def test_values_eq_approx():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
assert GpuArrayType.values_eq_approx(a, a)
b = a.copy()
b[0] = numpy.asarray(b[0]) + 1.
assert not GpuArrayType.values_eq_approx(a, b)
b = a.copy()
b[0] = -numpy.asarray(b[0])
assert not GpuArrayType.values_eq_approx(a, b)
def test_values_eq_approx():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False, ))('g')
assert GpuArrayType.values_eq_approx(a, a)
b = a.copy()
b[0] = numpy.asarray(b[0]) + 1.
assert not GpuArrayType.values_eq_approx(a, b)
b = a.copy()
b[0] = -numpy.asarray(b[0])
assert not GpuArrayType.values_eq_approx(a, b)
def test_deep_copy():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
f = theano.function([g], g)
assert isinstance(f.maker.fgraph.toposort()[0].op, DeepCopyOp)
res = f(a)
assert GpuArrayType.values_eq(res, a)
def test_deep_copy():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False, ))('g')
f = theano.function([g], g)
assert isinstance(f.maker.fgraph.toposort()[0].op, DeepCopyOp)
res = f(a)
assert GpuArrayType.values_eq(res, a)
def rand_cval(self, shp):
return rand_gpuarray(*shp, **dict(cls=gpuarray))
def rand_val(self, shp):
return rand_gpuarray(*shp, **dict(cls=gpuarray))
def test_specify_shape():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
f = theano.function([g], theano.tensor.specify_shape(g, [20]))
f(a)
def test_specify_shape():
a = rand_gpuarray(20, dtype='float32')
g = GpuArrayType(dtype='float32', broadcastable=(False, ))('g')
f = theano.function([g], theano.tensor.specify_shape(g, [20]))
f(a)
