start_time = time.time()
print >> sys.stderr, 'Initial det(W)', np.linalg.det(
generator.gen_l_out.W.get_value())
for batch_id in range(1, num_epochs + 1):
id1 = next(we_batches1)
id2 = next(we_batches2)
X[:HALF_BATCH_SIZE] = we1.vectors[id1]
gen_loss_val, recon_gen_loss_val, adv_gen_loss_val, X_gen = gen_train_fn(
X[:HALF_BATCH_SIZE])
X[:HALF_BATCH_SIZE] = X_gen
X[HALF_BATCH_SIZE:] = we2.vectors[id2]
loss_val = discriminator.train_fn(X, target_mat)
if batch_id > 10000 and gen_loss_val < gloss_min:
gloss_min = gen_loss_val
print >> sys.stderr, batch_id, gloss_min
save_model()
W = generator.gen_l_out.W.get_value()
print >> sys.stderr, 'recon_gen_loss_val', recon_gen_loss_val, '||W^T*W - I||', np.linalg.norm(
np.dot(W.T, W) - np.identity(d)), 'det(W)', np.linalg.det(W)
we1.transformed_vectors = np.dot(we1.vectors,
W) #这里换成generator.gen_out试试？
# we1.transformed_vectors = generation.get_value()
we1.save_transformed_vectors(dataDir + 'transformed-' + str(modelID) +
'.' + 'zh')
print >> sys.stderr, (time.time() - start_time) / 60, 'min'
loss_val, accuracy_val = discriminator.train_fn(X, target_mat)
if batch_id % print_every_n == 0:
W = gen_l_out.W.get_value()
print '%s %s %s %s %s %s %s %s' % (
batch_id, accuracy_val, loss_val, gen_loss_val, recon_gen_loss_val,
adv_gen_loss_val, grad_norm_val,
np.linalg.norm(np.dot(W.T, W) - np.identity(d)))
if args.num_save > 0:
if batch_id % (numBatches / args.num_save) == 0:
save_model()
W = gen_l_out.W.get_value()
print >> sys.stderr, 'recon_gen_loss_val', recon_gen_loss_val, '||W^T*W - I||', np.linalg.norm(
np.dot(W.T, W) - np.identity(d)), 'det(W)', np.linalg.det(W)
we1.transformed_vectors = np.dot(we1.vectors, W)
we1.save_transformed_vectors(dataDir + 'transformed-' +
str(modelID) + '.' + lang1)
modelID += 1
else:
if batch_id > 10000 and gen_loss_val < gloss_min:
gloss_min = gen_loss_val
print >> sys.stderr, batch_id, gloss_min
save_model()
W = gen_l_out.W.get_value()
print >> sys.stderr, 'recon_gen_loss_val', recon_gen_loss_val, '||W^T*W - I||', np.linalg.norm(
np.dot(W.T, W) - np.identity(d)), 'det(W)', np.linalg.det(W)
we1.transformed_vectors = np.dot(we1.vectors, W)
we1.save_transformed_vectors(dataDir + 'transformed-' +
str(modelID) + '.' + lang1)
print >> sys.stderr, (time.time() - start_time) / 60, 'min'