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)