Python tensor.dmatrix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 dmatrix [as 別名]
def test_max_pool_2d_2D(self):
        rng = numpy.random.RandomState(utt.fetch_seed())
        maxpoolshps = ((1, 1), (3, 2))
        imval = rng.rand(4, 5)
        images = tensor.dmatrix()

        for maxpoolshp, ignore_border, mode in product(maxpoolshps,
                                                       [True, False],
                                                       ['max', 'sum',
                                                        'average_inc_pad',
                                                        'average_exc_pad']):
                # print 'maxpoolshp =', maxpoolshp
                # print 'ignore_border =', ignore_border
                numpy_output_val = self.numpy_max_pool_2d(imval, maxpoolshp,
                                                          ignore_border,
                                                          mode=mode)
                output = pool_2d(images, maxpoolshp, ignore_border,
                                 mode=mode)
                output_val = function([images], output)(imval)
                utt.assert_allclose(output_val, numpy_output_val)

                def mp(input):
                    return pool_2d(input, maxpoolshp, ignore_border,
                                   mode=mode)
                utt.verify_grad(mp, [imval], rng=rng) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
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()] 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
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() 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [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 dmatrix [as 別名]
def test_adv_sub(self):
        admat = dmatrix()
        aivec = lvector()
        bivec = lvector()

        admat_val = rand(5, 4)
        aivec_val = [1, 3, 2]
        bivec_val = [0, 3, 3]
        self._compile_and_check([admat, aivec, bivec],
                                [admat[aivec, bivec]],
                                [admat_val, aivec_val, bivec_val], AdvancedSubtensor)
        # Test case that aren't implemented, but make sure they do not crash.
        self._compile_and_check([admat, aivec],
                                [admat[aivec, 1:3]],
                                [admat_val, aivec_val], AdvancedSubtensor,
                                check_topo=False)
        self._compile_and_check([admat, aivec],
                                [admat[1:3, aivec]],
                                [admat_val, aivec_val], AdvancedSubtensor,
                                check_topo=False) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
def test_borrow_input(self):
        """
        Tests that the contract for io.In is respected. When borrow=False, it should be
        impossible for outputs to be aliased to the input variables provided by the user,
        either through a view-map or a destroy map. New tests should be added in the future
        when borrow=True is implemented.
        """
        a = T.dmatrix()
        aval = numpy.random.rand(3, 3)

        # when borrow=False, test that a destroy map cannot alias output to input
        f = theano.function([In(a, borrow=False)], Out(a + 1, borrow=True))
        assert numpy.all(f(aval) == aval + 1)
        assert not numpy.may_share_memory(aval, f(aval))

        # when borrow=False, test that a viewmap cannot alias output to input
        f = theano.function([In(a, borrow=False)], Out(a[0, :], borrow=True))
        assert numpy.all(f(aval) == aval[0, :])
        assert not numpy.may_share_memory(aval, f(aval)) 

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

        # CHECK: two functions (f1 and f2) can share w
        w = shared(numpy.random.rand(2, 2), 'w')
        wval = w.get_value(borrow=False)

        x = dmatrix()
        out1 = w + x
        out2 = w * x
        f1 = pfunc([x], [out1])
        f2 = pfunc([x], [out2])
        xval = numpy.random.rand(2, 2)
        assert numpy.all(f1(xval) == xval + wval)
        assert numpy.all(f2(xval) == xval * wval)

        # CHECK: updating a shared value
        f3 = pfunc([x], out1, updates=[(w, (w - 1))])
        # f3 changes the value of w
        assert numpy.all(f3(xval) == xval + wval)
        # this same value is read by f1
        assert numpy.all(f1(xval) == xval + (wval - 1))

        w.set_value(w.get_value(borrow=True) * 10, borrow=True)
        # this same value is read by f1
        assert numpy.all(f1(xval) == xval + w.get_value(borrow=True)) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [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 dmatrix [as 別名]
def test_no_make_node(self):
        class DoubleOp(Op):
            """An Op without make_node"""

            __props__ = ()

            itypes = [T.dmatrix]
            otypes = [T.dmatrix]

            def perform(self, node, inputs, outputs):
                inp = inputs[0]
                output = outputs[0]
                output[0] = inp * 2

        x_input = T.dmatrix('x_input')
        f = theano.function([x_input], DoubleOp()(x_input))
        inp = numpy.random.rand(5, 4)
        out = f(inp)
        assert numpy.allclose(inp * 2, out) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
def __init__(self, nfeatures=100, noutputs=10, nhiddens=50, rng=None):
        if rng is None:
            rng = 0
        if isinstance(rng, integer_types):
            rng = np.random.RandomState(rng)
        self.rng = rng
        self.nfeatures = nfeatures
        self.noutputs = noutputs
        self.nhiddens = nhiddens

        x = T.dmatrix('x')
        wh = th.shared(self.rng.normal(0, 1, (nfeatures, nhiddens)),
                       borrow=True)
        bh = th.shared(np.zeros(nhiddens), borrow=True)
        h = T.nnet.sigmoid(T.dot(x, wh) + bh)

        wy = th.shared(self.rng.normal(0, 1, (nhiddens, noutputs)))
        by = th.shared(np.zeros(noutputs), borrow=True)
        y = T.nnet.softmax(T.dot(h, wy) + by)

        self.inputs = [x]
        self.outputs = [y] 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
def test_borrow_input(self):
        """
        Tests that the contract for io.In is respected. When borrow=False, it should be
        impossible for outputs to be aliased to the input variables provided by the user,
        either through a view-map or a destroy map. New tests should be added in the future
        when borrow=True is implemented.
        """
        a = T.dmatrix()
        aval = numpy.random.rand(3, 3)

        # when borrow=False, test that a destroy map cannot alias output to input
        f = theano.function([In(a, borrow=False)], Out(a+1, borrow=True))
        assert numpy.all(f(aval) == aval+1)
        assert not numpy.may_share_memory(aval, f(aval))

        # when borrow=False, test that a viewmap cannot alias output to input
        f = theano.function([In(a, borrow=False)], Out(a[0, :], borrow=True))
        assert numpy.all(f(aval) == aval[0, :])
        assert not numpy.may_share_memory(aval, f(aval)) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
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) 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
def __init__(self, nfeatures=100, noutputs=10, nhiddens=50, rng=None):
        if rng is None:
            rng = 0
        if isinstance(rng, int):
            rng = np.random.RandomState(rng)
        self.rng = rng
        self.nfeatures = nfeatures
        self.noutputs = noutputs
        self.nhiddens = nhiddens

        x = T.dmatrix('x')
        wh = th.shared(self.rng.normal(0, 1, (nfeatures, nhiddens)),
                       borrow=True)
        bh = th.shared(np.zeros(nhiddens), borrow=True)
        h = T.nnet.sigmoid(T.dot(x, wh) + bh)

        wy = th.shared(self.rng.normal(0, 1, (nhiddens, noutputs)))
        by = th.shared(np.zeros(noutputs), borrow=True)
        y = T.nnet.softmax(T.dot(h, wy) + by)

        self.inputs = [x]
        self.outputs = [y] 

# 需要導入模塊: from theano import tensor [as 別名]
# 或者: from theano.tensor import dmatrix [as 別名]
def test_multilayer_conv(self):
        # fixed parameters
        bsize = 10     # batch size
        imshp = (5, 5)
        kshp = ((3, 3), (2, 2))
        nkerns = (3, 6)  # per output pixel
        ssizes = (((1, 1), (2, 2)),)
        convmodes = ('full',)  # 'valid',)

        # symbolic stuff
        kerns = [tensor.dmatrix(), tensor.dmatrix()]
        input = tensor.dmatrix()
        rng = numpy.random.RandomState(3423489)

        # build actual input images
        img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
        img1d = img2d.reshape(bsize, -1)

        for mode in ('FAST_COMPILE', 'FAST_RUN'):
            for conv_mode in convmodes:
                for ss in ssizes:

                    l1hid, l1shp = sp.convolve(kerns[0], kshp[0],\
                            nkerns[0], input, imshp, ss[0], mode=conv_mode)
                    l1propup = function([kerns[0], input], l1hid, mode=mode)

                    #l1kernvals = numpy.random.rand(nkerns[0],numpy.prod(kshp[0]))
                    l1kernvals = numpy.arange(nkerns[0]*numpy.prod(kshp[0])).reshape(nkerns[0], numpy.prod(kshp[0]))
                    l1hidval = l1propup(l1kernvals, img1d)

                    # actual values
                    l2hid, l2shp = sp.convolve(kerns[1], kshp[1],\
                            nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode)
                    l2propup = function([kerns[1], l1hid], l2hid, mode=mode)

                    #l2kernvals = numpy.random.rand(nkerns[1],numpy.prod(kshp[1])*nkerns[0])
                    l2kernvals = numpy.arange(nkerns[1]*numpy.prod(kshp[1])*nkerns[0]).reshape(nkerns[1], numpy.prod(kshp[1])*nkerns[0])
                    # for debugging, we bring things back to integers
                    l1hidval = numpy.arange(numpy.size(l1hidval)).reshape(l1hidval.shape)

                    l2hidval = l2propup(l2kernvals, l1hidval)