Python tensor.dvector方法代碼示例

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def timeit_2vector_theano(init, nb_element=1e6, nb_repeat=3, nb_call=int(1e2), expr="a**2 + b**2 + 2*a*b"):
    t3 = timeit.Timer("tf(av,bv)",
                      """
import theano
import theano.tensor as T
import numexpr as ne
from theano.tensor import exp
%(init)s
av=a
bv=b
a=T.dvector()
b=T.dvector()
tf= theano.function([a,b],%(expr)s)
"""%locals()
)
    ret=t3.repeat(nb_repeat,nb_call)
    return np.asarray(ret) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_infer_shape(self):
        a = tensor.dvector()
        self._compile_and_check([a], [self.op(a, 16, 0)],
                                [numpy.random.rand(12)],
                               self.op_class)
        a = tensor.dmatrix()
        for var in [self.op(a, 16, 1), self.op(a, None, 1),
                     self.op(a, 16, None), self.op(a, None, None)]:
            self._compile_and_check([a], [var],
                                    [numpy.random.rand(12, 4)],
                                    self.op_class)
        b = tensor.iscalar()
        for var in [self.op(a, 16, b), self.op(a, None, b)]:
            self._compile_and_check([a, b], [var],
                                    [numpy.random.rand(12, 4), 0],
                                    self.op_class) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_merge_opt_runtime():
    """In the original merge optimization, the following graph took
    like caused the MERGE optimizer to exhibit really bad performance
    (quadratic? exponential?)

    Ironically, there is actually no merging to do in this graph.

    """
    x = T.dvector()
    for i in xrange(50):
        if i:
            r = r + r/10
        else:
            r = x
    t = time.time()
    f = theano.function([x], r, mode='FAST_COMPILE')
    # FAST_RUN does in-place optimizer which requires a lot of
    # toposorting, which is actually pretty slow at the moment.  This
    # test was designed to test MergeOptimizer... so I'm leaving
    # toposort optimizations for a later date.
    dt = time.time() - t

    # it should never take longer than 5 seconds to compile this graph
    assert dt < 5.0, dt 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_broadcast_arguments(self):
        random = RandomStreams(utt.fetch_seed())
        low = tensor.dvector()
        high = tensor.dcol()
        out = random.uniform(low=low, high=high)
        assert out.ndim == 2
        f = function([low, high], out)

        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
        numpy_rng = numpy.random.RandomState(int(rng_seed))
        val0 = f([-5, .5, 0, 1], [[1.]])
        val1 = f([.9], [[1.], [1.1], [1.5]])
        val2 = f([-5, .5, 0, 1], [[1.], [1.1], [1.5]])

        numpy_val0 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[1.])
        numpy_val1 = numpy_rng.uniform(low=[.9], high=[[1.], [1.1], [1.5]])
        numpy_val2 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[[1.], [1.1], [1.5]])

        assert numpy.all(val0 == numpy_val0)
        assert numpy.all(val1 == numpy_val1)
        assert numpy.all(val2 == numpy_val2) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_basic_usage(self):
        rf = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector)
        assert not rf.inplace
        assert getattr(rf, 'destroy_map', {}) == {}

        rng_R = random_state_type()

        # If calling RandomFunction directly, all args have to be specified,
        # because shape will have to be moved to the end
        post_r, out = rf(rng_R, (4,), 0., 1.)

        assert out.type == tensor.dvector

        f = compile.function([rng_R], out)

        rng_state0 = numpy.random.RandomState(utt.fetch_seed())

        f_0 = f(rng_state0)
        f_1 = f(rng_state0)

        assert numpy.all(f_0 == f_1) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_broadcast_arguments(self):
        rng_R = random_state_type()
        low = tensor.dvector()
        high = tensor.dcol()
        post_r, out = uniform(rng_R, low=low, high=high)
        assert out.ndim == 2
        f = compile.function([rng_R, low, high], [post_r, out],
                             accept_inplace=True)

        rng_state0 = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(utt.fetch_seed())
        post0, val0 = f(rng_state0, [-5, .5, 0, 1], [[1.]])
        post1, val1 = f(post0, [.9], [[1.], [1.1], [1.5]])
        post2, val2 = f(post1, [-5, .5, 0, 1], [[1.], [1.1], [1.5]])

        numpy_val0 = numpy_rng.uniform(low=[-5, .5, 0, 1], high=[1.])
        numpy_val1 = numpy_rng.uniform(low=[.9], high=[[1.], [1.1], [1.5]])
        numpy_val2 = numpy_rng.uniform(low=[-5, .5, 0, 1],
                                       high=[[1.], [1.1], [1.5]])

        assert numpy.all(val0 == numpy_val0), (val0, numpy_val0)
        assert numpy.all(val1 == numpy_val1)
        assert numpy.all(val2 == numpy_val2) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_param_strict(self):

        a = tensor.dvector()
        b = shared(7)
        out = a + b

        f = pfunc([In(a, strict=False)], [out])
        # works, rand generates float64 by default
        f(numpy.random.rand(8))
        # works, casting is allowed
        f(numpy.array([1, 2, 3, 4], dtype='int32'))

        f = pfunc([In(a, strict=True)], [out])
        try:
            # fails, f expects float64
            f(numpy.array([1, 2, 3, 4], dtype='int32'))
        except TypeError:
            pass 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_param_mutable(self):
        a = tensor.dvector()
        a_out = a * 2  # assuming the op which makes this "in place" triggers

        # using mutable=True will let fip change the value in aval
        fip = pfunc([In(a, mutable=True)], [a_out], mode='FAST_RUN')
        aval = numpy.random.rand(10)
        aval2 = aval.copy()
        assert numpy.all(fip(aval) == (aval2 * 2))
        assert not numpy.all(aval == aval2)

        # using mutable=False should leave the input untouched
        f = pfunc([In(a, mutable=False)], [a_out], mode='FAST_RUN')
        aval = numpy.random.rand(10)
        aval2 = aval.copy()
        assert numpy.all(f(aval) == (aval2 * 2))
        assert numpy.all(aval == aval2) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_infer_shape(self):
        x = dmatrix('x')
        x.tag.test_value = np.zeros((2, 2))
        y = dvector('y')
        y.tag.test_value = [0, 0]

        def infer_shape(node, shapes):
            x, y = shapes
            return [y]

        @as_op([dmatrix, dvector], dvector, infer_shape)
        def cumprod_plus(x, y):
            return np.cumprod(x) + y

        self._compile_and_check([x, y], [cumprod_plus(x, y)],
                                [[[1.5, 5], [2, 2]], [1, 100, 2, 200]],
                                cumprod_plus.__class__, warn=False) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_pydotprint_long_name():
    """This is a REALLY PARTIAL TEST.

    It prints a graph where there are variable and apply nodes whose long
    names are different, but not the shortened names.
    We should not merge those nodes in the dot graph.

    """

    # Skip test if pydot is not available.
    if not theano.printing.pydot_imported:
        raise SkipTest('pydot not available')

    x = tensor.dvector()
    mode = theano.compile.mode.get_default_mode().excluding("fusion")
    f = theano.function([x], [x * 2, x + x], mode=mode)
    f([1, 2, 3, 4])

    theano.printing.pydotprint(f, max_label_size=5,
                               print_output_file=False)
    theano.printing.pydotprint([x * 2, x + x],
                               max_label_size=5,
                               print_output_file=False) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_wrong_input(self):
        """
        Make sure errors are raised when image and kernel are not 4D tensors
        """
        self.assertRaises(Exception, self.validate, (3, 2, 8, 8), (4, 2, 5, 5),
                          'valid', input=T.dmatrix())
        self.assertRaises(Exception, self.validate, (3, 2, 8, 8), (4, 2, 5, 5),
                          'valid', filters=T.dvector())
        self.assertRaises(Exception, self.validate, (3, 2, 8, 8), (4, 2, 5, 5),
                          'valid', input=T.dtensor3()) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_fail(self):
        """
        Test that conv2d fails for dimensions other than 2 or 3.
        """
        self.assertRaises(Exception, conv.conv2d, T.dtensor4(), T.dtensor3())
        self.assertRaises(Exception, conv.conv2d, T.dtensor3(), T.dvector()) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test3(self):
        a = tensor.dvector()
        w2 = sort(a)
        f = theano.function([a], w2)
        gv = f(self.v_val)
        gt = np.sort(self.v_val)
        assert np.allclose(gv, gt) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_multiple_inplace(self):
        x = tensor.dmatrix('x')
        y = tensor.dvector('y')
        z = tensor.dvector('z')
        f = theano.function([x, y, z],
                            [tensor.dot(y, x), tensor.dot(z,x)],
                            mode=mode_blas_opt)
        vx = numpy.random.rand(3, 3)
        vy = numpy.random.rand(3)
        vz = numpy.random.rand(3)
        out = f(vx, vy, vz)
        assert numpy.allclose(out[0], numpy.dot(vy, vx))
        assert numpy.allclose(out[1], numpy.dot(vz, vx))
        assert len([n for n in f.maker.fgraph.apply_nodes
                    if isinstance(n.op, tensor.AllocEmpty)]) == 2 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dvector [as 別名]
def test_uniform_vector(self):
        random = RandomStreams(utt.fetch_seed())
        low = tensor.dvector()
        high = tensor.dvector()
        out = random.uniform(low=low, high=high)
        assert out.ndim == 1
        f = function([low, high], out)

        low_val = [.1, .2, .3]
        high_val = [1.1, 2.2, 3.3]
        seed_gen = numpy.random.RandomState(utt.fetch_seed())
        numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))

        # Arguments of size (3,)
        val0 = f(low_val, high_val)
        numpy_val0 = numpy_rng.uniform(low=low_val, high=high_val)
        print('THEANO', val0)
        print('NUMPY', numpy_val0)
        assert numpy.all(val0 == numpy_val0)

        # arguments of size (2,)
        val1 = f(low_val[:-1], high_val[:-1])
        numpy_val1 = numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1])
        print('THEANO', val1)
        print('NUMPY', numpy_val1)
        assert numpy.all(val1 == numpy_val1)

        # Specifying the size explicitly
        g = function([low, high], random.uniform(low=low, high=high, size=(3,)))
        val2 = g(low_val, high_val)
        numpy_rng = numpy.random.RandomState(int(seed_gen.randint(2**30)))
        numpy_val2 = numpy_rng.uniform(low=low_val, high=high_val, size=(3,))
        assert numpy.all(val2 == numpy_val2)
        self.assertRaises(ValueError, g, low_val[:-1], high_val[:-1])