Python theano.tensor.dvector() Examples

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) 

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 

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) 

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 

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) 

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) 

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) 

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) 

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) 

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) 

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) 

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) 

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 

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) 

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) 

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) 

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) 

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) 

def test_inplace_norun(self):
        rf = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector,
                            inplace=True)
        assert rf.inplace
        assert getattr(rf, 'destroy_map', {}) != {} 

def get_compiled_gTrig(angi, D, derivs=True):
    m = tt.dvector('x')      # n_samples x idims
    v = tt.dmatrix('x_cov')  # n_samples x idims x idims

    gt = gTrig2(m, v, angi, D, derivs=derivs)
    return theano.function([m, v], gt) 

def setUp(self):
        self.x = T.dvector('x')
        self.x.tag.test_value = np.linspace(0, 1, 20)
        self.y = T.dvector('y')
        self.y.tag.test_value = np.linspace(0, 1, 20)
        self.input = T.dvector('input')
        self.input.tag.test_value = np.linspace(0, 1, 20)
        self.length = 20
        self.nonlin = nonlinearity(self.input, self.x, self.y, self.length)
        self.f = theano.function([self.input, self.x, self.y], self.nonlin) 

def test_theano_operator():
    """Test the ODL->Theano operator wrapper."""
    # Define ODL operator
    matrix = np.random.rand(3, 2)
    odl_op = odl.MatrixOperator(matrix)

    # Define evaluation points
    x = [1., 2.]
    dy = [1., 2., 3.]

    # Create Theano placeholders
    x_theano = T.dvector()
    dy_theano = T.dvector()

    # Create Theano layer from odl operator
    odl_op_layer = odl.contrib.theano.TheanoOperator(odl_op)

    # Build computation graphs
    y_theano = odl_op_layer(x_theano)
    y_theano_func = theano.function([x_theano], y_theano)
    dy_theano_func = theano.function([x_theano, dy_theano],
                                     T.Rop(y_theano, x_theano, dy_theano))

    # Evaluate using Theano
    result = y_theano_func(x)
    expected = odl_op(x)

    assert all_almost_equal(result, expected)

    # Evaluate the adjoint of the derivative, called gradient in Theano
    result = dy_theano_func(x, dy)
    expected = odl_op.derivative(x).adjoint(dy)

    assert all_almost_equal(result, expected) 

def _theano_c_wrapper(self):

        Slownesses = self.get_slownesses()

        slownesses = tt.dvector('slownesses')
        slownesses.tag.test_value = Slownesses.flatten()

        nuc_x = tt.lscalar('nuc_x')
        nuc_x.tag.test_value = self.nuc_x

        nuc_y = tt.lscalar('nuc_y')
        nuc_y.tag.test_value = self.nuc_y

        cleanup = theanof.Sweeper(
            self.patch_size / km, self.n_patch_dip, self.n_patch_strike, 'c')

        start_times = cleanup(slownesses, nuc_y, nuc_x)

        t0 = time()
        f = function([slownesses, nuc_y, nuc_x], start_times)
        t1 = time()
        theano_c_wrap_start_times = f(
            Slownesses.flatten(), self.nuc_y, self.nuc_x)
        print('tc', theano_c_wrap_start_times)
        t2 = time()
        logger.info('Theano C wrapper compile time %f' % (t1 - t0))
        logger.info('done theano C wrapper fast_sweeping in %f' % (t2 - t1))
        print('Theano C wrapper compile time %f' % (t1 - t0))
        return theano_c_wrap_start_times 

def __init__(self,
            input=tensor.dvector('input'),
            target=tensor.dvector('target'),
            n_input=1, n_hidden=1, n_output=1, lr=1e-3, **kw):
        super(NNet, self).__init__(**kw)

        self.input = input
        self.target = target
        self.lr = shared(lr, 'learning_rate')
        self.w1 = shared(numpy.zeros((n_hidden, n_input)), 'w1')
        self.w2 = shared(numpy.zeros((n_output, n_hidden)), 'w2')
        # print self.lr.type

        self.hidden = sigmoid(tensor.dot(self.w1, self.input))
        self.output = tensor.dot(self.w2, self.hidden)
        self.cost = tensor.sum((self.output - self.target)**2)

        self.sgd_updates = {
                    self.w1: self.w1 - self.lr * tensor.grad(self.cost, self.w1),
                    self.w2: self.w2 - self.lr * tensor.grad(self.cost, self.w2)}

        self.sgd_step = pfunc(
                params=[self.input, self.target],
                outputs=[self.output, self.cost],
                updates=self.sgd_updates)

        self.compute_output = pfunc([self.input],  self.output)

        self.output_from_hidden = pfunc([self.hidden], self.output) 

def test_no_inplace(self):
        """Test that when not running inplace, the RandomState is
        not updated"""
        rf = RandomFunction('uniform', tensor.dvector)
        rng_R = random_state_type()

        post_r, out = rf(rng_R, (3,), 0., 1.)
        f = compile.function([rng_R], [post_r, out])
        rng = numpy.random.RandomState(utt.fetch_seed())

        rng0, val0 = f(rng)
        rng_ = numpy.random.RandomState(utt.fetch_seed())
        # rng should still be in a fresh state
        self.assertTrue(rng_R.type.values_eq(rng, rng_))
        # rng0 should be in an updated state
        self.assertFalse(rng_R.type.values_eq(rng, rng0))

        f2 = compile.function(
                [compile.In(rng_R,
                    value=rng,
                    update=post_r,
                    mutable=False)],
                [post_r, out])
        rng2, val2 = f2()
        # rng should be in a fresh state
        self.assertTrue(rng_R.type.values_eq(rng, rng_))
        # rng2 should be in an updated state
        self.assertFalse(rng_R.type.values_eq(rng, rng2))
        # The updated state should be the same for both functions
        self.assertTrue(rng_R.type.values_eq(rng2, rng0))

        rng3, val3 = f2()
        # rng2 should not have changed
        self.assertTrue(rng_R.type.values_eq(rng2, rng0))
        # rng3 should be an updated again version of rng2
        self.assertFalse(rng_R.type.values_eq(rng3, rng2))
        self.assertFalse(rng_R.type.values_eq(rng3, rng)) 

def test_inplace_optimization(self):
        """Test that FAST_RUN includes the random_make_inplace optimization"""
        #inplace = False
        rf2 = RandomFunction(numpy.random.RandomState.uniform, tensor.dvector)
        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_r2, out2 = rf2(rng_R, (4,), 0., 1.)

        f = compile.function(
                [compile.In(rng_R,
                    value=numpy.random.RandomState(utt.fetch_seed()),
                    update=post_r2,
                    mutable=True)],
                out2,
                mode='FAST_RUN')  # DEBUG_MODE can't pass the id-based
                                  # test below

        # test that the RandomState object stays the same from function call to
        # function call, but that the values returned change from call to call.

        id0 = id(f[rng_R])
        val0 = f()
        assert id0 == id(f[rng_R])
        val1 = f()
        assert id0 == id(f[rng_R])

        assert not numpy.allclose(val0, val1) 

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) 

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]) 

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 

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())