Python tensor.fmatrix方法代码示例

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_blocksparse_inplace_gemv_opt():
    b = tensor.fmatrix()
    W = tensor.ftensor4()
    h = tensor.ftensor3()
    iIdx = tensor.lmatrix()
    oIdx = tensor.lmatrix()

    o = sparse_block_dot(W, h, iIdx, b, oIdx)

    f = theano.function([W, h, iIdx, b, oIdx], o)
    assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')

    if theano.config.mode == "FAST_COMPILE":
        assert not f.maker.fgraph.toposort()[-1].op.inplace
    else:
        assert f.maker.fgraph.toposort()[-1].op.inplace 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_blocksparse_inplace_outer_opt():
    b = tensor.fmatrix()
    W = tensor.ftensor4()
    h = tensor.ftensor3()
    iIdx = tensor.lmatrix()
    oIdx = tensor.lmatrix()

    o = sparse_block_dot(W, h, iIdx, b, oIdx)

    theano.printing.debugprint(tensor.grad(o.sum(), wrt=W))

    f = theano.function([W, h, iIdx, b, oIdx],
                        [o, tensor.grad(o.sum(), wrt=W)])
    assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')

    if theano.config.mode == "FAST_COMPILE":
        assert not f.maker.fgraph.toposort()[-1].op.inplace
    else:
        assert f.maker.fgraph.toposort()[-1].op.inplace 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_sparseblockdot(self):
        """
        Compares the numpy version of sparseblockgemv to sparse_block_dot.
        """
        b = tensor.fmatrix()
        W = tensor.ftensor4()
        h = tensor.ftensor3()
        iIdx = tensor.imatrix()
        oIdx = tensor.imatrix()

        o = sparse_block_dot(W, h, iIdx, b, oIdx)

        f = theano.function([W, h, iIdx, b, oIdx], o, mode=self.mode)

        W_val, h_val, iIdx_val, b_val, oIdx_val = \
            BlockSparse_Gemv_and_Outer.gemv_data()

        th_out = f(W_val, h_val, iIdx_val, b_val, oIdx_val)

        ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
            b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)

        utt.assert_allclose(ref_out, th_out) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_sparseblockgemv(self):
        """
        Compares the numpy and theano versions of sparseblockgemv.
        """
        b = tensor.fmatrix()
        W = tensor.ftensor4()
        h = tensor.ftensor3()
        iIdx = tensor.imatrix()
        oIdx = tensor.imatrix()

        o = self.gemv_op(b.take(oIdx, axis=0), W, h, iIdx, oIdx)

        f = theano.function([W, h, iIdx, b, oIdx], o, mode=self.mode)

        W_val, h_val, iIdx_val, b_val, oIdx_val = \
            BlockSparse_Gemv_and_Outer.gemv_data()

        th_out = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
        ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
            b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)

        utt.assert_allclose(ref_out, th_out) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_1msigmoid(self):
        if not register_local_1msigmoid:
            return

        m = self.get_mode()
        x = T.fmatrix()

        # tests exp_over_1_plus_exp
        f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
        assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
        assert [node.op for node in f.maker.fgraph.toposort()] == [
            tensor.neg, sigmoid_inplace]

        # tests inv_1_plus_exp
        f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
        assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
        assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
                sigmoid_inplace] 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_dot22scalar_cast():
    """
    Test that in `dot22_to_dot22scalar` we properly cast integers to floats.
    """
    # Note that this test was failing before d5ff6904.
    A = T.dmatrix()
    for scalar_int_type in T.int_dtypes:
        y = T.scalar(dtype=scalar_int_type)
        f = theano.function([A, y], T.dot(A, A) * y, mode=mode_blas_opt)
        assert _dot22scalar in [x.op for x in f.maker.fgraph.toposort()]
    A = T.fmatrix()
    for scalar_int_type in T.int_dtypes:
        y = T.scalar(dtype=scalar_int_type)
        f = theano.function([A, y], T.dot(A, A) * y, mode=mode_blas_opt)
        if scalar_int_type in ['int32', 'int64']:
            assert _dot22 in [x.op for x in f.maker.fgraph.toposort()]
        else:
            assert _dot22scalar in [x.op for x in f.maker.fgraph.toposort()] 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def setUp(self):
        self.iv = T.tensor(dtype='int32', broadcastable=(False,))
        self.fv = T.tensor(dtype='float32', broadcastable=(False,))
        self.fv1 = T.tensor(dtype='float32', broadcastable=(True,))
        self.dv = T.tensor(dtype='float64', broadcastable=(False,))
        self.dv1 = T.tensor(dtype='float64', broadcastable=(True,))
        self.cv = T.tensor(dtype='complex64', broadcastable=(False,))
        self.zv = T.tensor(dtype='complex128', broadcastable=(False,))

        self.fv_2 = T.tensor(dtype='float32', broadcastable=(False,))
        self.fv1_2 = T.tensor(dtype='float32', broadcastable=(True,))
        self.dv_2 = T.tensor(dtype='float64', broadcastable=(False,))
        self.dv1_2 = T.tensor(dtype='float64', broadcastable=(True,))
        self.cv_2 = T.tensor(dtype='complex64', broadcastable=(False,))
        self.zv_2 = T.tensor(dtype='complex128', broadcastable=(False,))

        self.fm = T.fmatrix()
        self.dm = T.dmatrix()
        self.cm = T.cmatrix()
        self.zm = T.zmatrix()

        self.fa = T.fscalar()
        self.da = T.dscalar()
        self.ca = T.cscalar()
        self.za = T.zscalar() 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_local_remove_all_assert():
    x = theano.tensor.fmatrix()
    a = theano.tensor.opt.assert_op(x, theano.tensor.eq(x, 0).any())

    # By default `unsafe` should not be there
    f = theano.function([x], a, mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    a_op = [n for n in topo if isinstance(n.op, theano.tensor.opt.Assert)]
    assert len(a_op) == 1

    # Put `unsafe`
    f = theano.function([x], a, mode=mode_with_gpu.including('unsafe'))
    topo = f.maker.fgraph.toposort()
    a_op = [n for n in topo if isinstance(n.op, theano.tensor.opt.Assert)]
    assert len(a_op) == 0

    # Remove `unsafe`
    f = theano.function([x], a, mode=mode_with_gpu.excluding('unsafe'))
    topo = f.maker.fgraph.toposort()
    a_op = [n for n in topo if isinstance(n.op, theano.tensor.opt.Assert)]
    assert len(a_op) == 1 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_pdbbreakpoint_op():
    """ Test that PdbBreakpoint ops don't block gpu optimization"""
    b = tensor.fmatrix()

    # Create a function composed of a breakpoint followed by
    # some computation
    condition = tensor.gt(b.sum(), 0)
    b_monitored = PdbBreakpoint(name='TestBreakpoint')(condition, b)
    output = b_monitored ** 2

    f = theano.function([b], output, mode=mode_with_gpu)

    # Ensure that, in the compiled function, the computation following the
    # breakpoint has been moved to the gpu.
    topo = f.maker.fgraph.toposort()
    assert isinstance(topo[-2].op, cuda.GpuElemwise)
    assert topo[-1].op == cuda.host_from_gpu 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_local_gpu_elemwise_careduce():
    x = theano.tensor.fmatrix()
    o = (x * x).sum()
    f = theano.function([x], o, mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len(topo) == 3
    assert topo[1].op.pre_scalar_op == theano.scalar.sqr
    data = numpy.random.rand(3, 4).astype('float32')
    utt.assert_allclose(f(data), (data * data).sum())

    o = (x * x).sum(axis=1)
    f = theano.function([x], o, mode=mode_with_gpu)
    topo = f.maker.fgraph.toposort()
    assert len(topo) == 3
    assert topo[1].op.pre_scalar_op == theano.scalar.sqr
    utt.assert_allclose(f(data), (data * data).sum(axis=1)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [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: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_incsubtensor_mixed():

    # This catches a bug that occurred when incrementing
    # a float32 tensor by a float64 tensor.
    # The result is defined to be float32, so it is OK
    # to downcast the float64 increment in order to
    # transfer it to the GPU.
    # The bug was that the optimization called GpuFromHost
    # without casting first, causing the optimization to
    # fail.
    X = tensor.fmatrix()
    Y = tensor.dmatrix()
    Z = tensor.inc_subtensor(X[0:1, 0:1], Y)
    f = theano.function([X, Y], Z, mode=mode_with_gpu)
    packed, = f.maker.fgraph.inputs[1].clients
    client, idx = packed
    print(client)
    assert isinstance(client.op, tensor.Elemwise)
    assert isinstance(client.op.scalar_op, theano.scalar.Cast)
    packed, = client.outputs[0].clients
    client, idx = packed
    assert isinstance(client.op, cuda.GpuFromHost) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [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()) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_elemwise1():
    """ Several kinds of elemwise expressions with no broadcasting,
    non power-of-two shape """

    shape = (3, 4)
    a = tcn.shared_constructor(theano._asarray(numpy.random.rand(*shape),
                                               dtype='float32') + 0.5, 'a')
    b = tensor.fmatrix()

    # let debugmode catch any mistakes
    f = pfunc([b], [], updates=[(a, b ** a)], mode=mode_with_gpu)
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3)

    # let debugmode catch any mistakes
    f = pfunc([b], [], updates=[(a, tensor.exp(b ** a))], mode=mode_with_gpu)
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3)

    # let debugmode catch any mistakes
    f = pfunc([b], [], updates=[(a, a + b * tensor.exp(b ** a))],
              mode=mode_with_gpu)
    f(theano._asarray(numpy.random.rand(*shape), dtype='float32') + 0.3) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import fmatrix [as 别名]
def test_elemwise_comparaison_cast():
    """
    test if an elemwise comparaison followed by a cast to float32 are
    pushed to gpu.
    """

    a = tensor.fmatrix()
    b = tensor.fmatrix()
    av = theano._asarray(numpy.random.rand(4, 4), dtype='float32')
    bv = numpy.ones((4, 4), dtype='float32')

    for g, ans in [(tensor.lt, av < bv), (tensor.gt, av > bv),
                   (tensor.le, av <= bv), (tensor.ge, av >= bv)]:

        f = pfunc([a, b], tensor.cast(g(a, b), 'float32'), mode=mode_with_gpu)

        out = f(av, bv)
        assert numpy.all(out == ans)
        assert any([isinstance(node.op, cuda.GpuElemwise)
                    for node in f.maker.fgraph.toposort()])