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mkaczanowski/uTensor_epiphany Secret

Created Aug 30, 2020
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diff --git a/CMakeLists.txt b/CMakeLists.txt
index 77b74e6..c561541 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -6,6 +6,16 @@ set( CMAKE_VERBOSE_MAKEFILE on )
# set(CMAKE_CXX_FLAGS "-fPIC")
+set(CMAKE_C_COMPILER /opt/adapteva/esdk/tools/e-gnu/bin/e-gcc)
+set(CMAKE_CXX_COMPILER /opt/adapteva/esdk/tools/e-gnu/bin/e-g++)
+
+# add epiphany libs
+include_directories( /opt/adapteva/esdk/tools/e-gnu.armv7l/epiphany-elf/include )
+find_library(E_LIB e-lib HINTS /opt/adapteva/esdk/tools/e-gnu.armv7l/epiphany-elf/lib)
+
+add_compile_options(-g -s -O2 -Wl,--strip-all -fpermissive)
+add_link_options(-g -s -O2 -Wl,--strip-all -T /opt/adapteva/esdk/bsps/current/legacy.ldf)
+
# Find includes in corresponding build directories
set(CMAKE_INCLUDE_CURRENT_DIR ON)
@@ -14,7 +24,6 @@ include_directories(${PROJECT_SOURCE_DIR}/uTensor/loaders)
include_directories(${PROJECT_SOURCE_DIR}/uTensor/ops)
include_directories(${PROJECT_SOURCE_DIR}/uTensor/util)
-
file(GLOB SOURCES uTensor/*/*.cpp)
file(GLOB INCLUDES uTensor/*/*.hpp)
@@ -34,7 +43,7 @@ else()
endif()
# Build with code coverage
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -ggdb")
+#set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage -ggdb")
add_library(utensor STATIC ${SOURCES})
@@ -81,3 +90,4 @@ target_include_directories(utensor PUBLIC ${CMAKE_CURRENT_LIST_DIR}/uTensor/util
# Todo add install
set(UTENSOR_INCLUDES ${INCLUDES} PARENT_SCOPE)
+add_subdirectory(DigitRecognition)
diff --git a/DigitRecognition/CMakeLists.txt b/DigitRecognition/CMakeLists.txt
new file mode 100644
index 0000000..9264617
--- /dev/null
+++ b/DigitRecognition/CMakeLists.txt
@@ -0,0 +1,3 @@
+add_executable(DigitRecognition DigitRecognition.cpp deep_mlp.cpp deep_mlp_weight.hpp input_data.h)
+target_link_libraries(DigitRecognition PUBLIC utensor)
+#add_executable(DigitRecognition DigitRecognition.cpp)
diff --git a/DigitRecognition/DigitRecognition.cpp b/DigitRecognition/DigitRecognition.cpp
new file mode 100644
index 0000000..9d38ec0
--- /dev/null
+++ b/DigitRecognition/DigitRecognition.cpp
@@ -0,0 +1,29 @@
+#include <tensor.hpp>
+#include <stdio.h>
+
+#include "deep_mlp.hpp" //gernerated model file
+#include "input_data.h" //contains the first sample taken from the MNIST test set
+
+volatile unsigned long long *debug = (void *)0x7200;
+
+int main() {
+ (*debug) = 1;
+
+ Context ctx; //creating the context class, the stage where inferences take place
+ //wrapping the input data in a tensor class
+ Tensor* input_x = new WrappedRamTensor<float>({1, 784}, (float*) input_data);
+
+ (*debug) = 2;
+ get_deep_mlp_ctx(ctx, input_x); // pass the tensor to the context
+
+ (*debug) = 3;
+ S_TENSOR pred_tensor = ctx.get("y_pred:0"); // getting a reference to the output tensor
+ ctx.eval(); //trigger the inference
+
+ (*debug) = 4;
+ int pred_label = *(pred_tensor->read<int>(0, 0)); //getting the result back
+
+ *debug = pred_label;
+
+ return 0;
+}
diff --git a/DigitRecognition/deep_mlp.cpp b/DigitRecognition/deep_mlp.cpp
new file mode 100644
index 0000000..61a3763
--- /dev/null
+++ b/DigitRecognition/deep_mlp.cpp
@@ -0,0 +1,355 @@
+// Auto generated by utensor-cli
+
+#include "deep_mlp_weight.hpp"
+#include "../uTensor/ops/ArrayOps.hpp"
+#include "../uTensor/ops/MatrixOps.hpp"
+#include "deep_mlp.hpp"
+#include "../uTensor/ops/NnOps.hpp"
+#include "../uTensor/ops/MathOps.hpp"
+#include "../uTensor/core/tensor.hpp"
+#include "../uTensor/core/context.hpp"
+
+void get_deep_mlp_ctx(Context& ctx, Tensor* input_0) {
+
+
+{ // add tensor for placeholders
+ ctx.add(input_0, "x:0", 2);
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_MatMul_eightbit_x__port__0_reshape_dims_0),
+ "MatMul_eightbit/x__port__0/reshape_dims:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<float>(), "MatMul_eightbit/x__port__0/reshape:0", 2);
+ ctx.push(new ReshapeOp(),
+ { "x:0", "MatMul_eightbit/x__port__0/reshape_dims:0" },
+ { "MatMul_eightbit/x__port__0/reshape:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_MatMul_eightbit_x__port__0_reduction_dims_0),
+ "MatMul_eightbit/x__port__0/reduction_dims:0",
+ 2);
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "MatMul_eightbit/x__port__0/min:0", 1);
+ ctx.push(new MinOp(),
+ { "MatMul_eightbit/x__port__0/reshape:0", "MatMul_eightbit/x__port__0/reduction_dims:0" },
+ { "MatMul_eightbit/x__port__0/min:0" });
+ ctx.eval();
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "MatMul_eightbit/x__port__0/max:0", 1);
+ ctx.push(new MaxOp(),
+ { "MatMul_eightbit/x__port__0/reshape:0", "MatMul_eightbit/x__port__0/reduction_dims:0" },
+ { "MatMul_eightbit/x__port__0/max:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "MatMul_eightbit/x__port__0/quantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_eightbit/x__port__0/quantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_eightbit/x__port__0/quantize:2", 1);
+ ctx.push(new QuantizeV2Op(),
+ { "x:0", "MatMul_eightbit/x__port__0/min:0", "MatMul_eightbit/x__port__0/max:0" },
+ { "MatMul_eightbit/x__port__0/quantize:0", "MatMul_eightbit/x__port__0/quantize:1", "MatMul_eightbit/x__port__0/quantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<uint8_t>({784,32}, inline_Variable_quantized_const_0),
+ "Variable_quantized_const:0",
+ 1);
+}
+{
+ ctx.add(new BinaryTensor<float>({1}, inline_Variable_quantized_min_0),
+ "Variable_quantized_min:0",
+ 1);
+}
+{
+ ctx.add(new BinaryTensor<float>({1}, inline_Variable_quantized_max_0),
+ "Variable_quantized_max:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<int>(), "MatMul/eightbit:0", 2);
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit:1", 2);
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit:2", 2);
+ ctx.push(new QntMatMulOp<uint8_t, uint8_t, int>(),
+ { "MatMul_eightbit/x__port__0/quantize:0", "MatMul_eightbit/x__port__0/quantize:1", "MatMul_eightbit/x__port__0/quantize:2", "Variable_quantized_const:0", "Variable_quantized_min:0", "Variable_quantized_max:0" },
+ { "MatMul/eightbit:0", "MatMul/eightbit:1", "MatMul/eightbit:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit/requant_range:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit/requant_range:1", 1);
+ ctx.push(new Requantization_RangeOp(),
+ { "MatMul/eightbit:0", "MatMul/eightbit:1", "MatMul/eightbit:2" },
+ { "MatMul/eightbit/requant_range:0", "MatMul/eightbit/requant_range:1" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "MatMul/eightbit/requantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit/requantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul/eightbit/requantize:2", 1);
+ ctx.push(new RequantizeOp(),
+ { "MatMul/eightbit:0", "MatMul/eightbit:1", "MatMul/eightbit:2", "MatMul/eightbit/requant_range:0", "MatMul/eightbit/requant_range:1" },
+ { "MatMul/eightbit/requantize:0", "MatMul/eightbit/requantize:1", "MatMul/eightbit/requantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<float>({32}, inline_Variable_1_0),
+ "Variable_1:0",
+ 2);
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_zscore_eightbit_Variable_1__port__0_reshape_dims_0),
+ "zscore_eightbit/Variable_1__port__0/reshape_dims:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<float>(), "zscore_eightbit/Variable_1__port__0/reshape:0", 2);
+ ctx.push(new ReshapeOp(),
+ { "Variable_1:0", "zscore_eightbit/Variable_1__port__0/reshape_dims:0" },
+ { "zscore_eightbit/Variable_1__port__0/reshape:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_zscore_eightbit_Variable_1__port__0_reduction_dims_0),
+ "zscore_eightbit/Variable_1__port__0/reduction_dims:0",
+ 2);
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "zscore_eightbit/Variable_1__port__0/min:0", 1);
+ ctx.push(new MinOp(),
+ { "zscore_eightbit/Variable_1__port__0/reshape:0", "zscore_eightbit/Variable_1__port__0/reduction_dims:0" },
+ { "zscore_eightbit/Variable_1__port__0/min:0" });
+ ctx.eval();
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "zscore_eightbit/Variable_1__port__0/max:0", 1);
+ ctx.push(new MaxOp(),
+ { "zscore_eightbit/Variable_1__port__0/reshape:0", "zscore_eightbit/Variable_1__port__0/reduction_dims:0" },
+ { "zscore_eightbit/Variable_1__port__0/max:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "zscore_eightbit/Variable_1__port__0/quantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "zscore_eightbit/Variable_1__port__0/quantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "zscore_eightbit/Variable_1__port__0/quantize:2", 1);
+ ctx.push(new QuantizeV2Op(),
+ { "Variable_1:0", "zscore_eightbit/Variable_1__port__0/min:0", "zscore_eightbit/Variable_1__port__0/max:0" },
+ { "zscore_eightbit/Variable_1__port__0/quantize:0", "zscore_eightbit/Variable_1__port__0/quantize:1", "zscore_eightbit/Variable_1__port__0/quantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<int>(), "zscore/eightbit:0", 2);
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit:1", 2);
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit:2", 2);
+ ctx.push(new QuantizedAddOp<uint8_t, uint8_t, int>(),
+ { "MatMul/eightbit/requantize:0", "MatMul/eightbit/requantize:1", "MatMul/eightbit/requantize:2", "zscore_eightbit/Variable_1__port__0/quantize:0", "zscore_eightbit/Variable_1__port__0/quantize:1", "zscore_eightbit/Variable_1__port__0/quantize:2" },
+ { "zscore/eightbit:0", "zscore/eightbit:1", "zscore/eightbit:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit/requant_range:0", 1);
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit/requant_range:1", 1);
+ ctx.push(new Requantization_RangeOp(),
+ { "zscore/eightbit:0", "zscore/eightbit:1", "zscore/eightbit:2" },
+ { "zscore/eightbit/requant_range:0", "zscore/eightbit/requant_range:1" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "zscore/eightbit/requantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit/requantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "zscore/eightbit/requantize:2", 1);
+ ctx.push(new RequantizeOp(),
+ { "zscore/eightbit:0", "zscore/eightbit:1", "zscore/eightbit:2", "zscore/eightbit/requant_range:0", "zscore/eightbit/requant_range:1" },
+ { "zscore/eightbit/requantize:0", "zscore/eightbit/requantize:1", "zscore/eightbit/requantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "Relu/eightbit:0", 1);
+ ctx.add(new RamTensor<float>({1}), "Relu/eightbit:1", 1);
+ ctx.add(new RamTensor<float>({1}), "Relu/eightbit:2", 1);
+ ctx.push(new QuantizedReluOp<uint8_t, float, uint8_t>(),
+ { "zscore/eightbit/requantize:0", "zscore/eightbit/requantize:1", "zscore/eightbit/requantize:2" },
+ { "Relu/eightbit:0", "Relu/eightbit:1", "Relu/eightbit:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<float>({32,10}, inline_Variable_2_0),
+ "Variable_2:0",
+ 2);
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_MatMul_1_eightbit_Variable_2__port__0_reshape_dims_0),
+ "MatMul_1_eightbit/Variable_2__port__0/reshape_dims:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<float>(), "MatMul_1_eightbit/Variable_2__port__0/reshape:0", 2);
+ ctx.push(new ReshapeOp(),
+ { "Variable_2:0", "MatMul_1_eightbit/Variable_2__port__0/reshape_dims:0" },
+ { "MatMul_1_eightbit/Variable_2__port__0/reshape:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_MatMul_1_eightbit_Variable_2__port__0_reduction_dims_0),
+ "MatMul_1_eightbit/Variable_2__port__0/reduction_dims:0",
+ 2);
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "MatMul_1_eightbit/Variable_2__port__0/min:0", 1);
+ ctx.push(new MinOp(),
+ { "MatMul_1_eightbit/Variable_2__port__0/reshape:0", "MatMul_1_eightbit/Variable_2__port__0/reduction_dims:0" },
+ { "MatMul_1_eightbit/Variable_2__port__0/min:0" });
+ ctx.eval();
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "MatMul_1_eightbit/Variable_2__port__0/max:0", 1);
+ ctx.push(new MaxOp(),
+ { "MatMul_1_eightbit/Variable_2__port__0/reshape:0", "MatMul_1_eightbit/Variable_2__port__0/reduction_dims:0" },
+ { "MatMul_1_eightbit/Variable_2__port__0/max:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "MatMul_1_eightbit/Variable_2__port__0/quantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1_eightbit/Variable_2__port__0/quantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1_eightbit/Variable_2__port__0/quantize:2", 1);
+ ctx.push(new QuantizeV2Op(),
+ { "Variable_2:0", "MatMul_1_eightbit/Variable_2__port__0/min:0", "MatMul_1_eightbit/Variable_2__port__0/max:0" },
+ { "MatMul_1_eightbit/Variable_2__port__0/quantize:0", "MatMul_1_eightbit/Variable_2__port__0/quantize:1", "MatMul_1_eightbit/Variable_2__port__0/quantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<int>(), "MatMul_1/eightbit:0", 2);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit:1", 2);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit:2", 2);
+ ctx.push(new QntMatMulOp<uint8_t, uint8_t, int>(),
+ { "Relu/eightbit:0", "Relu/eightbit:1", "Relu/eightbit:2", "MatMul_1_eightbit/Variable_2__port__0/quantize:0", "MatMul_1_eightbit/Variable_2__port__0/quantize:1", "MatMul_1_eightbit/Variable_2__port__0/quantize:2" },
+ { "MatMul_1/eightbit:0", "MatMul_1/eightbit:1", "MatMul_1/eightbit:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit/requant_range:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit/requant_range:1", 1);
+ ctx.push(new Requantization_RangeOp(),
+ { "MatMul_1/eightbit:0", "MatMul_1/eightbit:1", "MatMul_1/eightbit:2" },
+ { "MatMul_1/eightbit/requant_range:0", "MatMul_1/eightbit/requant_range:1" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "MatMul_1/eightbit/requantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit/requantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "MatMul_1/eightbit/requantize:2", 1);
+ ctx.push(new RequantizeOp(),
+ { "MatMul_1/eightbit:0", "MatMul_1/eightbit:1", "MatMul_1/eightbit:2", "MatMul_1/eightbit/requant_range:0", "MatMul_1/eightbit/requant_range:1" },
+ { "MatMul_1/eightbit/requantize:0", "MatMul_1/eightbit/requantize:1", "MatMul_1/eightbit/requantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<float>({10}, inline_Variable_3_0),
+ "Variable_3:0",
+ 2);
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_logits_eightbit_Variable_3__port__0_reshape_dims_0),
+ "logits_eightbit/Variable_3__port__0/reshape_dims:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<float>(), "logits_eightbit/Variable_3__port__0/reshape:0", 2);
+ ctx.push(new ReshapeOp(),
+ { "Variable_3:0", "logits_eightbit/Variable_3__port__0/reshape_dims:0" },
+ { "logits_eightbit/Variable_3__port__0/reshape:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_logits_eightbit_Variable_3__port__0_reduction_dims_0),
+ "logits_eightbit/Variable_3__port__0/reduction_dims:0",
+ 2);
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "logits_eightbit/Variable_3__port__0/min:0", 1);
+ ctx.push(new MinOp(),
+ { "logits_eightbit/Variable_3__port__0/reshape:0", "logits_eightbit/Variable_3__port__0/reduction_dims:0" },
+ { "logits_eightbit/Variable_3__port__0/min:0" });
+ ctx.eval();
+}
+{
+ RamTensor<float>* out_tensor;
+ out_tensor = new RamTensor<float>({ 1 });
+ ctx.add(out_tensor, "logits_eightbit/Variable_3__port__0/max:0", 1);
+ ctx.push(new MaxOp(),
+ { "logits_eightbit/Variable_3__port__0/reshape:0", "logits_eightbit/Variable_3__port__0/reduction_dims:0" },
+ { "logits_eightbit/Variable_3__port__0/max:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "logits_eightbit/Variable_3__port__0/quantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "logits_eightbit/Variable_3__port__0/quantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "logits_eightbit/Variable_3__port__0/quantize:2", 1);
+ ctx.push(new QuantizeV2Op(),
+ { "Variable_3:0", "logits_eightbit/Variable_3__port__0/min:0", "logits_eightbit/Variable_3__port__0/max:0" },
+ { "logits_eightbit/Variable_3__port__0/quantize:0", "logits_eightbit/Variable_3__port__0/quantize:1", "logits_eightbit/Variable_3__port__0/quantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<int>(), "logits/eightbit:0", 2);
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit:1", 2);
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit:2", 2);
+ ctx.push(new QuantizedAddOp<uint8_t, uint8_t, int>(),
+ { "MatMul_1/eightbit/requantize:0", "MatMul_1/eightbit/requantize:1", "MatMul_1/eightbit/requantize:2", "logits_eightbit/Variable_3__port__0/quantize:0", "logits_eightbit/Variable_3__port__0/quantize:1", "logits_eightbit/Variable_3__port__0/quantize:2" },
+ { "logits/eightbit:0", "logits/eightbit:1", "logits/eightbit:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit/requant_range:0", 1);
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit/requant_range:1", 1);
+ ctx.push(new Requantization_RangeOp(),
+ { "logits/eightbit:0", "logits/eightbit:1", "logits/eightbit:2" },
+ { "logits/eightbit/requant_range:0", "logits/eightbit/requant_range:1" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<uint8_t>(), "logits/eightbit/requantize:0", 1);
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit/requantize:1", 1);
+ ctx.add(new RamTensor<float>({1}), "logits/eightbit/requantize:2", 1);
+ ctx.push(new RequantizeOp(),
+ { "logits/eightbit:0", "logits/eightbit:1", "logits/eightbit:2", "logits/eightbit/requant_range:0", "logits/eightbit/requant_range:1" },
+ { "logits/eightbit/requantize:0", "logits/eightbit/requantize:1", "logits/eightbit/requantize:2" });
+ ctx.eval();
+}
+{
+ ctx.add(new RamTensor<float>(), "logits:0", 1);
+ ctx.push(new DequantizeOp(),
+ { "logits/eightbit/requantize:0", "logits/eightbit/requantize:1", "logits/eightbit/requantize:2" },
+ { "logits:0" });
+ ctx.eval();
+}
+{
+ ctx.add(new BinaryTensor<int>({1}, inline_y_pred_dimension_0),
+ "y_pred/dimension:0",
+ 1);
+}
+{
+ ctx.add(new RamTensor<int>(), "y_pred:0");
+ ctx.push(new ArgMaxOp<float, int>(),
+ { "logits:0", "y_pred/dimension:0" },
+ { "y_pred:0" });
+}
+}
diff --git a/DigitRecognition/deep_mlp.hpp b/DigitRecognition/deep_mlp.hpp
new file mode 100644
index 0000000..7e0ec9d
--- /dev/null
+++ b/DigitRecognition/deep_mlp.hpp
@@ -0,0 +1,7 @@
+// Auto generated by utensor-cli
+
+#ifndef __MODELS_DEEP_MLP_H
+#define __MODELS_DEEP_MLP_H
+#include "../uTensor/core/context.hpp"
+void get_deep_mlp_ctx(Context& ctx, Tensor* input_0);
+#endif // __MODELS_DEEP_MLP_H
diff --git a/DigitRecognition/deep_mlp.pb b/DigitRecognition/deep_mlp.pb
new file mode 100644
index 0000000..f99487f
Binary files /dev/null and b/DigitRecognition/deep_mlp.pb differ
diff --git a/DigitRecognition/deep_mlp.py b/DigitRecognition/deep_mlp.py
new file mode 100644
index 0000000..cb50f85
--- /dev/null
+++ b/DigitRecognition/deep_mlp.py
@@ -0,0 +1,139 @@
+#!/usr/bin/python
+# -*- coding: utf8 -*-
+"""
+# This script is based on:
+# https://www.tensorflow.org/get_started/mnist/pros
+"""
+from __future__ import print_function
+import argparse
+import sys
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+from tensorflow.python.framework import graph_util as gu
+from tensorflow.tools.graph_transforms import TransformGraph
+
+FLAGS = None
+
+
+# helper functions
+def weight_variable(shape, name):
+ """weight_variable generates a weight variable of a given shape."""
+ initial = tf.truncated_normal(shape, stddev=0.1)
+ return tf.Variable(initial, name)
+
+
+def bias_variable(shape, name):
+ """bias_variable generates a bias variable of a given shape."""
+ initial = tf.constant(0.1, shape=shape)
+ return tf.Variable(initial, name)
+
+
+# Fully connected 2 layer NN
+def deepnn(x):
+ W_fc1 = weight_variable([784, 32], name='W_fc1')
+ b_fc1 = bias_variable([32], name='b_fc1')
+ a_fc1 = tf.add(tf.matmul(x, W_fc1), b_fc1, name="zscore")
+ h_fc1 = tf.nn.relu(a_fc1)
+ layer1 = tf.nn.dropout(h_fc1, 0.50)
+
+ W_fc2 = weight_variable([32, 10], name='W_fc3')
+ b_fc2 = bias_variable([10], name='b_fc3')
+ logits = tf.add(tf.matmul(layer1, W_fc2), b_fc2, name="logits")
+ y_pred = tf.argmax(logits, 1, name='y_pred')
+
+ return y_pred, logits
+
+
+def main(_):
+ # Import data
+ mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+
+ # Specify inputs, outputs, and a cost function
+ # placeholders
+ x = tf.placeholder(tf.float32, [None, 784], name="x")
+ y_ = tf.placeholder(tf.float32, [None, 10], name="y")
+
+ # Build the graph for the deep net
+ y_pred, logits = deepnn(x)
+
+ with tf.name_scope("Loss"):
+ cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_,
+ logits=logits)
+ loss = tf.reduce_mean(cross_entropy, name="cross_entropy_loss")
+ train_step = tf.train.AdamOptimizer(1e-4).minimize(loss, name="train_step")
+
+ with tf.name_scope("Prediction"):
+ correct_prediction = tf.equal(y_pred,
+ tf.argmax(y_, 1))
+ accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name="accuracy")
+
+ # Start training session
+ with tf.Session() as sess:
+ sess.run(tf.global_variables_initializer())
+ saver = tf.train.Saver()
+
+ # SGD
+ for i in range(1, FLAGS.num_iter + 1):
+ batch_images, batch_labels = mnist.train.next_batch(FLAGS.batch_size)
+ feed_dict = {x: batch_images, y_: batch_labels}
+ train_step.run(feed_dict=feed_dict)
+ if i % FLAGS.log_iter == 0:
+ train_accuracy = accuracy.eval(feed_dict=feed_dict)
+ print('step %d, training accuracy %g' % (i, train_accuracy))
+
+ print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images,
+ y_: mnist.test.labels}))
+ # Saving checkpoint and serialize the graph
+ ckpt_path = saver.save(sess, FLAGS.chkp)
+ print('saving checkpoint: %s' % ckpt_path)
+ out_nodes = [y_pred.op.name]
+ # Freeze graph and remove training nodes
+ sub_graph_def = gu.remove_training_nodes(sess.graph_def)
+ sub_graph_def = gu.convert_variables_to_constants(sess, sub_graph_def, out_nodes)
+ if FLAGS.no_quant:
+ graph_path = tf.train.write_graph(sub_graph_def,
+ FLAGS.output_dir,
+ FLAGS.pb_fname,
+ as_text=False)
+ else:
+ # # quantize the graph
+ # quant_graph_def = TransformGraph(sub_graph_def,
+ # [],
+ # out_nodes,
+ # ["quantize_weights", "quantize_nodes"])
+ graph_path = tf.train.write_graph(sub_graph_def,
+ FLAGS.output_dir,
+ FLAGS.pb_fname,
+ as_text=False)
+ print('written graph to: %s' % graph_path)
+ print('the output nodes: {!s}'.format(out_nodes))
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--data_dir', type=str,
+ default='mnist_data',
+ help='Directory for storing input data (default: %(default)s)')
+ parser.add_argument('--chkp', default='chkps/mnist_model',
+ help='session check point (default: %(default)s)')
+ parser.add_argument('-n', '--num-iteration', type=int,
+ dest='num_iter',
+ default=100000,
+ help='number of iterations (default: %(default)s)')
+ parser.add_argument('--batch-size', dest='batch_size',
+ default=150, type=int,
+ help='batch size (default: %(default)s)')
+ parser.add_argument('--log-every-iters', type=int,
+ dest='log_iter', default=1000,
+ help='logging the training accuracy per numbers of iteration %(default)s')
+ parser.add_argument('--output-dir', default='mnist_model',
+ dest='output_dir',
+ help='output directory directory (default: %(default)s)')
+ parser.add_argument('--no-quantization', action='store_true',
+ dest='no_quant',
+ help='save the output graph pb file without quantization')
+ parser.add_argument('-o', '--output', default='deep_mlp.pb',
+ dest='pb_fname',
+ help='output pb file (default: %(default)s)')
+ FLAGS, unparsed = parser.parse_known_args()
+ tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/DigitRecognition/deep_mlp_weight.hpp b/DigitRecognition/deep_mlp_weight.hpp
new file mode 100644
index 0000000..1491259
--- /dev/null
+++ b/DigitRecognition/deep_mlp_weight.hpp
@@ -0,0 +1,47 @@
+// Auto generated by utensor-cli
+
+#include <stdint.h>
+
+const int inline_MatMul_eightbit_x__port__0_reshape_dims_0 [ 1 ] = { -1, };
+#include <stdint.h>
+
+const int inline_MatMul_eightbit_x__port__0_reduction_dims_0 [ 1 ] = { 0, };
+#include <stdint.h>
+
+const uint8_t inline_Variable_quantized_const_0 [ 25088 ] = { 122, 139, 125, 121, 142, 136, 129, 113, 121, 140, 132, 112, 132, 119, 136, 133, 131, 132, 130, 133, 128, 124, 125, 135, 126, 130, 125, 138, 131, 128, 127, 127, 126, 121, 140, 121, 110, 123, 128, 121, 131, 139, 130, 142, 134, 138, 121, 122, 128, 130, 133, 121, 132, 118, 126, 124, 137, 125, 112, 135, 127, 124, 114, 125, 139, 129, 141, 116, 129, 134, 131, 129, 115, 121, 120, 120, 133, 131, 129, 111, 116, 123, 132, 127, 116, 129, 124, 134, 124, 142, 134, 133, 125, 120, 125, 138, 127, 129, 128, 120, 126, 121, 133, 125, 118, 119, 127, 126, 127, 138, 126, 131, 128, 111, 114, 124, 130, 138, 113, 129, 122, 126, 118, 131, 122, 132, 131, 129, 142, 129, 133, 140, 133, 138, 135, 133, 118, 129, 126, 129, 113, 127, 122, 126, 120, 135, 138, 126, 136, 139, 119, 137, 114, 137, 125, 124, 113, 127, 143, 130, 127, 140, 133, 130, 132, 120, 124, 122, 131, 120, 120, 127, 137, 132, 127, 123, 126, 136, 117, 135, 116, 127, 129, 111, 137, 122, 126, 127, 141, 138, 128, 122, 110, 127, 132, 128, 122, 141, 120, 139, 118, 131, 120, 120, 125, 129, 116, 111, 131, 134, 131, 117, 116, 127, 138, 129, 127, 127, 132, 117, 124, 131, 117, 119, 110, 121, 121, 126, 127, 127, 120, 131, 137, 130, 118, 138, 116, 124, 134, 126, 120, 124, 126, 134, 120, 118, 122, 123, 127, 131, 124, 128, 130, 138, 118, 135, 114, 132, 117, 117, 128, 121, 126, 122, 134, 132, 129, 128, 118, 137, 125, 122, 139, 134, 112, 117, 117, 120, 130, 118, 137, 124, 117, 124, 123, 123, 122, 122, 128, 136, 119, 122, 126, 136, 117, 133, 132, 132, 119, 125, 128, 135, 121, 117, 128, 132, 133, 133, 139, 121, 119, 137, 126, 128, 112, 126, 142, 110, 131, 119, 133, 133, 115, 132, 118, 131, 134, 131, 136, 124, 114, 134, 123, 123, 134, 140, 131, 119, 140, 142, 121, 130, 129, 126, 118, 133, 138, 135, 126, 144, 120, 135, 136, 132, 128, 124, 128, 127, 121, 131, 128, 134, 137, 128, 128, 120, 143, 127, 127, 128, 134, 128, 127, 136, 123, 128, 137, 122, 126, 137, 132, 123, 124, 128, 124, 136, 151, 137, 120, 132, 118, 117, 133, 132, 115, 118, 120, 116, 152, 136, 135, 139, 131, 138, 124, 122, 150, 142, 138, 123, 148, 119, 123, 131, 130, 126, 116, 142, 160, 126, 132, 125, 134, 115, 128, 116, 124, 143, 117, 117, 130, 133, 140, 145, 139, 140, 133, 128, 151, 137, 144, 138, 132, 125, 123, 131, 110, 126, 122, 128, 126, 124, 126, 138, 143, 120, 134, 138, 125, 138, 131, 120, 124, 135, 140, 125, 125, 115, 119, 124, 126, 111, 134, 124, 133, 119, 138, 143, 128, 126, 122, 136, 128, 126, 140, 131, 148, 116, 149, 144, 126, 131, 130, 122, 121, 112, 142, 113, 128, 126, 120, 134, 116, 120, 123, 129, 135, 121, 128, 141, 124, 136, 133, 116, 125, 132, 123, 131, 129, 133, 122, 135, 132, 130, 143, 134, 131, 116, 123, 135, 134, 119, 129, 128, 134, 120, 138, 126, 128, 122, 111, 125, 125, 142, 114, 143, 130, 136, 122, 128, 128, 124, 123, 126, 129, 116, 121, 122, 131, 137, 130, 114, 134, 120, 140, 127, 114, 143, 123, 130, 121, 125, 132, 116, 139, 141, 121, 122, 111, 128, 123, 125, 112, 119, 137, 134, 121, 141, 117, 132, 138, 135, 133, 126, 127, 120, 123, 121, 131, 120, 116, 141, 120, 123, 127, 128, 138, 126, 121, 124, 123, 122, 133, 130, 128, 120, 120, 123, 134, 133, 131, 127, 121, 135, 126, 138, 131, 127, 127, 135, 122, 133, 121, 131, 123, 122, 133, 121, 122, 115, 111, 139, 115, 126, 124, 117, 124, 118, 115, 139, 128, 116, 129, 134, 131, 136, 130, 129, 129, 127, 135, 121, 133, 139, 131, 119, 135, 125, 112, 139, 124, 139, 119, 137, 121, 129, 115, 110, 131, 124, 136, 116, 142, 131, 128, 125, 132, 132, 121, 116, 111, 121, 128, 127, 128, 142, 133, 126, 127, 132, 130, 125, 139, 112, 135, 125, 122, 123, 123, 123, 137, 128, 135, 133, 143, 127, 134, 119, 132, 135, 129, 140, 129, 129, 122, 126, 120, 131, 133, 134, 126, 132, 126, 113, 132, 129, 119, 136, 135, 127, 133, 119, 130, 139, 140, 114, 120, 127, 123, 126, 136, 136, 119, 122, 127, 132, 143, 125, 121, 142, 129, 130, 124, 127, 120, 137, 135, 138, 127, 134, 122, 120, 133, 125, 127, 131, 129, 117, 123, 136, 133, 113, 122, 119, 125, 127, 131, 141, 116, 124, 112, 125, 113, 118, 140, 117, 125, 133, 136, 133, 114, 122, 124, 117, 138, 115, 127, 114, 121, 125, 143, 120, 139, 136, 112, 119, 120, 127, 122, 141, 124, 124, 130, 110, 139, 122, 132, 139, 136, 137, 131, 133, 129, 128, 117, 142, 117, 139, 127, 129, 125, 136, 132, 120, 122, 129, 133, 127, 135, 137, 128, 127, 117, 125, 143, 128, 116, 125, 123, 138, 124, 125, 120, 135, 140, 125, 128, 115, 119, 132, 135, 132, 133, 113, 130, 116, 124, 115, 128, 116, 120, 120, 123, 124, 130, 138, 135, 123, 136, 129, 128, 138, 122, 133, 120, 136, 125, 123, 142, 137, 123, 127, 121, 129, 128, 127, 140, 130, 137, 128, 136, 129, 139, 117, 135, 121, 120, 129, 130, 125, 135, 130, 119, 136, 142, 128, 124, 123, 137, 129, 118, 137, 132, 115, 131, 132, 133, 122, 125, 134, 123, 129, 132, 131, 122, 128, 128, 129, 125, 133, 129, 141, 130, 133, 133, 125, 117, 136, 133, 130, 138, 131, 110, 132, 118, 120, 124, 138, 114, 127, 128, 133, 124, 118, 123, 116, 111, 123, 122, 124, 113, 121, 131, 124, 129, 121, 125, 133, 122, 133, 118, 121, 141, 126, 140, 121, 132, 125, 119, 122, 121, 132, 138, 142, 123, 122, 139, 115, 119, 136, 132, 123, 119, 141, 129, 110, 129, 115, 114, 121, 134, 136, 118, 115, 129, 145, 128, 137, 127, 129, 128, 125, 113, 121, 131, 129, 126, 124, 146, 125, 140, 131, 138, 124, 120, 125, 148, 135, 113, 133, 141, 139, 134, 110, 118, 136, 127, 142, 156, 151, 128, 128, 109, 130, 131, 124, 130, 135, 139, 121, 144, 111, 130, 135, 137, 133, 118, 126, 146, 154, 126, 128, 138, 112, 131, 123, 133, 132, 135, 152, 155, 163, 123, 102, 126, 108, 128, 129, 133, 136, 114, 127, 154, 118, 131, 142, 145, 139, 113, 125, 170, 151, 114, 119, 148, 118, 103, 129, 131, 107, 114, 173, 162, 174, 119, 102, 98, 110, 123, 126, 115, 140, 136, 133, 158, 137, 128, 137, 160, 172, 125, 132, 172, 155, 117, 123, 152, 126, 106, 125, 123, 109, 117, 158, 154, 147, 125, 104, 110, 109, 129, 136, 119, 147, 129, 112, 155, 134, 127, 123, 170, 153, 110, 115, 157, 160, 133, 135, 149, 143, 106, 125, 122, 102, 119, 156, 178, 146, 132, 118, 109, 117, 131, 139, 109, 127, 132, 124, 174, 116, 126, 164, 168, 154, 119, 117, 172, 152, 122, 138, 167, 137, 106, 122, 124, 108, 109, 158, 178, 176, 123, 105, 123, 123, 130, 117, 122, 134, 141, 112, 188, 117, 123, 164, 176, 172, 109, 112, 183, 180, 131, 135, 166, 120, 98, 125, 133, 123, 115, 196, 177, 176, 139, 109, 114, 115, 117, 131, 115, 169, 129, 114, 201, 125, 146, 191, 189, 136, 91, 100, 120, 108, 135, 127, 155, 130, 81, 111, 118, 115, 116, 196, 180, 180, 136, 111, 90, 128, 108, 149, 113, 191, 140, 127, 206, 120, 111, 184, 166, 117, 115, 114, 110, 98, 141, 130, 162, 132, 80, 144, 125, 111, 123, 161, 168, 184, 117, 113, 116, 112, 130, 149, 108, 175, 126, 117, 182, 133, 120, 192, 184, 126, 116, 107, 130, 105, 137, 128, 175, 143, 78, 133, 121, 115, 131, 130, 116, 127, 122, 131, 122, 127, 139, 136, 101, 166, 121, 126, 141, 143, 135, 152, 139, 122, 131, 126, 103, 116, 141, 133, 140, 138, 123, 115, 120, 139, 104, 182, 128, 134, 116, 119, 106, 116, 122, 149, 127, 153, 109, 103, 134, 123, 139, 134, 162, 123, 128, 119, 91, 89, 149, 135, 151, 139, 105, 105, 111, 126, 108, 178, 187, 168, 130, 122, 111, 113, 151, 141, 119, 156, 134, 130, 172, 118, 151, 97, 177, 97, 152, 105, 109, 87, 138, 125, 143, 126, 97, 110, 120, 124, 109, 191, 186, 177, 118, 119, 113, 115, 139, 146, 137, 142, 126, 132, 186, 112, 147, 118, 195, 117, 129, 93, 115, 95, 125, 140, 146, 111, 107, 133, 115, 127, 123, 179, 184, 183, 128, 117, 115, 109, 126, 133, 127, 148, 143, 131, 191, 122, 120, 167, 197, 178, 111, 112, 183, 185, 128, 135, 168, 121, 107, 128, 120, 128, 114, 162, 168, 158, 129, 126, 109, 114, 107, 136, 126, 163, 143, 123, 189, 111, 107, 182, 181, 193, 107, 125, 165, 178, 122, 129, 152, 134, 99, 120, 129, 117, 99, 174, 175, 177, 138, 112, 120, 128, 107, 134, 121, 155, 131, 104, 174, 106, 134, 185, 173, 169, 111, 133, 177, 174, 133, 124, 165, 121, 89, 125, 132, 128, 99, 163, 165, 148, 120, 129, 109, 123, 116, 140, 134, 137, 122, 100, 152, 108, 116, 149, 161, 148, 106, 138, 164, 165, 125, 138, 139, 126, 101, 117, 133, 118, 99, 135, 136, 160, 117, 123, 111, 109, 120, 134, 135, 147, 127, 125, 151, 95, 133, 150, 156, 150, 129, 113, 154, 143, 117, 133, 136, 115, 94, 130, 126, 136, 114, 143, 141, 142, 137, 125, 132, 116, 124, 123, 116, 135, 129, 121, 152, 115, 134, 143, 144, 159, 126, 110, 142, 141, 112, 119, 155, 114, 91, 141, 121, 133, 112, 128, 133, 140, 136, 130, 129, 113, 124, 116, 120, 110, 116, 134, 122, 124, 129, 137, 120, 125, 121, 122, 128, 129, 125, 132, 127, 123, 125, 118, 110, 123, 128, 126, 132, 127, 127, 124, 131, 132, 120, 140, 144, 117, 134, 139, 117, 120, 134, 142, 126, 132, 134, 126, 142, 131, 135, 118, 128, 138, 128, 128, 125, 128, 132, 139, 129, 117, 125, 126, 116, 125, 120, 126, 122, 113, 139, 125, 128, 143, 129, 132, 132, 130, 141, 118, 130, 125, 132, 126, 129, 126, 132, 120, 122, 131, 118, 123, 118, 122, 143, 122, 128, 112, 132, 132, 116, 132, 127, 122, 126, 119, 132, 121, 121, 128, 129, 126, 118, 135, 123, 128, 127, 131, 124, 126, 131, 129, 120, 130, 126, 128, 136, 134, 129, 128, 132, 127, 128, 121, 138, 117, 130, 128, 126, 120, 120, 120, 115, 116, 144, 139, 129, 131, 125, 116, 122, 123, 139, 115, 119, 116, 130, 128, 131, 118, 132, 134, 122, 142, 132, 136, 127, 122, 120, 132, 132, 140, 128, 139, 130, 137, 117, 135, 124, 130, 141, 121, 132, 142, 125, 132, 123, 144, 147, 128, 124, 111, 128, 129, 140, 141, 119, 120, 125, 123, 153, 132, 129, 140, 136, 145, 131, 125, 145, 142, 140, 121, 138, 128, 128, 117, 124, 124, 121, 135, 143, 158, 137, 140, 122, 137, 128, 144, 117, 145, 131, 114, 156, 106, 130, 139, 122, 156, 116, 127, 90, 104, 161, 130, 135, 124, 96, 118, 125, 122, 118, 143, 156, 157, 132, 123, 141, 118, 146, 149, 122, 148, 116, 120, 168, 118, 131, 155, 145, 153, 136, 137, 102, 113, 159, 147, 144, 132, 106, 129, 113, 132, 126, 145, 154, 168, 123, 135, 142, 125, 116, 127, 113, 147, 122, 109, 173, 121, 136, 152, 157, 142, 119, 125, 170, 176, 121, 124, 144, 121, 113, 128, 119, 132, 140, 179, 157, 189, 125, 109, 113, 121, 111, 129, 118, 140, 118, 123, 178, 139, 147, 158, 168, 149, 133, 130, 175, 177, 119, 125, 157, 137, 98, 120, 109, 107, 125, 187, 181, 189, 126, 96, 97, 116, 112, 156, 94, 165, 123, 105, 194, 119, 145, 152, 201, 141, 129, 131, 197, 178, 122, 144, 161, 130, 112, 131, 85, 97, 120, 215, 200, 198, 125, 89, 109, 94, 114, 134, 116, 144, 139, 96, 182, 106, 143, 112, 226, 144, 132, 118, 198, 183, 128, 121, 126, 103, 85, 128, 110, 110, 101, 214, 230, 159, 115, 116, 130, 88, 127, 120, 119, 122, 151, 97, 200, 93, 139, 95, 229, 125, 121, 136, 200, 162, 136, 109, 108, 120, 85, 106, 80, 130, 115, 198, 218, 186, 117, 78, 115, 84, 119, 122, 110, 132, 116, 93, 183, 97, 139, 137, 216, 146, 132, 132, 191, 166, 123, 121, 124, 110, 68, 116, 86, 110, 106, 197, 255, 184, 98, 99, 111, 86, 124, 117, 115, 122, 141, 75, 186, 87, 146, 146, 189, 124, 126, 113, 160, 148, 112, 107, 139, 109, 72, 128, 74, 115, 102, 204, 202, 179, 114, 109, 113, 77, 117, 136, 97, 110, 124, 66, 153, 86, 126, 171, 171, 147, 121, 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121, 158, 129, 118, 168, 194, 129, 114, 129, 110, 153, 171, 159, 123, 154, 167, 135, 164, 110, 136, 174, 138, 100, 124, 96, 158, 159, 123, 123, 169, 132, 114, 113, 180, 136, 119, 172, 186, 145, 129, 117, 102, 146, 164, 151, 125, 153, 164, 160, 149, 101, 135, 187, 146, 108, 118, 98, 174, 182, 132, 126, 169, 111, 125, 130, 178, 135, 103, 179, 169, 138, 127, 119, 101, 107, 186, 163, 118, 153, 190, 153, 169, 98, 115, 172, 124, 113, 142, 101, 179, 156, 140, 133, 153, 133, 126, 108, 143, 136, 87, 168, 173, 113, 118, 138, 135, 107, 174, 158, 134, 125, 184, 137, 158, 104, 116, 155, 137, 149, 134, 100, 152, 137, 131, 127, 133, 148, 121, 110, 121, 142, 136, 168, 184, 133, 111, 131, 116, 112, 170, 143, 123, 138, 180, 138, 174, 120, 129, 156, 144, 137, 144, 109, 160, 142, 129, 136, 145, 147, 120, 111, 139, 121, 136, 183, 222, 130, 118, 111, 96, 116, 180, 191, 117, 178, 233, 168, 196, 78, 128, 213, 135, 110, 121, 109, 201, 163, 111, 117, 168, 106, 132, 117, 144, 133, 112, 204, 230, 141, 119, 118, 119, 153, 192, 199, 111, 161, 207, 125, 171, 126, 110, 249, 149, 149, 92, 89, 218, 223, 146, 152, 193, 103, 134, 111, 189, 131, 116, 230, 222, 124, 112, 108, 141, 135, 176, 186, 120, 174, 197, 145, 163, 119, 112, 234, 144, 154, 107, 105, 196, 208, 149, 145, 181, 96, 133, 112, 196, 120, 135, 210, 223, 124, 114, 126, 116, 148, 175, 192, 118, 150, 187, 139, 165, 109, 119, 216, 140, 146, 129, 133, 181, 206, 121, 120, 168, 112, 121, 129, 210, 117, 103, 214, 213, 120, 127, 125, 88, 123, 157, 179, 120, 191, 208, 146, 192, 91, 123, 217, 144, 144, 126, 114, 192, 212, 123, 103, 185, 112, 122, 125, 195, 103, 93, 198, 187, 123, 127, 132, 101, 120, 156, 170, 130, 161, 184, 147, 158, 103, 103, 209, 132, 151, 129, 106, 158, 194, 128, 100, 181, 112, 121, 120, 172, 121, 112, 193, 167, 119, 123, 138, 83, 127, 154, 183, 134, 170, 182, 139, 167, 93, 126, 183, 142, 122, 131, 101, 159, 165, 145, 136, 165, 111, 133, 121, 168, 105, 96, 168, 151, 127, 107, 105, 116, 128, 148, 172, 109, 148, 143, 133, 162, 127, 150, 138, 108, 121, 127, 118, 132, 155, 159, 131, 157, 126, 135, 122, 145, 105, 100, 171, 146, 129, 132, 124, 126, 102, 87, 174, 132, 146, 129, 131, 142, 137, 109, 148, 107, 129, 132, 121, 108, 154, 154, 160, 141, 104, 135, 123, 144, 101, 138, 146, 163, 126, 139, 127, 113, 115, 97, 157, 120, 137, 117, 155, 141, 129, 112, 156, 136, 120, 128, 131, 103, 148, 152, 133, 154, 117, 129, 112, 148, 117, 150, 114, 141, 133, 138, 113, 121, 131, 116, 149, 137, 157, 153, 150, 126, 138, 104, 163, 121, 118, 121, 134, 116, 151, 135, 122, 161, 119, 126, 134, 148, 133, 132, 124, 134, 127, 134, 143, 124, 132, 118, 124, 138, 135, 126, 123, 136, 128, 128, 135, 133, 136, 121, 128, 121, 114, 116, 129, 120, 118, 129, 115, 129, 124, 131, 123, 134, 111, 136, 123, 121, 140, 125, 113, 128, 120, 126, 131, 126, 125, 139, 138, 120, 132, 128, 113, 137, 113, 133, 135, 130, 118, 125, 125, 133, 139, 118, 141, 131, 126, 125, 123, 133, 126, 120, 135, 121, 122, 135, 127, 129, 118, 117, 124, 127, 123, 126, 130, 132, 128, 122, 130, 128, 118, 114, 129, 139, 144, 133, 129, 110, 120, 127, 125, 116, 132, 131, 122, 134, 119, 123, 128, 120, 139, 134, 122, 137, 128, 133, 113, 116, 116, 126, 116, 125, 126, 118, 114, 127, 127, 123, 137, };
+#include <stdint.h>
+
+const float inline_Variable_quantized_min_0 [ 1 ] = { -1.4658213, };
+#include <stdint.h>
+
+const float inline_Variable_quantized_max_0 [ 1 ] = { 1.4827952, };
+#include <stdint.h>
+
+const float inline_Variable_1_0 [ 32 ] = { 0.5780225, -0.16384779, -0.4345745, -0.597377, -0.07290177, -0.054406535, 0.50055176, 0.17901272, 0.061275087, 0.7035889, 0.4821739, 0.71990424, 0.8626814, -0.33886588, 0.35639188, 0.18779205, 0.0122972, 0.31423524, 0.3264045, 0.43020993, -0.252126, 0.45105258, 0.19716854, 0.1755184, -0.6027231, -0.5300849, 0.0747934, 0.772581, 0.41220447, -0.035656765, -0.19516377, 0.23329337, };
+#include <stdint.h>
+
+const int inline_zscore_eightbit_Variable_1__port__0_reshape_dims_0 [ 1 ] = { -1, };
+#include <stdint.h>
+
+const int inline_zscore_eightbit_Variable_1__port__0_reduction_dims_0 [ 1 ] = { 0, };
+#include <stdint.h>
+
+const float inline_Variable_2_0 [ 320 ] = { 0.09988884, -1.1353989, -0.30359662, -0.56671315, 0.14865185, 0.07054141, -0.704112, 0.14634563, 0.038837895, 0.12827557, -0.64816165, 0.22390124, 0.2185188, -0.37734136, -0.4888368, -0.331484, 0.2816934, -0.16101772, 0.1772173, -1.536288, 0.090911604, -0.1976156, 0.12854636, 0.07009501, -0.6700738, 0.09005826, 0.1293878, -1.2707134, 0.106641725, -0.6959609, 0.2972734, -0.70892376, 0.07672069, -0.2427029, -0.5615418, 0.025082974, 0.30457148, -0.76232576, 0.21156852, 0.08285654, -1.5010211, 0.13247569, -1.0183421, 0.33674574, 0.104593806, 0.32479236, -0.8564378, -1.0494801, 0.20867094, 0.2168353, -0.8439878, 0.2557021, 0.264512, 0.25309193, 0.11172143, -0.76710767, -1.0080864, 0.111535, 0.1552014, -0.75241226, -1.405301, 0.24535958, 0.50746, 0.41373497, -1.1237661, -0.49714264, -1.193322, 0.46042937, -0.7896807, -1.5146972, -1.065499, -0.7908416, -0.36020097, 0.13737579, 0.1800311, 0.10458995, -1.2556252, 0.19812348, 0.11553663, 0.19312829, 0.19815284, -0.6666778, 0.38857913, 0.34162205, -0.32378697, -0.44522342, -1.1611727, 0.22871752, -0.29519376, -1.3071843, 0.5401799, 0.47966257, -0.7937667, -0.4779475, -0.57502764, 0.05691072, -0.37754378, 0.4417573, -0.53276825, -0.025525812, 0.22032534, -0.52696246, 0.006800288, 0.21865974, -1.3790156, 0.18081068, -1.1568668, 0.24767317, -0.40314373, 0.15539955, 0.4219837, -0.89859253, -0.27225167, -0.5556192, 0.39687112, 0.22608338, 0.25365105, 0.42373037, -0.696854, -0.5957061, 0.25737432, -0.29580608, 0.23977302, 0.17660421, -1.1174837, 0.15318531, -0.27639833, 0.14572532, -0.547503, -0.61896807, -0.85999936, 0.25975132, 0.010483696, 0.12721783, 0.24397285, -0.8340953, -1.1913066, -0.12690386, 0.1763387, 0.2419106, 0.24643797, -1.1318684, 0.2253065, -0.42647567, 0.23230341, -0.19417518, 0.2654986, -0.78949887, -0.3036996, 0.088061474, 0.08736932, -1.4195353, -0.11059197, -0.23896568, 0.14594136, 0.02509412, -0.69089675, 0.1775754, 0.11705143, 0.15467075, -1.1349207, 0.26591048, 0.3258521, 0.031622194, -1.1133165, 0.1874126, -0.15618077, -0.068399064, 0.22635035, -1.4113846, -1.0293227, 0.2676869, -0.6638573, -0.809697, 0.18838994, 0.2597897, 0.2623801, -0.71988785, 0.21033898, 0.06689028, 0.17471848, -0.60703725, 0.21527685, -0.11019864, 0.14076443, 0.15391845, 0.24682955, -0.8389134, 0.09058878, -0.8974278, -0.70003927, 0.48892072, 0.08466619, 0.37337586, 0.28916568, -0.5683443, 0.5530419, -0.029433832, -0.31237292, -1.0186286, -0.856211, 0.18168707, 0.24505684, 0.11122351, -1.0695484, -0.2974286, -0.9681376, 0.20844795, 0.1775028, -0.0049748956, -1.1505655, 0.34835112, -0.55756736, -0.023716582, -0.43805465, -0.5937699, -1.582205, 0.38626796, -0.31469446, 0.36865616, -0.5076476, -0.82651234, 0.31743962, -0.23834953, 0.29536465, -0.24033858, 0.30810896, -0.39118254, -0.68157536, 0.24319851, -0.54398525, -1.0657492, 0.25145695, -0.3252582, 0.27745238, -0.11717181, 0.3144103, -0.45244437, -0.763521, 0.22043528, 0.19075611, -0.5889457, 0.20152752, 0.19212854, -1.1147237, -0.20268336, -1.228045, 0.18554242, 0.13688831, 0.15359318, 0.24540433, -0.760812, 0.013307341, 0.16837452, -1.2520785, 0.16123922, -0.062990166, -0.5221762, 0.20173639, -0.00034856345, -0.9912086, -1.377334, -1.2250979, 0.403099, -1.2409686, 0.5818211, 0.48017198, -1.1191057, 0.03920581, -0.04006948, 0.29451925, 0.26080048, -0.113024294, 0.37046853, -1.0469614, 0.3173648, -0.59533584, -0.9986988, -0.70942324, -0.41003427, -0.91178864, -0.70455736, -0.36260656, -0.30002633, 0.29269257, -0.7386339, -0.7255059, 0.29854324, -1.1281329, 0.23479827, -1.6177104, -0.48460004, -0.8565176, 0.5298956, -0.8117683, 0.5291518, -1.2899235, -1.3870862, -0.7093207, 0.30185577, -1.7221317, -0.2104229, -0.6143567, 0.2010387, 0.2665378, 0.20631528, -0.84900254, -0.2459556, 0.1791449, 0.22730747, -0.64836246, 0.2167886, 0.21860307, 0.22896318, -0.32576513, -0.73935324, -1.8570166, 0.278421, -0.21250452, 0.22379157, };
+#include <stdint.h>
+
+const int inline_MatMul_1_eightbit_Variable_2__port__0_reshape_dims_0 [ 1 ] = { -1, };
+#include <stdint.h>
+
+const int inline_MatMul_1_eightbit_Variable_2__port__0_reduction_dims_0 [ 1 ] = { 0, };
+#include <stdint.h>
+
+const float inline_Variable_3_0 [ 10 ] = { 0.28487265, 1.0677086, -0.81945246, -0.8251177, 0.3759778, -0.023035817, 1.0077289, -0.10922931, 0.41795105, 0.02427876, };
+#include <stdint.h>
+
+const int inline_logits_eightbit_Variable_3__port__0_reshape_dims_0 [ 1 ] = { -1, };
+#include <stdint.h>
+
+const int inline_logits_eightbit_Variable_3__port__0_reduction_dims_0 [ 1 ] = { 0, };
+#include <stdint.h>
+
+const int inline_y_pred_dimension_0 [ 1 ] = { 1, };
diff --git a/DigitRecognition/input_data.h b/DigitRecognition/input_data.h
new file mode 100644
index 0000000..d6ece33
--- /dev/null
+++ b/DigitRecognition/input_data.h
@@ -0,0 +1,3 @@
+
+
+ const float input_data [ 784 ] = { 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.32941, 0.72549, 0.62353, 0.59216, 0.23529, 0.14118, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.87059, 0.99608, 0.99608, 0.99608, 0.99608, 0.94510, 0.77647, 0.77647, 0.77647, 0.77647, 0.77647, 0.77647, 0.77647, 0.77647, 0.66667, 0.20392, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.26275, 0.44706, 0.28235, 0.44706, 0.63922, 0.89020, 0.99608, 0.88235, 0.99608, 0.99608, 0.99608, 0.98039, 0.89804, 0.99608, 0.99608, 0.54902, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.06667, 0.25882, 0.05490, 0.26275, 0.26275, 0.26275, 0.23137, 0.08235, 0.92549, 0.99608, 0.41569, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.32549, 0.99216, 0.81961, 0.07059, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.08627, 0.91373, 1.00000, 0.32549, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.50588, 0.99608, 0.93333, 0.17255, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.23137, 0.97647, 0.99608, 0.24314, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.52157, 0.99608, 0.73333, 0.01961, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.03529, 0.80392, 0.97255, 0.22745, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.49412, 0.99608, 0.71373, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.29412, 0.98431, 0.94118, 0.22353, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.07451, 0.86667, 0.99608, 0.65098, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.01176, 0.79608, 0.99608, 0.85882, 0.13725, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.14902, 0.99608, 0.99608, 0.30196, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.12157, 0.87843, 0.99608, 0.45098, 0.00392, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.52157, 0.99608, 0.99608, 0.20392, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.23922, 0.94902, 0.99608, 0.99608, 0.20392, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.47451, 0.99608, 0.99608, 0.85882, 0.15686, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.47451, 0.99608, 0.81176, 0.07059, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000 };
diff --git a/uTensor/core/context.hpp b/uTensor/core/context.hpp
index 34c7116..240dd57 100644
--- a/uTensor/core/context.hpp
+++ b/uTensor/core/context.hpp
@@ -1,7 +1,7 @@
#ifndef UTENSOR_CTX_H
#define UTENSOR_CTX_H
-#include "uTensor/core/uTensorBase.hpp"
+#include "uTensorBase.hpp"
#include <memory>
#include <unordered_map>
#include <initializer_list>
diff --git a/uTensor/core/tensor.hpp b/uTensor/core/tensor.hpp
index 309eb69..542469b 100644
--- a/uTensor/core/tensor.hpp
+++ b/uTensor/core/tensor.hpp
@@ -1,7 +1,7 @@
#ifndef UTENSOR_TENSOR_H
#define UTENSOR_TENSOR_H
-#include "uTensor/util/uTensor_util.hpp"
+#include "../util/uTensor_util.hpp"
#include <initializer_list>
#include "utensor_string.hpp"
#include <memory>
diff --git a/uTensor/core/uTensorBase.hpp b/uTensor/core/uTensorBase.hpp
index c1b47a8..303269b 100644
--- a/uTensor/core/uTensorBase.hpp
+++ b/uTensor/core/uTensorBase.hpp
@@ -1,7 +1,7 @@
#ifndef UTENSOR_BASE_H
#define UTENSOR_BASE_H
-#include "uTensor/core/tensor.hpp"
+#include "tensor.hpp"
//isType() https://stackoverflow.com/questions/9974596/how-to-check-whether-two-pointers-point-to-the-same-object-or-not
//double dispatch
diff --git a/uTensor/ops/ArrayOps.hpp b/uTensor/ops/ArrayOps.hpp
index bfa4552..e5b002f 100644
--- a/uTensor/ops/ArrayOps.hpp
+++ b/uTensor/ops/ArrayOps.hpp
@@ -1,9 +1,9 @@
#ifndef UTENSOR_ARRAY_OPS
#define UTENSOR_ARRAY_OPS
-#include "uTensor/util/uTensor_util.hpp"
-#include "uTensor/util/quantization_utils.hpp"
-#include "uTensor/core/uTensorBase.hpp"
+#include "../util/uTensor_util.hpp"
+#include "../util/quantization_utils.hpp"
+#include "../core/uTensorBase.hpp"
#include <cstring>
#include <cmath>
#include <array>
diff --git a/uTensor/ops/MathOps.hpp b/uTensor/ops/MathOps.hpp
index b063139..72f1c21 100644
--- a/uTensor/ops/MathOps.hpp
+++ b/uTensor/ops/MathOps.hpp
@@ -1,9 +1,9 @@
#ifndef UTENSOR_MATH_OPS
#define UTENSOR_MATH_OPS
-#include "uTensor/util/quantization_utils.hpp"
-#include "uTensor/core/tensor.hpp"
-#include "uTensor/core/uTensorBase.hpp"
+#include "../util/quantization_utils.hpp"
+#include "../core/tensor.hpp"
+#include "../core/uTensorBase.hpp"
#include <climits>
#include <algorithm>
#include <vector>
diff --git a/uTensor/ops/MatrixOps.hpp b/uTensor/ops/MatrixOps.hpp
index a4b1576..0beef33 100644
--- a/uTensor/ops/MatrixOps.hpp
+++ b/uTensor/ops/MatrixOps.hpp
@@ -1,10 +1,10 @@
#ifndef UTENSOR_MATRIX_OPS
#define UTENSOR_MATRIX_OPS
-#include "uTensor/util/quantization_utils.hpp"
-#include "uTensor/core/tensor.hpp"
-#include "uTensor/core/uTensorBase.hpp"
-//#include "uTensor/ops/quantization.hpp" // Deprecated
+#include "../util/quantization_utils.hpp"
+#include "../core/tensor.hpp"
+#include "../core/uTensorBase.hpp"
+//#include "../ops/quantization.hpp" // Deprecated
#include <cmath>
#include <cstdlib>
#include <limits>
diff --git a/uTensor/ops/NnOps.hpp b/uTensor/ops/NnOps.hpp
index 0317d5e..5433640 100644
--- a/uTensor/ops/NnOps.hpp
+++ b/uTensor/ops/NnOps.hpp
@@ -1,9 +1,9 @@
#ifndef UTENSOR_NN_OPS
#define UTENSOR_NN_OPS
-#include "uTensor/util/quantization_utils.hpp"
-#include "uTensor/core/tensor.hpp"
-#include "uTensor/core/uTensorBase.hpp"
+#include "../util/quantization_utils.hpp"
+#include "../core/tensor.hpp"
+#include "../core/uTensorBase.hpp"
#include <math.h>
#include <algorithm>
diff --git a/uTensor/util/quantization_utils.hpp b/uTensor/util/quantization_utils.hpp
index 9e8592f..60f2b1f 100644
--- a/uTensor/util/quantization_utils.hpp
+++ b/uTensor/util/quantization_utils.hpp
@@ -1,7 +1,7 @@
#ifndef UTENSOR_QUANT_UTILS
#define UTENSOR_QUANT_UTILS
-#include "uTensor/core/tensor.hpp"
+#include "../core/tensor.hpp"
#include <math.h>
#include <limits>
#include <cstdlib>
diff --git a/uTensor/util/uTensor_util.cpp b/uTensor/util/uTensor_util.cpp
index 3127970..89ac0b5 100644
--- a/uTensor/util/uTensor_util.cpp
+++ b/uTensor/util/uTensor_util.cpp
@@ -1,8 +1,8 @@
#include "uTensor/util/uTensor_util.hpp"
-#ifndef TARGET_MBED
-#include <sys/stat.h>
-#include <dirent.h>
-#endif
+//#ifndef TARGET_MBED
+//#include <sys/stat.h>
+//#include <dirent.h>
+//#endif
#include <cstdlib>
void return_error(int ret_val) {
@@ -46,18 +46,18 @@ uint32_t ntoh32(uint32_t val) {
}
void init_env() {
- int status;
- DIR* dir = opendir("/fs/tmp");
- if (dir) {
- closedir(dir);
- return;
- } else {
+ //int status;
+ //DIR* dir = opendir("/fs/tmp");
+ //if (dir) {
+ // closedir(dir);
+ // return;
+ //} else {
- status = mkdir("/fs/tmp", 0777);
- if (status == -1) {
- ERR_EXIT("env setting failed");
- }
- }
+ // status = mkdir("/fs/tmp", 0777);
+ // if (status == -1) {
+ // ERR_EXIT("env setting failed");
+ // }
+ //}
}
void utensor_exit(void) {
diff --git a/uTensor/util/uTensor_util.hpp b/uTensor/util/uTensor_util.hpp
index 7c2713d..750239d 100644
--- a/uTensor/util/uTensor_util.hpp
+++ b/uTensor/util/uTensor_util.hpp
@@ -22,14 +22,11 @@ void return_error(int ret_val);
#define ON_ERR(FUNC, MSG) \
{ \
- printf(" * "); \
- printf(MSG); \
return_error(FUNC); \
}
#define DEBUG(MSG, ...) \
{ \
- printf(MSG, ##__VA_ARGS__); \
fflush(stdout); \
}
@@ -49,12 +46,9 @@ enum Padding {
void utensor_exit(void);
+
#define ERR_EXIT(MSG, ...) \
{ \
- printf("[Error] %s:%d @%s ", __FILE__, __LINE__, __func__); \
- printf(MSG, ##__VA_ARGS__); \
- fflush(stdout); \
- utensor_exit(); \
}
//typedef std::vector<uint32_t> Shape;
@@ -98,7 +92,7 @@ void printVector(std::vector<uint32_t> vec);
#elif defined(_POSIX_VERSION)
// POSIX
#else
- #include "mbed.h"
+ //#include "mbed.h"
//# error "Unknown compiler"
// little endian to big endian
//uint32_t htonl(uint32_t& val);
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