def train_epochs(self, start_epoch=1):
start_time = time.time()
self.megabatch = []
self.ep_loss = 0
self.curr_idx = 0
self.eval()
evaluate_sts(self, self.args)
self.train()
try:
for ep in range(start_epoch, self.args.epochs + 1):
self.mb = utils.get_minibatches_idx(len(self.data),
self.args.batchsize,
shuffle=True)
self.curr_idx = 0
self.ep_loss = 0
self.megabatch = []
cost = 0
counter = 0
while (cost is not None):
cost = pairing.compute_loss_one_batch(self)
if cost is None:
continue
self.ep_loss += cost.item()
counter += 1
print("Epoch {0}, Counter {1}/{2}".format(
ep, counter, len(self.mb)))
if self.save_interval > 0 and counter > 0:
if counter % self.save_interval == 0:
self.eval()
evaluate_sts(self, self.args)
self.train()
self.save_params(ep, counter=counter)
self.optimizer.zero_grad()
cost.backward()
torch.nn.utils.clip_grad_norm_(self.parameters,
self.args.grad_clip)
self.optimizer.step()
self.eval()
evaluate_sts(self, self.args)
self.train()
if self.args.save_every_epoch:
self.save_params(ep)
print('Epoch {0}\tCost: '.format(ep), self.ep_loss / counter)
except KeyboardInterrupt:
print("Training Interrupted")
if self.args.save_final:
self.save_params(ep)
end_time = time.time()
print("Total Time:", (end_time - start_time))
def fit(self, X, y, sess):
max_epochs = 20
# Split into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.33,
random_state=42)
for epoch in range(max_epochs):
start = time.time()
train_indices = get_minibatches_idx(len(X_train),
batch_size,
shuffle=True)
print("\nEpoch %d" % (epoch + 1))
train_accs = []
for c, it in enumerate(train_indices):
batch_train_x = [X_train[i] for i in it]
batch_train_y = [y_train[i] for i in it]
feed_dict = {
self.x: batch_train_x,
self.y: batch_train_y,
self.deterministic: False
}
_, acc = sess.run([self.train_step, self.accuracy], feed_dict)
train_accs.append(acc)
#print(c,len(train_indices),acc)
print("Training accuracy: %.3f" % np.mean(train_accs))
val_pred = self.predict(X_val, sess)
y = np.argmax(y_val, axis=1)
val_acc = np.mean(np.equal(val_pred, y))
print("Val accuracy: %.3f" % val_acc)
print("Time taken: %.3fs" % (time.time() - start))
return
def simple_test_batch(testloader, model, config, hidden=False):
model.eval()
total = 0.0
correct = 0.0
pred_np = []
hidden_vectors = []
minibatches_idx = get_minibatches_idx(
len(testloader),
minibatch_size=config['simple_test_batch_size'],
shuffle=False)
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([list(testloader[x][0].cpu().numpy())
for x in minibatch]))
targets = torch.Tensor(
np.array([list(testloader[x][1].cpu().numpy())
for x in minibatch]))
inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(
targets.cuda()).squeeze()
outputs = model(inputs)
if hidden:
hiddens = get_hidden(model, inputs)
hidden_vectors.extend(list(hiddens.cpu().data.numpy()))
pred_np.extend(list(outputs.cpu().data.numpy()))
predicted = (outputs >= 0.5).long().squeeze()
total += targets.size(0)
correct += predicted.eq(targets.long()).sum().item()
test_accuracy = correct / total
pred_np = np.array(pred_np)
if hidden:
return test_accuracy, pred_np, np.array(hidden_vectors)
return test_accuracy, pred_np
def simple_train_batch(trainloader, model, loss_function, optimizer, config):
model.train()
for epoch in range(config['epoch_num']):
if epoch == int(config['epoch_num'] / 3):
for g in optimizer.param_groups:
g['lr'] = config['lr'] / 10
print('divide current learning rate by 10')
elif epoch == int(config['epoch_num'] * 2 / 3):
for g in optimizer.param_groups:
g['lr'] = config['lr'] / 100
print('divide current learning rate by 10')
total_loss = 0
minibatches_idx = get_minibatches_idx(
len(trainloader),
minibatch_size=config['simple_train_batch_size'],
shuffle=True)
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([
list(trainloader[x][0].cpu().numpy()) for x in minibatch
]))
targets = torch.Tensor(
np.array([
list(trainloader[x][1].cpu().numpy()) for x in minibatch
]))
inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(
targets.long().cuda()).squeeze()
optimizer.zero_grad()
outputs = model(inputs).squeeze()
loss = loss_function(outputs, targets)
total_loss += loss
loss.backward()
optimizer.step()
print('epoch:', epoch, 'loss:', total_loss)
def do_eval(sess, train_q, train_a, train_lab):
train_correct = 0.0
# number_examples = len(train_q)
# print("valid examples:", number_examples)
eval_loss, eval_accc, eval_counter = 0.0, 0.0, 0
eval_true_positive, eval_false_positive, eval_true_negative, eval_false_negative = 0, 0, 0, 0
# batch_size = 1
weights_label = {} # weight_label[label_index]=(number,correct)
weights = np.ones((opt.batch_size))
kf_train = get_minibatches_idx(len(train_q),
opt.batch_size,
shuffle=True)
for _, train_index in kf_train:
train_sents_1 = [train_q[t] for t in train_index]
train_sents_2 = [train_a[t] for t in train_index]
train_labels = [train_lab[t] for t in train_index]
train_labels_array = np.array(train_labels)
# print("train_labels", train_labels.shape)
# train_labels = train_labels.reshape((len(train_labels), opt.category))
train_labels = np.eye(opt.category)[train_labels_array]
x_train_batch_1, x_train_mask_1 = prepare_data_for_emb(
train_sents_1, opt)
x_train_batch_2, x_train_mask_2 = prepare_data_for_emb(
train_sents_2, opt)
curr_eval_loss, curr_accc, logits = sess.run(
[loss_, accuracy_, logits_],
feed_dict={
x_1_: x_train_batch_1,
x_2_: x_train_batch_2,
x_mask_1_: x_train_mask_1,
x_mask_2_: x_train_mask_2,
y_: train_labels,
opt.weights_label: weights,
keep_prob: 1.0
})
true_positive, false_positive, true_negative, false_negative = compute_confuse_matrix(
logits, train_labels
) # logits:[batch_size,label_size]-->logits[0]:[label_size]
# write_predict_error_to_file(start,file_object,logits[0], evalY[start:end][0],vocabulary_index2word,evalX1[start:end],evalX2[start:end])
eval_loss, eval_accc, eval_counter = eval_loss + curr_eval_loss, eval_accc + curr_accc, eval_counter + 1 # 注意这里计算loss和accc的方法，计算累加值，然后归一化
weights_label = compute_labels_weights(
weights_label, logits, train_labels_array
) # compute_labels_weights(weights_label,logits,labels)
eval_true_positive, eval_false_positive = eval_true_positive + true_positive, eval_false_positive + false_positive
eval_true_negative, eval_false_negative = eval_true_negative + true_negative, eval_false_negative + false_negative
# weights_label = compute_labels_weights(weights_label, logits, evalY[start:end]) #compute_labels_weights(weights_label,logits,labels)
print("true_positive:", eval_true_positive, ";false_positive:",
eval_false_positive, ";true_negative:", eval_true_negative,
";false_negative:", eval_false_negative)
p = float(eval_true_positive) / float(eval_true_positive +
eval_false_positive)
r = float(eval_true_positive) / float(eval_true_positive +
eval_false_negative)
f1_score = (2 * p * r) / (p + r)
print("eval_counter:", eval_counter, ";eval_acc:", eval_accc)
return eval_loss / float(eval_counter), eval_accc / float(
eval_counter), f1_score, p, r, weights_label
def train(self, params, train, dev, test):
start_time = time.time()
counter = 0
try:
for eidx in xrange(params.epochs):
kf = utils.get_minibatches_idx(len(train), params.batchsize, shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [train[t] for t in train_index]
vocab = self.get_word_arr(batch)
y = self.get_y(batch)
x, xmask = self.prepare_data(self.populate_embeddings_words(batch, vocab))
idxs = self.get_idxs(xmask)
if params.nntype == "charlstm" or params.nntype == "charcnn":
char_indices = self.populate_embeddings_characters(vocab)
if params.nntype == "charagram":
char_hash = self.populate_embeddings_characters_charagram(vocab)
if params.nntype == "charlstm":
c, cmask = self.prepare_data(char_indices)
if params.nntype == "charcnn":
c = self.prepare_data_conv(char_indices)
if params.nntype == "charlstm":
cost = self.train_function(c, cmask, x, xmask, idxs, y)
if params.nntype == "charcnn":
cost = self.train_function(c, x, xmask, idxs, y)
if params.nntype == "charagram":
cost = self.train_function(char_hash, x, xmask, idxs, y)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
#print 'Epoch ', (eidx+1), 'Update ', (uidx+1), 'Cost ', cost
if(params.save):
counter += 1
utils.save_params(self, params.outfile+str(counter)+'.pickle')
if(params.evaluate):
devscore = self.evaluate(dev, params)
testscore = self.evaluate(test, params)
trainscore = self.evaluate(train, params)
print "accuracy: ", devscore, testscore, trainscore
print 'Epoch ', (eidx+1), 'Cost ', cost
except KeyboardInterrupt:
print "Training interrupted"
end_time = time.time()
print "total time:", (end_time - start_time)
def run_model(opt, X):
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:' +
str(params['Wemb'].shape) + ' opt: ' + str(
(opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:0'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
if opt.plot_type == 'ae':
x_lat_ = ae(x_, opt)
elif opt.plot_type == 'vae' or opt.plot_type == 'cyc':
mu_, z_ = vae(x_, opt)
x_lat_ = z_ if opt.use_z else mu_
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.3
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.trainable_variables()
#print([var.name[:-2] for var in t_vars])
loader = restore_from_save(t_vars, sess, opt)
except Exception as e:
print(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
X_emb = np.zeros([len(X), opt.z_dim], dtype='float32')
kf = get_minibatches_idx(len(X), opt.batch_size)
t = 0
for _, index in kf:
sents_b = [X[i] for i in index]
x_b = prepare_data_for_cnn(sents_b, opt)
x_lat = np.squeeze(sess.run(x_lat_, feed_dict={x_: x_b}))
X_emb[t * opt.batch_size:(t + 1) * opt.batch_size] = x_lat
if (t + 1) % 10 == 0:
print('%d / %d' % (t + 1, len(kf)))
t += 1
return X_emb
def train(model, data, words, params):
start_time = time.time()
counter = 0
try:
for eidx in xrange(params.epochs):
kf = utils.get_minibatches_idx(len(data), params.batchsize, shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [data[t] for t in train_index]
for i in batch:
i[0].populate_embeddings(words)
i[1].populate_embeddings(words)
(g1x, g1mask, g2x, g2mask, p1x, p1mask, p2x, p2mask) = getpairs(model, batch, params)
cost = model.train_function(g1x, g2x, p1x, p2x, g1mask, g2mask, p1mask, p2mask)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
if (utils.checkIfQuarter(uidx, len(kf))):
if (params.save):
counter += 1
utils.saveParams(model, params.outfile + str(counter) + '.pickle')
if (params.evaluate):
evaluate_all(model, words)
sys.stdout.flush()
#undo batch to save RAM
for i in batch:
i[0].representation = None
i[1].representation = None
i[0].unpopulate_embeddings()
i[1].unpopulate_embeddings()
#print 'Epoch ', (eidx+1), 'Update ', (uidx+1), 'Cost ', cost
if (params.save):
counter += 1
utils.saveParams(model, params.outfile + str(counter) + '.pickle')
if (params.evaluate):
evaluate_all(model, words)
print 'Epoch ', (eidx + 1), 'Cost ', cost
except KeyboardInterrupt:
print "Training interupted"
end_time = time.time()
print "total time:", (end_time - start_time)
def train(model, data, words, params):
start_time = time.time()
counter = 0
try:
for eidx in xrange(params.epochs):
kf = utils.get_minibatches_idx(len(data), params.batchsize, shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [data[t] for t in train_index]
for i in batch:
i[0].populate_embeddings(words)
i[1].populate_embeddings(words)
(g1x, g1mask, g2x, g2mask, p1x, p1mask, p2x, p2mask) = getpairs(model, batch, params)
cost = model.train_function(g1x, g2x, p1x, p2x, g1mask, g2mask, p1mask, p2mask)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
if (utils.checkIfQuarter(uidx, len(kf))):
if (params.save):
counter += 1
utils.saveParams(model, params.outfile + str(counter) + '.pickle')
if (params.evaluate):
evaluate_all(model, words)
sys.stdout.flush()
# undo batch to save RAM
for i in batch:
i[0].representation = None
i[1].representation = None
i[0].unpopulate_embeddings()
i[1].unpopulate_embeddings()
# print 'Epoch ', (eidx+1), 'Update ', (uidx+1), 'Cost ', cost
if (params.save):
counter += 1
utils.saveParams(model, params.outfile + str(counter) + '.pickle')
if (params.evaluate):
evaluate_all(model, words)
print 'Epoch ', (eidx + 1), 'Cost ', cost
except KeyboardInterrupt:
print "Training interupted"
end_time = time.time()
print "total time:", (end_time - start_time)
def predict(self, X, sess):
indices = get_minibatches_idx(len(X), batch_size, shuffle=False)
pred = []
for i in indices:
batch_x = [X[j] for j in i]
feed_dict = {self.x: batch_x, self.deterministic: True}
pred_batch = sess.run(self.pred, feed_dict)
pred.append(pred_batch)
pred = np.concatenate(pred, axis=0)
pred = np.argmax(pred, axis=1)
pred = np.reshape(pred, (-1))
return pred
def get_features(trainloader, model, config):
total_features = []
total_labels = []
minibatches_idx = get_minibatches_idx(
len(trainloader),
minibatch_size=config['simple_test_batch_size'],
shuffle=False)
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array(
[list(trainloader[x][0].cpu().numpy()) for x in minibatch]))
targets = torch.Tensor(
np.array(
[list(trainloader[x][1].cpu().numpy()) for x in minibatch]))
inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(
targets.cuda()).squeeze()
features = model.get_features(inputs)
total_features.extend(features.cpu().data.numpy().tolist())
total_labels.extend(targets.cpu().data.numpy().tolist())
total_features = np.array(total_features)
total_labels = np.array(total_labels)
print('total features', total_features.shape)
print('total labels', total_labels.shape)
avg_feature = np.mean(total_features, axis=0)
# print('avg feature', np.linalg.norm(avg_feature))
centralized_features = total_features - avg_feature
feature_norm = np.square(np.linalg.norm(centralized_features, axis=1))
class_features = []
feature_norm_list = []
for i in range(10):
mask_index = (total_labels == i)
mask_index = mask_index.reshape(len(mask_index), 1)
# print('mask index', mask_index)
if config['R'] == 'inf' and i == config['t1']:
break
class_features.append(
np.sum(total_features * mask_index, axis=0) /
np.sum(mask_index.reshape(-1)))
feature_norm_list.append(
np.sum(feature_norm * mask_index.reshape(-1)) /
np.sum(mask_index.reshape(-1)))
class_features = np.array(class_features)
# print('original class features', class_features)
class_features = np.array(class_features) - avg_feature
# print('centralized class features', class_features)
print('feature norm list', feature_norm_list)
print('avg square feature norm', np.mean(feature_norm_list))
return class_features
def run_epoch(sess, epoch, mode, print_freq=-1, train_writer=None):
fetches_ = {
'loss': loss_,
'accuracy': accuracy_
}
if mode == 'train':
x, y, is_train = train, train_lab, 1
fetches_['train_op'] = train_op_
fetches_['summary'] = merged
elif mode == 'val':
assert(print_freq == -1)
x, y, is_train = val, val_lab, None
elif mode == 'test':
assert(print_freq == -1)
x, y, is_train = test, test_lab, None
correct, acc_loss, acc_n = 0.0, 0.0, 0.0
local_t = 0
global_t = epoch*epoch_t # only used in train mode
start_time = time.time()
kf = get_minibatches_idx(len(x), opt.batch_size, shuffle=True)
for _, index in kf:
local_t += 1
global_t += 1
sents_b = [x[i] for i in index]
sents_b_n = add_noise(sents_b, opt)
y_b = [y[i] for i in index]
y_b = np.array(y_b)
y_b = y_b.reshape((len(y_b), 1))
x_b = prepare_data_for_cnn(sents_b_n, opt) # Batch L
feed_t = {x_: x_b, y_: y_b, is_train_: is_train}
fetches = sess.run(fetches_, feed_dict=feed_t)
batch_size = len(index)
acc_n += batch_size
acc_loss += fetches['loss']*batch_size
correct += fetches['accuracy']*batch_size
if print_freq>0 and local_t%print_freq==0:
print("%s Iter %d: loss %.4f, acc %.4f, time %.1fs" %
(mode, local_t, acc_loss/acc_n, correct/acc_n, time.time()-start_time))
if mode == 'train' and train_writer != None:
train_writer.add_summary(fetches['summary'], global_t)
print("%s Epoch %d: loss %.4f, acc %.4f, time %.1fs" %
(mode, epoch, acc_loss/acc_n, correct/acc_n, time.time()-start_time))
return acc_loss/acc_n, correct/acc_n
def reset(self):
"""
Resets the state of the environment, returning an initial observation.
Outputs
-------
observation : the initial observation of the space. (Initial reward is assumed to be 0.)
"""
t0 = time()
#for now lets get one sample with all.
kf = utils.get_minibatches_idx(len(self.qi),
len(self.qi),
shuffle=True)
_, train_index = kf[
0] #iterate if len(kf)>1 --> for _, train_index in kf:
print "kf", kf, len(self.qi)
print("Got minibatch index {}".format(time() - t0))
qi, qi_i, qi_lst, D_gt_id, D_gt_url = self.get_samples(
self.qi,
self.dt,
self.vocab,
train_index,
self.search.engine,
max_words_input=self.search.max_words_input)
current_queries = qi_lst
self.current_queries = qi_lst
self.D_gt_id = D_gt_id
print('current queries are', current_queries)
n_iterations = 1 # number of query reformulation iterations.
if n_iterations < self.search.q_0_fixed_until:
ones = np.ones((len(current_queries), self.search.max_words_input))
reformulated_query = ones
if n_iterations > 0:
# select everything from the original query in the first iteration.
reformulated_query = np.concatenate([ones, ones], axis=1)
print 'reformulated_query', reformulated_query.shape
# reformulated_query is our action!!!
actions = reformulated_query
state, reward, done = self.execute(actions)
print "state", state
print "actions", actions
print "rew", reward
return state
def train(self, X, training_epochs=10):
print("\nStarting training")
for epoch in range(training_epochs):
avg_cost = 0.0
train_indices = get_minibatches_idx(len(X),
batch_size,
shuffle=True)
for it in train_indices:
batch_x = [X[i] for i in it]
_, cost = self.sess.run((self.train_step, self.cost),
feed_dict={self.x: batch_x})
avg_cost += cost / n_samples * batch_size
print("Epoch:", '%d' % (epoch + 1), "cost=",
"{:.3f}".format(avg_cost))
def predict(test_inputs, model, config):
# test inputs: (T, D)
model.eval()
pred_np = []
minibatches_idx = get_minibatches_idx(
len(test_inputs),
minibatch_size=config['test_batch_size'],
shuffle=False)
for minibatch in minibatches_idx:
inputs = torch.Tensor(np.array([test_inputs[x] for x in minibatch]))
inputs = Variable(inputs.cuda().squeeze())
# (B, D)
outputs = model(inputs)
pred_np.extend(list(np.exp(outputs.cpu().data.numpy())))
# (B, C)
pred_np = np.array(pred_np)
# (T, C)
return pred_np
def simple_train_batch(trainloader, model, loss_function, optimizer, config):
model.train()
for epoch in range(config['epoch_num']):
if epoch % (config['epoch_num'] // 10) == 0:
print('current epoch: ', epoch)
total_loss = 0
minibatches_idx = get_minibatches_idx(
len(trainloader),
minibatch_size=config['simple_train_batch_size'],
shuffle=True)
# model.train()
# BCE, (100, 1, 1) doesn't matter
# MSE, (100, 1, 1) matter
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([
list(trainloader[x][0].cpu().numpy()) for x in minibatch
]))
targets = torch.Tensor(
np.array([
list(trainloader[x][1].cpu().numpy()) for x in minibatch
]))
inputs, targets = Variable(inputs.cuda()).squeeze(), Variable(
targets.float().cuda()).squeeze()
# inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(targets.float().cuda()).squeeze(1)
# print('inputs', inputs.size())
# print('targets', targets.size())
if config['model'] == 'CNN_MNIST':
inputs = inputs.unsqueeze(1)
optimizer.zero_grad()
# outputs = model(inputs)
outputs = model(inputs).squeeze()
loss = loss_function(outputs, targets)
# print('outputs', outputs.size())
# print('loss', loss)
total_loss += loss
loss.backward()
optimizer.step()
if epoch % (config['epoch_num'] // 10) == 0:
print('loss', total_loss)
def evaluate(self, data, params):
kf = utils.get_minibatches_idx(len(data), 100, shuffle=False)
preds = []
for _, train_index in kf:
batch = [data[t] for t in train_index]
vocab = self.get_word_arr(batch)
x, xmask = self.prepare_data(
self.populate_embeddings_words(batch, vocab))
idxs = self.get_idxs(xmask)
if params.nntype == "charlstm" or params.nntype == "charcnn":
char_indices = self.populate_embeddings_characters(vocab)
if params.nntype == "charagram":
char_hash = self.populate_embeddings_characters_charagram(
vocab)
if params.nntype == "charlstm":
c, cmask = self.prepare_data(char_indices)
if params.nntype == "charcnn":
c = self.prepare_data_conv(char_indices)
if params.nntype == "charlstm":
temp = self.scoring_function(c, cmask, x, xmask, idxs)
if params.nntype == "charcnn":
temp = self.scoring_function(c, x, xmask, idxs)
if params.nntype == "charagram":
temp = self.scoring_function(char_hash, x, xmask, idxs)
preds.extend(temp)
ys = []
for i in data:
for j in i[1]:
y = self.tags[j]
ys.append(y)
return accuracy_score(ys, preds)
def train(train_data, dev_data, model, loss_function, optimizer, ner_to_ix,
config):
total_loss_list = []
for epoch in range(config['epoch_num']):
model.train()
print('current epoch: ', epoch, end='\r\n')
total_loss = 0
minibatches_idx = get_minibatches_idx(
len(train_data['inputs']),
minibatch_size=config['train_batch_size'],
shuffle=True)
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([train_data['inputs'][x] for x in minibatch]))
targets = torch.Tensor(
np.array([train_data['labels'][x] for x in minibatch]))
confidences = torch.Tensor(
np.array([train_data['confidences'][x] for x in minibatch]))
inputs, targets = Variable(inputs.cuda()).squeeze(), Variable(
targets.cuda()).squeeze().long()
confidences = Variable(confidences.cuda(),
requires_grad=False).squeeze()
# inputs: (B, d), targets: B, confidences: B
optimizer.zero_grad()
outputs = model(inputs)
loss = loss_function(outputs, targets)
# print('loss', loss)
loss = torch.sum(loss * confidences)
total_loss += loss
loss.backward()
optimizer.step()
total_loss_list.append(total_loss.cpu().data.item())
print('train loss', total_loss)
# train_accuracy = evaluate(train_data, model, ner_to_ix, config)
# print('train accuracy', train_accuracy)
dev_accuracy = evaluate(dev_data, model, ner_to_ix, config)
print('dev accuracy', dev_accuracy)
def train_batch_autoencoder(trainloader, model, loss_function, optimizer,
config):
model.train()
for epoch in range(config['epoch_num']):
if epoch % (config['epoch_num'] // 10) == 0:
print('current epoch: ', epoch)
total_loss = 0
minibatches_idx = get_minibatches_idx(
len(trainloader),
minibatch_size=config['simple_train_batch_size'],
shuffle=True)
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([
list(trainloader[x][0].cpu().numpy()) for x in minibatch
]))
targets = torch.Tensor(
np.array([
list(trainloader[x][1].cpu().numpy()) for x in minibatch
]))
inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(
targets.float().cuda()).squeeze(1)
# print(inputs.size())
optimizer.zero_grad()
outputs = model(inputs)
# print(outputs.size())
loss = loss_function(outputs, inputs)
total_loss += loss
loss.backward()
optimizer.step()
if epoch % (config['epoch_num'] // 10) == 0:
pic = to_img(outputs.cpu().data)
save_image(
pic,
'/path/to/experiments/dir/figures/autoencoders/image_{}.png'.
format(epoch))
print('loss', total_loss)
def evaluate(self, data, params):
kf = utils.get_minibatches_idx(len(data), 100, shuffle=False)
preds = []
for _, train_index in kf:
batch = [data[t] for t in train_index]
vocab = self.get_word_arr(batch)
x, xmask = self.prepare_data(self.populate_embeddings_words(batch, vocab))
idxs = self.get_idxs(xmask)
if params.nntype == "charlstm" or params.nntype == "charcnn":
char_indices = self.populate_embeddings_characters(vocab)
if params.nntype == "charagram":
char_hash = self.populate_embeddings_characters_charagram(vocab)
if params.nntype == "charlstm":
c, cmask = self.prepare_data(char_indices)
if params.nntype == "charcnn":
c = self.prepare_data_conv(char_indices)
if params.nntype == "charlstm":
temp = self.scoring_function(c, cmask, x, xmask, idxs)
if params.nntype == "charcnn":
temp = self.scoring_function(c, x, xmask, idxs)
if params.nntype == "charagram":
temp = self.scoring_function(char_hash, x, xmask, idxs)
preds.extend(temp)
ys = []
for i in data:
for j in i[1]:
y = self.tags[j]
ys.append(y)
return accuracy_score(ys, preds)
def simple_test_batch(testloader, model, config):
model.eval()
total = 0.0
correct = 0.0
minibatches_idx = get_minibatches_idx(
len(testloader),
minibatch_size=config['simple_test_batch_size'],
shuffle=False)
y_true = []
y_pred = []
for minibatch in minibatches_idx:
inputs = torch.Tensor(
np.array([list(testloader[x][0].cpu().numpy())
for x in minibatch]))
targets = torch.Tensor(
np.array([list(testloader[x][1].cpu().numpy())
for x in minibatch]))
inputs, targets = Variable(inputs.cuda()).squeeze(1), Variable(
targets.cuda()).squeeze()
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
total += targets.size(0)
correct += predicted.eq(targets.long()).sum().item()
y_true.extend(targets.cpu().data.numpy().tolist())
y_pred.extend(predicted.cpu().data.numpy().tolist())
test_accuracy = correct / total
test_confusion_matrix = confusion_matrix(y_true, y_pred)
t1 = config['t1']
big_class_acc = np.sum([test_confusion_matrix[i, i] for i in range(t1)
]) / np.sum(test_confusion_matrix[:t1])
if t1 == 10:
small_class_acc = None
else:
small_class_acc = \
np.sum([test_confusion_matrix[i, i] for i in range(10)[t1:]]) / np.sum(test_confusion_matrix[t1:])
return test_accuracy, big_class_acc, small_class_acc, test_confusion_matrix
def main():
# global n_words
# Prepare training and testing data
loadpath = "./data/yahoo.p"
x = cPickle.load(open(loadpath, "rb"))
train, val, test = x[0], x[1], x[2]
train_lab, val_lab, test_lab = x[3], x[4], x[5]
wordtoix, ixtoword = x[6], x[7]
train_lab = np.array(train_lab, dtype='float32')
val_lab = np.array(val_lab, dtype='float32')
test_lab = np.array(test_lab, dtype='float32')
opt = Options()
opt.n_words = len(ixtoword)
del x
print(dict(opt))
print('Total words: %d' % opt.n_words)
if opt.part_data:
np.random.seed(123)
train_ind = np.random.choice(len(train),
int(len(train) * opt.portion),
replace=False)
train = [train[t] for t in train_ind]
train_lab = [train_lab[t] for t in train_ind]
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:' +
str(params['Wemb'].shape) + ' opt: ' +
str((opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.maxlen])
x_mask_ = tf.placeholder(tf.float32,
shape=[opt.batch_size, opt.maxlen])
keep_prob = tf.placeholder(tf.float32)
y_ = tf.placeholder(tf.float32, shape=[opt.batch_size, 10])
accuracy_, loss_, train_op, W_emb_ = emb_classifier(
x_, x_mask_, y_, keep_prob, opt)
# merged = tf.summary.merge_all()
uidx = 0
max_val_accuracy = 0.
max_test_accuracy = 0.
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.trainable_variables()
# print([var.name[:-2] for var in t_vars])
save_keys = tensors_key_in_file(opt.save_path)
# print(save_keys.keys())
ss = set([var.name for var in t_vars]) & set(
[s + ":0" for s in save_keys.keys()])
cc = {var.name: var for var in t_vars}
# only restore variables with correct shape
ss_right_shape = set(
[s for s in ss if cc[s].get_shape() == save_keys[s[:-2]]])
loader = tf.train.Saver(var_list=[
var for var in t_vars if var.name in ss_right_shape
])
loader.restore(sess, opt.save_path)
print("Loading variables from '%s'." % opt.save_path)
print("Loaded variables:" + str(ss))
except:
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
try:
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
kf = get_minibatches_idx(len(train),
opt.batch_size,
shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
x_labels = [train_lab[t] for t in train_index]
x_labels = np.array(x_labels)
x_labels = x_labels.reshape((len(x_labels), 10))
x_batch, x_batch_mask = prepare_data_for_emb(sents, opt)
_, loss = sess.run(
[train_op, loss_],
feed_dict={
x_: x_batch,
x_mask_: x_batch_mask,
y_: x_labels,
keep_prob: opt.drop_rate
})
if uidx % opt.valid_freq == 0:
train_correct = 0.0
kf_train = get_minibatches_idx(500,
opt.batch_size,
shuffle=True)
for _, train_index in kf_train:
train_sents = [train[t] for t in train_index]
train_labels = [train_lab[t] for t in train_index]
train_labels = np.array(train_labels)
train_labels = train_labels.reshape(
(len(train_labels), 10))
x_train_batch, x_train_batch_mask = prepare_data_for_emb(
train_sents, opt) # Batch L
train_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_train_batch,
x_mask_:
x_train_batch_mask,
y_: train_labels,
keep_prob: 1.0
})
train_correct += train_accuracy * len(train_index)
train_accuracy = train_correct / 500
print("Iteration %d: Training loss %f " % (uidx, loss))
print("Train accuracy %f " % train_accuracy)
val_correct = 0.0
kf_val = get_minibatches_idx(20000,
opt.batch_size,
shuffle=True)
for _, val_index in kf_val:
val_sents = [val[t] for t in val_index]
val_labels = [val_lab[t] for t in val_index]
val_labels = np.array(val_labels)
val_labels = val_labels.reshape(
(len(val_labels), 10))
x_val_batch, x_val_batch_mask = prepare_data_for_emb(
val_sents, opt)
val_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_val_batch,
x_mask_:
x_val_batch_mask,
y_: val_labels,
keep_prob: 1.0
})
val_correct += val_accuracy * len(val_index)
val_accuracy = val_correct / 20000
print("Validation accuracy %f " % val_accuracy)
if val_accuracy > max_val_accuracy:
max_val_accuracy = val_accuracy
test_correct = 0.0
kf_test = get_minibatches_idx(len(test),
opt.batch_size,
shuffle=True)
for _, test_index in kf_test:
test_sents = [test[t] for t in test_index]
test_labels = [test_lab[t] for t in test_index]
test_labels = np.array(test_labels)
test_labels = test_labels.reshape(
(len(test_labels), 10))
x_test_batch, x_test_batch_mask = prepare_data_for_emb(
test_sents, opt)
test_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_test_batch,
x_mask_:
x_test_batch_mask,
y_: test_labels,
keep_prob: 1.0
})
test_correct += test_accuracy * len(test_index)
test_accuracy = test_correct / len(test)
print("Test accuracy %f " % test_accuracy)
max_test_accuracy = test_accuracy
print("Epoch %d: Max Test accuracy %f" %
(epoch, max_test_accuracy))
emb = sess.run(W_emb_, feed_dict={x_: x_test_batch})
cPickle.dump([emb], open("yahoo_emb_max_300.p", "wb"))
print("Max Test accuracy %f " % max_test_accuracy)
except KeyboardInterrupt:
# print 'Training interupted'
print('Training interupted')
print("Max Test accuracy %f " % max_test_accuracy)
def run_model(opt, train, val, ixtoword):
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:'+ str(params['Wemb'].shape) + ' opt: ' + str((opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:0'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
x_org_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
is_train_ = tf.placeholder(tf.bool, name='is_train_')
res_, g_loss_, d_loss_, gen_op, dis_op = textGAN(x_, opt)
merged = tf.summary.merge_all()
# opt.is_train = False
# res_val_, loss_val_, _ = auto_encoder(x_, x_org_, opt)
# merged_val = tf.summary.merge_all()
#tensorboard --logdir=run1:/tmp/tensorflow/ --port 6006
#writer = tf.train.SummaryWriter(opt.log_path, graph=tf.get_default_graph())
uidx = 0
config = tf.ConfigProto(log_device_placement = False, allow_soft_placement=True,
graph_options=tf.GraphOptions(build_cost_model=1))
#config = tf.ConfigProto(device_count={'GPU':0})
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
run_metadata = tf.RunMetadata()
with tf.Session(config = config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train', sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
#pdb.set_trace()
t_vars = tf.trainable_variables()
#print([var.name[:-2] for var in t_vars])
loader = restore_from_save(t_vars, sess, opt)
except Exception as e:
print(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
for epoch in range(opt.max_epochs):
print("\nStarting epoch %d\n" % epoch)
# if epoch >= 10:
# print("Relax embedding ")
# opt.fix_emb = False
# opt.batch_size = 2
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
print "\rIter: %d" % uidx,
uidx += 1
sents = [train[t] for t in train_index]
sents_permutated = add_noise(sents, opt)
#sents[0] = np.random.permutation(sents[0])
x_batch = prepare_data_for_cnn(sents_permutated, opt) # Batch L
if x_batch.shape[0] == opt.batch_size:
d_loss = 0
g_loss = 0
if profile:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run([dis_op, d_loss_], feed_dict={x_: x_batch},options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),run_metadata=run_metadata)
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run([gen_op, g_loss_], feed_dict={x_: x_batch},options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),run_metadata=run_metadata)
else:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run([dis_op, d_loss_], feed_dict={x_: x_batch})
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run([gen_op, g_loss_], feed_dict={x_: x_batch})
if uidx % opt.valid_freq == 0:
is_train = True
# print('Valid Size:', len(val))
valid_index = np.random.choice(len(val), opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
x_val_batch = prepare_data_for_cnn(val_sents_permutated, opt)
d_loss_val = sess.run(d_loss_, feed_dict={x_: x_val_batch})
g_loss_val = sess.run(g_loss_, feed_dict={x_: x_val_batch})
res = sess.run(res_, feed_dict={x_: x_val_batch})
print("Validation d_loss %f, g_loss %f mean_dist %f" % (d_loss_val, g_loss_val, res['mean_dist']))
print "Sent:" + u' '.join([ixtoword[x] for x in res['syn_sent'][0] if x != 0]).encode('utf-8').strip()
print("MMD loss %f, GAN loss %f" % (res['mmd'], res['gan']))
np.savetxt('./text/rec_val_words.txt', res['syn_sent'], fmt='%i', delimiter=' ')
if opt.discrimination:
print ("Real Prob %f Fake Prob %f" % (res['prob_r'], res['prob_f']))
val_set = [prepare_for_bleu(s) for s in val_sents]
[bleu2s, bleu3s, bleu4s] = cal_BLEU([prepare_for_bleu(s) for s in res['syn_sent']], {0: val_set})
print 'Val BLEU (2,3,4): ' + ' '.join([str(round(it, 3)) for it in (bleu2s, bleu3s, bleu4s)])
summary = sess.run(merged, feed_dict={x_: x_val_batch})
test_writer.add_summary(summary, uidx)
if uidx % opt.print_freq == 0:
#pdb.set_trace()
res = sess.run(res_, feed_dict={x_: x_batch})
median_dis = np.sqrt(np.median([((x-y)**2).sum() for x in res['real_f'] for y in res['real_f']]))
print("Iteration %d: d_loss %f, g_loss %f, mean_dist %f, realdist median %f" % (uidx, d_loss, g_loss, res['mean_dist'], median_dis))
np.savetxt('./text/rec_train_words.txt', res['syn_sent'], fmt='%i', delimiter=' ')
print "Sent:" + u' '.join([ixtoword[x] for x in res['syn_sent'][0] if x != 0]).encode('utf-8').strip()
summary = sess.run(merged, feed_dict={x_: x_batch})
train_writer.add_summary(summary, uidx)
# print res['x_rec'][0][0]
# print res['x_emb'][0][0]
if profile:
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
saver.save(sess, opt.save_path, global_step=epoch)
def run_model(opt, train, val, ixtoword):
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:' +
str(params['Wemb'].shape) + ' opt: ' +
str((opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
x_org_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
# is_train_ = tf.placeholder(tf.bool, name='is_train_')
res_, g_loss_, d_loss_, gen_op, dis_op = textGAN(x_, x_org_, opt)
merged = tf.summary.merge_all()
# opt.is_train = False
# res_val_, loss_val_, _ = auto_encoder(x_, x_org_, opt)
# merged_val = tf.summary.merge_all()
#tensorboard --logdir=run1:/tmp/tensorflow/ --port 6006
#writer = tf.train.SummaryWriter(opt.log_path, graph=tf.get_default_graph())
uidx = 0
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True,
graph_options=tf.GraphOptions(build_cost_model=1))
#config = tf.ConfigProto(device_count={'GPU':0})
config.gpu_options.per_process_gpu_memory_fraction = 0.8
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
run_metadata = tf.RunMetadata()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
#pdb.set_trace()
t_vars = tf.trainable_variables()
#print([var.name[:-2] for var in t_vars])
loader = restore_from_save(t_vars, sess, opt)
print('\nload successfully\n')
except Exception as e:
print(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
# for i in range(34):
# valid_index = np.random.choice(
# len(val), opt.batch_size)
# val_sents = [val[t] for t in valid_index]
# val_sents_permutated = add_noise(val_sents, opt)
# x_val_batch = prepare_data_for_cnn(
# val_sents_permutated, opt)
# x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
# res = sess.run(res_, feed_dict={
# x_: x_val_batch, x_org_: x_val_batch_org})
# if i == 0:
# valid_text = res['syn_sent']
# else:
# valid_text = np.concatenate(
# (valid_text, res['syn_sent']), 0)
# np.savetxt('./text_news/vae_words.txt', valid_text, fmt='%i', delimiter=' ')
# pdb.set_trace()
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
# if epoch >= 10:
# print("Relax embedding ")
# opt.fix_emb = False
# opt.batch_size = 2
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
sents_permutated = add_noise(sents, opt)
#sents[0] = np.random.permutation(sents[0])
x_batch = prepare_data_for_cnn(sents_permutated,
opt) # Batch L
x_batch_org = prepare_data_for_rnn(sents, opt)
d_loss = 0
g_loss = 0
if profile:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run(
[dis_op, d_loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org
},
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run(
[gen_op, g_loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org
},
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
else:
if uidx % opt.dis_steps == 0:
_, d_loss = sess.run([dis_op, d_loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org
})
if uidx % opt.gen_steps == 0:
_, g_loss = sess.run([gen_op, g_loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org
})
''' validation '''
if uidx % opt.valid_freq == 0:
valid_index = np.random.choice(len(val), opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
x_val_batch = prepare_data_for_cnn(val_sents_permutated,
opt)
x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
d_loss_val = sess.run(d_loss_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org
})
g_loss_val = sess.run(g_loss_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org
})
res = sess.run(res_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org
})
print("Validation d_loss %f, g_loss %f mean_dist %f" %
(d_loss_val, g_loss_val, res['mean_dist']))
print("Sent:" + u' '.join([
ixtoword[x] for x in res['syn_sent'][0] if x != 0
])) #.encode('utf-8', 'ignore').decode("utf8").strip())
print("MMD loss %f, GAN loss %f" %
(res['mmd'], res['gan']))
# np.savetxt('./text_arxiv/syn_val_words.txt', res['syn_sent'], fmt='%i', delimiter=' ')
if opt.discrimination:
print("Real Prob %f Fake Prob %f" %
(res['prob_r'], res['prob_f']))
for i in range(4):
valid_index = np.random.choice(len(val),
opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
x_val_batch = prepare_data_for_cnn(
val_sents_permutated, opt)
x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
res = sess.run(res_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org
})
if i == 0:
valid_text = res['syn_sent']
else:
valid_text = np.concatenate(
(valid_text, res['syn_sent']), 0)
np.savetxt('./text_news/syn_val_words.txt',
valid_text,
fmt='%i',
delimiter=' ')
val_set = [prepare_for_bleu(s) for s in val_sents]
[bleu2s, bleu3s, bleu4s] = cal_BLEU(
[prepare_for_bleu(s) for s in res['syn_sent']],
{0: val_set})
print('Val BLEU (2,3,4): ' + ' '.join(
[str(round(it, 3))
for it in (bleu2s, bleu3s, bleu4s)]))
summary = sess.run(merged,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org
})
test_writer.add_summary(summary, uidx)
def main(opt):
# global n_words
# Prepare training and testing data
data_path = opt.data_dir + "/" + opt.data_name
print('loading '+data_path)
x = cPickle.load(open(data_path, "rb"))
train, val, test = x[0], x[1], x[2]
wordtoix, ixtoword = x[3], x[4]
opt.n_words = len(ixtoword)
print datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")
print dict(opt)
print('Total words: %d' % opt.n_words)
with tf.device('/gpu:1'):
x_1_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
x_2_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
y_ = tf.placeholder(tf.float32, shape=[opt.batch_size,])
l_temp_ = tf.placeholder(tf.float32, shape=[])
res_, loss_ ,train_op = cons_disc(x_1_, x_2_, y_, opt, l_temp_)
merged = tf.summary.merge_all()
uidx = 0
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.95
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train', sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer()) # feed_dict={x_: np.zeros([opt.batch_size, opt.sent_len]), x_org_: np.zeros([opt.batch_size, opt.sent_len])}
if opt.restore:
print('-'*20)
print("Loading variables from '%s'." % opt.load_path)
try:
#pdb.set_trace()
t_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) #tf.trainable_variables()
#print([var.name[:-2] for var in t_vars]
save_keys = tensors_key_in_file(opt.load_path)
ss = [var for var in t_vars if var.name[:-2] in save_keys.keys()]
ss = [var.name for var in ss if var.get_shape() == save_keys[var.name[:-2]]]
loader = tf.train.Saver(var_list= [var for var in t_vars if var.name in ss])
loader.restore(sess, opt.load_path)
print("Loaded variables:"+str(ss))
print('-'*20)
except Exception as e:
print 'Error: '+str(e)
exit()
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
# train
# if don't want to train, set max_epochs=0
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
opt.l_temp = min(opt.l_temp * opt.l_temp_factor, opt.l_temp_max)
print("Annealing temperature " + str(opt.l_temp))
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
indice = [rand_pair(opt.task, opt.data_name) for _ in range(opt.batch_size)]
if opt.task == 'L':
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[i][idx[1]] for i, idx in enumerate(indice)]
y_batch = [(i1-i2)%2 == 0 for i1,i2 in indice]
elif opt.task == 'C':
batch_indice = np.concatenate([np.random.permutation(opt.batch_size/2) , range(opt.batch_size/2, opt.batch_size)])
y_batch = (range(opt.batch_size) == batch_indice)
rn = np.random.choice(7,size = opt.batch_size)
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[batch_indice[i]][idx[1]] for i, idx in enumerate(indice)]
else: # G
batch_indice = np.concatenate([np.random.permutation(opt.batch_size/2) , range(opt.batch_size/2, opt.batch_size)])
y_batch = (range(opt.batch_size) == batch_indice)
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[batch_indice[i]][idx[1]] for i, idx in enumerate(indice)]
x_1_batch = prepare_data_for_cnn(x_1, opt) # Batch L
x_2_batch = prepare_data_for_cnn(x_2, opt) # Batch L
feed = {x_1_: x_1_batch, x_2_: x_2_batch, y_:np.float32(y_batch),l_temp_:opt.l_temp}
_, loss = sess.run([train_op, loss_], feed_dict=feed)
if uidx % opt.print_freq == 1:
print("Iteration %d: loss %f " % (uidx, loss))
res = sess.run(res_, feed_dict=feed)
if opt.verbose:
print("logits:" + str(res['logits']))
print("H1:" + str(res['H_1'][0]))
print("H2:" + str(res['H_2'][0]))
# print("H2:" + str(res['H_1'][0]*res['H_2'][0]-0.5))
acc = sum(np.equal(res['y_pred'],y_batch))/np.float(opt.batch_size)
print("Accuracy: %f" % (acc))
print("y_mean: %f" % (np.mean(y_batch)))
print("corr:" + str(res['corr']))
sys.stdout.flush()
summary = sess.run(merged, feed_dict=feed)
train_writer.add_summary(summary, uidx)
if uidx % opt.valid_freq == 1:
acc, loss_val, y_mean, corr = 0, 0, 0, 0
indice = [rand_pair(opt.task, opt.data_name) for _ in range(opt.batch_size)]
for i in range(100):
valid_index = np.random.choice(len(test), opt.batch_size)
sents = [test[t] for t in valid_index]
if opt.task == 'L':
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[i][idx[1]] for i, idx in enumerate(indice)]
y_batch = [(i1-i2)%2 == 0 for i1,i2 in indice]
elif opt.task == 'C':
batch_indice = np.concatenate([np.random.permutation(opt.batch_size/2) , range(opt.batch_size/2, opt.batch_size)])
y_batch = (range(opt.batch_size) == batch_indice)
rn = np.random.choice(7,size = opt.batch_size)
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[batch_indice[i]][idx[1]] for i, idx in enumerate(indice)]
else: # G
batch_indice = np.concatenate([np.random.permutation(opt.batch_size/2) , range(opt.batch_size/2, opt.batch_size)])
y_batch = (range(opt.batch_size) == batch_indice)
x_1 = [sents[i][idx[0]] for i, idx in enumerate(indice)]
x_2 = [sents[batch_indice[i]][idx[1]] for i, idx in enumerate(indice)]
x_1_batch = prepare_data_for_cnn(x_1, opt) # Batch L
x_2_batch = prepare_data_for_cnn(x_2, opt) # Batch L
feed = {x_1_: x_1_batch, x_2_: x_2_batch, y_:np.float32(y_batch),l_temp_:opt.l_temp}
loss_val += sess.run(loss_, feed_dict=feed)
res = sess.run(res_, feed_dict=feed)
acc += sum(np.equal(res['y_pred'],y_batch))/np.float(opt.batch_size)
y_mean += np.mean(y_batch)
corr += res['corr']
loss_val = loss_val / 100.0
acc = acc / 100.0
y_mean = y_mean / 100.0
corr = corr / 100.0
print("Validation loss %.4f " % (loss_val))
print("Validation accuracy: %.4f" % (acc))
print("Validation y_mean: %.4f" % (y_mean))
print("Validation corr: %.4f" % (corr))
print("")
sys.stdout.flush()
summary = sess.run(merged, feed_dict=feed)
test_writer.add_summary(summary, uidx)
saver.save(sess, opt.save_path, global_step=epoch)
# test
if opt.test:
print('Testing....')
iter_num = np.int(np.floor(len(test)/opt.batch_size))+1
for i in range(iter_num):
if i%100 == 0:
print('Iter %i/%i'%(i, iter_num))
test_index = range(i*opt.batch_size, (i+1)*opt.batch_size)
test_sents = [test[t%len(test)] for t in test_index]
indice = [(0,1),(2,3),(4,5),(6,7)]
for idx in indice:
x_1 = [test_sents[i][idx[0]] for i in range(opt.batch_size)]
x_2 = [test_sents[i][idx[1]] for i in range(opt.batch_size)]
y_batch = [True for i in range(opt.batch_size)]
x_1_batch = prepare_data_for_cnn(x_1, opt) # Batch L
x_2_batch = prepare_data_for_cnn(x_2, opt) # Batch L
feed = {x_1_: x_1_batch, x_2_: x_2_batch, y_:np.float32(y_batch), l_temp_:opt.l_temp}
res = sess.run(res_, feed_dict=feed)
for d in range(opt.batch_size):
with open(opt.log_path + '.feature.txt', "a") as myfile:
myfile.write(str(test_index[d]) + "\t" + str(idx[0]) + "\t" + " ".join([ixtoword[x] for x in x_1_batch[d] if x != 0]) + "\t" + " ".join(map(str,res['H_1'][d]))+ "\n")
myfile.write(str(test_index[d]) + "\t" + str(idx[1]) + "\t" + " ".join([ixtoword[x] for x in x_2_batch[d] if x != 0]) + "\t" + " ".join(map(str,res['H_2'][d]))+ "\n")
def run_epoch(sess,
epoch,
mode,
print_freq=-1,
display_sent=-1,
train_writer=None):
fetches_ = {'loss': loss_}
if mode == 'train':
x, is_train = train, 1
fetches_['train_op'] = train_op_
fetches_['summary'] = merged
elif mode == 'val':
assert (print_freq == -1)
x, is_train = val, None
elif mode == 'test':
assert (print_freq == -1)
x, is_train = test, None
acc_loss, acc_n = 0.0, 0.0
local_t = 0
global_t = epoch * epoch_t # only used in train mode
start_time = time.time()
kf = get_minibatches_idx(len(x), opt.batch_size, shuffle=True)
for _, index in kf:
local_t += 1
global_t += 1
sents_b = [x[i] for i in index]
sents_b_n = add_noise(sents_b, opt)
x_b_org = prepare_data_for_rnn(sents_b, opt) # Batch L
x_b = prepare_data_for_cnn(sents_b_n, opt) # Batch L
feed_t = {x_: x_b, x_org_: x_b_org, is_train_: is_train}
fetches = sess.run(fetches_, feed_dict=feed_t)
batch_size = len(index)
acc_n += batch_size
acc_loss += fetches['loss'] * batch_size
if print_freq > 0 and local_t % print_freq == 0:
print("%s Iter %d: loss %.4f, time %.1fs" %
(mode, local_t, acc_loss / acc_n,
time.time() - start_time))
sys.stdout.flush()
if mode == 'train' and train_writer != None:
train_writer.add_summary(fetches['summary'], global_t)
if display_sent > 0:
index_d = np.random.choice(len(x), opt.batch_size, replace=False)
sents_d = [x[i] for i in index_d]
sents_d_n = add_noise(sents_d, opt)
x_d_org = prepare_data_for_rnn(sents_d, opt) # Batch L
x_d = prepare_data_for_cnn(sents_d_n, opt) # Batch L
res = sess.run(res_,
feed_dict={
x_: x_d,
x_org_: x_d_org,
is_train_: is_train
})
for i in range(display_sent):
print(
"%s Org: " % mode + " ".join([
ixtoword[ix]
for ix in sents_d[i] if ix != 0 and ix != 2
]))
if mode == 'train':
print(
"%s Rec(feedy): " % mode + " ".join([
ixtoword[ix] for ix in res['rec_sents_feed_y'][i]
if ix != 0 and ix != 2
]))
print(
"%s Rec: " % mode + " ".join([
ixtoword[ix]
for ix in res['rec_sents'][i] if ix != 0 and ix != 2
]))
print("%s Epoch %d: loss %.4f, time %.1fs" %
(mode, epoch, acc_loss / acc_n, time.time() - start_time))
return acc_loss / acc_n
def main():
# Prepare training and testing data
opt = Options()
# load data
loadpath = "./data/mimic3.p"
embpath = "mimic3_emb.p"
opt.num_class = 50
x = cPickle.load(open(loadpath, "rb"))
train, train_text, train_lab = x[0], x[1], x[2]
val, val_text, val_lab = x[3], x[4], x[5]
test, test_text, test_lab = x[6], x[7], x[8]
wordtoix, ixtoword = x[10], x[9]
del x
print("load data finished")
train_lab = np.array(train_lab, dtype='float32')
val_lab = np.array(val_lab, dtype='float32')
test_lab = np.array(test_lab, dtype='float32')
opt.n_words = len(ixtoword)
if opt.part_data:
#np.random.seed(123)
train_ind = np.random.choice(len(train),
int(len(train) * opt.portion),
replace=False)
train = [train[t] for t in train_ind]
train_lab = [train_lab[t] for t in train_ind]
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.GPUID)
print(dict(opt))
print('Total words: %d' % opt.n_words)
try:
opt.W_emb = np.array(cPickle.load(open(embpath, 'rb')),
dtype='float32')
opt.W_class_emb = load_class_embedding(wordtoix, opt)
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_ = tf.placeholder(tf.int32,
shape=[opt.batch_size, opt.maxlen],
name='x_')
x_mask_ = tf.placeholder(tf.float32,
shape=[opt.batch_size, opt.maxlen],
name='x_mask_')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
y_ = tf.placeholder(tf.float32,
shape=[opt.batch_size, opt.num_class],
name='y_')
class_penalty_ = tf.placeholder(tf.float32, shape=())
accuracy_, loss_, train_op, W_norm_, global_step, logits_, prob_ = emb_classifier(
x_, x_mask_, y_, keep_prob, opt, class_penalty_)
uidx = 0
max_val_accuracy = 0.
max_test_accuracy = 0.
max_val_auc_mean = 0.
max_test_auc_mean = 0.
config = tf.ConfigProto(
log_device_placement=False,
allow_soft_placement=True,
)
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.trainable_variables()
save_keys = tensors_key_in_file(opt.save_path)
ss = set([var.name for var in t_vars]) & set(
[s + ":0" for s in save_keys.keys()])
cc = {var.name: var for var in t_vars}
# only restore variables with correct shape
ss_right_shape = set(
[s for s in ss if cc[s].get_shape() == save_keys[s[:-2]]])
loader = tf.train.Saver(var_list=[
var for var in t_vars if var.name in ss_right_shape
])
loader.restore(sess, opt.save_path)
print("Loading variables from '%s'." % opt.save_path)
print("Loaded variables:" + str(ss))
except:
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
try:
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
kf = get_minibatches_idx(len(train),
opt.batch_size,
shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
x_labels = [train_lab[t] for t in train_index]
x_labels = np.array(x_labels)
x_labels = x_labels.reshape((len(x_labels), opt.num_class))
x_batch, x_batch_mask = prepare_data_for_emb(sents, opt)
_, loss, step, = sess.run(
[train_op, loss_, global_step],
feed_dict={
x_: x_batch,
x_mask_: x_batch_mask,
y_: x_labels,
keep_prob: opt.dropout,
class_penalty_: opt.class_penalty
})
if uidx % opt.valid_freq == 0:
train_correct = 0.0
# sample evaluate accuaccy on 500 sample data
kf_train = get_minibatches_idx(500,
opt.batch_size,
shuffle=True)
for _, train_index in kf_train:
train_sents = [train[t] for t in train_index]
train_labels = [train_lab[t] for t in train_index]
train_labels = np.array(train_labels)
train_labels = train_labels.reshape(
(len(train_labels), opt.num_class))
x_train_batch, x_train_batch_mask = prepare_data_for_emb(
train_sents, opt)
train_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_train_batch,
x_mask_:
x_train_batch_mask,
y_: train_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
train_correct += train_accuracy * len(train_index)
train_accuracy = train_correct / 500
print("Iteration %d: Training loss %f " % (uidx, loss))
print("Train accuracy %f " % train_accuracy)
val_correct = 0.0
val_y = []
val_logits_list = []
val_prob_list = []
val_true_list = []
kf_val = get_minibatches_idx(len(val),
opt.batch_size,
shuffle=True)
for _, val_index in kf_val:
val_sents = [val[t] for t in val_index]
val_labels = [val_lab[t] for t in val_index]
val_labels = np.array(val_labels)
val_labels = val_labels.reshape(
(len(val_labels), opt.num_class))
x_val_batch, x_val_batch_mask = prepare_data_for_emb(
val_sents, opt)
val_accuracy, val_logits, val_probs = sess.run(
[accuracy_, logits_, prob_],
feed_dict={
x_: x_val_batch,
x_mask_: x_val_batch_mask,
y_: val_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
val_correct += val_accuracy * len(val_index)
val_y += np.argmax(val_labels, axis=1).tolist()
val_logits_list += val_logits.tolist()
val_prob_list += val_probs.tolist()
val_true_list += val_labels.tolist()
val_accuracy = val_correct / len(val)
val_logits_array = np.asarray(val_logits_list)
val_prob_array = np.asarray(val_prob_list)
val_true_array = np.asarray(val_true_list)
val_auc_list = []
val_auc_micro = roc_auc_score(y_true=val_true_array,
y_score=val_logits_array,
average='micro')
val_auc_macro = roc_auc_score(y_true=val_true_array,
y_score=val_logits_array,
average='macro')
for i in range(opt.num_class):
if np.max(val_true_array[:, i] > 0):
val_auc = roc_auc_score(
y_true=val_true_array[:, i],
y_score=val_logits_array[:, i],
)
val_auc_list.append(val_auc)
val_auc_mean = np.mean(val_auc)
# print("Validation accuracy %f " % val_accuracy)
print("val auc macro %f micro %f " %
(val_auc_macro, val_auc_micro))
if True:
test_correct = 0.0
test_y = []
test_logits_list = []
test_prob_list = []
test_true_list = []
kf_test = get_minibatches_idx(len(test),
opt.batch_size,
shuffle=True)
for _, test_index in kf_test:
test_sents = [test[t] for t in test_index]
test_labels = [test_lab[t] for t in test_index]
test_labels = np.array(test_labels)
test_labels = test_labels.reshape(
(len(test_labels), opt.num_class))
x_test_batch, x_test_batch_mask = prepare_data_for_emb(
test_sents, opt)
test_accuracy, test_logits, test_probs = sess.run(
[accuracy_, logits_, prob_],
feed_dict={
x_: x_test_batch,
x_mask_: x_test_batch_mask,
y_: test_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
test_correct += test_accuracy * len(test_index)
test_correct += test_accuracy * len(test_index)
test_y += np.argmax(test_labels,
axis=1).tolist()
test_logits_list += test_logits.tolist()
test_prob_list += test_probs.tolist()
test_true_list += test_labels.tolist()
test_accuracy = test_correct / len(test)
test_logits_array = np.asarray(test_logits_list)
test_prob_array = np.asarray(test_prob_list)
test_true_array = np.asarray(test_true_list)
test_auc_list = []
test_auc_micro = roc_auc_score(
y_true=test_true_array,
y_score=test_logits_array,
average='micro')
test_auc_macro = roc_auc_score(
y_true=test_true_array,
y_score=test_logits_array,
average='macro')
test_f1_micro = micro_f1(
test_prob_array.ravel() > 0.5,
test_true_array.ravel(),
)
test_f1_macro = macro_f1(
test_prob_array > 0.5,
test_true_array,
)
test_p5 = precision_at_k(test_logits_array,
test_true_array, 5)
for i in range(opt.num_class):
if np.max(test_true_array[:, i] > 0):
test_auc = roc_auc_score(
y_true=test_true_array[:, i],
y_score=test_logits_array[:, i],
)
test_auc_list.append(test_auc)
test_auc_mean = np.mean(test_auc)
print("Test auc macro %f micro %f " %
(test_auc_macro, test_auc_micro))
print("Test f1 macro %f micro %f " %
(test_f1_macro, test_f1_micro))
print("P5 %f" % test_p5)
# max_test_accuracy = test_accuracy
max_test_auc_mean = test_auc_mean
# print("Test accuracy %f " % test_accuracy)
# max_test_accuracy = test_accuracy
# print("Epoch %d: Max Test accuracy %f" % (epoch, max_test_accuracy))
print("Epoch %d: Max Test auc %f" % (epoch, max_test_auc_mean))
saver.save(sess, opt.save_path, global_step=epoch)
print("Max Test accuracy %f " % max_test_accuracy)
except KeyboardInterrupt:
print('Training interupted')
print("Max Test accuracy %f " % max_test_accuracy)
def train(model, train_triples, valid_triples, test_triples, sr_index, params):
rng = np.random
n_entities, n_relations = model.n_entities, model.n_relations
train_fn = model.train_fn(num_neg=params[NUM_NEG], lrate=params[LEARNING_RATE], marge=params[MARGE])
ranks_fn = model.ranks_fn()
scores_fn = model.scores_fn()
uidx = 1
best_p = None
history_valid_hits = []
history_test_hits = []
history_epoch_times = []
bins = [1, 11, 21, 31, 51, 101, 1001, 10001, 20000]
print("Training on {:d} triples".format(len(train_triples)))
num_batches = int(math.ceil(len(train_triples) / params[BATCH_SIZE]))
print("Batch size = {:d}, Number of batches = {:d}".format(params[BATCH_SIZE], num_batches))
print("The eval is being printed with number of items the bins -> %s" % bins)
try:
# We iterate over epochs:
train_start_time = time.time()
for epoch in range(params[NUM_EPOCHS]):
# In each epoch, we do a full pass over the training data:
epoch_start_time = time.time()
for _, train_index in utils.get_minibatches_idx(len(train_triples), params[BATCH_SIZE], False):
# Normalize the entity embeddings
if params[IS_NORMALIZED]:
model.normalize()
tmb = train_triples[train_index]
# generating negative examples replacing left entity
tmbln_list = [rng.randint(0, n_entities, tmb.shape[0]).astype(dtype=tmb.dtype) for i in xrange(params[NUM_NEG])]
# generating negative examples replacing right entity
tmbrn_list = [rng.randint(0, n_entities, tmb.shape[0]).astype(dtype=tmb.dtype) for i in xrange(params[NUM_NEG])]
cost = train_fn(*([tmb] + tmbln_list + tmbrn_list))[0]
if np.isnan(cost) or np.isinf(cost):
print('bad cost detected! Cost is ' + str(cost))
return get_best_metric(history_valid_hits)
if uidx % params[DISP_FREQ] == 0:
print('Epoch ', epoch, 'Iter', uidx, 'Cost ', cost)
if uidx % params[VALID_FREQ] == 0:
print('Epoch ', epoch, 'Iter', uidx, 'Cost ', cost)
# print("Epoch {} of {} uidx {} took {:.3f}s".format(
# epoch + 1, params[NUM_EPOCHS], uidx, time.time() - start_time))
if len(history_epoch_times) > 0:
print (" Average epoch time - {:.3f}s".format(np.mean(history_epoch_times)))
print(" Time since start - {:.3f}s".format(time.time() - train_start_time))
print(" Train Minibatch Metrics")
train_hits10 = get_batch_metrics(tmb, sr_index, scores_fn, False)
print('')
print(" Validation data Metrics")
valid_hits10 = get_batch_metrics(valid_triples, sr_index, scores_fn, True)
print('')
print(" Test data Metrics")
test_hits10 = get_batch_metrics(test_triples, sr_index, scores_fn, True)
if (best_p is None) or (len(history_valid_hits) > 0 and valid_hits10 >= np.max(history_valid_hits)):
print("found best params yet")
best_p = utils.get_params(model)
history_valid_hits.append(valid_hits10)
history_test_hits.append(test_hits10)
if uidx % params[SAVE_FREQ] == 0:
if best_p is None:
all_params = utils.get_params(model)
else:
all_params = best_p
utils.save(params[SAVETO_FILE], all_params)
uidx += 1
history_epoch_times.append(time.time() - epoch_start_time)
except KeyboardInterrupt:
print("training interrupted")
return model, get_best_metric(history_valid_hits), train_fn, scores_fn
def main():
loadpath = "./data/snli.p"
x = cPickle.load(open(loadpath, "rb"))
train, val, test = x[0], x[1], x[2]
wordtoix, ixtoword = x[4], x[5]
train_q, train_a, train_lab = train[0], train[1], train[2]
val_q, val_a, val_lab = val[0], val[1], val[2]
test_q, test_a, test_lab = test[0], test[1], test[2]
train_lab = np.array(train_lab, dtype='float32')
val_lab = np.array(val_lab, dtype='float32')
test_lab = np.array(test_lab, dtype='float32')
opt = Options()
opt.n_words = len(ixtoword)
del x
print(dict(opt))
print('Total words: %d' % opt.n_words)
if opt.part_data:
np.random.seed(123)
train_ind = np.random.choice(len(train_q), int(len(train_q)*opt.portion), replace=False)
train_q = [train_q[t] for t in train_ind]
train_a = [train_a[t] for t in train_ind]
train_lab = [train_lab[t] for t in train_ind]
try:
params = np.load('./data/snli_emb.p')
if params[0].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
#pdb.set_trace()
opt.W_emb = np.array(params[0], dtype='float32')
else:
print('Emb Dimension mismatch: param_g.npz:' + str(params[0].shape) + ' opt: ' + str(
(opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_1_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.maxlen])
x_2_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.maxlen])
x_mask_1_ = tf.placeholder(tf.float32, shape=[opt.batch_size, opt.maxlen])
x_mask_2_ = tf.placeholder(tf.float32, shape=[opt.batch_size, opt.maxlen])
y_ = tf.placeholder(tf.float32, shape=[opt.batch_size, opt.category])
keep_prob = tf.placeholder(tf.float32)
accuracy_, loss_, train_op_, W_emb_ = auto_encoder(x_1_, x_2_, x_mask_1_, x_mask_2_, y_, keep_prob, opt)
merged = tf.summary.merge_all()
uidx = 0
max_val_accuracy = 0.
max_test_accuracy = 0.
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1)
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train', sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
#pdb.set_trace()
t_vars = tf.trainable_variables()
# print([var.name[:-2] for var in t_vars])
save_keys = tensors_key_in_file(opt.save_path)
# pdb.set_trace()
# print(save_keys.keys())
ss = set([var.name for var in t_vars]) & set([s + ":0" for s in save_keys.keys()])
cc = {var.name: var for var in t_vars}
#pdb.set_trace()
# only restore variables with correct shape
ss_right_shape = set([s for s in ss if cc[s].get_shape() == save_keys[s[:-2]]])
loader = tf.train.Saver(var_list=[var for var in t_vars if var.name in ss_right_shape])
loader.restore(sess, opt.save_path)
print("Loading variables from '%s'." % opt.save_path)
print("Loaded variables:" + str(ss))
except:
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
try:
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
kf = get_minibatches_idx(len(train_q), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
sents_1 = [train_q[t] for t in train_index]
sents_2 = [train_a[t] for t in train_index]
x_labels = [train_lab[t] for t in train_index]
x_labels = np.array(x_labels)
x_labels = x_labels.reshape((len(x_labels), opt.category))
x_batch_1, x_batch_mask_1 = prepare_data_for_emb(sents_1, opt)
x_batch_2, x_batch_mask_2 = prepare_data_for_emb(sents_2, opt)
_, loss = sess.run([train_op_, loss_], feed_dict={x_1_: x_batch_1, x_2_: x_batch_2,
x_mask_1_: x_batch_mask_1, x_mask_2_: x_batch_mask_2, y_: x_labels, keep_prob: opt.dropout_ratio})
if uidx % opt.valid_freq == 0:
train_correct = 0.0
kf_train = get_minibatches_idx(3070, opt.batch_size, shuffle=True)
for _, train_index in kf_train:
train_sents_1 = [train_q[t] for t in train_index]
train_sents_2 = [train_a[t] for t in train_index]
train_labels = [train_lab[t] for t in train_index]
train_labels = np.array(train_labels)
train_labels = train_labels.reshape((len(train_labels), opt.category))
x_train_batch_1, x_train_mask_1 = prepare_data_for_emb(train_sents_1, opt)
x_train_batch_2, x_train_mask_2 = prepare_data_for_emb(train_sents_2, opt)
train_accuracy = sess.run(accuracy_,
feed_dict={x_1_: x_train_batch_1, x_2_: x_train_batch_2, x_mask_1_: x_train_mask_1, x_mask_2_: x_train_mask_2,
y_: train_labels, keep_prob: 1.0})
train_correct += train_accuracy * len(train_index)
train_accuracy = train_correct / 3070
# print("Iteration %d: Training loss %f, dis loss %f, rec loss %f" % (uidx,
# loss, dis_loss, rec_loss))
print("Train accuracy %f " % train_accuracy)
val_correct = 0.0
is_train = True
kf_val = get_minibatches_idx(len(val_q), opt.batch_size, shuffle=True)
for _, val_index in kf_val:
val_sents_1 = [val_q[t] for t in val_index]
val_sents_2 = [val_a[t] for t in val_index]
val_labels = [val_lab[t] for t in val_index]
val_labels = np.array(val_labels)
val_labels = val_labels.reshape((len(val_labels), opt.category))
x_val_batch_1, x_val_mask_1 = prepare_data_for_emb(val_sents_1, opt)
x_val_batch_2, x_val_mask_2 = prepare_data_for_emb(val_sents_2, opt)
val_accuracy = sess.run(accuracy_, feed_dict={x_1_: x_val_batch_1, x_2_: x_val_batch_2,
x_mask_1_: x_val_mask_1, x_mask_2_: x_val_mask_2, y_: val_labels, keep_prob: 1.0})
val_correct += val_accuracy * len(val_index)
val_accuracy = val_correct / len(val_q)
print("Validation accuracy %f " % val_accuracy)
if val_accuracy > max_val_accuracy:
max_val_accuracy = val_accuracy
test_correct = 0.0
kf_test = get_minibatches_idx(len(test_q), opt.batch_size, shuffle=True)
for _, test_index in kf_test:
test_sents_1 = [test_q[t] for t in test_index]
test_sents_2 = [test_a[t] for t in test_index]
test_labels = [test_lab[t] for t in test_index]
test_labels = np.array(test_labels)
test_labels = test_labels.reshape((len(test_labels), opt.category))
x_test_batch_1, x_test_mask_1 = prepare_data_for_emb(test_sents_1, opt)
x_test_batch_2, x_test_mask_2 = prepare_data_for_emb(test_sents_2, opt)
test_accuracy = sess.run(accuracy_, feed_dict={x_1_: x_test_batch_1, x_2_: x_test_batch_2,
x_mask_1_: x_test_mask_1, x_mask_2_: x_test_mask_2,
y_: test_labels, keep_prob: 1.0})
test_correct += test_accuracy * len(test_index)
test_accuracy = test_correct / len(test_q)
print("Test accuracy %f " % test_accuracy)
max_test_accuracy = test_accuracy
print("Epoch %d: Max Test accuracy %f" % (epoch, max_test_accuracy))
print("Max Test accuracy %f " % max_test_accuracy)
except KeyboardInterrupt:
print('Training interupted')
print("Max Test accuracy %f " % max_test_accuracy)
def main():
# Prepare training and testing data
opt = Options()
# load data
if opt.dataset == 'Tweet':
loadpath = "./data/langdetect_tweet0.7.p"
embpath = "./data/langdetect_tweet_emb.p"
opt.num_class = 4
opt.class_name = ['apple', 'google', 'microsoft', 'twitter']
if opt.dataset == 'N20short':
loadpath = "./data/N20short.p"
embpath = "./data/N20short_emb.p"
opt.class_name = [
'rec.autos', 'talk.politics.misc', 'sci.electronics',
'comp.sys.ibm.pc.hardware', 'talk.politics.guns', 'sci.med',
'rec.motorcycles', 'soc.religion.christian',
'comp.sys.mac.hardware', 'comp.graphics', 'sci.space',
'alt.atheism', 'rec.sport.baseball', 'comp.windows.x',
'talk.religion.misc', 'comp.os.ms-windows.misc', 'misc.forsale',
'talk.politics.mideast', 'sci.crypt', 'rec.sport.hockey'
]
opt.num_class = len(opt.class_name)
elif opt.dataset == 'agnews':
loadpath = "./data/ag_news.p"
embpath = "./data/ag_news_glove.p"
opt.num_class = 4
opt.class_name = ['World', 'Sports', 'Business', 'Science']
elif opt.dataset == 'dbpedia':
loadpath = "./data/dbpedia.p"
embpath = "./data/dbpedia_glove.p"
opt.num_class = 14
opt.class_name = [
'Company',
'Educational Institution',
'Artist',
'Athlete',
'Office Holder',
'Mean Of Transportation',
'Building',
'Natural Place',
'Village',
'Animal',
'Plant',
'Album',
'Film',
'Written Work',
]
elif opt.dataset == 'yelp_full':
loadpath = "./data/yelp_full.p"
embpath = "./data/yelp_full_glove.p"
opt.num_class = 5
opt.class_name = ['worst', 'bad', 'middle', 'good', 'best']
x = cPickle.load(open(loadpath, "rb"), encoding='iso-8859-1')
train, val, test = x[0], x[1], x[2]
print(len(val))
train_lab, val_lab, test_lab = x[3], x[4], x[5]
wordtoix, ixtoword = x[6], x[7]
del x
print("len of train,val,test:", len(train), len(val), len(test))
print("load data finished")
train_lab = np.array(train_lab, dtype='float32')
val_lab = np.array(val_lab, dtype='float32')
test_lab = np.array(test_lab, dtype='float32')
opt.n_words = len(ixtoword)
if opt.part_data:
#np.random.seed(123)
train_ind = np.random.choice(len(train),
int(len(train) * opt.portion),
replace=False)
train = [train[t] for t in train_ind]
train_lab = [train_lab[t] for t in train_ind]
os.environ['CUDA_VISIBLE_DEVICES'] = str(opt.GPUID)
print(dict(opt))
print('Total words: %d' % opt.n_words)
try:
opt.W_emb = np.array(cPickle.load(open(embpath, 'rb'),
encoding='iso-8859-1'),
dtype='float32')
opt.W_class_emb = load_class_embedding(wordtoix, opt)
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/cpu:0'):
x_ = tf.placeholder(tf.int32,
shape=[opt.batch_size, opt.maxlen],
name='x_')
x_mask_ = tf.placeholder(tf.float32,
shape=[opt.batch_size, opt.maxlen],
name='x_mask_')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
y_ = tf.placeholder(tf.float32,
shape=[opt.batch_size, opt.num_class],
name='y_')
class_penalty_ = tf.placeholder(tf.float32, shape=())
accuracy_, loss_, train_op, W_norm_, global_step, prob_ = emb_classifier(
x_, x_mask_, y_, keep_prob, opt, class_penalty_)
uidx = 0
max_val_accuracy = 0.
max_test_accuracy = 0.
config = tf.ConfigProto(
log_device_placement=False,
allow_soft_placement=True,
)
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.trainable_variables()
save_keys = tensors_key_in_file(opt.save_path)
ss = set([var.name for var in t_vars]) & set(
[s + ":0" for s in save_keys.keys()])
cc = {var.name: var for var in t_vars}
# only restore variables with correct shape
ss_right_shape = set(
[s for s in ss if cc[s].get_shape() == save_keys[s[:-2]]])
loader = tf.train.Saver(var_list=[
var for var in t_vars if var.name in ss_right_shape
])
loader.restore(sess, opt.save_path)
print("Loading variables from '%s'." % opt.save_path)
print("Loaded variables:" + str(ss))
except:
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
try:
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
kf = get_minibatches_idx(len(train),
opt.batch_size,
shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
x_labels = [train_lab[t] for t in train_index]
# print(x_labels)
x_labels = np.array(x_labels)
x_labels = x_labels.reshape((len(x_labels), opt.num_class))
# print(x_labels)
# exit()
x_batch, x_batch_mask = prepare_data_for_emb(sents, opt)
_, loss, step, = sess.run(
[train_op, loss_, global_step],
feed_dict={
x_: x_batch,
x_mask_: x_batch_mask,
y_: x_labels,
keep_prob: opt.dropout,
class_penalty_: opt.class_penalty
})
if uidx % opt.valid_freq == 0:
train_correct = 0.0
# sample evaluate accuaccy on 500 sample data
kf_train = get_minibatches_idx(500,
opt.batch_size,
shuffle=True)
for _, train_index in kf_train:
train_sents = [train[t] for t in train_index]
train_labels = [train_lab[t] for t in train_index]
train_labels = np.array(train_labels)
train_labels = train_labels.reshape(
(len(train_labels), opt.num_class))
x_train_batch, x_train_batch_mask = prepare_data_for_emb(
train_sents, opt)
train_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_train_batch,
x_mask_:
x_train_batch_mask,
y_: train_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
train_correct += train_accuracy * len(train_index)
train_accuracy = train_correct / 500
print("Iteration %d: Training loss %f " % (uidx, loss))
print("Train accuracy %f " % train_accuracy)
val_correct = 0.0
kf_val = get_minibatches_idx(len(val),
opt.batch_size,
shuffle=True)
for _, val_index in kf_val:
val_sents = [val[t] for t in val_index]
val_labels = [val_lab[t] for t in val_index]
val_labels = np.array(val_labels)
val_labels = val_labels.reshape(
(len(val_labels), opt.num_class))
x_val_batch, x_val_batch_mask = prepare_data_for_emb(
val_sents, opt)
val_accuracy = sess.run(accuracy_,
feed_dict={
x_: x_val_batch,
x_mask_:
x_val_batch_mask,
y_: val_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
val_correct += val_accuracy * len(val_index)
val_accuracy = val_correct / len(val)
print("Validation accuracy %f " % val_accuracy)
if val_accuracy > max_val_accuracy:
max_val_accuracy = val_accuracy
# test_correct = 0.0
#
# kf_test = get_minibatches_idx(len(test), opt.batch_size, shuffle=True)
# for _, test_index in kf_test:
# test_sents = [test[t] for t in test_index]
# test_labels = [test_lab[t] for t in test_index]
# test_labels = np.array(test_labels)
# test_labels = test_labels.reshape((len(test_labels), opt.num_class))
# x_test_batch, x_test_batch_mask = prepare_data_for_emb(test_sents, opt)
#
# test_accuracy,predict_prob = sess.run([accuracy_,prob_],feed_dict={x_: x_test_batch, x_mask_: x_test_batch_mask,y_: test_labels, keep_prob: 1.0, class_penalty_: 0.0})
# print(predict_prob)
# test_correct += test_accuracy * len(test_index)
#
# test_accuracy = test_correct / len(test)
# print("Test accuracy %f " % test_accuracy)
# max_test_accuracy = test_accuracy
# print("Epoch %d: Max Test accuracy %f" % (epoch, max_test_accuracy))
saver.save(sess, opt.save_path, global_step=epoch)
saver.save(sess, "save_model/model.ckpt")
# print("Max Test accuracy %f " % max_test_accuracy)
test_correct = 0.0
kf_test = get_minibatches_idx(len(test),
opt.batch_size,
shuffle=False)
for _, test_index in kf_test:
test_sents = [test[t] for t in test_index]
test_labels = [test_lab[t] for t in test_index]
test_labels = np.array(test_labels)
test_labels = test_labels.reshape(
(len(test_labels), opt.num_class))
x_test_batch, x_test_batch_mask = prepare_data_for_emb(
test_sents, opt)
test_accuracy, predict_prob = sess.run(
[accuracy_, prob_],
feed_dict={
x_: x_test_batch,
x_mask_: x_test_batch_mask,
y_: test_labels,
keep_prob: 1.0,
class_penalty_: 0.0
})
for prob in predict_prob:
topnlabel_onedoc = [0] * opt.num_class
for iter_topnlabel in range(opt.topnlabel):
index_label = np.argwhere(prob == max(prob))
topnlabel_onedoc[index_label[0]
[0]] = prob[index_label][0][0]
prob[index_label] = -1
topnlabel_docwithoutlabel.append(topnlabel_onedoc)
test_correct += test_accuracy * len(test_index)
print(topnlabel_docwithoutlabel)
test_accuracy = test_correct / len(test)
print("Predict accuracy %f " % test_accuracy)
max_test_accuracy = test_accuracy
filename = 'test'
file = open(filename, 'w')
file.write(str(len(test)))
file.write('\n')
# print(wordtoix.get('close'))
# exit()
for topic_prob in topnlabel_docwithoutlabel:
print(topic_prob)
for prob_each_label in topic_prob:
file.write(str(prob_each_label))
file.write(" ")
file.write('\n')
except KeyboardInterrupt:
print('Training interupted')
print("Max Test accuracy %f " % max_test_accuracy)
def run_model(opt, train, val, ixtoword):
try:
params = np.load('./param_g.npz')
if params['Wemb'].shape == (opt.n_words, opt.embed_size):
print('Use saved embedding.')
opt.W_emb = params['Wemb']
else:
print('Emb Dimension mismatch: param_g.npz:' +
str(params['Wemb'].shape) + ' opt: ' + str(
(opt.n_words, opt.embed_size)))
opt.fix_emb = False
except IOError:
print('No embedding file found.')
opt.fix_emb = False
with tf.device('/gpu:1'):
x_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
x_org_ = tf.placeholder(tf.int32, shape=[opt.batch_size, opt.sent_len])
is_train_ = tf.placeholder(tf.bool, name='is_train_')
res_, loss_, train_op = auto_encoder(x_, x_org_, is_train_, opt)
merged = tf.summary.merge_all()
# opt.is_train = False
# res_val_, loss_val_, _ = auto_encoder(x_, x_org_, opt)
# merged_val = tf.summary.merge_all()
#tensorboard --logdir=run1:/tmp/tensorflow/ --port 6006
#writer = tf.train.SummaryWriter(opt.log_path, graph=tf.get_default_graph())
uidx = 0
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True,
graph_options=tf.GraphOptions(build_cost_model=1))
#config = tf.ConfigProto(device_count={'GPU':0})
# config.gpu_options.per_process_gpu_memory_fraction = 0.8
config.gpu_options.allow_growth = True
np.set_printoptions(precision=3)
np.set_printoptions(threshold=np.inf)
saver = tf.train.Saver()
run_metadata = tf.RunMetadata()
with tf.Session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.log_path + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(opt.log_path + '/test', sess.graph)
sess.run(tf.global_variables_initializer())
if opt.restore:
try:
t_vars = tf.trainable_variables()
#print([var.name[:-2] for var in t_vars])
loader = restore_from_save(t_vars, sess, opt)
print('Load pretrain successfully')
except Exception as e:
print(e)
print("No saving session, using random initialization")
sess.run(tf.global_variables_initializer())
for epoch in range(opt.max_epochs):
print("Starting epoch %d" % epoch)
# if epoch >= 10:
# print("Relax embedding ")
# opt.fix_emb = False
# opt.batch_size = 2
kf = get_minibatches_idx(len(train), opt.batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
sents = [train[t] for t in train_index]
sents_permutated = add_noise(sents, opt)
#sents[0] = np.random.permutation(sents[0])
if opt.model != 'rnn_rnn' and opt.model != 'cnn_rnn':
x_batch_org = prepare_data_for_cnn(sents, opt) # Batch L
else:
x_batch_org = prepare_data_for_rnn(sents, opt) # Batch L
if opt.model != 'rnn_rnn':
x_batch = prepare_data_for_cnn(sents_permutated,
opt) # Batch L
else:
x_batch = prepare_data_for_rnn(sents_permutated,
opt,
is_add_GO=False) # Batch L
if profile:
_, loss = sess.run(
[train_op, loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org,
is_train_: 1
},
options=tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
else:
_, loss = sess.run([train_op, loss_],
feed_dict={
x_: x_batch,
x_org_: x_batch_org,
is_train_: 1
})
if uidx % opt.valid_freq == 0:
is_train = None
valid_index = np.random.choice(len(val), opt.batch_size)
val_sents = [val[t] for t in valid_index]
val_sents_permutated = add_noise(val_sents, opt)
if opt.model != 'rnn_rnn' and opt.model != 'cnn_rnn':
x_val_batch_org = prepare_data_for_cnn(val_sents, opt)
else:
x_val_batch_org = prepare_data_for_rnn(val_sents, opt)
if opt.model != 'rnn_rnn':
x_val_batch = prepare_data_for_cnn(
val_sents_permutated, opt)
else:
x_val_batch = prepare_data_for_rnn(
val_sents_permutated, opt, is_add_GO=False)
loss_val = sess.run(loss_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org,
is_train_: is_train
})
print("Validation loss %f " % (loss_val))
res = sess.run(res_,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org,
is_train_: is_train
})
np.savetxt(opt.save_txt + '/rec_val_words.txt',
res['rec_sents'],
fmt='%i',
delimiter=' ')
try:
print("Orig:" + u' '.join([
ixtoword[x]
for x in x_val_batch_org[0] if x != 0 and x != 1
])) #.encode('utf-8', 'ignore').strip()
print("Sent:" + u' '.join([
ixtoword[x] for x in res['rec_sents'][0] if x != 0
])) #.encode('utf-8', 'ignore').strip()
except:
pass
if opt.discrimination:
print("Real Prob %f Fake Prob %f" %
(res['prob_r'], res['prob_f']))
summary = sess.run(merged,
feed_dict={
x_: x_val_batch,
x_org_: x_val_batch_org,
is_train_: is_train
})
test_writer.add_summary(summary, uidx)
is_train = True
if uidx % opt.print_freq == 1:
#pdb.set_trace()
print("Iteration %d: loss %f " % (uidx, loss))
res = sess.run(res_,
feed_dict={
x_: x_batch,
x_org_: x_batch_org,
is_train_: 1
})
np.savetxt(opt.save_txt + '/rec_train_words.txt',
res['rec_sents'],
fmt='%i',
delimiter=' ')
try:
print("Orig:" + u' '.join([
ixtoword[x]
for x in x_batch_org[0] if x != 0 and x != 1
])) #.encode('utf-8').strip()
print("Sent:" + u' '.join([
ixtoword[x] for x in res['rec_sents'][0] if x != 0
])) #.encode('utf-8').strip()
except:
pass
summary = sess.run(merged,
feed_dict={
x_: x_batch,
x_org_: x_batch_org,
is_train_: 1
})
train_writer.add_summary(summary, uidx)
# print res['x_rec'][0][0]
# print res['x_emb'][0][0]
if profile:
tf.contrib.tfprof.model_analyzer.print_model_analysis(
tf.get_default_graph(),
run_meta=run_metadata,
tfprof_options=tf.contrib.tfprof.model_analyzer.
PRINT_ALL_TIMING_MEMORY)
saver.save(sess, opt.save_path, global_step=epoch)
def train_classifier(train,
valid,
test,
W,
n_p=10,
n_words=10000,
n_x=300,
n_h=200,
patience=10,
max_epochs=50,
lrate=0.001,
n_train=10000,
optimizer='RMSprop',
batch_size=50,
valid_batch_size=50,
dispFreq=10,
validFreq=100,
saveFreq=500,
eps=1e-3):
""" train, valid, test : datasets
W : the word embedding initialization
n_words : vocabulary size
n_x : word embedding dimension
n_h : LSTM/GRU number of hidden units
n_z : latent embedding sapce for a sentence
patience : Number of epoch to wait before early stop if no progress
max_epochs : The maximum number of epoch to run
lrate : learning rate
optimizer : methods to do optimization
batch_size : batch size during training
valid_batch_size : The batch size used for validation/test set
dispFreq : Display to stdout the training progress every N updates
validFreq : Compute the validation error after this number of update.
"""
options = {}
options['n_p'] = n_p
options['n_words'] = n_words
options['n_x'] = n_x
options['n_h'] = n_h
options['patience'] = patience
options['max_epochs'] = max_epochs
options['lrate'] = lrate
options['optimizer'] = optimizer
options['batch_size'] = batch_size
options['valid_batch_size'] = valid_batch_size
options['dispFreq'] = dispFreq
options['validFreq'] = validFreq
#if config.method in ['SVGD', 'SVGD_KFAC']: patience = 5
logger.info('Model options {}'.format(options))
logger.info('{} train examples'.format(len(train[0])))
logger.info('{} valid examples'.format(len(valid[0])))
logger.info('{} test examples'.format(len(test[0])))
logger.info('Building model...')
assert np.min(train[1]) == 0 and np.max(train[1]) == 1
n_y = np.max(train[1]) + 1
options['n_y'] = n_y
params = init_params(options, W)
tparams = init_tparams(params)
(use_noise, x, mask, y, f_pred_prob, f_pred, cost,
cache) = build_model(tparams, options)
lr_theano = tensor.scalar(name='lr')
ntrain_theano = tensor.scalar(name='ntrain')
if config.method == 'pSGLD':
f_grad_shared, f_update = pSGLD(tparams, cost, [x, mask, y],
ntrain_theano, lr_theano)
elif config.method == 'SGLD':
f_grad_shared, f_update = SGLD(tparams, cost, [x, mask, y],
ntrain_theano, lr_theano)
elif config.method == 'RMSprop':
f_grad_shared, f_update = RMSprop(tparams, cost, [x, mask, y],
lr_theano)
elif config.method == 'SVGD':
f_grad_shared, f_update = SVGD(tparams,
cost, [x, mask, y],
ntrain_theano,
lr_theano,
kfac=False)
elif config.method == 'SVGD_KFAC':
f_grad_shared, f_update = SVGD(tparams,
cost, [x, mask, y],
ntrain_theano,
lr_theano,
kfac=True,
average=True,
cache=cache,
eps=eps,
n_p=n_p)
elif config.method == 'MIXTURE_KFAC':
f_grad_shared, f_update = SVGD(tparams,
cost, [x, mask, y],
ntrain_theano,
lr_theano,
kfac=True,
average=False,
cache=cache,
eps=eps,
n_p=n_p)
#print 'Training model...'
logger.info('Training model...')
kf_valid = get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = get_minibatches_idx(len(test[0]), valid_batch_size)
estop = False # early stop
history_errs = []
best_train_err, best_valid_err, best_test_err = 0., 0., 0.
bad_counter = 0
uidx = 0 # the number of update done
start_time = time.time()
n_average = 0
train_probs = np.zeros((len(train[0]), n_y))
valid_probs = np.zeros((len(valid[0]), n_y))
test_probs = np.zeros((len(test[0]), n_y))
try:
for eidx in xrange(max_epochs):
print tparams.keys()
from optimizers import sqr_dist
##['Wemb', 'lstm_encoder_W', 'lstm_encoder_U', 'lstm_encoder_rev_W', 'lstm_encoder_rev_U', 'Wy']
tv = tensor.flatten(tparams['Wy'], 2)
ftv = theano.function([], sqr_dist(tv, tv))
otv = ftv()
print(np.min(otv), np.max(otv), np.mean(otv), np.median(otv),
np.sum(otv**2) / n_p)
n_samples = 0
kf = get_minibatches_idx(len(train[0]), batch_size, shuffle=True)
for _, train_index in kf:
uidx += 1
#use_noise.set_value(0.5)
use_noise.set_value(config.dropout)
y = [train[1][t] for t in train_index]
x = [train[0][t] for t in train_index]
x, mask, y = prepare_data(x, y)
n_samples += x.shape[1]
cost = f_grad_shared(x, mask, y)
if config.method == 'RMSprop':
f_update(lrate)
elif config.method in ['SVGD', 'pSGLD', 'SGLD']:
f_update(lrate, n_train)
elif config.method in ['SVGD_KFAC', 'MIXTURE_KFAC']:
f_update(lrate, n_train, x, mask, y)
if np.isnan(cost) or np.isinf(cost):
logger.info('NaN detected')
estop = True
break
return 1., 1., 1.
if np.mod(uidx, dispFreq) == 0:
logger.info('Epoch {} Update {} Cost {}'.format(
eidx, uidx, cost))
if np.mod(uidx, saveFreq) == 0:
logger.info('Saving ...')
saveto = 'results/%s.npz' % save_prefix
np.savez(saveto, history_errs=history_errs)
logger.info('Done ...')
if np.mod(uidx, validFreq) == 0:
use_noise.set_value(0.)
if eidx < 1:
train_err = pred_error(f_pred, prepare_data, train, kf)
valid_err = pred_error(f_pred, prepare_data, valid,
kf_valid)
test_err = pred_error(f_pred, prepare_data, test,
kf_test)
history_errs.append([valid_err, test_err, train_err])
else:
train_probs_curr = pred_probs(f_pred_prob,
prepare_data, train, kf,
options)
valid_probs_curr = pred_probs(f_pred_prob,
prepare_data, valid,
kf_valid, options)
test_probs_curr = pred_probs(f_pred_prob, prepare_data,
test, kf_test, options)
train_probs = (n_average * train_probs +
train_probs_curr) / (n_average + 1)
valid_probs = (n_average * valid_probs +
valid_probs_curr) / (n_average + 1)
test_probs = (n_average * test_probs +
test_probs_curr) / (n_average + 1)
n_average += 1
train_pred = train_probs.argmax(axis=1)
valid_pred = valid_probs.argmax(axis=1)
test_pred = test_probs.argmax(axis=1)
train_err = (train_pred == np.array(train[1])).sum()
train_err = 1. - numpy_floatX(train_err) / len(
train[0])
valid_err = (valid_pred == np.array(valid[1])).sum()
valid_err = 1. - numpy_floatX(valid_err) / len(
valid[0])
test_err = (test_pred == np.array(test[1])).sum()
test_err = 1. - numpy_floatX(test_err) / len(test[0])
history_errs.append([valid_err, test_err, train_err])
if (uidx == 0 or
valid_err <= np.array(history_errs)[:, 0].min()):
best_train_err = train_err
best_valid_err = valid_err
best_test_err = test_err
bad_counter = 0
logger.info('Train {} Valid {} Test {}'.format(
train_err, valid_err, test_err))
if (len(history_errs) > patience and valid_err >=
np.array(history_errs)[:-patience, 0].min()):
#valid_err >= np.array(history_errs)[:-patience,0].mean()):
bad_counter += 1
#valid_err >= np.array(history_errs)[:-patience,0].mean()):
if bad_counter > patience:
logger.info('Early Stop!')
estop = True
break
logger.info('Seen {} samples'.format(n_samples))
if estop:
break
except KeyboardInterrupt:
logger.info('Training interupted')
end_time = time.time()
logger.info('Train {} Valid {} Test {}'.format(best_train_err,
best_valid_err,
best_test_err))
saveto = 'results/%s.npz' % save_prefix
np.savez(saveto,
train_err=best_train_err,
valid_err=best_valid_err,
test_err=best_test_err,
history_errs=history_errs)
logger.info('The code run for {} epochs, with {} sec/epochs'.format(
eidx + 1, (end_time - start_time) / (1. * (eidx + 1))))
#print >> sys.stderr, ('Training took %.1fs' %
# (end_time - start_time))
return best_train_err, best_valid_err, best_test_err
train = transform(train, w2i)
dev = transform(dev, w2i)
test = transform(test, w2i)
def evaluate(model, dev, params):
_, g1x, g1mask, g2x, g2mask = utils.get_prepare_data(dev, params.nout)
golds = [score for sa, sb, score in dev]
scores = model.scoring_function(g1x, g2x, g1mask, g2mask)
preds = np.squeeze(scores)
return pearsonr(preds, golds)[0], spearmanr(preds, golds)[0]
for epoch in range(300):
process_bar = pyprind.ProgPercent(len(train))
kf = utils.get_minibatches_idx(len(train), params.batchsize, shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [train[t] for t in train_index]
scores, g1x, g1mask, g2x, g2mask = utils.get_prepare_data(
batch, params.nout)
# print scores[:2], g1x[:2], g1mask[:2], g2x[:2], g2mask[:2]
cost = model.train_function(scores, g1x, g2x, g1mask, g2mask)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
print 'Epoch ', (epoch + 1), 'Update ', (uidx + 1), 'Cost ', cost
def main(gpu, mem_frac, batch_size, alpha, gamma, omega, euler_ord, max_len,
optimizer, keep_prob, learning_rate, margin, norm_type, balancing):
# ========================================================================== #
# =============================== Parameters =============================== #
# ========================================================================== #
params = {
'gpu': gpu,
'mem_frac': mem_frac,
'batch_size': batch_size,
'alpha': alpha, # threshold for euler angle
'gamma': gamma, # weight factor for twist loss
'omega': omega, # weight factor for smooth loss
'euler_ord': euler_ord,
'max_len': max_len,
'optimizer': optimizer,
'keep_prob': keep_prob,
'learning_rate': learning_rate,
'margin': margin,
'norm_type': norm_type,
'balancing': balancing
}
prefix = "pmnet"
for k, v in params.items():
if (k != 'gpu' and k != 'mem_frac' and k != 'euler_ord'):
prefix += "_" + k + "=" + str(v)
# ========================================================================== #
# =============================== Load Data ================================ #
# ========================================================================== #
data_path = "../datasets/train/"
stats_path = "../data/"
# Mixamo joint configuration
parents = np.array([
-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 0, 10, 11, 12, 3, 14, 15, 16, 3, 18, 19,
20
])
all_local = []
all_global = []
all_skel = []
all_names = []
t_skel = []
folders = [
f for f in listdir(data_path) if not f.startswith(".")
and not f.endswith("py") and not f.endswith(".npz")
]
for folder_name in folders:
files = [
f for f in listdir(data_path + folder_name)
if not f.startswith(".") and f.endswith("_seq.npy")
]
for cfile in files:
file_name = cfile[:-8]
# Real joint positions
positions = np.load(data_path + folder_name + "/" + file_name +
"_skel.npy")
# After processed (Maybe, last 4 elements are dummy values)
sequence = np.load(data_path + folder_name + "/" + file_name +
"_seq.npy")
# Processed global positions (#frames, 4)
offset = sequence[:, -8:-4]
# Processed local positions (#frames, #joints, 3)
sequence = np.reshape(sequence[:, :-8], [sequence.shape[0], -1, 3])
positions[:, 0, :] = sequence[:, 0, :] # root joint
all_local.append(sequence)
all_global.append(offset)
all_skel.append(positions)
all_names.append(folder_name)
# Joint positions before processed
train_skel = all_skel
# After processed, relative position
train_local = all_local
train_global = all_global
# T-pose (real position)
for tt in train_skel:
t_skel.append(tt[0:1])
# Total training samples
all_frames = np.concatenate(train_local)
ntotal_samples = all_frames.shape[0]
ntotal_sequences = len(train_local)
print("Number of sequences: " + str(ntotal_sequences))
# ========================================================================== #
# ============================= Data Normalize ============================= #
# ========================================================================== #
# Calculate total mean and std
allframes_n_skel = np.concatenate(train_local + t_skel)
local_mean = allframes_n_skel.mean(axis=0)[None, :]
global_mean = np.concatenate(train_global).mean(axis=0)[None, :]
local_std = allframes_n_skel.std(axis=0)[None, :]
global_std = np.concatenate(train_global).std(axis=0)[None, :]
# Save the data stats
np.save(stats_path + "mixamo_local_motion_mean.npy", local_mean)
np.save(stats_path + "mixamo_local_motion_std.npy", local_std)
np.save(stats_path + "mixamo_global_motion_mean.npy", global_mean)
np.save(stats_path + "mixamo_global_motion_std.npy", global_std)
# Normalize the data (whitening)
n_joints = all_local[0].shape[-2]
local_std[local_std == 0] = 1
for i in xrange(len(train_local)):
train_local[i] = (train_local[i] - local_mean) / local_std
train_global[i] = (train_global[i] - global_mean) / global_std
train_skel[i] = (train_skel[i] - local_mean) / local_std
# ========================================================================== #
# =============================== Load Model =============================== #
# ========================================================================== #
models_dir = "../data/models/" + prefix
logs_dir = "../data/logs/" + prefix
if not exists(models_dir):
makedirs(models_dir)
if not exists(logs_dir):
makedirs(logs_dir)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_frac)
with tf.device("/gpu:%d" % gpu):
net = pmnet_model(batch_size, alpha, gamma, omega, euler_ord, n_joints,
max_len, parents, keep_prob, learning_rate,
optimizer, local_mean, local_std, global_mean,
global_std, logs_dir, margin, norm_type, balancing)
# ========================================================================== #
# ================================ Training ================================ #
# ========================================================================== #
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
loaded, model_name = net.load(sess, models_dir)
if loaded:
print("[*] Load SUCCESSFUL")
iteration = int(model_name.split("-")[-1])
else:
print("[!] Starting from scratch ...")
iteration = 0
net.saver = tf.train.Saver(max_to_keep=10)
max_iter = 15000
while iteration < max_iter:
mini_batches = get_minibatches_idx(len(train_local),
batch_size,
shuffle=True)
for _, batch_idxs in mini_batches:
start_time = time.time()
if len(batch_idxs) == batch_size:
steps = np.repeat(max_len, batch_size)
localA_batch = []
globalA_batch = []
skelA_batch = []
localB_batch = []
globalB_batch = []
skelB_batch = []
mask_batch = np.zeros((batch_size, max_len),
dtype="float32")
aeReg_batch = np.zeros((batch_size, 1), dtype="float32")
inp_height_batch = np.zeros((batch_size, 1),
dtype="float32")
tgt_height_batch = np.zeros((batch_size, 1),
dtype="float32")
# Make minibatch
for bb in xrange(batch_size):
low = 0
high = train_local[batch_idxs[bb]].shape[0] - max_len
if low >= high:
stidx = 0
else:
stidx = np.random.randint(low=low, high=high)
clocalA = train_local[batch_idxs[bb]][stidx:(stidx +
max_len)]
mask_batch[
bb, :np.min([max_len, clocalA.shape[0]])] = 1.0
if clocalA.shape[0] < max_len:
clocalA = np.concatenate(
(clocalA,
np.zeros((max_len - clocalA.shape[0],
n_joints, 3))))
cglobalA = train_global[batch_idxs[bb]][stidx:(
stidx + max_len)]
if cglobalA.shape[0] < max_len:
cglobalA = np.concatenate(
(cglobalA,
np.zeros((max_len - cglobalA.shape[0],
n_joints, 3))))
cskelA = train_skel[batch_idxs[bb]][stidx:(stidx +
max_len)]
if cskelA.shape[0] < max_len:
cskelA = np.concatenate(
(cskelA,
np.zeros((max_len - cskelA.shape[0], n_joints,
3))))
rnd_idx = np.random.randint(len(train_local))
cskelB = train_skel[rnd_idx][0:max_len]
if cskelB.shape[0] < max_len:
cskelB = np.concatenate(
(cskelB,
np.zeros((max_len - cskelB.shape[0], n_joints,
3))))
joints_a = cskelA[0].copy()
joints_a = joints_a[None]
joints_a = (joints_a * local_std) + local_mean
height_a = get_height_from_skel(joints_a[0])
height_a = height_a / 100
joints_b = cskelB[0].copy()
joints_b = joints_b[None]
joints_b = (joints_b * local_std + local_mean)
height_b = get_height_from_skel(joints_b[0])
height_b = height_b / 100
aeReg_on = np.random.binomial(1, p=0.5)
if aeReg_on:
cskelB = cskelA.copy()
aeReg_batch[bb, 0] = 1
inp_height_batch[bb, 0] = height_a
tgt_height_batch[bb, 0] = height_a
else:
aeReg_batch[bb, 0] = 0
inp_height_batch[bb, 0] = height_a
tgt_height_batch[bb, 0] = height_b
localA_batch.append(clocalA)
globalA_batch.append(cglobalA)
skelA_batch.append(cskelA)
localB_batch.append(clocalA)
globalB_batch.append(cglobalA)
skelB_batch.append(cskelB)
localA_batch = np.array(localA_batch).reshape(
(batch_size, max_len, -1))
globalA_batch = np.array(globalA_batch).reshape(
(batch_size, max_len, -1))
seqA_batch = np.concatenate((localA_batch, globalA_batch),
axis=-1)
skelA_batch = np.array(skelA_batch).reshape(
(batch_size, max_len, -1))
localB_batch = np.array(localB_batch).reshape(
(batch_size, max_len, -1))
globalB_batch = np.array(globalB_batch).reshape(
(batch_size, max_len, -1))
seqB_batch = np.concatenate((localB_batch, globalB_batch),
axis=-1)
skelB_batch = np.array(skelB_batch).reshape(
(batch_size, max_len, -1))
mid_time = time.time()
mf, mr, mg, shape, base = net.train(
sess, seqA_batch, skelA_batch, seqB_batch, skelB_batch,
mask_batch, aeReg_batch, inp_height_batch,
tgt_height_batch, iteration)
print("step=%d/%d, time=%.2f+%.2f" %
(iteration, max_iter, mid_time - start_time,
time.time() - mid_time))
if np.isnan(mg) or np.isinf(mg):
return
if iteration >= 1000 and iteration % 5000 == 0:
net.save(sess, models_dir, iteration)
iteration = iteration + 1
net.save(sess, models_dir, iteration)
def main(gpu, batch_size, alpha, beta, gamma, omega, margin, d_arch, d_rand,
euler_ord, max_steps, min_steps, num_layer, gru_units, optim,
norm_type, mem_frac, keep_prob, learning_rate):
prefix = "Online_Retargeting_Mixamo_Cycle_Adv"
for kk, vv in locals().iteritems():
if (kk != "prefix" and kk != "mem_frac" and kk != "batch_size"
and kk != "min_steps" and kk != "max_steps" and kk != "gpu"):
prefix += "_" + kk + "=" + str(vv)
layers_units = []
for i in range(num_layer):
layers_units.append(gru_units)
data_path = "./datasets/train/"
alllocal = []
allglobal = []
allskel = []
allnames = []
folders = [
f for f in listdir(data_path) if not f.startswith(".")
and not f.endswith("py") and not f.endswith(".npz")
]
for folder in folders:
files = [
f for f in listdir(data_path + folder)
if not f.startswith(".") and f.endswith("_seq.npy")
]
for cfile in files:
positions = np.load(data_path + folder + "/" + cfile[:-8] +
"_skel.npy")
if positions.shape[0] >= min_steps:
sequence = np.load(data_path + folder + "/" + cfile[:-8] +
"_seq.npy")
offset = sequence[:, -8:-4]
sequence = np.reshape(sequence[:, :-8],
[sequence.shape[0], -1, 3])
positions[:, 0, :] = sequence[:, 0, :]
alllocal.append(sequence)
allglobal.append(offset)
allskel.append(positions)
allnames.append(folder)
trainlocal = alllocal
trainskel = allskel
trainglobal = allglobal
print("Number of examples: " + str(len(trainlocal)))
tskel = []
for tt in trainskel:
tskel.append(tt[0:1])
allframes_n_skel = np.concatenate(trainlocal + tskel)
min_root = allframes_n_skel[:, 0:1].min(axis=0)
max_root = allframes_n_skel[:, 0:1].max(axis=0)
local_mean = allframes_n_skel.mean(axis=0)[None, :]
global_mean = np.concatenate(trainglobal).mean(axis=0)[None, :]
local_std = allframes_n_skel.std(axis=0)[None, :]
global_std = np.concatenate(trainglobal).std(axis=0)[None, :]
np.save(data_path[:-6] + "mixamo_local_motion_mean.npy", local_mean)
np.save(data_path[:-6] + "mixamo_local_motion_std.npy", local_std)
local_std[local_std == 0] = 1
np.save(data_path[:-6] + "mixamo_global_motion_mean.npy", global_mean)
np.save(data_path[:-6] + "mixamo_global_motion_std.npy", global_std)
n_joints = alllocal[0].shape[-2]
for i in xrange(len(trainlocal)):
trainlocal[i] = (trainlocal[i] - local_mean) / local_std
trainglobal[i] = (trainglobal[i] - global_mean) / global_std
trainskel[i] = (trainskel[i] - local_mean) / local_std
models_dir = "./models/" + prefix
logs_dir = "./logs/" + prefix
parents = np.array([
-1, 0, 1, 2, 3, 4, 0, 6, 7, 8, 0, 10, 11, 12, 3, 14, 15, 16, 3, 18, 19,
20
])
with tf.device("/gpu:%d" % gpu):
gru = EncoderDecoderGRU(batch_size, alpha, beta, gamma, omega,
euler_ord, n_joints, layers_units, max_steps,
local_mean, local_std, global_mean, global_std,
parents, keep_prob, logs_dir, learning_rate,
optim, margin, d_arch, d_rand, norm_type)
if not exists(models_dir):
makedirs(models_dir)
if not exists(logs_dir):
makedirs(logs_dir)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=mem_frac)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
loaded, model_name = gru.load(sess, models_dir)
if loaded:
print("[*] Load SUCCESSFUL")
step = int(model_name.split("-")[-1])
else:
print("[!] Starting from scratch ...")
step = 0
total_steps = 50000
gru.saver = tf.train.Saver(max_to_keep=10)
while step < total_steps:
mini_batches = get_minibatches_idx(len(trainlocal),
batch_size,
shuffle=True)
for _, batchidx in mini_batches:
start_time = time.time()
if len(batchidx) == batch_size:
if min_steps >= max_steps:
steps = np.repeat(max_steps, batch_size)
else:
steps = np.random.randint(low=min_steps,
high=max_steps + 1,
size=(batch_size, ))
realLocal_batch = []
realSkel_batch = []
realGlobal_batch = []
localA_batch = []
globalA_batch = []
skelA_batch = []
localB_batch = []
globalB_batch = []
skelB_batch = []
aeReg_batch = np.zeros((batch_size, 1), dtype="float32")
mask_batch = np.zeros((batch_size, max_steps),
dtype="float32")
for b in xrange(batch_size):
low = 0
high = trainlocal[batchidx[b]].shape[0] - max_steps
if low >= high:
stidx = 0
else:
stidx = np.random.randint(low=low, high=high)
clocalA = trainlocal[batchidx[b]][stidx:stidx +
max_steps]
mask_batch[
b, :np.min([steps[b], clocalA.shape[0]])] = 1.0
if clocalA.shape[0] < max_steps:
clocalA = np.concatenate(
(clocalA,
np.zeros((max_steps - clocalA.shape[0],
n_joints, 3))))
cglobalA = trainglobal[batchidx[b]][stidx:stidx +
max_steps]
if cglobalA.shape[0] < max_steps:
cglobalA = np.concatenate(
(cglobalA,
np.zeros((max_steps - cglobalA.shape[0], 4))))
cskelA = trainskel[batchidx[b]][stidx:stidx +
max_steps]
if cskelA.shape[0] < max_steps:
cskelA = np.concatenate(
(cskelA,
np.zeros((max_steps - cskelA.shape[0],
n_joints, 3))))
rnd_idx = np.random.randint(len(trainlocal))
cskelB = trainskel[rnd_idx][stidx:stidx + max_steps]
if cskelB.shape[0] < max_steps:
cskelB = np.concatenate(
(cskelB,
np.zeros((max_steps - cskelB.shape[0],
n_joints, 3))))
tgtname = allnames[rnd_idx]
rnd_idx = np.random.randint(len(trainlocal))
while tgtname != allnames[rnd_idx]:
rnd_idx = np.random.randint(len(trainlocal))
low = 0
high = trainlocal[rnd_idx].shape[0] - max_steps
if low >= high:
stidx = 0
else:
stidx = np.random.randint(low=low, high=high)
crealLocal = trainlocal[rnd_idx][stidx:stidx +
max_steps]
crealGlobal = trainglobal[rnd_idx][stidx:stidx +
max_steps]
crealSkel = trainskel[rnd_idx][stidx:stidx + max_steps]
regon = np.random.binomial(1, p=0.2)
if regon:
cskelB = cskelA.copy()
aeReg_batch[b, 0] = 1
else:
aeReg_batch[b, 0] = 0
localA_batch.append(clocalA)
globalA_batch.append(cglobalA)
skelA_batch.append(cskelA)
localB_batch.append(clocalA)
globalB_batch.append(cglobalA)
skelB_batch.append(cskelB)
realLocal_batch.append(crealLocal)
realGlobal_batch.append(crealGlobal)
realSkel_batch.append(crealSkel)
localA_batch = np.array(localA_batch).reshape(
(batch_size, max_steps, -1))
globalA_batch = np.array(globalA_batch).reshape(
(batch_size, max_steps, -1))
seqA_batch = np.concatenate((localA_batch, globalA_batch),
axis=-1)
skelA_batch = np.array(skelA_batch).reshape(
(batch_size, max_steps, -1))
localB_batch = np.array(localB_batch).reshape(
(batch_size, max_steps, -1))
globalB_batch = np.array(globalB_batch).reshape(
(batch_size, max_steps, -1))
seqB_batch = np.concatenate((localB_batch, globalB_batch),
axis=-1)
skelB_batch = np.array(skelB_batch).reshape(
(batch_size, max_steps, -1))
realLocal_batch = np.array(realLocal_batch).reshape(
(batch_size, max_steps, -1))
realGlobal_batch = np.array(realGlobal_batch).reshape(
(batch_size, max_steps, -1))
realSeq_batch = np.concatenate(
(realLocal_batch, realGlobal_batch), axis=-1)
realSkel_batch = np.array(realSkel_batch).reshape(
(batch_size, max_steps, -1))
mid_time = time.time()
dlf, dlr, gl, lc = gru.train(sess, realSeq_batch,
realSkel_batch, seqA_batch,
skelA_batch, seqB_batch,
skelB_batch, aeReg_batch,
mask_batch, step)
print(
"step=%d/%d, g_loss=%.5f, d_loss=%.5f, cyc_loss=%.5f, "
"time=%.2f+%.2f" %
(step, total_steps, gl, dlf + dlr, lc,
mid_time - start_time, time.time() - mid_time))
if np.isnan(gl) or np.isinf(gl):
return
if step >= 1000 and step % 1000 == 0:
gru.save(sess, models_dir, step)
step = step + 1
gru.save(sess, models_dir, step)
def main(options):
C.update(options)
model = ResnetModel()
x_train, y_train, x_validate, y_validate, x_test, y_test, train_size, validate_size, test_size = \
pre_process_CIFAR10_data()
learning_rate = 0.1
for epoch in range(C['num_epoch']):
start_time = time.time()
kf = get_minibatches_idx(train_size, C['batch_size'], shuffle=True)
train_loss = 0.0
train_batches = 0
for _, train_index in kf:
inputs = x_train[train_index]
targets = y_train[train_index]
inputs, targets = prepare_CIFAR10_data(inputs, targets)
loss = model.f_grad_shared(inputs, targets)
model.f_update(learning_rate)
train_loss += loss
train_batches += 1
kf_valid = get_minibatches_idx(validate_size, C['valid_batch_size'], shuffle=False)
valid_loss = 0.0
valid_accuracy = 0.0
valid_batches = 0
for _, valid_index in kf_valid:
inputs = x_validate[valid_index]
targets = y_validate[valid_index]
inputs, targets = prepare_CIFAR10_data(inputs, targets)
loss, accuracy = model.f_validate(inputs, targets)
valid_loss += loss
valid_accuracy += accuracy
valid_batches += 1
print(
'''\
Epoch {} of {} took {:.3f}s
training loss: {:.6f}
validation loss: {:.6f}
validation accuracy: {:.2f} %'''.format(
epoch, C['num_epoch'], time.time() - start_time,
train_loss / train_batches,
valid_loss / valid_batches,
valid_accuracy / valid_batches * 100.0,
)
)
if epoch + 1 == 41 or epoch + 1 == 61:
learning_rate *= 0.1
print('Discount learning rate to', learning_rate)
print('Saving model...', end='')
np.savez('cifar10_deep_residual_model.npz', *lasagne.layers.get_all_param_values(model.network))
print('Done')
