Python theano._asarray方法代码示例

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def c_code(self, node, name, inp, out, sub):
        x, = inp
        z, = out
        # These constants were obtained by looking at the output of
        # python commands like:
        #  for i in xrange(750):
        #      print i, repr(numpy.log1p(numpy.exp(theano._asarray([i,-i], dtype=dt))))
        # the boundary checks prevent us from generating inf

        # float16 limits: -17.0, 6.0
        # We use the float32 limits for float16 for now as the
        # computation will happend in float32 anyway.
        if (node.inputs[0].type == scalar.float32 or
                node.inputs[0].type == scalar.float16):
            return """%(z)s = %(x)s < -103.0f ? 0.0 : %(x)s > 14.0f ? %(x)s : log1p(exp(%(x)s));""" % locals()
        elif node.inputs[0].type == scalar.float64:
            return """%(z)s = %(x)s < -745.0 ? 0.0 : %(x)s > 16.0 ? %(x)s : log1p(exp(%(x)s));""" % locals()
        else:
            raise NotImplementedError('only floatingpoint is implemented') 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_givens(self):
        x = shared(0)
        assign = pfunc([], x, givens={x: 3})
        assert assign() == 3
        assert x.get_value(borrow=True) == 0

        y = tensor.ivector()
        f = pfunc([y], (y * x), givens={x: 6})
        assert numpy.all(f([1, 1, 1]) == [6, 6, 6])
        assert x.get_value() == 0

        z = tensor.ivector()
        c = z * y
        f = pfunc([y], (c + 7),
                  givens={z: theano._asarray([4, 4, 4], dtype='int32')})
        assert numpy.all(f([1, 1, 1]) == [11, 11, 11])
        assert x.get_value() == 0 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_host_to_device():
    #print >>sys.stdout, 'starting test_host_to_dev'
    for shape in ((), (3,), (2, 3), (3, 4, 5, 6)):
        a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
        b = cuda_ndarray.CudaNdarray(a)
        c = numpy.asarray(b)
        assert numpy.all(a == c)

        # test with float32 dtype
        d = numpy.asarray(b, dtype='float32')
        assert numpy.all(a == d)

        # test with not float32 dtype
        try:
            numpy.asarray(b, dtype='int8')
            assert False
        except TypeError:
            pass 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_exp():
    #print >>sys.stdout, 'starting test_exp'
    for shape in ((), (3,), (2, 3),
                  (1, 10000000), (10, 1000000),
                  (100, 100000), (1000, 10000), (10000, 1000)):
        a0 = theano._asarray(numpy.random.rand(*shape), dtype='float32')
        a1 = a0.copy()
        b0 = cuda_ndarray.CudaNdarray(a0)
        b1 = cuda_ndarray.CudaNdarray(a1)
        t0 = time.time()
        bsum = b0.exp()
        t1 = time.time()
        gpu_dt = t1 - t0
        t0 = time.time()
        asum = numpy.exp(a1)
        t1 = time.time()
        cpu_dt = t1 - t0
        # print shape, 'adding ', a0.size, 'cpu', cpu_dt, 'advantage', advantage(cpu_dt, gpu_dt)
        #c = numpy.asarray(b0+b1)
        if asum.shape:
            assert numpy.allclose(asum, numpy.asarray(bsum)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_nvcc_bug():
    """
    The fct k_elemwise_unary_rowmajor_copy(used by cuda.copy()) in cuda_ndarray.cu
    is not well compiled with nvcc 3.0 and 3.1 beta. We found a workaround, so it
    sould work correctly. Without the workaround, this test fail.
    """
    shape = (5, 4)
    aa = theano._asarray(numpy.random.rand(*shape), dtype='float32')
    a = aa[::, ::-1]

    b = cuda_ndarray.CudaNdarray(aa)[::, ::-1]
    c = copy.copy(b)
    d = copy.deepcopy(b)

    assert numpy.allclose(a, numpy.asarray(b))
    assert numpy.allclose(a, numpy.asarray(c))
    assert numpy.allclose(a, numpy.asarray(d))
    b += b
    assert numpy.allclose(a+a, numpy.asarray(b))
    assert numpy.allclose(a+a, numpy.asarray(c))
    assert numpy.allclose(a, numpy.asarray(d)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_getshape():
    shapelist = [
            ((1, 2, 3), (1, 2, 3)),
            ((1,), (1,)),
            ((1, 2, 3), (3, 2, 1)),
            ((1, 2, 3), (6,)),
            ((1, 2, 3, 2), (6, 2)),
            ((2, 3, 2), (6, 2))
             ]

    def subtest(shape):
        a = theano._asarray(numpy.random.rand(*shape_1), dtype='float32')
        b = cuda_ndarray.CudaNdarray(a)
        assert b.shape == a.shape

    for shape_1, shape_2 in shapelist:
        subtest(shape_1)
        subtest(shape_2) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_stride_manipulation():

    a = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float32')
    b = cuda_ndarray.CudaNdarray(a)
    v = b.view()
    v._dev_data += 0
    c = numpy.asarray(v)
    assert numpy.all(a == c)

    sizeof_float = 4
    offset = 0

    b_strides = b._strides
    for i in xrange(len(b.shape)):
        offset += (b.shape[i]-1) * b_strides[i]
        v._set_stride(i, -b_strides[i])

    v._dev_data += offset * sizeof_float
    c = numpy.asarray(v)

    assert numpy.all(c == [[5, 4, 3], [2, 1, 0]]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_matrixscalar0():
    a = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray(8, dtype='float32')
    _b = cuda_ndarray.CudaNdarray(b)

    # set an element to 8
    _a[1, 1] = _b
    a[1, 1] = b
    assert numpy.allclose(a, numpy.asarray(_a))

    # test direct transfert from numpy
    _a[1, 1] = theano._asarray(888, dtype='float32')
    a[1, 1] = theano._asarray(888, dtype='float32')
    assert numpy.allclose(a, numpy.asarray(_a))

    # broadcast a 0
    _a[1, 1] = 0
    _a[0:2] = 0
    _a[1:] = 0 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_matrixvector1():
    a = theano._asarray([[0, 1, 2], [3, 4, 5]], dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray([8, 9], dtype='float32')
    _b = cuda_ndarray.CudaNdarray(b)

    # set second column to 8,9
    _a[:, 1] = _b
    a[:, 1] = b
    assert numpy.allclose(a, numpy.asarray(_a))

    # test direct transfert from numpy
    _a[:, 1] =  b*100
    a[:, 1] =  b*100
    assert numpy.allclose(a, numpy.asarray(_a))

    row = theano._asarray([777, 888, 999], dtype='float32')
    _a[1, :] = row
    a[1, :] = row
    assert numpy.allclose(a, numpy.asarray(_a)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_matrix_bad_shape():
    a = numpy.arange(27)
    a.resize((3, 3, 3))
    a = theano._asarray(a, dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray([7, 8], dtype='float32')
    _b = cuda_ndarray.CudaNdarray(b)

    try:
        # attempt to assign the ndarray b with setitem
        _a[:, 1, 1] = _b
        assert False
    except ValueError as e:
        # print e
        assert True

    # test direct transfert from numpy
    try:
        # attempt to assign the ndarray b with setitem
        _a[1, 1, :] = b
        assert False
    except ValueError as e:
        # print e
        assert True 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_matrix_bad_ndim():
    a = numpy.arange(27)
    a.resize((3, 3, 3))
    a = theano._asarray(a, dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray([7, 8], dtype='float32')
    _b = cuda_ndarray.CudaNdarray(b)

    try:
        # attempt to assign the ndarray b with setitem
        _a[:, :, 1] = _b
        assert False
    except ValueError as e:
        # print e
        assert True

    # test direct transfert from numpy
    try:
        # attempt to assign the ndarray b with setitem
        _a[1, :, :] = b
        assert False
    except ValueError as e:
        # print e
        assert True 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_matrix_bad_type():
    a = numpy.arange(27)
    a.resize((3, 3, 3))
    a = theano._asarray(a, dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray([7, 8], dtype='float64')

    # test direct transfert from numpy
    try:
        # attempt to assign the ndarray b with setitem
        _a[1, :, :] = b
        assert False
    except TypeError as e:
        # print e
        assert True 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_setitem_assign_to_slice():
    a = numpy.arange(27)
    a.resize((3, 3, 3))
    a = theano._asarray(a, dtype='float32')
    _a = cuda_ndarray.CudaNdarray(a)

    b = theano._asarray([7, 8, 9], dtype='float32')
    _b = cuda_ndarray.CudaNdarray(b)

    # first get a slice of a
    _c = _a[:, :, 1]

    # set middle row through cube to 7,8,9
    # (this corresponds to middle row of matrix _c)
    _c[:, 1] = _b

    a[:, :, 1][:, 1] = b
    assert numpy.allclose(a, numpy.asarray(_a))

    # test direct transfert from numpy
    _d = _a[1, :, :]
    _d[1, :] = b*10
    a[1, :, :][1, :] = b*10
    assert numpy.allclose(a, numpy.asarray(_a)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_elemwise_fusion():
    """ Test the the GpuElemwise fusion work correctly"""
    shape = (3, 4)
    a = cuda.shared_constructor(theano._asarray(numpy.random.rand(*shape),
                                                dtype='float32'), 'a')
    b = tensor.fmatrix()
    c = tensor.fmatrix()
    f = pfunc([b, c], [a + b + c], mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    for i, node in enumerate(topo):
        print(i, node, file=sys.stdout)
    assert len(topo) == 4
    assert isinstance(topo[2].op.scalar_op, theano.scalar.basic.Composite)
    # let debugmode catch errors
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32'),
      theano._asarray(numpy.random.rand(*shape), dtype='float32')) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import _asarray [as 别名]
def test_elemwise0():

    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(4, 4),
                                               dtype='float32'), 'a')

    b = tensor.fmatrix()

    f = pfunc([b], [], updates=[(a, a + b)], mode=mode_with_gpu)

    # check that we work inplace.
    assert (list(f.maker.fgraph.toposort()[1].op.destroy_map.items())
            == [(0, [0])])

    a0 = a.get_value() * 1.0
    f(numpy.ones((4, 4), dtype='float32'))

    assert numpy.all(a0 + 1.0 == a.get_value())