diff options
64 files changed, 1016 insertions, 298 deletions
diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD index 7b8cfc56ed..48fbaa479a 100644 --- a/tensorflow/cc/BUILD +++ b/tensorflow/cc/BUILD @@ -2,7 +2,9 @@ # TensorFlow is a computational framework, primarily for use in machine # learning applications. -package(default_visibility = ["//tensorflow:internal"]) +package( + default_visibility = ["//tensorflow:internal"], +) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 72bd33dab5..cb26384ebe 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2,7 +2,9 @@ # TensorFlow is a computational framework, primarily for use in machine # learning applications. -package(default_visibility = ["//tensorflow:internal"]) +package( + default_visibility = ["//tensorflow:internal"], +) package_group(name = "friends") diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc index 32109157aa..1821289f4b 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc @@ -40,13 +40,13 @@ EventMgr::~EventMgr() { delete e; } while (!used_events_.empty()) { - InUse* ue = &used_events_[0]; - delete ue->event; - delete ue->mem; - if (ue->bufrec.buf) { - ue->bufrec.alloc->DeallocateRaw(ue->bufrec.buf); + delete used_events_[0].event; + delete used_events_[0].mem; + if (used_events_[0].bufrec.buf) { + used_events_[0].bufrec.alloc->DeallocateRaw(used_events_[0].bufrec.buf); } - if (ue->func != nullptr) threadpool_.Schedule(ue->func); + if (used_events_[0].func != nullptr) + threadpool_.Schedule(used_events_[0].func); used_events_.pop_front(); } } @@ -60,17 +60,15 @@ EventMgr::~EventMgr() { void EventMgr::PollLoop() { while (!stop_polling_.HasBeenNotified()) { Env::Default()->SleepForMicroseconds(1 * 1000); - ToFreeVector to_free; { mutex_lock l(mu_); - PollEvents(true, &to_free); + PollEvents(true); } - FreeMemory(to_free); } polling_stopped_.Notify(); } -void EventMgr::QueueInUse(gpu::Stream* stream, InUse iu, gpu::Event** e) { +void EventMgr::QueueInUse(gpu::Stream* stream, InUse iu) { VLOG(2) << "QueueInUse free_events_ " << free_events_.size() << " used_events_ " << used_events_.size(); // Events are created on demand, and repeatedly reused. There is no @@ -79,9 +77,10 @@ void EventMgr::QueueInUse(gpu::Stream* stream, InUse iu, gpu::Event** e) { free_events_.push_back(new gpu::Event(exec_)); free_events_.back()->Init(); } - *e = free_events_.back(); + gpu::Event* e = free_events_.back(); free_events_.pop_back(); - iu.event = *e; + stream->ThenRecordEvent(e); + iu.event = e; used_events_.push_back(iu); } @@ -104,8 +103,7 @@ void EventMgr::QueueInUse(gpu::Stream* stream, InUse iu, gpu::Event** e) { // GPU memory use to spike needlessly. An alternative strategy would // be to throttle new Op execution until the pending event queue // clears. -void EventMgr::PollEvents(bool is_dedicated_poller, - gtl::InlinedVector<InUse, 4>* to_free) { +void EventMgr::PollEvents(bool is_dedicated_poller) { VLOG(2) << "PollEvents free_events_ " << free_events_.size() << " used_events_ " << used_events_.size(); // Sweep the remaining events in order. If this is the dedicated @@ -125,9 +123,11 @@ void EventMgr::PollEvents(bool is_dedicated_poller, if (!is_dedicated_poller) return; // quit processing queue break; case gpu::Event::Status::kComplete: - // Make a copy of the InUse record so we can free it after releasing - // the lock - to_free->push_back(iu); + delete iu.mem; + if (iu.bufrec.buf) iu.bufrec.alloc->DeallocateRaw(iu.bufrec.buf); + // The function must be called in another thread, outside of + // the mutex held here. + if (iu.func != nullptr) threadpool_.Schedule(iu.func); free_events_.push_back(iu.event); // Mark this InUse record as completed. iu.event = nullptr; diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h index f2a1ea2603..5fe9fd782d 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h @@ -18,10 +18,8 @@ limitations under the License. #include <deque> #include <vector> -#include "tensorflow/stream_executor/stream.h" #include "tensorflow/core/lib/core/notification.h" #include "tensorflow/core/lib/core/threadpool.h" -#include "tensorflow/core/lib/gtl/inlined_vector.h" #include "tensorflow/core/platform/port.h" #include "tensorflow/core/platform/thread_annotations.h" #include "tensorflow/core/public/tensor.h" @@ -49,15 +47,9 @@ class EventMgr { // currently enqueued on *stream have completed. inline void ThenDeleteTensors(perftools::gputools::Stream* stream, std::vector<Tensor>* tensors) { - ToFreeVector to_free; - ::perftools::gputools::Event* e; - { - mutex_lock l(mu_); - QueueTensors(stream, tensors, &e); - PollEvents(false, &to_free); - } - stream->ThenRecordEvent(e); - FreeMemory(to_free); + mutex_lock l(mu_); + QueueTensors(stream, tensors); + PollEvents(false); } struct BufRec { @@ -69,28 +61,16 @@ class EventMgr { // on it as soon as all events currently enqueued on *stream have completed. inline void ThenDeleteBuffer(perftools::gputools::Stream* stream, BufRec bufrec) { - ToFreeVector to_free; - ::perftools::gputools::Event* e; - { - mutex_lock l(mu_); - QueueBuffer(stream, bufrec, &e); - PollEvents(false, &to_free); - } - stream->ThenRecordEvent(e); - FreeMemory(to_free); + mutex_lock l(mu_); + QueueBuffer(stream, bufrec); + PollEvents(false); } inline void ThenExecute(perftools::gputools::Stream* stream, std::function<void()> func) { - ToFreeVector to_free; - ::perftools::gputools::Event* e; - { - mutex_lock l(mu_); - QueueFunc(stream, func, &e); - PollEvents(false, &to_free); - } - stream->ThenRecordEvent(e); - FreeMemory(to_free); + mutex_lock l(mu_); + QueueFunc(stream, func); + PollEvents(false); } private: @@ -105,50 +85,32 @@ class EventMgr { std::function<void()> func; }; - typedef gtl::InlinedVector<InUse, 4> ToFreeVector; - - void FreeMemory(const ToFreeVector& to_free) { - for (const auto& iu : to_free) { - delete iu.mem; - if (iu.bufrec.buf) iu.bufrec.alloc->DeallocateRaw(iu.bufrec.buf); - // The function must be called in another thread. - if (iu.func != nullptr) threadpool_.Schedule(iu.func); - } - } - // Stream-enqueue an unused Event and save with it a collection of // Tensors and/or a BufRec to be deleted only after the Event // records. - void QueueInUse(perftools::gputools::Stream* stream, InUse in_use, - ::perftools::gputools::Event** e) + void QueueInUse(perftools::gputools::Stream* stream, InUse in_use) EXCLUSIVE_LOCKS_REQUIRED(mu_); void QueueTensors(perftools::gputools::Stream* stream, - std::vector<Tensor>* tensors, - ::perftools::gputools::Event** e) + std::vector<Tensor>* tensors) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - QueueInUse(stream, {nullptr, tensors, BufRec(), nullptr}, e); + QueueInUse(stream, {nullptr, tensors, BufRec(), nullptr}); } - void QueueBuffer(perftools::gputools::Stream* stream, BufRec bufrec, - ::perftools::gputools::Event** e) + void QueueBuffer(perftools::gputools::Stream* stream, BufRec bufrec) EXCLUSIVE_LOCKS_REQUIRED(mu_) { - QueueInUse(stream, {nullptr, nullptr, bufrec, nullptr}, e); + QueueInUse(stream, {nullptr, nullptr, bufrec, nullptr}); } void QueueFunc(perftools::gputools::Stream* stream, - std::function<void()> func, ::perftools::gputools::Event** e) - EXCLUSIVE_LOCKS_REQUIRED(mu_) { - QueueInUse(stream, {nullptr, nullptr, BufRec(), func}, e); + std::function<void()> func) EXCLUSIVE_LOCKS_REQUIRED(mu_) { + QueueInUse(stream, {nullptr, nullptr, BufRec(), func}); } // This function should be called at roughly the same tempo as // QueueTensors() to check whether pending events have recorded, - // and then retire them. It appends InUse elements that need cleanup - // to "*to_free". The caller should call FreeMemory(to_free) - // when this returns. - void PollEvents(bool is_dedicated_poller, ToFreeVector* to_free) - EXCLUSIVE_LOCKS_REQUIRED(mu_); + // and then retire them. + void PollEvents(bool is_dedicated_poller) EXCLUSIVE_LOCKS_REQUIRED(mu_); // An internal polling loop that runs at a low frequency to clear // straggler Events. diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc b/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc index c6893c91e7..6956ead643 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc @@ -42,21 +42,13 @@ class TEST_EventMgrHelper { void QueueTensors(perftools::gputools::Stream* stream, std::vector<Tensor>* tensors) { - ::perftools::gputools::Event* e; - { - mutex_lock l(em_->mu_); - em_->QueueTensors(stream, tensors, &e); - } - stream->ThenRecordEvent(e); + mutex_lock l(em_->mu_); + em_->QueueTensors(stream, tensors); } void PollEvents(bool is_dedicated_poller) { - EventMgr::ToFreeVector to_free; - { - mutex_lock l(em_->mu_); - em_->PollEvents(is_dedicated_poller, &to_free); - } - em_->FreeMemory(to_free); + mutex_lock l(em_->mu_); + em_->PollEvents(is_dedicated_poller); } private: diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc index c40acac6ff..6e4eab8c05 100644 --- a/tensorflow/core/framework/node_def_util.cc +++ b/tensorflow/core/framework/node_def_util.cc @@ -79,7 +79,10 @@ Status AttrSlice::Find(const string& attr_name, return Status::OK(); } Status s = errors::NotFound("No attr named '", attr_name, "' in NodeDef:"); - if (ndef_) { + // Skip AttachDef for internal attrs since it is a little bit + // expensive and it is common for them to correctly not be included + // in a NodeDef. + if (!StringPiece(attr_name).starts_with("_") && ndef_) { s = AttachDef(s, *ndef_); } return s; diff --git a/tensorflow/core/kernels/cholesky_op.cc b/tensorflow/core/kernels/cholesky_op.cc index 1a06b124c7..2f6ad43cc2 100644 --- a/tensorflow/core/kernels/cholesky_op.cc +++ b/tensorflow/core/kernels/cholesky_op.cc @@ -46,7 +46,7 @@ class CholeskyOp const int64 rows = input_matrix_shape.dim_size(0); if (rows > (1LL << 20)) { // A big number to cap the cost in case overflow. - return kint32max; + return kint64max; } else { return rows * rows * rows; } @@ -69,8 +69,9 @@ class CholeskyOp // Perform the actual LL^T Cholesky decomposition. This will only use // the lower triangular part of data_in by default. The upper triangular // part of the matrix will not be read. - Eigen::LLT<Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, - Eigen::RowMajor>> llt_decomposition(input); + Eigen::LLT< + Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> + llt_decomposition(input); // Output the lower triangular in a dense form. *output = llt_decomposition.matrixL(); diff --git a/tensorflow/core/kernels/determinant_op.cc b/tensorflow/core/kernels/determinant_op.cc index 20988b5830..852a36bcd4 100644 --- a/tensorflow/core/kernels/determinant_op.cc +++ b/tensorflow/core/kernels/determinant_op.cc @@ -44,7 +44,7 @@ class DeterminantOp const int64 rows = input_matrix_shape.dim_size(0); if (rows > (1LL << 20)) { // A big number to cap the cost in case overflow. - return kint32max; + return kint64max; } else { return rows * rows * rows; } diff --git a/tensorflow/core/kernels/matrix_inverse_op.cc b/tensorflow/core/kernels/matrix_inverse_op.cc index 3d7ade4b51..d7c8149ceb 100644 --- a/tensorflow/core/kernels/matrix_inverse_op.cc +++ b/tensorflow/core/kernels/matrix_inverse_op.cc @@ -45,7 +45,7 @@ class MatrixInverseOp const int64 rows = input_matrix_shape.dim_size(0); if (rows > (1LL << 20)) { // A big number to cap the cost in case overflow. - return kint32max; + return kint64max; } else { return rows * rows * rows; } diff --git a/tensorflow/core/kernels/reduction_ops_sum.cc b/tensorflow/core/kernels/reduction_ops_sum.cc index ecd9c1a56b..e715ca6664 100644 --- a/tensorflow/core/kernels/reduction_ops_sum.cc +++ b/tensorflow/core/kernels/reduction_ops_sum.cc @@ -44,7 +44,10 @@ REGISTER_GPU_KERNELS(float); #undef REGISTER_GPU_KERNELS REGISTER_KERNEL_BUILDER( - Name("Sum").Device(DEVICE_GPU).TypeConstraint<complex64>("T"), + Name("Sum") + .Device(DEVICE_GPU) + .TypeConstraint<complex64>("T") + .HostMemory("reduction_indices"), ReductionOp<GPUDevice, complex64, Eigen::internal::SumReducer<complex64>>); #endif diff --git a/tensorflow/core/kernels/self_adjoint_eig_op.cc b/tensorflow/core/kernels/self_adjoint_eig_op.cc new file mode 100644 index 0000000000..9db1a5c518 --- /dev/null +++ b/tensorflow/core/kernels/self_adjoint_eig_op.cc @@ -0,0 +1,89 @@ +/* Copyright 2015 Google Inc. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// See docs in ../ops/linalg_ops.cc. + +#include <cmath> + +#include "third_party/eigen3/Eigen/Core" +#include "third_party/eigen3/Eigen/Eigenvalues" + +#include "tensorflow/core/framework/kernel_def_builder.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/kernels/linalg_ops_common.h" +#include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/port.h" +#include "tensorflow/core/public/tensor_shape.h" + +namespace tensorflow { + +template <class Scalar, bool SupportsBatchOperationT> +class SelfAdjointEigOp + : public UnaryLinearAlgebraOp<Scalar, SupportsBatchOperationT> { + public: + explicit SelfAdjointEigOp(OpKernelConstruction* context) + : UnaryLinearAlgebraOp<Scalar, SupportsBatchOperationT>(context) {} + + TensorShape GetOutputMatrixShape( + const TensorShape& input_matrix_shape) override { + int64 d = input_matrix_shape.dim_size(0); + return TensorShape({d + 1, d}); + } + + int64 GetCostPerUnit(const TensorShape& input_matrix_shape) override { + const int64 rows = input_matrix_shape.dim_size(0); + if (rows > (1LL << 20)) { + // A big number to cap the cost in case overflow. + return kint64max; + } else { + return rows * rows * rows; + } + } + + using + typename UnaryLinearAlgebraOp<Scalar, SupportsBatchOperationT>::MatrixMap; + using typename UnaryLinearAlgebraOp<Scalar, + SupportsBatchOperationT>::ConstMatrixMap; + + void ComputeMatrix(OpKernelContext* context, const ConstMatrixMap& input, + MatrixMap* output) override { + OP_REQUIRES(context, input.rows() == input.cols(), + errors::InvalidArgument("Input matrix must be square.")); + if (input.rows() == 0) { + // If X is an empty matrix (0 rows, 0 col), X * X' == X. + // Therefore, we return X. + return; + } + + Eigen::SelfAdjointEigenSolver< + Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>> + es(input); + output->row(0) = es.eigenvalues().transpose(); + output->bottomRows(input.rows()) = es.eigenvectors(); + OP_REQUIRES(context, es.info() == Eigen::Success, + errors::InvalidArgument("Self Adjoint Eigen decomposition was" + "not successful. " + "The input might not be valid.")); + } +}; + +REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<float, false>), float); +REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<double, false>), double); +REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<float, true>), + float); +REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<double, true>), + double); +} // namespace tensorflow diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 8287d758f0..99cd3e61ce 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -326,7 +326,7 @@ If `indices` is a permutation and `len(indices) == params.shape[0]` then this operation will permute `params` accordingly. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/Gather.png" alt> +<img style="width:100%" src="../../images/Gather.png" alt> </div> )doc"); diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc index 59a7876289..c9d782f1c5 100644 --- a/tensorflow/core/ops/data_flow_ops.cc +++ b/tensorflow/core/ops/data_flow_ops.cc @@ -57,7 +57,7 @@ For example: outputs[1] = [30, 40] <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/DynamicPartition.png" alt> +<img style="width:100%" src="../../images/DynamicPartition.png" alt> </div> partitions: Any shape. Indices in the range `[0, num_partitions)`. @@ -108,7 +108,7 @@ For example: [51, 52], [61, 62]] <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/DynamicStitch.png" alt> +<img style="width:100%" src="../../images/DynamicStitch.png" alt> </div> )doc"); diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc index 9b577e7562..a4b0b0aebf 100644 --- a/tensorflow/core/ops/linalg_ops.cc +++ b/tensorflow/core/ops/linalg_ops.cc @@ -123,4 +123,41 @@ output: Shape is `[..., M, M]`. T: The type of values in the input and output. )doc"); +REGISTER_OP("SelfAdjointEig") + .Input("input: T") + .Output("output: T") + .Attr("T: {double, float}") + .Doc(R"doc( +Calculates the Eigen Decomposition of a square Self-Adjoint matrix. + +Only the lower-triangular part of the input will be used in this case. The +upper-triangular part will not be read. + +The result is a M+1 x M matrix whose first row is the eigenvalues, and +subsequent rows are eigenvectors. + +input: Shape is `[M, M]`. +output: Shape is `[M+1, M]`. +T: The type of values in the input and output. +)doc"); + +REGISTER_OP("BatchSelfAdjointEig") + .Input("input: T") + .Output("output: T") + .Attr("T: {double, float}") + .Doc(R"doc( +Calculates the Eigen Decomposition of a batch of square self-adjoint matrices. + +The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions +form square matrices, with the same constraints as the single matrix +SelfAdjointEig. + +The result is a '[..., M+1, M] matrix with [..., 0,:] containing the +eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. + +input: Shape is `[..., M, M]`. +output: Shape is `[..., M+1, M]`. +T: The type of values in the input and output. +)doc"); + } // namespace tensorflow diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index a1f1db5f7f..61080bc763 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -649,7 +649,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentSum.png" alt> +<img style="width:100%" src="../../images/SegmentSum.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s @@ -678,7 +678,7 @@ over `j` such that `segment_ids[j] == i` and `N` is the total number of values summed. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMean.png" alt> +<img style="width:100%" src="../../images/SegmentMean.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s @@ -706,7 +706,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentProd.png" alt> +<img style="width:100%" src="../../images/SegmentProd.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s @@ -734,7 +734,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMin.png" alt> +<img style="width:100%" src="../../images/SegmentMin.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s @@ -761,7 +761,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMax.png" alt> +<img style="width:100%" src="../../images/SegmentMax.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s @@ -796,7 +796,7 @@ If the sum is empty for a given segment ID `i`, `output[i] = 0`. `num_segments` should equal the number of distinct segment IDs. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/UnsortedSegmentSum.png" alt> +<img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt> </div> segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index b529ef9c76..59a0ee62ee 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -1225,6 +1225,32 @@ op { summary: "Gradients for batch normalization." } op { + name: "BatchSelfAdjointEig" + input_arg { + name: "input" + description: "Shape is `[..., M, M]`." + type_attr: "T" + } + output_arg { + name: "output" + description: "Shape is `[..., M+1, M]`." + type_attr: "T" + } + attr { + name: "T" + type: "type" + description: "The type of values in the input and output." + allowed_values { + list { + type: DT_DOUBLE + type: DT_FLOAT + } + } + } + summary: "Calculates the Eigen Decomposition of a batch of square self-adjoint matrices." + description: "The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions\nform square matrices, with the same constraints as the single matrix\nSelfAdjointEig.\n\nThe result is a \'[..., M+1, M] matrix with [..., 0,:] containing the\neigenvalues, and subsequent [...,1:, :] containing the eigenvectors." +} +op { name: "BiasAdd" input_arg { name: "value" @@ -6520,6 +6546,32 @@ op { description: "The `condition`, `t`, and `e` tensors must all have the same shape,\nand the output will also have that shape. The `condition` tensor acts\nas an element-wise mask that chooses, based on the value at each\nelement, whether the corresponding element in the output should be\ntaken from `t` (if true) or `e` (if false). For example:\n\nFor example:\n\n```prettyprint\n# \'condition\' tensor is [[True, False]\n# [True, False]]\n# \'t\' is [[1, 1],\n# [1, 1]]\n# \'e\' is [[2, 2],\n# [2, 2]]\nselect(condition, t, e) ==> [[1, 2],\n [1, 2]]\n```" } op { + name: "SelfAdjointEig" + input_arg { + name: "input" + description: "Shape is `[M, M]`." + type_attr: "T" + } + output_arg { + name: "output" + description: "Shape is `[M+1, M]`." + type_attr: "T" + } + attr { + name: "T" + type: "type" + description: "The type of values in the input and output." + allowed_values { + list { + type: DT_DOUBLE + type: DT_FLOAT + } + } + } + summary: "Calculates the Eigen Decomposition of a square Self-Adjoint matrix." + description: "Only the lower-triangular part of the input will be used in this case. The\nupper-triangular part will not be read.\n\nThe result is a M+1 x M matrix whose first row is the eigenvalues, and\nsubsequent rows are eigenvectors." +} +op { name: "Shape" input_arg { name: "input" diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc index 0855c16786..a5a3d14a07 100644 --- a/tensorflow/core/ops/state_ops.cc +++ b/tensorflow/core/ops/state_ops.cc @@ -188,7 +188,7 @@ override earlier entries. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterUpdate.png" alt> +<img style="width:100%" src="../../images/ScatterUpdate.png" alt> </div> ref: Should be from a `Variable` node. @@ -231,7 +231,7 @@ the same location, their contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterAdd.png" alt> +<img style="width:100%" src="../../images/ScatterAdd.png" alt> </div> ref: Should be from a `Variable` node. @@ -272,7 +272,7 @@ the same location, their (negated) contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterSub.png" alt> +<img style="width:100%" src="../../images/ScatterSub.png" alt> </div> ref: Should be from a `Variable` node. diff --git a/tensorflow/examples/android/BUILD b/tensorflow/examples/android/BUILD index 872aeeb6a2..042e5e6e14 100644 --- a/tensorflow/examples/android/BUILD +++ b/tensorflow/examples/android/BUILD @@ -1,7 +1,9 @@ # Description: # Tensorflow camera demo app for Android. -package(default_visibility = ["//visibility:public"]) +package( + default_visibility = ["//visibility:public"], +) licenses(["notice"]) # Apache 2.0 diff --git a/tensorflow/g3doc/api_docs/python/array_ops.md b/tensorflow/g3doc/api_docs/python/array_ops.md index 09485d671e..d763864300 100644 --- a/tensorflow/g3doc/api_docs/python/array_ops.md +++ b/tensorflow/g3doc/api_docs/python/array_ops.md @@ -904,7 +904,7 @@ If `indices` is a permutation and `len(indices) == params.shape[0]` then this operation will permute `params` accordingly. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/Gather.png" alt> +<img style="width:100%" src="../../images/Gather.png" alt> </div> ##### Args: @@ -954,7 +954,7 @@ For example: outputs[1] = [30, 40] <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/DynamicPartition.png" alt> +<img style="width:100%" src="../../images/DynamicPartition.png" alt> </div> ##### Args: @@ -1013,7 +1013,7 @@ For example: [51, 52], [61, 62]] <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/DynamicStitch.png" alt> +<img style="width:100%" src="../../images/DynamicStitch.png" alt> </div> ##### Args: diff --git a/tensorflow/g3doc/api_docs/python/image.md b/tensorflow/g3doc/api_docs/python/image.md index 3d8c51d520..e9fd048b3f 100644 --- a/tensorflow/g3doc/api_docs/python/image.md +++ b/tensorflow/g3doc/api_docs/python/image.md @@ -192,7 +192,7 @@ The convenience function [`resize_images()`](#resize_images) supports both 4-D and 3-D tensors as input and output. 4-D tensors are for batches of images, 3-D tensors for individual images. -Other resizing Ops only support 3-D individual images as input: +Other resizing Ops only support 4-D batches of images as input: [`resize_area`](#resize_area), [`resize_bicubic`](#resize_bicubic), [`resize_bilinear`](#resize_bilinear), [`resize_nearest_neighbor`](#resize_nearest_neighbor). @@ -200,9 +200,9 @@ Other resizing Ops only support 3-D individual images as input: Example: ```python -# Decode a JPG image and resize it to 299 by 299. +# Decode a JPG image and resize it to 299 by 299 using default method. image = tf.image.decode_jpeg(...) -resized_image = tf.image.resize_bilinear(image, [299, 299]) +resized_image = tf.image.resize_images(image, 299, 299) ``` - - - diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md index b46ef9ffbc..5f5ae1e5ea 100644 --- a/tensorflow/g3doc/api_docs/python/index.md +++ b/tensorflow/g3doc/api_docs/python/index.md @@ -68,6 +68,7 @@ * [`uniform_unit_scaling_initializer`](../../api_docs/python/state_ops.md#uniform_unit_scaling_initializer) * [`update_checkpoint_state`](../../api_docs/python/state_ops.md#update_checkpoint_state) * [`Variable`](../../api_docs/python/state_ops.md#Variable) + * [`variable_op_scope`](../../api_docs/python/state_ops.md#variable_op_scope) * [`variable_scope`](../../api_docs/python/state_ops.md#variable_scope) * [`zeros_initializer`](../../api_docs/python/state_ops.md#zeros_initializer) @@ -356,20 +357,3 @@ * [`write_graph`](../../api_docs/python/train.md#write_graph) * [`zero_fraction`](../../api_docs/python/train.md#zero_fraction) -<div class="sections-order" style="display: none;"> -<!-- -<!-- framework.md --> -<!-- constant_op.md --> -<!-- state_ops.md --> -<!-- array_ops.md --> -<!-- math_ops.md --> -<!-- control_flow_ops.md --> -<!-- image.md --> -<!-- sparse_ops.md --> -<!-- io_ops.md --> -<!-- python_io.md --> -<!-- nn.md --> -<!-- client.md --> -<!-- train.md --> ---> -</div> diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md index 346c2fbf95..0e98516038 100644 --- a/tensorflow/g3doc/api_docs/python/math_ops.md +++ b/tensorflow/g3doc/api_docs/python/math_ops.md @@ -1340,7 +1340,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentSum.png" alt> +<img style="width:100%" src="../../images/SegmentSum.png" alt> </div> ##### Args: @@ -1374,7 +1374,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentProd.png" alt> +<img style="width:100%" src="../../images/SegmentProd.png" alt> </div> ##### Args: @@ -1408,7 +1408,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMin.png" alt> +<img style="width:100%" src="../../images/SegmentMin.png" alt> </div> ##### Args: @@ -1441,7 +1441,7 @@ Computes a tensor such that that `segment_ids[j] == i`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMax.png" alt> +<img style="width:100%" src="../../images/SegmentMax.png" alt> </div> ##### Args: @@ -1476,7 +1476,7 @@ over `j` such that `segment_ids[j] == i` and `N` is the total number of values summed. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/SegmentMean.png" alt> +<img style="width:100%" src="../../images/SegmentMean.png" alt> </div> ##### Args: @@ -1517,7 +1517,7 @@ If the sum is empty for a given segment ID `i`, `output[i] = 0`. `num_segments` should equal the number of distinct segment IDs. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/UnsortedSegmentSum.png" alt> +<img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt> </div> ##### Args: diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md index 1ce124a835..1a0b2df9ff 100644 --- a/tensorflow/g3doc/api_docs/python/nn.md +++ b/tensorflow/g3doc/api_docs/python/nn.md @@ -59,7 +59,7 @@ Computes Rectified Linear 6: `min(max(features, 0), 6)`. Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. -See [Fast and Aaccurate Deep Network Learning by Exponential Linear Units (ELUs) +See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) ](http://arxiv.org/abs/1511.07289) ##### Args: @@ -872,7 +872,7 @@ See [Noise-contrastive estimation: A new estimation principle for unnormalized statistical models] (http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf). Also see our [Candidate Sampling Algorithms Reference] -(http://www.tensorflow.org/extras/candidate_sampling.pdf) +(../../extras/candidate_sampling.pdf) Note: In the case where `num_true` > 1, we assign to each target class the target probability 1 / `num_true` so that the target probabilities @@ -906,7 +906,7 @@ with an otherwise unused class. `True`, this is a "Sampled Logistic" loss instead of NCE, and we are learning to generate log-odds instead of log probabilities. See our [Candidate Sampling Algorithms Reference] - (http://www.tensorflow.org/extras/candidate_sampling.pdf). + (../../extras/candidate_sampling.pdf). Default is False. * <b>`name`</b>: A name for the operation (optional). @@ -931,7 +931,7 @@ At inference time, you can compute full softmax probabilities with the expression `tf.nn.softmax(tf.matmul(inputs, weights) + biases)`. See our [Candidate Sampling Algorithms Reference] -(http://www.tensorflow.org/extras/candidate_sampling.pdf) +(../../extras/candidate_sampling.pdf) Also see Section 3 of http://arxiv.org/abs/1412.2007 for the math. diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md index 82c2047e2c..de8d81f959 100644 --- a/tensorflow/g3doc/api_docs/python/state_ops.md +++ b/tensorflow/g3doc/api_docs/python/state_ops.md @@ -915,6 +915,58 @@ Returns the current variable scope. - - - +### `tf.variable_op_scope(values, name, default_name, initializer=None)` {#variable_op_scope} + +Returns a context manager for defining an op that creates variables. + +This context manager validates that the given `values` are from the +same graph, ensures that that graph is the default graph, and pushes a +name scope and a variable scope. + +If `name` is not None, it is used as is in the variable scope. If `name` +is None, then `default_name` is used. In that case, if the same name has been +previously used in the same scope, it will made unique be appending `_N` to +it. + +This is intended to be used when defining generic ops and so reuse is always +inherited. + +For example, to define a new Python op called `my_op_with_vars`: + +```python +def my_op_with_vars(a, b, name=None): + with tf.variable_op_scope([a, b], name, "MyOp") as scope: + a = tf.convert_to_tensor(a, name="a") + b = tf.convert_to_tensor(b, name="b") + c = tf.get_variable('c') + # Define some computation that uses `a`, `b`, and `c`. + return foo_op(..., name=scope) +``` + +##### Args: + + +* <b>`values`</b>: The list of `Tensor` arguments that are passed to the op function. +* <b>`name`</b>: The name argument that is passed to the op function, this name is not + uniquified in the variable scope. +* <b>`default_name`</b>: The default name to use if the `name` argument is `None`, this + name will be uniquified. +* <b>`initializer`</b>: A default initializer to pass to variable scope. + +##### Returns: + + A context manager for use in defining a Python op. + +##### Raises: + + +* <b>`ValueError`</b>: when trying to reuse within a create scope, or create within + a reuse scope, or if reuse is not `None` or `True`. +* <b>`TypeError`</b>: when the types of some arguments are not appropriate. + + +- - - + ### `tf.variable_scope(name_or_scope, reuse=None, initializer=None)` {#variable_scope} Returns a context for variable scope. @@ -983,7 +1035,7 @@ then all its sub-scopes become reusing as well. well as all sub-scopes; if `None`, we just inherit the parent scope reuse. * <b>`initializer`</b>: default initializer for variables within this scope. -##### Yields: +##### Returns: A scope that can be to captured and reused. @@ -1167,7 +1219,7 @@ override earlier entries. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterUpdate.png" alt> +<img style="width:100%" src="../../images/ScatterUpdate.png" alt> </div> ##### Args: @@ -1215,7 +1267,7 @@ the same location, their contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterAdd.png" alt> +<img style="width:100%" src="../../images/ScatterAdd.png" alt> </div> ##### Args: @@ -1262,7 +1314,7 @@ the same location, their (negated) contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="../images/ScatterSub.png" alt> +<img style="width:100%" src="../../images/ScatterSub.png" alt> </div> ##### Args: diff --git a/tensorflow/g3doc/get_started/index.md b/tensorflow/g3doc/get_started/index.md index e9e0bcd0cb..c00954000e 100644 --- a/tensorflow/g3doc/get_started/index.md +++ b/tensorflow/g3doc/get_started/index.md @@ -60,10 +60,10 @@ suggest skimming blue, then red. <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row"> <a href="../tutorials/mnist/beginners/index.md" title="MNIST for ML Beginners tutorial"> - <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="blue_pill.png" alt="MNIST for machine learning beginners tutorial" /> + <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="../images/blue_pill.png" alt="MNIST for machine learning beginners tutorial" /> </a> <a href="../tutorials/mnist/pros/index.md" title="Deep MNIST for ML Experts tutorial"> - <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="red_pill.png" alt="Deep MNIST for machine learning experts tutorial" /> + <img style="flex-grow:1; flex-shrink:1; border: 1px solid black;" src="../images/red_pill.png" alt="Deep MNIST for machine learning experts tutorial" /> </a> </div> <p style="font-size:10px;">Images licensed CC BY-SA 4.0; original by W. Carter</p> @@ -77,12 +77,3 @@ TensorFlow features. * [Download and Setup](../get_started/os_setup.md) * [Basic Usage](../get_started/basic_usage.md) * [TensorFlow Mechanics 101](../tutorials/mnist/tf/index.md) - - -<div class='sections-order' style="display: none;"> -<!-- -<!-- os_setup.md --> -<!-- basic_usage.md --> ---> -</div> - diff --git a/tensorflow/g3doc/how_tos/graph_viz/index.md b/tensorflow/g3doc/how_tos/graph_viz/index.md index fd66e9ce87..fa79b7fa87 100644 --- a/tensorflow/g3doc/how_tos/graph_viz/index.md +++ b/tensorflow/g3doc/how_tos/graph_viz/index.md @@ -2,7 +2,7 @@ TensorFlow computation graphs are powerful but complicated. The graph visualization can help you understand and debug them. Here's an example of the visualization at work. - + *Visualization of a TensorFlow graph.* To see your own graph, run TensorBoard pointing it to the log directory of the job, click on the graph tab on the top pane and select the appropriate run using the menu at the upper left corner. For in depth information on how to run TensorBoard and make sure you are logging all the necessary information, see [TensorBoard: Visualizing Learning](../../how_tos/summaries_and_tensorboard/index.md). @@ -43,10 +43,10 @@ expanded states. <table width="100%;"> <tr> <td style="width: 50%;"> - <img src="./pool1_collapsed.png" alt="Unexpanded name scope" title="Unexpanded name scope" /> + <img src="../../images/pool1_collapsed.png" alt="Unexpanded name scope" title="Unexpanded name scope" /> </td> <td style="width: 50%;"> - <img src="./pool1_expanded.png" alt="Expanded name scope" title="Expanded name scope" /> + <img src="../../images/pool1_expanded.png" alt="Expanded name scope" title="Expanded name scope" /> </td> </tr> <tr> @@ -86,10 +86,10 @@ information since these nodes are usually related to bookkeeping functions. <table width="100%;"> <tr> <td style="width: 50%;"> - <img src="./conv_1.png" alt="conv_1 is part of the main graph" title="conv_1 is part of the main graph" /> + <img src="../../images/conv_1.png" alt="conv_1 is part of the main graph" title="conv_1 is part of the main graph" /> </td> <td style="width: 50%;"> - <img src="./save.png" alt="save is extracted as auxiliary node" title="save is extracted as auxiliary node" /> + <img src="../../images/save.png" alt="save is extracted as auxiliary node" title="save is extracted as auxiliary node" /> </td> </tr> <tr> @@ -111,10 +111,10 @@ with hierarchical nodes, double-clicking expands the series. <table width="100%;"> <tr> <td style="width: 50%;"> - <img src="./series.png" alt="Sequence of nodes" title="Sequence of nodes" /> + <img src="../../images/series.png" alt="Sequence of nodes" title="Sequence of nodes" /> </td> <td style="width: 50%;"> - <img src="./series_expanded.png" alt="Expanded sequence of nodes" title="Expanded sequence of nodes" /> + <img src="../../images/series_expanded.png" alt="Expanded sequence of nodes" title="Expanded sequence of nodes" /> </td> </tr> <tr> @@ -132,15 +132,15 @@ for constants and summary nodes. To summarize, here's a table of node symbols: Symbol | Meaning --- | --- - | *High-level* node representing a name scope. Double-click to expand a high-level node. - | Sequence of numbered nodes that are not connected to each other. - | Sequence of numbered nodes that are connected to each other. - | An individual operation node. - | A constant. - | A summary node. - | Edge showing the data flow between operations. - | Edge showing the control dependency between operations. - | A reference edge showing that the outgoing operation node can mutate the incoming tensor. + | *High-level* node representing a name scope. Double-click to expand a high-level node. + | Sequence of numbered nodes that are not connected to each other. + | Sequence of numbered nodes that are connected to each other. + | An individual operation node. + | A constant. + | A summary node. + | Edge showing the data flow between operations. + | Edge showing the control dependency between operations. + | A reference edge showing that the outgoing operation node can mutate the incoming tensor. ## Interaction @@ -158,10 +158,10 @@ right corner of the visualization. <table width="100%;"> <tr> <td style="width: 50%;"> - <img src="./infocard.png" alt="Info card of a name scope" title="Info card of a name scope" /> + <img src="../../images/infocard.png" alt="Info card of a name scope" title="Info card of a name scope" /> </td> <td style="width: 50%;"> - <img src="./infocard_op.png" alt="Info card of operation node" title="Info card of operation node" /> + <img src="../../images/infocard_op.png" alt="Info card of operation node" title="Info card of operation node" /> </td> </tr> <tr> @@ -194,10 +194,10 @@ The images below give an illustration for a piece of a real-life graph. <table width="100%;"> <tr> <td style="width: 50%;"> - <img src="./colorby_structure.png" alt="Color by structure" title="Color by structure" /> + <img src="../../images/colorby_structure.png" alt="Color by structure" title="Color by structure" /> </td> <td style="width: 50%;"> - <img src="./colorby_device.png" alt="Color by device" title="Color by device" /> + <img src="../../images/colorby_device.png" alt="Color by device" title="Color by device" /> </td> </tr> <tr> diff --git a/tensorflow/g3doc/how_tos/index.md b/tensorflow/g3doc/how_tos/index.md index c63a4e19b8..748ecfd398 100644 --- a/tensorflow/g3doc/how_tos/index.md +++ b/tensorflow/g3doc/how_tos/index.md @@ -84,19 +84,3 @@ different locations in the model construction code. The "Variable Scope" mechanism is designed to facilitate that. [View Tutorial](../how_tos/variable_scope/index.md) - -<div class='sections-order' style="display: none;"> -<!-- -<!-- variables/index.md --> -<!-- ../tutorials/mnist/tf/index.md --> -<!-- summaries_and_tensorboard/index.md --> -<!-- graph_viz/index.md --> -<!-- reading_data/index.md --> -<!-- threading_and_queues/index.md --> -<!-- adding_an_op/index.md --> -<!-- new_data_formats/index.md --> -<!-- using_gpu/index.md --> -<!-- variable_scope/index.md --> ---> -</div> - diff --git a/tensorflow/g3doc/how_tos/reading_data/index.md b/tensorflow/g3doc/how_tos/reading_data/index.md index 907652952a..e8ad141fea 100644 --- a/tensorflow/g3doc/how_tos/reading_data/index.md +++ b/tensorflow/g3doc/how_tos/reading_data/index.md @@ -311,7 +311,7 @@ operations, so that our training loop can dequeue examples from the example queue. <div style="width:70%; margin-left:12%; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="AnimatedFileQueues.gif"> +<img style="width:100%" src="../../images/AnimatedFileQueues.gif"> </div> The helpers in `tf.train` that create these queues and enqueuing operations add diff --git a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md index 23ca971224..a102344a9a 100644 --- a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md +++ b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md @@ -8,7 +8,7 @@ your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. When TensorBoard is fully configured, it looks like this: - + ## Serializing the data diff --git a/tensorflow/g3doc/how_tos/threading_and_queues/index.md b/tensorflow/g3doc/how_tos/threading_and_queues/index.md index 6b71310e13..68bb7626fa 100644 --- a/tensorflow/g3doc/how_tos/threading_and_queues/index.md +++ b/tensorflow/g3doc/how_tos/threading_and_queues/index.md @@ -14,7 +14,7 @@ that takes an item off the queue, adds one to that item, and puts it back on the end of the queue. Slowly, the numbers on the queue increase. <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="IncremeterFifoQueue.gif"> +<img style="width:100%" src="../../images/IncremeterFifoQueue.gif"> </div> `Enqueue`, `EnqueueMany`, and `Dequeue` are special nodes. They take a pointer diff --git a/tensorflow/g3doc/resources/index.md b/tensorflow/g3doc/resources/index.md index 3ae4896656..018ad44822 100644 --- a/tensorflow/g3doc/resources/index.md +++ b/tensorflow/g3doc/resources/index.md @@ -55,14 +55,3 @@ https://github.com/tensorflow/tensorflow/issues) on GitHub. If you need help with using TensorFlow, please do not use the issue tracker for that. Instead, direct your questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow). -<div class='sections-order' style="display: none;"> -<!-- -<!-- bib.md --> -<!-- uses.md --> -<!-- faq.md --> -<!-- glossary.md --> -<!-- dims_types.md --> ---> -</div> - - diff --git a/tensorflow/g3doc/tutorials/deep_cnn/index.md b/tensorflow/g3doc/tutorials/deep_cnn/index.md index 59d106680e..66614d402f 100644 --- a/tensorflow/g3doc/tutorials/deep_cnn/index.md +++ b/tensorflow/g3doc/tutorials/deep_cnn/index.md @@ -9,7 +9,7 @@ CIFAR-10 classification is a common benchmark problem in machine learning. The problem is to classify RGB 32x32 pixel images across 10 categories: ```airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.``` - + For more details refer to the [CIFAR-10 page](http://www.cs.toronto.edu/~kriz/cifar.html) and a [Tech Report](http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) @@ -135,7 +135,7 @@ so that we may visualize them in TensorBoard. This is a good practice to verify that inputs are built correctly. <div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;"> - <img style="width:70%" src="./cifar_image_summary.png"> + <img style="width:70%" src="../../images/cifar_image_summary.png"> </div> Reading images from disk and distorting them can use a non-trivial amount of @@ -164,7 +164,7 @@ Layer Name | Description Here is a graph generated from TensorBoard describing the inference operation: <div style="width:15%; margin:auto; margin-bottom:10px; margin-top:20px;"> - <img style="width:100%" src="./cifar_graph.png"> + <img style="width:100%" src="../../images/cifar_graph.png"> </div> > **EXERCISE**: The output of `inference` are un-normalized logits. Try editing @@ -199,7 +199,7 @@ loss and all these weight decay terms, as returned by the `loss()` function. We visualize it in TensorBoard with a [`scalar_summary`](../../api_docs/python/train.md#scalar_summary): - + We train the model using standard [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) @@ -208,7 +208,7 @@ with a learning rate that [exponentially decays](../../api_docs/python/train.md#exponential_decay) over time. - + The `train()` function adds the operations needed to minimize the objective by calculating the gradient and updating the learned variables (see @@ -289,8 +289,8 @@ For instance, we can watch how the distribution of activations and degree of sparsity in `local3` features evolve during training: <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px; display: flex; flex-direction: row"> - <img style="flex-grow:1; flex-shrink:1;" src="./cifar_sparsity.png"> - <img style="flex-grow:1; flex-shrink:1;" src="./cifar_activations.png"> + <img style="flex-grow:1; flex-shrink:1;" src="../../images/cifar_sparsity.png"> + <img style="flex-grow:1; flex-shrink:1;" src="../../images/cifar_activations.png"> </div> Individual loss functions, as well as the total loss, are particularly @@ -372,7 +372,7 @@ processing a batch of data. Here is a diagram of this model: <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;"> - <img style="width:100%" src="./Parallelism.png"> + <img style="width:100%" src="../../images/Parallelism.png"> </div> Note that each GPU computes inference as well as the gradients for a unique diff --git a/tensorflow/g3doc/tutorials/index.md b/tensorflow/g3doc/tutorials/index.md index 84215c72f1..ee22806f69 100644 --- a/tensorflow/g3doc/tutorials/index.md +++ b/tensorflow/g3doc/tutorials/index.md @@ -107,19 +107,3 @@ visual hallucination software. COMING SOON -<div class='sections-order' style="display: none;"> -<!-- -<!-- mnist/beginners/index.md --> -<!-- mnist/pros/index.md --> -<!-- mnist/tf/index.md --> -<!-- deep_cnn/index.md --> -<!-- word2vec/index.md --> -<!-- recurrent/index.md --> -<!-- seq2seq/index.md --> -<!-- mandelbrot/index.md --> -<!-- pdes/index.md --> -<!-- mnist/download/index.md --> ---> -</div> - - diff --git a/tensorflow/g3doc/tutorials/mandelbrot/index.md b/tensorflow/g3doc/tutorials/mandelbrot/index.md index ee8a95d12b..d88e7b5502 100755 --- a/tensorflow/g3doc/tutorials/mandelbrot/index.md +++ b/tensorflow/g3doc/tutorials/mandelbrot/index.md @@ -110,7 +110,7 @@ Let's see what we've got. DisplayFractal(ns.eval()) ``` - + Not bad! diff --git a/tensorflow/g3doc/tutorials/mnist/beginners/index.md b/tensorflow/g3doc/tutorials/mnist/beginners/index.md index 569cbef9e9..99453d065d 100644 --- a/tensorflow/g3doc/tutorials/mnist/beginners/index.md +++ b/tensorflow/g3doc/tutorials/mnist/beginners/index.md @@ -3,7 +3,7 @@ *This tutorial is intended for readers who are new to both machine learning and TensorFlow. If you already know what MNIST is, and what softmax (multinomial logistic) regression is, -you might prefer this [faster paced tutorial](../../../tutorials/mnist/pros/index.md).* +you might prefer this [faster paced tutorial](../pros/index.md).* When one learns how to program, there's a tradition that the first thing you do is print "Hello World." Just like programming has Hello World, machine learning @@ -13,7 +13,7 @@ MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these: <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/MNIST.png"> +<img style="width:100%" src="../../../images/MNIST.png"> </div> It also includes labels for each image, telling us which digit it is. For @@ -61,7 +61,7 @@ Each image is 28 pixels by 28 pixels. We can interpret this as a big array of numbers: <div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/MNIST-Matrix.png"> +<img style="width:100%" src="../../../images/MNIST-Matrix.png"> </div> We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't @@ -83,7 +83,7 @@ the pixel intensity between 0 and 1, for a particular pixel in a particular image. <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/mnist-train-xs.png"> +<img style="width:100%" src="../../../images/mnist-train-xs.png"> </div> The corresponding labels in MNIST are numbers between 0 and 9, describing @@ -97,7 +97,7 @@ Consequently, `mnist.train.labels` is a `[55000, 10]` array of floats. <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/mnist-train-ys.png"> +<img style="width:100%" src="../../../images/mnist-train-ys.png"> </div> We're now ready to actually make our model! @@ -128,7 +128,7 @@ classes. Red represents negative weights, while blue represents positive weights. <div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/softmax-weights.png"> +<img style="width:100%" src="../../../images/softmax-weights.png"> </div> We also add some extra evidence called a bias. Basically, we want to be able @@ -175,13 +175,13 @@ although with a lot more \\(x\\)s. For each output, we compute a weighted sum of the \\(x\\)s, add a bias, and then apply softmax. <div style="width:55%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/softmax-regression-scalargraph.png"> +<img style="width:100%" src="../../../images/softmax-regression-scalargraph.png"> </div> If we write that out as equations, we get: <div style="width:52%; margin-left:25%; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/softmax-regression-scalarequation.png"> +<img style="width:100%" src="../../../images/softmax-regression-scalarequation.png"> </div> We can "vectorize" this procedure, turning it into a matrix multiplication @@ -189,7 +189,7 @@ and vector addition. This is helpful for computational efficiency. (It's also a useful way to think.) <div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/softmax-regression-vectorequation.png"> +<img style="width:100%" src="../../../images/softmax-regression-vectorequation.png"> </div> More compactly, we can just write: diff --git a/tensorflow/g3doc/tutorials/mnist/download/index.md b/tensorflow/g3doc/tutorials/mnist/download/index.md index 70e6e37ef6..3cb9528f34 100644 --- a/tensorflow/g3doc/tutorials/mnist/download/index.md +++ b/tensorflow/g3doc/tutorials/mnist/download/index.md @@ -19,7 +19,7 @@ MNIST is a classic problem in machine learning. The problem is to look at greyscale 28x28 pixel images of handwritten digits and determine which digit the image represents, for all the digits from zero to nine. - + For more information, refer to [Yann LeCun's MNIST page](http://yann.lecun.com/exdb/mnist/) or [Chris Olah's visualizations of MNIST](http://colah.github.io/posts/2014-10-Visualizing-MNIST/). diff --git a/tensorflow/g3doc/tutorials/mnist/input_data.py b/tensorflow/g3doc/tutorials/mnist/input_data.py index 3078137f2e..ae3727c82e 100644 --- a/tensorflow/g3doc/tutorials/mnist/input_data.py +++ b/tensorflow/g3doc/tutorials/mnist/input_data.py @@ -42,7 +42,7 @@ def maybe_download(filename, work_directory): def _read32(bytestream): dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt) + return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] def extract_images(filename): diff --git a/tensorflow/g3doc/tutorials/mnist/pros/index.md b/tensorflow/g3doc/tutorials/mnist/pros/index.md index 6df9e430a3..4d8b5e84bd 100644 --- a/tensorflow/g3doc/tutorials/mnist/pros/index.md +++ b/tensorflow/g3doc/tutorials/mnist/pros/index.md @@ -9,7 +9,7 @@ while constructing a deep convolutional MNIST classifier. *This introduction assumes familiarity with neural networks and the MNIST dataset. If you don't have a background with them, check out the -[introduction for beginners](../../../tutorials/mnist/beginners/index.md).* +[introduction for beginners](../beginners/index.md).* ## Setup diff --git a/tensorflow/g3doc/tutorials/mnist/tf/index.md b/tensorflow/g3doc/tutorials/mnist/tf/index.md index f7cd3b527c..373b8968c5 100644 --- a/tensorflow/g3doc/tutorials/mnist/tf/index.md +++ b/tensorflow/g3doc/tutorials/mnist/tf/index.md @@ -31,7 +31,7 @@ MNIST is a classic problem in machine learning. The problem is to look at greyscale 28x28 pixel images of handwritten digits and determine which digit the image represents, for all the digits from zero to nine. - + For more information, refer to [Yann LeCun's MNIST page](http://yann.lecun.com/exdb/mnist/) or [Chris Olah's visualizations of MNIST](http://colah.github.io/posts/2014-10-Visualizing-MNIST/). @@ -90,7 +90,7 @@ loss. and apply gradients. <div style="width:95%; margin:auto; margin-bottom:10px; margin-top:20px;"> - <img style="width:100%" src="./mnist_subgraph.png"> + <img style="width:100%" src="../../../images/mnist_subgraph.png"> </div> ### Inference @@ -401,7 +401,7 @@ summary_writer.add_summary(summary_str, step) When the events files are written, TensorBoard may be run against the training folder to display the values from the summaries. - + **NOTE**: For more info about how to build and run Tensorboard, please see the accompanying tutorial [Tensorboard: Visualizing Your Training](../../../how_tos/summaries_and_tensorboard/index.md). diff --git a/tensorflow/g3doc/tutorials/pdes/index.md b/tensorflow/g3doc/tutorials/pdes/index.md index ca6f84985e..9d922320cb 100755 --- a/tensorflow/g3doc/tutorials/pdes/index.md +++ b/tensorflow/g3doc/tutorials/pdes/index.md @@ -92,7 +92,7 @@ for n in range(40): DisplayArray(u_init, rng=[-0.1, 0.1]) ``` - + Now let's specify the details of the differential equation. @@ -137,7 +137,7 @@ for i in range(1000): DisplayArray(U.eval(), rng=[-0.1, 0.1]) ``` - + Look! Ripples! diff --git a/tensorflow/g3doc/tutorials/seq2seq/index.md b/tensorflow/g3doc/tutorials/seq2seq/index.md index 6cbbf6a3bd..63542dc6ba 100644 --- a/tensorflow/g3doc/tutorials/seq2seq/index.md +++ b/tensorflow/g3doc/tutorials/seq2seq/index.md @@ -41,7 +41,7 @@ processes the input and a *decoder* that generates the output. This basic architecture is depicted below. <div style="width:80%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="basic_seq2seq.png" /> +<img style="width:100%" src="../../images/basic_seq2seq.png" /> </div> Each box in the picture above represents a cell of the RNN, most commonly @@ -61,7 +61,7 @@ decoding step. A multi-layer sequence-to-sequence network with LSTM cells and attention mechanism in the decoder looks like this. <div style="width:80%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="attention_seq2seq.png" /> +<img style="width:100%" src="../../images/attention_seq2seq.png" /> </div> ## TensorFlow seq2seq Library diff --git a/tensorflow/g3doc/tutorials/word2vec/index.md b/tensorflow/g3doc/tutorials/word2vec/index.md index 3dc632c1bc..f026d8c5b5 100644 --- a/tensorflow/g3doc/tutorials/word2vec/index.md +++ b/tensorflow/g3doc/tutorials/word2vec/index.md @@ -51,7 +51,7 @@ means that we may need more data in order to successfully train statistical models. Using vector representations can overcome some of these obstacles. <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/audio-image-text.png" alt> +<img style="width:100%" src="../../images/audio-image-text.png" alt> </div> [Vector space models](https://en.wikipedia.org/wiki/Vector_space_model) (VSMs) @@ -124,7 +124,7 @@ probability using the score for all other \\(V\\) words \\(w'\\) in the current context \\(h\\), *at every training step*. <div style="width:60%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/softmax-nplm.png" alt> +<img style="width:100%" src="../../images/softmax-nplm.png" alt> </div> On the other hand, for feature learning in word2vec we do not need a full @@ -135,7 +135,7 @@ same context. We illustrate this below for a CBOW model. For skip-gram the direction is simply inverted. <div style="width:60%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/nce-nplm.png" alt> +<img style="width:100%" src="../../images/nce-nplm.png" alt> </div> Mathematically, the objective (for each example) is to maximize @@ -232,7 +232,7 @@ below (see also for example [Mikolov et al., 2013](http://www.aclweb.org/anthology/N13-1090)). <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/linear-relationships.png" alt> +<img style="width:100%" src="../../images/linear-relationships.png" alt> </div> This explains why these vectors are also useful as features for many canonical @@ -329,7 +329,7 @@ After training has finished we can visualize the learned embeddings using t-SNE. <div style="width:100%; margin:auto; margin-bottom:10px; margin-top:20px;"> -<img style="width:100%" src="img/tsne.png" alt> +<img style="width:100%" src="../../images/tsne.png" alt> </div> Et voila! As expected, words that are similar end up clustering nearby each diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 0c75bd8dc7..1fd5c539b8 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -1,7 +1,9 @@ # Description: # Python support for TensorFlow. -package(default_visibility = ["//tensorflow:internal"]) +package( + default_visibility = ["//tensorflow:internal"], +) licenses(["notice"]) # Apache 2.0 @@ -49,13 +51,23 @@ py_library( ) py_tests( - name = "platform_tests", + name = "default_platform_tests", srcs = glob(["platform/default/*_test.py"]), additional_deps = [ ":platform", ":platform_test", ], - prefix = "platform", + prefix = "default_platform", +) + +py_tests( + name = "google_platform_tests", + srcs = glob(["platform/google/*_test.py"]), + additional_deps = [ + ":platform", + ":platform_test", + ], + prefix = "google_platform", ) cc_library( @@ -843,6 +855,7 @@ cpu_only_kernel_test_list = glob([ "kernel_tests/random_shuffle_queue_test.py", "kernel_tests/save_restore_ops_test.py", "kernel_tests/segment_reduction_ops_test.py", + "kernel_tests/self_adjoint_eig_op_test.py", "kernel_tests/sparse_concat_op_test.py", "kernel_tests/sparse_matmul_op_test.py", "kernel_tests/sparse_reorder_op_test.py", diff --git a/tensorflow/python/framework/docs.py b/tensorflow/python/framework/docs.py index 145b8049ad..38e72deb30 100644 --- a/tensorflow/python/framework/docs.py +++ b/tensorflow/python/framework/docs.py @@ -98,12 +98,6 @@ class Index(Document): print(" * %s" % link, file=f) print("", file=f) - # actually include the files right here - print('<div class="sections-order" style="display: none;">\n<!--', file=f) - for filename, _ in self._filename_to_library_map: - print("<!-- %s -->" % filename, file=f) - print("-->\n</div>", file=f) - def collect_members(module_to_name): """Collect all symbols from a list of modules. diff --git a/tensorflow/python/kernel_tests/batch_matmul_op_test.py b/tensorflow/python/kernel_tests/batch_matmul_op_test.py index 809b23bd7d..25e0abe014 100644 --- a/tensorflow/python/kernel_tests/batch_matmul_op_test.py +++ b/tensorflow/python/kernel_tests/batch_matmul_op_test.py @@ -151,14 +151,14 @@ class BatchMatmulOpTest(tf.test.TestCase): self._randComplex([10, 30, 75]), True, True) def testEmpty(self): - self._compare(np.empty([0, 3, 2]).astype(np.float32), - np.empty([0, 2, 4]).astype(np.float32), False, False) - self._compare(np.empty([3, 2, 0]).astype(np.float32), - np.empty([3, 0, 5]).astype(np.float32), False, False) - self._compare(np.empty([3, 0, 2]).astype(np.float32), - np.empty([3, 2, 5]).astype(np.float32), False, False) - self._compare(np.empty([3, 3, 2]).astype(np.float32), - np.empty([3, 2, 0]).astype(np.float32), False, False) + self._compare(np.zeros([0, 3, 2]).astype(np.float32), + np.zeros([0, 2, 4]).astype(np.float32), False, False) + self._compare(np.zeros([3, 2, 0]).astype(np.float32), + np.zeros([3, 0, 5]).astype(np.float32), False, False) + self._compare(np.zeros([3, 0, 2]).astype(np.float32), + np.zeros([3, 2, 5]).astype(np.float32), False, False) + self._compare(np.zeros([3, 3, 2]).astype(np.float32), + np.zeros([3, 2, 0]).astype(np.float32), False, False) class BatchMatmulGradientTest(tf.test.TestCase): diff --git a/tensorflow/python/kernel_tests/reduction_ops_test.py b/tensorflow/python/kernel_tests/reduction_ops_test.py index 3867034dc1..7ff3851da7 100644 --- a/tensorflow/python/kernel_tests/reduction_ops_test.py +++ b/tensorflow/python/kernel_tests/reduction_ops_test.py @@ -115,7 +115,7 @@ class SumReductionTest(tf.test.TestCase): self._compareAll(np_arr, [1, 2, 3, 4]) self._compareAll(np_arr, [0, 1, 2, 3, 4]) - # Simple tests for various tf. + # Simple tests for various types. def testDoubleReduce1D(self): np_arr = np.arange(1, 6).reshape([5]).astype(np.float64) self._compare(np_arr, [], False) @@ -126,6 +126,11 @@ class SumReductionTest(tf.test.TestCase): self._compare(np_arr, [], False) self._compare(np_arr, [0], False) + def testComplex64Reduce1D(self): + np_arr = np.arange(1, 6).reshape([5]).astype(np.complex64) + self._compare(np_arr, [], False) + self._compare(np_arr, [0], False) + def testInvalidIndex(self): np_arr = np.arange(0, 10).reshape([2, 5]).astype(np.float32) input_tensor = tf.convert_to_tensor(np_arr) diff --git a/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py new file mode 100644 index 0000000000..c6a42296c4 --- /dev/null +++ b/tensorflow/python/kernel_tests/self_adjoint_eig_op_test.py @@ -0,0 +1,105 @@ +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""Tests for tensorflow.ops.tf.self_adjoint_eig.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +# pylint: disable=g-bad-import-order,wildcard-import,unused-import + +import tensorflow.python.platform +import numpy as np +from six.moves import xrange # pylint: disable=redefined-builtin +import tensorflow as tf + + +class SelfAdjointEigOpTest(tf.test.TestCase): + + def _testEigs(self, x, d, tf_ans, use_gpu=False): + np_eig_val, np_eig_vec = np.linalg.eig(x) + + # First check the eigenvalues + self.assertAllClose(sorted(np_eig_val), sorted(tf_ans[0, :])) + + # need to make things canonical. This test may still fail in case there are + # two equal eigenvalues, so that there is indeterminacy in the eigenvectors. + # For now, assume that we will only test matrices with distinct eigenvalues. + np_arg = np.argsort(np_eig_val) + tf_arg = np.argsort(tf_ans[0, :]) + + np_eig_vecs_sorted = np.array([np_eig_vec[:, i] for i in np_arg]).T + tf_eig_vecs_sorted = np.array([tf_ans[1:, i] for i in tf_arg]).T + np_eig_vecs_signed_sorted = np.array([np_eig_vecs_sorted[:, i] * + np.sign(np_eig_vecs_sorted[0, i]) + for i in xrange(d)]).T + tf_eig_vecs_signed_sorted = np.array([tf_eig_vecs_sorted[:, i] * + np.sign(tf_eig_vecs_sorted[0, i]) + for i in xrange(d)]).T + self.assertAllClose(np_eig_vecs_signed_sorted, tf_eig_vecs_signed_sorted) + + def _compareSelfAdjointEig(self, x, use_gpu=False): + with self.test_session() as sess: + tf_eig = tf.self_adjoint_eig(tf.constant(x)) + tf_eig_out = sess.run([tf_eig])[0] + + d, _ = x.shape + self.assertEqual([d+1, d], tf_eig.get_shape().dims) + self._testEigs(x, d, tf_eig_out, use_gpu) + + def _compareBatchSelfAdjointEigRank3(self, x, use_gpu=False): + with self.test_session() as sess: + tf_eig = tf.batch_self_adjoint_eig(tf.constant(x)) + tf_out = sess.run([tf_eig])[0] + dlist = x.shape + d = dlist[-2] + + self.assertEqual([d+1, d], tf_eig.get_shape().dims[-2:]) + # not testing the values. + self.assertEqual(dlist[0], tf_eig.get_shape().dims[0]) + + for i in xrange(dlist[0]): + self._testEigs(x[i], d, tf_out[i]) + + def testBasic(self): + self._compareSelfAdjointEig( + np.array([[3., 0., 1.], [0., 2., -2.], [1., -2., 3.]])) + + def testBatch(self): + simple_array = np.array([[[1., 0.], [0., 5.]]]) # shape (1, 2, 2) + self._compareBatchSelfAdjointEigRank3(simple_array) + self._compareBatchSelfAdjointEigRank3(np.vstack((simple_array, simple_array))) + odd_sized_array = np.array([[[3., 0., 1.], [0., 2., -2.], [1., -2., 3.]]]) + self._compareBatchSelfAdjointEigRank3( + np.vstack((odd_sized_array, odd_sized_array))) + + # Generate random positive-definite matrices. + matrices = np.random.rand(10, 5, 5) + for i in xrange(10): + matrices[i] = np.dot(matrices[i].T, matrices[i]) + self._compareBatchSelfAdjointEigRank3(matrices) + + def testNonSquareMatrix(self): + with self.assertRaises(ValueError): + tf.self_adjoint_eig(tf.constant(np.array([[1., 2., 3.], [3., 4., 5.]]))) + + def testWrongDimensions(self): + tensor3 = tf.constant([1., 2.]) + with self.assertRaises(ValueError): + tf.self_adjoint_eig(tensor3) + + +if __name__ == "__main__": + tf.test.main() diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py index 953835d55e..457bc894b4 100644 --- a/tensorflow/python/ops/linalg_ops.py +++ b/tensorflow/python/ops/linalg_ops.py @@ -79,3 +79,24 @@ def _BatchMatrixInverseShape(op): # The matrices in the batch must be square. input_shape[-1].assert_is_compatible_with(input_shape[-2]) return [input_shape] + + +@ops.RegisterShape("SelfAdjointEig") +def _SelfAdjointEigShape(op): + input_shape = op.inputs[0].get_shape().with_rank(2) + # The matrix must be square. + input_shape[0].assert_is_compatible_with(input_shape[1]) + d = input_shape.dims[0] + out_shape = tensor_shape.TensorShape([d+1, d]) + return [out_shape] + + +@ops.RegisterShape("BatchSelfAdjointEig") +def _BatchSelfAdjointEigShape(op): + input_shape = op.inputs[0].get_shape().with_rank_at_least(3) + # The matrices in the batch must be square. + input_shape[-1].assert_is_compatible_with(input_shape[-2]) + dlist = input_shape.dims + dlist[-2] += 1 + out_shape = tensor_shape.TensorShape(dlist) + return [out_shape] diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py index 2555b4ba17..b5ed02d693 100644 --- a/tensorflow/python/ops/math_ops.py +++ b/tensorflow/python/ops/math_ops.py @@ -70,6 +70,9 @@ mathematical functions for matrices to your graph. @@cholesky @@batch_cholesky +@@self_adjoint_eig +@@batch_self_adjoint_eig + ## Complex Number Functions TensorFlow provides several operations that you can use to add complex number diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py index 065552c7ed..e8880d59a8 100644 --- a/tensorflow/python/ops/nn.py +++ b/tensorflow/python/ops/nn.py @@ -711,7 +711,7 @@ def nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, unnormalized statistical models] (http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf). Also see our [Candidate Sampling Algorithms Reference] - (http://www.tensorflow.org/extras/candidate_sampling.pdf) + (../../extras/candidate_sampling.pdf) Note: In the case where `num_true` > 1, we assign to each target class the target probability 1 / `num_true` so that the target probabilities @@ -743,7 +743,7 @@ def nce_loss(weights, biases, inputs, labels, num_sampled, num_classes, `True`, this is a "Sampled Logistic" loss instead of NCE, and we are learning to generate log-odds instead of log probabilities. See our [Candidate Sampling Algorithms Reference] - (http://www.tensorflow.org/extras/candidate_sampling.pdf). + (../../extras/candidate_sampling.pdf). Default is False. name: A name for the operation (optional). @@ -782,7 +782,7 @@ def sampled_softmax_loss(weights, biases, inputs, labels, num_sampled, expression `tf.nn.softmax(tf.matmul(inputs, weights) + biases)`. See our [Candidate Sampling Algorithms Reference] - (http://www.tensorflow.org/extras/candidate_sampling.pdf) + (../../extras/candidate_sampling.pdf) Also see Section 3 of http://arxiv.org/abs/1412.2007 for the math. diff --git a/tensorflow/python/platform/default/_resource_loader.py b/tensorflow/python/platform/default/_resource_loader.py index 0fe18aef15..f42a52094e 100644 --- a/tensorflow/python/platform/default/_resource_loader.py +++ b/tensorflow/python/platform/default/_resource_loader.py @@ -36,10 +36,15 @@ def load_resource(path): Raises: IOError: If the path is not found, or the resource can't be opened. """ - path = os.path.join('tensorflow', path) + tensorflow_root = ( + os.path.join( + os.path.dirname(__file__), os.pardir, os.pardir, + os.pardir)) + path = os.path.join(tensorflow_root, path) path = os.path.abspath(path) try: with open(path, 'rb') as f: return f.read() except IOError as e: logging.warning('IOError %s on path %s', e, path) + raise e diff --git a/tensorflow/python/platform/default/_resource_loader_test.py b/tensorflow/python/platform/default/_resource_loader_test.py new file mode 100644 index 0000000000..28d8ee1d60 --- /dev/null +++ b/tensorflow/python/platform/default/_resource_loader_test.py @@ -0,0 +1,31 @@ +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.platform import googletest +from tensorflow.python.platform.default import _resource_loader as resource_loader + + +class DefaultResourceLoaderTest(googletest.TestCase): + + def test_exception(self): + with self.assertRaises(IOError): + resource_loader.load_resource("/fake/file/path/dne") + +if __name__ == "__main__": + googletest.main() diff --git a/tensorflow/python/summary/impl/event_file_loader_test.py b/tensorflow/python/summary/impl/event_file_loader_test.py index 4f6ac75d83..1f961b4b78 100644 --- a/tensorflow/python/summary/impl/event_file_loader_test.py +++ b/tensorflow/python/summary/impl/event_file_loader_test.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import os +import tempfile from tensorflow.python.framework import test_util from tensorflow.python.platform import googletest @@ -32,8 +33,7 @@ class EventFileLoaderTest(test_util.TensorFlowTestCase): b'\xd5A\x1a\rbrain.Event:1\xec\xf32\x8d') def _WriteToFile(self, filename, data): - path = os.path.join(self.get_temp_dir(), filename) - with open(path, 'ab') as f: + with open(filename, 'ab') as f: f.write(data) def _LoaderForTestFile(self, filename): @@ -41,36 +41,41 @@ class EventFileLoaderTest(test_util.TensorFlowTestCase): os.path.join(self.get_temp_dir(), filename)) def testEmptyEventFile(self): - self._WriteToFile('empty_event_file', b'') - loader = self._LoaderForTestFile('empty_event_file') + filename = tempfile.NamedTemporaryFile().name + self._WriteToFile(filename, b'') + loader = self._LoaderForTestFile(filename) self.assertEqual(len(list(loader.Load())), 0) def testSingleWrite(self): - self._WriteToFile('single_event_file', EventFileLoaderTest.RECORD) - loader = self._LoaderForTestFile('single_event_file') + filename = tempfile.NamedTemporaryFile().name + self._WriteToFile(filename, EventFileLoaderTest.RECORD) + loader = self._LoaderForTestFile(filename) events = list(loader.Load()) self.assertEqual(len(events), 1) self.assertEqual(events[0].wall_time, 1440183447.0) self.assertEqual(len(list(loader.Load())), 0) def testMultipleWrites(self): - self._WriteToFile('staggered_event_file', EventFileLoaderTest.RECORD) - loader = self._LoaderForTestFile('staggered_event_file') + filename = tempfile.NamedTemporaryFile().name + self._WriteToFile(filename, EventFileLoaderTest.RECORD) + loader = self._LoaderForTestFile(filename) self.assertEqual(len(list(loader.Load())), 1) - self._WriteToFile('staggered_event_file', EventFileLoaderTest.RECORD) + self._WriteToFile(filename, EventFileLoaderTest.RECORD) self.assertEqual(len(list(loader.Load())), 1) def testMultipleLoads(self): - self._WriteToFile('multiple_loads_event_file', EventFileLoaderTest.RECORD) - loader = self._LoaderForTestFile('multiple_loads_event_file') + filename = tempfile.NamedTemporaryFile().name + self._WriteToFile(filename, EventFileLoaderTest.RECORD) + loader = self._LoaderForTestFile(filename) loader.Load() loader.Load() self.assertEqual(len(list(loader.Load())), 1) def testMultipleWritesAtOnce(self): - self._WriteToFile('multiple_event_file', EventFileLoaderTest.RECORD) - self._WriteToFile('multiple_event_file', EventFileLoaderTest.RECORD) - loader = self._LoaderForTestFile('staggered_event_file') + filename = tempfile.NamedTemporaryFile().name + self._WriteToFile(filename, EventFileLoaderTest.RECORD) + self._WriteToFile(filename, EventFileLoaderTest.RECORD) + loader = self._LoaderForTestFile(filename) self.assertEqual(len(list(loader.Load())), 2) diff --git a/tensorflow/tensorboard/tfgraph-demo-index.html b/tensorflow/tensorboard/components/tf-graph/demo/index.html index 84b7aaa94c..30231cb6fa 100644 --- a/tensorflow/tensorboard/tfgraph-demo-index.html +++ b/tensorflow/tensorboard/components/tf-graph/demo/index.html @@ -14,9 +14,9 @@ subject to an additional IP rights grant found at http://polymer.github.io/PATEN <meta name="viewport" content="width=device-width, minimum-scale=1.0, initial-scale=1.0, user-scalable=yes"> <title>tf-graph Demo</title> <!-- Libraries that should be imported in TensorBoard when the Graph visualizer ports to TensorBoard --> - <script src="bower_components/webcomponentsjs/webcomponents-lite.min.js"></script> - <script src="bower_components/es6-promise/promise.min.js"></script> - <link rel="import" href="components/tf-graph/demo/tf-graph-demo.html"> + <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script> + <script src="../../es6-promise/promise.min.js"></script> + <link rel="import" href="tf-graph-demo.html"> <style> html { width: 100%; diff --git a/tensorflow/tensorboard/tensorboard.py b/tensorflow/tensorboard/tensorboard.py index 07593a8323..a571cd06c3 100644 --- a/tensorflow/tensorboard/tensorboard.py +++ b/tensorflow/tensorboard/tensorboard.py @@ -112,11 +112,6 @@ class ThreadedHTTPServer(SocketServer.ThreadingMixIn, def main(unused_argv=None): - # Change current working directory to tensorflow/'s parent directory. - server_root = os.path.join(os.path.dirname(__file__), - os.pardir, os.pardir) - os.chdir(server_root) - if FLAGS.debug: logging.set_verbosity(logging.DEBUG) diff --git a/tensorflow/tools/ci_build/Dockerfile.android b/tensorflow/tools/ci_build/Dockerfile.android new file mode 100644 index 0000000000..10b5f3a20f --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.android @@ -0,0 +1,28 @@ +FROM tensorflow:ci_build.cpu + +MAINTAINER Jan Prach <jendap@google.com> + +# Install Android SDK. +ENV ANDROID_SDK_FILENAME android-sdk_r24.4.1-linux.tgz +ENV ANDROID_SDK_URL http://dl.google.com/android/${ANDROID_SDK_FILENAME} +ENV ANDROID_API_LEVEL 23 +ENV ANDROID_BUILD_TOOLS_VERSION 23.0.2 +ENV ANDROID_HOME /opt/android-sdk-linux +ENV PATH ${PATH}:${ANDROID_HOME}/tools:${ANDROID_HOME}/platform-tools +RUN cd /opt && \ + wget -q ${ANDROID_SDK_URL} && \ + tar -xzf ${ANDROID_SDK_FILENAME} && \ + rm ${ANDROID_SDK_FILENAME} && \ + echo y | android update sdk --no-ui -a --filter tools,platform-tools,android-${ANDROID_API_LEVEL},build-tools-${ANDROID_BUILD_TOOLS_VERSION} + +# Install Android NDK. +ENV ANDROID_NDK_FILENAME android-ndk-r10e-linux-x86_64.bin +ENV ANDROID_NDK_URL http://dl.google.com/android/ndk/${ANDROID_NDK_FILENAME} +ENV ANDROID_NDK_HOME /opt/android-ndk +ENV PATH ${PATH}:${ANDROID_NDK_HOME} +RUN cd /opt && \ + wget -q ${ANDROID_NDK_URL} && \ + chmod +x ${ANDROID_NDK_FILENAME} && \ + ./${ANDROID_NDK_FILENAME} -o/opt && \ + rm ${ANDROID_NDK_FILENAME} && \ + bash -c 'ln -s /opt/android-ndk-* /opt/android-ndk' diff --git a/tensorflow/tools/ci_build/Dockerfile.cpu b/tensorflow/tools/ci_build/Dockerfile.cpu new file mode 100644 index 0000000000..f162fcb552 --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.cpu @@ -0,0 +1,52 @@ +FROM ubuntu:14.04 + +MAINTAINER Jan Prach <jendap@google.com> + +# Install dependencies for bazel. +RUN apt-get update && apt-get install -y \ + g++ \ + pkg-config \ + python-dev \ + python-numpy \ + python-pip \ + software-properties-common \ + swig \ + unzip \ + wget \ + zip \ + zlib1g-dev \ + && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +# Install openjdk 8 for bazel from PPA (it is not available in 14.04). +RUN add-apt-repository -y ppa:openjdk-r/ppa && \ + apt-get update && \ + apt-get install -y openjdk-8-jdk openjdk-8-jre-headless && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +# Install the most recent bazel release. +ENV BAZEL_VERSION 0.1.1 +RUN mkdir /bazel && \ + cd /bazel && \ + wget https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \ + wget -O /bazel/LICENSE.txt https://raw.githubusercontent.com/bazelbuild/bazel/master/LICENSE.txt && \ + chmod +x bazel-*.sh && \ + ./bazel-$BAZEL_VERSION-installer-linux-x86_64.sh && \ + rm -f /bazel/bazel-$BAZEL_VERSION-installer-linux-x86_64.sh + +# Enable bazel auto completion. +RUN echo "source /usr/local/lib/bazel/bin/bazel-complete.bash" >> ~/.bashrc + +# Running bazel inside a `docker build` command causes trouble, cf: +# https://github.com/bazelbuild/bazel/issues/134 +# The easiest solution is to set up a bazelrc file forcing --batch. +RUN echo "startup --batch" >>/root/.bazelrc +# Similarly, we need to workaround sandboxing issues: +# https://github.com/bazelbuild/bazel/issues/418 +RUN echo "build --spawn_strategy=standalone --genrule_strategy=standalone" \ + >>/root/.bazelrc +# Force bazel output to use colors (good for jenkins). +RUN echo "common --color=yes" >>/root/.bazelrc +ENV BAZELRC /root/.bazelrc diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu new file mode 100644 index 0000000000..4b0933ae8c --- /dev/null +++ b/tensorflow/tools/ci_build/Dockerfile.gpu @@ -0,0 +1,23 @@ +FROM tensorflow:ci_build.cpu + +MAINTAINER Jan Prach <jendap@google.com> + +# Install Cuda. +RUN cd /tmp && \ + wget http://developer.download.nvidia.com/compute/cuda/7_0/Prod/local_installers/cuda_7.0.28_linux.run && \ + chmod +x *.run && ./cuda_*_linux.run -extract=`pwd` && \ + ./NVIDIA-Linux-x86_64-*.run -s --no-kernel-module && \ + ./cuda-linux64-rel-*.run -noprompt && \ + rm -rf * + +# Set up CUDA variables in .bashrc +RUN echo "CUDA_PATH=/usr/local/cuda" >>~/.bash_profile && \ + echo "LD_LIBRARY_PATH=/usr/local/cuda/lib64:/tensorflow_extra_deps/cudnn-6.5-linux-x64-v2" >>~/.bash_profile + +# Set up cuda variables. +ENV CUDA_PATH /usr/local/cuda +ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/tensorflow_extra_deps/cudnn-6.5-linux-x64-v2 + +# Set up variables fo tensorflow to use cuda. +ENV CUDA_TOOLKIT_PATH /usr/local/cuda +ENV CUDNN_INSTALL_PATH /tensorflow_extra_deps/cudnn-6.5-linux-x64-v2 diff --git a/tensorflow/tools/ci_build/README.md b/tensorflow/tools/ci_build/README.md new file mode 100644 index 0000000000..d557106301 --- /dev/null +++ b/tensorflow/tools/ci_build/README.md @@ -0,0 +1,104 @@ +# TensorFlow.org Continuous Integration + +This directory contains all the files and setup instructions to run +continuous integration [ci.tensorflow.org](http://ci.tensorflow.org). + + + +## How it works + +We use [jenkins](https://jenkins-ci.org/) as our continuous integration. +It is running at [ci.tensorflow.org](http://ci.tensorflow.org). +All the jobs are run within [docker](http://www.docker.com/) containers. + +Builds can be triggered by push to master, push a change set or manually. +The build started in jenkins will first pull the git tree. Then jenkins builds +a docker container (using one of those Dockerfile.* files in this directory). +The build itself is run within the container itself. + +Source tree lives in jenkins job workspace. Docker container for jenkins +are transient - deleted after the build. Containers build very fast thanks +to docker caching. Individual builds are fast thanks to bazel caching. + + + +## Implementation Details + +* The unusual `bazel-user-cache-for-docker` directory is mapped to docker + container performing the build using docker's --volume parameter. + This way we cache bazel output between builds. + +* The `$HOME/.tensorflow_extra_deps` directory contains + [cudnn](https://developer.nvidia.com/cudnn). + Unfortunatelly this require you to agree a license to download. + +* The builds directory hithin this folder contains shell scripts to run within + the container. They essentially contains workarounds for current limitations + of bazel. + + + +## Run It Yourself + +1. Install [Docker](http://www.docker.com/). Follow instructions + [on the Docker site](https://docs.docker.com/installation/). + +2. Clone tensorflow repository. + + ```bash +git clone https://github.com/tensorflow/tensorflow.git +``` + +3. Go to tensorflow directory + + ```bash +cd tensorflow +``` + +4. Build what you want, for example + + ```bash +tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/... +``` + +**Note**: For GPU you have to create `$HOME/.tensorflow_extra_deps` and manually +install there required dependencies (i.e. cudnn) for which you have to agree +to licences manually. + + +#### CUDNN + +For GPU download the [cudnn](https://developer.nvidia.com/cudnn). +You will download `cudnn-6.5-linux-x64-v2.tgz`. Run + +```bash +mkdir -p $HOME/.tensorflow_extra_deps +tar xzf cudnn-6.5-linux-x64-v2.tgz -C $HOME/.tensorflow_extra_deps +``` + + + +## Jobs + +The jobs run by [ci.tensorflow.org](http://ci.tensorflow.org) include following: + +```bash +# Note: You can run the following one-liners yourself if you have Docker. + +# build and run cpu tests +tensorflow/tools/ci_build/ci_build.sh CPU bazel test --test_timeout=1800 //tensorflow/... + +# build gpu +tensorflow/tools/ci_build/ci_build.sh GPU tensorflow/tools/ci_build/builds/gpu.sh + +# build pip with gpu support +tensorflow/tools/ci_build/ci_build.sh GPU tensorflow/tools/ci_build/builds/gpu_pip.sh + +# build android example app +tensorflow/tools/ci_build/ci_build.sh ANDROID tensorflow/tools/ci_build/builds/android.sh +``` + +**Note**: The set of jobs and how they are triggered is still evolving. +There are builds for master branch on cpu, gpu and android. There is a build +for incoming gerrit changes. Gpu tests and benchmark are coming soon. Check +[ci.tensorflow.org](http://ci.tensorflow.org) for current jobs. diff --git a/tensorflow/tools/ci_build/builds/android.sh b/tensorflow/tools/ci_build/builds/android.sh new file mode 100755 index 0000000000..2919537a89 --- /dev/null +++ b/tensorflow/tools/ci_build/builds/android.sh @@ -0,0 +1,59 @@ +#!/usr/bin/env bash +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +# Download model file. +# Note: This is workaround. This should be done by bazel. +model_file_name="inception5h.zip" +tmp_model_file_name="${HOME}/.cache/tensorflow_models/${model_file_name}" +mkdir -p $(dirname ${tmp_model_file_name}) +[ -e "${tmp_model_file_name}" ] || wget -c "https://storage.googleapis.com/download.tensorflow.org/models/${model_file_name}" -O "${tmp_model_file_name}" +unzip -o "${tmp_model_file_name}" -d tensorflow/examples/android/assets/ + +# Modify the WORKSPACE file. +# Note: This is workaround. This should be done by bazel. +if grep -q '^android_sdk_repository' WORKSPACE && grep -q '^android_ndk_repository' WORKSPACE; then + echo "You probably have your WORKSPACE file setup for Android." +else + if [ -z "${ANDROID_API_LEVEL}" -o -z "${ANDROID_BUILD_TOOLS_VERSION}" ] || \ + [ -z "${ANDROID_HOME}" -o -z "${ANDROID_NDK_HOME}" ]; then + echo "ERROR: Your WORKSPACE file does not seems to have proper android" + echo " configuration and not all the environment variables expected" + echo " inside ci_build android docker container are set." + echo " Please configure it manually. See: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android/README.md" + else + cat << EOF >> WORKSPACE +android_sdk_repository( + name = "androidsdk", + api_level = ${ANDROID_API_LEVEL}, + build_tools_version = "${ANDROID_BUILD_TOOLS_VERSION}", + path = "${ANDROID_HOME}", +) + +android_ndk_repository( + name="androidndk", + path="${ANDROID_NDK_HOME}", + api_level=21) +EOF + fi +fi + +# Build Android demo app. +bazel build -c opt --copt=-mfpu=neon //tensorflow/examples/android:tensorflow_demo + +# Cleanup workarounds. +rm -rf tensorflow/examples/android/assets/ diff --git a/tensorflow/tools/ci_build/builds/gpu.sh b/tensorflow/tools/ci_build/builds/gpu.sh new file mode 100755 index 0000000000..b252012b74 --- /dev/null +++ b/tensorflow/tools/ci_build/builds/gpu.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +export TF_NEED_CUDA=1 +./configure +bazel build -c opt --config=cuda //tensorflow/... diff --git a/tensorflow/tools/ci_build/builds/gpu_pip.sh b/tensorflow/tools/ci_build/builds/gpu_pip.sh new file mode 100755 index 0000000000..d2530abb75 --- /dev/null +++ b/tensorflow/tools/ci_build/builds/gpu_pip.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +export TF_NEED_CUDA=1 +./configure +bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package +rm -rf /root/.cache/tensorflow-pip +bazel-bin/tensorflow/tools/pip_package/build_pip_package /root/.cache/tensorflow-pip diff --git a/tensorflow/tools/ci_build/ci_build.sh b/tensorflow/tools/ci_build/ci_build.sh new file mode 100755 index 0000000000..79de732603 --- /dev/null +++ b/tensorflow/tools/ci_build/ci_build.sh @@ -0,0 +1,88 @@ +#!/usr/bin/env bash +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +# Get the command line arguments. +CONTAINER_TYPE=$( echo "$1" | tr '[:upper:]' '[:lower:]' ) +shift 1 +COMMAND=("$@") + +# Validate command line arguments. +if [ "$#" -lt 1 ] || [[ ! "${CONTAINER_TYPE}" =~ ^(cpu|gpu|android)$ ]]; then + >&2 echo "Usage: $(basename $0) CONTAINER_TYPE COMMAND" + >&2 echo " CONTAINER_TYPE can be 'CPU' or 'GPU'" + >&2 echo " COMMAND is a command (with arguments) to run inside" + >&2 echo " the container." + >&2 echo "" + >&2 echo "Example (run all tests on CPU):" + >&2 echo "$0 CPU bazel test //tensorflow/..." + exit 1 +fi + + +# Figure out the directory where this script is. +SCRIPT_DIR=$( cd ${0%/*} && pwd -P ) + +# Helper function to traverse directories up until given file is found. +function upsearch () { + test / == "$PWD" && return || \ + test -e "$1" && echo "$PWD" && return || \ + cd .. && upsearch "$1" +} + +# Set up WORKSPACE and BUILD_TAG. Jenkins will set them for you or we pick +# reasonable defaults if you run it outside of Jenkins. +WORKSPACE="${WORKSPACE:-$(upsearch WORKSPACE)}" +BUILD_TAG="${BUILD_TAG:-tf_ci}" + +# Additional configuration. You can customize it by modifying +# env variable. +EXTRA_DEPS_DIR="${EXTRA_DEPS_DIR:-${HOME}/.tensorflow_extra_deps}" + + +# Print arguments. +echo "CONTAINER_TYPE: ${CONTAINER_TYPE}" +echo "COMMAND: ${COMMAND[@]}" +echo "WORKSAPCE: ${WORKSPACE}" +echo "BUILD_TAG: ${BUILD_TAG}" +echo " (docker container name will be ${BUILD_TAG}.${CONTAINER_TYPE})" +echo "EXTRA_DEPS_DIR: ${EXTRA_DEPS_DIR}" +echo "" + +# Build the docker containers. +echo "Building CPU container (${BUILD_TAG}.cpu)..." +docker build -t ${BUILD_TAG}.cpu -f ${SCRIPT_DIR}/Dockerfile.cpu ${SCRIPT_DIR} +if [ "${CONTAINER_TYPE}" != "cpu" ]; then + echo "Building container ${BUILD_TAG}.${CONTAINER_TYPE}..." + tmp_dockerfile="${SCRIPT_DIR}/Dockerfile.${CONTAINER_TYPE}.${BUILD_TAG}" + # we need to generate temporary dockerfile with overwritten FROM directive + sed "s/^FROM .*/FROM ${BUILD_TAG}.cpu/" \ + ${SCRIPT_DIR}/Dockerfile.${CONTAINER_TYPE} > ${tmp_dockerfile} + docker build -t ${BUILD_TAG}.${CONTAINER_TYPE} \ + -f ${tmp_dockerfile} ${SCRIPT_DIR} + rm ${tmp_dockerfile} +fi + + +# Run the command inside the container. +echo "Running '${COMMAND[@]}' inside ${BUILD_TAG}.${CONTAINER_TYPE}..." +mkdir -p ${WORKSPACE}/bazel-user-cache-for-docker +docker run \ + -v ${WORKSPACE}/bazel-user-cache-for-docker:/root/.cache \ + -v ${WORKSPACE}:/tensorflow \ + -v ${EXTRA_DEPS_DIR}:/tensorflow_extra_deps \ + -w /tensorflow \ + ${BUILD_TAG}.${CONTAINER_TYPE} \ + "${COMMAND[@]}" diff --git a/util/python/python_config.sh b/util/python/python_config.sh index a52f6f7030..27b20949e8 100755 --- a/util/python/python_config.sh +++ b/util/python/python_config.sh @@ -1,4 +1,18 @@ #!/bin/bash +# Copyright 2015 Google Inc. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== set -e -o errexit |