def load_tf_model(path, embedding_dim=128, graph_size=20, n_encode_layers=2):
"""Load model weights from hd5 file
"""
# https://stackoverflow.com/questions/51806852/cant-save-custom-subclassed-model
CAPACITIES = {10: 20., 20: 30., 50: 40., 100: 50.}
data_random = [
tf.random.uniform((
2,
2,
),
minval=0,
maxval=1,
dtype=tf.dtypes.float32),
tf.random.uniform((2, graph_size, 2),
minval=0,
maxval=1,
dtype=tf.dtypes.float32),
tf.cast(
tf.random.uniform(
minval=1, maxval=10, shape=(2, graph_size), dtype=tf.int32),
tf.float32) / tf.cast(CAPACITIES[graph_size], tf.float32)
]
model_loaded = AttentionModel(embedding_dim,
n_encode_layers=n_encode_layers)
set_decode_type(model_loaded, "greedy")
_, _ = model_loaded(data_random)
model_loaded.load_weights(path)
return model_loaded
def __init__(self, input_dim, second_hidden_size, minute_hidden_size, rnn_layers, batch_size, bidirectional, use_lstm):
super(SafetyModel, self).__init__()
self.batch_size = batch_size
self.rnn_layers = rnn_layers
self.use_lstm = use_lstm
self.second_hidden_size = second_hidden_size
self.minute_hidden_size = minute_hidden_size
self.second_att_net = AttentionModel(input_dim, second_hidden_size, bidirectional, use_lstm)
if bidirectional:
second_hidden_size *= 2
self.minute_att_net = AttentionModel(second_hidden_size, minute_hidden_size, bidirectional, use_lstm)
if bidirectional:
minute_hidden_size *= 2
self.fc = nn.Linear(minute_hidden_size, 2)
self.init_hidden()
def copy_of_tf_model(model, embedding_dim=128, graph_size=20):
"""Copy model weights to new model
"""
# https://stackoverflow.com/questions/56841736/how-to-copy-a-network-in-tensorflow-2-0
CAPACITIES = {10: 20., 20: 30., 50: 40., 100: 50.}
data_random = [
tf.random.uniform((
2,
2,
),
minval=0,
maxval=1,
dtype=tf.dtypes.float32),
tf.random.uniform((2, graph_size, 2),
minval=0,
maxval=1,
dtype=tf.dtypes.float32),
tf.cast(
tf.random.uniform(
minval=1, maxval=10, shape=(2, graph_size), dtype=tf.int32),
tf.float32) / tf.cast(CAPACITIES[graph_size], tf.float32)
]
new_model = AttentionModel(embedding_dim)
set_decode_type(new_model, "sampling")
_, _ = new_model(data_random)
for a, b in zip(new_model.variables, model.variables):
a.assign(b)
return new_model
def main():
args = parse_args()
print(args)
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
if args.enable_ce:
fluid.default_main_program().random_seed = 102
framework.default_startup_program().random_seed = 102
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
# Training process
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
loss = model.build_graph()
inference_program = train_program.clone(for_test=True)
clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=max_grad_norm)
lr = args.learning_rate
opt_type = args.optimizer
if opt_type == "sgd":
optimizer = fluid.optimizer.SGD(lr, grad_clip=clip)
elif opt_type == "adam":
optimizer = fluid.optimizer.Adam(lr, grad_clip=clip)
else:
print("only support [sgd|adam]")
raise Exception("opt type not support")
optimizer.minimize(loss)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(startup_program)
device_count = len(fluid.cuda_places()) if args.use_gpu else len(
fluid.cpu_places())
CompiledProgram = fluid.CompiledProgram(train_program).with_data_parallel(
loss_name=loss.name)
train_data_prefix = args.train_data_prefix
eval_data_prefix = args.eval_data_prefix
test_data_prefix = args.test_data_prefix
vocab_prefix = args.vocab_prefix
src_lang = args.src_lang
tar_lang = args.tar_lang
print("begin to load data")
raw_data = reader.raw_data(src_lang, tar_lang, vocab_prefix,
train_data_prefix, eval_data_prefix,
test_data_prefix, args.max_len)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, epoch_id=0, with_lr=True):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1]))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1]))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
res['src'] = src_ids
res['tar'] = in_tar
res['label'] = label_tar
res['src_sequence_length'] = src_mask
res['tar_sequence_length'] = tar_mask
return res, np.sum(tar_mask)
# get train epoch size
def eval(data, epoch_id=0):
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id,
with_lr=False)
fetch_outs = exe.run(inference_program,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
ppl = np.exp(total_loss / word_count)
return ppl
def train():
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
start_time = time.time()
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
batch_times = []
time_interval = 0.0
batch_start_time = time.time()
epoch_word_count = 0.0
total_reader_cost = 0.0
batch_read_start = time.time()
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id=epoch_id)
word_count += word_num
total_reader_cost += time.time() - batch_read_start
fetch_outs = exe.run(program=CompiledProgram,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=True)
cost_train = np.mean(fetch_outs[0])
# print(cost_train)
total_loss += cost_train * batch_size
batch_end_time = time.time()
batch_time = batch_end_time - batch_start_time
batch_times.append(batch_time)
time_interval += batch_time
epoch_word_count += word_num
if batch_id > 0 and batch_id % 100 == 0:
print(
"-- Epoch:[%d]; Batch:[%d]; Time: %.5f s; ppl: %.5f; reader cost: %0.5f s; ips: %0.5f tokens/sec"
% (epoch_id, batch_id, batch_time,
np.exp(total_loss / word_count), total_reader_cost /
100, word_count / time_interval))
ce_ppl.append(np.exp(total_loss / word_count))
total_loss = 0.0
word_count = 0.0
time_interval = 0.0
total_reader_cost = 0.0
# profiler tools
if args.profile and epoch_id == 0 and batch_id == 100:
profiler.reset_profiler()
elif args.profile and epoch_id == 0 and batch_id == 105:
return
batch_start_time = time.time()
batch_read_start = time.time()
end_time = time.time()
epoch_time = end_time - start_time
ce_time.append(epoch_time)
print(
"\nTrain epoch:[%d]; Epoch Time: %.5f; avg_time: %.5f s/step; ips: %0.5f tokens/sec\n"
% (epoch_id, epoch_time, sum(batch_times) / len(batch_times),
epoch_word_count / sum(batch_times)))
if not args.profile:
save_path = os.path.join(args.model_path,
"epoch_" + str(epoch_id),
"checkpoint")
print("begin to save", save_path)
fluid.save(train_program, save_path)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
with profile_context(args.profile, args.profiler_path):
train()
def train():
args = parse_args()
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
if args.enable_ce:
fluid.default_main_program().random_seed = 102
framework.default_startup_program().random_seed = 102
# Training process
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
loss = model.build_graph()
# clone from default main program and use it as the validation program
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByGlobalNorm(
clip_norm=max_grad_norm))
lr = args.learning_rate
opt_type = args.optimizer
if opt_type == "sgd":
optimizer = fluid.optimizer.SGD(lr)
elif opt_type == "adam":
optimizer = fluid.optimizer.Adam(lr)
else:
print("only support [sgd|adam]")
raise Exception("opt type not support")
optimizer.minimize(loss)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
train_data_prefix = args.train_data_prefix
eval_data_prefix = args.eval_data_prefix
test_data_prefix = args.test_data_prefix
vocab_prefix = args.vocab_prefix
src_lang = args.src_lang
tar_lang = args.tar_lang
print("begin to load data")
raw_data = reader.raw_data(src_lang, tar_lang, vocab_prefix,
train_data_prefix, eval_data_prefix,
test_data_prefix, args.max_len)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, epoch_id=0, with_lr=True):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1], 1))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1], 1))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
res['src'] = src_ids
res['tar'] = in_tar
res['label'] = label_tar
res['src_sequence_length'] = src_mask
res['tar_sequence_length'] = tar_mask
return res, np.sum(tar_mask)
# get train epoch size
def eval(data, epoch_id=0):
eval_data_iter = reader.get_data_iter(data, batch_size, mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch,
epoch_id,
with_lr=False)
fetch_outs = exe.run(inference_program,
feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=False)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
ppl = np.exp(total_loss / word_count)
return ppl
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
start_time = time.time()
print("epoch id", epoch_id)
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id=epoch_id)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[loss.name],
use_program_cache=True)
cost_train = np.array(fetch_outs[0])
total_loss += cost_train * batch_size
word_count += word_num
if batch_id > 0 and batch_id % 100 == 0:
print("ppl", batch_id, np.exp(total_loss / word_count))
ce_ppl.append(np.exp(total_loss / word_count))
total_loss = 0.0
word_count = 0.0
end_time = time.time()
time_gap = end_time - start_time
ce_time.append(time_gap)
dir_name = args.model_path + "/epoch_" + str(epoch_id)
print("begin to save", dir_name)
fluid.io.save_params(exe, dir_name)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
from attention_model import AttentionModel
if __name__ == '__main__':
debug = 0
if len(sys.argv) > 1:
if sys.argv[1] == '--debug':
debug = 1
print('-> Starting Bot!')
f = open("token.txt", "r")
token = f.read().strip()
if debug == 0:
checkpoint_path = "models/char_att7/"
global model
model = AttentionModel(checkpoint_path=checkpoint_path,
load_model=True).model
elif debug == 1:
model = 'yolo'
# with CustomObjectScope({'SeqSelfAttention': SeqSelfAttention,
# 'MultiHead': MultiHead,
# 'root_mean_squared_error': root_mean_squared_error}):
# model = keras.models.load_model('models/char_18_epoch_5.h5')
# global graph
# graph = tf.get_default_graph()
graph = None
pp = PicklePersistence(filename='data/conversationbot')
updater = Updater(token, persistence=pp, use_context=True)
bot = NeuralBot(updater, model, graph)
updater.dispatcher.add_handler(CommandHandler('hello', bot.hello))
def train(self):
self.max_acc = 1
self.is_training = True
with tf.Graph().as_default():
data_processor = DataProcessor()
vocab_size = data_processor.get_vocabulary_size(FLAGS.vocab_path)
vocab, revocab = DataProcessor.initialize_vocabulary(
FLAGS.vocab_path)
data_processor.get_init(FLAGS.input_training_data_path,
FLAGS.input_validation_data_path, vocab,
vocab_size, FLAGS.max_length, revocab)
models = AttentionModel()
input_q = tf.placeholder(tf.int32,
shape=(None, FLAGS.max_length),
name="input_x1") # FLAGS.train_batch_size
input_ap = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
input_an = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
# input_k = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
# input_v = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
q_encode = models.embed(
inputs=input_q,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units) # embedding size plus 1 for padding
ap_encode = models.embed(inputs=input_ap,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units)
an_encode = models.embed(inputs=input_an,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units)
# k_encode = models.embed(input_k, vocab_size=vocab_size,num_units=hp.hidden_units)
# v_encode = models.embed(input_v, vocab_size=vocab_size,num_units=hp.hidden_units)
# apply dropout
q_encode = tf.layers.dropout(q_encode,
hp.dropout_rate,
training=tf.convert_to_tensor(
self.is_training))
ap_encode = tf.layers.dropout(ap_encode,
hp.dropout_rate,
training=tf.convert_to_tensor(
self.is_training))
an_encode = tf.layers.dropout(an_encode,
hp.dropout_rate,
training=tf.convert_to_tensor(
self.is_training))
# k_encode = tf.layers.dropout(k_encode, hp.dropout_rate, training=tf.convert_to_tensor(self.is_training))
# v_encode = tf.layers.dropout(v_encode, hp.dropout_rate, training=tf.convert_to_tensor(self.is_training))
# multihead blocks
for i in range(hp.num_blocks):
with tf.variable_scope("num_blocks_{}".format(i)):
q_encode = models.multihead_attention(
query=q_encode,
key=q_encode,
value=q_encode,
num_heads=hp.num_heads,
is_training=tf.convert_to_tensor(self.is_training),
dropout_rate=hp.dropout_rate,
mask_future=False)
q_encode = models.feed_forward(
q_encode, units=[hp.hidden_units * 4, hp.hidden_units])
ap_encode = models.multihead_attention(
query=ap_encode,
key=ap_encode,
value=ap_encode,
num_heads=hp.num_heads,
is_training=tf.convert_to_tensor(self.is_training),
dropout_rate=hp.dropout_rate,
mask_future=False)
ap_encode = models.feed_forward(
ap_encode,
units=[hp.hidden_units * 4, hp.hidden_units])
an_encode = models.multihead_attention(
query=an_encode,
key=an_encode,
value=an_encode,
num_heads=hp.num_heads,
is_training=tf.convert_to_tensor(self.is_training),
dropout_rate=hp.dropout_rate,
mask_future=False)
an_encode = models.feed_forward(
an_encode,
units=[hp.hidden_units * 4, hp.hidden_units])
## output layer
with tf.name_scope('output_layer'):
dims = q_encode.get_shape().as_list()
q_encode = tf.reshape(q_encode, [-1, dims[1] * dims[2]])
ap_encode = tf.reshape(ap_encode, [-1, dims[1] * dims[2]])
an_encode = tf.reshape(an_encode, [-1, dims[1] * dims[2]])
weight = tf.get_variable(
'output_weight',
[q_encode.get_shape().as_list()[-1], hp.hidden_units])
q_encode = tf.matmul(q_encode, weight)
ap_encode = tf.matmul(ap_encode, weight)
an_encode = tf.matmul(an_encode, weight)
q_encode = models.vec_normalize(q_encode)
ap_encode = models.vec_normalize(ap_encode)
an_encode = models.vec_normalize(an_encode)
## calculate similarity and loss
cos_12 = tf.reduce_sum(tf.multiply(q_encode, ap_encode),
1) # wisely multiple vectors
cos_13 = tf.reduce_sum(tf.multiply(q_encode, an_encode), 1)
zero = tf.constant(0,
shape=[FLAGS.train_batch_size],
dtype=tf.float32)
margin = tf.constant(FLAGS.loss_margin,
shape=[FLAGS.train_batch_size],
dtype=tf.float32)
losses = tf.maximum(
zero, tf.subtract(margin, tf.subtract(cos_12, cos_13)))
loss_sum = tf.reduce_sum(losses)
loss_avg = tf.div(loss_sum, FLAGS.train_batch_size)
correct = tf.equal(zero, losses)
accuracy = tf.reduce_mean(tf.cast(correct, "float"),
name="accuracy")
global_step = tf.Variable(
0, name="global_step", trainable=False
) # The global step will be automatically incremented by one every time you execute a train loop
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
# optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(loss_avg)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
saver = tf.train.Saver(tf.global_variables())
# session start point
with tf.Session() as session:
session.run(tf.local_variables_initializer())
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
# meta_path = FLAGS.output_model_path + '/step6500_loss0.0_trainAcc1.0_evalAcc0.36.meta'
# model_path = FLAGS.output_model_path + '/step6500_loss0.0_trainAcc1.0_evalAcc0.36'
# saver = tf.train.import_meta_graph(meta_path)
# print('graph imported')
# saver.restore(session, model_path)
# print('variables restored!')
# Load pre-trained model
ckpt = tf.train.get_checkpoint_state(
FLAGS.input_previous_model_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(session, ckpt.model_checkpoint_path)
print("Load Model From ", ckpt.model_checkpoint_path)
else:
print("No model found")
print("Begin to train model.")
max_acc = 0
for step in range(FLAGS.training_steps):
train_data_batch = data_processor.next_batch_train_random(
FLAGS.train_batch_size)
train_q_vec_b, train_q_vec_len_b, train_d_vec_b, train_d_vec_len_b, train_dneg_vec_b, train_dneg_vec_len_b = train_data_batch
feed_dict = {
input_q: train_q_vec_b,
input_ap: train_d_vec_b,
input_an: train_dneg_vec_b
}
_, loss_avg_, accuracy_, step_ = session.run(
[train_op, loss_avg, accuracy, global_step],
feed_dict=feed_dict)
print('=' * 10 + 'step{}, loss_avg = {}, acc={}'.format(
step_, loss_avg_, accuracy_)) # loss for all batches
if step_ % FLAGS.eval_every == 0:
print('\n============================> begin to test ')
eval_size = FLAGS.validation_size
def test_step(input_y1, input_y2, input_y3, label_list,
sess):
feed_dict = {
input_q: input_y1,
input_ap: input_y2,
input_an: input_y3
}
correct_flag = 0
cos_12_ = sess.run(cos_12, feed_dict)
cos_max = max(cos_12_)
index_max = list(cos_12_).index(cos_max)
if label_list[index_max] == '1':
correct_flag = 1
# cos_pos_, cos_neg_, accuracy_ = sess.run([cos_12, cos_13, accuracy], feed_dict)
# data_processor.saveFeatures(cos_pos_, cos_neg_, test_loss_, accuracy_)
return correct_flag
def evaluate(eval_size):
correct_num = int(0)
for i in range(eval_size):
print('evaluation step %d ' % i)
batches = data_processor.loadValData_step(
vocab, vocab_size,
FLAGS.input_validation_data_path,
FLAGS.max_length,
eval_size) # batch_size*seq_len
# 显示/保存测试数据
# save_test_data(batch_y1, batch_y2, label_list)
batch_y1, batch_y2, label_list = batches[i]
correct_flag = test_step(
batch_y1, batch_y2, batch_y2, label_list,
session)
correct_num += correct_flag
print('correct_num', correct_num)
acc = correct_num / float(eval_size)
return acc
self.is_training = False
acc_ = evaluate(eval_size=eval_size)
self.is_training = True
print(
'--------The test result among the test data sets: acc = {0}, test size = {1}----------'
.format(acc_, eval_size))
if acc_ >= max_acc:
max_acc = acc_
# # acc = test_for_bilstm.test()
path = saver.save(
session, FLAGS.output_model_path + '/step' +
str(step_) + '_loss' + str(loss_avg_) +
'_trainAcc' + str(accuracy_) + '_evalAcc' +
str(acc_))
saver.export_meta_graph(FLAGS.output_model_path +
'/meta_' + 'step' +
str(step_) + '_loss' +
str(loss_avg_) +
'_trainAcc' +
str(accuracy_) +
'_evalAcc' + str(acc_))
print("Save checkpoint(model) to {}".format(path))
def main():
args = parse_args()
print(args)
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
#args.enable_ce = True
if args.enable_ce:
fluid.default_startup_program().random_seed = 102
fluid.default_main_program().random_seed = 102
np.random.seed(102)
random.seed(102)
# Training process
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
gloabl_norm_clip = GradientClipByGlobalNorm(max_grad_norm)
lr = args.learning_rate
opt_type = args.optimizer
if opt_type == "sgd":
optimizer = fluid.optimizer.SGD(lr,
parameter_list=model.parameters(),
grad_clip=gloabl_norm_clip)
elif opt_type == "adam":
optimizer = fluid.optimizer.Adam(lr,
parameter_list=model.parameters(),
grad_clip=gloabl_norm_clip)
else:
print("only support [sgd|adam]")
raise Exception("opt type not support")
train_data_prefix = args.train_data_prefix
eval_data_prefix = args.eval_data_prefix
test_data_prefix = args.test_data_prefix
vocab_prefix = args.vocab_prefix
src_lang = args.src_lang
tar_lang = args.tar_lang
print("begin to load data")
raw_data = reader.raw_data(src_lang, tar_lang, vocab_prefix,
train_data_prefix, eval_data_prefix,
test_data_prefix, args.max_len)
print("finished load data")
train_data, valid_data, test_data, _ = raw_data
def prepare_input(batch, epoch_id=0):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1]))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1]))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
inputs = [src_ids, in_tar, label_tar, src_mask, tar_mask]
return inputs, np.sum(tar_mask)
# get train epoch size
def eval(data, epoch_id=0):
model.eval()
eval_data_iter = reader.get_data_iter(data,
batch_size,
mode='eval')
total_loss = 0.0
word_count = 0.0
for batch_id, batch in enumerate(eval_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id)
loss = model(input_data_feed)
total_loss += loss * batch_size
word_count += word_num
ppl = np.exp(total_loss.numpy() / word_count)
model.train()
return ppl
ce_time = []
ce_ppl = []
max_epoch = args.max_epoch
for epoch_id in range(max_epoch):
epoch_start = time.time()
model.train()
if args.enable_ce:
train_data_iter = reader.get_data_iter(train_data,
batch_size,
enable_ce=True)
else:
train_data_iter = reader.get_data_iter(train_data, batch_size)
total_loss = 0
word_count = 0.0
batch_times = []
total_reader_cost = 0.0
interval_time_start = time.time()
batch_start = time.time()
for batch_id, batch in enumerate(train_data_iter):
batch_reader_end = time.time()
total_reader_cost += batch_reader_end - batch_start
input_data_feed, word_num = prepare_input(batch,
epoch_id=epoch_id)
word_count += word_num
loss = model(input_data_feed)
loss.backward()
optimizer.minimize(loss)
model.clear_gradients()
total_loss += loss * batch_size
total_loss_value = total_loss.numpy()
batch_times.append(time.time() - batch_start)
if batch_id > 0 and batch_id % 100 == 0:
print(
"-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, batch_cost: %.5f sec, reader_cost: %.5f sec, ips: %.5f words/sec"
% (epoch_id, batch_id,
np.exp(total_loss_value / word_count),
(time.time() - interval_time_start) / 100,
total_reader_cost / 100, word_count /
(time.time() - interval_time_start)))
ce_ppl.append(np.exp(total_loss_value / word_count))
total_loss = 0.0
word_count = 0.0
total_reader_cost = 0.0
interval_time_start = time.time()
batch_start = time.time()
train_epoch_cost = time.time() - epoch_start
print(
"\nTrain epoch:[%d]; epoch_cost: %.5f sec; avg_batch_cost: %.5f s/step\n"
% (epoch_id, train_epoch_cost,
sum(batch_times) / len(batch_times)))
ce_time.append(train_epoch_cost)
dir_name = os.path.join(args.model_path, "epoch_" + str(epoch_id))
print("begin to save", dir_name)
paddle.fluid.save_dygraph(model.state_dict(), dir_name)
print("save finished")
dev_ppl = eval(valid_data)
print("dev ppl", dev_ppl)
test_ppl = eval(test_data)
print("test ppl", test_ppl)
if args.enable_ce:
card_num = get_cards()
_ppl = 0
_time = 0
try:
_time = ce_time[-1]
_ppl = ce_ppl[-1]
except:
print("ce info error")
print("kpis\ttrain_duration_card%s\t%s" % (card_num, _time))
print("kpis\ttrain_ppl_card%s\t%f" % (card_num, _ppl))
set up model, loss criterion, optimizer
'''
# Instantiate the model
Exp_model = TextEncoder(embedding_dim=1024,
hidden_size=256,
num_layers=1,
bidir=True,
dropout1=0.5)
Query_model = TextEncoder(embedding_dim=1024,
hidden_size=256,
num_layers=1,
bidir=True,
dropout1=0.5)
Attn_model = AttentionModel(para_encoder_input_dim=512,
query_dim=512,
output_dim=256)
para_encoder_attn_model = AttentionModel(para_encoder_input_dim=512,
query_dim=512,
output_dim=512)
para_encoder = ParaEncoder(input_dim=1024,
hidden_size=256,
num_layers=1,
attn_model=para_encoder_attn_model,
bidir=True,
dropout1=0.5)
linearfc = LinearFC(num_classes=2,
encoded_embedding_dim=512,
context_dim=512,
dropout1=0.2)
def train():
args = parse_args()
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
# inference process
print("src", src_vocab_size)
# dropout type using upscale_in_train, dropout can be remove in inferecen
# So we can set dropout to 0
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=0.0)
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=0.0)
beam_size = args.beam_size
trans_res = model.build_graph(mode='beam_search', beam_size=beam_size)
# clone from default main program and use it as the validation program
main_program = fluid.default_main_program()
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = Executor(place)
exe.run(framework.default_startup_program())
source_vocab_file = args.vocab_prefix + "." + args.src_lang
infer_file = args.infer_file
infer_data = reader.raw_mono_data(source_vocab_file, infer_file)
def prepare_input(batch, epoch_id=0, with_lr=True):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1], 1))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1], 1))
in_tar = np.zeros_like(in_tar, dtype='int64')
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
label_tar = np.zeros_like(label_tar, dtype='int64')
res['src'] = src_ids
res['tar'] = in_tar
res['label'] = label_tar
res['src_sequence_length'] = src_mask
res['tar_sequence_length'] = tar_mask
return res, np.sum(tar_mask)
dir_name = args.reload_model
print("dir name", dir_name)
fluid.io.load_params(exe, dir_name)
train_data_iter = reader.get_data_iter(infer_data, 1, mode='eval')
tar_id2vocab = []
tar_vocab_file = args.vocab_prefix + "." + args.tar_lang
with open(tar_vocab_file, "r") as f:
for line in f.readlines():
tar_id2vocab.append(line.strip())
infer_output_file = args.infer_output_file
out_file = open(infer_output_file, 'w')
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id=0)
fetch_outs = exe.run(feed=input_data_feed,
fetch_list=[trans_res.name],
use_program_cache=False)
res = [tar_id2vocab[e] for e in fetch_outs[0].reshape(-1)]
res = res[1:]
new_res = []
for ele in res:
if ele == "</s>":
break
new_res.append(ele)
out_file.write(' '.join(new_res))
out_file.write('\n')
out_file.close()
def main():
h5_list = [
h5py.File('data/processed0.h5', 'r'),
h5py.File('data/processed1.h5', 'r'),
h5py.File('data/processed3.h5', 'r'),
h5py.File('data/processed4.h5', 'r')
]
h5_list_test = [h5py.File('data/processed2.h5', 'r')]
batch_size = 100
bg_train = BatchGenerator(h5_list, batch_size)
bg_test = BatchGenerator(h5_list_test, batch_size) #, maxlen = 400000)
# if we want to change batch size during training
# dynamic_batch = True
dynamic_batch = False
n_epochs = 30
opt = keras.optimizers.Adam()
# opt = keras.optimizers.Adadelta()
# opt = keras.optimizers.RMSprop(lr=0.001)
# which iteration of models to load
# next_it = 2
# # encoder_output, attention_weights = SelfAttention(size=50,
# # num_hops=16,
# # use_penalization=False)(x)
checkpoint_path = "models/char_att7/"
# Create checkpoint callback
cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_accuracy',
verbose=1,
save_weights_only=True,
mode='max',
save_best_only=True)
load_model = False
# load_model = True
am = AttentionModel(
checkpoint_path=checkpoint_path,
rnn_size=512,
rnn_style='GRU', #'CuDNNLSTM',
# bidirectional = True,
dropout_rate=0.4,
load_model=load_model)
# am.model.save(checkpoint_path + 'model.h5')
# am.build_model()
am.model.compile(
optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy', perplexity, categorical_accuracy],
)
# print(am.model.summary())
am.save_config()
# am.model.fit(X_train,
# y_train,
# batch_size = batch_size,
# validation_data=(X_test, y_test),
# callbacks = [cp_callback],
# epochs=n_epochs)
# fit using the batch generators
am.model.fit_generator(
bg_train,
validation_data=bg_test,
callbacks=[cp_callback],
# use_multiprocessing=True,
# workers=4,
epochs=n_epochs)
default_value=0)
train_x, train_y = load_data(train_df)
splitVal = int(len(train_y) * VAL_RATE)
val_x, val_y = train_x[splitVal:], train_y[splitVal:]
src_dataset = tf.contrib.data.Dataset.from_tensor_slices(
(train_x, train_y))
val_dataset = tf.contrib.data.Dataset.from_tensor_slices((val_x, val_y))
src_iterator = train_iterator(src_dataset,
src_vocab_table,
BATCH_SIZE,
max_length=500)
val_iterator = train_iterator(val_dataset,
src_vocab_table,
BATCH_SIZE,
max_length=500)
attention_model = AttentionModel(src_iterator, val_iterator, 4, 200, 200,
False)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
for i in range(MAX_EPOCHS):
sess.run(src_iterator.initializer)
sess.run(val_iterator.initializer)
print("epoch:", i)
step = 0
while True:
try:
attention_model.train(sess, step)
step += 1
def infer():
args = parse_args()
num_layers = args.num_layers
src_vocab_size = args.src_vocab_size
tar_vocab_size = args.tar_vocab_size
batch_size = args.batch_size
dropout = args.dropout
init_scale = args.init_scale
max_grad_norm = args.max_grad_norm
hidden_size = args.hidden_size
# inference process
print("src", src_vocab_size)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
with fluid.dygraph.guard(place):
# dropout type using upscale_in_train, dropout can be remove in inferecen
# So we can set dropout to 0
if args.attention:
model = AttentionModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
beam_size=args.beam_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=0.0,
mode='beam_search')
else:
model = BaseModel(hidden_size,
src_vocab_size,
tar_vocab_size,
batch_size,
beam_size=args.beam_size,
num_layers=num_layers,
init_scale=init_scale,
dropout=0.0,
mode='beam_search')
source_vocab_file = args.vocab_prefix + "." + args.src_lang
infer_file = args.infer_file
infer_data = reader.raw_mono_data(source_vocab_file, infer_file)
def prepare_input(batch, epoch_id=0):
src_ids, src_mask, tar_ids, tar_mask = batch
res = {}
src_ids = src_ids.reshape((src_ids.shape[0], src_ids.shape[1]))
in_tar = tar_ids[:, :-1]
label_tar = tar_ids[:, 1:]
in_tar = in_tar.reshape((in_tar.shape[0], in_tar.shape[1]))
label_tar = label_tar.reshape(
(label_tar.shape[0], label_tar.shape[1], 1))
inputs = [src_ids, in_tar, label_tar, src_mask, tar_mask]
return inputs, np.sum(tar_mask)
dir_name = args.reload_model
print("dir name", dir_name)
state_dict, _ = fluid.dygraph.load_dygraph(dir_name)
model.set_dict(state_dict)
model.eval()
train_data_iter = reader.get_data_iter(infer_data,
batch_size,
mode='infer')
tar_id2vocab = []
tar_vocab_file = args.vocab_prefix + "." + args.tar_lang
with io.open(tar_vocab_file, "r", encoding='utf-8') as f:
for line in f.readlines():
tar_id2vocab.append(line.strip())
infer_output_file = args.infer_output_file
infer_output_dir = infer_output_file.split('/')[0]
if not os.path.exists(infer_output_dir):
os.mkdir(infer_output_dir)
with io.open(infer_output_file, 'w', encoding='utf-8') as out_file:
for batch_id, batch in enumerate(train_data_iter):
input_data_feed, word_num = prepare_input(batch, epoch_id=0)
# import ipdb; ipdb.set_trace()
outputs = model(input_data_feed)
for i in range(outputs.shape[0]):
ins = outputs[i].numpy()
res = [tar_id2vocab[int(e)] for e in ins[:, 0].reshape(-1)]
new_res = []
for ele in res:
if ele == "</s>":
break
new_res.append(ele)
out_file.write(space_tok.join(new_res))
out_file.write(line_tok)
def main():
# load test data
filename = "data/mixed.txt"
raw_text = open(filename, 'r', encoding='utf-8').read()
print('-> Raw text length:', len(raw_text))
raw_text = raw_text.lower()[:700000]
raw_text = re.sub('\n', " ", raw_text)
# create mapping of unique chars to integers
chars = sorted(list(set(raw_text)))
# print('-> chars:', chars)
char_to_int = dict((c, i) for i, c in enumerate(chars))
int_to_char = dict((i, c) for i, c in enumerate(chars))
print('-> int to char:', int_to_char)
# print('-> char to int:', char_to_int)
# print(char_to_int)
# char_to_int = {' ': 0, '!': 1, '%': 2, '&': 3, "'": 4, ',': 5, '-': 6, '.': 7, '/': 8, '0': 9, '1': 10, '2': 11, '3': 12, '4': 13, '5': 14, '6': 15, '7': 16, '8': 17, '9': 18, ':': 19, ';': 20, '<': 21, '>': 22, '?': 23, 'a': 24, 'b': 25, 'c': 26, 'd': 27, 'e': 28, 'f': 29, 'g': 30, 'h': 31, 'i': 32, 'j': 33, 'k': 34, 'l': 35, 'm': 36, 'n': 37, 'o': 38, 'p': 39, 'q': 40, 'r': 41, 's': 42, 't': 43, 'u': 44, 'v': 45, 'w': 46, 'x': 47, 'y': 48, 'z': 49, '~': 50, '—': 51}
char_list = list(char_to_int.keys())
raw_text = ''.join([i for i in raw_text if i in char_list])
print('-> char to int:', char_to_int)
# summarize the loaded data
n_chars = len(raw_text)
n_vocab = len(char_list)
print("-> Total Characters: ", n_chars)
print("-> Total Vocab: ", n_vocab)
# prepare the dataset of input to output pairs encoded as integers
seq_length = 100
sentences = []
next_chars = []
for i in range(0, n_chars - seq_length, 1):
sentences.append(raw_text[i:i + seq_length])
next_chars.append(raw_text[i + seq_length])
n_patterns = len(sentences)
print("Total Patterns: ", n_patterns)
X = np.zeros((n_patterns, seq_length, n_vocab), dtype=np.bool)
y = np.zeros((n_patterns, n_vocab), dtype=np.bool)
for i, sentence in enumerate(sentences):
for t, char in enumerate(sentence):
X[i, t, char_to_int[char]] = 1
y[i, char_to_int[next_chars[i]]] = 1
print('- Input:', X[0, :, :].shape)
print('- Output:', y[0].shape)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
batch_size = 100
# if we want to change batch size during training
# dynamic_batch = True
dynamic_batch = False
n_epochs = 40
# opt = keras.optimizers.Adam()
# opt = keras.optimizers.Adadelta()
opt = keras.optimizers.RMSprop(lr=0.001)
sen_len = seq_length
emb_len = n_vocab
# which iteration of models to load
# next_it = 2
# # encoder_output, attention_weights = SelfAttention(size=50,
# # num_hops=16,
# # use_penalization=False)(x)
checkpoint_path = "models/char_att4/"
# Create checkpoint callback
cp_callback = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_accuracy',
verbose=1,
save_weights_only=True,
mode='max',
save_best_only=True)
load_model = False
# load_model = True
am = AttentionModel(checkpoint_path=checkpoint_path,
rnn_size=512,
rnn_style='CuDNNLSTM',
dropout_rate=0.3,
load_model=load_model)
# am.build_model()
am.model.compile(
optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy', perplexity, categorical_accuracy],
)
# print(am.model.summary())
am.save_config()
am.model.fit(X_train,
y_train,
batch_size=batch_size,
validation_data=(X_test, y_test),
callbacks=[cp_callback],
epochs=n_epochs)
def main():
start_time = time.time()
random.seed(42)
# os.environ["CUDA_VISIBLE_DEVICES"] = '-1'
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=int, help='Preprocess or execute the data.', default=None)
args = vars(parser.parse_args()) # Convert the arguments to a dict
if args['mode'] == 1:
train = pd.read_csv('../data/proppy_1.0.train.tsv', sep='\t', header=None)
train_processed = Preprocessing.pipeline(train[train.columns[0]])
train_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
train_processed_df['text_stem'], train_processed_df['text_join'] = train_processed
train_processed_df['label'] = train[train.columns[len(train.columns) - 1]]
train_processed_df['text'] = Preprocessing.pipeline_simple(train[train.columns[0]])
# train_processed_df['embedding'] = train_embeddings
train_processed_df.to_csv('../data/train_preprocessed.tsv', sep='\t', index=False,
index_label=False)
test = pd.read_csv('../data/proppy_1.0.test.tsv', sep='\t', header=None)
test_processed = Preprocessing.pipeline(test[test.columns[0]])
test_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
test_processed_df['text_stem'], test_processed_df['text_join'] = test_processed
test_processed_df['label'] = test[test.columns[len(test.columns) - 1]]
test_processed_df['text'] = Preprocessing.pipeline_simple(test[test.columns[0]])
# train_processed_df['embedding'] = train_embeddings
test_processed_df.to_csv('../data/test_preprocessed.tsv', sep='\t', index=False,
index_label=False)
dev = pd.read_csv('../data/proppy_1.0.dev.tsv', sep='\t', header=None)
dev_processed = Preprocessing.pipeline(dev[dev.columns[0]])
dev_processed_df = pd.DataFrame(columns=['text_stem', 'text_join', 'text', 'label'])
dev_processed_df['text_stem'], dev_processed_df['text_join'] = dev_processed
dev_processed_df['label'] = dev[dev.columns[len(dev.columns) - 1]]
dev_processed_df['text'] = Preprocessing.pipeline_simple(dev[dev.columns[0]])
# train_processed_df['embedding'] = train_embeddings
dev_processed_df.to_csv('../data/dev_preprocessed.tsv', sep='\t', index=False,
index_label=False)
elif args['mode'] == 2:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.AttentionConfig.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units'],
att_units=config['att_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
elif args['mode'] == 3:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.TrainEmbeddings.value
model = BiLSTMModel(batch_size=config['batch_size'], epochs=config['epochs'], vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'], length_type=config['length_type'],
dense_units=config['dense_units'], concat=config['concat']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
# model.fit_as_tensors(with_validation=False)
print('Previo a predict')
# model.predict_test_dev()
# print('Se guarda historial del loss:')
# model.save_plot_history()
elif args['mode'] == 4:
config = ModelConfig.SecondExperiment.value
model = BiLSTMModel(batch_size=config['batch_size'], epochs=config['epochs'], vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'], length_type=config['length_type'],
dense_units=config['dense_units'], concat=config['concat']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict()
elif args['mode'] == 5:
config = ModelConfig.BertConfig.value
model = BertModel(max_len=config['max_len'], path_train=config['path_train'], path_test=config['path_test'],
path_dev=config['path_dev'], epochs=config['epochs'], optimizer=config['optimizer'],
load_embeddings=False, batch_size=config['batch_size'],
max_sequence_len=config['max_sequence_len'],
rate=config['rate'], learning_rate=config['learning_rate'], length_type=config['length_type']
)
print('Loading the data.')
model.load_data()
print('Creating the model.')
model.call()
print('Fitting the model.')
# model.fit(with_validation=True)
print('Predict the test set.')
# model.predict()
elif args['mode'] == 6:
config = ModelConfig.TransformerConfig.value
model = TransformerModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'], dense_units=config['dense_units'],
attheads=config['attheads'], att_layers=config['att_layers']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=True)
print('Previo a predict')
model.predict()
elif args['mode'] == 7:
# Creación del modelo con embeddings de fasttext o glove
config = ModelConfig.MeanModelConfig.value
model = LocalAttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
print('Se muestra la atención:')
# model.plot_attention()
elif args['mode'] == 8:
config = ModelConfig.SecondExperiment.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=True)
print('Previo a predict')
model.predict()
elif args['mode'] == 9:
config = ModelConfig.AttentionConfig.value
model = AttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'], kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'], emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units'], both_embeddings=config['both_embeddings'],
att_units=config['att_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
elif args['mode'] == 10:
config = ModelConfig.BertConfigSecondExp.value
model = BertModel(max_len=config['max_len'], path_train=config['path_train'], path_test=config['path_test'],
epochs=config['epochs'], optimizer=config['optimizer'],
load_embeddings=False, batch_size=config['batch_size'],
max_sequence_len=config['max_sequence_len'],
rate=config['rate'], learning_rate=config['learning_rate'], length_type=config['length_type']
)
print('Loading the data.')
model.load_data()
print('Creating the model.')
model.call()
print('Fitting the model.')
model.fit(with_validation=False)
print('Predict the test set.')
model.predict()
elif args['mode'] == 11:
config = ModelConfig.SecondExperiment.value
model = LocalAttentionModel(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'], load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=None, emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data_as_tensors()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit_as_tensors(with_validation=False)
print('Previo a predict')
model.predict()
elif args['mode'] == 12:
config = ModelConfig.MeanModelConfig.value
model = LocalAttentionModelNela(batch_size=config['batch_size'], epochs=config['epochs'],
vocab_size=config['vocab_size'],
max_len=config['max_len'], filters=config['filters'],
kernel_size=config['kernel_size'],
optimizer=config['optimizer'], learning_rate=config['learning_rate'],
max_sequence_len=config['max_sequence_len'], lstm_units=config['lstm_units'],
embedding_size=config['embedding_size'],
load_embeddings=config['load_embeddings'],
pool_size=config['pool_size'], path_train=config['path_train'],
path_test=config['path_test'], path_dev=config['path_dev'],
emb_type=config['emb_type'],
buffer_size=config['buffer_size'], rate=config['rate'],
length_type=config['length_type'],
dense_units=config['dense_units']
)
model.prepare_data()
print('Building the model.')
model.call()
print('Previo a fit')
model.fit(with_validation=False)
print('Previo a predict')
model.predict_test_dev()
else:
print('No other mode implemented yed.')
elapsed_time = time.time() - start_time
print('The execution took: ' + str(elapsed_time) + ' seconds.')
print('End of execution.')
def sample(preds, temperature=1.0):
# helper function to sample an index from a probability array
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
if __name__ == '__main__':
print('-> Loading att model')
checkpoint_path = "models/char_att7/"
model = AttentionModel(checkpoint_path=checkpoint_path,
load_model=True).model
filename = "data/mixed.txt"
raw_text = open(filename, 'r', encoding='utf-8').read()
raw_text = raw_text.lower()
raw_text = re.sub('\n', " ", raw_text)[:10000]
# create mapping of unique chars to integers
chars = sorted(list(set(raw_text)))
# char_to_int = dict((c, i) for i, c in enumerate(chars))
# int_to_char = dict((i, c) for i, c in enumerate(chars))
int_to_char = {
0: ' ',
1: '!',
2: '"',
3: "'",
def eval(self):
self.max_acc = 1
self.is_training = False
with tf.Graph().as_default():
data_processor = DataProcessor()
vocab_size = data_processor.get_vocabulary_size(FLAGS.vocab_path)
vocab, revocab = DataProcessor.initialize_vocabulary(
FLAGS.vocab_path)
data_processor.get_init(FLAGS.input_training_data_path,
FLAGS.input_validation_data_path, vocab,
vocab_size, FLAGS.max_length, revocab)
models = AttentionModel()
input_q = tf.placeholder(tf.int32,
shape=(None, FLAGS.max_length),
name="input_x1") # FLAGS.train_batch_size
input_ap = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
input_an = tf.placeholder(tf.int32, shape=(None, FLAGS.max_length))
q_encode = models.embed(
inputs=input_q,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units) # embedding size plus 1 for padding
ap_encode = models.embed(inputs=input_ap,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units)
an_encode = models.embed(inputs=input_an,
vocab_size=vocab_size + 1,
num_units=hp.hidden_units)
# multihead blocks
for i in range(hp.num_blocks):
with tf.variable_scope("num_blocks_{}".format(i)):
q_encode = models.multihead_attention(
query=q_encode,
key=q_encode,
value=q_encode,
num_heads=hp.num_heads,
mask_future=False)
q_encode = models.feed_forward(
q_encode, units=[hp.hidden_units * 4, hp.hidden_units])
ap_encode = models.multihead_attention(
query=ap_encode,
key=ap_encode,
value=ap_encode,
num_heads=hp.num_heads,
mask_future=False)
ap_encode = models.feed_forward(
ap_encode,
units=[hp.hidden_units * 4, hp.hidden_units])
an_encode = models.multihead_attention(
query=an_encode,
key=an_encode,
value=an_encode,
num_heads=hp.num_heads,
mask_future=False)
an_encode = models.feed_forward(
an_encode,
units=[hp.hidden_units * 4, hp.hidden_units])
## output layer
with tf.name_scope('output_layer'):
dims = q_encode.get_shape().as_list()
q_encode = tf.reshape(q_encode, [-1, dims[1] * dims[2]])
ap_encode = tf.reshape(ap_encode, [-1, dims[1] * dims[2]])
an_encode = tf.reshape(an_encode, [-1, dims[1] * dims[2]])
weight = tf.get_variable(
'output_weight',
[q_encode.get_shape().as_list()[-1], hp.hidden_units])
q_encode = tf.matmul(q_encode, weight)
ap_encode = tf.matmul(ap_encode, weight)
an_encode = tf.matmul(an_encode, weight)
q_encode = models.vec_normalize(q_encode)
ap_encode = models.vec_normalize(ap_encode)
an_encode = models.vec_normalize(an_encode)
## calculate similarity and loss
cos_12 = tf.reduce_sum(tf.multiply(q_encode, ap_encode),
1) # wisely multiple vectors
cos_13 = tf.reduce_sum(tf.multiply(q_encode, an_encode), 1)
zero = tf.constant(0,
shape=[FLAGS.train_batch_size],
dtype=tf.float32)
margin = tf.constant(FLAGS.loss_margin,
shape=[FLAGS.train_batch_size],
dtype=tf.float32)
losses = tf.maximum(
zero, tf.subtract(margin, tf.subtract(cos_12, cos_13)))
loss_sum = tf.reduce_sum(losses)
loss_avg = tf.div(loss_sum, FLAGS.train_batch_size)
correct = tf.equal(zero, losses)
accuracy = tf.reduce_mean(tf.cast(correct, "float"),
name="accuracy")
global_step = tf.Variable(
0, name="global_step", trainable=False
) # The global step will be automatically incremented by one every time you execute a train loop
optimizer = tf.train.GradientDescentOptimizer(FLAGS.learning_rate)
# optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(loss_avg)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
saver = tf.train.Saver(tf.global_variables())
# session start point
with tf.Session() as session:
session.run(tf.local_variables_initializer())
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
# Load pre-trained model
ckpt = tf.train.get_checkpoint_state(
FLAGS.input_previous_model_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(session, ckpt.model_checkpoint_path)
print("Load Model From ", ckpt.model_checkpoint_path)
else:
print("No model found and exit.")
exit()
print(
'\n============================> begin to evaluate model. '
)
eval_size = FLAGS.evaluation_size
def evaluate_all():
correct_num = int(0)
batches = data_processor.loadValData_step(
vocab,
vocab_size,
FLAGS.input_validation_data_path,
FLAGS.max_length,
eval_size=0) # batch_size*seq_len
for i in range(eval_size):
# 显示/保存测试数据
# save_test_data(batch_y1, batch_y2, label_list)
batch_y1, batch_y2, label_list = batches[i]
correct_flag = test_step(batch_y1, batch_y2, batch_y2,
label_list, session)
correct_num += correct_flag
if (correct_flag == 1):
print('step %d ==== correct prediction' % i)
else:
print('step %d ==== wrong prediction' % i)
print('correct_num', correct_num)
acc = correct_num / float(eval_size)
return acc
def test_step(input_y1, input_y2, input_y3, label_list, sess):
feed_dict = {
input_q: input_y1,
input_ap: input_y2,
input_an: input_y3
}
correct_flag = 0
cos_12_ = sess.run(cos_12, feed_dict)
cos_max = max(cos_12_)
index_max = list(cos_12_).index(cos_max)
if label_list[index_max] == '1':
correct_flag = 1
return correct_flag
def evaluate(eval_size):
correct_num = int(0)
batches = data_processor.loadValData_step(
vocab, vocab_size, FLAGS.input_validation_data_path,
FLAGS.max_length, eval_size) # batch_size*seq_len
for i in range(eval_size):
# 显示/保存测试数据
# save_test_data(batch_y1, batch_y2, label_list)
batch_y1, batch_y2, label_list = batches[i]
correct_flag = test_step(batch_y1, batch_y2, batch_y2,
label_list, session)
correct_num += correct_flag
if (correct_flag == 1):
print('step %d ==== correct prediction' % i)
else:
print('step %d ==== wrong prediction' % i)
print('correct_num', correct_num)
acc = correct_num / float(eval_size)
return acc
acc_ = evaluate(eval_size=eval_size)
print(
'--------The test result among the test data sets: acc = {0}, test size = {1}----------'
.format(acc_, eval_size))
exit()
json.dump(vars(opts), f, indent=True)
# Load data from load_path
load_data = {}
if opts.load_path is not None:
print(' [*] Loading data from {}'.format(opts.load_path))
load_data = torch.load(
opts.load_path,
map_location=lambda storage, loc: storage) # Load on CPU
# Initialize model
model = maybe_cuda_model(
AttentionModel(opts.embedding_dim,
opts.hidden_dim,
problem,
n_encode_layers=opts.n_encode_layers,
mask_inner=True,
mask_logits=True,
normalization=opts.normalization,
tanh_clipping=opts.tanh_clipping), opts.use_cuda)
# Overwrite model parameters by parameters to load
model.load_state_dict({**model.state_dict(), **load_data.get('model', {})})
# Initialize baseline
if opts.baseline == 'exponential':
baseline = ExponentialBaseline(opts.exp_beta)
elif opts.baseline == 'critic':
baseline = CriticBaseline(
maybe_cuda_model(
CriticNetwork(problem.NODE_DIM, opts.embedding_dim,
opts.hidden_dim, opts.n_encode_layers,
wordlist = Word2VecUtil.review_to_wordlist(review)
train_x.append(' '.join(wordlist))
return train_x
if __name__ == '__main__':
test_df = pd.read_csv("data/testData.tsv", delimiter="\t", quoting=3)
src_vocab_table = lookup_ops.index_table_from_file('data/vocab.txt',
default_value=0)
test_x = load_data(test_df)
test_dataset = tf.contrib.data.Dataset.from_tensor_slices(test_x)
test_iterator = infer_iterator(test_dataset,
src_vocab_table,
BATCH_SIZE,
max_length=500)
attention_model = AttentionModel(None, test_iterator, 4, 200, 200, False)
sess_config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
attention_model.load_model(sess, "model/attention_model.ckpt")
sess.run(tf.tables_initializer())
sess.run(test_iterator.initializer)
test_preds = []
while True:
try:
batch_preds = attention_model.test_infer(sess)
test_preds.append(batch_preds)
except tf.errors.OutOfRangeError:
test_preds = (np.concatenate(test_preds, axis=0))[:, 1]
submission = pd.DataFrame({
