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+# tkDNN
+tkDNN is a Deep Neural Network library built with cuDNN primitives specifically thought to work on NVIDIA TK1 board.<br>
+The main scope is to do high performance inference on already trained models.
+Currently supports the following layers:
+
+* Dense, fully interconnected
+* Activation (RELU, ELU, SIGMOID, TANH)
+* Convolutional 2D
+* Convolutional 3D
+* Max and Average Pooling
+* Flatten 
+* Data preprocessing
+
+## Workflow
+The recommended workflow follow these step:
+* Build and train a model in Keras (on any PC)
+* Export weights and bias 
+* Define the model on tkDNN
+* Do inference (on TK1)
+
+## Compile the library
+Build with cmake
+```
+mkdir build
+cd build
+cmake ..
+make
+```
+
+## Test
+There is a ready to use example on *test* directory, to try it you must generate the weights with Keras
+```
+cd tests
+python test_model.py
+```
+And then execute the inference on build directory
+```
+cd build
+./tkDNNtest 
+```
+this should output the same prediction as Keras.
+
+## Simple example
+Here is a example of the entire workflow on a simple model.
+Using the following Keras model save it to a file
+```python
+model = Sequential()
+model.add(Reshape((20, 1), input_shape=(20)))
+model.add(Dense(256))
+model.compile()
+
+# save model
+model.save("path/to/model.h5")
+```
+
+After the model is created the weights can be exported for tkDNN inference
+```
+python weights_exporter model.h5 dense --output=weights/path
+```
+the exporter take as arguments, in order:
+* input model
+* layer type ["dense", "conv2d", conv3d"]
+* { layer type ["dense", "conv2d", conv3d"] for each layer to export }
+* optional argument --output define path where export weights   
+
+Then we can create a c++ program to do inference on tk1
+```c++
+#include<tkdnn.h>   //library include
+
+//Network object 
+tkDNN::Network net;
+//input dimension
+tkDNN::dataDim_t dim(1, 20, 1, 1, 1);
+//Dense layer
+tkDNN::Dense d0(&net, dim, 256, "weights/path", "bias/path");
+
+//here load the input data to CUDA
+//value_type is an alias of "float"
+value_type *data_d = [...] 
+
+//do inference
+value_type *output_d = d0.infer(dim, data_d);
+//dim will be updated with the output dimension
+``` 
+The result is finally stored on output_d in device memory.
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diff --git a/tests/simple_dense.py b/tests/test_model.py
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