def main():
args = parse_args()
print('Audio socket classifier')
print("Restoring model: ", args.model)
mdl = model.restore(args.model)
if mdl is None:
print("Can't classify without an existing model")
return
endpoints.serverFromString(reactor, "tcp:80").listen(PubFactory(mdl, args))
reactor.run()
def main():
"""
Loads an existing model, opens audio input stream, classifies input
"""
args = parse_args()
print('Audio stream classifier')
print("Restoring model: ", args.model)
mdl = model.restore(args.model)
if mdl is None:
print("Can't classify data without an existing model.")
return
print("Opening audio input..")
audio = pyaudio.PyAudio()
stream = audio.open(format=pyaudio.paFloat32,
channels=1,
rate=args.sample_rate,
input=True,
frames_per_buffer=args.frame_size)
label_a = label_b = ""
if args.labels is not None:
label_a = args.labels[0]
label_b = args.labels[1]
while True:
# Peel off [frame_size] bytes from the audio stream
stream_data = stream.read(args.frame_size)
# Unpack the binary stream and expand
data = struct.unpack("%df" % args.frame_size, stream_data)
data = np.expand_dims([data], axis=2)
avg = model.classify(mdl, data)
steps = 20
a = int(math.ceil(avg * steps))
b = steps - a
print(label_a + " [" + ("." * a) + "|" + ("." * b) + "] " + label_b +
" - " + str(avg),
end='\r')
import numpy as np
import model
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("diffLatentSpace", 6)
data = np.load("./npz/diffs.npz")["arr_1"]
model = model.emptyModel("DIFF_17jan_ls6_g",
inputsShape=list(data.shape[1:]),
use="diff",
log=False)
model.restore("DIFF_17jan_ls6_f")
code = [[0, 0, 0, 0, 0, 0]]
result = model.generate(code, data[0:1])
print(result)
print(result.shape)
parser.add_argument('--connected-components-threshold', type=float, default=0.05)
parser.add_argument('--width', type=int, default=768, help='input image width')
parser.add_argument('--height', type=int, default=1024, help='input image height')
opts = parser.parse_args()
# feed data through an explicit placeholder to avoid using tf.data
imgs = tf.placeholder(dtype=tf.float32, shape=(1, opts.height, opts.width, 3), name='input_imgs')
# restore model
model = model.Model(imgs,
is_training=False,
use_skip_connections=not opts.no_use_skip_connections,
base_filter_size=opts.base_filter_size,
use_batch_norm=not opts.no_use_batch_norm)
sess = tf.Session()
model.restore(sess, "ckpts/%s" % opts.run)
if opts.output_label_db:
db = LabelDB(label_db_file=opts.output_label_db)
db.create_if_required()
else:
db = None
if opts.export_pngs:
export_dir = "predict_examples/%s" % opts.run
print("exporting prediction samples to [%s]" % export_dir)
if not os.path.exists(export_dir):
os.makedirs(export_dir)
# TODO: make this batched to speed it up for larger runs
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 50.0)
SET_HYPERPARAMETER("diffLatentSpace", 30)
data = np.load("./npz/diffsWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateSmoothDiff",
inputsShape=list(goods[0].shape),
use="diff",
log=False)
model.restore("28jan_ls30")
trainDiffs = model.reproduce(goods)
testDiffs = model.reproduce(bads)
np.savez("./npz/smoothDiffsWithNames_ls30.npz", trainDiffs, testDiffs,
data["arr_2"], data["arr_3"])
import numpy as np
import os
import jieba
import config
C = config.Config()
jieba.load_userdict('data/coal_dict.txt')
model = model.Model()
labels = util.labelGenerator()
compute_graph = tf.get_default_graph()
with compute_graph.as_default():
model.lstm_crf_predict() #Define graph
model.load_word2vec() #Initialize all variables
model.restore()
trainHelper = util.trainHelper(
model.word2vector) #Train helper do the padding
# for x,y in batchLoader:
# x_raw = x.copy()
# x,y,sequence_length,seq_max_len = trainHelper.generateBatch(x,y)
# viterbi_sequence = model.predict(x,sequence_length,seq_max_len)
# print(x_raw[0])
# print(labels.ID2label(viterbi_sequence[0][0]))
while True:
seg_X = input("Input your sentence :").replace(' ',
'').replace('\n', '')
x = list(jieba.cut(seg_X))
x_raw = x.copy()
x, sequence_length, seq_max_len = trainHelper.generateData4Predict(x)
x = np.reshape(x, (1, x.shape[0]))
from model import restore restore()
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 300.0)
SET_HYPERPARAMETER("learningRate", 0.0005)
SET_HYPERPARAMETER("diffLatentSpace", 5)
SET_HYPERPARAMETER("normalize", "individual")
files = np.load("./npz/diffsWithNames.npz")
goods = files["arr_0"]
bads = files["arr_1"]
data = np.concatenate([bads, goods])
model = model.emptyModel("5feb-ls5-inorm_f",
inputsShape=list(data.shape[1:]),
use="diff")
model.restore("5feb-ls5-inorm_e")
model.train(epoch=100, dataset=data)
model.save()
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 300.0)
SET_HYPERPARAMETER("diffLatentSpace", 12)
SET_HYPERPARAMETER("normalize", "individual")
data = np.load("./npz/diffsWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateEncode",
use="diff",
log=False,
inputsShape=list(goods[0].shape))
model.restore("5feb-ls12-inorm_d")
testEncoded = model.encode(bads)
trainEncoded = model.encode(goods)
np.savez("./npz/codesWithNames_inorm.npz", trainEncoded, testEncoded,
data["arr_2"], data["arr_3"])
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("contrast", 50.0)
SET_HYPERPARAMETER("diffLatentSpace", 8)
data = np.load("./npz/diffsWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateFeatures",
use="diff",
log=False,
inputsShape=list(goods[0].shape))
model.restore("DIFF_23jan_ls8_e")
testFeatures = model.getFeatures(bads)
trainFeatures = model.getFeatures(goods)
np.savez("./npz/featuresWithNames_ls8.npz", trainFeatures, testFeatures,
data["arr_2"], data["arr_3"])
testdata.read(testpath, wikidata.char_id)
webdata = data.Dataset(params)
webdata.read(webpath, wikidata.char_id)
print('data loaded')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
model = model.Model(params)
model.model_build(sess)
print('model built')
model.model_initialize(sess)
print('model initialize')
# model.lattice_train(wikidata, sess, 300000)
# print('wiki_lattice finished')
# model.train(traindata, sess, 40000)
# print('all_train finished')
# model.evaluate(testdata, sess)
# model.train(traindata, sess, 40000)
# print('train finished')
# model.evaluate(testdata, sess)
# print('lattice and text at the same time')
model.restore('./calc_data/lattice2textlattice2divide_data/all_finished_save/model.ckpt', sess)
wiki = [ x for x in open('hoge', 'r') ]
for text in wiki:
model.demo(text.strip(), wikidata.char_id, sess)
import model
import numpy as np
from model import SET_HYPERPARAMETER
SET_HYPERPARAMETER("latentSpace", 1)
data = np.load("./npz/dataWithNames.npz")
goods = data["arr_0"]
bads = data["arr_1"]
model = model.emptyModel("generateDiff",
inputsShape=list(goods[0].shape),
log=False,
use="jtekt")
model.restore("4feb-ls1")
trainDiffs = model.getDiff(goods)
testDiffs = model.getDiff(bads)
np.savez("./npz/diffsWithNames.npz", trainDiffs, testDiffs, data["arr_2"],
data["arr_3"])
import os
import time
import tensorflow as tf
import util
import model
import ujson as json
if __name__ == "__main__":
test_data = list()
print('Start to process data...')
with open('test.jsonlines', 'r') as f:
for line in f:
tmp_example = json.loads(line)
test_data.append(tmp_example)
print('finish processing data')
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
config = util.initialize_from_env()
model = model.KnowledgePronounCorefModel(config)
with tf.Session() as session:
model.restore(session)
# print('we are working on NP-NP')
model.evaluate(session, test_data, official_stdout=True)
# model.evaluate(session)
# model.evaluate_baseline_methods(session)
print('end')
def main():
args = parse_args()
print('Audio classifier')
output_prefix = os.path.splitext(args.data_file)[0]
if not os.path.exists(args.logdir):
os.mkdir(args.logdir)
# create / restore model
mdl = None
if args.model:
print("Restoring model..")
print(" input:", args.model)
mdl = model.restore(args.model)
elif model.model_exists(args.logdir) and not args.force:
print("Restoring last created model..")
file = model.most_recent(args.logdir)
print(" input:", file)
mdl = model.restore(file)
if not args.predict:
# training task
print("Loading training data..")
print(" input:", args.data_file)
# load data from file
X, Y = data.load_labeled_data(args.data_file)
w, h = np.shape(X)
print(" size: {}x{}".format(w, h))
# encode labels
Y, Dict = data.encode_labels(Y)
print(" labels: ", Dict)
# save labels with data
# model.save_labels(Dict, args.logdir + output_prefix + "_labels.txt")
if mdl is None:
print("Creating new model..")
mdl = model.create(h)
train(mdl, (X, Y), args.logdir + output_prefix, args.kfolds,
args.epochs)
else:
# prediction task
print("Loading classification data..")
print(" input:", args.data_file)
X = data.load_unlabeled_data(args.data_file)
w, h = np.shape(X)
print(" size: {}x{}".format(w, h))
if mdl is None:
print("Can't classify without an existing model")
return
classify(mdl, X)
