在PaddlePaddle中出现报错张量类型不正确

• 问题描述：使用PaddlePaddle编写好网络运行时，报张量类型不正确的错误，我反复检查了自己的网络结构中张量类型相关的定义，并打印了传入数据的类型，类型都相匹配，但运行时就是报张量类似错误，逐步排查后，发现是训练代码段会抛出该错误，不知道如果修改？

• 报错信息：

EnforceNotMet: Tensor holds the wrong type, it holds l at [/paddle/paddle/fluid/framework/tensor_impl.h:29]
PaddlePaddle Call Stacks: 
0       0x7fe0624486b6p paddle::platform::EnforceNotMet::EnforceNotMet(std::__exception_ptr::exception_ptr, char const*, int) + 486
1       0x7fe0624501c0p float const* paddle::framework::Tensor::data<float>() const + 192
2       0x7fe06278f813p void paddle::operators::ElementwiseComputeEx<paddle::operators::SubFunctor<float>, paddle::platform::CPUDeviceContext, float, float>(paddle::framework::ExecutionContext const&, paddle::framework::Tensor const*, paddle::framework::Tensor const*, int, paddle::operators::SubFunctor<float>, paddle::framework::Tensor*) + 67
3       0x7fe062936e53p paddle::operators::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, float>::Compute(paddle::framework::ExecutionContext const&) const + 323
4       0x7fe062936ed3p std::_Function_handler<void (paddle::framework::ExecutionContext const&), paddle::framework::OpKernelRegistrarFunctor<paddle::platform::CPUPlace, false, 0ul, paddle::operators::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, float>, paddle::operators::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, double>, paddle::operators::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, int>, paddle::operators::ElementwiseSubKernel<paddle::platform::CPUDeviceContext, long> >::operator()(char const*, char const*) const::{
     lambda(paddle::framework::ExecutionContext const&)#1}>::_M_invoke(std::_Any_data const&, paddle::framework::ExecutionContext const&) + 35
5       0x7fe062fc52ecp 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 + 492
6       0x7fe062fc171fp 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
7       0x7fe0625094eap paddle::framework::Executor::RunPreparedContext(paddle::framework::ExecutorPrepareContext*, paddle::framework::Scope*, bool, bool, bool) + 298
8       0x7fe062509ee0p paddle::framework::Executor::Run(paddle::framework::ProgramDesc const&, paddle::framework::Scope*, int, bool, bool) + 128
9       0x7fe06242ff5dp
10      0x7fe06247ade4p pybind11::cpp_function::dispatcher(_object*, _object*, _object*) + 2596
11            0x4e9ba7p PyCFunction_Call + 119
12            0x53c6d5p PyEval_EvalFrameEx + 23029

• 问题复现

def train_program():
    # feature vector of length 13
    x = fluid.layers.data(name='x', shape=[11], dtype='float32')
    y_predict = fluid.layers.fc(input=x, size=1, act=None)

    y = fluid.layers.data(name='y', shape=[1], dtype='int64')
    loss = fluid.layers.square_error_cost(input=y_predict, label=y)
    avg_loss = fluid.layers.mean(loss)

    return avg_loss

• 问题分析：张量类型错误有多种可能，但就提供的代码而言，可能是损失函数定义的类型不满足当前的网络结构，出错代码中使用了square_error_cost，即平方差损失，导致代码报错。

• 问题解决：
  使用交叉熵损失函数代替平方差损失函数

paddle.fluid.layers.cross_entropy(input, label, soft_label=False, ignore_index=-100)

• 问题拓展：
  损失函数（loss function）是用来估量你模型的预测值f(x)与真实值Y的不一致程度，它是一个非负实值函数,通常使用L(Y, f(x))来表示，损失函数越小，模型的鲁棒性就越好。损失函数是经验风险函数的核心部分，也是结构风险函数重要组成部分。损失函数有多种，选择适合的损失函数对模型的训练有很大的帮助。