Python tensor.vector方法代码示例

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_softmax():

    from keras.activations import softmax as s

    # Test using a reference implementation of softmax
    def softmax(values):
        m = max(values)
        values = numpy.array(values)
        e = numpy.exp(values - m)
        dist = list(e / numpy.sum(e))

        return dist

    x = T.vector()
    exp = s(x)
    f = theano.function([x], exp)
    test_values=get_standard_values()

    result = f(test_values)
    expected = softmax(test_values)

    print(str(result))
    print(str(expected))

    list_assert_equal(result, expected) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_relu():
    '''
    Relu implementation doesn't depend on the value being
    a theano variable. Testing ints, floats and theano tensors.
    '''

    from keras.activations import relu as r

    assert r(5) == 5
    assert r(-5) == 0
    assert r(-0.1) == 0
    assert r(0.1) == 0.1

    x = T.vector()
    exp = r(x)
    f = theano.function([x], exp)

    test_values = get_standard_values()
    result = f(test_values)

    list_assert_equal(result, test_values) # because no negatives in test values 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_tanh():

    from keras.activations import tanh as t
    test_values = get_standard_values()

    x = T.vector()
    exp = t(x)
    f = theano.function([x], exp)

    result = f(test_values)
    expected = [math.tanh(v) for v in test_values]

    print(result)
    print(expected)

    list_assert_equal(result, expected) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def __init__(self):
        X_in = T.matrix('X_in')
        u = T.matrix('u')
        s = T.vector('s')
        eps = T.scalar('eps')

        X_ = X_in - T.mean(X_in, 0)
        sigma = T.dot(X_.T, X_) / X_.shape[0]
        self.sigma = theano.function([X_in], sigma, allow_input_downcast=True)

        Z = T.dot(T.dot(u, T.nlinalg.diag(1. / T.sqrt(s + eps))), u.T)
        X_zca = T.dot(X_, Z.T)
        self.compute_zca = theano.function([X_in, u, s, eps], X_zca, allow_input_downcast=True)

        self._u = None
        self._s = None 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def var(name, label=None, observed=False, const=False, vector=False, lower=None, upper=None):
    if vector and not observed:
        raise ValueError('Currently, only observed variables can be vectors')

    if observed and const:
        raise ValueError('Observed variables are automatically const')

    if vector:
        var = T.vector(name)
    else:
        var = T.scalar(name)

    var._name = name
    var._label = label
    var._observed = observed
    var._const = observed or const
    var._lower = lower or -np.inf
    var._upper = upper or np.inf

    return var 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def __init__(self):
        super(M, self).__init__()

        x = T.matrix('x') # input, target
        self.w = module.Member(T.matrix('w')) # weights
        self.a = module.Member(T.vector('a')) # hid bias
        self.b = module.Member(T.vector('b')) # output bias

        self.hid = T.tanh(T.dot(x, self.w) + self.a)
        hid = self.hid

        self.out = T.tanh(T.dot(hid, self.w.T) + self.b)
        out = self.out

        self.err = 0.5 * T.sum((out - x)**2)
        err = self.err

        params = [self.w, self.a, self.b]

        gparams = T.grad(err, params)

        updates = [(p, p - 0.01 * gp) for p, gp in zip(params, gparams)]

        self.step = module.Method([x], err, updates=dict(updates)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_local_csm_properties_csm():
    data = tensor.vector()
    indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
                              tensor.ivector())
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_csm_properties_csm")
    for CS, cast in [(sparse.CSC, sp.csc_matrix),
                     (sparse.CSR, sp.csr_matrix)]:
        f = theano.function([data, indices, indptr, shape],
                            sparse.csm_properties(
                                CS(data, indices, indptr, shape)),
                            mode=mode)
        assert not any(
            isinstance(node.op, (sparse.CSM, sparse.CSMProperties))
            for node in f.maker.fgraph.toposort())
        v = cast(random_lil((10, 40),
                            config.floatX, 3))
        f(v.data, v.indices, v.indptr, v.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_local_csm_grad_c():
    raise SkipTest("Opt disabled as it don't support unsorted indices")
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    data = tensor.vector()
    indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
                              tensor.ivector())
    mode = theano.compile.mode.get_default_mode()

    if theano.config.mode == 'FAST_COMPILE':
        mode = theano.compile.Mode(linker='c|py', optimizer='fast_compile')

    mode = mode.including("specialize", "local_csm_grad_c")
    for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
        cost = tensor.sum(sparse.DenseFromSparse()(CS(data, indices, indptr, shape)))
        f = theano.function(
            [data, indices, indptr, shape],
            tensor.grad(cost, data),
            mode=mode)
        assert not any(isinstance(node.op, sparse.CSMGrad) for node
                       in f.maker.fgraph.toposort())
        v = cast(random_lil((10, 40),
                            config.floatX, 3))
        f(v.data, v.indices, v.indptr, v.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_local_mul_s_v():
    if not theano.config.cxx:
        raise SkipTest("G++ not available, so we need to skip this test.")
    mode = theano.compile.mode.get_default_mode()
    mode = mode.including("specialize", "local_mul_s_v")

    for sp_format in ['csr']:  # Not implemented for other format
        inputs = [getattr(theano.sparse, sp_format + '_matrix')(),
                  tensor.vector()]

        f = theano.function(inputs,
                            sparse.mul_s_v(*inputs),
                            mode=mode)

        assert not any(isinstance(node.op, sparse.MulSV) for node
                       in f.maker.fgraph.toposort()) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_csm_grad(self):
        for sparsetype in ('csr', 'csc'):
            x = tensor.vector()
            y = tensor.ivector()
            z = tensor.ivector()
            s = tensor.ivector()
            call = getattr(sp, sparsetype + '_matrix')
            spm = call(random_lil((300, 400), config.floatX, 5))
            out = tensor.grad(dense_from_sparse(
                CSM(sparsetype)(x, y, z, s)
            ).sum(), x)
            self._compile_and_check([x, y, z, s],
                                    [out],
                                    [spm.data, spm.indices, spm.indptr,
                                     spm.shape],
                                    (CSMGrad, CSMGradC)
                                   ) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_csr_dense(self):
        x = theano.sparse.csr_matrix('x')
        y = theano.tensor.matrix('y')
        v = theano.tensor.vector('v')

        for (x, y, x_v, y_v) in [(x, y, self.x_csr, self.y),
                                 (x, v, self.x_csr, self.v_100),
                                 (v, x, self.v_10, self.x_csr)]:
            f_a = theano.function([x, y], theano.sparse.dot(x, y))
            f_b = lambda x, y: x * y

            utt.assert_allclose(f_a(x_v, y_v), f_b(x_v, y_v))

            # Test infer_shape
            self._compile_and_check([x, y], [theano.sparse.dot(x, y)],
                                    [x_v, y_v],
                                    (Dot, Usmm, UsmmCscDense)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_csc_dense(self):
        x = theano.sparse.csc_matrix('x')
        y = theano.tensor.matrix('y')
        v = theano.tensor.vector('v')

        for (x, y, x_v, y_v) in [(x, y, self.x_csc, self.y),
                                 (x, v, self.x_csc, self.v_100),
                                 (v, x, self.v_10, self.x_csc)]:

            f_a = theano.function([x, y], theano.sparse.dot(x, y))
            f_b = lambda x, y: x * y

            utt.assert_allclose(f_a(x_v, y_v), f_b(x_v, y_v))

            # Test infer_shape
            self._compile_and_check([x, y], [theano.sparse.dot(x, y)],
                                    [x_v, y_v],
                                    (Dot, Usmm, UsmmCscDense)) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_mul_s_v(self):
        sp_types = {'csc': sp.csc_matrix,
                    'csr': sp.csr_matrix}

        for format in ['csr', 'csc']:
            for dtype in ['float32', 'float64']:
                x = theano.sparse.SparseType(format, dtype=dtype)()
                y = tensor.vector(dtype=dtype)
                f = theano.function([x, y], mul_s_v(x, y))

                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)

                out = f(spmat, mat)

                utt.assert_allclose(spmat.toarray() * mat, out.toarray()) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_structured_add_s_v(self):
        sp_types = {'csc': sp.csc_matrix,
                    'csr': sp.csr_matrix}

        for format in ['csr', 'csc']:
            for dtype in ['float32', 'float64']:
                x = theano.sparse.SparseType(format, dtype=dtype)()
                y = tensor.vector(dtype=dtype)
                f = theano.function([x, y], structured_add_s_v(x, y))

                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
                spones = spmat.copy()
                spones.data = numpy.ones_like(spones.data)
                mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)

                out = f(spmat, mat)

                utt.assert_allclose(as_ndarray(spones.multiply(spmat + mat)),
                                    out.toarray()) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import vector [as 别名]
def test_broadcast(self):
        # test that we don't raise an error during optimization for no good
        # reason as softmax_with_bias don't support correctly some/all
        # broadcasted inputs pattern
        initial_W = numpy.asarray([[0.1, 0.1, 0.1],
                                   [0.1, 0.1, 0.1],
                                   [0.1, 0.1, 0.1]],
                                  dtype=theano.config.floatX)
        W = theano.shared(value=initial_W, name='W')
        vbias = theano.shared(value=0.1, name='vbias')  # 0.01
        hid = T.vector('hid')
        f = theano.function([hid],
                            T.nnet.softmax_op(T.dot(hid, W.T) + vbias))
        ops = [node.op for node in f.maker.fgraph.toposort()]
        assert softmax_with_bias not in ops
        assert softmax_op in ops

        f([0, 1, 0])
        # print f.maker.fgraph.toposort()