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diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h
deleted file mode 100644
index 0e72173536..0000000000
--- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h
+++ /dev/null
@@ -1,289 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
-// Copyright (C) 2015 Jianwei Cui <thucjw@gmail.com>
-//
-// This Source Code Form is subject to the terms of the Mozilla
-// Public License v. 2.0. If a copy of the MPL was not distributed
-// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
-
-#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
-#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
-
-namespace Eigen {
-
-/** \class TensorConvolutionByFFT
-  * \ingroup CXX11_Tensor_Module
-  *
-  * \brief Tensor convolution class.
-  *
-  *
-  */
-namespace internal {
-
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct traits<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> >
-{
-  // Type promotion to handle the case where the types of the lhs and the rhs are different.
-  typedef typename promote_storage_type<typename InputXprType::Scalar,
-                                        typename KernelXprType::Scalar>::ret Scalar;
-  typedef typename packet_traits<Scalar>::type Packet;
-  typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
-                                        typename traits<KernelXprType>::StorageKind>::ret StorageKind;
-  typedef typename promote_index_type<typename traits<InputXprType>::Index,
-                                      typename traits<KernelXprType>::Index>::type Index;
-  typedef typename InputXprType::Nested LhsNested;
-  typedef typename KernelXprType::Nested RhsNested;
-  typedef typename remove_reference<LhsNested>::type _LhsNested;
-  typedef typename remove_reference<RhsNested>::type _RhsNested;
-  static const int NumDimensions = traits<InputXprType>::NumDimensions;
-  static const int Layout = traits<InputXprType>::Layout;
-
-  enum {
-    Flags = 0,
-  };
-};
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct eval<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
-{
-  typedef const TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>& type;
-};
-
-template<typename Dimensions, typename InputXprType, typename KernelXprType>
-struct nested<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> >::type>
-{
-  typedef TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> type;
-};
-
-}  // end namespace internal
-
-
-
-template<typename Indices, typename InputXprType, typename KernelXprType>
-class TensorConvolutionByFFTOp : public TensorBase<TensorConvolutionByFFTOp<Indices, InputXprType, KernelXprType> >
-{
-  public:
-  typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Scalar Scalar;
-  typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Packet Packet;
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-  typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
-                                                  typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
-  typedef typename internal::promote_storage_type<typename InputXprType::PacketReturnType,
-                                                  typename KernelXprType::PacketReturnType>::ret PacketReturnType;
-  typedef typename Eigen::internal::nested<TensorConvolutionByFFTOp>::type Nested;
-  typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::StorageKind StorageKind;
-  typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Index Index;
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionByFFTOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
-      : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const Indices& indices() const { return m_indices; }
-
-    /** \returns the nested expressions */
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<typename InputXprType::Nested>::type&
-    inputExpression() const { return m_input_xpr; }
-
-    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-    const typename internal::remove_all<typename KernelXprType::Nested>::type&
-    kernelExpression() const { return m_kernel_xpr; }
-
-  protected:
-    typename InputXprType::Nested m_input_xpr;
-    typename KernelXprType::Nested m_kernel_xpr;
-    const Indices m_indices;
-};
-
-
-template<typename Indices, typename InputArgType, typename KernelArgType, typename Device>
-struct TensorEvaluator<const TensorConvolutionByFFTOp<Indices, InputArgType, KernelArgType>, Device>
-{
-  typedef TensorConvolutionByFFTOp<Indices, InputArgType, KernelArgType> XprType;
-
-  typedef typename XprType::Scalar Scalar;
-  typedef typename XprType::CoeffReturnType CoeffReturnType;
-  typedef typename XprType::PacketReturnType PacketReturnType;
-
-  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
-
-  static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
-  static const int NumKernelDims = internal::array_size<Indices>::value;
-  typedef typename XprType::Index Index;
-  typedef DSizes<Index, NumDims> Dimensions;
-
-  enum {
-    IsAligned = TensorEvaluator<InputArgType, Device>::IsAligned &
-                TensorEvaluator<KernelArgType, Device>::IsAligned,
-    PacketAccess = false,
-    BlockAccess = false,
-    Layout = TensorEvaluator<InputArgType, Device>::Layout,
-    CoordAccess = false,  // to be implemented
-  };
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
-      : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
-  {
-    EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
-
-    const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
-    const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
-
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      m_inputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
-      }
-    } else {
-      m_inputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
-      }
-    }
-
-    m_dimensions = m_inputImpl.dimensions();
-    if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
-      for (int i = 0; i < NumKernelDims; ++i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i > 0) {
-          m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
-        } else {
-          m_kernelStride[0] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[0] = 1;
-      for (int i = 1; i < NumDims; ++i) {
-        m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
-      }
-    } else {
-      for (int i = NumKernelDims - 1; i >= 0; --i) {
-        const Index index = op.indices()[i];
-        const Index input_dim = input_dims[index];
-        const Index kernel_dim = kernel_dims[i];
-        const Index result_dim = input_dim - kernel_dim + 1;
-        m_dimensions[index] = result_dim;
-        if (i < NumKernelDims - 1) {
-          m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
-        } else {
-          m_kernelStride[NumKernelDims - 1] = 1;
-        }
-        m_indexStride[i] = m_inputStride[index];
-      }
-
-      m_outputStride[NumDims - 1] = 1;
-      for (int i = NumDims - 2; i >= 0; --i) {
-        m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1];
-      }
-    }
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
-
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
-    m_inputImpl.evalSubExprsIfNeeded(NULL);
-    m_kernelImpl.evalSubExprsIfNeeded(NULL);
-
-    typedef typename internal::traits<InputArgType>::Index TensorIndex;
-
-    Tensor<Scalar, NumDims, Layout, TensorIndex> input(m_inputImpl.dimensions());
-    for (int i = 0; i < m_inputImpl.dimensions().TotalSize(); ++i) {
-      input.data()[i] = m_inputImpl.coeff(i);
-    }
-
-    Tensor<Scalar, NumDims, Layout, TensorIndex> kernel(m_kernelImpl.dimensions());
-    for (int i = 0; i < m_kernelImpl.dimensions().TotalSize(); ++i) {
-      kernel.data()[i] = m_kernelImpl.coeff(i);
-    }
-
-    array<std::pair<ptrdiff_t, ptrdiff_t>, NumDims> paddings;
-    for (int i = 0; i < NumDims; ++i) {
-      paddings[i] = std::make_pair(0, m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i]);
-    }
-
-    Eigen::array<bool, NumKernelDims> reverse;
-    for (int i = 0; i < NumKernelDims; ++i) {
-      reverse[i] = true;
-    }
-
-    Eigen::array<bool, NumDims> fft;
-    for (int i = 0; i < NumDims; ++i) {
-      fft[i] = i;
-    }
-
-    Eigen::DSizes<TensorIndex, NumDims> slice_offsets;
-    for (int i = 0; i < NumDims; ++i) {
-      slice_offsets[i] = m_kernelImpl.dimensions()[i] - 1;
-    }
-
-    Eigen::DSizes<TensorIndex, NumDims> slice_extents;
-    for (int i = 0; i < NumDims; ++i) {
-      slice_extents[i] = m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i] + 1;
-    }
-
-    Tensor<Scalar, NumDims, Layout, TensorIndex> kernel_variant =  kernel.reverse(reverse).pad(paddings);
-    Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> kernel_fft =  kernel_variant.template fft<Eigen::BothParts, FFT_FORWARD>(fft);
-    //Tensor<std::complex<Scalar>, NumDims, Layout|IndexType> kernel_fft =  kernel.reverse(reverse).pad(paddings).template fft<2>(fft);
-    Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> input_fft = input.template fft<Eigen::BothParts, FFT_FORWARD>(fft);
-    Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> prod = (input_fft * kernel_fft).template fft<Eigen::BothParts, FFT_REVERSE>(fft);
-    Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> tensor_result = prod.slice(slice_offsets, slice_extents);
-
-    for (int i = 0; i < tensor_result.size(); ++i) {
-      data[i] = std::real(tensor_result.data()[i]);
-    }
-    return false;
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
-    m_inputImpl.cleanup();
-    if (m_local_kernel) {
-      m_device.deallocate((void*)m_kernel);
-      m_local_kernel = false;
-    }
-    m_kernel = NULL;
-  }
-
-  void evalTo(typename XprType::Scalar* buffer) {
-    evalSubExprsIfNeeded(NULL);
-    for (int i = 0; i < dimensions().TotalSize(); ++i) {
-      buffer[i] += coeff(i);
-    }
-    cleanup();
-  }
-
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
-  {
-    CoeffReturnType result = CoeffReturnType(0);
-    return result;
-  }
-
-  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
-
- private:
-  array<Index, NumDims> m_inputStride;
-  array<Index, NumDims> m_outputStride;
-
-  array<Index, NumKernelDims> m_indexStride;
-  array<Index, NumKernelDims> m_kernelStride;
-  TensorEvaluator<InputArgType, Device> m_inputImpl;
-  TensorEvaluator<KernelArgType, Device> m_kernelImpl;
-  Dimensions m_dimensions;
-
-  KernelArgType m_kernelArg;
-  const Scalar* m_kernel;
-  bool m_local_kernel;
-  const Device& m_device;
-};
-
-} // end namespace Eigen
-
-#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H