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pytensor
提交 1451d214 authored 作者: James Bergstra's avatar James Bergstra
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removing vast commented test cases in tensor test_basic

上级 410405cf
隐藏空白字符变更
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正在显示 包含 0 行增加 和 319 行删除
theano/tensor/tests/test_basic.py
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......@@ -1255,24 +1255,6 @@ def _approx_eq(a,b,eps=1.0e-4):
return True
_approx_eq.debug = 0
# def check_eq(self, node_in, node_out, arg_in, arg_out):
# fn = Function([node_in], node_out)
# self.assertTrue( numpy.all(fn(arg_in) == arg_out), (arg_in, arg_out))
# def check_eq2(self, inputs, output, args_in, arg_out):
# fn = Function(inputs, output)
# val = fn(*args_in)
# self.assertTrue( numpy.all(val == arg_out), (val, arg_out))
# def check_eq2_c(self, inputs, output, args_in, arg_out):
# fn = Function(inputs, [output], linker_cls = gof.CLinker)
# val = fn(*args_in)
# self.assertTrue( numpy.all(val == arg_out), (val, arg_out))
# def check_eq2_both(self, inputs, output, args_in, arg_out):
# fn = Function(inputs, [output], linker_cls = lambda env: gof.DualLinker(env, _numpy_checker))
# val = fn(*args_in)
# self.assertTrue( numpy.all(val == arg_out), (val, arg_out))
def test_tensor_values_eq_approx():
#test, inf, -inf and nan equal themself
......@@ -3214,173 +3196,6 @@ class T_mean(unittest.TestCase):
data = numpy.asarray(numpy.random.rand(50), dtype=config.floatX)
assert numpy.allclose(f(data), numpy.mean(data))
# class T_abs(unittest.TestCase):
# def test_impl(self):
# t = as_tensor_variable(1.0)
# check_eq(self, t, abs(t), 1.0, 1.0)
# check_eq(self, t, abs(t), -1.0, 1.0)
# for shape in (2,), (3,4):
# t = as_tensor_variable(numpy.ones(shape))
# d = numpy.random.rand(*shape)*2-1.0
# check_eq(self, t, abs(t), d, abs(d))
# check_eq(self, t, abs(t), -d, abs(-d))
# def test_grad(self):
# utt.verify_grad(Abs, [numpy.ones(())])
# utt.verify_grad(Abs, [numpy.ones(3)])
# class AbsBadGrad(Abs):
# def grad(self, (x, ), (gz, )):
# return mul(gz * sgn(x),0.9),
# def test_badgrad(self):
# try:
# utt.verify_grad(T_abs.AbsBadGrad, [numpy.ones(())])
# except Exception, e:
# self.assertTrue(str(e) == utt.verify_grad.E_grad, str(e))
# return
# self.fail()
# class T_fill(unittest.TestCase):
# def test0(self):
# t = fill(numpy.asarray([1,2,3]), 9)
# self.assertTrue(t.owner.__class__ == Fill)
# o = t.owner
# self.assertTrue(o.inputs[0].broadcastable == (0,))
# # self.assertTrue(o.inputs[0].dtype[0:3] == 'int')
# self.assertTrue(o.inputs[1].broadcastable == (1,))
# # self.assertTrue(o.inputs[1].dtype[0:3] == 'flo')
# self.assertTrue(o.outputs[0].broadcastable == (0,))
# # self.assertTrue(o.outputs[0].dtype[0:3] == 'flo')
# self.assertTrue(numpy.all(eval_outputs([t]) == [9,9,9]))
# def test1(self):
# x = as_tensor_variable(numpy.ones((4,5)))
# l = ones_like(x[:,0:1])
# r = ones_like(x[0:1,:])
# xx = x + dot(l,r)
# self.assertTrue(numpy.mean(eval_outputs([xx]) == 2.0))
# class T_sum(unittest.TestCase):
# def test_impl(self):
# t = as_tensor_variable(0.0)
# check_eq(self, t, Sum(t).out, 1.0, 1.0)
# check_eq(self, t, Sum(t).out, -1.0, -1.0)
# t = as_tensor_variable([0.0, 0.0])
# d = numpy.asarray([-0.4, 1.2])
# check_eq(self, t, Sum(t).out, d, numpy.sum(d))
# check_eq(self, t, Sum(t).out, -d, -numpy.sum(d))
# class T_mul(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test_elemwise(self):
# a = as_tensor_variable(0.0)
# b = as_tensor_variable(0.0)
# check_eq2_both(self, [a,b], mul(a,b), [3.0, 4.0], 12.0)
# check_eq2_both(self, [a,b], mul(b,a), [-1.0,2.0], -2.0)
# a = as_tensor_variable(numpy.ones(2))
# b = as_tensor_variable(numpy.ones(2))
# aa = numpy.asarray([-0.5, 4.0])
# bb = numpy.asarray([-0.5, 2.0])
# check_eq2_both(self, [a,b], mul(a,b), [aa,bb], numpy.asarray([0.25, 8.0]))
# check_eq2_both(self, [a,b], mul(a,b), [bb,aa], numpy.asarray([0.25, 8.0]))
# def test_scalar(self):
# r = numpy.random.rand(2,3)
# a = as_tensor_variable(r)
# b = as_tensor_variable(2.0)
# check_eq2_both(self, [a,b], mul(a,b), [r, 2.0], r*2.0)
# check_eq2_both(self, [a,b], mul(a,b), [r, 4.0], r*4.0)
# self.assertTrue(b.data == 2.0)
# def test_rowcol(self):
# r1 = numpy.random.rand(3,5)
# r2 = numpy.random.rand(1,5)
# r3 = numpy.random.rand(3,1)
# a1, a2, a3 = as_tensor_variable(r1), as_tensor_variable(r2), as_tensor_variable(r3)
# check_eq2_both(self, [a1,a2], mul(a1,a2), [r1, r2], r1*r2)
# check_eq2_both(self, [a1,a3], mul(a1,a3), [r1, r3], r1*r3)
# def test_grad_elemwise(self):
# utt.verify_grad(Mul, [numpy.random.rand(3,4), numpy.random.rand(3,4)])
# def test_grad_scalar_l(self):
# utt.verify_grad(Mul, [numpy.asarray([3.0]), numpy.random.rand(3)])
# def test_grad_scalar_r(self):
# utt.verify_grad(Mul, [numpy.random.rand(3), numpy.asarray([3.0])])
# def test_grad_row(self):
# utt.verify_grad(Mul, [numpy.random.rand(3, 5), numpy.random.rand(1, 5)])
# def test_grad_row2(self):
# op = lambda x, y: Mul(x, DimShuffle(y, ['x', 0]).out)
# utt.verify_grad(op, [numpy.random.rand(3, 5), numpy.random.rand(5)])
# def test_grad_col(self):
# utt.verify_grad(Mul, [numpy.random.rand(3, 5), numpy.random.rand(3, 1)])
# def test_wrong_shapes(self):
# a = as_tensor_variable(numpy.ones(3))
# b = as_tensor_variable(numpy.ones(4))
# try:
# check_eq2(self, [a,b], Mul(a,b).out,
# [numpy.ones(3), numpy.ones(4)], 1.0)
# self.fail()
# except ValueError, e:
# self.assertTrue('shape mismatch' in str(e))
# try:
# check_eq2_c(self, [a,b], Mul(a,b).out,
# [numpy.ones(3), numpy.ones(4)], 1.0)
# self.fail()
# except ValueError, e:
# pass
# class T_div(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test_grad_e(self):
# utt.verify_grad(Div, [numpy.random.rand(3), numpy.ones(3)])
# utt.verify_grad(Div, [numpy.random.rand(3,5), numpy.random.rand(3,5)+0.1])
# utt.verify_grad(Div, [numpy.ones(()), numpy.ones(())])
# def test_grad_sl(self):
# utt.verify_grad(Div, [numpy.ones((3, 5)), numpy.ones((1, 1))])
# utt.verify_grad(Div, [numpy.random.rand(3), numpy.ones((1, ))])
# utt.verify_grad(Div, [numpy.random.rand(3,5), numpy.random.rand(1,1)])
# class T_log2(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test0(self):
# utt.verify_grad(Log2, [numpy.random.rand(3,1)+0.0001])
# class T_log(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test0(self):
# utt.verify_grad(Log, [numpy.random.rand(3,1)+0.0001])
# def test1(self):
# a = as_tensor_variable(numpy.ones(2))
# b = as_tensor_variable(numpy.ones(2))
# aa = numpy.asarray([0.5, 4.0])
# bb = numpy.asarray([0.5, 2.0])
# check_eq2(self, [a], log(a), [aa], numpy.log(numpy.asarray(aa)))
# class T_pow(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test_elemwise(self):
# utt.verify_grad(Div, [numpy.random.rand(3,4), numpy.random.rand(3,4)+0.1])
# utt.verify_grad(Pow, [numpy.random.rand(3,4), numpy.random.rand(3,4)])
# def test_scalar_l(self):
# utt.verify_grad(Pow, [numpy.asarray([3.0]), numpy.random.rand(3)])
# def test_scalar_r(self):
# utt.verify_grad(Pow, [numpy.random.rand(3), numpy.asarray([3.0])])
# def test_row(self):
# utt.verify_grad(Pow, [numpy.random.rand(3, 5), numpy.random.rand(1, 5)])
# def test_col(self):
# utt.verify_grad(Pow, [numpy.random.rand(3, 5), numpy.random.rand(3, 1)])
class test_matinv(unittest.TestCase):
......@@ -3646,140 +3461,6 @@ class T_scalarfromtensor(unittest.TestCase):
self.assertTrue(isinstance(v, numpy.int64))
self.assertTrue(v.shape == (),v.shape)
# def _tensor(data, broadcastable=None, name=None):
# """Return a TensorType containing given data"""
# data = numpy.asarray(data)
# if broadcastable is None:
# broadcastable = [s==1 for s in data.shape]
# elif broadcastable in [0, 1]:
# broadcastable = [broadcastable] * len(data.shape)
# rval = TensorType(data.dtype, broadcastable, name)
# rval.data = data # will raise if broadcastable was mis-specified
# return rval
# class T_tensor(unittest.TestCase):
# def setUp(self):
# utt.seed_rng()
# def test0(self): # allocate from a scalar float
# t = _tensor(1.0)
# self.assertTrue(isinstance(t, TensorType))
# self.assertTrue(t.dtype == 'float64')
# self.assertTrue(t.broadcastable == ())
# self.assertTrue(t.role == None)
# self.assertTrue(isinstance(t.data, numpy.ndarray))
# self.assertTrue(str(t.data.dtype) == 'float64')
# self.assertTrue(t.data == 1.0)
# def test0_int(self): # allocate from a scalar float
# t = _tensor(1)
# self.assertTrue(isinstance(t, TensorType))
# self.assertTrue(t.dtype == 'int64' or t.dtype == 'int32')
# def test1(self): # allocate from a vector of ints, not broadcastable
# t = _tensor(numpy.ones(5,dtype='int32'))
# self.assertTrue(isinstance(t, TensorType))
# self.assertTrue(t.dtype == 'int32')
# self.assertTrue(t.broadcastable == (0,))
# self.assertTrue(isinstance(t.data, numpy.ndarray))
# self.assertTrue(str(t.data.dtype) == 'int32')
# def test2(self): # allocate from a column matrix of complex with name
# t = _tensor(numpy.ones((5,1),dtype='complex64'),name='bart')
# self.assertTrue(isinstance(t, TensorType))
# self.assertTrue(t.dtype == 'complex64')
# self.assertTrue(t.broadcastable == (0,1))
# self.assertTrue(isinstance(t.data, numpy.ndarray))
# self.assertTrue(t.name == 'bart')
# def test2b(self): # allocate from a column matrix, not broadcastable
# t = _tensor(numpy.ones((5,1),dtype='complex64'),broadcastable=0)
# self.assertTrue(isinstance(t, TensorType))
# self.assertTrue(t.dtype == 'complex64')
# self.assertTrue(t.broadcastable == (0,0))
# self.assertTrue(isinstance(t.data, numpy.ndarray))
# f = Function([t], [t], linker_cls=gof.CLinker)
# self.assertTrue(numpy.all(t.data == f(t.data)))
# def test_data_normal(self): #test that assigning to .data works when it should
# t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
# o27 = numpy.ones((2,7), dtype='complex64')
# t.data = o27
# lst = t._data
# self.assertTrue(t.data.shape == (2,7))
# self.assertTrue(t.data is o27)
# self.assertTrue(t._data is lst)
# def test_data_badrank0(self):
# t = _tensor(numpy.ones((5,1),dtype='complex64'), broadcastable=0)
# try:
# t.data = numpy.ones((2,7,1))
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_rank)
# try:
# t.data = numpy.ones(1)
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_rank)
# def test_data_badrank1(self):
# t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
# try:
# t.data = numpy.ones((1,1,1))
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_rank)
# try:
# t.data = numpy.ones(1)
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_rank)
# def test_data_badshape0(self):
# t = _tensor(numpy.ones((1,1),dtype='complex64'), broadcastable=1)
# try:
# t.data = numpy.ones((1,2))
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_shape)
# try:
# t.data = numpy.ones((0,1))
# self.fail()
# except ValueError, e:
# self.assertTrue(e[0] is TensorType.filter.E_shape)
# def test_cast0(self):
# t = TensorType('float32', [0])
# t.data = numpy.random.rand(4) > 0.5
# self.assertTrue(str(t.data.dtype) == t.dtype)
# class T_stdlib(unittest.TestCase):
# def test0(self):
# t = _tensor(1.0)
# tt = t.clone(False)
# self.assertTrue(t.dtype == tt.dtype)
# self.assertTrue(t.broadcastable is tt.broadcastable)
# self.assertTrue(tt.data is None)
# self.assertTrue(t.data == 1.0)
# def test0b(self):
# t = _tensor(1.0)
# tt = t.clone()
# self.assertTrue(t.dtype == tt.dtype)
# self.assertTrue(t.broadcastable is tt.broadcastable)
# self.assertTrue(tt.data is None)
# self.assertTrue(t.data == 1.0)
# def test1(self):
# t = _tensor(1.0)
# tt = t.clone(True)
# self.assertTrue(t.dtype == tt.dtype)
# self.assertTrue(t.broadcastable is tt.broadcastable)
# self.assertTrue(tt.data == 1.0)
# self.assertTrue(t.data == 1.0)
# self.assertTrue(t.data is not tt.data)
# def test1b(self):
# t = _tensor(1.0)
# tt = copy(t)
# self.assertTrue(t.dtype == tt.dtype)
# self.assertTrue(t.broadcastable is tt.broadcastable)
# self.assertTrue(tt.data == 1.0)
# self.assertTrue(t.data == 1.0)
# self.assertTrue(t.data is not tt.data)
class test_grad(unittest.TestCase):
class O(gof.op.Op):
......
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