.config.floatX)
we_batches_it = we_it.sample_batches(batch_size=HALF_BATCH_SIZE, random_state=rng)
print >> sys.stderr, 'Loading English embeddings...'
we_en = WordEmbeddings()
we_en.load_from_word2vec('./en')
we_en.downsample_frequent_words()
skn_en = StandardScaler()
we_en.vectors = skn_en.fit_transform(we_en.vectors).astype(theano.config.floatX)
we_batches_en = we_en.sample_batches(batch_size=HALF_BATCH_SIZE, random_state=rng)
print >> sys.stderr, 'Ready to train.'
print >> sys.stderr, 'Training...'
for i in xrange(10000000):
train_batch(i+1, 100)
def save_model():
params_vals = lasagne.layers.get_all_param_values(
[discriminator_0.l_out, discriminator_1.l_out, gen_l_out])
cPickle.dump(params_vals,
open(MODEL_FILENAME, 'wb'),
protocol=cPickle.HIGHEST_PROTOCOL)
print >> sys.stderr, 'Loading German embeddings...'
we_it = WordEmbeddings()
we_it.load_from_word2vec('./de')
we_it.downsample_frequent_words()
skn_it = StandardScaler()
we_it.vectors = skn_it.fit_transform(we_it.vectors).astype(
theano.config.floatX)
we_batches_it = we_it.sample_batches(batch_size=HALF_BATCH_SIZE,
random_state=rng)
print >> sys.stderr, 'Loading English embeddings...'
we_en = WordEmbeddings()
we_en.load_from_word2vec('./en')
we_en.downsample_frequent_words()
skn_en = StandardScaler()
we_en.vectors = skn_en.fit_transform(we_en.vectors).astype(
theano.config.floatX)
we_batches_en = we_en.sample_batches(batch_size=HALF_BATCH_SIZE,
random_state=rng)
print >> sys.stderr, 'Ready to train.'