Python theano.Mode方法代碼示例

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_borrow_output(self):
        a = T.dmatrix()
        f = function([a], Out(a, borrow=False))
        o = N.ones((3, 3))
        assert o is not f(o)  # function no longer permits aliasing outputs to inputs

        f = function([a], Out(a * 4, borrow=False))
        o = N.ones((3, 3))
        four = f(o)
        assert numpy.all(four == 4)
        f(o + .1)  # should not clobber the memory used to store four
        assert numpy.all(four == 4)

        f = function([a], Out(a * 4, borrow=True), mode=theano.Mode('c|py_nogc', 'fast_run'))
        o = N.ones((3, 3))
        four = f(o)
        assert numpy.all(four == 4)
        f(o + .1)  # should clobber the memory used to store four
        if theano.config.cxx:
            assert not numpy.all(four == 4)
        else:
            # The Elemwise.perform method don't reuse memory
            # as some numpy version don't support that correctly.
            assert numpy.all(four == 4) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_no_output_from_implace():

    x = T.matrix()
    y = T.matrix()
    a = T.dot(x, y)
    b = T.tanh(a)

    # Ensure that the elemwise op that produces the output is inplace when
    # using a mode that does not include the optimization
    fct_no_opt = theano.function([x, y], b, mode="FAST_RUN")
    op = fct_no_opt.maker.fgraph.outputs[0].owner.op
    assert (hasattr(op, 'destroy_map') and 0 in op.destroy_map)

    # Ensure that the elemwise op that produces the output is not inplace when
    # using a mode that includes the optimization
    opt = AddFeatureOptimizer(NoOutputFromInplace())
    mode_opt = Mode(linker="cvm", optimizer="fast_run").register((opt, 49.9))

    fct_opt = theano.function([x, y], b, mode=mode_opt)
    op = fct_opt.maker.fgraph.outputs[0].owner.op
    assert (not hasattr(op, 'destroy_map') or 0 not in op.destroy_map) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_c_thunks():
    a = tensor.scalars('a')
    b, c = tensor.vectors('bc')
    cases = [False]
    if theano.config.cxx:
        cases.append(True)
    for c_thunks in cases:
        f = function([a, b, c], ifelse(a, a * b, b * c),
                     mode=Mode(
                         optimizer=None,
                         linker=vm.VM_Linker(c_thunks=c_thunks,
                                             use_cloop=False)))
        f(1, [2], [3, 2])
        from nose.tools import assert_raises
        assert_raises(ValueError, f, 0, [2], [3, 4])
        assert any([hasattr(t, 'cthunk') for t in f.fn.thunks]) == c_thunks 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_no_leak_many_graphs():
        # Verify no memory leaks when creating and deleting a lot of functions

        # This isn't really a unit test, you have to run it and look at top to
        # see if there's a leak
        for i in xrange(10000):
            x = tensor.vector()
            z = x
            for d in range(10):
                z = tensor.sin(-z + 1)

            f = function([x], z, mode=Mode(optimizer=None, linker='cvm'))
            if not i % 100:
                print(gc.collect())
            sys.stdout.flush()

            gc.collect()
            if 1:
                f([2.0])
                f([3.0])
                f([4.0])
                f([5.0]) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_vm_gc():
    """This already caused a bug in the trunk of Theano.

    The bug was introduced in the trunk on July 5th, 2012 and fixed on
    July 30th.

    """
    x = theano.tensor.vector()
    p = RunOnce()(x)
    mode = theano.Mode(linker=theano.gof.vm.VM_Linker(lazy=True))
    f = theano.function([theano.In(x, mutable=True)], [p + 1, p + 2],
                        mode=mode)
    f([1, 2, 3])

    p = RunOnce()(x)
    pp = p + p
    f = theano.function([x], [pp + pp],
                        mode=mode)
    f([1, 2, 3]) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def dnn_version():
    """Return the current cuDNN version we compile with.

    This returns a tuple with the header version and the library
    version we link with. For older cudnn version without version
    information, we return -1.

    """
    if not dnn_available():
        raise Exception(
            "We can't determine the cudnn version as it is not available",
            dnn_available.msg)

    if dnn_version.v is None:
        f = theano.function([], DnnVersion()(),
                            theano.Mode(optimizer=None),
                            profile=False)
        dnn_version.v = f()
    return dnn_version.v 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_deepcopy():
    a = cuda.fmatrix()
    a_v = cuda.CudaNdarray(numpy.zeros((3, 4), dtype='float32'))

    # We force the c code to check that we generate c code
    mode = theano.Mode("c", mode_with_gpu.optimizer)
    f = theano.function([a], a, mode=mode)
    theano.printing.debugprint(f)
    out = f(a_v)
    assert out is not a_v
    assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out))

    # We force the python linker as the default code should work for this op
    mode = theano.Mode("py", mode_with_gpu.optimizer)
    f = theano.function([a], a, mode=mode)
    theano.printing.debugprint(f)
    out = f(a_v)
    assert out is not a_v
    assert numpy.allclose(numpy.asarray(a_v), numpy.asarray(out)) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_borrow_output(self):
        a = T.dmatrix()
        f = function([a], Out(a, borrow=False))
        o = N.ones((3, 3))
        assert o is not f(o)  # function no longer permits aliasing outputs to inputs

        f = function([a], Out(a*4, borrow=False))
        o = N.ones((3, 3))
        four = f(o)
        assert numpy.all(four == 4)
        f(o+.1)  # should not clobber the memory used to store four
        assert numpy.all(four == 4)

        f = function([a], Out(a*4, borrow=True), mode=theano.Mode('c|py_nogc', 'fast_run'))
        o = N.ones((3, 3))
        four = f(o)
        assert numpy.all(four == 4)
        f(o+.1)  # should clobber the memory used to store four
        if theano.config.cxx:
            assert not numpy.all(four == 4)
        else:
            # The Elemwise.perform method don't reuse memory
            # as some numpy version don't support that correctly.
            assert numpy.all(four == 4) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def __init__(self, network, config=None, batch_size=20):
        """
        Create a SGD trainer.
        :type network:
        :type config: deepy.conf.TrainerConfig
        :return:
        """
        super(DelayedBatchSGDTrainer, self).__init__(network, config)

        self.learning_rate = self.config.learning_rate
        self.batch_size = batch_size

        logging.info('compiling %s learning function', self.__class__.__name__)

        network_updates = list(network.updates) + list(network._learning_updates)
        learning_updates = list(self.learning_updates())
        update_list = network_updates + learning_updates
        logging.info("network updates: %s" % " ".join(map(str, [x[0] for x in network_updates])))
        logging.info("learning updates: %s" % " ".join(map(str, [x[0] for x in learning_updates])))

        self.learning_func = theano.function(
            network.inputs,
            self.training_variables,
            updates=update_list, allow_input_downcast=True, mode=theano.Mode(linker=THEANO_LINKER)) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def speed(self):
        n_calls = 20000
        print("n_calls", n_calls)
        for border_mode in ['valid', 'full']:
            print()
            print(border_mode)
            for openmp in [False, True]:
                print("OpenMP", openmp)
                image_shapes = [(1, 5, 6, 6),
                                (10, 5, 6, 6),
                                #(10, 10, 16, 16),
                                #(10, 10, 32, 32)
                ]
                print("image_shape", image_shapes)
                for image_shape in image_shapes:
                    filter_shapes = [(1, 5, 4, 4), (2, 5, 4, 4), (5, 5, 4, 4)]
                    print("filter_shapes", filter_shapes)
                    for filter_shape in filter_shapes:

                        input = theano.shared(numpy.random.random(image_shape))
                        filters = theano.shared(numpy.random.random(filter_shape))

                        output = self.conv2d(input, filters,
                                             image_shape, filter_shape,
                                             border_mode,
                                             unroll_patch=True,
                                             openmp=openmp)
                        mode = theano.Mode(linker=theano.gof.vm.VM_Linker(
                            allow_gc=False,
                            use_cloop=True))
                        theano_conv = theano.function([], output, mode=mode)
                        t1 = time.time()
                        theano_conv.fn(n_calls=n_calls)
                        t2 = time.time()
                        print(t2 - t1, end=' ')
                    print() 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_including():
    mode = theano.Mode(optimizer='merge')
    mode.including('fast_compile') 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_ifelse():
    a = T.scalar()
    b = generic()
    c = generic()

    notimpl = NotImplementedOp()
    lazys = [True]
    # We need lazy to end up being True for this test.
    if theano.config.vm.lazy in [True, None]:
        lazys = [True, None]
    cloops = [True, False]
    if theano.config.cxx == "":
        cloops = [False]
    for cloop in cloops:
        for lazy in lazys:
            linker = theano.gof.vm.VM_Linker(use_cloop=cloop, lazy=lazy)
            f = function([a, b, c], ifelse(a, notimpl(b), c),
                         mode=Mode(linker=linker, optimizer='fast_run'))

            try:
                # print "case 1"
                f(1, 'a', 'b')
                assert False
            except NotImplementedOp.E:
                pass
            # print "... passed"

            # print "case 2"
            # print f(0, 'a', 'b')
            assert f(0, 'a', 'b') == 'b'
            # print "... passed" 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def more_complex_test():
    notimpl = NotImplementedOp()
    ifelseifelseif = IfElseIfElseIf()

    x1 = T.scalar('x1')
    x2 = T.scalar('x2')
    c1 = T.scalar('c1')
    c2 = T.scalar('c2')
    t1 = ifelse(c1, x1, notimpl(x2))
    t1.name = 't1'
    t2 = t1 * 10
    t2.name = 't2'
    t3 = ifelse(c2, t2, x1 + t1)
    t3.name = 't3'
    t4 = ifelseifelseif(T.eq(x1, x2), x1, T.eq(x1, 5), x2, c2, t3, t3 + 0.5)
    t4.name = 't4'

    f = function([c1, c2, x1, x2], t4, mode=Mode(linker='vm',
                                                 optimizer='fast_run'))
    if theano.config.vm.lazy is False:
        try:
            f(1, 0, numpy.array(10, dtype=x1.dtype), 0)
            assert False
        except NotImplementedOp.E:
            pass
    else:
        print(f(1, 0, numpy.array(10, dtype=x1.dtype), 0))
        assert f(1, 0, numpy.array(10, dtype=x1.dtype), 0) == 20.5
    print('... passed') 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_callback(self):
        a, b, c = tensor.scalars('abc')
        f = function([a, b, c], (a + b) + c,
                     mode=Mode(
                         optimizer=None,
                         linker=vm.VM_Linker(callback=self.callback)))

        f(1, 2, 3)
        assert sum(self.n_callbacks.values()) == len(f.maker.fgraph.toposort())
        f(1, 2, 3)
        assert (sum(self.n_callbacks.values()) ==
                len(f.maker.fgraph.toposort()) * 2) 

# 需要導入模塊: import theano [as 別名]
# 或者: from theano import Mode [as 別名]
def test_callback_with_ifelse(self):
        a, b, c = tensor.scalars('abc')
        f = function([a, b, c], ifelse(a, 2 * b, 2 * c),
                     mode=Mode(
                         optimizer=None,
                         linker=vm.VM_Linker(callback=self.callback)))

        f(1, 2, 3)
        assert self.n_callbacks['IfElse'] == 2