Python theano.tensor 模块，true_div() 实例源码

def fit(self, weights, o_error, tpo ):

        gradients = T.grad(o_error ,weights)
        updates = []
        for c, v, w, g in zip(self.t_cache, self.t_velocity, weights,gradients):
            new_velocity = T.sub( T.mul(tpo["momentum_rate"], v) , T.mul(tpo["learn_rate"], g) )
            new_cache = T.add( T.mul(tpo["decay_rate"] , c) , T.mul(T.sub( 1, tpo["decay_rate"]) , T.sqr(g)))
            new_weights = T.sub(T.add(w , new_velocity) , T.true_div( T.mul(g,tpo["learn_rate"]) , T.sqrt(T.add(new_cache,0.1**8))))
            updates.append((w, new_weights))
            updates.append((v, new_velocity))
            updates.append((c, new_cache))

        return updates


######                 Nesterov momentum
########################################

def test_local_div_switch_sink(self):
        c = T.dscalar()
        idx = 0
        for condition in [(T.dmatrix('cond'), self.condm), (T.dvector('cond'), self.condv), (T.dscalar('cond'), self.conds)]:
            for x in [(T.dmatrix('x'), self.xm), (T.dvector('x'), self.xv), (T.dscalar('x'), self.xs)]:
                y = T.true_div(T.switch(condition[0] > 0, 1. *
                    x[0], 0.*x[0]), T.switch(condition[0] > 0, 1.*x[0], T.log(c)*x[0]))
                f = theano.function([condition[0], x[0], c]
                    , [y], mode=self.mode)
                if type(condition[1]) is list:
                    for i in xrange(len(condition[1])):
                        res = f(condition[1][i], x[1], -1)
                        assert (res == numpy.
                            asarray(self.resm[idx][i])).sum() == self.resm[idx][i].size
                else:
                    res = f(condition[1], x[1], -1)
                    assert (res == numpy.asarray(self.
                        resm[idx])).sum() == self.resm[idx].size
                idx += 1

def test_local_zero_div():
    """Tests 0/x -> 0"""
    mode = theano.compile.mode.get_default_mode().including("local_zero_div")
    for t in (T.scalar, T.ivector, T.ftensor4):
        x = t('x')
        for op in (T.int_div, T.true_div):
            y = op(0, x)
            g = optimize(FunctionGraph([x], [y]))
            # the division should be gone
            divs = [node for node in g.toposort()
                    if isinstance(node.op, T.elemwise.Elemwise) and
                    isinstance(node.op.scalar_op, type(op.scalar_op))]
            assert len(divs) == 0
            # the output type should match the unoptimized one
            output = g.outputs[0]
            assert output.ndim == y.ndim
            assert output.type == y.type
            # and the output should be zero
            assert theano.tensor.get_scalar_constant_value(output) == 0

def test_impls(self):
        i = iscalar()
        ii = lscalar()
        d = dscalar()
        f = fscalar()
        c = cscalar()

        assert numpy.allclose(function([i, d], i / d)(5, 7.0), (5.0 / 7.0))
        assert numpy.allclose(function([i, d], d / i)(5, 7.0), (7.0 / 5.0))
        assert numpy.allclose(function([i, f], i / f)(5, 11.0), (5.0 / 11.0))
        assert numpy.allclose(function([i, f], f / i)(5, 11.0), (11.0 / 5.0))
        assert numpy.allclose(function([i, ii], i // ii)(5, 3), (5 // 3))
        assert numpy.allclose(function([i, ii], ii // i)(5, 3), (3 // 5))
        assert numpy.allclose(function([i, ii], true_div(i, ii))(5, 3),
                (5. / 3.))
        assert numpy.allclose(function([i, ii], true_div(ii, i))(5, 3),
                (3. / 5.))
        assert numpy.allclose(function([i, c], i / c)(5, numpy.complex(5, 3)),
                (5. / (5 + 3j)))
        assert numpy.allclose(function([i, c], c / i)(5, numpy.complex(5, 3)),
                ((5 + 3j) / 5.))

def SlopeLin(slope):
    """
    Linear unit with different slopes
    :param slope: slope of negative quadrant
    :return: x if x > 0 else x/slope
    """
    import theano.tensor as T

    def inner(x):
        return T.switch(T.gt(x, 0), x, T.true_div(x, slope))
    return inner

def SlopeLin2(x, slope):
    """
    Linear unit with different slopes
    :param slope: slope of negative quadrant
    :return: x if x > 0 else x/slope
    """

    import theano.tensor as T
    return T.switch(T.gt(x, 0), x, T.true_div(x, slope))

def get_output_for(self, inputs, deterministic=False, **kwargs):
        alpha,beta = inputs
        # return 2*T.true_div(alpha,T.add(alpha,beta)+1e-8)-1
        return 2*(alpha/(alpha+beta+1e-8))-1

# Convenience Function to produce a residual pre-activation MDCL block

def avg_pool(input_layer, **kwargs):
    # hack to work around https://github.com/Theano/Theano/issues/3776
    norm = nn.layers.ExpressionLayer(input_layer, lambda X: T.ones_like(X))
    norm = nn.layers.Pool2DLayer(norm, mode='average_inc_pad', **kwargs)
    l = nn.layers.Pool2DLayer(input_layer, mode='average_inc_pad', **kwargs)
    l = nn.layers.ElemwiseMergeLayer([l, norm], T.true_div)
    return l

def test_elemwise(self):
        # float Ops
        mats = theano.tensor.matrices('cabxy')
        c, a, b, x, y = mats
        s1 = T.switch(c, a, b)
        s2 = T.switch(c, x, y)
        for op in (T.add, T.sub, T.mul, T.true_div, T.int_div, T.floor_div,
                   T.minimum, T.maximum, T.gt, T.lt, T.ge, T.le, T.eq, T.neq,
                   T.pow):
            g = optimize(FunctionGraph(mats, [op(s1, s2)]))
            assert str(g).count('Switch') == 1
        # integer Ops
        mats = theano.tensor.imatrices('cabxy')
        c, a, b, x, y = mats
        s1 = T.switch(c, a, b)
        s2 = T.switch(c, x, y)
        for op in (T.and_, T.or_, T.xor,
                   T.bitwise_and, T.bitwise_or, T.bitwise_xor):
            g = optimize(FunctionGraph(mats, [op(s1, s2)]))
            assert str(g).count('Switch') == 1
        # add/mul with more than two inputs
        u, v = theano.tensor.matrices('uv')
        s3 = T.switch(c, u, v)
        for op in (T.add, T.mul):
            g = optimize(FunctionGraph(mats + [u, v], [op(s1, s2, s3)]))
            assert str(g).count('Switch') == 1

def test_local_upcast_elemwise_constant_inputs():
    s = dvector("s")
    x = tensor.sum(tensor.log(10 ** s))
    f = function([s], [tensor.grad(x, s)])
    f([-42, -2.1, -1, -0.5, 0, 0.2, 1, 2, 12])

    # This test a corner where the optimization should not be applied.
    old = theano.config.floatX
    theano.config.floatX = 'float32'
    try:
        v = lvector()
        function([v], theano.tensor.basic.true_div(v, 2))
    finally:
        theano.config.floatX = old