def prepare(self, loc_count, user_count, hidden_size, gru_factory, device):
self.hidden_size = hidden_size
if self.use_user_embedding:
self.model = RNN_user(loc_count, user_count, hidden_size,
gru_factory).to(device)
else:
self.model = RNN(loc_count, hidden_size, gru_factory).to(device)
def load_checkpoint(filename, dir=GOOGLE_COLAB_CHECKPOINT_DIR):
checkpoint = torch.load(os.path.join(dir, filename))
model = RNN(hidden_size=checkpoint['hidden_size'],
output_size=checkpoint['output_size'],
n_layers=checkpoint['n_layers'],
batch_size=checkpoint['batch_size'],
bidirectional=checkpoint['bidirectional'])
model.load_state_dict(checkpoint['state_dict'])
model.num_epochs_trained = checkpoint['num_epochs_trained']
return model
def main():
with open('data/wsj00-18.pos') as f:
training_data = f.readlines()
with open('data/wsj19-21.pos') as f:
test_data = f.readlines()
model = word2vec.KeyedVectors.load_word2vec_format(
'./data/GoogleNews-vectors-negative300.bin', binary=True)
print(model.wv.most_similar(positive=['woman', 'king'], negative=['man']))
# print(model['hgoehgoehgoehg'])
# print(len(model['hogehgoehgoe']))
labels = ('NNP', ',', 'CD', 'NNS', 'JJ', 'MD', 'VB', 'DT', 'NN', 'IN', '.',
'VBZ', 'VBG', 'CC', 'VBD', 'VBN', 'RB', 'TO', 'PRP', 'RBR',
'WDT', 'VBP', 'RP', 'PRP$', 'JJS', 'POS', '``', 'EX', "''", 'WP',
':', 'JJR', 'WRB', '$', 'NNPS', 'WP$', '-LRB-', '-RRB-', 'PDT',
'RBS', 'FW', 'UH', 'SYM', 'LS', '#')
rnn = RNN(300, 1000, labels)
training_vector_data = [line for line in training_data]
test_vector_data = [line for line in test_data]
manager = NetworkEvaluator(rnn, training_vector_data, test_vector_data)
def run(checkpoint=None, dir=CHECKPOINT_DIR):
gc.collect()
batch_size = 1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# torch.autograd.set_detect_anomaly(True)
clean_train_loader = DataLoader(clean_train_data,
batch_size=batch_size,
shuffle=True,
num_workers=2,
pin_memory=True)
clean_test_loader = DataLoader(clean_test_data,
batch_size=batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
if checkpoint:
model = load_checkpoint(checkpoint, dir=dir).to(device)
else:
model = RNN(hidden_size=80,
output_size=5,
n_layers=2,
batch_size=batch_size,
bidirectional=False).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss = torch.nn.CrossEntropyLoss()
losses = train(clean_train_loader,
clean_test_loader,
500,
model,
optimizer,
loss,
device,
checkpoint_dir=CHECKPOINT_DIR)
return model, losses
class BprTrainer(Trainer):
def greeter(self):
if not self.use_user_embedding:
return 'Use BPR training.'
return 'Use BPR training with user embeddings.'
def prepare(self, loc_count, user_count, hidden_size, gru_factory, device):
self.hidden_size = hidden_size
if self.use_user_embedding:
self.model = RNN_user(loc_count, user_count, hidden_size,
gru_factory).to(device)
else:
self.model = RNN(loc_count, hidden_size, gru_factory).to(device)
def evaluate(self, x, t, s, y_t, y_s, h, active_users):
seq_length = x.shape[0]
user_length = x.shape[1]
out, h = self.model(x, h, active_users)
out_t = out.transpose(0, 1)
response = []
Q = self.model.encoder.weight
for j in range(user_length):
out_j = out_t[j].transpose(0, 1)
o = torch.matmul(Q, out_j).cpu().detach()
o = o.transpose(0, 1)
o = o.contiguous().view(seq_length, -1)
response.append(o)
return response, h
def loss(self, x, t, s, y, y_t, y_s, h, active_users):
out, h = self.model(x, h, active_users)
y_emb = self.model.encoder(y)
# reshape
out = out.view(-1, self.hidden_size)
out_t = out.transpose(0, 1)
y_emb = y_emb.contiguous().view(-1, self.hidden_size)
Q = self.model.encoder.weight
neg_o = torch.matmul(Q, out_t)
pos_o = torch.matmul(y_emb, out_t).diag()
l = torch.log(1 + torch.exp(-(pos_o - neg_o)))
l = torch.mean(l)
return l, h
class RESTService:
def __init__(self, host='0.0.0.0', port=8080):
self._host = host
self._port = port
self._model = RNN()
self._app = Bottle()
self._route()
def _route(self):
self._app.route('/predict', method="POST", callback=self._predict)
def _predict(self):
data = request.json
category = self._model.predict(data)
response.content_type = 'text/plain'
return str(category)
def start(self):
self._app.run(host=self._host, port=self._port)
def setUpClass(cls):
cls.W_in = np.eye(2)
cls.W_rec = np.eye(2)
cls.W_out = np.eye(2)
cls.W_FB = -np.ones((2, 2)) + np.eye(2)
cls.b_rec = np.zeros(2)
cls.b_out = np.zeros(2)
cls.rnn = RNN(cls.W_in,
cls.W_rec,
cls.W_out,
cls.b_rec,
cls.b_out,
activation=identity,
alpha=1,
output=softmax,
loss=softmax_cross_entropy)
cls.rnn.a = np.ones(2)
cls.rnn.error = np.ones(2) * 0.5
def test_mimic_task(self):
"""Verifies that the proper RNN output is returned as label in a simple
case where the RNN simply counts the number of time steps."""
from network import RNN
from functions import identity, mean_squared_error
n_in = 2
n_h = 2
n_out = 2
W_in_target = np.eye(n_in)
W_rec_target = np.eye(n_h)
W_out_target = np.eye(n_out)
b_rec_target = np.zeros(n_h)
b_out_target = np.zeros(n_out)
alpha = 1
rnn_target = RNN(W_in_target,
W_rec_target,
W_out_target,
b_rec_target,
b_out_target,
activation=identity,
alpha=alpha,
output=identity,
loss=mean_squared_error)
task = Mimic_RNN(rnn_target, p_input=1, tau_task=1)
data = task.gen_data(100, 0)
y = np.arange(1, 101)
y_correct = np.array([y, y]).T
self.assertTrue(np.isclose(data['train']['Y'], y_correct).all())
def train_rnn(file, batch_size, layers, learning_rate, dropout, num_steps,
cell_size, epochs, cell, test_seed, delim, save):
""" Train neural network """
model_name = "cell-{}-size-{}-batch-{}-steps-{}-layers-{}-lr-{}-dropout-{}".format(
cell, cell_size, batch_size, num_steps, layers, learning_rate, dropout)
ds = Dataset(file,
batch_size=batch_size,
num_steps=num_steps,
with_delim=delim)
n = RNN(data=ds,
cell=cell,
num_layers=layers,
dropout=dropout,
learning_rate=learning_rate,
cell_size=cell_size,
num_epochs=epochs)
n.train(save=save,
model_name=model_name,
test_output=True,
test_seed=test_seed,
with_delim=delim)
if save:
n.save(model_name)
def __init__(self, host='0.0.0.0', port=8080):
self._host = host
self._port = port
self._model = RNN()
self._app = Bottle()
self._route()
from torch.utils.data import DataLoader
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
from dataset import dataset, dataset2
from network import RNN
import os
model_dir = '/home/lixiaoyu/project/airQuality/Analysis-of-Air-Quality-and-Outpatient-Quantity/ckpt/'
TIME_STEP = 120
INPUT_SIZE = 7
HIDDEN_SIZE = 32
LR = 0.01
EPOCH = 1000
rnn = RNN(INPUT_SIZE=INPUT_SIZE, HIDDEN_SIZE=HIDDEN_SIZE)
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)
loss_func = nn.MSELoss()
LoadModel = False
def train(train_loader, num_e):
torch.manual_seed(1)
if LoadModel:
checkpoint = torch.load(model_dir + '{}.ckpt'.format(num_e))
rnn.load_state_dict(checkpoint['state_dict'])
print('Loading model~~~~~~~~~~', num_e)
for e in range(EPOCH):
print('epoch>>>>>>> ', e)
def test(word2vec, dataset, parameters, loadpath):
print "1"
device_string = "/gpu:{}".format(
parameters["gpu"]) if parameters["gpu"] else "/cpu:0"
with tf.device(device_string):
print "2"
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config_proto = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
sess = tf.Session(config=config_proto)
premises_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="premises")
hypothesis_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="hypothesis")
targets_ph = tf.placeholder(tf.int32, shape=[None], name="targets")
keep_prob_ph = tf.placeholder(tf.float32, name="keep_prob")
_projecter = TensorFlowTrainable()
projecter = _projecter.get_4Dweights(
filter_height=1,
filter_width=parameters["embedding_dim"],
in_channels=1,
out_channels=parameters["num_units"],
name="projecter")
with tf.variable_scope(name_or_scope="premise"):
premise = RNN(cell=LSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
projecter=projecter,
keep_prob=keep_prob_ph)
premise.process(sequence=premises_ph)
with tf.variable_scope(name_or_scope="hypothesis"):
hypothesis = RNN(cell=AttentionLSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
hiddens=premise.hiddens,
states=premise.states,
projecter=projecter,
keep_prob=keep_prob_ph)
hypothesis.process(sequence=hypothesis_ph)
loss, loss_summary, accuracy, accuracy_summary = hypothesis.loss(
targets=targets_ph)
loader = tf.train.Saver()
loader.restore(sess, loadpath)
batcher = Batcher(word2vec=word2vec, settings=parameters)
test_batches = batcher.batch_generator(
dataset=dataset["test"],
num_epochs=1,
batch_size=parameters["batch_size"]["test"],
sequence_length=parameters["sequence_length"])
print "2.5"
for test_step, (test_batch, _) in enumerate(test_batches):
print "3"
feed_dict = {
premises_ph: np.transpose(test_batch["premises"], (1, 0, 2)),
hypothesis_ph: np.transpose(test_batch["hypothesis"],
(1, 0, 2)),
targets_ph: test_batch["targets"],
keep_prob_ph: 1.,
}
test_loss, test_accuracy = sess.run([loss, accuracy],
feed_dict=feed_dict)
print "\nTEST | loss={0:.2f}, accuracy={1:.2f}% ".format(
test_loss, 100. * test_accuracy)
print ""
training_loader = DataLoader(training_set, args.batch_size, shuffle=True)
validation_data, validation_labels = joblib.load('%s/validation-%d.data' % (args.datapath, args.n))
validation_data = th.from_numpy(validation_data)
validation_labels = onehot_sequence(th.from_numpy(validation_labels), 10, cuda)
validation_set = TensorDataset(validation_data, validation_labels)
validation_loader = DataLoader(validation_set, args.batch_size)
test_data, test_labels = joblib.load('%s/test-%d.data' % (args.datapath, args.n))
test_data = th.from_numpy(test_data)
test_labels = onehot_sequence(th.from_numpy(test_labels), 10, cuda)
test_set = TensorDataset(test_data, test_labels)
test_loader = DataLoader(test_set, args.batch_size)
cnn_path = args.pretrained_cnn_path if args.pretrained_cnn else None
model = RNN(args.n_units, 10, cnn_path, cuda)
if args.gpu > -1:
model.cuda()
if args.criterion == 'regression_loss':
from criterions import regression_loss
criterion = regression_loss(args.entropy_scale)
else:
criterion = getattr(__import__('criterions'), args.criterion)()
if args.gpu > -1:
criterion.cuda()
optimizer = Adam(model.parameters(), lr=1e-3)
vis = visdom.Visdom()
tb_path = args.tensorboard_path
if args.tensorboard_log:
tb_path += '/%s' % args.tensorboard_log
TensorboardVisualizer.configure(tb_path)
def train(word2vec, dataset, parameters):
modeldir = os.path.join(parameters["runs_dir"], parameters["model_name"])
if not os.path.exists(modeldir):
os.mkdir(modeldir)
logdir = os.path.join(modeldir, "log")
if not os.path.exists(logdir):
os.mkdir(logdir)
logdir_train = os.path.join(logdir, "train")
if not os.path.exists(logdir_train):
os.mkdir(logdir_train)
logdir_test = os.path.join(logdir, "test")
if not os.path.exists(logdir_test):
os.mkdir(logdir_test)
logdir_dev = os.path.join(logdir, "dev")
if not os.path.exists(logdir_dev):
os.mkdir(logdir_dev)
savepath = os.path.join(modeldir, "save")
device_string = "/gpu:{}".format(parameters["gpu"]) if parameters["gpu"] else "/cpu:0"
with tf.device(device_string):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config_proto = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
sess = tf.Session(config=config_proto)
premises_ph = tf.placeholder(tf.float32, shape=[parameters["sequence_length"], None, parameters["embedding_dim"]], name="premises")
hypothesis_ph = tf.placeholder(tf.float32, shape=[parameters["sequence_length"], None, parameters["embedding_dim"]], name="hypothesis")
targets_ph = tf.placeholder(tf.int32, shape=[None], name="targets")
keep_prob_ph = tf.placeholder(tf.float32, name="keep_prob")
_projecter = TensorFlowTrainable()
projecter = _projecter.get_4Dweights(filter_height=1, filter_width=parameters["embedding_dim"], in_channels=1, out_channels=parameters["num_units"], name="projecter")
optimizer = tf.train.AdamOptimizer(learning_rate=parameters["learning_rate"], name="ADAM", beta1=0.9, beta2=0.999)
with tf.variable_scope(name_or_scope="premise"):
premise = RNN(cell=LSTMCell, num_units=parameters["num_units"], embedding_dim=parameters["embedding_dim"], projecter=projecter, keep_prob=keep_prob_ph)
premise.process(sequence=premises_ph)
with tf.variable_scope(name_or_scope="hypothesis"):
hypothesis = RNN(cell=AttentionLSTMCell, num_units=parameters["num_units"], embedding_dim=parameters["embedding_dim"], hiddens=premise.hiddens, states=premise.states, projecter=projecter, keep_prob=keep_prob_ph)
hypothesis.process(sequence=hypothesis_ph)
loss, loss_summary, accuracy, accuracy_summary = hypothesis.loss(targets=targets_ph)
weight_decay = tf.reduce_sum([tf.reduce_sum(parameter) for parameter in premise.parameters + hypothesis.parameters])
global_loss = loss + parameters["weight_decay"] * weight_decay
train_summary_op = tf.merge_summary([loss_summary, accuracy_summary])
train_summary_writer = tf.train.SummaryWriter(logdir_train, sess.graph)
test_summary_op = tf.merge_summary([loss_summary, accuracy_summary])
test_summary_writer = tf.train.SummaryWriter(logdir_test)
saver = tf.train.Saver(max_to_keep=10)
summary_writer = tf.train.SummaryWriter(logdir)
tf.train.write_graph(sess.graph_def, modeldir, "graph.pb", as_text=False)
loader = tf.train.Saver(tf.all_variables())
optimizer = tf.train.AdamOptimizer(learning_rate=parameters["learning_rate"], name="ADAM", beta1=0.9, beta2=0.999)
train_op = optimizer.minimize(global_loss)
sess.run(tf.initialize_all_variables())
batcher = Batcher(word2vec=word2vec)
train_batches = batcher.batch_generator(dataset=dataset["train"], num_epochs=parameters["num_epochs"], batch_size=parameters["batch_size"]["train"], sequence_length=parameters["sequence_length"])
num_step_by_epoch = int(math.ceil(len(dataset["train"]["targets"]) / parameters["batch_size"]["train"]))
for train_step, (train_batch, epoch) in enumerate(train_batches):
feed_dict = {
premises_ph: np.transpose(train_batch["premises"], (1, 0, 2)),
hypothesis_ph: np.transpose(train_batch["hypothesis"], (1, 0, 2)),
targets_ph: train_batch["targets"],
keep_prob_ph: parameters["keep_prob"],
}
_, summary_str, train_loss, train_accuracy = sess.run([train_op, train_summary_op, loss, accuracy], feed_dict=feed_dict)
train_summary_writer.add_summary(summary_str, train_step)
if train_step % 100 == 0:
sys.stdout.write("\rTRAIN | epoch={0}/{1}, step={2}/{3} | loss={4:.2f}, accuracy={5:.2f}% ".format(epoch + 1, parameters["num_epochs"], train_step % num_step_by_epoch, num_step_by_epoch, train_loss, 100. * train_accuracy))
sys.stdout.flush()
if train_step % 5000 == 0:
test_batches = batcher.batch_generator(dataset=dataset["test"], num_epochs=1, batch_size=parameters["batch_size"]["test"], sequence_length=parameters["sequence_length"])
for test_step, (test_batch, _) in enumerate(test_batches):
feed_dict = {
premises_ph: np.transpose(test_batch["premises"], (1, 0, 2)),
hypothesis_ph: np.transpose(test_batch["hypothesis"], (1, 0, 2)),
targets_ph: test_batch["targets"],
keep_prob_ph: 1.,
}
summary_str, test_loss, test_accuracy = sess.run([test_summary_op, loss, accuracy], feed_dict=feed_dict)
print"\nTEST | loss={0:.2f}, accuracy={1:.2f}% ".format(test_loss, 100. * test_accuracy)
print ""
test_summary_writer.add_summary(summary_str, train_step)
break
if train_step % 5000 == 0:
saver.save(sess, save_path=savepath, global_step=train_step)
print ""
def train(word2vec, dataset, parameters):
modeldir = os.path.join(parameters["runs_dir"], parameters["model_name"])
if not os.path.exists(modeldir):
os.mkdir(modeldir)
logdir = os.path.join(modeldir, "log")
if not os.path.exists(logdir):
os.mkdir(logdir)
logdir_train = os.path.join(logdir, "train")
if not os.path.exists(logdir_train):
os.mkdir(logdir_train)
logdir_test = os.path.join(logdir, "test")
if not os.path.exists(logdir_test):
os.mkdir(logdir_test)
logdir_dev = os.path.join(logdir, "dev")
if not os.path.exists(logdir_dev):
os.mkdir(logdir_dev)
savepath = os.path.join(modeldir, "save")
device_string = "/gpu:{}".format(
parameters["gpu"]) if parameters["gpu"] else "/cpu:0"
with tf.device(device_string):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config_proto = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
sess = tf.Session(config=config_proto)
premises_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="premises")
hypothesis_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="hypothesis")
targets_ph = tf.placeholder(tf.int32, shape=[None], name="targets")
keep_prob_ph = tf.placeholder(tf.float32, name="keep_prob")
_projecter = TensorFlowTrainable()
projecter = _projecter.get_4Dweights(
filter_height=1,
filter_width=parameters["embedding_dim"],
in_channels=1,
out_channels=parameters["num_units"],
name="projecter")
# optimizer = tf.train.AdamOptimizer(learning_rate=parameters["learning_rate"], name="ADAM", beta1=0.9, beta2=0.999)
with tf.variable_scope(name_or_scope="premise"):
premise = RNN(cell=LSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
projecter=projecter,
keep_prob=keep_prob_ph)
premise.process(sequence=premises_ph)
with tf.variable_scope(name_or_scope="hypothesis"):
hypothesis = RNN(cell=AttentionLSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
hiddens=premise.hiddens,
states=premise.states,
projecter=projecter,
keep_prob=keep_prob_ph)
hypothesis.process(sequence=hypothesis_ph)
loss, loss_summary, accuracy, accuracy_summary = hypothesis.loss(
targets=targets_ph)
weight_decay = tf.reduce_sum([
tf.reduce_sum(parameter)
for parameter in premise.parameters + hypothesis.parameters
])
global_loss = loss + parameters["weight_decay"] * weight_decay
train_summary_op = tf.merge_summary([loss_summary, accuracy_summary])
# train_summary_op = tf.summary.merge([loss_summary, accuracy_summary])
train_summary_writer = tf.train.SummaryWriter(logdir_train, sess.graph)
# train_summary_writer = tf.summary.FileWriter(logdir_train, sess.graph)
# test_summary_op = tf.merge_summary([loss_summary, accuracy_summary])
dev_summary_op = tf.merge_summary([loss_summary, accuracy_summary])
# test_summary_writer = tf.train.SummaryWriter(logdir_test)
dev_summary_writer = tf.train.SummaryWriter(logdir_dev)
saver = tf.train.Saver(max_to_keep=10)
# summary_writer = tf.train.SummaryWriter(logdir)
tf.train.write_graph(sess.graph_def,
modeldir,
"graph.pb",
as_text=False)
optimizer = tf.train.AdamOptimizer(
learning_rate=parameters["learning_rate"],
name="ADAM",
beta1=0.9,
beta2=0.999)
train_op = optimizer.minimize(global_loss)
sess.run(tf.initialize_all_variables())
# sess.run(tf.global_variables_initializer())
batcher = Batcher(word2vec=word2vec, settings=parameters)
#train_split = "train"
#train_batches = batcher.batch_generator(dataset=dataset[train_split], num_epochs=parameters["num_epochs"],
# batch_size=parameters["batch_size"]["train"],
# sequence_length=parameters["sequence_length"])
#print("train data size: %d" % len(dataset["train"]["targets"]))
#num_step_by_epoch = int(math.ceil(len(dataset[train_split]["targets"]) / parameters["batch_size"]["train"]))
#best_dev_accuracy = 0
print("train data size: %d" % len(dataset["train"]["targets"]))
best_dev_accuracy = 0.0
total_loss = 0.0
timestamp = time.time()
for epoch in range(parameters["num_epochs"]):
print("epoch %d" % epoch)
train_batches = batcher.batch_generator(
dataset=dataset["train"],
num_epochs=1,
batch_size=parameters["batch_size"]["train"],
sequence_length=parameters["sequence_length"])
steps = len(dataset["train"]
["targets"]) / parameters["batch_size"]["train"]
# progress bar http://stackoverflow.com/a/3002114
bar = progressbar.ProgressBar(maxval=steps / 10 + 1,
widgets=[
progressbar.Bar('=', '[', ']'),
' ',
progressbar.Percentage()
])
bar.start()
for step, (train_batch, train_epoch) in enumerate(train_batches):
feed_dict = {
premises_ph:
np.transpose(train_batch["premises"], (1, 0, 2)),
hypothesis_ph:
np.transpose(train_batch["hypothesis"], (1, 0, 2)),
targets_ph:
train_batch["targets"],
keep_prob_ph:
parameters["keep_prob"],
}
_, summary_str, train_loss, train_accuracy = sess.run(
[train_op, train_summary_op, loss, accuracy],
feed_dict=feed_dict)
total_loss += train_loss
train_summary_writer.add_summary(summary_str, step)
if step % 100 == 0: # eval 1 random dev batch
# eval 1 random dev batch
dev_batches = batcher.batch_generator(
dataset=dataset["dev"],
num_epochs=1,
batch_size=parameters["batch_size"]["dev"],
sequence_length=parameters["sequence_length"])
for dev_step, (dev_batch, _) in enumerate(dev_batches):
feed_dict = {
premises_ph:
np.transpose(dev_batch["premises"], (1, 0, 2)),
hypothesis_ph:
np.transpose(dev_batch["hypothesis"], (1, 0, 2)),
targets_ph:
dev_batch["targets"],
keep_prob_ph:
1.,
}
summary_str, dev_loss, dev_accuracy = sess.run(
[dev_summary_op, loss, accuracy],
feed_dict=feed_dict)
dev_summary_writer.add_summary(summary_str, step)
break
bar.update(step / 10 + 1)
bar.finish()
# eval on all dev
dev_batches = batcher.batch_generator(
dataset=dataset["dev"],
num_epochs=1,
batch_size=len(dataset["dev"]["targets"]),
sequence_length=parameters["sequence_length"])
dev_accuracy = 0
for dev_step, (dev_batch, _) in enumerate(dev_batches):
feed_dict = {
premises_ph: np.transpose(dev_batch["premises"],
(1, 0, 2)),
hypothesis_ph: np.transpose(dev_batch["hypothesis"],
(1, 0, 2)),
targets_ph: dev_batch["targets"],
keep_prob_ph: 1.,
}
summary_str, dev_loss, dev_accuracy = sess.run(
[dev_summary_op, loss, accuracy], feed_dict=feed_dict)
print "\nDEV full | loss={0:.2f}, accuracy={1:.2f}% ".format(
dev_loss, 100. * dev_accuracy)
print ""
if dev_accuracy > best_dev_accuracy:
saver.save(sess,
save_path=savepath + '_best',
global_step=(epoch + 1) * steps)
break
saver.save(sess,
save_path=savepath,
global_step=(epoch + 1) * steps)
current_time = time.time()
print("Iter %3d Loss %-8.3f Dev Acc %-6.2f Time %-5.2f at %s" %
(epoch, total_loss, dev_accuracy,
(current_time - timestamp) / 60.0,
str(datetime.datetime.now())))
total_loss = 0.0
print ""
""" make predictions using the network
"""
import torch
import torch.nn
import os
import dataset
from network import RNN
dataset = dataset.Dataset()
max_length = 20
rnn = RNN(dataset.n_letters, 128, dataset.n_letters, dataset.n_categories)
rnn.eval()
# load weights
if os.path.exists("models/gen_names.pkl"):
checkpoint = torch.load("models/gen_names.pkl")
rnn.load_state_dict(checkpoint['nn_state_dict'])
print("checkpoint loaded")
def sample(category, start_char):
with torch.no_grad():
category_tensor_var = dataset.category_tensor(category)
input = dataset.input_tensor(start_char)
hidden = rnn.init_hidden()
output_name = start_char
def test_small_lr_case(self):
alpha = 1
self.rnn_1 = RNN(self.W_in,
self.W_rec,
self.W_out,
self.b_rec,
self.b_out,
activation=tanh,
alpha=alpha,
output=softmax,
loss=softmax_cross_entropy)
self.rnn_2 = RNN(self.W_in,
self.W_rec,
self.W_out,
self.b_rec,
self.b_out,
activation=tanh,
alpha=alpha,
output=softmax,
loss=softmax_cross_entropy)
self.rnn_3 = RNN(self.W_in,
self.W_rec,
self.W_out,
self.b_rec,
self.b_out,
activation=tanh,
alpha=alpha,
output=softmax,
loss=softmax_cross_entropy)
lr = 0.00001
self.optimizer_1 = Stochastic_Gradient_Descent(lr=lr)
self.learn_alg_1 = RTRL(self.rnn_1)
self.optimizer_2 = Stochastic_Gradient_Descent(lr=lr)
self.learn_alg_2 = Future_BPTT(self.rnn_2, 25)
self.optimizer_3 = Stochastic_Gradient_Descent(lr=lr)
self.learn_alg_3 = Efficient_BPTT(self.rnn_3, 100)
monitors = []
np.random.seed(1)
self.sim_1 = Simulation(self.rnn_1)
self.sim_1.run(self.data,
learn_alg=self.learn_alg_1,
optimizer=self.optimizer_1,
monitors=monitors,
verbose=False)
np.random.seed(1)
self.sim_2 = Simulation(self.rnn_2)
self.sim_2.run(self.data,
learn_alg=self.learn_alg_2,
optimizer=self.optimizer_2,
monitors=monitors,
verbose=False)
np.random.seed(1)
self.sim_3 = Simulation(self.rnn_3)
self.sim_3.run(self.data,
learn_alg=self.learn_alg_3,
optimizer=self.optimizer_3,
monitors=monitors,
verbose=False)
#Assert networks learned similar weights with a small tolerance.
assert_allclose(self.rnn_1.W_rec, self.rnn_2.W_rec, atol=1e-4)
assert_allclose(self.rnn_2.W_rec, self.rnn_3.W_rec, atol=1e-4)
#But that there was some difference from initialization
self.assertFalse(
np.isclose(self.rnn_1.W_rec, self.W_rec, atol=1e-4).all())
from options import options
options = options()
opts = options.parse()
#data loader
data_loader = data.dataloader(opts)
train_loader = util.create_dataset(
[data_loader.train_data, data_loader.train_label], data_loader.wordIdx,
data_loader.labelIdx, opts)
from network import RNN
from train import train
from test import test
'''RNN model'''
RNN = RNN(opts, data_loader.wordIdx, data_loader.labelIdx,
len(data_loader.labelIdx.items())).to(device)
if opts.print_model:
print(RNN)
'''Optimizers'''
import torch.optim as optim
RNN_optim = optim.Adam(RNN.parameters(),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
'''run training'''
trainer = train(opts, RNN, RNN_optim, train_loader)
trainer.trainer()
test_loader = util.create_dataset(
[data_loader.test_data, data_loader.test_label], data_loader.wordIdx,
def test_kernl_reduce_rflo(self):
"""Verifies that KeRNL reduces to RFLO in special case.
If beta is initialized to the identity while the gammas are all
initialized to the network inverse time constant alpha, and the KeRNL
optimizer has 0 learning rate (i.e. beta and gamma do not change), then
KeRNL should produce the same gradients as RFLO if the approximate
KeRNL of (1 - alpha) (rather than exp(-alpha)) is used."""
self.task = Add_Task(4, 6, deterministic=True, tau_task=2)
self.data = self.task.gen_data(100, 0)
alpha = 0.3
self.rnn_1 = RNN(self.W_in,
self.W_rec,
self.W_out,
self.b_rec,
self.b_out,
activation=tanh,
alpha=alpha,
output=softmax,
loss=softmax_cross_entropy)
self.rnn_2 = RNN(self.W_in,
self.W_rec,
self.W_out,
self.b_rec,
self.b_out,
activation=tanh,
alpha=alpha,
output=softmax,
loss=softmax_cross_entropy)
#RFLO
np.random.seed(1)
self.optimizer_1 = Stochastic_Gradient_Descent(lr=0.001)
self.learn_alg_1 = RFLO(self.rnn_1, alpha)
#KeRNL with beta and gamma fixed to RFLO values
np.random.seed(1)
self.optimizer_2 = Stochastic_Gradient_Descent(lr=0.001)
self.KeRNL_optimizer = Stochastic_Gradient_Descent(lr=0)
A = np.eye(self.rnn_2.n_h)
alpha_i = np.ones(self.rnn_2.n_h) * alpha
self.learn_alg_2 = KeRNL(self.rnn_2,
self.KeRNL_optimizer,
A=A,
alpha=alpha_i)
monitors = []
np.random.seed(2)
self.sim_1 = Simulation(self.rnn_1)
self.sim_1.run(self.data,
learn_alg=self.learn_alg_1,
optimizer=self.optimizer_1,
monitors=monitors,
verbose=False)
np.random.seed(2)
self.sim_2 = Simulation(self.rnn_2)
self.sim_2.run(self.data,
learn_alg=self.learn_alg_2,
optimizer=self.optimizer_2,
monitors=monitors,
verbose=False)
#Assert networks learned the same weights
assert_allclose(self.rnn_1.W_rec, self.rnn_2.W_rec)
#Assert networks' parameters changed appreciably, despite a large
#tolerance for closeness.
self.assertFalse(np.isclose(self.W_rec, self.rnn_2.W_rec).all())
transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=opts.batch_size,
shuffle=True)
testset = datasets.MNIST('MNIST_data/',
download=True,
train=False,
transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=1, shuffle=False)
from network import RNN
from train import trainer
from test import tester
'''RNN model'''
RNN = RNN(opts).to(device)
if opts.print_model:
print(RNN)
'''Optimizers'''
import torch.optim as optim
RNN_optim = optim.Adam(RNN.parameters(),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
'''Criterion'''
criterion = nn.CrossEntropyLoss() # the target label is not one-hotted
'''run training'''
trainer(opts, RNN, RNN_optim, criterion, trainloader)
'''test'''
tester(opts, RNN, testloader)
def generate(model_path):
with open('{}/rnn.pickle'.format(model_path)) as f:
config = pickle.load(f)
n = RNN(training=False, **config)
print n.gen_text(sess=None, model_path=model_path)
options = options()
opts = options.parse()
#data loader
data_loader = data.dataloader(opts)
train_loader = util.create_dataset(data_loader.train_data,
data_loader.letteridx, data_loader.labelidx,
opts)
test_loader = util.create_dataset(data_loader.test_data, data_loader.letteridx,
data_loader.labelidx, opts)
from network import RNN
from train import trainer
from test import tester
'''RNN model'''
RNN = RNN(opts, data_loader.letteridx).to(device)
if opts.print_model:
print(RNN)
'''Optimizers'''
import torch.optim as optim
RNN_optim = optim.Adam(RNN.parameters(),
lr=opts.lr,
betas=(opts.beta1, opts.beta2))
'''Criterion'''
criterion = nn.NLLLoss()
'''run training'''
trainer(opts, RNN, RNN_optim, criterion, train_loader)
'''test'''
tester(opts, RNN, test_loader)
def train(word2vec, dataset, parameters, class_weights):
modeldir = os.path.join(parameters["runs_dir"], parameters["model_name"])
if not os.path.exists(modeldir):
os.mkdir(modeldir)
logdir = os.path.join(modeldir, "log")
if not os.path.exists(logdir):
os.mkdir(logdir)
logdir_train = os.path.join(logdir, "train")
if not os.path.exists(logdir_train):
os.mkdir(logdir_train)
logdir_test = os.path.join(logdir, "test")
if not os.path.exists(logdir_test):
os.mkdir(logdir_test)
# logdir_dev = os.path.join(logdir, "dev")
# if not os.path.exists(logdir_dev):
# os.mkdir(logdir_dev)
savepath = os.path.join(modeldir, "save")
#device_string = "/gpu:{}".format(parameters["gpu"]) if parameters["gpu"] else "/cpu:0"
device_string = "/cpu:0"
with tf.device(device_string):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config_proto = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
sess = tf.Session(config=config_proto)
headline_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="headline")
body_ph = tf.placeholder(tf.float32,
shape=[
parameters["sequence_length"], None,
parameters["embedding_dim"]
],
name="body")
targets_ph = tf.placeholder(tf.int32, shape=[None], name="targets")
keep_prob_ph = tf.placeholder(tf.float32, name="keep_prob")
_projecter = TensorFlowTrainable()
projecter = _projecter.get_4Dweights(
filter_height=1,
filter_width=parameters["embedding_dim"],
in_channels=1,
out_channels=parameters["num_units"],
name="projecter")
optimizer = tf.train.AdamOptimizer(
learning_rate=parameters["learning_rate"],
name="ADAM",
beta1=0.9,
beta2=0.999)
with tf.variable_scope(name_or_scope="headline"):
headline = RNN(cell=LSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
projecter=projecter,
keep_prob=keep_prob_ph,
class_weights=class_weights)
headline.process(sequence=headline_ph)
with tf.variable_scope(name_or_scope="body"):
body = RNN(cell=AttentionLSTMCell,
num_units=parameters["num_units"],
embedding_dim=parameters["embedding_dim"],
hiddens=headline.hiddens,
states=headline.states,
projecter=projecter,
keep_prob=keep_prob_ph,
class_weights=class_weights)
body.process(sequence=body_ph)
loss, loss_summary, accuracy, accuracy_summary = body.loss(
targets=targets_ph)
weight_decay = tf.reduce_sum([
tf.reduce_sum(parameter)
for parameter in headline.parameters + body.parameters
])
global_loss = loss + parameters["weight_decay"] * weight_decay
train_summary_op = tf.summary.merge([loss_summary, accuracy_summary])
train_summary_writer = tf.summary.FileWriter(logdir_train, sess.graph)
test_summary_op = tf.summary.merge([loss_summary, accuracy_summary])
test_summary_writer = tf.summary.FileWriter(logdir_test)
saver = tf.train.Saver(max_to_keep=10)
summary_writer = tf.summary.FileWriter(logdir)
tf.train.write_graph(sess.graph_def,
modeldir,
"graph.pb",
as_text=False)
loader = tf.train.Saver(tf.global_variables())
optimizer = tf.train.AdamOptimizer(
learning_rate=parameters["learning_rate"],
name="ADAM",
beta1=0.9,
beta2=0.999)
train_op = optimizer.minimize(global_loss)
sess.run(tf.global_variables_initializer())
batcher = Batcher(word2vec=word2vec)
train_batches = batcher.batch_generator(
dataset=dataset["train"],
num_epochs=parameters["num_epochs"],
batch_size=parameters["batch_size"]["train"],
sequence_length=parameters["sequence_length"])
num_step_by_epoch = int(
math.ceil(
len(dataset["train"]["targets"]) /
parameters["batch_size"]["train"]))
for train_step, (train_batch, epoch) in enumerate(train_batches):
feed_dict = {
headline_ph: np.transpose(train_batch["headline"], (1, 0, 2)),
body_ph: np.transpose(train_batch["body"], (1, 0, 2)),
targets_ph: train_batch["targets"],
keep_prob_ph: parameters["keep_prob"],
}
_, summary_str, train_loss, train_accuracy = sess.run(
[train_op, train_summary_op, loss, accuracy],
feed_dict=feed_dict)
train_summary_writer.add_summary(summary_str, train_step)
if train_step % 10 == 0:
sys.stdout.write(
"\rTRAIN | epoch={0}/{1}, step={2}/{3} | loss={4:.2f}, accuracy={5:.2f}% "
.format(epoch + 1, parameters["num_epochs"],
train_step % num_step_by_epoch, num_step_by_epoch,
train_loss, 100. * train_accuracy))
sys.stdout.flush()
if train_step % 500 == 0:
test_batches = batcher.batch_generator(
dataset=dataset["test"],
num_epochs=1,
batch_size=parameters["batch_size"]["test"],
sequence_length=parameters["sequence_length"])
for test_step, (test_batch, _) in enumerate(test_batches):
feed_dict = {
headline_ph:
np.transpose(test_batch["headline"], (1, 0, 2)),
body_ph:
np.transpose(test_batch["body"], (1, 0, 2)),
targets_ph:
test_batch["targets"],
keep_prob_ph:
1.,
}
summary_str, test_loss, test_accuracy = sess.run(
[test_summary_op, loss, accuracy], feed_dict=feed_dict)
print "\nTEST | loss={0:.2f}, accuracy={1:.2f}% ".format(
test_loss, 100. * test_accuracy)
print ""
test_summary_writer.add_summary(summary_str, train_step)
break
if train_step % 5000 == 0:
saver.save(sess, save_path=savepath, global_step=train_step)
print ""
keys = yaml.load(open("twitter_keys", "r"))
consumer_key = keys["consumer_key"]
consumer_secret = keys["consumer_secret"]
access_token = keys["access_token"]
access_secret = keys["access_secret"]
handler = OAuthHandler(consumer_key, consumer_secret)
handler.set_access_token(access_token, access_secret)
a, b, c, d, inv_vocab = load_dataset()
mx = len(inv_vocab)
api = API(handler)
rnn = RNN(mx, rnn_size, False)
model = L.Classifier(rnn)
serializers.load_hdf5("mymodel.h5", model)
while True:
nxt = np.random.randint(0, mx)
result = ""
for i in range(40):
nxt = np.array([nxt], np.int32)
prob = F.softmax(model.predictor(nxt))
nxt = np.argmax(prob.data)
s = inv_vocab[nxt]
if s == "。":
break
result += s
