编辑：对现有问题的修改。我有以下代码：
#include <iostream>
#include <stdlib.h>
#include <fstream>
#include <string.h>
#include <vector>
#include <sstream>
#include <typeinfo>
#include <sys/time.h>
#include "tensorflow/cc/client/client_session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
using namespace std;
using namespace tensorflow;
using namespace tensorflow::ops;
Session* session; // tensorflow session
Status status; // tensorflow status
long get_prediction(Tensor a);
long get_prediction(Tensor a) {
long prediction;
vector<Tensor> outputs;
// preparing input
std::vector<std::pair<string, tensorflow::Tensor>> inputs = {{"InputDataLayer/InputXPlaceHolder", a}, {"InputDataLayer/LabelYPlaceHolder", a}}; //TODO: Update this line
// getting prediction for test data
status = session->Run(inputs, {"OutputLayer/outputLogits"}, {}, &outputs); //TODO: Update this line
if (!status.ok()) {
cout<<"Error@get_prediction: "<<status.ToString()<<"\n";
return 1l;
}
prediction = outputs[0].scalar<long>()(0);
return prediction;
}
int main(int argc, char *argv[]) {
ifstream f;
string line = "";
string token = "";
double temp = 0.0;
double matches = 0.0, accuracy = 0.0;
int row_no=0, col_no=0;
long prediction = 0l, actual = 0l;
status = NewSession(SessionOptions(), &session);
if (!status.ok()) {
std::cout << status.ToString() << "\n";
return 1;
}
GraphDef graph_def;
status = ReadTextProto(Env::Default(), "/home/userj/Desktop/tensorflow/tensorflow/loader/models.pbtxt", &graph_def);
if (!status.ok()) {
std::cout << status.ToString() << "\n";
return 1;
}
status = session->Create(graph_def);
if (!status.ok()) {
std::cout << status.ToString() << "\n";
return 1;
}
session->Run({}, {}, {"init_all_vars_op"}, nullptr); //Initializes all the variables in C++
auto a = Tensor(DT_DOUBLE, TensorShape({1, 9})); //TODO: Update this line?
f.open("/home/userj/Desktop/tensorflow/tensorflow/loader/signals.csv");
while(getline(f, line)) {
if (row_no == 0) {
row_no += 1;
continue;
}
istringstream iss(line);
while(getline(iss, token, ',')) {
const char * item = token.c_str();
if (strlen(item) == 0 && col_no != 2) {
a.matrix<double>()(0, col_no) = 0;
col_no += 1;
continue;
}
temp = stod(token.c_str()); //breaking on the missing values
if (col_no == 1) { //skip the time column
col_no += 1;
continue;
}
// filling feature tensors
if(col_no != 2)
a.matrix<double>()(0, col_no) = temp;
// filling label tensor
if(col_no == 2)
actual = (long) temp;
col_no += 1;
}
col_no = 0;
row_no += 1;
// getting prediction
prediction = get_prediction(a);
// if actual and prediction matches, increment matches
if(actual == prediction)
matches += 1;
}
accuracy = matches / (row_no);
cout<<"Total Rows: "<<(row_no)<<endl;
cout<<"Accuracy: "<<accuracy<<endl;
session->Close();
return 0;
}
当我在 bazel 中运行它时出现此错误：
Invalid argument: Expects arg[1] to be int32 but double is provided
*** Error in `./ml': malloc(): memory corruption (fast): 0x0000000005602fd0 ***
下面是我的 .pbtxt 文件。InputDataLayer/InputXPlaceHolder 和 InputDataLayer/InputLabelYPlaceHolder 是输入，OutputLayer/outputLogits 是输出。有谁知道我做错了什么？我确信它是多方面的，包括运行功能。
node {
name: "InputDataLayer/InputXPlaceHolder"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_DOUBLE
}
}
attr {
key: "shape"
value {
shape {
dim {
size: -1
}
dim {
size: -1
}
dim {
size: 9
}
}
}
}
}
node {
name: "InputDataLayer/LabelYPlaceHolder"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
dim {
size: -1
}
}
}
}
}
node {
name: "RNNLayers/ones_like/Shape"
op: "Shape"
input: "InputDataLayer/InputXPlaceHolder"
attr {
key: "T"
value {
type: DT_DOUBLE
}
}
attr {
key: "out_type"
value {
type: DT_INT32
}
}
}
node {
name: "RNNLayers/ones_like/Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT16
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT16
tensor_shape {
}
int_val: 1
}
}
}
}
node {
name: "RNNLayers/ones_like"
op: "Fill"
input: "RNNLayers/ones_like/Shape"
input: "RNNLayers/ones_like/Const"
attr {
key: "T"
value {
type: DT_INT16
}
}
attr {
key: "index_type"
value {
type: DT_INT32
}
}
}
node {
name: "RNNLayers/Max/reduction_indices"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 2
}
}
}
}
node {
name: "RNNLayers/Max"
op: "Max"
input: "RNNLayers/ones_like"
input: "RNNLayers/Max/reduction_indices"
attr {
key: "T"
value {
type: DT_INT16
}
}
attr {
key: "Tidx"
value {
type: DT_INT32
}
}
attr {
key: "keep_dims"
value {
b: false
}
}
}
node {
name: "RNNLayers/Sum/reduction_indices"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 0
}
}
}
}
node {
name: "RNNLayers/Sum"
op: "Sum"
input: "RNNLayers/Max"
input: "RNNLayers/Sum/reduction_indices"
attr {
key: "T"
value {
type: DT_INT16
}
}
attr {
key: "Tidx"
value {
type: DT_INT32
}
}
attr {
key: "keep_dims"
value {
b: false
}
}
}
node {
name: "RNNLayers/Shape"
op: "Shape"
input: "InputDataLayer/InputXPlaceHolder"
attr {
key: "T"
value {
type: DT_DOUBLE
}
}
attr {
key: "out_type"
value {
type: DT_INT32
}
}
}
node {
name: "RNNLayers/strided_slice/stack"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
dim {
size: 1
}
}
int_val: 1
}
}
}
}
node {
name: "RNNLayers/strided_slice/stack_1"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
dim {
size: 1
}
}
int_val: 2
}
}
}
}
node {
name: "RNNLayers/strided_slice/stack_2"
op: "Const"
attr {
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value {
type: DT_INT32
}
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attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
dim {
size: 1
}
}
int_val: 1
}
}
}
}
node {
name: "RNNLayers/strided_slice"
op: "StridedSlice"
input: "RNNLayers/Shape"
input: "RNNLayers/strided_slice/stack"
input: "RNNLayers/strided_slice/stack_1"
input: "RNNLayers/strided_slice/stack_2"
attr {
key: "Index"
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type: DT_INT32
}
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type: DT_INT32
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attr {
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value {
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key: "ellipsis_mask"
value {
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}
}
attr {
key: "end_mask"
value {
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}
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attr {
key: "new_axis_mask"
value {
i: 0
}
}
attr {
key: "shrink_axis_mask"
value {
i: 1
}
}
}
node {
name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims/dim"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
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int_val: 0
}
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node {
name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims"
op: "ExpandDims"
input: "RNNLayers/strided_slice"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims/dim"
attr {
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op: "Const"
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tensor_shape {
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node {
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tensor_shape {
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node {
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op: "ConcatV2"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/Const"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/concat/axis"
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node {
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tensor_shape {
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double_val: 0.0
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node {
name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/zeros"
op: "Fill"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/concat"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/zeros/Const"
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op: "Const"
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node {
name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims_1"
op: "ExpandDims"
input: "RNNLayers/strided_slice"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims_1/dim"
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op: "ExpandDims"
input: "RNNLayers/strided_slice"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims_2/dim"
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key: "T"
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key: "Tdim"
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name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/Const_2"
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op: "Const"
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tensor_shape {
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name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/concat_1"
op: "ConcatV2"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/ExpandDims_2"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/Const_2"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/concat_1/axis"
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name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/zeros_1/Const"
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tensor_shape {
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node {
name: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/zeros_1"
op: "Fill"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/concat_1"
input: "RNNLayers/MultiRNNCellZeroState/LSTMCellZeroState/zeros_1/Const"
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输出是这样的。
node {
name: "OutputLayer/outputLogits"
op: "Sigmoid"
input: "OutputLayer/Add"
attr {
key: "T"
value {
type: DT_DOUBLE
}
}
}
node {
name: "save/Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_STRING
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_STRING
tensor_shape {
}
string_val: "model"
}
}
}
}