提交 efcd1c08 编写于 作者: F frankwhzhang

fix model style

上级 ee6bd53b
......@@ -133,12 +133,28 @@ class Model(object):
print(">>>>>>>>>>>.learnig rate: %s" % learning_rate)
return self._build_optimizer(optimizer, learning_rate)
@abc.abstractmethod
def input_data(self, is_infer=False):
return None
def net(self, is_infer=False):
return None
def train_net(self):
"""R
"""
pass
input_data = self.input_data(is_infer=False)
self._data_var = input_data
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
self.net(input_data, is_infer=False)
@abc.abstractmethod
def infer_net(self):
pass
input_data = self.input_data(is_infer=True)
self._infer_data_var = input_data
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
self.net(input_data, is_infer=True)
......@@ -56,68 +56,12 @@ class Model(ModelBase):
inputs = [user_slot_names] + [item_slot_names] + [lens] + [labels]
# demo: hot to use is_infer:
if is_infer:
self._infer_data_var = inputs
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
return inputs
else:
self._data_var = inputs
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
return inputs
def _fluid_sequence_pad(self, input, pad_value, maxlen=None):
"""
args:
input: (batch*seq_len, dim)
returns:
(batch, max_seq_len, dim)
"""
pad_value = fluid.layers.cast(
fluid.layers.assign(input=np.array([pad_value], 'float32')),
input.dtype)
input_padded, _ = fluid.layers.sequence_pad(
input, pad_value,
maxlen=maxlen) # (batch, max_seq_len, 1), (batch, 1)
# TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
return input_padded
def _fluid_sequence_get_pos(self, lodtensor):
"""
args:
lodtensor: lod = [[0,4,7]]
return:
pos: lod = [[0,4,7]]
data = [0,1,2,3,0,1,3]
shape = [-1, 1]
"""
lodtensor = fluid.layers.reduce_sum(lodtensor, dim=1, keep_dim=True)
assert lodtensor.shape == (-1, 1), (lodtensor.shape())
ones = fluid.layers.cast(lodtensor * 0 + 1,
'float32') # (batch*seq_len, 1)
ones_padded = self._fluid_sequence_pad(ones,
0) # (batch, max_seq_len, 1)
ones_padded = fluid.layers.squeeze(ones_padded,
[2]) # (batch, max_seq_len)
seq_len = fluid.layers.cast(
fluid.layers.reduce_sum(
ones_padded, 1, keep_dim=True), 'int64') # (batch, 1)
seq_len = fluid.layers.squeeze(seq_len, [1])
pos = fluid.layers.cast(
fluid.layers.cumsum(
ones_padded, 1, exclusive=True), 'int64')
pos = fluid.layers.sequence_unpad(pos, seq_len) # (batch*seq_len, 1)
pos.stop_gradient = True
return pos
def net(self, inputs, is_infer=False):
# user encode
user_embedding = fluid.embedding(
......@@ -225,10 +169,55 @@ class Model(ModelBase):
self._cost = loss
self._metrics['auc'] = auc_val
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def _fluid_sequence_pad(self, input, pad_value, maxlen=None):
"""
args:
input: (batch*seq_len, dim)
returns:
(batch, max_seq_len, dim)
"""
pad_value = fluid.layers.cast(
fluid.layers.assign(input=np.array([pad_value], 'float32')),
input.dtype)
input_padded, _ = fluid.layers.sequence_pad(
input, pad_value,
maxlen=maxlen) # (batch, max_seq_len, 1), (batch, 1)
# TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
return input_padded
def _fluid_sequence_get_pos(self, lodtensor):
"""
args:
lodtensor: lod = [[0,4,7]]
return:
pos: lod = [[0,4,7]]
data = [0,1,2,3,0,1,3]
shape = [-1, 1]
"""
lodtensor = fluid.layers.reduce_sum(lodtensor, dim=1, keep_dim=True)
assert lodtensor.shape == (-1, 1), (lodtensor.shape())
ones = fluid.layers.cast(lodtensor * 0 + 1,
'float32') # (batch*seq_len, 1)
ones_padded = self._fluid_sequence_pad(ones,
0) # (batch, max_seq_len, 1)
ones_padded = fluid.layers.squeeze(ones_padded,
[2]) # (batch, max_seq_len)
seq_len = fluid.layers.cast(
fluid.layers.reduce_sum(
ones_padded, 1, keep_dim=True), 'int64') # (batch, 1)
seq_len = fluid.layers.squeeze(seq_len, [1])
pos = fluid.layers.cast(
fluid.layers.cumsum(
ones_padded, 1, exclusive=True), 'int64')
pos = fluid.layers.sequence_unpad(pos, seq_len) # (batch*seq_len, 1)
pos.stop_gradient = True
return pos
#def train_net(self):
# input_data = self.input_data()
# self.net(input_data)
def infer_net(self):
input_data = self.input_data(is_infer=True)
self.net(input_data, is_infer=True)
#def infer_net(self):
# input_data = self.input_data(is_infer=True)
# self.net(input_data, is_infer=True)
......@@ -44,11 +44,7 @@ class EvaluateReader(Reader):
length = [self.item_len] * self.batch_size
label = np.random.randint(
2, size=(self.batch_size, self.item_len)).tolist()
output = []
output.append(user_slot_name)
output.append(item_slot_name)
output.append(length)
output.append(label)
output = [user_slot_name, item_slot_name, length, label]
yield output
......
......@@ -44,11 +44,7 @@ class TrainReader(Reader):
length = [self.item_len] * self.batch_size
label = np.random.randint(
2, size=(self.batch_size, self.item_len)).tolist()
output = []
output.append(user_slot_name)
output.append(item_slot_name)
output.append(length)
output.append(label)
output = [user_slot_name, item_slot_name, length, label]
yield output
......
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