def Setup(self):
utils.say("Oooo 'ello, I'm Mrs. Premise!")
self.Params = utils.read_params()
try:
self.Semaphore = sysv_ipc.Semaphore(self.Params["KEY"],
sysv_ipc.IPC_CREX)
except sysv_ipc.ExistentialError as err:
self.Logger.debug(err)
# One of my peers created the semaphore already
self.Semaphore = sysv_ipc.Semaphore(self.Params["KEY"])
# Waiting for that peer to do the first acquire or release
while not self.Semaphore.o_time:
time.sleep(.1)
else:
# Initializing sem.o_time to nonzero value
self.Semaphore.release()
# Now the semaphore is safe to use.
try:
self.Memory = sysv_ipc.SharedMemory(self.Params["KEY"],
sysv_ipc.IPC_CREX)
except sysv_ipc.ExistentialError as err:
self.Logger.debug(err)
self.Memory = sysv_ipc.SharedMemory(self.Params["KEY"])
else:
self.Memory.release()
self.Logger.debug("Setup done")
return True
def p_challenge_answer():
print("------ 挑战答题得分任务 ------")
p = say("完成挑战答题，输入 x 退出查询")
p.wait()
while True:
p = say("请输入关键字：")
keyWord = input()
p.kill()
if keyWord == 'x':
break
searchRet = tiaozhanSearch(keyWord)
answerList = []
remove_str = "温馨提示：相似题目，注意区分"
for sr in searchRet:
for s in sr.split('· '):
s = s.replace(remove_str, '')
s = s.replace("\r\n\r\n", '')
s = s.replace("【", "\033[1;31m【")
s = s.replace("】", "】\033[0m")
answerList.append(s)
answerList = list(filter(None, answerList))
p = say("搜索到" + str(len(answerList)) + "个结果")
for i in range(0, len(answerList)):
print('\033[1;36m <' + str(i + 1) + '>\033[0m ' + answerList[i])
p.wait()
beep('coin')
p = say("恭喜已完成该任务")
p.wait()
def __init__(self, n_d, vocab, oov="<unk>", embs=None, fix_init_embs=True):
if embs is not None:
lst_words = [ ]
vocab_map = {}
emb_vals = [ ]
for word, vector in embs:
assert word not in vocab_map, "Duplicate words in initial embeddings"
vocab_map[word] = len(vocab_map)
emb_vals.append(vector)
lst_words.append(word)
self.init_end = len(emb_vals) if fix_init_embs else -1
if n_d != len(emb_vals[0]):
say("WARNING: n_d ({}) != init word vector size ({}). Use {} instead.\n".format(
n_d, len(emb_vals[0]), len(emb_vals[0])
))
n_d = len(emb_vals[0])
say("{} pre-trained embeddings loaded.\n".format(len(emb_vals)))
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
emb_vals.append(random_init((n_d,))*(0.001 if word != oov else 0.0))
lst_words.append(word)
emb_vals = np.vstack(emb_vals).astype(theano.config.floatX)
self.vocab_map = vocab_map
self.lst_words = lst_words
else:
lst_words = [ ]
vocab_map = {}
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
lst_words.append(word)
self.lst_words = lst_words
self.vocab_map = vocab_map
emb_vals = random_init((len(self.vocab_map), n_d))
self.init_end = -1
if oov is not None and oov is not False:
assert oov in self.vocab_map, "oov {} not in vocab".format(oov)
self.oov_tok = oov
self.oov_id = self.vocab_map[oov]
else:
self.oov_tok = None
self.oov_id = -1
self.embeddings = create_shared(emb_vals)
if self.init_end > -1:
self.embeddings_trainable = self.embeddings[self.init_end:]
else:
self.embeddings_trainable = self.embeddings
self.n_V = len(self.vocab_map)
self.n_d = n_d
def evaluate_data(self, batches, eval_func):
tot_loss, tot_acc, tot = 0.0, 0.0, 0.0
n_c = self.nclasses
tot_pred, tot_gold, tot_corr = [0.0]*n_c, [0.0]*n_c, [0.0]*n_c
tot_rel_loss, tot_dom_loss, tot_adv_loss = 0.0, 0.0, 0.0
tot_recon_loss = 0.0
for b in batches:
bx, by, brel, bid = b
prob, lab_loss, rel_loss, dom_loss, adv_loss, recon_loss = eval_func(bx, by, brel, bid)
tot_loss += lab_loss
tot_rel_loss += rel_loss
tot_dom_loss += dom_loss
tot_adv_loss += adv_loss
tot_recon_loss += recon_loss
for gold_y, p in zip(by, prob):
pred = np.argmax(p)
tot += 1
tot_pred[pred] += 1
tot_gold[gold_y] += 1
if pred == gold_y:
tot_acc += 1
tot_corr[pred] += 1
n = len(batches)
f1 = []
for p, g, c in zip(tot_pred, tot_gold, tot_corr):
pre = c / p if p > 0 else 0.0
rec = c / g if g > 0 else 0.0
f1.append((2*pre*rec)/(pre+rec+1e-8))
tot_data = sum([len(b[1]) for b in batches])
say(("\tEvaluate data: {}\n").format(str(tot_data)))
return tot_loss/n, tot_rel_loss/n, tot_dom_loss/n, tot_adv_loss/n/self.rho.get_value(), tot_recon_loss/n, tot_acc/tot, f1
def __init__(self, n_d, vocab, oov="<unk>", embs=None, fix_init_embs=True):
if embs is not None:
lst_words = [ ]
vocab_map = {}
emb_vals = [ ]
for word, vector in embs:
assert word not in vocab_map, "Duplicate words in initial embeddings"
vocab_map[word] = len(vocab_map)
emb_vals.append(vector)
lst_words.append(word)
self.init_end = len(emb_vals) if fix_init_embs else -1
if n_d != len(emb_vals[0]):
say("WARNING: n_d ({}) != init word vector size ({}). Use {} instead.\n".format(
n_d, len(emb_vals[0]), len(emb_vals[0])
))
n_d = len(emb_vals[0])
say("{} pre-trained embeddings loaded.\n".format(len(emb_vals)))
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
emb_vals.append(random_init((n_d,))*(0.001 if word != oov else 0.0))
lst_words.append(word)
emb_vals = np.vstack(emb_vals).astype(theano.config.floatX)
self.vocab_map = vocab_map
self.lst_words = lst_words
else:
lst_words = [ ]
vocab_map = {}
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
lst_words.append(word)
self.lst_words = lst_words
self.vocab_map = vocab_map
emb_vals = random_init((len(self.vocab_map), n_d))
self.init_end = -1
if oov is not None and oov is not False:
assert oov in self.vocab_map, "oov {} not in vocab".format(oov)
self.oov_tok = oov
self.oov_id = self.vocab_map[oov]
else:
self.oov_tok = None
self.oov_id = -1
self.embeddings = create_shared(emb_vals)
if self.init_end > -1:
self.embeddings_trainable = self.embeddings[self.init_end:]
else:
self.embeddings_trainable = self.embeddings
self.n_V = len(self.vocab_map)
self.n_d = n_d
def join_game(self, player_name):
if player_name not in self.players:
new_player = Player(player_name)
new_player.workers[0].position = self.world.random_coords()
self.players.append(new_player)
say(player_name, '%s: now playing' % player_name)
else:
say(player_name, '%s: already playing' % player_name)
def ready(self, args, train):
# len * batch
self.idxs = T.imatrix()
self.idys = T.imatrix()
self.init_state = T.matrix(dtype=theano.config.floatX)
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
self.dropout = theano.shared(dropout_prob)
self.n_d = args["hidden_dim"]
embedding_layer = EmbeddingLayer(n_d=self.n_d,
vocab=set(w for w in train))
self.n_V = embedding_layer.n_V
say("Vocab size: {}\tHidden dim: {}\n".format(self.n_V, self.n_d))
activation = get_activation_by_name(args["activation"])
rnn_layer = LSTM(n_in=self.n_d, n_out=self.n_d, activation=activation)
output_layer = Layer(
n_in=self.n_d,
n_out=self.n_V,
activation=T.nnet.softmax,
)
# (len*batch) * n_d
x_flat = embedding_layer.forward(self.idxs.ravel())
# len * batch * n_d
x = apply_dropout(x_flat, self.dropout)
x = x.reshape((self.idxs.shape[0], self.idxs.shape[1], self.n_d))
# len * batch * (n_d+n_d)
h = rnn_layer.forward_all(x, self.init_state, return_c=True)
self.last_state = h[-1]
h = h[:, :, self.n_d:]
h = apply_dropout(h, self.dropout)
self.p_y_given_x = output_layer.forward(h.reshape(x_flat.shape))
idys = self.idys.ravel()
self.nll = -T.log(self.p_y_given_x[T.arange(idys.shape[0]), idys])
#self.nll = T.nnet.categorical_crossentropy(
# self.p_y_given_x,
# idys
# )
self.layers = [embedding_layer, rnn_layer, output_layer]
#self.params = [ x_flat ] + rnn_layer.params + output_layer.params
self.params = embedding_layer.params + rnn_layer.params + output_layer.params
self.num_params = sum(
len(x.get_value(borrow=True).ravel()) for l in self.layers
for x in l.params)
say("# of params in total: {}\n".format(self.num_params))
def new_round(self):
self.slow = False
self.new_challenge()
self.text = self.word['challenge']
self.txt.set_text(self.text)
self.input_enabled = True
utils.say(self.word['speak'],
self.slow,
audio_options=self.audio_options)
def settle(self, player_name, group_name):
player = self.get_player(player_name)
# Enough resources
if group.resources > [1000, 1000, 1000, 1000]:
group.resources = [resource - 1000 for resource in group.resources]
self.world.grid[group.position[0]][group.position[1]] = Town(player.name)
say(player, 'You have settled in position %s.' % group.position)
else:
say(player, 'Could not settle in position %s. Make sure your worker group'
' has enough resources.' % group.position)
def handle_input(self, key):
if isinstance(key, str) or type(key) == 'unicode':
if key == 'enter':
if len(self.text) > 0:
utils.say(self.text, audio_options=self.audio_options)
self.text = ""
elif key == 'backspace':
self.text = self.text[:-1]
else:
self.text = self.text + key
self.txt.set_text(self.text.upper())
def main():
args = get_args()
# set defaults
if args.out is None:
args.out = args.fasta + ".annotated"
# translate fasta?
query = args.fasta
if args.seqtype == "cds":
query = os.path.split(query)[1]
query = os.path.join(args.temp, query)
query = query + ".translated"
say("Translating input fasta to:\n ", query)
translate_fasta(args.fasta, query)
args.seqtype = "prot"
# perform uniref90 search
uniref90hits = uniref_search(
diamond=args.diamond,
database=args.uniref90db,
query=query,
seqtype=args.seqtype,
temp=args.temp,
diamond_options=args.diamond_options,
force_search=args.force_search,
)
uniref90map = parse_results(uniref90hits)
# perform uniref50 search
uniref50hits = uniref_search(
diamond=args.diamond,
database=args.uniref50db,
query=query,
seqtype=args.seqtype,
temp=args.temp,
diamond_options=args.diamond_options,
force_search=args.force_search,
)
uniref50map = parse_results(uniref50hits)
# override mappings?
overrides = {}
if args.transitive_map is not None:
overrides = trans_mapping(uniref90map, args.transitive_map)
# reannoate the fasta
reannotate(
query=args.fasta,
out=args.out,
uniref90map=uniref90map,
uniref50map=uniref50map,
overrides=overrides,
)
# done
say("Finished successfully.")
def destroy_shm_semaphore(semaphore, mapfile):
params = utils.read_params()
utils.say("Destroying semaphore and shared memory.")
mapfile.close()
# I could call memory.unlink() here but in order to demonstrate
# unlinking at the module level I'll do it that way.
posix_ipc.unlink_shared_memory(params["SHARED_MEMORY_NAME"])
semaphore.release()
# I could also unlink the semaphore by calling
# posix_ipc.unlink_semaphore() but I'll do it this way instead.
semaphore.unlink()
def p_watch_video(times=6, ts=60):
print("------ 视听学习得分任务 ------")
goon("观看" + str(times) + "个新视频，每个观看" + str(ts) + "秒， \n请选择一个未观看过的视频")
t = 1
while True:
say("观看第" + str(t) + "个视频")
time.sleep(int(ts))
t = t + 1
if t > times:
break
goon("请选择下一个视频")
beep('coin')
p = say("恭喜已完成该任务")
p.wait()
def trans_mapping(uniref90map, p_trans_map):
say("Loading transitive mapping file:\n ", p_trans_map)
check_path(p_trans_map)
overrides = {}
uniref90map_r = {}
for header, uniref90 in uniref90map.items():
uniref90map_r.setdefault(uniref90, set()).add(header)
with try_open(p_trans_map) as fh:
for row in csv.reader(fh, csv.excel_tab):
uniref90, uniref50 = row
headers = uniref90map_r.get(uniref90, set())
for h in headers:
overrides[h] = uniref50
return overrides
def p_read_article(times=6, ts=60):
print("------ 文章学习得分任务 ------")
goon("阅读" + str(times) + "篇新文章，每篇阅读" + str(ts) + "秒，\n请选择一篇未阅读过的文章")
t = 1
while True:
say("阅读第" + str(t) + "篇文章")
time.sleep(int(ts))
t = t + 1
if t > times:
break
goon("请选择下一篇文章")
beep('coin')
p = say("恭喜已完成该任务")
p.wait()
def parse_results(results):
say("Parsing results file:\n ", results)
check_path(results)
mapping = {}
mode = get_mode(results)
min_pident = float(mode.replace("uniref", ""))
with try_open(results) as fh:
for row in csv.reader(fh, csv.excel_tab):
h = Hit(row, config=c_output_format)
if h.qseqid not in mapping:
if h.pident >= min_pident and h.mcov >= c_min_coverage:
uniref = h.sseqid.split("|")[0]
mapping[h.qseqid] = uniref
return mapping
def read_annotations(path):
data_x, data_y = [], []
fopen = gzip.open if path.endswith(".gz") else open
with fopen(path) as fin:
for line in fin:
y, sep, x = line.partition("\t")
x, y = x.split(), y.split()
if len(x) == 0: continue
y = np.asarray([float(v) for v in y], dtype=theano.config.floatX)
data_x.append(x)
data_y.append(y)
say("{} examples loaded from {}\n".format(len(data_x), path))
say("max text length: {}\n".format(max(len(x) for x in data_x)))
return data_x, data_y
def parse(self, command):
# Play music
if command[0] == 'music':
say('Playing music.')
self.startPlayback()
sv.playing = True
# Play playlist ...
elif command[0] == 'playlist':
searchterm = ' '.join(command[1:])
say('Playing' + searchterm)
self.searchPlaylist(searchterm)
sv.playing = True
# Play ... playlist
elif command[-1] == 'playlist':
searchterm = ' '.join(command[:-1])
say('Playing' + searchterm)
self.searchPlaylist(searchterm)
sv.playing = True
# Play ...
else:
searchterm = ' '.join(command)
say('Playing' + searchterm)
self.searchTrack(searchterm)
sv.playing = True
def main(args):
assert args.train, "Training set required"
assert args.dev, "Dev set required"
assert args.test, "Test set required"
assert args.emb, "Pre-trained word embeddings required."
assert args.aspect_seeds, "Aspect seeds required."
print args
seeds = load_lis(args.aspect_seeds)
say("loaded {} aspect seeds\n".format(len(seeds)))
embedding_layer = EmbeddingLayer(
n_d = 100,
vocab = [ "<unk>" ],
pre_embs = load_embedding_iterator(args.emb),
)
seeds_id = np.array(map(lambda seed: embedding_layer.map_to_ids(seed.strip().split()).tolist(), seeds), dtype = np.int32)
if args.train:
train_x, train_y = load_doc_corpus(embedding_layer, args.train)
if args.dev:
dev_x, dev_y = load_doc_corpus(embedding_layer, args.dev)
if args.test:
test_x, test_y = load_doc_corpus(embedding_layer, args.test)
if args.train:
model = Model(
args = args,
embedding_layer = embedding_layer,
num_aspects = len(seeds_id),
query = seeds_id
)
if args.load:
print 'loading model...'
model.load_model(args.load)
else:
model.ready()
print 'training...'
model.train(
(train_x, train_y),
(dev_x, dev_y) if args.dev else None,
(test_x, test_y) if args.test else None
)
def p_likesome():
print("------ 评论、转发、订阅得分任务 ------")
print("提示：强国号多订阅无效")
goon("选择一篇文章，评论 1 次，转发 2 次, 选择订阅 2 个新的强国号, \n请完成后")
beep('coin')
p = say("恭喜已完成该任务")
p.wait()
def __init__(self,
vocab,
n_d=300,
oov="<unk>",
pre_embs=None,
fix_init_embs=False):
# print vocab
if pre_embs is not None:
t_word = ''
t_vector = []
for word, vector in pre_embs:
if n_d != len(vector):
say("WARNING: n_d ({}) != init word vector size ({}). Use {} instead.\n"
.format(n_d, len(vector), len(vector)))
n_d = len(vector)
t_word = word
t_vector = vector
break
embs = random_init((len(vocab) + 1, n_d)) * 0.01
cnt = 0
t_word = t_word.decode('utf8')
if t_word in vocab:
embs[vocab[t_word]] = t_vector
cnt = 1
for word, vector in pre_embs:
uword = word.decode('utf8', 'ignore')
if len(vector) != n_d:
continue
if uword in vocab:
if vocab[uword] >= len(vocab):
continue
embs[vocab[uword]] = vector
cnt += 1
say("{} pre-trained embeddings loaded.\n".format(cnt))
embs[len(vocab)] = random_init((n_d, )) * 0.0 # for oov embs
emb_vals = embs # np.vstack(embs).astype(theano.config.floatX)
else:
emb_vals = random_init((len(vocab) + 1, n_d)) * 0.5
self.init_end = len(vocab) if fix_init_embs else -1
self.embs = create_shared(emb_vals)
self.n_d = n_d
def read_annotations(path):
data_x, data_y = [ ], [ ]
fopen = gzip.open if path.endswith(".gz") else open
with fopen(path) as fin:
for line in fin:
y, sep, x = line.partition("\t")
x, y = x.split(), y.split()
if len(x) == 0: continue
y = np.asarray([ float(v) for v in y ], dtype = theano.config.floatX)
data_x.append(x)
data_y.append(y)
say("{} examples loaded from {}\n".format(
len(data_x), path
))
say("max text length: {}\n".format(
max(len(x) for x in data_x)
))
return data_x, data_y
def p_weekend_answer():
print("------ 每周答题得分任务 ------")
print("提示：本系统默认设置为每周一完成每周答题任务")
wd = datetime.datetime.now().weekday()
if wd == 0: # 周一
goon("完成每周答题，要小心别手残答错哟, \n请完成后")
beep('coin')
p = say("恭喜已完成该任务")
p.wait()
def handle_input(self, key):
if self.input_enabled and isinstance(key, str) and len(key) == 1:
self.input_enabled = False
self.text = self.text.replace(Game.MISSING_LETTER, key.upper(), 1)
self.txt.set_text(self.text)
if (not (Game.MISSING_LETTER in self.text)):
if self.word['screen'] == self.text:
self.txt.set_text(self.text_decorator(
self.text, "success"))
self.loop.set_alarm_in(1, self.alarm_new_round)
else:
self.txt.set_text(self.text_decorator(self.text, "error"))
self.loop.set_alarm_in(2, self.alarm_reset_text)
utils.say(self.word['speak'],
self.toggle_slow(),
audio_options=self.audio_options)
else:
self.input_enabled = True
def wake_me_up():
"""
function to execute to wake up. reads a wikipedia article then gives useful information
"""
t = get_date()
#getting article
page = get_rand_wiki_page()
while len(page["text"]) < LENGTH:
print("too short")
page = get_rand_wiki_page()
article = page["title"] + ". " + remove_extra_whitespace(
soft_cut_string(page["text"], LENGTH).replace("\n", " "))
#loading parameters
d = get_json("wake_up.json")
#constants for now
temp = "25"
conditions = "sunny"
planning = "shopping, going to school"
#none constant info
weekday = get_weekday_name(get_weekday_number(t))
intro = random.choice(d["intro"])
outro = random.choice(d["outro"])
salute = random.choice(d["salute"]).format(d["user_name"])
weather = random.choice(d["weather"]).format(temp, d["temp_scale"],
conditions)
day = random.choice(d["day"]).format(weekday, planning)
text = " ".join([intro, article, outro, salute, weather, day])
print(text)
say(text)
end_time = get_date()
total_time = end_time - t
print(total_time)
def uniref_search(diamond=None,
database=None,
query=None,
seqtype=None,
temp=None,
diamond_options=None,
force_search=False):
if which(diamond) is None:
die("<diamond> is not executable as: {}".format(diamond))
for path in [database, query, temp]:
check_path(path)
binary = {"nuc": "blastx", "prot": "blastp"}[seqtype]
mode = get_mode(database)
results = os.path.split(query)[1]
results = os.path.join(temp, results)
results = ".".join([results, mode, "hits"])
command = [
diamond,
binary,
"--db",
database,
"--query",
query,
"--outfmt",
c_output_format,
"--tmpdir",
temp,
"--out",
results,
"--id",
get_mode(results).replace("uniref", ""),
c_diamond_filters,
]
command = " ".join([str(k) for k in command])
command += (" " + diamond_options) if diamond_options is not None else ""
if force_search or not os.path.exists(results):
say("Executing:\n ", command)
os.system(command)
else:
say("Using existing results file:\n ", results)
return results
def _register():
say('登録を始めます')
say('正面に立ってラズベリーマークを見てください')
with picamera.PiCamera() as camera:
camera.resolution = (1024, 768)
camera.start_preview()
time.sleep(2) # 輝度調整のために最低2秒sleepする必要がある
camera.capture('front.jpg')
say('次にゆっくりと左右に顔を振ってください。５秒かけて左右にふるイメージです。')
for i in range(10):
camera.capture(f"side_{i}.jpg")
time.sleep(0.5)
say('撮影が完了しました。')
camera.stop_preview()
def p_guide_all():
print("------ 视听学习得分任务 ------")
goon("请打开电台，选择一个未听过的大于6首歌曲的专辑，并后台播放")
p_daily_answer()
p_weekend_answer()
p_specific_answer()
p_challenge_answer()
p_likesome()
p_read_article_by_num(5) # 这里只需要看 5 篇，另外 1 篇在点赞任务中完成
p_read_article_by_time()
p_watch_local_video()
while True:
r = goon("请检查视听学习次数任务是否完成", "请输入未得分数，全部完成输入回车键")
if r:
try:
ri = int(r)
except:
goon("输入错误，请重新输入")
else:
if ri <= 0 or ri > 6:
goon("输入错误，请重新输入")
continue
p_watch_video_by_num(ri)
break
else:
break
while True:
r = goon("请检查视听学习时间任务是否完成", "请输入未得分数，全部完成输入回车键")
if r:
try:
ri = int(r)
except:
goon("输入错误，请重新输入")
else:
if ri < 0 or ri > 6:
goon("输入错误，请重新输入")
continue
p_watch_video_by_time(ri)
break
else:
break
beep('coin')
p = say("恭喜已完成全部任务")
p.wait()
def main(semaphore, mapfile, what_i_wrote =""):
vehicle = connection_uav()
semaphore.release()
semaphore.acquire()
s = utils.read_from_memory(mapfile)
print s
# I keep checking the shared memory until something new has
# been written.
while s == what_i_wrote:
# Nothing new; give Mrs. Conclusion another chance to respond.
utils.say("Releasing the semaphore")
semaphore.release()
utils.say("Waiting to acquire the semaphore")
semaphore.acquire()
s = utils.read_from_memory(mapfile)
what_i_wrote = vehicle.attitude
utils.write_to_memory(mapfile, what_i_wrote)
return what_i_wrote
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
z = self.z = T.bmatrix()
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
if layer_type == "rcnn":
l = ExtRCNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = ExtLSTM(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds-y)**2
loss = self.loss = T.mean(loss_mat)
pred_diff = self.pred_diff = T.mean(T.max(preds, axis=1) - T.min(preds, axis=1))
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
cost = self.cost = loss * 10 + l2_cost
# hashlib is only available in Python >= 2.5. I still want to support
# older Pythons so I import md5 if hashlib is not available. Fortunately
# md5 can masquerade as hashlib for my purposes.
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
utils.say("Oooo 'ello, I'm Mrs. Premise!")
params = utils.read_params()
# Create the shared memory and the semaphore.
memory = posix_ipc.SharedMemory(params["SHARED_MEMORY_NAME"], posix_ipc.O_CREX,
size=params["SHM_SIZE"])
semaphore = posix_ipc.Semaphore(params["SEMAPHORE_NAME"], posix_ipc.O_CREX)
# MMap the shared memory
mapfile = mmap.mmap(memory.fd, memory.size)
# Once I've mmapped the file descriptor, I can close it without
# interfering with the mmap.
memory.close_fd()
def run_command(message_data):
sender = message_data['sender']
said = message_data['said']
# '#channel' if room, 'sender' if private message
current_channel = message_data['current_channel']
params = message_data['params']
# Get title from web pages
if 'http://' in said:
url = extract_url(said)
title = get_title(url)
if title:
say(current_channel, 'Title: %s' % title)
# Get link to Wikipedia article
if '[[' in said:
for article_name in extract_article(said):
say(current_channel, get_link(article_name))
# Reply to mention with a random quote
if nickname in said:
say(current_channel, random_quote(sender))
## IRC commands ##
search_term = '+'.join(params)
# List all commands
if said.find('@help') == 0:
say(sender, 'Search engines: google, wa, ddg, drae, dpd, en, es')
say(sender, 'Misc: random [list], conv (unit) to (unit), fetch (wikipedia_article), link <start|get|check|stop>, calc (expression)')
# Google
elif said.find('@google') == 0:
say(current_channel, 'https://www.google.com/search?q=%s' % search_term)
# Wolfram Alpha
elif said.find('@wa') == 0:
say(current_channel, 'http://www.wolframalpha.com/input/?i=%s' % search_term)
# DuckDuckGo
elif said.find('@ddg') == 0:
say(current_channel, 'http://duckduckgo.com/?q=%s' % search_term)
# DRAE
elif said.find('@drae') == 0:
say(current_channel, 'http://lema.rae.es/drae/?val=%s' % search_term)
# DPD
elif said.find('@dpd') == 0:
say(current_channel, 'http://lema.rae.es/dpd/?key=%s' % search_term)
# Jisho kanji lookup
elif said.find('@kan') == 0:
escaped_term = urllib2.quote(search_term)
say(current_channel, 'http://jisho.org/kanji/details/%s' % escaped_term)
# EN > JP
elif said.find('@ei') == 0:
say(current_channel, 'http://jisho.org/words?jap=&eng=%s&dict=edict' % search_term)
# JP > EN
elif said.find('@ni') == 0:
escaped_term = urllib2.quote(search_term)
say(current_channel, 'http://jisho.org/words?jap=%s&eng=&dict=edict' % escaped_term)
# EN > ES
elif said.find('@en') == 0:
say(current_channel, 'http://www.wordreference.com/es/translation.asp?tranword=%s' % search_term)
# ES > EN
elif said.find('@es') == 0:
say(current_channel, 'http://www.wordreference.com/es/en/translation.asp?spen=%s' % search_term)
# Random choice
elif said.find('@random') == 0:
if len(params) == 1:
say(current_channel, 'f****t')
elif len(params) > 1:
say(current_channel, random.choice(said.split(',').strip()))
else:
say(current_channel, random.choice([0, 1]))
# Unit converter
elif said.find('@conv') == 0:
if 'to' not in params:
return
index = params.index('to')
amount = params[0]
unit_from = params[1:index]
unit_from = urllib2.quote(' '.join(unit_from))
# 'to' == params[index]
unit_to = params[index + 1:]
unit_to = urllib2.quote(' '.join(unit_to))
conversion_url = 'http://www.google.com/ig/calculator?hl=en&q='
conversion = fetch_url(conversion_url + amount + unit_from + '=?' + unit_to).read()
parsed_conversion = conversion.split('"')
# Check for errors
if len(parsed_conversion[5]) == 0:
unit_result = urllib2.unquote(unit_to)
say(current_channel, '%s %s' % (parsed_conversion[3].split()[0], unit_result))
# Linkrace module
elif said.find('@link') == 0:
# Get race links
if params[0] == 'get':
url = 'http://es.wikipedia.org/wiki/%s'
start, end = random_pair()
starturl = url % urllib2.quote(start)
endurl = url % urllib2.quote(end)
say(current_channel, 'Start article is %s' % starturl)
say(current_channel, 'Goal article is %s' % endurl)
# Check if chain is valid
elif params[0] == 'check':
chain = ' '.join(params[1:])
broken_links = check_chain(chain)
if not broken_links:
say(current_channel, 'The chain is valid.')
else:
error_list = ' | '.join(broken_links)
say(current_channel, error_list)
say(current_channel, 'The chain is not valid.')
# Calculator
elif said.find('@calc') == 0:
expression = ''.join(params)
result = str(calculate(expression))
say(current_channel, result)
# Wikipedia fetch
elif said.find('@fetch') == 0:
article_name = ' '.join(params)
extract = fetch(article_name)
say(current_channel, extract)
# Text game
elif said.find('@dicks') == 0:
global game
# Commands available for everyone
if params[0] == 'join':
game.join_game(sender)
elif params[0] == 'players':
say(current_channel, [player.name for player in game.players])
# Commands available for players
if sender in [player.name for player in game.players]:
if params[0] == 'panel':
panel_url = sprunge(game.panel(sender))
say(sender, '[i] Uploading panel')
say(sender, panel_url)
elif params[0] == 'settle':
group = params[1]
game.settle(sender, group)
elif params[0] == 'move':
troop = params[1]
new_position = [params[2], params[3]]
game.move(sender, troop, new_position)
## Owner commands ##
if sender == owner:
# Disconnect
if said == '.quit':
execute('QUIT')
sys.exit(0)
# Send message from bot
elif said.find('.say') == 0:
if len(params) > 1:
say(params[0], ' '.join(params[1:]))
# Print userlist
elif said.find('.users') == 0:
say(current_channel, str(users))
# Bot joins
elif said.find('.join') == 0:
channel = params[0]
execute('JOIN %s' % channel)
# Bot parts
elif said.find('.part') == 0:
execute('PART %s' % current_channel)
del users[current_channel]
# Bot kicks
elif said.find('.kick') == 0:
user = params[0]
reason = ' '.join(params[1:])
if not reason:
reason = 'huh'
bot_kick(current_channel, user, reason)
def train(self):
args = self.args
train_x, train_y = self.train_set
dev_x, dev_y = self.dev_set
test_x, test_y = self.test_set
updates, lr, gnorm = create_optimization_updates(
cost = self.cost,
params = self.params,
lr = args.learning_rate,
rho = args.rho,
beta1 = args.beta1,
beta2 = args.beta2,
momentum = args.momentum,
gamma = args.gamma,
method = args.learning
)[:3]
batch = args.batch
index = self.index
x = self.x
y = self.y
train_func = theano.function(
inputs = [ index ],
outputs = [ self.cost, gnorm ],
givens = {
x: train_x[index*batch:(index+1)*batch],
y: train_y[index*batch:(index+1)*batch]
},
updates = updates
)
dev_func = theano.function(
inputs = [ index ],
outputs = [ self.err, self.loss ],
givens = {
x: dev_x[index*batch:(index+1)*batch],
y: dev_y[index*batch:(index+1)*batch]
}
)
test_func = theano.function(
inputs = [ index ],
outputs = [ self.err, self.loss ],
givens = {
x: test_x[index*batch:(index+1)*batch],
y: test_y[index*batch:(index+1)*batch]
}
)
decay_lr = args.decay_lr and args.learning.lower() != "adadelta" and \
args.learning.lower() != "adagrad"
lr_0 = args.learning_rate
iter_cnt = 0
N = train_x.get_value(borrow=True).shape[0]
num_batches = (N-1)/batch + 1
processed = 0
period = args.eval_period
best_dev_err = 1.0
max_epochs = args.max_epochs
for epoch in xrange(max_epochs):
start_time = time.time()
tot_cost = 0
for i in xrange(num_batches):
iter_cnt += 1
if decay_lr:
lr.set_value(np.float32(lr_0/iter_cnt**0.5))
cost, grad_norm = train_func(i)
tot_cost += cost
if math.isnan(cost):
say("NaN !!\n")
return
ed = min(N, (i+1)*batch)
prev = processed/period
processed += ed-i*batch
if (i == num_batches-1) or (processed/period > prev):
say("Epoch={:.1f} Sample={} cost={:.4f} |g|={:.2f}\t[{:.1f}m]\n".format(
epoch + (i+1.0)/num_batches,
processed,
tot_cost/(i+1),
float(grad_norm),
(time.time()-start_time)/60.0
))
dev_err, dev_loss = self.evaluate(dev_func, dev_x)
best_dev_err = min(best_dev_err, dev_err)
say("\tdev_err={:.4f} dev_loss={:.4f} best_dev={:.4f}\n".format(
dev_err, dev_loss, best_dev_err))
if dev_err == best_dev_err:
test_err, test_loss = self.evaluate(test_func, test_x)
say("\ttest_err={:.4f} test_loss={:.4f}\n".format(
test_err, test_loss))
say("\n")
import utils
if PY_MAJOR_VERSION > 2:
import utils_for_3 as flex_utils
else:
import utils_for_2 as flex_utils
params = utils.read_params()
# Mrs. Premise has already created the message queue. I just need a handle
# to it.
mq = sysv_ipc.MessageQueue(params["KEY"])
what_i_sent = ""
for i in range(0, params["ITERATIONS"]):
utils.say("iteration %d" % i)
s, _ = mq.receive()
s = s.decode()
utils.say("Received %s" % s)
while s == what_i_sent:
# Nothing new; give Mrs. Premise another chance to respond.
mq.send(s)
s, _ = mq.receive()
s = s.decode()
utils.say("Received %s" % s)
if what_i_sent:
if PY_MAJOR_VERSION > 2:
# hashlib is only available in Python >= 2.5. I still want to support
# older Pythons so I import md5 if hashlib is not available. Fortunately
# md5 can masquerade as hashlib for my purposes.
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
utils.say("Oooo 'ello, I'm Mrs. Conclusion!")
params = utils.read_params()
# Mrs. Premise has already created the message queue. I just need a handle
# to it.
mq = posix_ipc.MessageQueue(params["MESSAGE_QUEUE_NAME"])
what_i_sent = ""
for i in range(0, params["ITERATIONS"]):
utils.say("iteration %d" % i)
s, _ = mq.receive()
s = s.decode()
utils.say("Received %s" % s)
def ready(self):
generator = self.generator
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = generator.dropout
# len*batch
x = generator.x
z = generator.z_pred
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
if layer_type == "rcnn":
l = ExtRCNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = ExtLSTM(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# len*batch*n_e
embs = generator.word_embs
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev, z)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(h_next[-1]) # last state
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
preds = self.preds = output_layer.forward(h_final)
# batch
loss_mat = self.loss_mat = (preds-y)**2
pred_diff = self.pred_diff = T.mean(T.max(preds, axis=1) - T.min(preds, axis=1))
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
self.cost_g = cost_logpz * 10 + generator.l2_cost
self.cost_e = loss * 10 + l2_cost
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(
n_in = n_e,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = LSTM(
n_in = n_e,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch
masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = ZLayer(
n_in = size,
n_hidden = args.hidden_dimension2,
activation = activation
)
# sample z given text (i.e. x)
z_pred, sample_updates = output_layer.sample_all(h_final)
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
self.sample_updates = sample_updates
print "z_pred", z_pred.ndim
probs = output_layer.forward_all(h_final, z_pred)
print "probs", probs.ndim
logpz = - T.nnet.binary_crossentropy(probs, z_pred) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
def train(self, train, dev, test):
args = self.args
dropout = self.dropout
padding_id = self.embedding_layer.vocab_map["<padding>"]
if dev is not None:
dev_batches_x, dev_batches_y = myio.create_batches(
dev[0], dev[1], args.batch, padding_id
)
if test is not None:
test_batches_x, test_batches_y = myio.create_batches(
test[0], test[1], args.batch, padding_id
)
start_time = time.time()
train_batches_x, train_batches_y = myio.create_batches(
train[0], train[1], args.batch, padding_id
)
say("{:.2f}s to create training batches\n\n".format(
time.time()-start_time
))
updates_e, lr_e, gnorm_e = create_optimization_updates(
cost = self.encoder.cost_e,
params = self.encoder.params,
method = args.learning,
lr = args.learning_rate
)[:3]
updates_g, lr_g, gnorm_g = create_optimization_updates(
cost = self.encoder.cost_g,
params = self.generator.params,
method = args.learning,
lr = args.learning_rate
)[:3]
sample_generator = theano.function(
inputs = [ self.x ],
outputs = self.z
)
get_loss_and_pred = theano.function(
inputs = [ self.x, self.y ],
outputs = [ self.encoder.loss_vec, self.encoder.preds, self.z ]
)
train_generator = theano.function(
inputs = [ self.x, self.y ],
outputs = [ self.encoder.obj, self.encoder.loss, \
self.encoder.sparsity_cost, self.z, gnorm_e, gnorm_g ],
updates = updates_e.items() + updates_g.items(),
)
eval_func = theano.function(
inputs = [ self.x, self.y ],
outputs = [ self.z, self.encoder.obj, self.true_pos, self.tot_pos, self.tot_true ]
)
eval_period = args.eval_period
unchanged = 0
best_dev = 1e+2
best_dev_e = 1e+2
last_train_avg_cost = None
last_dev_avg_cost = None
tolerance = 0.10 + 1e-3
dropout_prob = np.float64(args.dropout).astype(theano.config.floatX)
for epoch in xrange(args.max_epochs):
unchanged += 1
if unchanged > 50: return
train_batches_x, train_batches_y = myio.create_batches(
train[0], train[1], args.batch, padding_id
)
more = True
if args.decay_lr:
param_bak = [ p.get_value(borrow=False) for p in self.params ]
while more:
processed = 0
train_cost = 0.0
train_loss = 0.0
train_sparsity_cost = 0.0
p1 = 0.0
start_time = time.time()
N = len(train_batches_x)
for i in xrange(N):
if (i+1) % 100 == 0:
say("\r{}/{} {:.2f} ".format(i+1,N,p1/(i+1)))
bx, by = train_batches_x[i], train_batches_y[i]
mask = bx != padding_id
cost, loss, sparsity_cost, bz, gl2_e, gl2_g = train_generator(bx, by)
k = len(by)
processed += k
train_cost += cost
train_loss += loss
train_sparsity_cost += sparsity_cost
p1 += np.sum(bz*mask) / (np.sum(mask)+1e-8)
cur_train_avg_cost = train_cost / N
if dev:
self.dropout.set_value(0.0)
dev_obj, dev_prec, dev_recall, dev_f1, dev_p1 = self.evaluate_data(
dev_batches_x, dev_batches_y, eval_func)
self.dropout.set_value(dropout_prob)
cur_dev_avg_cost = dev_obj
more = False
if args.decay_lr and last_train_avg_cost is not None:
if cur_train_avg_cost > last_train_avg_cost*(1+tolerance):
more = True
say("\nTrain cost {} --> {}\n".format(
last_train_avg_cost, cur_train_avg_cost
))
if dev and cur_dev_avg_cost > last_dev_avg_cost*(1+tolerance):
more = True
say("\nDev cost {} --> {}\n".format(
last_dev_avg_cost, cur_dev_avg_cost
))
if more:
lr_val = lr_g.get_value()*0.5
lr_val = np.float64(lr_val).astype(theano.config.floatX)
lr_g.set_value(lr_val)
lr_e.set_value(lr_val)
say("Decrease learning rate to {}\n".format(float(lr_val)))
for p, v in zip(self.params, param_bak):
p.set_value(v)
continue
last_train_avg_cost = cur_train_avg_cost
if dev: last_dev_avg_cost = cur_dev_avg_cost
say("\n")
say(("Generator Epoch {:.2f} costg={:.4f} scost={:.4f} lossg={:.4f} " +
"p[1]={:.3f} |g|={:.4f} {:.4f}\t[{:.2f}m / {:.2f}m]\n").format(
epoch+(i+1.0)/N,
train_cost / N,
train_sparsity_cost / N,
train_loss / N,
p1 / N,
float(gl2_e),
float(gl2_g),
(time.time()-start_time)/60.0,
(time.time()-start_time)/60.0/(i+1)*N
))
say("\t"+str([ "{:.2f}".format(np.linalg.norm(x.get_value(borrow=True))) \
for x in self.encoder.params ])+"\n")
say("\t"+str([ "{:.2f}".format(np.linalg.norm(x.get_value(borrow=True))) \
for x in self.generator.params ])+"\n")
if dev:
if dev_obj < best_dev:
best_dev = dev_obj
unchanged = 0
if args.dump and test:
self.dump_rationales(args.dump, test_batches_x, test_batches_y,
get_loss_and_pred, sample_generator)
say(("\tdevg={:.4f} f1g={:.4f} preg={:.4f} recg={:.4f}" +
" p[1]g={:.3f} best_dev={:.4f}\n").format(
dev_obj,
dev_f1,
dev_prec,
dev_recall,
dev_p1,
best_dev
))
if test is not None:
self.dropout.set_value(0.0)
test_obj, test_prec, test_recall, test_f1, test_p1 = self.evaluate_data(
test_batches_x, test_batches_y, eval_func)
self.dropout.set_value(dropout_prob)
say(("\ttestt={:.4f} f1t={:.4f} pret={:.4f} rect={:.4f}" +
" p[1]t={:.3f}\n").format(
test_obj,
test_f1,
test_prec,
test_recall,
test_p1
))
def __init__(self,
n_d,
vocab,
oov="<unk>",
pre_embs=None,
fix_init_embs=False):
if pre_embs is not None:
vocab_map = {}
words = []
embs = []
for word, vector in pre_embs:
if word in vocab_map:
continue
vocab_map[word] = len(vocab_map)
embs.append(vector)
words.append(word)
self.init_end = len(embs) if fix_init_embs else -1
if n_d != len(embs[0]):
say("WARNING: n_d ({}) != init word vector size ({}). Use {} instead.\n"
.format(n_d, len(embs[0]), len(embs[0])))
n_d = len(embs[0])
say("{} pre-trained embeddings loaded.\n".format(len(embs)))
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
embs.append(
random_init((n_d, )) * (0.01 if word != oov else 0))
words.append(word)
self.emb_vals = np.vstack(embs).astype(theano.config.floatX)
self.vocab_map = vocab_map
self.words = words
else:
words = []
vocab_map = {}
for word in vocab:
if word not in vocab_map:
vocab_map[word] = len(vocab_map)
words.append(word)
self.words = words
self.vocab_map = vocab_map
self.emb_vals = random_init((len(self.vocab_map), n_d)) * 0.01
self.init_end = -1
if oov is not None and oov is not False:
assert oov in self.vocab_map, "oov {} not in vocab".format(oov)
self.oov_tok = oov
self.oov_id = self.vocab_map[oov]
else:
self.oov_tok = None
self.oov_id = -1
self.embs = create_shared(self.emb_vals)
if self.init_end > -1:
self.embs_trainable = self.embs[self.init_end:]
else:
self.embs_trainable = self.embs
self.n_vocab = len(self.vocab_map)
self.n_d = n_d
def ready(self):
args = self.args
embedding_layer = self.embedding_layer
self.n_hidden = args.hidden_dim
self.n_in = embedding_layer.n_d
dropout = self.dropout = theano.shared(
np.float64(args.dropout_rate).astype(theano.config.floatX)
)
# x is length * batch_size
# y is batch_size
self.x = T.imatrix('x')
self.y = T.ivector('y')
x = self.x
y = self.y
n_hidden = self.n_hidden
n_in = self.n_in
# fetch word embeddings
# (len * batch_size) * n_in
slices = embedding_layer.forward(x.ravel())
self.slices = slices
# 3-d tensor, len * batch_size * n_in
slices = slices.reshape( (x.shape[0], x.shape[1], n_in) )
# stacking the feature extraction layers
pooling = args.pooling
depth = args.depth
layers = self.layers = [ ]
prev_output = slices
prev_output = apply_dropout(prev_output, dropout, v2=True)
size = 0
softmax_inputs = [ ]
activation = get_activation_by_name(args.act)
for i in range(depth):
if args.layer.lower() == "lstm":
layer = LSTM(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden
)
elif args.layer.lower() == "strcnn":
layer = StrCNN(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden,
activation = activation,
decay = args.decay,
order = args.order
)
elif args.layer.lower() == "rcnn":
layer = RCNN(
n_in = n_hidden if i > 0 else n_in,
n_out = n_hidden,
activation = activation,
order = args.order,
mode = args.mode
)
else:
raise Exception("unknown layer type: {}".format(args.layer))
layers.append(layer)
prev_output = layer.forward_all(prev_output)
if pooling:
softmax_inputs.append(T.sum(prev_output, axis=0)) # summing over columns
else:
softmax_inputs.append(prev_output[-1])
prev_output = apply_dropout(prev_output, dropout)
size += n_hidden
# final feature representation is the concatenation of all extraction layers
if pooling:
softmax_input = T.concatenate(softmax_inputs, axis=1) / x.shape[0]
else:
softmax_input = T.concatenate(softmax_inputs, axis=1)
softmax_input = apply_dropout(softmax_input, dropout, v2=True)
# feed the feature repr. to the softmax output layer
layers.append( Layer(
n_in = size,
n_out = self.nclasses,
activation = softmax,
has_bias = False
) )
for l,i in zip(layers, range(len(layers))):
say("layer {}: n_in={}\tn_out={}\n".format(
i, l.n_in, l.n_out
))
# unnormalized score of y given x
self.p_y_given_x = layers[-1].forward(softmax_input)
self.pred = T.argmax(self.p_y_given_x, axis=1)
self.nll_loss = T.mean( T.nnet.categorical_crossentropy(
self.p_y_given_x,
y
))
# adding regularizations
self.l2_sqr = None
self.params = [ ]
for layer in layers:
self.params += layer.params
for p in self.params:
if self.l2_sqr is None:
self.l2_sqr = args.l2_reg * T.sum(p**2)
else:
self.l2_sqr += args.l2_reg * T.sum(p**2)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in self.params)
say("total # parameters: {}\n".format(nparams))
# Python modules
import mmap
import os
import sys
import hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
PY_MAJOR_VERSION = sys.version_info[0]
utils.say("Oooo 'ello, I'm Mrs. Conclusion!")
params = utils.read_params()
# Mrs. Premise has already created the semaphore and shared memory.
# I just need to get handles to them.
memory = posix_ipc.SharedMemory(params["SHARED_MEMORY_NAME"])
semaphore = posix_ipc.Semaphore(params["SEMAPHORE_NAME"])
# MMap the shared memory
mapfile = mmap.mmap(memory.fd, memory.size)
# Once I've mmapped the file descriptor, I can close it without
# interfering with the mmap. This also demonstrates that os.close() is a
# perfectly legitimate alternative to the SharedMemory's close_fd() method.
os.close(memory.fd)
def train(self, train, dev, test, rationale_data):
args = self.args
dropout = self.dropout
padding_id = self.embedding_layer.vocab_map["<padding>"]
if dev is not None:
dev_batches_x, dev_batches_y = myio.create_batches(
dev[0], dev[1], args.batch, padding_id
)
if test is not None:
test_batches_x, test_batches_y = myio.create_batches(
test[0], test[1], args.batch, padding_id
)
if rationale_data is not None:
valid_batches_x, valid_batches_y = myio.create_batches(
[ u["xids"] for u in rationale_data ],
[ u["y"] for u in rationale_data ],
args.batch,
padding_id,
sort = False
)
start_time = time.time()
train_batches_x, train_batches_y = myio.create_batches(
train[0], train[1], args.batch, padding_id
)
say("{:.2f}s to create training batches\n\n".format(
time.time()-start_time
))
updates_e, lr_e, gnorm_e = create_optimization_updates(
cost = self.generator.cost_e,
params = self.encoder.params,
method = args.learning,
lr = args.learning_rate
)[:3]
updates_g, lr_g, gnorm_g = create_optimization_updates(
cost = self.generator.cost,
params = self.generator.params,
method = args.learning,
lr = args.learning_rate
)[:3]
sample_generator = theano.function(
inputs = [ self.x ],
outputs = self.z_pred,
#updates = self.generator.sample_updates
#allow_input_downcast = True
)
get_loss_and_pred = theano.function(
inputs = [ self.x, self.z, self.y ],
outputs = [ self.generator.loss_vec, self.encoder.preds ]
)
eval_generator = theano.function(
inputs = [ self.x, self.y ],
outputs = [ self.z, self.generator.obj, self.generator.loss,
self.encoder.pred_diff ],
givens = {
self.z : self.generator.z_pred
},
#updates = self.generator.sample_updates,
#no_default_updates = True
)
train_generator = theano.function(
inputs = [ self.x, self.y ],
outputs = [ self.generator.obj, self.generator.loss, \
self.generator.sparsity_cost, self.z, gnorm_g, gnorm_e ],
givens = {
self.z : self.generator.z_pred
},
#updates = updates_g,
updates = updates_g.items() + updates_e.items() #+ self.generator.sample_updates,
#no_default_updates = True
)
eval_period = args.eval_period
unchanged = 0
best_dev = 1e+2
best_dev_e = 1e+2
dropout_prob = np.float64(args.dropout).astype(theano.config.floatX)
for epoch in xrange(args.max_epochs):
unchanged += 1
if unchanged > 10: return
train_batches_x, train_batches_y = myio.create_batches(
train[0], train[1], args.batch, padding_id
)
processed = 0
train_cost = 0.0
train_loss = 0.0
train_sparsity_cost = 0.0
p1 = 0.0
start_time = time.time()
N = len(train_batches_x)
for i in xrange(N):
if (i+1) % 100 == 0:
say("\r{}/{} ".format(i+1,N))
bx, by = train_batches_x[i], train_batches_y[i]
mask = bx != padding_id
cost, loss, sparsity_cost, bz, gl2_g, gl2_e = train_generator(bx, by)
k = len(by)
processed += k
train_cost += cost
train_loss += loss
train_sparsity_cost += sparsity_cost
p1 += np.sum(bz*mask) / (np.sum(mask)+1e-8)
if (i == N-1) or (eval_period > 0 and processed/eval_period >
(processed-k)/eval_period):
say("\n")
say(("Generator Epoch {:.2f} costg={:.4f} scost={:.4f} lossg={:.4f} " +
"p[1]={:.2f} |g|={:.4f} {:.4f}\t[{:.2f}m / {:.2f}m]\n").format(
epoch+(i+1.0)/N,
train_cost / (i+1),
train_sparsity_cost / (i+1),
train_loss / (i+1),
p1 / (i+1),
float(gl2_g),
float(gl2_e),
(time.time()-start_time)/60.0,
(time.time()-start_time)/60.0/(i+1)*N
))
say("\t"+str([ "{:.1f}".format(np.linalg.norm(x.get_value(borrow=True))) \
for x in self.encoder.params ])+"\n")
say("\t"+str([ "{:.1f}".format(np.linalg.norm(x.get_value(borrow=True))) \
for x in self.generator.params ])+"\n")
if dev:
self.dropout.set_value(0.0)
dev_obj, dev_loss, dev_diff, dev_p1 = self.evaluate_data(
dev_batches_x, dev_batches_y, eval_generator, sampling=True)
if dev_obj < best_dev:
best_dev = dev_obj
unchanged = 0
if args.dump and rationale_data:
self.dump_rationales(args.dump, valid_batches_x, valid_batches_y,
get_loss_and_pred, sample_generator)
if args.save_model:
self.save_model(args.save_model, args)
say(("\tsampling devg={:.4f} mseg={:.4f} avg_diffg={:.4f}" +
" p[1]g={:.2f} best_dev={:.4f}\n").format(
dev_obj,
dev_loss,
dev_diff,
dev_p1,
best_dev
))
if rationale_data is not None:
r_mse, r_p1, r_prec1, r_prec2 = self.evaluate_rationale(
rationale_data, valid_batches_x,
valid_batches_y, eval_generator)
say(("\trationale mser={:.4f} p[1]r={:.2f} prec1={:.4f}" +
" prec2={:.4f}\n").format(
r_mse,
r_p1,
r_prec1,
r_prec2
))
self.dropout.set_value(dropout_prob)
def main():
print args
assert args.embedding, "Pre-trained word embeddings required."
embedding_layer = myio.create_embedding_layer(
args.embedding
)
max_len = args.max_len
if args.train:
train_x, train_y = myio.read_annotations(args.train)
train_x = [ embedding_layer.map_to_ids(x)[:max_len] for x in train_x ]
if args.dev:
dev_x, dev_y = myio.read_annotations(args.dev)
dev_x = [ embedding_layer.map_to_ids(x)[:max_len] for x in dev_x ]
if args.load_rationale:
rationale_data = myio.read_rationales(args.load_rationale)
for x in rationale_data:
x["xids"] = embedding_layer.map_to_ids(x["x"])
if args.train:
model = Model(
args = args,
embedding_layer = embedding_layer,
nclasses = len(train_y[0])
)
model.ready()
#debug_func2 = theano.function(
# inputs = [ model.x, model.z ],
# outputs = model.generator.logpz
# )
#theano.printing.debugprint(debug_func2)
#return
model.train(
(train_x, train_y),
(dev_x, dev_y) if args.dev else None,
None, #(test_x, test_y),
rationale_data if args.load_rationale else None
)
if args.load_model and args.dev and not args.train:
model = Model(
args = None,
embedding_layer = embedding_layer,
nclasses = -1
)
model.load_model(args.load_model)
say("model loaded successfully.\n")
# compile an evaluation function
eval_func = theano.function(
inputs = [ model.x, model.y ],
outputs = [ model.z, model.encoder.obj, model.encoder.loss,
model.encoder.pred_diff ],
updates = model.generator.sample_updates
)
# compile a predictor function
pred_func = theano.function(
inputs = [ model.x ],
outputs = [ model.z, model.encoder.preds ],
updates = model.generator.sample_updates
)
# batching data
padding_id = embedding_layer.vocab_map["<padding>"]
dev_batches_x, dev_batches_y = myio.create_batches(
dev_x, dev_y, args.batch, padding_id
)
# disable dropout
model.dropout.set_value(0.0)
dev_obj, dev_loss, dev_diff, dev_p1 = model.evaluate_data(
dev_batches_x, dev_batches_y, eval_func, sampling=True)
say("{} {} {} {}\n".format(dev_obj, dev_loss, dev_diff, dev_p1))
def train(self, args, train, dev, test=None):
embedding_layer = self.layers[0]
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
batch_size = args["batch_size"]
unroll_size = args["unroll_size"]
train = create_batches(train, embedding_layer.map_to_ids, batch_size)
dev = create_batches(dev, embedding_layer.map_to_ids, batch_size)
if test is not None:
test = create_batches(test, embedding_layer.map_to_ids, batch_size)
cost = T.sum(self.nll) / self.idxs.shape[1]
updates, lr, gnorm = create_optimization_updates(
cost = cost,
params = self.params,
lr = args["learning_rate"],
beta1 = args["beta1"],
beta2 = args["beta2"],
rho = args["rho"],
momentum = args["momentum"],
gamma = args["gamma"],
eps = args["eps"],
method = args["learning"]
)[:3]
#if args["learning"] == "adadelta":
# lr.set_value(args["learning_rate"])
train_func = theano.function(
inputs = [ self.idxs, self.idys, self.init_state ],
outputs = [cost, self.last_state, gnorm ],
updates = updates
)
eval_func = theano.function(
inputs = [ self.idxs, self.idys, self.init_state ],
outputs = [self.nll, self.last_state ]
)
N = (len(train[0])-1)/unroll_size + 1
say(" train: {} tokens, {} mini-batches\n".format(
len(train[0].ravel()), N
))
say(" dev: {} tokens\n".format(len(dev[0].ravel())))
say("\tp_norm: {}\n".format(
self.get_pnorm_stat()
))
decay_lr = args["decay_lr"] and args["learning"].lower() != "adadelta" and \
args["learning"].lower() != "adagrad"
lr_0 = args["learning_rate"]
iter_cnt = 0
unchanged = 0
best_dev = 1e+10
start_time = 0
max_epoch = args["max_epoch"]
for epoch in xrange(max_epoch):
if unchanged > 5: break
start_time = time.time()
prev_state = np.zeros((batch_size, self.n_d*2),
dtype=theano.config.floatX)
train_loss = 0.0
for i in xrange(N):
# get current batch
x = train[0][i*unroll_size:(i+1)*unroll_size]
y = train[1][i*unroll_size:(i+1)*unroll_size]
iter_cnt += 1
if decay_lr:
lr.set_value(np.float32(lr_0/iter_cnt**0.5))
cur_loss, prev_state, grad_norm = train_func(x, y, prev_state)
train_loss += cur_loss/len(x)
if math.isnan(cur_loss) or math.isnan(grad_norm):
say("\nNaN !!\n")
return
if i % 10 == 0:
say("\r{}".format(i))
if i == N-1:
self.dropout.set_value(0.0)
dev_preds = self.evaluate(eval_func, dev, batch_size, unroll_size)
dev_loss = evaluate_average(
predictions = dev_preds,
masks = None
)
dev_ppl = np.exp(dev_loss)
self.dropout.set_value(dropout_prob)
say("\r\n")
say( ( "Epoch={} lr={:.3f} train_loss={:.3f} train_ppl={:.1f} " \
+"dev_loss={:.3f} dev_ppl={:.1f}\t|g|={:.3f}\t[{:.1f}m]\n" ).format(
epoch,
float(lr.get_value(borrow=True)),
train_loss/N,
np.exp(train_loss/N),
dev_loss,
dev_ppl,
float(grad_norm),
(time.time()-start_time)/60.0
))
say("\tp_norm: {}\n".format(
self.get_pnorm_stat()
))
# halve the learning rate
#if args["learning"] == "sgd" and dev_ppl > best_dev-1:
# lr.set_value(np.max([lr.get_value()/2.0, np.float32(0.0001)]))
if dev_ppl < best_dev:
best_dev = dev_ppl
if test is None: continue
self.dropout.set_value(0.0)
test_preds = self.evaluate(eval_func, test, batch_size, unroll_size)
test_loss = evaluate_average(
predictions = test_preds,
masks = None
)
test_ppl = np.exp(test_loss)
self.dropout.set_value(dropout_prob)
say("\tbest_dev={:.1f} test_loss={:.3f} test_ppl={:.1f}\n".format(
best_dev, test_loss, test_ppl))
if best_dev > 200: unchanged += 1
say("\n")
def print_norms(self, lst=None):
if lst is None: lst = self.params
say("\t{}\n".format(
[ "{:.4f}".format(np.linalg.norm(p.get_value(borrow=True))) \
for p in lst ]
))
def ready(self, args, train):
# len * batch
self.idxs = T.imatrix()
self.idys = T.imatrix()
self.init_state = T.matrix(dtype=theano.config.floatX)
dropout_prob = np.float64(args["dropout"]).astype(theano.config.floatX)
self.dropout = theano.shared(dropout_prob)
self.n_d = args["hidden_dim"]
embedding_layer = EmbeddingLayer(
n_d = self.n_d,
vocab = set(w for w in train)
)
self.n_V = embedding_layer.n_V
say("Vocab size: {}\tHidden dim: {}\n".format(
self.n_V, self.n_d
))
activation = get_activation_by_name(args["activation"])
rnn_layer = LSTM(
n_in = self.n_d,
n_out = self.n_d,
activation = activation
)
output_layer = Layer(
n_in = self.n_d,
n_out = self.n_V,
activation = T.nnet.softmax,
)
# (len*batch) * n_d
x_flat = embedding_layer.forward(self.idxs.ravel())
# len * batch * n_d
x = apply_dropout(x_flat, self.dropout)
x = x.reshape( (self.idxs.shape[0], self.idxs.shape[1], self.n_d) )
# len * batch * (n_d+n_d)
h = rnn_layer.forward_all(x, self.init_state, return_c=True)
self.last_state = h[-1]
h = h[:,:,self.n_d:]
h = apply_dropout(h, self.dropout)
self.p_y_given_x = output_layer.forward(h.reshape(x_flat.shape))
idys = self.idys.ravel()
self.nll = -T.log(self.p_y_given_x[T.arange(idys.shape[0]), idys])
#self.nll = T.nnet.categorical_crossentropy(
# self.p_y_given_x,
# idys
# )
self.layers = [ embedding_layer, rnn_layer, output_layer ]
#self.params = [ x_flat ] + rnn_layer.params + output_layer.params
self.params = embedding_layer.params + rnn_layer.params + output_layer.params
self.num_params = sum(len(x.get_value(borrow=True).ravel())
for l in self.layers for x in l.params)
say("# of params in total: {}\n".format(self.num_params))
def train(self, train, dev, test):
args = self.args
trainx, trainy = train
batch_size = args.batch
if dev:
dev_batches_x, dev_batches_y = create_batches(
range(len(dev[0])),
dev[0],
dev[1],
batch_size
)
if test:
test_batches_x, test_batches_y = create_batches(
range(len(test[0])),
test[0],
test[1],
batch_size
)
cost = self.nll_loss + self.l2_sqr
updates, lr, gnorm = create_optimization_updates(
cost = cost,
params = self.params,
lr = args.learning_rate,
method = args.learning
)[:3]
train_model = theano.function(
inputs = [self.x, self.y],
outputs = [ cost, gnorm ],
updates = updates,
allow_input_downcast = True
)
eval_acc = theano.function(
inputs = [self.x],
outputs = self.pred,
allow_input_downcast = True
)
unchanged = 0
best_dev = 0.0
dropout_prob = np.float64(args.dropout_rate).astype(theano.config.floatX)
start_time = time.time()
eval_period = args.eval_period
perm = range(len(trainx))
say(str([ "%.2f" % np.linalg.norm(x.get_value(borrow=True)) for x in self.params ])+"\n")
for epoch in xrange(args.max_epochs):
unchanged += 1
if unchanged > 20: return
train_loss = 0.0
random.shuffle(perm)
batches_x, batches_y = create_batches(perm, trainx, trainy, batch_size)
N = len(batches_x)
for i in xrange(N):
if i % 100 == 0:
sys.stdout.write("\r%d" % i)
sys.stdout.flush()
x = batches_x[i]
y = batches_y[i]
va, grad_norm = train_model(x, y)
train_loss += va
# debug
if math.isnan(va):
print ""
print i-1, i
print x
print y
return
if (i == N-1) or (eval_period > 0 and (i+1) % eval_period == 0):
self.dropout.set_value(0.0)
say( "\n" )
say( "Epoch %.1f\tloss=%.4f\t|g|=%s [%.2fm]\n" % (
epoch + (i+1)/(N+0.0),
train_loss / (i+1),
float(grad_norm),
(time.time()-start_time) / 60.0
))
say(str([ "%.2f" % np.linalg.norm(x.get_value(borrow=True)) for x in self.params ])+"\n")
if dev:
preds = [ eval_acc(x) for x in dev_batches_x ]
nowf_dev = self.eval_accuracy(preds, dev_batches_y)
if nowf_dev > best_dev:
unchanged = 0
best_dev = nowf_dev
if args.save:
self.save_model(args.save, args)
say("\tdev accuracy=%.4f\tbest=%.4f\n" % (
nowf_dev,
best_dev
))
if args.test and nowf_dev == best_dev:
preds = [ eval_acc(x) for x in test_batches_x ]
nowf_test = self.eval_accuracy(preds, test_batches_y)
say("\ttest accuracy=%.4f\n" % (
nowf_test,
))
if best_dev > nowf_dev + 0.05:
return
self.dropout.set_value(dropout_prob)
start_time = time.time()
def finish_movement():
troop.position = new_position
say(player_name, "The troop %s is now at position %s." % (troop_name, new_position))
def ready(self):
generator = self.generator
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
unk_id = embedding_layer.vocab_map["<unk>"]
unk_vec = embedding_layer.embeddings[unk_id]
dropout = generator.dropout
# len*batch
x = generator.x
z = generator.z_pred
z = z.dimshuffle((0,1,"x"))
# batch*nclasses
y = self.y = T.fmatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
depth = args.depth
layer_type = args.layer.lower()
for i in xrange(depth):
l = CNN(
n_in = n_e if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
layers.append(l)
# len * batch * 1
masks = T.cast(T.neq(x, padding_id).dimshuffle((0,1,"x")) * z, theano.config.floatX)
# batch * 1
cnt_non_padding = T.sum(masks, axis=0) + 1e-8
# len*batch*n_e
embs = generator.word_embs*z + unk_vec.dimshuffle(('x','x',0))*(1-z)
pooling = args.pooling
lst_states = [ ]
h_prev = embs
for l in layers:
# len*batch*n_d
h_next = l.forward_all(h_prev)
if pooling:
# batch * n_d
masked_sum = T.sum(h_next * masks, axis=0)
lst_states.append(masked_sum/cnt_non_padding) # mean pooling
else:
lst_states.append(T.max(h_next, axis=0))
h_prev = apply_dropout(h_next, dropout)
if args.use_all:
size = depth * n_d
# batch * size (i.e. n_d*depth)
h_final = T.concatenate(lst_states, axis=1)
else:
size = n_d
h_final = lst_states[-1]
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = self.nclasses,
activation = sigmoid
)
# batch * nclasses
p_y_given_x = self.p_y_given_x = output_layer.forward(h_final)
preds = self.preds = p_y_given_x > 0.5
print preds, preds.dtype
print self.nclasses
# batch
loss_mat = T.nnet.binary_crossentropy(p_y_given_x, y)
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
self.true_pos = T.sum(preds*y)
self.tot_pos = T.sum(preds)
self.tot_true = T.sum(y)
zsum = generator.zsum
zdiff = generator.zdiff
logpz = generator.logpz
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
if not args.fix_emb:
params += embedding_layer.params
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
self.cost_g = cost_logpz + generator.l2_cost
self.cost_e = loss + l2_cost
# md5 can masquerade as hashlib for my purposes.
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
PY_MAJOR_VERSION = sys.version_info[0]
utils.say("Oooo 'ello, I'm Mrs. Conclusion!")
params = utils.read_params()
# Mrs. Premise has already created the semaphore and shared memory.
# I just need to get handles to them.
memory = posix_ipc.SharedMemory(params["SHARED_MEMORY_NAME"])
semaphore = posix_ipc.Semaphore(params["SEMAPHORE_NAME"])
# MMap the shared memory
mapfile = mmap.mmap(memory.fd, memory.size)
# Once I've mmapped the file descriptor, I can close it without
# interfering with the mmap. This also demonstrates that os.close() is a
# perfectly legitimate alternative to the SharedMemory's close_fd() method.
os.close(memory.fd)
def ready(self):
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = theano.shared(
np.float64(args.dropout).astype(theano.config.floatX)
)
# len*batch
x = self.x = T.imatrix()
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(1):
l = CNN(
n_in = n_e,
n_out = n_d,
activation = activation,
order = args.order
)
layers.append(l)
# len * batch
masks = T.cast(T.neq(x, padding_id), "int8").dimshuffle((0,1,'x'))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
self.word_embs = embs
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h_final = h1
size = n_d
h_final = apply_dropout(h_final, dropout)
output_layer = self.output_layer = Layer(
n_in = size,
n_out = 1,
activation = sigmoid
)
# len*batch*1
probs = output_layer.forward(h_final)
# len*batch
self.MRG_rng = MRG_RandomStreams()
z_pred_dim3 = self.MRG_rng.binomial(size=probs.shape, p=probs, dtype="int8")
z_pred = z_pred_dim3.reshape(x.shape)
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
z_pred = self.z_pred = theano.gradient.disconnected_grad(z_pred)
print "z_pred", z_pred.ndim
#logpz = - T.nnet.binary_crossentropy(probs, z_pred_dim3) * masks
logpz = - T.nnet.binary_crossentropy(probs, z_pred_dim3)
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
z = z_pred
self.zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
self.zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
self.l2_cost = l2_cost
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import sysv_ipc
# Utils for this demo
import utils
if PY_MAJOR_VERSION > 2:
import utils_for_3 as flex_utils
else:
import utils_for_2 as flex_utils
utils.say("Oooo 'ello, I'm Mrs. Premise!")
params = utils.read_params()
# Create the semaphore & shared memory. I read somewhere that semaphores
# and shared memory have separate key spaces, so one can safely use the
# same key for each. This seems to be true in my experience.
# For purposes of simplicity, this demo code makes no allowance for the
# failure of the semaphore or memory constructors. This is unrealistic
# because one can never predict whether or not a given key will be available,
# so your code must *always* be prepared for these functions to fail.
semaphore = sysv_ipc.Semaphore(params["KEY"], sysv_ipc.IPC_CREX)
memory = sysv_ipc.SharedMemory(params["KEY"], sysv_ipc.IPC_CREX)
def ready(self):
encoder = self.encoder
embedding_layer = self.embedding_layer
args = self.args
padding_id = embedding_layer.vocab_map["<padding>"]
dropout = self.dropout = encoder.dropout
# len*batch
x = self.x = encoder.x
z = self.z = encoder.z
n_d = args.hidden_dimension
n_e = embedding_layer.n_d
activation = get_activation_by_name(args.activation)
layers = self.layers = [ ]
layer_type = args.layer.lower()
for i in xrange(2):
if layer_type == "rcnn":
l = RCNN(
n_in = n_e,# if i == 0 else n_d,
n_out = n_d,
activation = activation,
order = args.order
)
elif layer_type == "lstm":
l = LSTM(
n_in = n_e,# if i == 0 else n_d,
n_out = n_d,
activation = activation
)
layers.append(l)
# len * batch
#masks = T.cast(T.neq(x, padding_id), theano.config.floatX)
masks = T.cast(T.neq(x, padding_id), "int8").dimshuffle((0,1,"x"))
# (len*batch)*n_e
embs = embedding_layer.forward(x.ravel())
# len*batch*n_e
embs = embs.reshape((x.shape[0], x.shape[1], n_e))
embs = apply_dropout(embs, dropout)
flipped_embs = embs[::-1]
# len*bacth*n_d
h1 = layers[0].forward_all(embs)
h2 = layers[1].forward_all(flipped_embs)
h_final = T.concatenate([h1, h2[::-1]], axis=2)
h_final = apply_dropout(h_final, dropout)
size = n_d * 2
output_layer = self.output_layer = Layer(
n_in = size,
n_out = 1,
activation = sigmoid
)
# len*batch*1
probs = output_layer.forward(h_final)
# len*batch
probs2 = probs.reshape(x.shape)
self.MRG_rng = MRG_RandomStreams()
z_pred = self.z_pred = T.cast(self.MRG_rng.binomial(size=probs2.shape, p=probs2), "int8")
# we are computing approximated gradient by sampling z;
# so should mark sampled z not part of the gradient propagation path
#
self.z_pred = theano.gradient.disconnected_grad(z_pred)
z2 = z.dimshuffle((0,1,"x"))
logpz = - T.nnet.binary_crossentropy(probs, z2) * masks
logpz = self.logpz = logpz.reshape(x.shape)
probs = self.probs = probs.reshape(x.shape)
# batch
zsum = T.sum(z, axis=0, dtype=theano.config.floatX)
zdiff = T.sum(T.abs_(z[1:]-z[:-1]), axis=0, dtype=theano.config.floatX)
loss_mat = encoder.loss_mat
if args.aspect < 0:
loss_vec = T.mean(loss_mat, axis=1)
else:
assert args.aspect < self.nclasses
loss_vec = loss_mat[:,args.aspect]
self.loss_vec = loss_vec
coherent_factor = args.sparsity * args.coherent
loss = self.loss = T.mean(loss_vec)
sparsity_cost = self.sparsity_cost = T.mean(zsum) * args.sparsity + \
T.mean(zdiff) * coherent_factor
cost_vec = loss_vec + zsum * args.sparsity + zdiff * coherent_factor
cost_logpz = T.mean(cost_vec * T.sum(logpz, axis=0))
self.obj = T.mean(cost_vec)
params = self.params = [ ]
for l in layers + [ output_layer ]:
for p in l.params:
params.append(p)
nparams = sum(len(x.get_value(borrow=True).ravel()) \
for x in params)
say("total # parameters: {}\n".format(nparams))
l2_cost = None
for p in params:
if l2_cost is None:
l2_cost = T.sum(p**2)
else:
l2_cost = l2_cost + T.sum(p**2)
l2_cost = l2_cost * args.l2_reg
cost = self.cost = cost_logpz * 10 + l2_cost
print "cost.dtype", cost.dtype
self.cost_e = loss * 10 + encoder.l2_cost
# hashlib is only available in Python >= 2.5. I still want to support
# older Pythons so I import md5 if hashlib is not available. Fortunately
# md5 can masquerade as hashlib for my purposes.
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
utils.say("Oooo 'ello, I'm Mrs. Premise!")
params = utils.read_params()
# Create the message queue.
mq = posix_ipc.MessageQueue(params["MESSAGE_QUEUE_NAME"], posix_ipc.O_CREX)
# The first message is a random string (the current time).
s = time.asctime()
utils.say("Sending %s" % s)
mq.send(s)
what_i_sent = s
for i in range(0, params["ITERATIONS"]):
utils.say("iteration %d" % i)
PY_MAJOR_VERSION = sys.version_info[0]
# hashlib is only available in Python >= 2.5. I still want to support
# older Pythons so I import md5 if hashlib is not available. Fortunately
# md5 can masquerade as hashlib for my purposes.
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import posix_ipc
# Utils for this demo
import utils
utils.say("Oooo 'ello, I'm Mrs. Conclusion!")
params = utils.read_params()
# Mrs. Premise has already created the message queue. I just need a handle
# to it.
mq = posix_ipc.MessageQueue(params["MESSAGE_QUEUE_NAME"])
what_i_sent = ""
for i in range(0, params["ITERATIONS"]):
utils.say("iteration %d" % i)
s, _ = mq.receive()
s = s.decode()
utils.say("Received %s" % s)
try:
import hashlib
except ImportError:
import md5 as hashlib
# 3rd party modules
import sysv_ipc
# Utils for this demo
import utils
if PY_MAJOR_VERSION > 2:
import utils_for_3 as flex_utils
else:
import utils_for_2 as flex_utils
utils.say("Oooo 'ello, I'm Mrs. Conclusion!")
params = utils.read_params()
semaphore = sysv_ipc.Semaphore(params["KEY"])
memory = sysv_ipc.SharedMemory(params["KEY"])
utils.say("memory attached at %d" % memory.address)
what_i_wrote = ""
s = ""
for i in range(0, params["ITERATIONS"]):
utils.say("i = %d" % i)
if not params["LIVE_DANGEROUSLY"]:
# Wait for Mrs. Premise to free up the semaphore.
