Created by: jaegerstar

错误信息如下：

paddle.fluid.core.EnforceNotMet: Enforce failed. Expected framework::slice_ddim(x_dims, 0, rank - 1) == framework::slice_ddim(label_dims, 0, rank - 1), but received framework::slice_ddim(x_dims, 0, rank - 1):487 != framework::slice_ddim(label_dims, 0, rank - 1):128.
Input(X) and Input(Label) shall have the same shape except the last dimension. at [/home/tangjian/paddle-release-docer/paddle/fluid/operators/cross_entropy_op.cc:37]
PaddlePaddle Call Stacks: 
0       0x7feba2bb5f96p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const*, int) + 486
1       0x7feba35230dbp paddle::operators::CrossEntropyOp::InferShape(paddle::framework::InferShapeContext*) const + 1451
2       0x7feba3610939p paddle::framework::OperatorWithKernel::RunImpl(paddle::framework::Scope const&, boost::variant<paddle::platform::CUDAPlace, paddle::platform::CPUPlace, paddle::platform::CUDAPinnedPlace, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_> const&) const + 73
3       0x7feba360cf6fp paddle::framework::OperatorBase::Run(paddle::framework::Scope const&, boost::variant<paddle::platform::CUDAPlace, paddle::platform::CPUPlace, paddle::platform::CUDAPinnedPlace, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_, boost::detail::variant::void_> const&) + 255
4       0x7feba2c7658dp paddle::framework::Executor::RunPreparedContext(paddle::framework::ExecutorPrepareContext*, paddle::framework::Scope*, bool, bool, bool) + 301
5       0x7feba2c77320p paddle::framework::Executor::Run(paddle::framework::ProgramDesc const&, paddle::framework::Scope*, int, bool, bool) + 128
6       0x7feba2b9df3dp
7       0x7feba2be81dcp pybind11::cpp_function::dispatcher(_object*, _object*, _object*) + 2540
8       0x7fec074e63d4p PyEval_EvalFrameEx + 25956
9       0x7fec074e7120p PyEval_EvalCodeEx + 2240
10      0x7fec074e5491p PyEval_EvalFrameEx + 22049
11      0x7fec074e7120p PyEval_EvalCodeEx + 2240
12      0x7fec074e5491p PyEval_EvalFrameEx + 22049
13      0x7fec074e7120p PyEval_EvalCodeEx + 2240
14      0x7fec074e5491p PyEval_EvalFrameEx + 22049
15      0x7fec074e7120p PyEval_EvalCodeEx + 2240
16      0x7fec074e7232p PyEval_EvalCode + 50
17      0x7fec0750161cp
18      0x7fec075016f0p PyRun_FileExFlags + 144
19      0x7fec07502bfcp PyRun_SimpleFileExFlags + 220
20      0x7fec075144bcp Py_Main + 3164
21        0x318ae1ecddp __libc_start_main + 253
22            0x400659p

网络代码如下：

class TextCNN_v3:
    """TextCNN_v3:
    This version is with double layers CNN and without pooling
    """

    def __init__(self, config):
        self._config = config

    def __call__(self, query, label):
        return self.body(query, label, self._config)

    def body(self, query, label, config):
        """Body function"""
        def block(input, kernel_size):
            bn_1 = fluid.layers.batch_norm(input=input, fuse_with_relu=True)
            conv_1 = fluid.layers.sequence_conv(
                input=bn_1,
                num_filters=config.kernel_count,
                filter_size=kernel_size,
                padding=True)
            bn_2 = fluid.layers.batch_norm(input=conv_1, fuse_with_relu=True)
            conv_2 = fluid.layers.sequence_conv(
                input=bn_2,
                num_filters=config.kernel_count,
                filter_size=kernel_size,
                padding=True)
            bn_3 = fluid.layers.batch_norm(input=conv_2, fuse_with_relu=True)

            return bn_3

        def MLP(vec):
            for dim in config.mlp_hid_dim:
                vec = fluid.layers.fc(vec, size=dim, act='relu')
                # vec = fluid.layers.dropout(vec, dropout_prob=config.droprate_fc)
            return vec

        emb_para = fluid.ParamAttr(name='emb_s', initializer=fluid.initializer.Xavier, trainable=False)
        embed = fluid.layers.embedding(input=query, size=[config.dict_dim, config.emb_dim], param_attr=emb_para)
        block_res = []
        for i, size in enumerate(config.kernel_size):
            locals()['conv_' + str(i + 1)] = block(embed, kernel_size=size)
            block_res.append(locals()['conv_' + str(i + 1)])
        concated_seq = fluid.layers.concat(input=block_res, axis=1)
        mlp_res = MLP(concated_seq)
        prediction = fluid.layers.fc(mlp_res, size=config.class_dim, act='softmax')
        loss = fluid.layers.cross_entropy(input=prediction, label=label)
        avg_cost = fluid.layers.mean(x=loss)
        acc = fluid.layers.accuracy(input=prediction, label=label)
        auc = fluid.layers.auc(input=prediction, label=label)
        return avg_cost, acc, prediction