def testMutate0():
lib = mkDefaultLib()
# region #+ Add recogFive to library
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive', recogDigitType, None))
# endregion
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkListSort(mkBoolTensorSort([1, 1]))
sort = mkFuncSort(inType, outType)
prog = PPFuncApp(fn=PPVar(name='lib.map_l'),
args=[PPVar(name='lib.recogFive')])
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
print(repr_py(newProg))
isort = inferType(newProg, lib)
assert isort is not None
assert sort == isort
def getLib():
libSynth = FnLibrary()
A = PPSortVar('A')
B = PPSortVar('B')
C = PPSortVar('C')
tr5 = mkRealTensorSort([5])
tb5 = mkBoolTensorSort([5])
ti5 = mkIntTensorSort([5])
ppint = PPInt()
cnts = PPEnumSort(2, 50)
libSynth.addItems([
PPLibItem('map', func(func(A, B), func(lst(A), lst(B))), None),
PPLibItem('fold', func(func(B, A, B), B, func(lst(A), B)), None),
PPLibItem('conv', func(func(A, lst(A), A), func(lst(A), lst(A))),
None),
PPLibItem('compose', func(func(B, C), func(A, B), func(A, C)), None),
PPLibItem('repeat', func(cnts, func(A, A), func(A, A)), None),
PPLibItem('zeros', func(PPDimVar('a'), mkRealTensorSort([1, 'a'])),
None),
PPLibItem('nn_fun_0', func(tr5, tr5), None),
PPLibItem('nn_fun_1', func(tr5, tb5), None),
PPLibItem('nn_fun_2', func(tb5, ti5), None),
])
return libSynth
def get_synth_lib():
libSynth = FnLibrary()
A = PPSortVar('A')
B = PPSortVar('B')
C = PPSortVar('C')
tr5 = mkRealTensorSort([5])
tb5 = mkBoolTensorSort([5])
ti5 = mkIntTensorSort([5])
ppint = PPInt()
repeatEnum = PPEnumSort(10, 10)
libSynth.addItems([
PPLibItem('compose', func(func(B, C), func(A, B), func(A, C)), None),
PPLibItem('map_l', func(func(A, B), func(lst(A), lst(B))), None),
PPLibItem('fold_l', func(func(B, A, B), B, func(lst(A), B)), None),
PPLibItem('conv_l', func(func(lst(A), B), func(lst(A), lst(B))), None),
PPLibItem('conv_g', func(func(lst(A), B), func(graph(A), graph(B))),
None),
PPLibItem('map_g', func(func(A, B), func(graph(A), graph(B))), None),
PPLibItem('fold_g', func(func(B, A, B), B, func(graph(A), B)), None),
PPLibItem('zeros', func(PPDimVar('a'), mkRealTensorSort([1, 'a'])),
None),
PPLibItem('repeat', func(repeatEnum, func(A, A), func(A, A)), None),
PPLibItem(
'regress_speed_mnist',
func(mkRealTensorSort([1, 3, 32, 32]), mkRealTensorSort([1, 2])),
None),
# PPLibItem('nav_mnist', func(mkGraphSort(mkRealTensorSort([1, 3, 32, 32])),
# mkGraphSort(mkRealTensorSort([1, 2]))), None),
])
return libSynth
def test_synthesizer_count_5s():
libSynth = getLib()
tr1_1_28_28 = mkRealTensorSort([1, 1, 28, 28])
tr1_1 = mkRealTensorSort([1, 1])
tb1_1 = mkBoolTensorSort([1, 1])
libSynth.addItems([
PPLibItem('recog_5', func(tr1_1_28_28, tb1_1), None),
])
fn_sort = func(lst(tr1_1_28_28), tr1_1)
synth = SymbolicSynthesizer(libSynth, fn_sort)
I = 10000
i = 0
for prog, unkMap in synth.genProgs():
i = i + 1
if i > I:
break
if i % 100 == 0:
print(i)
unks = ASTUtils.getUnks(prog)
if len(unks) > 1:
continue
for unk in unks:
if ASTUtils.isAbstract(unk.sort):
continue
print(repr_py_ann(prog))
def __init__(self, settings, toy_class, seq, dbg_learn_parameters):
input_type = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
output_type = mkRealTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
super(CountToysTask, self).__init__(fn_sort, settings, seq, dbg_learn_parameters)
self.toy_class = toy_class
def testMutate2():
"""
Unkprog
"""
lib = mkDefaultLib()
addRecogFive(lib)
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = \
PPTermUnk(name='nn_fun_x_906', sort=sort)
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
# print(repr_py(newProg))
# print('newProg: ', newProg)
# print('newProgReprPy: ', repr_py(newProg))
isort = inferType(newProg, lib)
if isort is None:
print(repr_py(newProg))
print('newProg: ', newProg)
continue
assert sort == isort
def __init__(self, settings, digit, seq, dbg_learn_parameters):
input_type = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
# input_type = mkListSort(mkBoolTensorSort([1, 1]))
# input_type = mkBoolTensorSort([1, 1])
output_type = mkRealTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
super(CountDigitOccTask, self).__init__(fn_sort, settings, seq, dbg_learn_parameters)
self.digit = digit
def __init__(self, settings, toy, seq, dbg_learn_parameters):
input_type = mkRealTensorSort([1, 1, 28, 28])
output_type = mkBoolTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
self.toy = toy
super(RecognizeToyTask, self).__init__(fn_sort, settings, seq, dbg_learn_parameters)
def test5():
def mk_recognise_5s():
res = NetCNN("recognise_5s",
input_ch=1,
output_dim=1,
output_activation=F.sigmoid)
res.load('../Interpreter/Models/is5_classifier.pth.tar')
return res
libSynth = FnLibrary()
t = PPSortVar('T')
t1 = PPSortVar('T1')
t2 = PPSortVar('T2')
libSynth.addItems([
PPLibItem(
'recognise_5s',
mkFuncSort(mkTensorSort(PPReal(), ['a', 1, 28, 28]),
mkTensorSort(PPReal(), ['a', 1])), mk_recognise_5s()),
PPLibItem(
'map',
mkFuncSort(mkFuncSort(t1, t2), mkListSort(t1), mkListSort(t2)),
pp_map),
])
ioExamples = None
img = mkRealTensorSort([1, 1, 28, 28])
imgList = mkListSort(img)
isFive = mkRealTensorSort([1, 1])
imgToIsFive = mkFuncSort(img, isFive)
isFiveList = mkListSort(isFive)
fnSort = mkFuncSort(imgList, isFiveList)
interpreter = None
"""targetProg = lambda inputs: map(lib.recognise_5s, inputs)"""
solver = SymbolicSynthesizer(interpreter, libSynth, fnSort, ioExamples,
ioExamples)
solver.setEvaluate(False)
# TODO: use "search" instead of "solve"
solution, score = solver.solve()
print(solution)
print(score)
def _mkNSynth(self):
ea_synthesis_mode = self.settings.synthesizer == 'evolutionary'
interpreter = Interpreter(self.seq.lib,
epochs=self.settings.epochs,
batch_size=self.settings.batch_size)
nnprefix = self.seq.sname() + self.sname()
if self.settings.synthesizer == 'enumerative':
# concrete_types = [mkRealTensorSort([1, 64, 4, 4]), mkRealTensorSort([1, 50])]
concreteTypes = [
mkRealTensorSort([1, 64, 4, 4]),
mkBoolTensorSort([1, 1]),
mkRealTensorSort([1, 50])
]
synth = SymbolicSynthesizer(self.seq.lib, self.fn_sort, nnprefix,
concreteTypes)
ns_settings = NeuralSynthesizerSettings(self.settings.N,
self.settings.M,
self.settings.K)
assert self.seq.lib is not None
nsynth = NeuralSynthesizer(interpreter, synth, self.seq.lib,
self.fn_sort,
self.settings.dbg_learn_parameters,
ns_settings)
return nsynth
elif self.settings.synthesizer == 'evolutionary':
concreteTypes = [
mkRealTensorSort([1, 64, 4, 4]),
mkBoolTensorSort([1, 1]),
mkRealTensorSort([1, 50])
]
synth = SymbolicSynthesizerEA(self.seq.lib, self.fn_sort, nnprefix,
concreteTypes)
# TODO: Do not hardcode G
NUM_GENERATIIONS = 100
ns_settings = NeuralSynthesizerEASettings(G=NUM_GENERATIIONS,
M=self.settings.M,
K=self.settings.K)
assert self.seq.lib is not None
nsynth = NeuralSynthesizerEA(interpreter, synth, self.seq.lib,
self.fn_sort, ns_settings)
return nsynth
def test_synthesizer_graph():
libSynth = get_synth_lib()
input_type = mkGraphSort(mkRealTensorSort([1, 3, 32, 32]))
output_type = mkGraphSort(mkRealTensorSort([1, 2]))
fn_sort = mkFuncSort(input_type, output_type)
synth = SymbolicSynthesizer(libSynth, fn_sort)
I = 20
i = 0
for prog, unkMap in synth.genProgs():
if i > I:
break
if NeuralSynthesizer.is_evaluable(prog)[0]:
i = i + 1
# print(i, repr_py_ann(prog))
print(i, repr_py(prog))
def test_zeros():
# IO Examples
train, val = split_into_train_and_validation(0, 10)
train_io_examples = get_batch_count_iseven(digits_to_count=[5],
count_up_to=10,
batch_size=100,
digit_dictionary=train)
val_io_examples = get_batch_count_iseven(digits_to_count=[5],
count_up_to=10,
batch_size=20,
digit_dictionary=val)
def mk_recognise_5s():
res = NetCNN("recognise_5s",
input_ch=1,
output_dim=1,
output_activation=F.sigmoid)
res.load('../Interpreter/Models/is5_classifier.pth.tar')
return res
# Library
libSynth = FnLibrary()
t = PPSortVar('T')
t1 = PPSortVar('T1')
t2 = PPSortVar('T2')
libSynth.addItems([
PPLibItem('zeros', mkFuncSort(PPDimVar('a'),
mkRealTensorSort([1, 'a'])), pp_map),
# PPLibItem('zeros2', mkFuncSort(PPDimVar('a'), PPDimVar('b'), mkRealTensorSort(['a', 'b'])), pp_map),
])
fnSort = mkFuncSort(PPDimConst(2), mkRealTensorSort([2]))
interpreter = Interpreter(libSynth)
solver = SymbolicSynthesizer(interpreter, libSynth, fnSort,
train_io_examples, val_io_examples)
solver.setEvaluate(False)
solution, score = solver.solve()
def testMutate1():
lib = mkDefaultLib()
addRecogFive(lib)
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
sort = mkFuncSort(inType, outType)
prog = \
PPFuncApp(fn=PPVar(name='lib.compose'),
args=[PPFuncApp(fn=PPVar(name='lib.fold_l'),
args=[PPTermUnk(name='nn_fun_x_906',
sort=PPFuncSort(
args=[PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]),
PPTensorSort(param_sort=PPBool(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)])],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))),
PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=1)])]),
PPFuncApp(fn=PPVar(name='lib.map_l'), args=[PPVar(name='lib.recogFive')])])
for i in range(100):
newProg = mutate(prog, lib)
if newProg is None:
continue
# print(repr_py(newProg))
# print('newProg: ', newProg)
# print('newProgReprPy: ', repr_py(newProg))
isort = inferType(newProg, lib)
if isort is None:
print(repr_py(newProg))
print('newProg: ', newProg)
continue
assert sort == isort
def main():
input_type = mkRealTensorSort([1, 1, 28, 28])
output_type = mkBoolTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
lib = mkDefaultLib()
pg = ProgramGenerator(lib)
progs = []
while len(progs) < 30:
prog = pg.genProg(fn_sort)
if prog is not None and prog not in progs:
print(repr_py(prog))
progs.append(prog)
else:
pass
def test_synthesizer_sum_digits():
libSynth = getLib()
input_type = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
output_type = mkRealTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
tr1_1_28_28 = mkRealTensorSort([1, 1, 28, 28])
tb_1_10 = mkBoolTensorSort([1, 10])
classify_digit = mkFuncSort(tr1_1_28_28, tb_1_10)
libSynth.addItems([
PPLibItem('classify_digit', classify_digit, None),
])
synth = SymbolicSynthesizer(libSynth, fn_sort)
I = 10000
i = 0
for prog, unkMap in synth.genProgs():
i = i + 1
if i > I:
break
if i % 100 == 0:
print(i)
unks = ASTUtils.getUnks(prog)
if len(unks) > 1:
continue
for unk in unks:
if ASTUtils.isAbstract(unk.sort):
continue
print(repr_py_ann(prog))
def add_libitems_to_repo():
def func(*lst):
return mkFuncSort(*lst)
def lst(t):
return mkListSort(t)
A = PPSortVar('A')
B = PPSortVar('B')
C = PPSortVar('C')
real_tensor_2d = mkTensorSort(PPReal(), ['a', 'b'])
bool_tensor_2d = mkTensorSort(PPBool(), ['a', 'b'])
add_lib_item(
PPLibItem('compose', func(func(B, C), func(A, B), func(A, C)),
pp_compose))
add_lib_item(
PPLibItem('repeat', func(PPEnumSort(9, 10), func(A, A), func(A, A)),
pp_repeat))
add_lib_item(
PPLibItem('map_l', func(func(A, B), func(lst(A), lst(B))),
pp_map_list))
add_lib_item(
PPLibItem('fold_l', func(func(B, A, B), B, func(lst(A), B)),
pp_reduce_list))
add_lib_item(
PPLibItem('conv_l', func(func(lst(A), B), func(lst(A), lst(B))),
pp_conv_list))
add_lib_item(
PPLibItem('zeros', func(PPDimVar('a'), mkRealTensorSort([1, 'a'])),
pp_get_zeros))
def graph(t):
return mkGraphSort(t)
add_lib_item(
PPLibItem('conv_g', func(func(lst(A), B), func(graph(A), graph(B))),
pp_conv_graph))
add_lib_item(
PPLibItem('map_g', func(func(A, B), func(graph(A), graph(B))),
pp_map_g))
add_lib_item(
PPLibItem('fold_g', func(func(B, A, B), B, func(graph(A), B)),
pp_reduce_graph))
add_lib_item(
PPLibItem('flatten_2d_list', func(func(B, C), func(A, B), func(A, C)),
pp_flatten_2d_list))
def addRecogFive2(lib):
inType = mkRealTensorSort([1, 1, 28, 28])
outType = mkBoolTensorSort([1, 1])
recogDigitType = mkFuncSort(inType, outType)
lib.addItem(PPLibItem('recogFive2', recogDigitType, None))
def test6():
t = PPSortVar('T')
t1 = PPSortVar('T1')
t2 = PPSortVar('T2')
def mk_recognise_5s():
res = NetCNN("recognise_5s",
input_ch=1,
output_dim=1,
output_activation=F.sigmoid)
res.load('../Interpreter/Models/is5_classifier.pth.tar')
return res
libSynth = FnLibrary()
real_tensor_2d = mkTensorSort(PPReal(), ['a', 'b'])
libSynth.addItems([
PPLibItem(
'recognise_5s',
mkFuncSort(mkTensorSort(PPReal(), ['a', 1, 28, 28]),
mkTensorSort(PPReal(), ['a', 1])), mk_recognise_5s()),
PPLibItem(
'map',
mkFuncSort(mkFuncSort(t1, t2), mkListSort(t1), mkListSort(t2)),
pp_map),
PPLibItem('reduce', mkFuncSort(mkFuncSort(t, t, t), mkListSort(t), t),
pp_reduce),
PPLibItem('add',
mkFuncSort(real_tensor_2d, real_tensor_2d, real_tensor_2d),
lambda x, y: x + y),
])
train, val = split_into_train_and_validation(0, 10)
val_ioExamples = get_batch_count_iseven(digits_to_count=[5],
count_up_to=10,
batch_size=20,
digit_dictionary=val)
img = mkRealTensorSort([1, 1, 28, 28])
isFive = mkRealTensorSort([1, 1])
imgToIsFive = mkFuncSort(img, isFive)
imgList = mkListSort(img)
isFiveList = mkListSort(isFive)
sumOfFives = mkRealTensorSort([1, 1])
fnSort = mkFuncSort(imgList, sumOfFives)
interpreter = Interpreter(libSynth)
"""
targetProg =
lambda inputs.
reduce(
add,
map(lib.recognise_5s, inputs))
"""
# TODO: use "search" instead of "solve"
solver = SymbolicSynthesizer(interpreter, libSynth, fnSort, val_ioExamples,
val_ioExamples)
# solver.setEvaluate(False)
solution, score = solver.solve()
print(solution)
print(score)
def addImageFunctionsToLibrary(libSynth: FnLibrary, load_recognise_5s=True):
real_tensor_2d = mkTensorSort(PPReal(), ['a', 'b'])
bool_tensor_2d = mkTensorSort(PPBool(), ['a', 'b'])
libSynth.addItems([
PPLibItem('add',
mkFuncSort(real_tensor_2d, real_tensor_2d, real_tensor_2d),
pp_add),
PPLibItem('add1',
mkFuncSort(real_tensor_2d, bool_tensor_2d, real_tensor_2d),
pp_add),
PPLibItem(
'map',
mkFuncSort(mkFuncSort(t1, t2), mkListSort(t1), mkListSort(t2)),
pp_map),
PPLibItem(
'map2d',
mkFuncSort(mkFuncSort(t1, t2), mkListSort(mkListSort(t1)),
mkListSort(mkListSort(t2))), pp_map2d),
# question ^ should we transform map's definition into using vectors? is this not enough?
# we don't know the type of the tensor output, w/o knowing the function.
# PPLibItem('cat', mkFuncSort(mkTensorSort(PPReal(), ['a', 'b']), mkTensorSort(PPReal(), ['a', 'c']),
# mkTensorSort(PPReal(), ['a', 'd'])), pp_cat), # TODO: d = b + c
# Question: can we write 'b+c'? I'm not sure if it's useful
# Also, the input types don't have to be PPReal, but for not it should suffice to just leave it like this?
# ^ It can accept a tuple of tensors of different shapes, but maybe we can restrict it to tuple of 2 for now.
# PPLibItem('zeros', mkFuncSort(PPInt(), mkTensorSort(PPReal(), ['a', 'b']), mkTensorSort(PPReal(), ['a', 'c'])), pp_get_zeros),
# PPLibItem('zeros', mkFuncSort(PPInt(), PPInt(), mkTensorSort(PPReal(), ['a', 'c'])), pp_get_zeros),
# 4, [2, 5] -> [2, 4]
# 7, [2, 5] -> [2, 7]
# Question: How do we say that the ints are the same number, PPInt() == 'c'
# Also, The input tensor type doesn't have to be PPReal, can be int or bool as well
# Also, the input tensor can be of any type, doesn't need to be float
PPLibItem('zeros', mkFuncSort(PPDimVar('a'), mkRealTensorSort([1,
'a'])),
pp_get_zeros),
PPLibItem('reduce_general',
mkFuncSort(mkFuncSort(t, t1, t), mkListSort(t1), t, t),
pp_reduce),
PPLibItem('reduce', mkFuncSort(mkFuncSort(t, t, t), mkListSort(t), t),
pp_reduce),
# pp_get_zeros
# PPLibItem('reduce_with_init_zeros', mkFuncSort(mkFuncSort(t, t1, t), mkListSort(t1), t), pp_reduce_w_zeros_init),
# Question : the initializer is only optional. How do we encode this information?
# The following are just test functions for evaluation, not properly typed.
# ,PPLibItem('mult_range09', mkFuncSort(mkFuncSort(t, t1, t), mkListSort(t1), t, t), get_multiply_by_range09())
# ,PPLibItem('argmax', mkFuncSort(mkFuncSort(t, t1, t), mkListSort(t1), t, t), argmax)
# PPLibItem('split', mkFuncSort(PPImageSort(), mkListSort(PPImageSort())), split),
# PPLibItem('join', mkFuncSort(mkListSort(PPImageSort()), PPImageSort()), None),
])
if load_recognise_5s:
libSynth.addItems([
PPLibItem(
'recognise_5s',
mkFuncSort(mkTensorSort(PPReal(), ['a', 1, 28, 28]),
mkTensorSort(PPBool(), ['a', 1])),
mk_recognise_5s())
])
# set the neural libraries to evaluation mode
# TODO: need to make sure we're properly switching between eval and train everywhere
libSynth.recognise_5s.eval()
def test_sum_digits():
libSynth = getLib()
input_type = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
output_type = mkRealTensorSort([1, 1])
fn_sort = mkFuncSort(input_type, output_type)
tr1_1_28_28 = mkRealTensorSort([1, 1, 28, 28])
tb_1_10 = mkBoolTensorSort([1, 10])
classify_digit = mkFuncSort(tr1_1_28_28, tb_1_10)
libSynth.addItems([
PPLibItem('classify_digit', classify_digit, None),
])
t = PPTermNT("Z", fn_sort)
assert (t == PPTermNT(
name='Z',
sort=PPFuncSort(args=[
PPListSort(param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))))
printTerm(t)
# (Z: (List[Tensor[real][1,1,28,28]] --> Tensor[real][1,1]))
t = Rules.expandToFuncApp(libSynth, t, 1, 'compose')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[PPSortVar(name='B')],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPSortVar(name='B')))
]))
printTerm(t)
# lib.compose((Z: (B --> Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> B)))
t = Rules.expandToFuncApp(libSynth, t, 1, 'fold')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Z: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), (Z: Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToUnk(t, 1)
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), (Z: Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToFuncApp(libSynth, t, 1, 'zeros')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(
fn=PPVar(name='lib.zeros'),
args=[PPTermNT(name='Z', sort=PPDimConst(value=1))])
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), lib.zeros((Z: 1))), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandDimConst(t, 1)
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), lib.zeros(1)), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToFuncApp(libSynth, t, 1, 'map')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='B')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPFuncApp(fn=PPVar(name='lib.map'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
])
],
rtpe=PPSortVar(name='B')))
])
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], B) --> Tensor[real][1,1])), lib.zeros(1)), lib.map((Z: (Tensor[real][1,1,28,28] --> B))))
t = Rules.expandToVar(libSynth, t, 1, 'classify_digit')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(value=1),
PPDimConst(value=10)
])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPFuncApp(fn=PPVar(name='lib.map'),
args=[PPVar(name='lib.classify_digit')])
]))
printTerm(t)
def test_count_5s():
libSynth = getLib()
tr1_1_28_28 = mkRealTensorSort([1, 1, 28, 28])
tr1_1 = mkRealTensorSort([1, 1])
tb1_1 = mkBoolTensorSort([1, 1])
libSynth.addItems([
PPLibItem('recog_5', func(tr1_1_28_28, tb1_1), None),
])
fn_sort = func(lst(tr1_1_28_28), tr1_1)
t = PPTermNT("Z", fn_sort)
assert (t == PPTermNT(
name='Z',
sort=PPFuncSort(args=[
PPListSort(param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=1)]))))
printTerm(t)
# (Z: (List[Tensor[real][1,1,28,28]] --> Tensor[real][1,1]))
t = Rules.expandToFuncApp(libSynth, t, 1, 'compose')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[PPSortVar(name='B')],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPSortVar(name='B')))
]))
printTerm(t)
# lib.compose((Z: (B --> Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> B)))
t = Rules.expandToFuncApp(libSynth, t, 1, 'fold')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Z: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), (Z: Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToUnk(t, 1)
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPTermNT(name='Z',
sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), (Z: Tensor[real][1,1])), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToFuncApp(libSynth, t, 1, 'zeros')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(
fn=PPVar(name='lib.zeros'),
args=[PPTermNT(name='Z', sort=PPDimConst(value=1))])
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), lib.zeros((Z: 1))), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandDimConst(t, 1)
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='A')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPListSort(
param_sort=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
]))
],
rtpe=PPListSort(param_sort=PPSortVar(
name='A'))))
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], A) --> Tensor[real][1,1])), lib.zeros(1)), (Z: (List[Tensor[real][1,1,28,28]] --> List[A])))
t = Rules.expandToFuncApp(libSynth, t, 1, 'map')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPSortVar(name='B')
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPFuncApp(fn=PPVar(name='lib.map'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1),
PPDimConst(value=28),
PPDimConst(value=28)
])
],
rtpe=PPSortVar(name='B')))
])
]))
printTerm(t)
# lib.compose(lib.fold((Unk: ((Tensor[real][1,1], B) --> Tensor[real][1,1])), lib.zeros(1)), lib.map((Z: (Tensor[real][1,1,28,28] --> B))))
t = Rules.expandToVar(libSynth, t, 1, 'recog_5')
assert (t == PPFuncApp(
fn=PPVar(name='lib.compose'),
args=[
PPFuncApp(fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(
name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]),
PPTensorSort(param_sort=PPBool(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=1)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPIntConst(value=1)])
]),
PPFuncApp(fn=PPVar(name='lib.map'),
args=[PPVar(name='lib.recog_5')])
]))
printTerm(t)
def test_fold():
tr15 = mkRealTensorSort([1, 5])
tb51 = mkBoolTensorSort([1, 5])
ti15 = mkIntTensorSort([1, 5])
libSynth = getLib()
t = PPTermNT("Z", func(lst(tr15), tr15))
printTerm(t)
# (Z: (List[Tensor[real][1,5]] --> Tensor[real][1,5]))
t = Rules.expandToFuncApp(libSynth, t, 1, 'fold')
assert (t == PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)]),
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)])
],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=5)
]))),
PPTermNT(name='Z',
sort=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)]))
]))
printTerm(t)
# lib.fold((Z: ((Tensor[real][1,5], Tensor[real][1,5]) --> Tensor[real][1,5])), (Z: Tensor[real][1,5]))
t = Rules.expandToFuncApp(libSynth, t, 2, 'zeros')
assert (t == PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)]),
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)])
],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=5)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'),
args=[PPTermNT(name='Z', sort=PPDimConst(value=5))])
]))
printTerm(t)
# lib.fold((Z: ((Tensor[real][1,5], Tensor[real][1,5]) --> Tensor[real][1,5])), lib.zeros((Z: 5)))
t = Rules.expandDimConst(t, 2)
assert (t == PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)]),
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)])
],
rtpe=PPTensorSort(param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=5)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=5)])
]))
printTerm(t)
# lib.fold((Z: ((Tensor[real][1,5], Tensor[real][1,5]) --> Tensor[real][1,5])), lib.zeros(5))
t = Rules.expandToUnk(t, 1)
assert (t == PPFuncApp(
fn=PPVar(name='lib.fold'),
args=[
PPTermUnk(name='Unk',
sort=PPFuncSort(args=[
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)]),
PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=1),
PPDimConst(value=5)])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[
PPDimConst(value=1),
PPDimConst(value=5)
]))),
PPFuncApp(fn=PPVar(name='lib.zeros'), args=[PPIntConst(value=5)])
]))
printTerm(t)
PPLibItem('map', func(func(A, B), func(lst(A), lst(B))), None),
PPLibItem('fold', func(func(B, A, B), B, func(lst(A), B)), None),
PPLibItem('conv', func(func(A, lst(A), A), func(lst(A), lst(A))),
None),
PPLibItem('compose', func(func(B, C), func(A, B), func(A, C)), None),
PPLibItem('repeat', func(cnts, func(A, A), func(A, A)), None),
PPLibItem('zeros', func(PPDimVar('a'), mkRealTensorSort([1, 'a'])),
None),
PPLibItem('nn_fun_0', func(tr5, tr5), None),
PPLibItem('nn_fun_1', func(tr5, tb5), None),
PPLibItem('nn_fun_2', func(tb5, ti5), None),
])
return libSynth
tr5 = mkRealTensorSort([5])
tb5 = mkBoolTensorSort([5])
ti5 = mkIntTensorSort([5])
def test_repeat():
libSynth = getLib()
t = PPTermNT("Z", func(tr5, tr5))
assert (t == PPTermNT(name='Z',
sort=PPFuncSort(args=[
PPTensorSort(param_sort=PPReal(),
shape=[PPDimConst(value=5)])
],
rtpe=PPTensorSort(
param_sort=PPReal(),
shape=[PPDimConst(value=5)]))))
def getSort():
inType = mkListSort(mkRealTensorSort([1, 1, 28, 28]))
outType = mkRealTensorSort([1, 1])
return mkFuncSort(inType, outType)
def test_search_space():
# type shortcuts
timg = mkRealTensorSort([1, 1, 28, 28])
treal = mkRealTensorSort([1, 1])
tbool = mkBoolTensorSort([1, 1])
tbool10 = mkBoolTensorSort([1, 10])
treal2 = mkRealTensorSort([1, 2])
def mklst(t):
return mkListSort(t)
def mkfn(t1, t2):
return mkFuncSort(t1, t2)
def mkgr(t):
return mkGraphSort(t)
targetSize = 6
print('start')
print('targetSize = %d' % targetSize)
add_sort = mkFuncSort(treal, tbool, treal)
testSeqs = ['cs1']
if 'cs1' in testSeqs:
sname = 'cs1'
libSynth = getBaseLibrary()
tname = sname + '_recog_digit_d1'
print(tname)
fn_sort = mkfn(timg, tbool)
print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(lfn, libSynth, tname)
tname = sname + '_recog_digit_d2'
print(tname)
fn_sort = mkfn(timg, tbool)
print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(lfn, libSynth, tname)
tname = sname + '_count_digit_d1'
print(tname)
fn_sort = mkfn(mklst(timg), treal)
print_progs(fn_sort, libSynth, targetSize)
lfn = add_sort
add_to_lib(lfn, libSynth, 'nn_add')
tname = sname + '_count_digit_d2'
print(tname)
fn_sort = mkfn(mklst(timg), treal)
print_progs(fn_sort, libSynth, targetSize)
if 'cs2' in testSeqs:
sname = 'cs2'
libSynth = getBaseLibrary()
tname = sname + '_recog_digit_d1'
print(tname)
fn_sort = mkfn(timg, tbool)
print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(lfn, libSynth, tname)
tname = sname + '_count_digit_d1'
print(tname)
fn_sort = mkfn(mklst(timg), treal)
print_progs(fn_sort, libSynth, targetSize)
lfn = add_sort
add_to_lib(fn_sort, libSynth, 'nn_add')
tname = sname + '_count_digit_d2'
print(tname)
fn_sort = mkfn(mklst(timg), treal)
print_progs(fn_sort, libSynth, targetSize)
lfn = mkfn(timg, tbool)
add_to_lib(fn_sort, libSynth, 'nn_recog_digit_d2')
tname = sname + '_recog_digit_d2'
print(tname)
fn_sort = mkfn(timg, tbool)
print_progs(fn_sort, libSynth, targetSize)
if 'ss' in testSeqs:
sname = 'ss'
libSynth = getBaseLibrary()
tname = sname + '_classify_digit'
print(tname)
fn_sort = mkfn(timg, tbool10)
print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(fn_sort, libSynth, tname)
tname = sname + '_sum_digits'
print(tname)
fn_sort = mkfn(mklst(timg), treal)
print_progs(fn_sort, libSynth, targetSize)
if 'gs1' in testSeqs:
sname = 'gs1'
libSynth = getBaseLibrary()
tname = sname + '_regress_speed'
print(tname)
fn_sort = mkfn(timg, treal2)
print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(fn_sort, libSynth, tname)
tname = sname + '_shortest_path_street'
print(tname)
fn_sort = mkfn(mkgr(timg), mkgr(treal2))
print_progs(fn_sort, libSynth, targetSize)
if 'gs2' in testSeqs:
sname = 'gs2'
libSynth = getBaseLibrary()
tname = sname + '_regress_mnist'
print(tname)
fn_sort = mkfn(timg, treal2)
# print_progs(fn_sort, libSynth, targetSize)
lfn = fn_sort
add_to_lib(lfn, libSynth, tname)
tname = sname + '_shortest_path_mnist'
print(tname)
fn_sort = mkfn(mkgr(timg), mkgr(treal2))
# print_progs(fn_sort, libSynth, targetSize)
lfn = mkfn(mklst(treal2), treal2)
add_to_lib(lfn, libSynth, 'nn_relax')
tname = sname + '_shortest_path_street'
print(tname)
fn_sort = mkfn(mkgr(timg), mkgr(treal2))
print_progs(fn_sort, libSynth, targetSize)
