Remove stale trainer example.
PiperOrigin-RevId: 359854240 Change-Id: Ic18e62c79b42c25aeb6069711e4c5d39cee22a78
This commit is contained in:
Russell Power
TensorFlower Gardener
parent
70b6d7a275
commit
4af6daae77
@ -6,7 +6,6 @@ load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")
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load(
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load(
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"//tensorflow:tensorflow.bzl",
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"//tensorflow:tensorflow.bzl",
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"cc_library_with_android_deps",
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"cc_library_with_android_deps",
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"tf_cc_binary",
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"tf_cc_test",
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"tf_cc_test",
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"tf_copts",
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"tf_copts",
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"transitive_hdrs",
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"transitive_hdrs",
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@ -748,36 +747,6 @@ tf_gen_op_wrappers_cc(
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],
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],
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)
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)
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tf_cc_binary(
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name = "tutorials_example_trainer",
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srcs = ["tutorials/example_trainer.cc"],
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copts = tf_copts(),
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linkopts = select({
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"//tensorflow:windows": [],
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"//tensorflow:macos": [
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"-lm",
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"-lpthread",
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],
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"//tensorflow:ios": [
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"-lm",
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"-lpthread",
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],
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"//conditions:default": [
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"-lm",
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"-lpthread",
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"-lrt",
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],
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}),
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deps = [
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":cc_ops",
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"//tensorflow/core:core_cpu",
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"//tensorflow/core:framework",
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"//tensorflow/core:lib",
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"//tensorflow/core:protos_all_cc",
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"//tensorflow/core:tensorflow",
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],
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)
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cc_library(
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cc_library(
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name = "queue_runner",
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name = "queue_runner",
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srcs = ["training/queue_runner.cc"],
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srcs = ["training/queue_runner.cc"],
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@ -1,234 +0,0 @@
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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <cstdio>
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#include <functional>
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#include <string>
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#include <vector>
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#include "tensorflow/cc/ops/standard_ops.h"
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#include "tensorflow/core/framework/graph.pb.h"
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#include "tensorflow/core/framework/tensor.h"
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#include "tensorflow/core/graph/default_device.h"
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#include "tensorflow/core/graph/graph_def_builder.h"
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#include "tensorflow/core/lib/core/threadpool.h"
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#include "tensorflow/core/lib/strings/str_util.h"
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#include "tensorflow/core/lib/strings/stringprintf.h"
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#include "tensorflow/core/platform/init_main.h"
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#include "tensorflow/core/platform/logging.h"
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#include "tensorflow/core/platform/types.h"
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#include "tensorflow/core/public/session.h"
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using tensorflow::string;
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using tensorflow::int32;
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namespace tensorflow {
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namespace example {
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struct Options {
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int num_concurrent_sessions = 1; // The number of concurrent sessions
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int num_concurrent_steps = 10; // The number of concurrent steps
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int num_iterations = 100; // Each step repeats this many times
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bool use_gpu = false; // Whether to use gpu in the training
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};
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// A = [3 2; -1 0]; x = rand(2, 1);
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// We want to compute the largest eigenvalue for A.
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// repeat x = y / y.norm(); y = A * x; end
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GraphDef CreateGraphDef() {
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// TODO(jeff,opensource): This should really be a more interesting
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// computation. Maybe turn this into an mnist model instead?
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Scope root = Scope::NewRootScope();
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using namespace ::tensorflow::ops; // NOLINT(build/namespaces)
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// A = [3 2; -1 0]. Using Const<float> means the result will be a
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// float tensor even though the initializer has integers.
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auto a = Const<float>(root, {{3, 2}, {-1, 0}});
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// x = [1.0; 1.0]
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auto x = Const(root.WithOpName("x"), {{1.f}, {1.f}});
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// y = A * x
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auto y = MatMul(root.WithOpName("y"), a, x);
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// y2 = y.^2
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auto y2 = Square(root, y);
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// y2_sum = sum(y2). Note that you can pass constants directly as
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// inputs. Sum() will automatically create a Const node to hold the
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// 0 value.
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auto y2_sum = Sum(root, y2, 0);
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// y_norm = sqrt(y2_sum)
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auto y_norm = Sqrt(root, y2_sum);
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// y_normalized = y ./ y_norm
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Div(root.WithOpName("y_normalized"), y, y_norm);
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GraphDef def;
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TF_CHECK_OK(root.ToGraphDef(&def));
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return def;
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}
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string DebugString(const Tensor& x, const Tensor& y) {
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CHECK_EQ(x.NumElements(), 2);
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CHECK_EQ(y.NumElements(), 2);
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auto x_flat = x.flat<float>();
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auto y_flat = y.flat<float>();
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// Compute an estimate of the eigenvalue via
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// (x' A x) / (x' x) = (x' y) / (x' x)
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// and exploit the fact that x' x = 1 by assumption
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Eigen::Tensor<float, 0, Eigen::RowMajor> lambda = (x_flat * y_flat).sum();
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return strings::Printf("lambda = %8.6f x = [%8.6f %8.6f] y = [%8.6f %8.6f]",
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lambda(), x_flat(0), x_flat(1), y_flat(0), y_flat(1));
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}
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void ConcurrentSteps(const Options* opts, int session_index) {
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// Creates a session.
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SessionOptions options;
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std::unique_ptr<Session> session(NewSession(options));
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GraphDef def = CreateGraphDef();
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if (options.target.empty()) {
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graph::SetDefaultDevice(opts->use_gpu ? "/device:GPU:0" : "/cpu:0", &def);
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}
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TF_CHECK_OK(session->Create(def));
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// Spawn M threads for M concurrent steps.
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const int M = opts->num_concurrent_steps;
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std::unique_ptr<thread::ThreadPool> step_threads(
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new thread::ThreadPool(Env::Default(), "trainer", M));
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for (int step = 0; step < M; ++step) {
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step_threads->Schedule([&session, opts, session_index, step]() {
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// Randomly initialize the input.
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Tensor x(DT_FLOAT, TensorShape({2, 1}));
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auto x_flat = x.flat<float>();
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x_flat.setRandom();
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Eigen::Tensor<float, 0, Eigen::RowMajor> inv_norm =
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x_flat.square().sum().sqrt().inverse();
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x_flat = x_flat * inv_norm();
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// Iterations.
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std::vector<Tensor> outputs;
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for (int iter = 0; iter < opts->num_iterations; ++iter) {
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outputs.clear();
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TF_CHECK_OK(
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session->Run({{"x", x}}, {"y:0", "y_normalized:0"}, {}, &outputs));
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CHECK_EQ(size_t{2}, outputs.size());
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const Tensor& y = outputs[0];
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const Tensor& y_norm = outputs[1];
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// Print out lambda, x, and y.
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std::printf("%06d/%06d %s\n", session_index, step,
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DebugString(x, y).c_str());
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// Copies y_normalized to x.
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x = y_norm;
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}
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});
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}
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// Delete the threadpool, thus waiting for all threads to complete.
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step_threads.reset(nullptr);
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TF_CHECK_OK(session->Close());
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}
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void ConcurrentSessions(const Options& opts) {
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// Spawn N threads for N concurrent sessions.
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const int N = opts.num_concurrent_sessions;
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// At the moment our Session implementation only allows
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// one concurrently computing Session on GPU.
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CHECK_EQ(1, N) << "Currently can only have one concurrent session.";
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thread::ThreadPool session_threads(Env::Default(), "trainer", N);
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for (int i = 0; i < N; ++i) {
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session_threads.Schedule(std::bind(&ConcurrentSteps, &opts, i));
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}
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}
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} // end namespace example
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} // end namespace tensorflow
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namespace {
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bool ParseInt32Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
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int32* dst) {
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if (absl::ConsumePrefix(&arg, flag) && absl::ConsumePrefix(&arg, "=")) {
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char extra;
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return (sscanf(arg.data(), "%d%c", dst, &extra) == 1);
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}
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return false;
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}
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bool ParseBoolFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag,
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bool* dst) {
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if (absl::ConsumePrefix(&arg, flag)) {
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if (arg.empty()) {
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*dst = true;
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return true;
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}
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if (arg == "=true") {
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*dst = true;
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return true;
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} else if (arg == "=false") {
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*dst = false;
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return true;
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}
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}
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return false;
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}
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} // namespace
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int main(int argc, char* argv[]) {
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tensorflow::example::Options opts;
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std::vector<char*> unknown_flags;
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for (int i = 1; i < argc; ++i) {
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if (string(argv[i]) == "--") {
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while (i < argc) {
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unknown_flags.push_back(argv[i]);
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++i;
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}
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break;
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}
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if (ParseInt32Flag(argv[i], "--num_concurrent_sessions",
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&opts.num_concurrent_sessions) ||
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ParseInt32Flag(argv[i], "--num_concurrent_steps",
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&opts.num_concurrent_steps) ||
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ParseInt32Flag(argv[i], "--num_iterations", &opts.num_iterations) ||
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ParseBoolFlag(argv[i], "--use_gpu", &opts.use_gpu)) {
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continue;
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}
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fprintf(stderr, "Unknown flag: %s\n", argv[i]);
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return -1;
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}
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// Passthrough any unknown flags.
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int dst = 1; // Skip argv[0]
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for (char* f : unknown_flags) {
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argv[dst++] = f;
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}
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argv[dst++] = nullptr;
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argc = static_cast<int>(unknown_flags.size() + 1);
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tensorflow::port::InitMain(argv[0], &argc, &argv);
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tensorflow::example::ConcurrentSessions(opts);
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}
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