def main():
#pretrained_embedding = utils.load_word_embedding()
num_gpu = torch.cuda.device_count()
model = Classifier(embedding, args.lstm_hidden_dim, args.num_class,
args.n_layers, args.bidirectional,
args.dropout_keep_prob)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# for GPU usage
if num_gpu > 1:
model = nn.DataParallel(model)
model.to(device)
loss_ft.cuda()
val_iter = utils.data_iter(args.seq_len, args.batch_size, args.vocab_size,
False, 'valid')
for epoch in range(args.num_epochs):
train_iter = utils.data_iter(args.seq_len, args.batch_size, True,
'train')
start = time.time()
train_loss, train_acc = train_model(model, train_iter, epoch)
logger.info("Training elapsed time: %.4fs per epoch\n" %
(time.time() - start))
val_loss, val_acc = eval_model(model, val_iter, epoch, True)
logger.info('--- Epoch: %d ---' % (epoch + 1))
logger.info('Train Acc: %.2f, Train Loss: %.4f' %
(100 * train_acc, train_loss))
logger.info('Val Acc: %.2f, Val Loss: %.4f' %
(100 * val_acc, val_loss))
def evaluate_loss(model, data, criterion, pad_idx=constants.PAD, cuda=True):
model.eval()
total_loss = 0.
total_tgt_words = 0
total_correct_tokens = 0
for src_sents, tgt_sents in data_iter(data, batch_size=model.args.valid_batch_size, batch_type=model.args.batch_type, shuffle=False):
pred_tgt_word_num = sum(len(s[1:]) for s in tgt_sents)
src = to_input_variable(src_sents, model.vocab.src, cuda=cuda)
tgt = to_input_variable([item[:-1] for item in tgt_sents], model.vocab.tgt, cuda=cuda)
gold_tgt_sents, _ = to_input_variable([item[1:] for item in tgt_sents], model.vocab.tgt, cuda=cuda)
gold_tgt_tokens_flatten = gold_tgt_sents.view(-1)
scores = model(src, tgt)
loss = criterion(scores, gold_tgt_tokens_flatten)
_, argmax_idxs = torch.max(scores, dim=-1)
equals_batch_tokens = argmax_idxs.eq(gold_tgt_tokens_flatten)
padding_mask = gold_tgt_tokens_flatten.ne(pad_idx)
equals_batch_tokens_padding = equals_batch_tokens.long() * padding_mask.long()
correct_tokens = torch.sum(equals_batch_tokens_padding)
total_loss += loss.item()
total_tgt_words += pred_tgt_word_num
total_correct_tokens += correct_tokens.item()
loss = total_loss / total_tgt_words
ppl = math.exp(loss)
acc = 1.0 * total_correct_tokens / total_tgt_words * 100
return loss, ppl, acc
def fit(self, X, Y, batch_size, coef_init=None):
# coef_init validation
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64, order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (X.shape[1], ):
raise ValueError("Provided coef_init does not match dataset.")
coef_init = np.r_[np.zeros(1), coef_init]
else:
coef_init = np.zeros((X.shape[1] + 1, ))
# label validation
Y = check_array(Y, ensure_2d=False)
# Check that X and Y have correct shape
X, Y = check_X_y(X, Y, y_numeric=True)
# add bias to X
X = np.c_[np.ones((X.shape[0])), X]
self.t_ = 0
self.n_iter_ = 0
self.loss_hist_ = []
self.loss_epoch_ = []
loss_count = 0
loss = float("inf")
theta = coef_init
for i in range(int(self.max_iter)):
self.n_iter_ += 1
loss_epoch = []
for x, y in data_iter(X, Y, batch_size):
self.t_ += 1
error = x.dot(theta) - y
loss_prev = loss
loss = error.dot(error) / x.shape[0]
loss_epoch.append(loss)
gradient = (x.T.dot(error)) / x.shape[0]
theta = theta - (self.eta0 /
(self.t_**self.power_t)) * gradient
if self.loss_hist_:
loss_prev = min(self.loss_hist_[-self.n_iter_no_change + 1:])
else:
loss_prev = float("inf")
loss = np.average(loss_epoch)
self.loss_epoch_.append(loss_epoch)
self.loss_hist_.append(loss)
if loss + self.tol > loss_prev:
if loss_count == self.n_iter_no_change - 1:
break
else:
loss_count += 1
else:
loss_count = 0
self.coef_ = theta
return self
def inference(print_datetime):
best_model = Classifier(embedding, args.lstm_hidden_dim, args.num_class,
args.n_layers, args.bidirectional,
args.dropout_keep_prob)
ckpts = glob.glob('./ckpt/%s/*' % print_datetime)
path = ckpts[np.argsort(
[float(ckpt.replace('.pt', '').split('_')[-1]) for ckpt in ckpts])[-1]]
print('Best Model: %s' % path)
best_model.load_state_dict(torch.load(path))
test_iter = utils.data_iter(args.seq_len, args.batch_size, args.vocab_size,
False, 'test')
test_loss, test_acc = eval_model(best_model, test_iter)
print('Test Acc: %.2f, Test Loss: %.4f' % (100 * test_acc, test_loss))
def _train(self, epoch):
self.optimizer.zero_grad()
self.model.train()
start_time = time.time()
epoch_start_time = time.time()
overall_losses = 0
losses = 0
batch_idx = 1
y_pred = []
y_true = []
for batch_data in data_iter(self.train_data,
train_batch_size,
shuffle=True):
torch.cuda.empty_cache()
batch_inputs, batch_labels = self.batch2tensor(batch_data)
batch_outputs = self.model(batch_inputs)
def optimize(batch_size, lr, mom, num_epochs, log_interval):
[w, b], vs = init_params()
y_vals = [squared_loss(net(X, w, b), y).mean().asnumpy()]
print('batch_size', batch_size)
for epoch in range(1, num_epochs + 1):
# 学习率自我衰减
if epoch > 2:
lr *= .1
for batch_i, (features, label) in enumerate(
utils.data_iter(batch_size, num_examples, X, y)):
with autograd.record():
output = net(features, w, b)
loss = squared_loss(output, label)
loss.backward()
sgd_momentum([w, b], vs, lr, mom, batch_size)
if batch_i * batch_size % log_interval == 0:
y_vals.append(squared_loss(net(X, w, b), y).mean().asnumpy())
print('epoch %d, learning_rate %f, loss %.4e' %
(epoch, lr, y_vals[-1]))
# 为了便于打印，改变输出形状并转化成numpy数组
print('w:', w.reshape((1, -1)).asnumpy(), 'b:', b.asscalar(), '\n')
x_vals = np.linspace(0, num_epochs, len(y_vals), endpoint=True)
utils.semilogy(x_vals, y_vals, 'epoch', 'loss')
def optimize(batch_size, lr, mom, num_epochs, log_interval):
num_examples = 1000
X, y = genData(num_examples)
[w, b], vs = init_params()
y_vals = [utils.squared_loss(utils.linreg(X, w, b), y).mean().asnumpy()]
for epoch in range(1, num_epochs + 1):
# 学习率自我衰减。
if epoch > 2:
lr *= 0.1
for batch_i, (features, label) in enumerate(
utils.data_iter(batch_size, num_examples, X, y)):
with autograd.record():
output = utils.linreg(features, w, b)
loss = utils.squared_loss(output, label)
loss.backward()
sgd_momentum([w, b], lr, batch_size, vs, mom)
if batch_i * batch_size % log_interval == 0:
y_vals.append(
utils.squared_loss(utils.linreg(X, w, b),
y).mean().asnumpy())
print('w:', w, '\nb:', b, '\n')
x_vals = np.linspace(0, num_epochs, len(y_vals), endpoint=True)
utils.semilogy(x_vals, y_vals, 'epoch', 'loss')
import paddle
import numpy as np
from paddle.fluid.dygraph.base import to_variable
from paddle.static import InputSpec
import grpc
import yaml
import utils
if __name__ == "__main__":
paddle.enable_static()
exe = paddle.static.Executor(paddle.CPUPlace())
inference_program, feed_target_names, fetch_targets = \
paddle.static.load_inference_model(
path_prefix="whole/static",
executor=exe)
for i, item in enumerate(utils.data_iter("data/input.json")):
uid, x1, x2, label = item
feed = {"x1": x1, "x2": x2}
fetch_vars = exe.run(program=inference_program,
feed=feed,
fetch_list=fetch_targets,
return_numpy=False)
print(np.array(fetch_vars))
def main(args):
""" Main function """
# training data
print("****** Train Set *****")
train_data_src = read_corpus(args.train_src, source='src')
train_data_tgt = read_corpus(args.train_tgt, source='tgt')
print()
# valid data
print("****** Valid Set *****")
valid_data_src = read_corpus(args.valid_src, source='src')
valid_data_tgt = read_corpus(args.valid_tgt, source='tgt')
print()
# merge data for source and target
train_data = zipped(train_data_src, train_data_tgt)
valid_data = zipped(valid_data_src, valid_data_tgt)
vocab, transformer, optimizer, cross_entropy_loss = init_training(args)
print("[Transformer Config] ", )
print(transformer)
epoch = 0
checkpoint = codecs.open(args.checkpoint, "w", encoding="utf-8")
transformer.train()
while epoch < args.epochs:
total_loss = 0.
total_tgt_words = 0
total_correct_tokens = 0
freq = 0
start_epoch = start_batch = time.time()
for src_sents, tgt_sents in data_iter(train_data,
batch_size=args.batch_size,
batch_type=args.batch_type):
# sum for predicting target words per batch(no padding)
pred_tgt_word_num = sum(len(s[1:]) for s in tgt_sents)
optimizer.zero_grad()
# format data for source and target(add padding)
src = to_input_variable(src_sents, vocab.src, cuda=args.cuda)
tgt = to_input_variable([item[:-1] for item in tgt_sents],
vocab.tgt,
cuda=args.cuda)
# scores for predicting(before softmax)
scores = transformer(src, tgt)
gold_tgt_sents, _ = to_input_variable(
[item[1:] for item in tgt_sents], vocab.tgt, cuda=args.cuda)
gold_tgt_tokens_flatten = gold_tgt_sents.view(-1)
# get loss according cross_entropy(one_hot distribution)
weight_loss = cross_entropy_loss(scores, gold_tgt_tokens_flatten)
mean_loss = weight_loss / pred_tgt_word_num
# get loss according cross_entropy(smoothing distribution)
if args.label_smoothing:
smoothing_loss = label_smoothing_loss(
scores,
gold_tgt_tokens_flatten,
epsilon=args.label_smoothing_rate)
smoothing_mean_loss = smoothing_loss / pred_tgt_word_num
_, pred_idxs = torch.max(scores, dim=-1)
is_target = gold_tgt_tokens_flatten.ne(constants.PAD)
correct_tokens = torch.sum(
gold_tgt_tokens_flatten.eq(pred_idxs).float() *
is_target.float())
if args.label_smoothing:
smoothing_mean_loss.backward()
else:
mean_loss.backward()
optimizer.step()
if args.optimizer == "Warmup_Adam":
optimizer.update_learning_rate()
total_loss += mean_loss.item()
total_correct_tokens += correct_tokens.item()
total_tgt_words += pred_tgt_word_num
freq += 1
if freq % args.displayFreq == 0:
end_batch = time.time()
total_time = end_batch - start_batch
aver_per_word_loss = total_loss / args.displayFreq
acc = 1.0 * total_correct_tokens / total_tgt_words * 100
print(
"[%d] [loss:%5.2f] [acc:%5.2f%%] [ppl:%5.2f] [speed:%5.2f words/s] [time:%5.2fs]"
% (freq, aver_per_word_loss, acc,
math.exp(aver_per_word_loss),
total_tgt_words / total_time, total_time))
total_loss = 0.
total_tgt_words = 0
total_correct_tokens = 0
start_batch = end_batch
if freq % args.validFreq == 0:
t0 = time.time()
if torch.cuda.device_count() > 1:
valid_loss, ppl, acc = evaluate_loss(
transformer.module, valid_data, cross_entropy_loss)
else:
valid_loss, ppl, acc = evaluate_loss(
transformer, valid_data, cross_entropy_loss)
t1 = time.time()
print(
"[Valid] [loss:%5.2f] [acc:%5.2f%%] [ppl:%5.2f] [time:%5.2fs]"
% (valid_loss, acc, ppl, t1 - t0))
epoch += 1
end_epoch = time.time()
print("[Epoch %d] is ending... [total_time:%.2f min]" %
(epoch, (end_epoch - start_epoch) / 60))
print("Saving model...")
if not os.path.isdir(args.save_to):
os.makedirs(args.save_to)
if args.finetune:
torch.save(
transformer.state_dict(),
args.finetune_model_path + "_finetune_epoch%d" % (epoch))
checkpoint.write(args.finetune_model_path + "_finetune_epoch%d\n" %
(epoch))
else:
torch.save(transformer.state_dict(),
args.save_to + "transformer_epoch%d" % (epoch))
checkpoint.write(args.save_to + "transformer_epoch%d\n" % (epoch))
checkpoint.flush()
print("Saving finish...\n")
checkpoint.close()
def train(self,
sess,
vocab_size,
epoch=25,
data_dir="data",
dataset_name="cnn",
log_dir='log/tmp/',
load_path=None,
data_size=3000,
eval_every=1500,
val_rate=0.1,
dropout_rate=0.9):
print(" [*] Building Network...")
start = time.time()
self.prepare_model()
print(" [*] Preparing model finished. Use %4.4f" %
(time.time() - start))
# Summary
writer = tf.train.SummaryWriter(log_dir, sess.graph)
print(" [*] Writing log to %s" % log_dir)
# Saver and Load
self.saver = tf.train.Saver(max_to_keep=15)
if load_path is not None:
if os.path.isdir(load_path):
fname = tf.train.latest_checkpoint(
os.path.join(load_path, 'ckpts'))
assert fname is not None
else:
fname = load_path
print(" [*] Loading %s" % fname)
self.saver.restore(sess, fname)
print(" [*] Checkpoint is loaded.")
else:
sess.run(tf.initialize_all_variables())
print(" [*] No checkpoint to load, all variable inited")
counter = 0
vcounter = 0
start_time = time.time()
ACC = []
LOSS = []
train_files, validate_files = fetch_files(data_dir, dataset_name,
vocab_size)
if data_size:
train_files = train_files[:data_size]
validate_size = int(
min(max(20.0,
float(len(train_files)) * val_rate), len(validate_files)))
print(" [*] Validate_size %d" % validate_size)
for epoch_idx in xrange(epoch):
# load data
train_iter = data_iter(train_files,
self.max_nsteps,
self.max_query_length,
batch_size=self.batch_size,
vocab_size=self.vocab_size,
shuffle_data=True)
tsteps = train_iter.next()
# train
running_acc = 0
running_loss = 0
for batch_idx, docs, d_end, queries, q_end, y in train_iter:
_, summary_str, cost, accuracy = sess.run(
[self.train_op, self.train_sum, self.loss, self.accuracy],
feed_dict={
self.document: docs,
self.query: queries,
self.d_end: d_end,
self.q_end: q_end,
self.y: y,
self.dropout: dropout_rate,
})
writer.add_summary(summary_str, counter)
running_acc += accuracy
running_loss += np.mean(cost)
if counter % 10 == 0:
print(
"Epoch: [%2d] [%4d/%4d] time: %4.4f, loss: %.8f, accuracy: %.8f"
%
(epoch_idx, batch_idx, tsteps, time.time() -
start_time, running_loss / 10.0, running_acc / 10.0))
running_loss = 0
running_acc = 0
counter += 1
if (counter + 1) % eval_every == 0:
# validate
running_acc = 0
running_loss = 0
idxs = np.random.choice(len(validate_files),
size=validate_size)
files = [validate_files[idx] for idx in idxs]
validate_iter = data_iter(files,
self.max_nsteps,
self.max_query_length,
batch_size=self.batch_size,
vocab_size=self.vocab_size,
shuffle_data=True)
vsteps = validate_iter.next()
for batch_idx, docs, d_end, queries, q_end, y in validate_iter:
validate_sum_str, cost, accuracy = sess.run(
[self.validate_sum, self.loss, self.accuracy],
feed_dict={
self.document: docs,
self.query: queries,
self.d_end: d_end,
self.q_end: q_end,
self.y: y,
self.dropout: 1.0,
})
writer.add_summary(validate_sum_str, vcounter)
running_acc += accuracy
running_loss += np.mean(cost)
vcounter += 1
ACC.append(running_acc / vsteps)
LOSS.append(running_loss / vsteps)
vcounter += vsteps
print(
"Epoch: [%2d] Validation time: %4.4f, loss: %.8f, accuracy: %.8f"
% (epoch_idx, time.time() - start_time,
running_loss / vsteps, running_acc / vsteps))
# save
self.save(sess, log_dir, global_step=counter)
print('\n\n')
import paddle.fluid as fluid
import numpy as np
import grpc
import yaml
import paddle
import network
import utils
if __name__ == "__main__":
paddle.enable_static()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_program, feed_target_names, fetch_targets = \
fluid.io.load_inference_model(
dirname="whole_program/inference",
executor=exe)
for batch_id, data in enumerate(utils.data_iter("../data/input.json")):
_, x1, x2, _ = data
data = {
"Host|x1": x1,
"Customer|x2": x2,
}
results = exe.run(
program=inference_program,
feed=data,
fetch_list=fetch_targets)
print("result: {}".format(np.array(results)))
for name in columns[1:]:
select += "," + name
return select
dfMean = pd.read_csv("data\MEAN.csv")
dfStd = pd.read_csv("data\STD.csv")
df = pd.read_sql("SELECT {} from Bank1PercentPredict".format(
getSQLColumnString(featureNames + predictNames)),
conn,
columns=featureNames + predictNames)
cursor.close()
conn.close()
for columnName in featureNames:
if not columnName.startswith('Next'):
df[columnName] = (df[columnName] -
dfMean[columnName][0]) / dfStd[columnName][0]
y_Predict = nd.array(df[predictCategory][:].as_matrix())
X_Predict = nd.array(df.loc[:, 'Day1OpenPrice':'Week4Exchange'][:].as_matrix())
batch_size = 16
predict_acc, predict_1acc = utils.evaluate_accuracy(
utils.data_iter(X_Predict, y_Predict, batch_size), net, ctx)
print("Predict acc: %f,Predict True Value acc: %f" %
(predict_acc, predict_1acc))
