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103
src/tensor.h
103
src/tensor.h
@ -1,6 +1,5 @@
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#pragma once
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#include <cudnn.h>
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#include <cublas_v2.h>
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#include <thrust/device_vector.h>
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#include <thrust/functional.h>
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@ -13,27 +12,27 @@
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namespace marian {
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struct Handles {
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cudnnHandle_t cudnnHandle;
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cublasHandle_t cublasHandle;
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cudnnOpTensorDescriptor_t add;
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Handles() {
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cudnnCreate(&cudnnHandle);
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cublasCreate(&cublasHandle);
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cudnnCreateOpTensorDescriptor(&add);
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cudnnSetOpTensorDescriptor(add, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN);
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}
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~Handles() {
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cudnnDestroy(cudnnHandle);
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cublasDestroy(cublasHandle);
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cudnnDestroyOpTensorDescriptor(add);
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}
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};
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const Handles handles;
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//struct Handles {
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// //cudnnHandle_t cudnnHandle;
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// //cublasHandle_t cublasHandle;
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//
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// //cudnnOpTensorDescriptor_t add;
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//
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// Handles() {
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// cudnnCreate(&cudnnHandle);
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// cublasCreate(&cublasHandle);
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// cudnnCreateOpTensorDescriptor(&add);
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// cudnnSetOpTensorDescriptor(add, CUDNN_OP_TENSOR_ADD, CUDNN_DATA_FLOAT, CUDNN_NOT_PROPAGATE_NAN);
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// }
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//
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// ~Handles() {
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// cudnnDestroy(cudnnHandle);
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// cublasDestroy(cublasHandle);
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// cudnnDestroyOpTensorDescriptor(add);
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// }
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//};
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//
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//const Handles handles;
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// typedef std::vector<int> Shape;
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@ -60,17 +59,17 @@ class TensorImpl {
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private:
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Shape shape_;
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thrust::device_vector<Float> data_;
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cudnnTensorDescriptor_t desc_;
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//cudnnTensorDescriptor_t desc_;
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size_t tno_;
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static size_t tensorCounter;
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cudnnDataType_t dataType() {
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switch(sizeof(Float)) {
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case 2: return CUDNN_DATA_HALF;
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case 8: return CUDNN_DATA_DOUBLE;
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default: return CUDNN_DATA_FLOAT;
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}
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}
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//cudnnDataType_t dataType() {
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// switch(sizeof(Float)) {
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// case 2: return CUDNN_DATA_HALF;
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// case 8: return CUDNN_DATA_DOUBLE;
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// default: return CUDNN_DATA_FLOAT;
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// }
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//}
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public:
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typedef Float value_type;
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@ -90,28 +89,28 @@ class TensorImpl {
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int size = GetTotalSize(shape_);
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data_.resize(size, value);
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cudnnCreateTensorDescriptor(&desc_);
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switch (shape_.size()) {
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case 1:
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cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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shape_[0], 1, 1, 1); break;
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case 2:
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cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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shape_[0], shape_[1], 1, 1); break;
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case 3:
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cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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shape_[0], shape_[1], shape_[2], 1); break;
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case 4:
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cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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shape_[0], shape_[1], shape_[2], shape_[3]); break;
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}
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//cudnnCreateTensorDescriptor(&desc_);
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//switch (shape_.size()) {
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// case 1:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], 1, 1, 1); break;
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// case 2:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], 1, 1); break;
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// case 3:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], shape_[2], 1); break;
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// case 4:
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// cudnnSetTensor4dDescriptor(desc_, CUDNN_TENSOR_NCHW, dataType(),
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// shape_[0], shape_[1], shape_[2], shape_[3]); break;
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//}
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}
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TensorImpl(const TensorImpl&) = delete;
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TensorImpl(TensorImpl&&) = delete;
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~TensorImpl() {
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cudnnDestroyTensorDescriptor(desc_);
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//cudnnDestroyTensorDescriptor(desc_);
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}
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value_type operator[](size_t i) const {
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@ -146,9 +145,9 @@ class TensorImpl {
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return thrust::raw_pointer_cast(data_.data());
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}
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cudnnTensorDescriptor_t desc() const {
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return desc_;
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}
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//cudnnTensorDescriptor_t desc() const {
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// return desc_;
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//}
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size_t id() const {
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return tno_;
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@ -246,9 +245,9 @@ class Tensor {
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return pimpl_->shape();
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}
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cudnnTensorDescriptor_t desc() const {
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return pimpl_->desc();
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}
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//cudnnTensorDescriptor_t desc() const {
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// return pimpl_->desc();
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//}
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void set(value_type value) {
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pimpl_->set(value);
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@ -130,7 +130,11 @@ Tensor Prod(cublasHandle_t handle, Tensor C, const Tensor A, const Tensor B,
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Tensor Prod(Tensor C, const Tensor A, const Tensor B,
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bool transA, bool transB, Float beta) {
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return Prod(handles.cublasHandle, C, A, B, transA, transB, beta);
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cublasHandle_t cublasHandle;
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cublasCreate(&cublasHandle);
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Tensor temp = Prod(cublasHandle, C, A, B, transA, transB, beta);
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cublasDestroy(cublasHandle);
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return temp;
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}
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}
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207
src/test.cu
207
src/test.cu
@ -2,9 +2,9 @@
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#include "marian.h"
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#include "mnist.h"
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using namespace std;
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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/*int numImg = 0;*/
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/*auto images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numImg);*/
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/*auto labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numImg);*/
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@ -12,117 +12,104 @@ int main(int argc, char** argv) {
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using namespace marian;
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using namespace keywords;
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const size_t BATCH_SIZE = 500;
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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Expr x = input(shape={1, 2});
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Expr y = input(shape={1, 2});
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Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
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Expr b = param(shape={1, LABEL_SIZE}, name="b0");
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Expr z = dot(x, w) + b;
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Expr lr = softmax(z, axis=1, name="pred");
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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//cerr << "x=" << Debug(lr.val().shape()) << endl;
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Expr w = param(shape={2, 2}, name="W0");
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//Expr b = param(shape={1, 2}, name="b0");
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int numofdata;
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//vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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//vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
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cerr << "numofdata=" << numofdata << endl;
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size_t startInd = 0;
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size_t startIndData = 0;
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while (startInd < numofdata) {
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size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
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cerr << "startInd=" << startInd
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<< " startIndData=" << startIndData
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<< " batchSize=" << batchSize << endl;
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Tensor tx({numofdata, IMAGE_SIZE}, 1);
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Tensor ty({numofdata, LABEL_SIZE}, 1);
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tx.Load(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
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ty.Load(labels.begin() + startInd, labels.begin() + startInd + batchSize);
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//cerr << "tx=" << Debug(tx.shape()) << endl;
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//cerr << "ty=" << Debug(ty.shape()) << endl;
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x = tx;
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y = ty;
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cerr << "x=" << Debug(x.val().shape()) << endl;
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cerr << "y=" << Debug(y.val().shape()) << endl;
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graph.forward(batchSize);
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cerr << "w=" << Debug(w.val().shape()) << endl;
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cerr << "b=" << Debug(b.val().shape()) << endl;
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std::cerr << "z: " << Debug(z.val().shape()) << endl;
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std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
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std::cerr << "Log-likelihood: " << Debug(graph.val().shape()) << endl ;
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//std::cerr << "scores=" << scores.val().Debug() << endl;
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//std::cerr << "lr=" << lr.val().Debug() << endl;
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graph.backward();
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//std::cerr << graph["pred"].val()[0] << std::endl;
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startInd += batchSize;
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startIndData += batchSize * IMAGE_SIZE;
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}
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std::cerr << "Building model...";
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auto predict = softmax(dot(x, w),
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axis=1, name="pred");
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auto graph = -mean(sum(y * log(predict), axis=1),
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axis=0, name="cost");
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// XOR
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/*
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Expr x = input(shape={whatevs, 2}, name="X");
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Expr y = input(shape={whatevs, 2}, name="Y");
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Expr w = param(shape={2, 1}, name="W0");
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Expr b = param(shape={1, 1}, name="b0");
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Expr n5 = dot(x, w);
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Expr n6 = n5 + b;
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Expr lr = softmax(n6, axis=1, name="pred");
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cerr << "lr=" << lr.Debug() << endl;
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Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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Tensor tx({4, 2}, 1);
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Tensor ty({4, 1}, 1);
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cerr << "tx=" << tx.Debug() << endl;
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cerr << "ty=" << ty.Debug() << endl;
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tx.Load("../examples/xor/train.txt");
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ty.Load("../examples/xor/label.txt");
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*/
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#if 0
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hook0(graph);
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graph.autodiff();
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std::cerr << graph["cost"].val()[0] << std::endl;
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//hook1(graph);
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for(auto p : graph.params()) {
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auto update = _1 = _1 - alpha * _2;
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Element(update, p.val(), p.grad());
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}
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hook2(graph);
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Tensor x1t({1, 2});
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std::vector<float> xv = { 0.6, 0.1 };
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thrust::copy(xv.begin(), xv.end(), x1t.begin());
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Tensor x2t({1, 2});
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std::vector<float> yv = { 0, 1 };
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thrust::copy(yv.begin(), yv.end(), x2t.begin());
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x = x1t;
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y = x2t;
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graph.forward(1);
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graph.backward();
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std::cerr << graph.val().Debug() << std::endl;
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std::cerr << w.grad().Debug() << std::endl;
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//std::cerr << b.grad().Debug() << std::endl;
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// using namespace marian;
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// using namespace keywords;
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//
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// const size_t BATCH_SIZE = 500;
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// const size_t IMAGE_SIZE = 784;
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// const size_t LABEL_SIZE = 10;
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//
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// Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
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// Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
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//
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// Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
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// Expr b = param(shape={1, LABEL_SIZE}, name="b0");
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//
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// Expr z = dot(x, w) + b;
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// Expr lr = softmax(z, axis=1, name="pred");
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// Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
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// //cerr << "x=" << Debug(lr.val().shape()) << endl;
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//
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// int numofdata;
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// //vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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// //vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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// vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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// vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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// cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
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// cerr << "numofdata=" << numofdata << endl;
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//
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// size_t startInd = 0;
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// size_t startIndData = 0;
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// while (startInd < numofdata) {
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// size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
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// cerr << "startInd=" << startInd
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// << " startIndData=" << startIndData
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// << " batchSize=" << batchSize << endl;
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//
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// Tensor tx({numofdata, IMAGE_SIZE}, 1);
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// Tensor ty({numofdata, LABEL_SIZE}, 1);
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//
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// tx.Load(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
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// ty.Load(labels.begin() + startInd, labels.begin() + startInd + batchSize);
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//
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// //cerr << "tx=" << Debug(tx.shape()) << endl;
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// //cerr << "ty=" << Debug(ty.shape()) << endl;
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//
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// x = tx;
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// y = ty;
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//
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// cerr << "x=" << Debug(x.val().shape()) << endl;
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// cerr << "y=" << Debug(y.val().shape()) << endl;
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//
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//
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// graph.forward(batchSize);
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//
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// cerr << "w=" << Debug(w.val().shape()) << endl;
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// cerr << "b=" << Debug(b.val().shape()) << endl;
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// std::cerr << "z: " << Debug(z.val().shape()) << endl;
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// std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
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// std::cerr << "Log-likelihood: " << Debug(graph.val().shape()) << endl ;
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//
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// //std::cerr << "scores=" << scores.val().Debug() << endl;
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// //std::cerr << "lr=" << lr.val().Debug() << endl;
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//
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// //graph.backward();
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//
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// //std::cerr << graph["pred"].val()[0] << std::endl;
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//
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// startInd += batchSize;
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// startIndData += batchSize * IMAGE_SIZE;
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// }
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auto opt = adadelta(cost_function=cost,
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eta=0.9, gamma=0.1,
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set_batch=set,
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before_update=before,
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after_update=after,
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set_valid=valid,
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validation_freq=100,
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verbose=1, epochs=3, early_stopping=10);
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opt.run();
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#endif
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return 0;
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}
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@ -7,13 +7,16 @@ using namespace marian;
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using namespace keywords;
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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int numofdata;
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std::cerr << "Loading test set...";
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std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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std::vector<float>testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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std::cerr << "\tDone." << std::endl;
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std::cerr << "Loading model params...";
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@ -27,11 +30,11 @@ int main(int argc, char** argv) {
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Shape bShape;
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converter.Load("bias", bData, bShape);
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auto initW = [&wData](Tensor t) {
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auto initW = [wData](Tensor t) {
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thrust::copy(wData.begin(), wData.end(), t.begin());
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};
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auto initB = [&bData](Tensor t) {
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auto initB = [bData](Tensor t) {
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thrust::copy(bData.begin(), bData.end(), t.begin());
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};
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@ -39,24 +42,35 @@ int main(int argc, char** argv) {
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Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
|
||||
|
||||
Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
|
||||
|
||||
Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0", init=initW);
|
||||
Expr b = param(shape={1, LABEL_SIZE}, name="b0", init=initB);
|
||||
|
||||
std::cerr << "Building model...";
|
||||
auto scores = dot(x, w) + b;
|
||||
auto predict = softmax(scores, axis=1, name="pred");
|
||||
auto predict = softmax(dot(x, w) + b,
|
||||
axis=1, name="pred");
|
||||
auto graph = -mean(sum(y * log(predict), axis=1),
|
||||
axis=0, name="cost");
|
||||
|
||||
std::cerr << "\tDone." << std::endl;
|
||||
|
||||
Tensor xt({numofdata, IMAGE_SIZE});
|
||||
xt.Load(testImages);
|
||||
|
||||
predict.forward(numofdata);
|
||||
|
||||
|
||||
Tensor yt({numofdata, LABEL_SIZE});
|
||||
yt.Load(testLabels);
|
||||
|
||||
x = xt;
|
||||
y = yt;
|
||||
|
||||
graph.forward(numofdata);
|
||||
auto results = predict.val();
|
||||
graph.backward();
|
||||
|
||||
std::cerr << b.grad().Debug() << std::endl;
|
||||
|
||||
size_t acc = 0;
|
||||
|
||||
for (size_t i = 0; i < testLabels.size(); i += LABEL_SIZE) {
|
||||
size_t correct = 0;
|
||||
size_t predicted = 0;
|
||||
@ -65,11 +79,11 @@ int main(int argc, char** argv) {
|
||||
if (results[i + j] > results[i + predicted]) predicted = j;
|
||||
}
|
||||
acc += (correct == predicted);
|
||||
std::cerr << "corect: " << correct << " | " << predicted << "(";
|
||||
for (size_t j = 0; j < LABEL_SIZE; ++j) {
|
||||
std::cerr << results[i+j] << " ";
|
||||
}
|
||||
std::cerr << std::endl;
|
||||
//std::cerr << "corect: " << correct << " | " << predicted << "(";
|
||||
//for (size_t j = 0; j < LABEL_SIZE; ++j) {
|
||||
// std::cerr << results[i+j] << " ";
|
||||
//}
|
||||
//std::cerr << std::endl;
|
||||
}
|
||||
std::cerr << "ACC: " << float(acc)/numofdata << std::endl;
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user