def train_discriminator(discriminator, dis_opt, real_data_samples, generator,
oracle, d_steps, epochs):
"""
Training the discriminator on real_data_samples (positive) and generated samples from generator (negative).
Samples are drawn d_steps times, and the discriminator is trained for epochs epochs.
"""
#生成一小部分验证数据和我们的真实数据对比
# generating a small validation set before training (using oracle and generator)
pos_val = oracle.sample(100)
neg_val = generator.sample(100)
val_inp, val_target = helpers.prepare_discriminator_data(pos_val,
neg_val,
gpu=CUDA)
#训练
for d_step in range(d_steps):
s = helpers.batchwise_sample(generator, POS_NEG_SAMPLES, BATCH_SIZE)
dis_inp, dis_target = helpers.prepare_discriminator_data(
real_data_samples, s, gpu=CUDA)
for epoch in range(epochs):
print('d-step %d epoch %d : ' % (d_step + 1, epoch + 1), end='')
sys.stdout.flush()
total_loss = 0
total_acc = 0
for i in range(0, 2 * POS_NEG_SAMPLES, BATCH_SIZE):
inp, target = dis_inp[i:i + BATCH_SIZE], dis_target[i:i +
BATCH_SIZE]
dis_opt.zero_grad()
out = discriminator.batchClassify(inp)
loss_fn = nn.BCELoss()
loss = loss_fn(out, target)
loss.backward()
dis_opt.step()
total_loss += loss.data.item()
total_acc += torch.sum(
(out > 0.5) == (target > 0.5)).data.item()
if (i / BATCH_SIZE) % ceil(
ceil(2 * POS_NEG_SAMPLES / float(BATCH_SIZE)) /
10.) == 0: # roughly every 10% of an epoch
print('.', end='')
sys.stdout.flush()
total_loss /= ceil(2 * POS_NEG_SAMPLES / float(BATCH_SIZE))
total_acc /= float(2 * POS_NEG_SAMPLES)
val_pred = discriminator.batchClassify(val_inp)
print(' average_loss = %.4f, train_acc = %.4f, val_acc = %.4f' %
(total_loss, total_acc,
torch.sum(
(val_pred > 0.5) == (val_target > 0.5)).data.item() /
200.))
def train_discriminator(discriminator,dis_opt,real_data_samples,generator,oracle,d_steps,epochs):
#通过鉴别器对真实数据和生成器生成的数据进行训练
#样本通过d步得到，鉴别器通过epochs次的训练
#生成一小部分验证集
pos_val = oracle.sample(100)
neg_val = generator.sample(100)
val_inp,val_target = helpers.prepare_discriminator_data(pos_val,neg_val,gpu=CUDA)
for d_step in range(d_steps):
s = helpers.batchwise_sample(generator,POS_NEG_SAMPLES,BATCH_SIZE)
dis_inp,dis_target = helpers.prepare_discriminator_data(real_data_samples,s,gpu=CUDA)
for epoch in range(epochs):
print('d_step %d epoch %d:' %(d_step+1,epoch+1),end='')
sys.stdout.flush()
total_loss = 0
total_acc = 0
for i in range(0,2*POS_NEG_SAMPLES,BATCH_SIZE):
inp,target = dis_inp[i:i+BATCH_SIZE],dis_target[i:i+BATCH_SIZE]
dis_opt.zero_grad()
out = discriminator.batchClassify(inp)
loss_fn = nn.BCELoss()
loss = loss_fn(out,target)
loss.backward()
dis_opt.step()
total_loss += loss.data.item()
total_acc += torch.sum((out>0.5)==(target>0.5)).data.item()
if(i/BATCH_SIZE) % ceil(ceil(2*POS_NEG_SAMPLES/float(BATCH_SIZE))/10) == 0:
print('.',end='')
sys.stdout.flush()
total_acc /= ceil(2*POS_NEG_SAMPLES/float(BATCH_SIZE))
total_acc /= float(2*POS_NEG_SAMPLES)
val_pred = discriminator.batchClassify(val_inp)
print(' average_loss = %.4f, train_acc = %.4f, val_acc = %.4f' % (
total_loss, total_acc, torch.sum((val_pred > 0.5) == (val_target > 0.5)).data.item() / 200.))
def train_discriminator(dis, dis_opt, real_data_samples, gen, d_steps, epochs):
"""
Training the discriminator on real_data_samples (positive) and generated samples from generator (negative).
Samples are drawn d_steps times, and the discriminator is trained for epochs epochs.
"""
for d_step in range(d_steps):
s = helpers.batchwise_sample(gen, POS_NEG_SAMPLES, BATCH_SIZE)
dis_inp, dis_target = helpers.prepare_discriminator_data(
real_data_samples, s, gpu=CUDA)
for epoch in range(epochs):
print('d-step %d epoch %d : ' % (d_step + 1, epoch + 1), end='')
sys.stdout.flush()
total_loss = 0
total_acc = 0
for i in range(0, 2 * POS_NEG_SAMPLES, BATCH_SIZE):
inp, target = dis_inp[i:i + BATCH_SIZE], dis_target[i:i +
BATCH_SIZE]
dis_opt.zero_grad()
out = dis.batchClassify(inp)
loss_fn = nn.BCELoss()
loss = loss_fn(out, target)
loss.backward()
dis_opt.step()
total_loss += loss.data[0]
total_acc += torch.sum((out > 0.5) == (target > 0.5)).data[0]
if (i / BATCH_SIZE) % ceil(
ceil(2 * POS_NEG_SAMPLES / float(BATCH_SIZE)) /
10.) == 0: # roughly every 10% of an epoch
print('.', end='')
sys.stdout.flush()
total_loss /= ceil(2 * POS_NEG_SAMPLES / float(BATCH_SIZE))
total_acc /= float(2 * POS_NEG_SAMPLES)
def train_discriminator(discriminator, dis_opt, real_data_samples, generator,
oracle, d_steps, epochs, args):
"""
Training the discriminator on real_data_samples (positive) and generated samples from generator (negative).
Samples are drawn d_steps times, and the discriminator is trained for epochs epochs.
"""
# generating a small validation set before training (using oracle and generator)
pos_val = oracle.sample(100)
neg_val = generator.sample(100)
val_inp, val_target = helpers.prepare_discriminator_data(pos_val,
neg_val,
gpu=args.cuda)
val_buffer = torch.zeros(200 * args.max_seq_len, args.vocab_size)
if args.cuda:
val_buffer = val_buffer.cuda()
val_inp_oh = helpers.get_oh(val_inp, val_buffer)
inp_buf = torch.zeros(args.d_bsz * args.max_seq_len, args.vocab_size)
if args.cuda:
inp_buf = inp_buf.cuda()
num_data = len(real_data_samples)
for d_step in range(d_steps):
s = helpers.batchwise_sample(generator, args.num_data)
dis_inp, dis_target = helpers.prepare_discriminator_data(
real_data_samples, s, gpu=args.cuda)
for epoch in range(epochs):
print('d-step %d epoch %d : ' % (d_step + 1, epoch + 1), end='')
sys.stdout.flush()
total_loss = 0
total_acc = 0
for i in range(0, 2 * num_data, args.d_bsz):
if i + args.d_bsz > 2 * num_data:
break
inp, target = dis_inp[i:i + args.d_bsz], dis_target[i:i +
args.d_bsz]
inp_oh = helpers.get_oh(inp, inp_buf)
dis_opt.zero_grad()
out = discriminator.batchClassify(Variable(inp_oh))
loss_fn = nn.BCELoss()
loss = loss_fn(out, Variable(target))
loss.backward()
dis_opt.step()
total_loss += loss.data[0]
total_acc += torch.sum(
(out > 0.5) == (Variable(target) > 0.5)).data[0]
if (i / args.d_bsz) % ceil(
ceil(2 * num_data / float(args.d_bsz)) /
10.) == 0: # roughly every 10% of an epoch
print('.', end='')
sys.stdout.flush()
total_loss /= ceil(2 * num_data / float(args.d_bsz))
total_acc /= float(2 * num_data)
val_pred = discriminator.batchClassify(Variable(val_inp_oh))
print(' average_loss = %.4f, train_acc = %.4f, val_acc = %.4f' %
(total_loss, total_acc,
torch.sum((val_pred > 0.5) ==
(Variable(val_target) > 0.5)).data[0] / 200.))
args.max_seq_len,
gpu=args.cuda,
)
dis = discriminator.Discriminator(args.d_emb_dim,
args.d_hid_dim,
args.vocab_size,
args.max_seq_len,
gpu=args.cuda)
if args.cuda:
oracle = oracle.cuda()
gen = gen.cuda()
dis = dis.cuda()
oracle_samples = helpers.batchwise_sample(oracle, args.num_data)
if args.oracle_save is not None:
torch.save(oracle.state_dict(), args.oracle_save)
logger = logger.Logger(args.log_dir)
# GENERATOR MLE TRAINING
gen_optimizer = optim.Adam(gen.parameters(), lr=1e-2)
if args.pre_g_load is not None:
print("Load pretrained MLE gen")
gen.load_state_dict(torch.load(args.pre_g_load))
else:
print('Starting Generator MLE Training...')
for epoch in range(args.mle_epochs):
print('epoch %d : ' % (epoch + 1), end='')
sys.stdout.flush()
