提交 7e6def58 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #1756 from qingqing01/v2_api_multi_leaf_node

Add extra_layers in paddle.trainer.SGD.
......@@ -53,20 +53,29 @@ import data_type
__all__ = ['parse_network', 'data']
def parse_network(*outputs):
def parse_network(output_layers, extra_layers=None):
"""
Parse all output layers and then generate a ModelConfig object.
Parse all layers in the neural network graph and
then generate a ModelConfig object.
.. note::
This function is used internally in paddle.v2 module. User should never
invoke this method.
:param outputs: Output layers.
:type outputs: Layer
:param output_layers: Output layers.
:type output_layers: Layer
:param extra_layers: Some layers in the neural network graph are not in the
path of output_layers.
:type extra_layers: Layer
:return: A ModelConfig object instance.
:rtype: ModelConfig
"""
if not isinstance(output_layers, collections.Sequence):
output_layers = [output_layers]
if extra_layers is not None and not isinstance(extra_layers,
collections.Sequence):
extra_layers = [extra_layers]
def __real_func__():
"""
......@@ -74,7 +83,11 @@ def parse_network(*outputs):
the plain old paddle configuration function.
"""
context = dict()
real_output = [each.to_proto(context=context) for each in outputs]
real_output = [each.to_proto(context=context) for each in output_layers]
if extra_layers is not None:
extra_output = [
each.to_proto(context=context) for each in extra_layers
]
conf_helps.outputs(real_output)
return __parse__(__real_func__)
......
......@@ -59,13 +59,13 @@ class ImageLayerTest(unittest.TestCase):
num_channels=16,
pool_type=pooling.Max())
maxout = layer.maxout(input=conv, num_channels=16, groups=4)
print layer.parse_network(maxpool, spp, maxout)
print layer.parse_network([maxpool, spp, maxout])
def test_norm_layer(self):
norm1 = layer.img_cmrnorm(input=conv, size=5)
norm2 = layer.batch_norm(input=conv)
norm3 = layer.sum_to_one_norm(input=conv)
print layer.parse_network(norm1, norm2, norm3)
print layer.parse_network([norm1, norm2, norm3])
class AggregateLayerTest(unittest.TestCase):
......@@ -78,7 +78,8 @@ class AggregateLayerTest(unittest.TestCase):
first_seq = layer.first_seq(input=pixel)
concat = layer.concat(input=[last_seq, first_seq])
seq_concat = layer.seq_concat(a=last_seq, b=first_seq)
print layer.parse_network(pool, last_seq, first_seq, concat, seq_concat)
print layer.parse_network(
[pool, last_seq, first_seq, concat, seq_concat])
class MathLayerTest(unittest.TestCase):
......@@ -95,8 +96,10 @@ class MathLayerTest(unittest.TestCase):
tensor = layer.tensor(a=pixel, b=pixel, size=1000)
cos_sim = layer.cos_sim(a=pixel, b=pixel)
trans = layer.trans(input=tensor)
print layer.parse_network(addto, linear_comb, interpolation, power,
scaling, slope, tensor, cos_sim, trans)
print layer.parse_network([
addto, linear_comb, interpolation, power, scaling, slope, tensor,
cos_sim, trans
])
class ReshapeLayerTest(unittest.TestCase):
......@@ -110,7 +113,8 @@ class ReshapeLayerTest(unittest.TestCase):
repeat = layer.repeat(input=pixel, num_repeats=4)
reshape = layer.seq_reshape(input=pixel, reshape_size=4)
rotate = layer.rotate(input=pixel, height=16, width=49)
print layer.parse_network(block_expand, expand, repeat, reshape, rotate)
print layer.parse_network(
[block_expand, expand, repeat, reshape, rotate])
class RecurrentLayerTest(unittest.TestCase):
......@@ -119,7 +123,7 @@ class RecurrentLayerTest(unittest.TestCase):
recurrent = layer.recurrent(input=word)
lstm = layer.lstmemory(input=word)
gru = layer.grumemory(input=word)
print layer.parse_network(recurrent, lstm, gru)
print layer.parse_network([recurrent, lstm, gru])
class CostLayerTest(unittest.TestCase):
......@@ -139,10 +143,10 @@ class CostLayerTest(unittest.TestCase):
cost10 = layer.sum_cost(input=inference)
cost11 = layer.huber_cost(input=score, label=label)
print layer.parse_network(cost1, cost2)
print layer.parse_network(cost3, cost4)
print layer.parse_network(cost5, cost6)
print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
print layer.parse_network([cost1, cost2])
print layer.parse_network([cost3, cost4])
print layer.parse_network([cost5, cost6])
print layer.parse_network([cost7, cost8, cost9, cost10, cost11])
crf = layer.crf(input=inference, label=label)
crf_decoding = layer.crf_decoding(input=inference, size=3)
......@@ -151,8 +155,8 @@ class CostLayerTest(unittest.TestCase):
nce = layer.nce(input=inference, label=label, num_classes=3)
hsigmoid = layer.hsigmoid(input=inference, label=label, num_classes=3)
print layer.parse_network(crf, crf_decoding, ctc, warp_ctc, nce,
hsigmoid)
print layer.parse_network(
[crf, crf_decoding, ctc, warp_ctc, nce, hsigmoid])
class OtherLayerTest(unittest.TestCase):
......@@ -160,7 +164,7 @@ class OtherLayerTest(unittest.TestCase):
maxid = layer.max_id(input=inference)
sampling_id = layer.sampling_id(input=inference)
eos = layer.eos(input=maxid, eos_id=5)
print layer.parse_network(maxid, sampling_id, eos)
print layer.parse_network([maxid, sampling_id, eos])
def test_slicing_joining_layer(self):
pad = layer.pad(input=conv, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
......
......@@ -53,14 +53,26 @@ class Topology(object):
and network configs.
"""
def __init__(self, layers):
def __init__(self, layers, extra_layers=None):
def __check__(layers):
if not isinstance(layers, collections.Sequence):
__check_layer_type__(layers)
layers = [layers]
for layer in layers:
__check_layer_type__(layer)
return layers
layers = __check__(layers)
self.layers = layers
self.__model_config__ = v2_layer.parse_network(*layers)
if extra_layers is not None:
extra_layers = __check__(extra_layers)
self.__model_config__ = v2_layer.parse_network(
layers, extra_layers=extra_layers)
if extra_layers is not None:
self.layers.extend(extra_layers)
assert isinstance(self.__model_config__, ModelConfig)
def proto(self):
......
......@@ -37,9 +37,12 @@ class SGD(object):
:type cost: paddle.v2.config_base.Layer
:param parameters: The parameters dictionary.
:type parameters: paddle.v2.parameters.Parameters
:param extra_layers: Some layers in the neural network graph are not
in the path of cost layer.
:type extra_layers: paddle.v2.config_base.Layer
"""
def __init__(self, cost, parameters, update_equation):
def __init__(self, cost, parameters, update_equation, extra_layers=None):
if not isinstance(parameters, v2_parameters.Parameters):
raise TypeError('parameters should be parameters')
......@@ -47,7 +50,7 @@ class SGD(object):
if not isinstance(update_equation, v2_optimizer.Optimizer):
raise TypeError("update equation parameter must be "
"paddle.v2.optimizer.Optimizer")
topology = Topology(cost)
topology = Topology(cost, extra_layers=extra_layers)
self.__optimizer__ = update_equation
self.__topology__ = topology
self.__parameters__ = parameters
......
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