def test_text_seq2seq_model(self):
# init token_dicts
token_dicts = TokenDicts('tests/data/dicts', {'query': 0})
data_field_list = []
# param = ["name", "processor", "type", "dtype", "shape", "max_len", "token_dict_name"]
data_field_list.append(
DataSchema(name='query',
processor='to_tokenid',
dtype='int32',
shape=(None, ),
is_with_len=False,
token_dict_name='query'))
label_field = DataSchema(name='label',
processor='to_tokenid',
dtype='int32',
shape=(None, ),
is_with_len=True,
token_dict_name='query')
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data/raw_datasets',
file_suffix='text_seq2seq.input')
dataset = generator.generate_dataset(batch_size=12,
num_epochs=1,
is_shuffle=False)
for (batchs, (inputs, targets)) in enumerate(dataset):
print('bacths', batchs, 'inputs', inputs, 'targets', targets)
def test_raw_query_float_dataset(self):
# init token_dicts
token_dicts = TokenDicts('tests/data/dicts', {'query': 0})
data_field_list = []
# param = ["name", "processor", "type", "dtype", "shape", "max_len", "token_dict_name"]
data_field_list.append(
DataSchema(name='query',
processor='to_tokenid',
dtype='int32',
shape=(None, ),
is_with_len=True,
token_dict_name='query'))
data_field_list.append(
DataSchema(name='width',
processor='to_np',
dtype='float32',
shape=(4)))
label_field = DataSchema(name='label',
processor='to_np',
dtype='float32',
shape=(1, ))
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data/raw_datasets',
file_suffix='query_float.input')
dataset = generator.generate_dataset(batch_size=12,
num_epochs=1,
is_shuffle=False)
for _ in enumerate(dataset):
pass
def test_text_seq2seq_model(self):
"""Test something."""
# init token_dicts
gpus = tf.config.experimental.list_physical_devices('GPU')
print(gpus)
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
token_dicts = TokenDicts('tests/data/dicts', {'query': 0})
data_field_list = []
#param = ["name", "processor", "type", "dtype", "shape", "max_len", "token_dict_name"]
data_field_list.append(
DataSchema(name='query',
processor='to_tokenid',
dtype='int32',
shape=(None, ),
is_with_len=False,
token_dict_name='query'))
label_field = DataSchema(name='label',
processor='to_np',
dtype='float32',
shape=(1, ),
is_with_len=False)
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data',
file_suffix='text_classifier.input')
dataset = generator.generate_dataset(batch_size=16,
num_epochs=20,
is_shuffle=False)
for (batchs, (inputs, targets)) in enumerate(dataset):
print('bacths', batchs, 'inputs', inputs, 'targets', targets)
if batchs > 3:
break
query_vocab_size = token_dicts.dict_size_by_name('query')
print('query_size', query_vocab_size)
optimizer = tf.keras.optimizers.Adam()
model = ClassiferModel(optimizer=optimizer,
loss='binary_crossentropy',
model=self.create_model(query_vocab_size))
# model.summary()
model.fit(dataset, 12, epochs=8, bar_step=10)
model.model.save_weights('model.h5')
def test_text_seq2seq_model(self):
"""Test something."""
# init token_dicts
gpus = tf.config.experimental.list_physical_devices('GPU')
print(gpus)
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
token_dicts = TokenDicts('tests/data/dicts', {'query': 0})
data_field_list = []
#param = ["name", "processor", "type", "dtype", "shape", "max_len", "token_dict_name"]
data_field_list.append(DataSchema(name='query', processor='to_tokenid',
dtype='int32', shape=(None,), is_with_len=False, token_dict_name='query'))
label_field = DataSchema(
name='label', processor='to_tokenid', dtype='int32', shape=(None,), is_with_len=True, token_dict_name='query')
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser, file_path='tests/data', file_suffix='text_seq2seq.input')
dataset = generator.generate_dataset(batch_size=64, num_epochs=900, is_shuffle=False)
# for (batchs, (inputs, targets)) in enumerate(dataset):
# #print('bacths', batchs, 'inputs', inputs, 'targets', targets)
# if batchs > 3:
# break
query_vocab_size = token_dicts.dict_size_by_name('query')
print('query_size', query_vocab_size)
print('<s>', token_dicts.to_id('query', '<s>'))
print(r'<\s>', token_dicts.to_id('query', r'<\s>'))
encoder = GRUEncoder(64, query_vocab_size)
decoder = GRUDecoder(64, query_vocab_size, query_vocab_size)
optimizer = tf.keras.optimizers.Adam()
model = Seq2seqModel(optimizer, seq2seq_cross_entropy_loss, encoder, decoder, feature_fields=data_field_list, label_fields=[label_field])
# model.model.summary()
#plot_model(model.model, 'model.png', show_shapes=True)
model.model.load_weights('model.h5')
#model.fit(dataset, 64, epochs=20, bar_step=20)
#model.model.save_weights('model.h5')
for (batchs, (inputs, targets)) in enumerate(dataset):
#result = model.predict(inputs)
result = model.predict_beam(inputs)
print('target', targets, 'predicts', result)
if batchs > 1:
break
def test_session_parser_dataset(self):
# init token_dicts
token_dicts = TokenDicts('tests/data/dicts', {'query': 0})
data_field_list = []
# param = ["name", "processor", "type", "dtype", "shape", "max_len", "token_dict_name"]
data_field_list.append(
DataSchema(name='query',
processor='to_tokenid',
dtype='int32',
shape=(
None,
None,
),
is_with_len=False,
token_dict_name='query',
has_session=True))
data_field_list.append(
DataSchema(name='width',
processor='to_np',
dtype='float32',
shape=(
None,
4,
),
has_session=True))
label_field = DataSchema(name='label',
processor='to_np',
dtype='float32',
shape=(1, ))
parser = SeqParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data/seq_datasets',
file_suffix='simple_seq.input')
print('Shapes', generator._get_shapes(is_training=True))
dataset = generator.generate_dataset(batch_size=12,
num_epochs=1,
is_shuffle=False)
for _ in enumerate(dataset):
pass
def processor(filepath, filename, tfrecord_filename):
token_dicts = None
data_field_list = []
data_field_list.append(
DataSchema(name='query',
processor='to_np',
type=tf.int32,
dtype='int32',
shape=(None, ),
is_with_len=True))
label_field = DataSchema(name='label',
processor='to_np',
type=tf.float32,
dtype='float32',
shape=(1, ),
is_with_len=False)
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path=filepath,
file_suffix=filename)
generator.to_tfrecords(tfrecord_filename)
return tfrecord_filename
def test_raw_dataset_varnum(self):
token_dicts = None
data_field_list = []
data_field_list.append(
DataSchema(name='query',
processor='to_np',
dtype='int32',
shape=(None, ),
is_with_len=True))
label_field = DataSchema(name='label',
processor='to_np',
dtype='float32',
shape=(1, ),
is_with_len=False)
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data/raw_datasets',
file_suffix='varnum.input')
self.pass_allway_dataset(generator, 4)
def test_tfrecord_dataset_varnum_writer_and_reader(self):
token_dicts = None
data_field_list = []
data_field_list.append(
DataSchema(name='query',
processor='to_np',
dtype='int32',
shape=(None, ),
is_with_len=True))
label_field = DataSchema(name='label',
processor='to_np',
dtype='float32',
shape=(1, ),
is_with_len=False)
parser = TextlineParser(token_dicts, data_field_list, label_field)
generator = TFDataset(parser=parser,
file_path='tests/data/raw_datasets',
file_suffix='varnum.input')
if not os.path.exists('outputs'):
os.mkdir('outputs')
generator.to_tfrecords('outputs/file.tfrecord')
generator = TFDataset(parser=parser,
file_path='outputs',
file_suffix='file.tfrecord',
file_system='tfrecord')
dataset = generator.generate_dataset(batch_size=1, num_epochs=1)
for d in dataset.take(4):
print(d)
def weight(_):
return 1.2
parser = TextlineParser(token_dicts,
data_field_list,
label_field,
weight_fn=weight)
generator = TFDataset(parser=parser,
file_path='tests/data/raw_datasets',
file_suffix='varnum.input')
if not os.path.exists('outputs'):
os.mkdir('outputs')
generator.to_tfrecords('outputs/file_weight.tfrecord')
generator = TFDataset(parser=parser,
file_path='outputs',
file_suffix='file_weight.tfrecord',
file_system='tfrecord')
dataset = generator.generate_dataset(batch_size=1, num_epochs=1)
for d in dataset.take(4):
print(d)