def print_everything(some_json, alias_name):
stuff = json.loads(some_json)
aliases = create_type_alias(stuff, type_alias_name=alias_name)
decoders = [create_decoder(alias, has_snakecase=True, prefix='decode') for alias in aliases ]
encoders = [create_encoder(alias, has_snakecase=True, prefix='encode') for alias in aliases ]
print('\n'.join(aliases))
print('\n'.join(decoders))
print('\n'.join(encoders))
def from_elm_file(file_text):
aliases = find_type_aliases(file_text)
unions = find_union_types(file_text)
decoders = [create_decoder(alias, has_snakecase=True, prefix='decode') for alias in aliases ]
decoders.extend(create_union_type_decoder(union_type) for union_type in unions)
encoders = [create_encoder(alias, has_snakecase=True, prefix='encode') for alias in aliases ]
encoders.extend(create_union_type_encoder(union_type) for union_type in unions)
print('\n'.join(decoders))
print('\n'.join(encoders))
def __init__(self,units,vocab_size,use_att= False,dropout_rate=0.5,isBidirectional=True):
super(JointModel,self).__init__()
self._dropout_rate = dropout_rate
self._isBidirectional = isBidirectional
self._units = units
## encoder进行特征抽取
self._encoder = create_encoder(self._units,dropout_rate = dropout_rate,isBidirectional=isBidirectional)
if use_att:
name = 'ATT'
else:
name = 'basic'
## decoder进行序列解析
self._decoder = create_decoder(name,self._units,dropout_rate,False)
## classification的decoder
self._clas_decoder = create_decoder(name,self._units,dropout_rate,False,vocab_size)
def print_everything(some_json, alias_name):
stuff = json.loads(some_json)
aliases = create_type_alias(stuff, type_alias_name=alias_name)
decoders = [
create_decoder(alias, has_snakecase=True, prefix='decode')
for alias in aliases
]
encoders = [
create_encoder(alias, has_snakecase=True, prefix='encode')
for alias in aliases
]
print('\n'.join(aliases))
print('\n'.join(decoders))
print('\n'.join(encoders))
def from_elm_file(file_text):
aliases = find_type_aliases(file_text)
unions = find_union_types(file_text)
decoders = [
create_decoder(alias, has_snakecase=True, prefix='decode')
for alias in aliases
]
decoders.extend(
create_union_type_decoder(union_type) for union_type in unions)
encoders = [
create_encoder(alias, has_snakecase=True, prefix='encode')
for alias in aliases
]
encoders.extend(
create_union_type_encoder(union_type) for union_type in unions)
print('\n'.join(decoders))
print('\n'.join(encoders))
def test():
testJson = """
{ "name" : "Noah"
, "age" : 23
, "location" :
{ "name" : "sweden"
, "days" : 45
}
}
"""
stuff = json.loads(testJson)
print('Creating type alias')
aliases = create_type_alias(stuff, type_alias_name='Assignment')
print('\n'.join(aliases))
print('Creating decoder')
decoders = []
for alias in aliases:
decoder = create_decoder(alias, has_snakecase=True, prefix='decode')
decoders.append(decoder)
print('\n'.join(decoders))
print_everything(
"""
{ "name" : "Noah"
, "age" : 23
, "location" :
{ "name" : "sweden"
, "days" : 45
}
}
"""
, alias_name = "Person")
print(create_union_type_decoder('type Action = Run | Hide | Noop'))
print(create_union_type_encoder('type Action = Run | Hide | Noop'))
def test():
testJson = """
{ "name" : "Noah"
, "age" : 23
, "location" :
{ "name" : "sweden"
, "days" : 45
}
}
"""
stuff = json.loads(testJson)
print('Creating type alias')
aliases = create_type_alias(stuff, type_alias_name='Assignment')
print('\n'.join(aliases))
print('Creating decoder')
decoders = []
for alias in aliases:
decoder = create_decoder(alias, has_snakecase=True, prefix='decode')
decoders.append(decoder)
print('\n'.join(decoders))
print_everything("""
{ "name" : "Noah"
, "age" : 23
, "location" :
{ "name" : "sweden"
, "days" : 45
}
}
""",
alias_name="Person")
print(create_union_type_decoder('type Action = Run | Hide | Noop'))
print(create_union_type_encoder('type Action = Run | Hide | Noop'))
def __init__(self,pathObj,m,n,scale = True,feature_set='basic',use_att=True,seperate_static=False,isBidirectional=False,use_l2 = True):
## 文件的路径
self._pathObj = pathObj
self._m = m
self._n = n
## 是否对数据进行归一化
self._scale = scale
## 特征集合 basic-author-structure三类
self._feature_set = feature_set
##使用attention
self._use_att = use_att
## 静态变量是否直接进行串联
self._seperate_static = seperate_static
## 是否使用bi-directional的GRU
self._isBidirectional = isBidirectional
## 是否使用l2的weight
self._use_l2 = use_l2
if self._use_l2:
self._l2_weight = 0.001
## 加载数据
if not self._seperate_static:
self._train_X,self._test_X,self._valid_X,self._dx_mean,self._dx_std,\
self._train_Y,self._test_Y,self._valid_Y,self._y_mean,self._y_std,\
self._test_sorted_ids = construct_RNN_datasets(pathObj,m,n,self._scale,self._feature_set)
else:
self._train_X,self._test_X,self._valid_X,self._dx_mean,self._dx_std,\
self._train_SX,self._test_SX,self._valid_SX,self._sx_mean,self._sx_std,\
self._train_Y,self._test_Y,self._valid_Y,self._y_mean,self._y_std,\
self._test_sorted_ids = construct_RNN_datasets(pathObj,m,n,self._scale,self._feature_set,seperate_static=self._seperate_static)
## 数据集
self._batch_sz = 512
self._buffer_size = len(self._train_Y)
self._n_batchs =self._buffer_size//self._batch_sz
if not self._seperate_static:
self._dataset = tf.data.Dataset.from_tensor_slices((self._train_X,self._train_Y)).shuffle(self._buffer_size)
else:
self._dataset = tf.data.Dataset.from_tensor_slices((self._train_X,self._train_SX,self._train_Y)).shuffle(self._buffer_size)
self._dataset = self._dataset.batch(self._batch_sz, drop_remainder=True)
### test
self._test_buffer_size = len(self._test_Y)
self._n_test_batchs = self._test_buffer_size//self._batch_sz if self._test_buffer_size%self._batch_sz==0 else self._test_buffer_size//self._batch_sz+1
if not self._seperate_static:
self._test_dataset = tf.data.Dataset.from_tensor_slices((self._test_X,self._test_Y)).shuffle(self._buffer_size)
else:
self._test_dataset = tf.data.Dataset.from_tensor_slices((self._test_X,self._test_SX,self._test_Y)).shuffle(self._test_buffer_size)
self._test_dataset = self._test_dataset.batch(self._batch_sz, drop_remainder=True)
self._valid_buffer_size = len(self._valid_Y)
self._n_valid_batchs = self._valid_buffer_size//self._batch_sz if self._valid_buffer_size%self._batch_sz==0 else self._valid_buffer_size//self._batch_sz+1
if not self._seperate_static:
self._valid_dataset = tf.data.Dataset.from_tensor_slices((self._valid_X,self._valid_Y)).shuffle(self._buffer_size)
else:
self._valid_dataset = tf.data.Dataset.from_tensor_slices((self._valid_X,self._valid_SX,self._valid_Y)).shuffle(self._valid_buffer_size)
self._valid_dataset = self._valid_dataset.batch(self._batch_sz, drop_remainder=True)
## dropout rate
self._dropout_rate = 0.5
self._units = 128
## 初始化encoder以及decoder
self._model_name = self.gen_model_name()
self._encoder = create_encoder(self._units,self._dropout_rate,self._isBidirectional)
self._decoder = create_decoder(self._model_name,self._units,self._dropout_rate,self._seperate_static)
print('train model {}.'.format(self._model_name))
## optimizer
self._optimizer = tf.keras.optimizers.Adam(learning_rate=5e-4,beta_2=0.95)
## 模型的保存位置
self._checkpoint_dir = './trainning_checkpoints_{}_{}_{}'.format(self._model_name, m,n)
self._checkpoint_prefix = os.path.join(self._checkpoint_dir, "ckpt")
self._trackables = {}
self._trackables['optimizer']=self._optimizer
self._trackables['encoder']=self._encoder
self._trackables['decoder']=self._decoder
self._checkpoint = tf.train.Checkpoint(**self._trackables)
