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9ecebb2d
编写于
10月 27, 2017
作者:
Y
Yu Yang
提交者:
GitHub
10月 27, 2017
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Remove test_mnist, since we replace it with compile time concepts (#5144)
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python/paddle/v2/framework/tests/test_mnist.py
python/paddle/v2/framework/tests/test_mnist.py
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python/paddle/v2/framework/tests/test_mnist.py
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import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
import
numpy
import
paddle.v2
as
paddle
exit
(
0
)
# FIXME(yuyang18): InferShape has been removed, this unittest should be changed until compile time is ready
BATCH_SIZE
=
100
scope
=
core
.
Scope
()
place
=
core
.
CPUPlace
()
# if you want to test GPU training, you can use gpu place
# place = core.GPUPlace(0)
dev_ctx
=
core
.
DeviceContext
.
create
(
place
)
init_net
=
core
.
Net
.
create
()
forward_net
=
core
.
Net
.
create
()
backward_net
=
None
optimize_net
=
core
.
Net
.
create
()
def
atomic_id
():
id
=
0
while
True
:
yield
id
id
+=
1
uniq_id
=
atomic_id
().
next
def
data_layer
(
name
,
dims
):
var
=
scope
.
var
(
name
)
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
(
dims
)
# 1 is batch size holder.
return
name
def
feed_data
(
name
,
data
):
assert
isinstance
(
data
,
numpy
.
ndarray
)
tensor
=
scope
.
find_var
(
name
).
get_tensor
()
tensor
.
set_dims
(
data
.
shape
)
if
data
.
dtype
==
numpy
.
dtype
(
"int32"
):
tensor
.
alloc_int
(
place
)
elif
data
.
dtype
==
numpy
.
dtype
(
"float32"
):
tensor
.
alloc_float
(
place
)
else
:
raise
ValueError
(
"data type not supported"
)
tensor
.
set
(
data
,
place
)
def
grad_var_name
(
var_name
):
return
var_name
+
"@GRAD"
def
sgd_optimizer
(
net
,
param_name
,
learning_rate
=
0.005
):
grad_name
=
grad_var_name
(
param_name
)
optimize_op
=
Operator
(
"sgd"
,
param
=
param_name
,
grad
=
grad_name
,
param_out
=
param_name
,
learning_rate
=
learning_rate
)
net
.
append_op
(
optimize_op
)
# should use operator and add these to the init_network
def
init_param
(
net
,
param_name
,
dims
):
scope
.
var
(
param_name
)
op
=
Operator
(
"uniform_random"
,
Out
=
param_name
,
dims
=
dims
,
min
=-
0.5
,
max
=
0.5
,
seed
=
10
)
op
.
infer_shape
(
scope
)
net
.
append_op
(
op
)
# fc_layer
def
fc_layer
(
net
,
input
,
size
,
act
=
"softmax"
,
bias
=
True
,
param
=
None
,
name
=
None
):
"""
The fully connected layer.
:param input: The name of input variable.
:type input: str
:param size: The size of fully connected layer.
:param act: The name of activation.
:param param: The attribute of learnable parameter which can be used to
modify initialization mean and std of the parameter.
:param bias: The attribute of bias. If set False, this layer does not have
a bias.
:param name: The name of this layer. If it is not set explictly, a name
will be generated automatically.
:return: The name of the output variable.
"""
if
name
is
None
:
name
=
"fc_%d"
%
uniq_id
()
if
not
isinstance
(
name
,
str
):
raise
ValueError
(
"The name of a layer should be a string."
)
input_dims
=
scope
.
find_var
(
input
).
get_tensor
().
get_dims
()
w_name
=
param
or
name
+
".w"
init_param
(
net
=
init_net
,
param_name
=
w_name
,
dims
=
[
input_dims
[
1
],
size
])
sgd_optimizer
(
net
=
optimize_net
,
param_name
=
w_name
,
learning_rate
=
0.01
)
pre_activation
=
name
+
".mul.out"
scope
.
var
(
pre_activation
)
mul_op
=
Operator
(
"mul"
,
X
=
input
,
Y
=
w_name
,
Out
=
pre_activation
)
net
.
append_op
(
mul_op
)
# create bias variable if needed
if
bias
:
bias_name
=
name
+
".b"
init_param
(
net
=
init_net
,
param_name
=
bias_name
,
dims
=
[
size
])
sgd_optimizer
(
net
=
optimize_net
,
param_name
=
bias_name
,
learning_rate
=
0.001
)
bias_out
=
name
+
".rowwise_add.out"
scope
.
var
(
bias_out
)
rowwise_append_op
=
Operator
(
"rowwise_add"
,
X
=
pre_activation
,
b
=
bias_name
,
Out
=
bias_out
)
net
.
append_op
(
rowwise_append_op
)
pre_activation
=
bias_out
activation_op
=
Operator
(
act
,
X
=
pre_activation
,
Y
=
name
)
net
.
append_op
(
activation_op
)
scope
.
var
(
name
)
net
.
infer_shape
(
scope
)
return
name
def
cross_entropy_layer
(
net
,
input
,
label
):
cost_name
=
"cross_entropy_%d"
%
uniq_id
()
cross_entropy_op
=
Operator
(
"cross_entropy"
,
X
=
input
,
Label
=
label
,
Y
=
cost_name
)
net
.
append_op
(
cross_entropy_op
)
scope
.
var
(
cost_name
)
net
.
infer_shape
(
scope
)
return
cost_name
def
create_backward_net
(
forward_net
):
net
=
core
.
Operator
.
backward
(
forward_net
,
set
())
for
input
in
net
.
inputs
()[
"all"
]:
var
=
scope
.
var
(
input
)
var
.
get_tensor
()
for
output
in
net
.
outputs
()[
"all"
]:
var
=
scope
.
var
(
output
)
var
.
get_tensor
()
return
net
def
debug_print_op
(
op
):
print
(
"==============="
+
op
.
type
()
+
"=============="
)
print
(
"***inputs:***"
)
for
input
in
op
.
inputs
()[
"all"
]:
print
input
,
scope
.
find_var
(
input
).
get_tensor
().
get_dims
()
print
(
"
\n
***outputs:***"
)
for
output
in
op
.
outputs
()[
"all"
]:
print
output
,
scope
.
find_var
(
output
).
get_tensor
().
get_dims
()
print
(
""
)
print
(
""
)
def
set_cost
(
cost
):
cost_shape
=
numpy
.
array
(
scope
.
find_var
(
cost
).
get_tensor
()).
shape
cost_grad
=
\
scope
.
find_var
(
grad_var_name
(
cost
)).
get_tensor
()
cost_grad
.
set_dims
(
cost_shape
)
cost_grad
.
alloc_float
(
place
)
cost_grad
.
set
(
numpy
.
ones
(
cost_shape
).
astype
(
"float32"
),
place
)
def
get_cost_mean
(
cost
):
cost_data
=
numpy
.
array
(
scope
.
find_var
(
cost
).
get_tensor
())
return
cost_data
.
sum
()
/
len
(
cost_data
)
def
error_rate
(
predict
,
label
):
predict_var
=
numpy
.
array
(
scope
.
find_var
(
predict
).
get_tensor
()).
argmax
(
axis
=
1
)
label
=
numpy
.
array
(
scope
.
find_var
(
label
).
get_tensor
())
error_num
=
numpy
.
sum
(
predict_var
!=
label
)
return
error_num
/
float
(
len
(
label
))
images
=
data_layer
(
name
=
"pixel"
,
dims
=
[
BATCH_SIZE
,
784
])
labels
=
data_layer
(
name
=
"label"
,
dims
=
[
BATCH_SIZE
,
1
])
fc1
=
fc_layer
(
net
=
forward_net
,
input
=
images
,
size
=
100
,
act
=
"sigmoid"
)
fc2
=
fc_layer
(
net
=
forward_net
,
input
=
fc1
,
size
=
100
,
act
=
"sigmoid"
)
predict
=
fc_layer
(
net
=
forward_net
,
input
=
fc2
,
size
=
10
,
act
=
"softmax"
)
cost
=
cross_entropy_layer
(
net
=
forward_net
,
input
=
predict
,
label
=
labels
)
init_net
.
complete_add_op
(
True
)
forward_net
.
complete_add_op
(
True
)
backward_net
=
create_backward_net
(
forward_net
)
optimize_net
.
complete_add_op
(
True
)
print
(
init_net
)
print
(
forward_net
)
print
(
backward_net
)
print
(
optimize_net
)
debug_print_op
(
forward_net
)
debug_print_op
(
backward_net
)
debug_print_op
(
optimize_net
)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
BATCH_SIZE
)
def
test
(
cost_name
):
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
BATCH_SIZE
)
cost
=
[]
error
=
[]
for
data
in
test_reader
():
image_data
=
numpy
.
array
(
map
(
lambda
x
:
x
[
0
],
data
)).
astype
(
"float32"
)
label_data
=
numpy
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int32"
)
label_data
=
numpy
.
expand_dims
(
label_data
,
axis
=
1
)
feed_data
(
images
,
image_data
)
feed_data
(
labels
,
label_data
)
forward_net
.
infer_shape
(
scope
)
forward_net
.
run
(
scope
,
dev_ctx
)
cost
.
append
(
get_cost_mean
(
cost_name
))
error
.
append
(
error_rate
(
predict
,
"label"
))
print
(
"cost="
+
str
(
sum
(
cost
)
/
float
(
len
(
cost
)))
+
" error_rate="
+
str
(
sum
(
error
)
/
float
(
len
(
error
))))
PASS_NUM
=
1
init_net
.
run
(
scope
,
dev_ctx
)
for
pass_id
in
range
(
PASS_NUM
):
batch_id
=
0
for
data
in
train_reader
():
image_data
=
numpy
.
array
(
map
(
lambda
x
:
x
[
0
],
data
)).
astype
(
"float32"
)
label_data
=
numpy
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int32"
)
label_data
=
numpy
.
expand_dims
(
label_data
,
axis
=
1
)
feed_data
(
images
,
image_data
)
feed_data
(
labels
,
label_data
)
forward_net
.
infer_shape
(
scope
)
forward_net
.
run
(
scope
,
dev_ctx
)
set_cost
(
cost
)
backward_net
.
infer_shape
(
scope
)
backward_net
.
run
(
scope
,
dev_ctx
)
optimize_net
.
run
(
scope
,
dev_ctx
)
if
batch_id
%
100
==
0
:
print
(
"pass["
+
str
(
pass_id
)
+
"] batch_id["
+
str
(
batch_id
)
+
"]"
)
test
(
cost
)
batch_id
=
batch_id
+
1
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