def __call__(self, input):
"""
Transform ImageSet or TextSet.
"""
# move the import here to break circular import
if "zoo.feature.image.imageset.ImageSet" not in sys.modules:
from zoo.feature.image import ImageSet
if "zoo.feature.text.text_set.TextSet" not in sys.modules:
from zoo.feature.text import TextSet
# if type(input) is ImageSet:
if isinstance(input, ImageSet):
jset = callBigDlFunc(self.bigdl_type, "transformImageSet", self.value, input)
return ImageSet(jvalue=jset)
elif isinstance(input, TextSet):
jset = callBigDlFunc(self.bigdl_type, "transformTextSet", self.value, input)
return TextSet(jvalue=jset)
def predict(self, x, batch_per_thread=4, distributed=True):
"""
Use a model to do prediction.
# Arguments
x: Prediction data. A Numpy array or RDD of Sample or ImageSet.
batch_per_thread:
The default value is 4.
When distributed is True,the total batch size is batch_per_thread * rdd.getNumPartitions.
When distributed is False the total batch size is batch_per_thread * numOfCores.
distributed: Boolean. Whether to do prediction in distributed mode or local mode.
Default is True. In local mode, x must be a Numpy array.
"""
if isinstance(x, ImageSet) or isinstance(x, TextSet):
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
x,
batch_per_thread)
return ImageSet(results) if isinstance(x, ImageSet) else TextSet(results)
if distributed:
if isinstance(x, np.ndarray):
data_rdd = to_sample_rdd(x, np.zeros([x.shape[0]]))
elif isinstance(x, RDD):
data_rdd = x
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
data_rdd,
batch_per_thread)
return results.map(lambda result: Layer.convert_output(result))
else:
if isinstance(x, np.ndarray) or isinstance(x, list):
results = callBigDlFunc(self.bigdl_type, "zooPredict",
self.value,
self._to_jtensors(x),
batch_per_thread)
return [Layer.convert_output(result) for result in results]
else:
raise TypeError("Unsupported prediction data type: %s" % type(x))
parser.add_option("--training_split", dest="training_split", default="0.8")
parser.add_option("-b", "--batch_size", dest="batch_size", default="128")
parser.add_option("-e", "--nb_epoch", dest="nb_epoch", default="20")
parser.add_option("-l",
"--learning_rate",
dest="learning_rate",
default="0.01")
parser.add_option("--log_dir",
dest="log_dir",
default="/tmp/.analytics-zoo")
parser.add_option("-m", "--model", dest="model")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("Text Classification Example")
text_set = TextSet.read(path=options.data_path).to_distributed(
sc, int(options.partition_num))
print("Processing text dataset...")
transformed = text_set.tokenize().normalize()\
.word2idx(remove_topN=10, max_words_num=int(options.max_words_num))\
.shape_sequence(len=int(options.sequence_length)).generate_sample()
train_set, val_set = transformed.random_split(
[float(options.training_split), 1 - float(options.training_split)])
if options.model:
model = TextClassifier.load_model(options.model)
else:
token_length = int(options.token_length)
if not (token_length == 50 or token_length == 100
or token_length == 200 or token_length == 300):
raise ValueError(
'token_length for GloVe can only be 50, 100, 200, 300, but got '
parser.add_option("--data_path", dest="data_path")
parser.add_option("--embedding_file", dest="embedding_file")
parser.add_option("--question_length", dest="question_length", default="10")
parser.add_option("--answer_length", dest="answer_length", default="40")
parser.add_option("--partition_num", dest="partition_num", default="4")
parser.add_option("-b", "--batch_size", dest="batch_size", default="200")
parser.add_option("-e", "--nb_epoch", dest="nb_epoch", default="30")
parser.add_option("-l", "--learning_rate", dest="learning_rate", default="0.001")
parser.add_option("-m", "--model", dest="model")
parser.add_option("--output_path", dest="output_path")
(options, args) = parser.parse_args(sys.argv)
sc = init_nncontext("QARanker Example")
q_set = TextSet.read_csv(options.data_path + "/question_corpus.csv",
sc, int(options.partition_num)).tokenize().normalize()\
.word2idx(min_freq=2).shape_sequence(int(options.question_length))
a_set = TextSet.read_csv(options.data_path+"/answer_corpus.csv",
sc, int(options.partition_num)).tokenize().normalize()\
.word2idx(min_freq=2, existing_map=q_set.get_word_index())\
.shape_sequence(int(options.answer_length))
train_relations = Relations.read(options.data_path + "/relation_train.csv",
sc, int(options.partition_num))
train_set = TextSet.from_relation_pairs(train_relations, q_set, a_set)
validate_relations = Relations.read(options.data_path + "/relation_valid.csv",
sc, int(options.partition_num))
validate_set = TextSet.from_relation_lists(validate_relations, q_set, a_set)
if options.model:
knrm = KNRM.load_model(options.model)
