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move test module

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avx_docs
dangqingqing 8 years ago
parent 9b73a602c4
commit 4c85f955d7
2 changed files with 90 additions and 131 deletions
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129
python/paddle/v2/layer.py
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@ -347,132 +347,3 @@ operator_list = [
]
for op in operator_list:
globals()[op[0]] = __convert_to_v2__(op[0], parent_names=op[1])
def test_projection():
"""
TODO: move to tests file
"""
input = data(name='data', type=data_type.dense_vector(784))
word = data(name='word', type=data_type.integer_value_sequence(10000))
fc0 = fc(input=input, size=100, act=conf_helps.SigmoidActivation())
fc1 = fc(input=input, size=200, act=conf_helps.SigmoidActivation())
mixed0 = mixed(
size=256,
input=[
full_matrix_projection(input=fc0), full_matrix_projection(input=fc1)
])
with mixed(size=200) as mixed1:
mixed1 += full_matrix_projection(input=fc0)
mixed1 += identity_projection(input=fc1)
table = table_projection(input=word)
emb0 = mixed(size=512, input=table)
with mixed(size=512) as emb1:
emb1 += table
scale = scaling_projection(input=fc0)
scale0 = mixed(size=100, input=scale)
with mixed(size=100) as scale1:
scale1 += scale
dotmul = dotmul_projection(input=fc0)
dotmul0 = mixed(size=100, input=dotmul)
with mixed(size=100) as dotmul1:
dotmul1 += dotmul
context = context_projection(input=fc0, context_len=5)
context0 = mixed(size=100, input=context)
with mixed(size=100) as context1:
context1 += context
conv = conv_projection(
input=input,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = mixed(input=conv, bias_attr=True)
with mixed(bias_attr=True) as conv1:
conv1 += conv
print parse_network(mixed0)
print parse_network(mixed1)
print parse_network(emb0)
print parse_network(emb1)
print parse_network(scale0)
print parse_network(scale1)
print parse_network(dotmul0)
print parse_network(dotmul1)
print parse_network(conv0)
print parse_network(conv1)
def test_operator():
"""
TODO: move to tests file
"""
ipt0 = data(name='data', type=data_type.dense_vector(784))
ipt1 = data(name='word', type=data_type.dense_vector(128))
fc0 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation())
fc1 = fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation())
dotmul_op = dotmul_operator(a=fc0, b=fc1)
dotmul0 = mixed(input=dotmul_op)
with mixed() as dotmul1:
dotmul1 += dotmul_op
conv = conv_operator(
img=ipt0,
filter=ipt1,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = mixed(input=conv)
with mixed() as conv1:
conv1 += conv
print parse_network(dotmul0)
print parse_network(dotmul1)
print parse_network(conv0)
print parse_network(conv1)
def test_cost(pixel, label, weight, score):
hidden = fc(input=pixel,
size=100,
act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden'))
inference = fc(input=hidden, size=10, act=activation.Softmax())
maxid = max_id(input=inference)
cost1 = classification_cost(input=inference, label=label)
cost2 = classification_cost(input=inference, label=label, weight=weight)
cost3 = cross_entropy_cost(input=inference, label=label)
cost4 = cross_entropy_with_selfnorm_cost(input=inference, label=label)
cost5 = regression_cost(input=inference, label=label)
cost6 = regression_cost(input=inference, label=label, weight=weight)
cost7 = multi_binary_label_cross_entropy_cost(input=inference, label=label)
cost8 = rank_cost(left=score, right=score, label=score)
cost9 = lambda_cost(input=inference, score=score)
cost10 = sum_cost(input=inference)
cost11 = huber_cost(input=score, label=label)
print parse_network(cost1, cost2)
print parse_network(cost3, cost4)
print parse_network(cost5, cost6)
print parse_network(cost7, cost8, cost9, cost10, cost11)
print parse_network(inference, maxid)
if __name__ == '__main__':
pixel = data(name='pixel', type=data_type.dense_vector(784))
label = data(name='label', type=data_type.integer_value(10))
weight = data(name='weight', type=data_type.dense_vector(10))
score = data(name='score', type=data_type.dense_vector(1))
test_cost(pixel, label, weight, score)
test_projection()
test_operator()

92
python/paddle/v2/tests/test_layer.py
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@ -19,8 +19,6 @@ import paddle.v2.activation as activation
import paddle.v2.attr as attr
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as parse_network
pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
label = layer.data(name='label', type=data_type.integer_value(10))
@ -58,6 +56,96 @@ class CostLayerTest(unittest.TestCase):
#print layer.parse_network(cost5, cost6)
#print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
def test_projection(self):
input = layer.data(name='data', type=data_type.dense_vector(784))
word = layer.data(
name='word', type=data_type.integer_value_sequence(10000))
fc0 = layer.fc(input=input,
size=100,
act=conf_helps.SigmoidActivation())
fc1 = layer.fc(input=input,
size=200,
act=conf_helps.SigmoidActivation())
mixed0 = layer.mixed(
size=256,
input=[
layer.full_matrix_projection(input=fc0),
layer.full_matrix_projection(input=fc1)
])
with layer.mixed(size=200) as mixed1:
mixed1 += layer.full_matrix_projection(input=fc0)
mixed1 += layer.identity_projection(input=fc1)
table = layer.table_projection(input=word)
emb0 = layer.mixed(size=512, input=table)
with layer.mixed(size=512) as emb1:
emb1 += table
scale = layer.scaling_projection(input=fc0)
scale0 = layer.mixed(size=100, input=scale)
with layer.mixed(size=100) as scale1:
scale1 += scale
dotmul = layer.dotmul_projection(input=fc0)
dotmul0 = layer.mixed(size=100, input=dotmul)
with layer.mixed(size=100) as dotmul1:
dotmul1 += dotmul
context = layer.context_projection(input=fc0, context_len=5)
context0 = layer.mixed(size=100, input=context)
with layer.mixed(size=100) as context1:
context1 += context
conv = layer.conv_projection(
input=input,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv, bias_attr=True)
with layer.mixed(bias_attr=True) as conv1:
conv1 += conv
print layer.parse_network(mixed0)
print layer.parse_network(mixed1)
print layer.parse_network(emb0)
print layer.parse_network(emb1)
print layer.parse_network(scale0)
print layer.parse_network(scale1)
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
def test_operator(self):
ipt0 = layer.data(name='data', type=data_type.dense_vector(784))
ipt1 = layer.data(name='word', type=data_type.dense_vector(128))
fc0 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation())
fc1 = layer.fc(input=ipt0, size=100, act=conf_helps.SigmoidActivation())
dotmul_op = layer.dotmul_operator(a=fc0, b=fc1)
dotmul0 = layer.mixed(input=dotmul_op)
with layer.mixed() as dotmul1:
dotmul1 += dotmul_op
conv = layer.conv_operator(
img=ipt0,
filter=ipt1,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv)
with layer.mixed() as conv1:
conv1 += conv
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
if __name__ == '__main__':
unittest.main()

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