diff --git a/Project2-Character-Recognition/README.md b/Project2-Character-Recognition/README.md
index 4503fac..1f59c38 100644
--- a/Project2-Character-Recognition/README.md
+++ b/Project2-Character-Recognition/README.md
@@ -1,14 +1,114 @@
-CUDA Character Recognition
-======================
+## Project 2 Part 2 - CUDA Character Recognition
+**University of Pennsylvania
+CIS 565: GPU Programming and Architecture**
 
-**University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2**
+* Author: Chhavi Sharma ([LinkedIn](https://www.linkedin.com/in/chhavi275/))
+* Tested on: Windows 10, Intel Core(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, 
+             NVIDIA Quadro P1000 4GB (MOORE100B-06)
 
-* (TODO) YOUR NAME HERE
-  * (TODO) [LinkedIn](), [personal website](), [twitter](), etc.
-* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)
+ <p align="center">
+  <img src="img/title.png">
+</p>
 
-### (TODO: Your README)
 
-Include analysis, etc. (Remember, this is public, so don't put
-anything here that you don't want to share with the world.)
+### Index
 
+- [Introduction](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#introduciton )
+- [Implementation Details](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#implementation-details)
+- [Training MLP to Predict a 2x2 XOR](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#training-mlp-to-predict-a-2x2-xor)
+- [Training MLP for Character Recognition](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#training-mlp-for-character-recognition)
+- [Observations and Comments](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#observations-and-comments)
+- [Extra Credit](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Character-Recognition/README.md#extra-credit)
+
+
+### Introduciton
+
+In this project, we implementa Multi Layer Perceptron using CUDA. 
+Training a neural network involves computing a sequence of operations that are highly parallelizable. 
+We implment computations at each stage of the forward and backward pass using CUDA kernels and compare performance. 
+
+A multilayer perceptron (MLP) is a class of feedforward artificial neural network. An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable such as an XOR function.
+
+After each a forward pass is run, an error in the difference between the actual output and the theoretical output due to each specific weight is calculated. This value is then multiplied by a negative lambda value to give the delta to be added to that specific weight to lessen the error in the overall system. Running this over multiple epochs allows us to find the right weights that describe the dataset classifiction.
+
+ ![](./img/MLP.png)
+
+### Implementation Details
+
+  ![](img/MLPmine.PNG)
+ 
+ We design our MLP network to train on batches of data. Therefore, the input dimension is NxD where N is the number of examples in a batch and D is the dimentionality of each example. After the hidden layer (NxH) is computed, the values are activated useing a Sigmoid activation function. Finally, the ouput layer, of dimension NxC where C is the numbe of classes, is fed into a softmax layer to compute probabilties over classes. We compute loss over these class probablity vectors using Multi Class Cross Entropy. 
+
+Forward Pass:
+```
+h = W1*X1
+X2 = sigmod(h)
+O = W2*X2
+Probab = Softmax(O)
+Loss = CrossEntropy(Probab)
+
+```
+Once the Loss is computed, the gradients with respect to W1 and W2 are computed using the chain rule and are reduced to simple matrix forms. See the code comments for more details about loss simplification.
+
+Backward Pass:
+```
+//Compute Derivatives Using Chain Rule
+dL/dw1 = dL/Pr * dPr/dSc * dSc/x2 * dx2/dh * dh/w1;
+dL/dw2 = dL/Pr * dPr/dSc * dSc/x2;
+
+//Update Weights 
+w1 = w1 - LearningRate*dw1;
+w2 = w2 - LearningRate*dw2;
+```
+
+### Training MLP to Predict a 2x2 XOR
+ 
+ <p align="center">
+  <img src="img/xor-table.png">
+</p>
+ 
+We train an MLP to predict output of a two bit XOR gate. Given 4 2-bit inputs and ground truth labels, we train the network as follows:
+ ```
+ Epochs: 1000
+ Learning Rate: 0.1
+ N = 4               //Number of examples per batch
+ D = 2 + 1(bias)     //Feature Dimension
+ H = 2               //Hidden Layer Nodes
+ C = 2               //Number of classes
+ ```
+ 
+ The network learns to classify XOR inputs over a 100 epochs correctly.
+ The loss per epoch reduces consistanctly
+ 
+ ![](img/XorStats.PNG)
+ 
+ ![](img/XorLoss.PNG)
+ 
+ The learned weights have been added to the repo at Project2-Number-Algorithms/Project2-Character-Recognition/build/
+.
+ 
+### Training MLP for Character Recognition
+ 
+We train an MLP to predict characters from images in this task. Given 52 images of characters ground truth labels, we train the network as follows:
+   ```
+   Epochs: 5000
+   Learning Rate: 0.1
+   N = 52               //Number of examples per batch
+   D = 10201            //Feature Dimension (101 x 101)
+   H = 10               //Hidden Layer Nodes
+   C = 52               //Number of classes
+  ```
+ 
+ ![](img/CharRecStats.PNG)
+ 
+ ![](img/CharRecLoss.PNG)
+  The training is stable as the loss reduces consistanly at every uweight update.
+  The learned weights have been added to the repo Project2-Number-Algorithms/Project2-Character-Recognition/build/
+.
+
+### Observations and Comments
+- The MLP describled above is able to learn XOR, a non-linearly seperable function, only when a bias term is added to the input.
+- In case of character recognition, we only have one image sample per class of characters, therefore the networks learns to memorise the data mapping to the classes and would not generalise well. In order to build a robust character recongition network, an ideal dataset of charcters per class would need to have enough variation in font intentsity. 
+
+### Extra Credit
+We implemented batch processing of data instead of running one exampe at a time. This required simplifying gradinets at the matrix level. These have been laid out in detail in the code. 
diff --git a/Project2-Character-Recognition/build/cr_W1_DxH_10201_x_10.txt b/Project2-Character-Recognition/build/cr_W1_DxH_10201_x_10.txt
new file mode 100644
index 0000000..9d28e48
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diff --git a/Project2-Character-Recognition/build/cr_W2_HxC_10_x_52.txt b/Project2-Character-Recognition/build/cr_W2_HxC_10_x_52.txt
new file mode 100644
index 0000000..44b3708
--- /dev/null
+++ b/Project2-Character-Recognition/build/cr_W2_HxC_10_x_52.txt
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diff --git a/Project2-Character-Recognition/build/cr_losses.txt b/Project2-Character-Recognition/build/cr_losses.txt
new file mode 100644
index 0000000..15e73c2
--- /dev/null
+++ b/Project2-Character-Recognition/build/cr_losses.txt
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diff --git a/Project2-Character-Recognition/build/xor_W1_DxH_2_x_4.txt b/Project2-Character-Recognition/build/xor_W1_DxH_2_x_4.txt
new file mode 100644
index 0000000..8cf7cf2
--- /dev/null
+++ b/Project2-Character-Recognition/build/xor_W1_DxH_2_x_4.txt
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diff --git a/Project2-Character-Recognition/build/xor_W2_HxC_4_x_2.txt b/Project2-Character-Recognition/build/xor_W2_HxC_4_x_2.txt
new file mode 100644
index 0000000..707b05d
--- /dev/null
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diff --git a/Project2-Character-Recognition/build/xor_losses.txt b/Project2-Character-Recognition/build/xor_losses.txt
new file mode 100644
index 0000000..62c1c96
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diff --git a/Project2-Character-Recognition/character_recognition/CMakeLists.txt b/Project2-Character-Recognition/character_recognition/CMakeLists.txt
index 7446175..9e834c1 100644
--- a/Project2-Character-Recognition/character_recognition/CMakeLists.txt
+++ b/Project2-Character-Recognition/character_recognition/CMakeLists.txt
@@ -7,5 +7,5 @@ set(SOURCE_FILES
 
 cuda_add_library(character_recognition
     ${SOURCE_FILES}
-    OPTIONS -arch=sm_20
+    OPTIONS -arch=sm_61
     )
diff --git a/Project2-Character-Recognition/character_recognition/common.cu b/Project2-Character-Recognition/character_recognition/common.cu
index 2a754d4..b15a87a 100644
--- a/Project2-Character-Recognition/character_recognition/common.cu
+++ b/Project2-Character-Recognition/character_recognition/common.cu
@@ -5,11 +5,13 @@ void checkCUDAErrorFn(const char *msg, const char *file, int line) {
     if (cudaSuccess == err) {
         return;
     }
-
-    fprintf(stderr, "CUDA error");
-    if (file) {
+	fprintf(stderr, "CUDA error");
+    
+	if (file) {
         fprintf(stderr, " (%s:%d)", file, line);
     }
-    fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err));
+    
+	fprintf(stderr, ": %s: %s\n", msg, cudaGetErrorString(err));
     exit(EXIT_FAILURE);
 }
+
diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu b/Project2-Character-Recognition/character_recognition/mlp.cu
index 5a3ed7f..486418c 100644
--- a/Project2-Character-Recognition/character_recognition/mlp.cu
+++ b/Project2-Character-Recognition/character_recognition/mlp.cu
@@ -3,25 +3,582 @@
 #include "common.h"
 #include "mlp.h"
 
+#include <curand.h>
+#include <curand_kernel.h>
+
+#define blockSize 128
+#define blockWidth 16
+
+
 namespace CharacterRecognition {
-    using Common::PerformanceTimer;
-    PerformanceTimer& timer()
-    {
-        static PerformanceTimer timer;
-        return timer;
-    }
-        
-    // TODO: __global__
-
-    /**
-        * Example of use case (follow how you did it in stream compaction)
-        */
-    /*void scan(int n, int *odata, const int *idata) {
-        timer().startGpuTimer();
-        // TODO
-        timer().endGpuTimer();
-    }
-    */
-
-	// TODO: implement required elements for MLP sections 1 and 2 here
+	using Common::PerformanceTimer;
+	PerformanceTimer& timer()
+	{
+		static PerformanceTimer timer;
+		return timer;
+	}
+
+	//=====Initlialiations=======
+
+	//layers
+	double *dev_iLayer;
+	double *dev_hLayer;
+	double *dev_oLayer;
+
+	double *dev_losses;
+	double *dev_LossAvg;
+
+	// gtruth and preds
+	int *dev_gtruth;
+	int *dev_preds;
+	double * dev_preds_probab;
+
+	//weights
+	double *dev_w_kj;
+	double *dev_w_ji;
+
+	//Derivatives
+	double *dev_dL_dw_ji;
+	double *dev_dL_dw_kj;
+	double *dev_dL_dscores;
+	double *dev_dL_dscores_2;
+
+	double *dev_hLayer_T;
+	double *dev_iLayer_T;
+	double *dev_w_ji_T;
+
+
+	//=============================================
+	// Rnadom Number Generation using cuRand on GPU
+	//=============================================
+	curandState *devState;
+
+	__global__ void kernInitCurand(curandState *state, int N, unsigned long seed) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N) {
+			curand_init(seed, tid, 0, &state[tid]);
+		}
+	}
+
+	__global__ void KernGenRand(curandState *state, int N, double *w) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N) {
+			w[tid] = (2.0*curand_uniform(&state[tid]) - 1.0); // Between -1 and 1
+		}
+	}
+
+	//===================================================================
+	//=====KERNEL DEFNITIONS FOR Forward and Backward====================
+	//===================================================================
+
+
+	void printArray(int n, int *a, bool abridged = false) {
+		printf("    [ ");
+		for (int i = 0; i < n; i++) {
+			if (abridged && i + 2 == 15 && n > 16) {
+				i = n - 2;
+				printf("... ");
+			}
+			printf("%3d ", a[i]);
+		}
+		printf("]\n");
+	}
+	void printFloatArray(int n, double *a, bool abridged = false) {
+		printf("    [ ");
+		for (int i = 0; i < n; i++) {
+			if (abridged && i + 2 == 15 && n > 16) {
+				i = n - 2;
+				printf("... ");
+			}
+			printf("%0.2f ", a[i]);
+		}
+		printf("]\n");
+	}
+
+
+	// Kernel for Gradient update on Weights
+	__global__ void kernUpdateWeights(int N, double *dev_dw, double *dev_w, double LR) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_w[tid] = dev_w[tid] - (LR * dev_dw[tid]);
+		}
+	}
+
+	// Kernel for derivative of sigmoid
+	__global__ void kernGradSigmoid(int N, int H, double *dev_hLayer) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N*H) {
+			dev_hLayer[tid] = dev_hLayer[tid] * (1 - dev_hLayer[tid]);
+		}
+	}
+
+	// Matrix Transpose
+	__global__ void kernMatrixTranspose(int rows, int cols, double *matrix, double *matrix_T) {
+
+		int idy = blockIdx.y * blockDim.y + threadIdx.y;
+		int idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (idx < cols && idy < rows) {
+			int pos = idy * cols + idx;
+			int tpos = idx * rows + idy;
+
+			matrix_T[tpos] = matrix[pos];
+		}
+	}
+
+	// Divide by N
+	__global__ void kernDivNdscores(int N, int C, double *dev_dL_dscores) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N*C) {
+			dev_dL_dscores[tid] /= N;
+		}
+	}
+
+	// Compute dscores gradient
+	__global__ void kernSetdscores(int N, int C, double *dev_dL_dscores, int *dev_gtruth) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_dL_dscores[tid*C + dev_gtruth[tid]] -= 1;
+		}
+	}
+
+	// compute predictions
+	__global__ void kernPredsN(int N, int C, double* dev_oLayer, int* dev_gtruth, int* dev_preds, double * dev_preds_probab) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_preds[tid] = dev_oLayer[tid*C + dev_gtruth[tid]] > 0.5 ? dev_gtruth[tid] : (dev_gtruth[tid] == 0 ? 1 : 0);
+			dev_preds_probab[tid] = dev_oLayer[tid*C + dev_gtruth[tid]];
+		}
+	}
+
+	// compute loss per example
+	__global__ void kernLossPerN(int N, int C, double* dev_oLayer, int* dev_gtruth, double* dev_losses) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N) {
+			dev_losses[tid] = -log(dev_oLayer[tid*C + dev_gtruth[tid]]);
+		}
+	}
+
+	// kernel to compute exp softmax
+	__global__ void kernSoftmax(int N, int C, double* scores) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			double sums = 0.0;
+
+			for (int i = 0; i < C; i++) {
+				sums += exp(scores[tid*C + i]);
+			}
+
+			for (int i = 0; i < C; i++) {
+				scores[tid*C + i] = exp(scores[tid*C + i]) / sums;
+			}
+		}
+	}
+
+	// kern for sigmoid // f(x) = 1/(1 + e^-x).
+	__global__ void kernSigmoid(int N, double *idata) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			idata[tid] = 1.0 / (1.0 + exp(-1*idata[tid]));
+		}
+	}
+
+	// kern for element wise product 
+	__global__ void kernElementProduct(int N, double *matrixA, double* matrixB) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			matrixA[tid] = matrixA[tid] * matrixB[tid];
+		}
+	}
+
+
+	// kernel to to matmul // A mxn // B nxk // C mxk
+	__global__ void kernMatrixMultiply(const double *dev_A, const double *dev_B, double *dev_C, int m, int n, int k) {
+
+		int row = blockIdx.y * blockDim.y + threadIdx.y;
+		int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+		double sum = 0;
+		if (col < k && row < m)
+		{
+			for (int i = 0; i < n; i++)
+				sum += dev_A[row * n + i] * dev_B[i * k + col];
+			dev_C[row * k + col] = sum;
+		}
+	}
+
+	// Dumb reduction
+	__global__ void kernReduction(int N, double *dev_losses, double *dev_LossAvg) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+		double sum = 0.0;
+		if (tid == 0) {
+			for (int i = 0; i < N; i++) {
+				sum += dev_losses[i];
+			}
+			dev_LossAvg[0] = sum / N;
+		}
+
+	}
+
+	// Ele wise addition A = A+B
+	__global__ void kernAddition(int N, double *dev_A, double *dev_B) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			dev_A[tid] += dev_B[tid];
+		}
+
+	}
+
+	void trainMLP(int N, int D, int H, int C, double *idata, int *preds, int *gtruth, int epochs, 
+		          double *lossAvgPerEpoch, const double LR, double *w1, double *w2, unsigned long seed) {
+
+		timer().startGpuTimer();
+
+		// N = number of examples
+		// D = dim of each example 
+		// H = Hidden state nodes
+		// C = number of classes
+
+		// NETWORK DEFITION_____________
+		// Compute f1		= W1*X1
+		// Compute X2		= Sig(f1)
+		// Compute Scroes S = W2*X2
+		// Compute Probab P = Softmax(S)
+		// Compute Loss   L = CEntropy(P)
+
+		//================================================================
+		//======================INITIALIZATIONS===========================
+		//================================================================
+
+		// Allocate input layer
+		cudaMalloc((void**)&dev_iLayer, N*D * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_iLayer failed!");
+
+		cudaMemcpy(dev_iLayer, idata, N*D * sizeof(double), cudaMemcpyHostToDevice);
+		checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_iLayer failed!");
+
+
+		// Allocate hidden layer
+		cudaMalloc((void**)&dev_hLayer, N*H* sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer failed!");
+
+
+		// Allocate output layer
+		cudaMalloc((void**)&dev_oLayer, N*C* sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_oLayer failed!");
+
+
+		// Allocate losses holder
+		cudaMalloc((void**)&dev_losses, N * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_losses failed!");
+
+		cudaMalloc((void**)&dev_LossAvg, 1*sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_LossAvg failed!");
+
+
+		// Allocate gtruth and preds
+		cudaMalloc((void**)&dev_gtruth, N * sizeof(int));
+		checkCUDAErrorFn("cudaMalloc dev_gtruth failed!");
+
+		cudaMemcpy(dev_gtruth, gtruth, N * sizeof(int), cudaMemcpyHostToDevice);
+		checkCUDAErrorFn("cudaMemcpyToSymbol from gtruth to dev_gtruth failed!");
+
+		cudaMalloc((void**)&dev_preds, N * sizeof(int));
+		checkCUDAErrorFn("cudaMalloc dev_preds failed!");
+
+		cudaMalloc((void**)&dev_preds_probab, N * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_preds_probab failed!");
+
+		// Allocate Weights
+		cudaMalloc((void**)&dev_w_kj, D*H * sizeof(double)); //w1
+		checkCUDAErrorFn("cudaMalloc dev_w_kj failed!");
+
+		cudaMalloc((void**)&dev_w_ji, C*H * sizeof(double)); //w2
+		checkCUDAErrorFn("cudaMalloc dev_w_ji failed!");
+
+
+		// Allocate Derivatives
+		cudaMalloc((void**)&dev_dL_dw_kj, D*H * sizeof(double)); //dw1
+		checkCUDAErrorFn("cudaMalloc dev_w_kj failed!");
+
+		cudaMalloc((void**)&dev_dL_dw_ji, C*H * sizeof(double)); //dw2
+		checkCUDAErrorFn("cudaMalloc dev_w_ji failed!");
+
+		cudaMalloc((void**)&dev_dL_dscores, N*C * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_dL_dscores failed!");
+
+		cudaMalloc((void**)&dev_dL_dscores_2, N*C * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_dL_dscores_2 failed!");
+
+
+		// Allocate transposes
+		cudaMalloc((void**)&dev_hLayer_T, N*H * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_iLayer_T, N*D * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_w_ji_T, C*H * sizeof(double));
+		checkCUDAErrorFn("cudaMalloc dev_w_ji_T failed!");
+
+		//==============================
+		// Initialise Weights
+		//==============================
+		cudaMalloc((void**)&devState, H*D * sizeof(curandState));
+
+		kernInitCurand << <((D*H + blockSize - 1) / blockSize), blockSize >> > (devState, D*H, seed);
+		checkCUDAErrorFn("KernInitCurand failed!");
+		KernGenRand << <((D*H + blockSize - 1) / blockSize), blockSize >> > (devState, D*H, dev_w_kj);//w1
+		checkCUDAErrorFn("KernGenRand dev_w_kj failed!");
+
+		kernInitCurand << <((H*C + blockSize - 1) / blockSize), blockSize >> > (devState, H*C, seed); 
+		checkCUDAErrorFn("KernInitCurand failed!");
+		KernGenRand << <((H*C + blockSize - 1) / blockSize), blockSize >> > (devState, H*C, dev_w_ji);//w2
+		checkCUDAErrorFn("KernGenRand dev_w_kj failed!");
+
+
+		//================================================================
+		//======================TRAINING LOOP=============================
+		//================================================================
+		double *tmp = new double[N*D];
+		double *tmp2 = new double[N*D];
+		double *lossesN = new double[N];
+		printf("--------------------------------------------\n");
+		printf("One Hidden Layer MLP | Configuration \n");
+		printf("--------------------------------------------\n");
+		printf("Number of Examples            | N = %d \n",N);
+		printf("Dimensionality of each Example| D = %d \n",D);
+		printf("Number of Hidden Layer Nodes  | H = %d \n",H);
+		printf("Total Number of Classes       | C = %d \n",C);
+		printf("Activation    = Sigmoid \n");
+		printf("Loss Function = Cross Entropy \n");
+		printf("--------------------------------------------\n");
+
+		//printf("\nInput DATA  ");
+		//printf("\nInput DATA  ");
+		//printFloatArray(N*D, idata, true);
+		dim3 dimBlock(blockWidth, blockWidth);
+		dim3 dimGrid;
+
+		for (int i = 0; i < epochs; i++) {
+
+			//================================================================
+			//========================= FORWARD ==============================
+
+			// STEP 1
+			// f1 = W1*X1 (Matrix Mul)
+			//=================================
+			// dev_hLayer = dev_iLayer*dev_w_kj 
+			//   NxH      =    NxD         DxH 
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_iLayer, dev_w_kj, dev_hLayer, N, D, H);
+
+			// STEP 2
+			// X2         = Sigmoid(f1) 
+			// dev_hLayer = sigmoid(dev_hLayer)
+			//   NxH     =    NxH 
+			kernSigmoid << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_hLayer);
+
+
+			// STEP 3
+			// Scores S = W2*X2 (Matrix Mul)
+			// dev_oLayer = dev_hLayer*dev_w_ji 
+			//   NxC      =    NxH         HxC
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_hLayer, dev_w_ji, dev_oLayer, N, H, C);
+			checkCUDAErrorFn("kernMatrixMultiply failed!");
+
+
+			// STEP 4
+			// P = Softmax(S) 
+			// dev_smaxDen = Sum_Over_classses(dev_olayer)
+			// dev_olayer = dev_olayer/Sum_Over_classses
+			//   NxC      =    NxC         1
+			kernSoftmax << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_oLayer);
+			checkCUDAErrorFn("kernSoftmax failed!");
+
+			// STEP 5
+			// Compute Losses | Cross Entropy Loss
+			kernLossPerN << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_oLayer, dev_gtruth, dev_losses);
+			checkCUDAErrorFn("kernLossPerN  failed!");
+
+			// Cpoy loss to CPU
+			//cudaMemcpy(lossesN, dev_losses, N * sizeof(double), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_losses to lossesN failed!");
+			//printf("Post dev_losses [Loss = CEntropy(P)]\n");
+			//printFloatArray(N, lossesN, true);
+
+
+			// Compute Predictions
+			kernPredsN << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_oLayer, dev_gtruth, dev_preds, dev_preds_probab);
+			cudaMemcpy(preds, dev_preds, N * sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyDeviceToHost from dev_preds to preds failed!");
+			cudaMemcpy(tmp2, dev_preds_probab, N * sizeof(double), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyDeviceToHost from dev_preds_probab to tmp failed!");
+
+
+			// STEP 5.2
+			// Compute Avg of Losses
+			kernReduction << <((N + blockSize - 1) / blockSize), blockSize >> > (N, dev_losses, dev_LossAvg);
+			// Copy back to cpu
+			cudaMemcpy(lossAvgPerEpoch + i, dev_LossAvg, sizeof(double), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_LossAvg to tmp failed!");
+			
+			if (i % 1000 == 0) {
+				printf("Epoch : %3d | LossAvg %3f \n", i, lossAvgPerEpoch[i]);
+				printf("GroundTruth :");
+				printArray(N, gtruth, true);
+				printf("Predictions :");
+				printArray(N, preds, true);
+				printf("Confidence  :");
+				printFloatArray(N, tmp2, true);
+				printf("\n");
+			}
+
+			//=================================================================
+			//========================= BACKPROP ==============================
+
+			//===============================
+			// STEP 1 : Gradient wrt w_kj W2
+			//===============================
+			// dW_ji = Probs_k - [1](gth == k) dev_dL_dscores;
+
+			cudaMemcpy(dev_dL_dscores, dev_oLayer, N*C * sizeof(double), cudaMemcpyDeviceToDevice);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from probabs to dev_dL_dscores failed!");
+
+			kernSetdscores << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_dL_dscores, dev_gtruth);
+			checkCUDAErrorFn("kernSetdscores failed!");
+
+			kernDivNdscores << <((N*C + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_dL_dscores);
+			checkCUDAErrorFn("kernDivNdscores failed!");
+
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (N, H, dev_hLayer, dev_hLayer_T);
+
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (H + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_hLayer_T, dev_dL_dscores, dev_dL_dw_ji, H, N, C);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dL_dw_ji failed!");
+
+			//===============================
+			// STEP 2 : Gradient wrt w_kj W1
+			//===============================
+
+			// Transpose Wji (W2)
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (H + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (H, C, dev_w_ji, dev_w_ji_T);
+
+			// Transpose Input Data
+			dimGrid.x = (D + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (N, D, dev_iLayer, dev_iLayer_T);
+
+			// Mul dev_dL_dscores * dev_w_kj_T == dev_dL_dscores_2
+			//             NxC          CxH             NxH
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_dL_dscores, dev_w_ji_T, dev_dL_dscores_2, N, C, H);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dL_dscores_2 failed!");
+
+			// compute sig gradient on dev_hlayer N*H [IN PLACE]
+			kernGradSigmoid << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N, H, dev_hLayer);
+			checkCUDAErrorFn("kernGradSigmoid failed!");
+
+			//Element wise mul dev_dL_dscores_2 [INPLACE] = dev_dL_dscores_2 . dev_hlayer[sig gradient] 
+			kernElementProduct << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_dL_dscores_2, dev_hLayer);
+			checkCUDAErrorFn("kernElementProduct failed!");
+
+			// matrix Mul final with Xi_T
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (D + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_iLayer_T, dev_dL_dscores_2, dev_dL_dw_kj, D, N, H);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dL_dw_kj failed!");
+
+
+			//=================================================================
+			// STEP 3 : Update Weights ========================================
+			//=================================================================
+
+			// Update weights kj W1
+			kernUpdateWeights << <((D*H + blockSize - 1) / blockSize), blockSize >> > (D*H, dev_dL_dw_kj, dev_w_kj, LR);
+			checkCUDAErrorFn("kernUpdateWeights dev_w_kj failed!");
+
+			// InitUpdate weights ji W2
+			kernUpdateWeights << <((H*C + blockSize - 1) / blockSize), blockSize >> > (H*C, dev_dL_dw_ji, dev_w_ji, LR);
+			checkCUDAErrorFn("kernUpdateWeights dev_w_ji failed!");
+
+			//printf("\n-----------------------------------------------------\n\n");
+		}
+
+
+		printf("Finished training.\n");
+		float count = 0.0;
+		for (int n = 0; n < N; n++) {
+			if (preds[n] == gtruth[n]) {
+				count += 1;
+			}
+		}
+		float acc = count / N;
+		printf("Accuracy: %0.2f Percent \n", acc*100.0);
+
+		// SAVE WEIGHTS
+		cudaMemcpy(w1, dev_w_kj, H*D*sizeof(double), cudaMemcpyDeviceToHost);
+		checkCUDAErrorFn("cudaMemcpyFromSymbol from  dev_w_kj to w1 failed!");
+
+		cudaMemcpy(w2, dev_w_ji, H*C*sizeof(double), cudaMemcpyDeviceToHost);
+		checkCUDAErrorFn("cudaMemcpyFromSymbol from  dev_w_ji to w2 failed!");
+
+		//printf("losses:\n");
+		//printFloatArray(epochs, lossAvgPerEpoch, true);
+
+		//====================
+		// CleanUp
+		//====================
+		cudaFree(dev_iLayer);
+		cudaFree(dev_hLayer);
+		cudaFree(dev_oLayer);
+
+		cudaFree(dev_losses);
+
+		cudaFree(dev_gtruth);
+		cudaFree(dev_preds);
+		cudaFree(dev_preds_probab);
+
+		cudaFree(dev_w_kj);
+		cudaFree(dev_w_ji);
+
+		cudaFree(dev_dL_dw_ji);
+		cudaFree(dev_dL_dw_kj);
+
+		cudaFree(dev_dL_dscores);
+		cudaFree(dev_dL_dscores_2);
+
+		cudaFree(dev_hLayer_T);
+		cudaFree(dev_iLayer_T);
+
+		delete(tmp);
+		delete(tmp2);
+		delete(lossesN);
+
+		timer().endGpuTimer();
+	}
 }
diff --git a/Project2-Character-Recognition/character_recognition/mlp.cu_back b/Project2-Character-Recognition/character_recognition/mlp.cu_back
new file mode 100644
index 0000000..210e535
--- /dev/null
+++ b/Project2-Character-Recognition/character_recognition/mlp.cu_back
@@ -0,0 +1,679 @@
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include "common.h"
+#include "mlp.h"
+
+#include <curand.h>
+#include <curand_kernel.h>
+
+#define blockSize 128
+#define blockWidth 16
+
+
+namespace CharacterRecognition {
+	using Common::PerformanceTimer;
+	PerformanceTimer& timer()
+	{
+		static PerformanceTimer timer;
+		return timer;
+	}
+
+	// Initlialiations
+
+	//layers
+	float *dev_iLayer;
+	float *dev_hLayer;
+	float *dev_oLayer;
+
+	//float *dev_b1;
+	//float *dev_b2;
+	//float *dev_db1;
+	//float *dev_db2;
+
+	float *dev_losses;
+	float *dev_LossAvg;
+	
+	// gtruth and preds
+	int *dev_gtruth;
+	int *dev_preds;
+	float * dev_preds_probab;
+
+	//weights
+	float *dev_w1;
+	float *dev_w2;
+
+	//Derivatives
+	float *dev_dw2;
+	float *dev_dw1;
+	float *dev_dscores;
+	float *dev_dscores_2;
+
+	float *dev_hLayer_T;
+	float *dev_iLayer_T;
+	float *dev_w2_T;
+
+
+	//=============================================
+	// Rnadom Number Generation using cuRand on GPU
+	//=============================================
+	curandState *devState;
+
+	__global__ void kernInitCurand(curandState *state, int N, unsigned long seed) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N) {
+			curand_init(seed, tid, 0, &state[tid]);
+		}
+	}
+
+	__global__ void KernGenRand(curandState *state, int N, float *w) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N) {
+			w[tid] = ((2.0*curand_uniform(&state[tid])) - 1.0); // Between -1 and 1 
+		}
+	}
+
+	//===================================================================
+	//=====KERNEL DEFNITIONS FOR Forward and Backward====================
+	//===================================================================
+
+
+	void printArray(int n, int *a, bool abridged = false) {
+		printf("    [ ");
+		for (int i = 0; i < n; i++) {
+			if (abridged && i + 2 == 15 && n > 16) {
+				i = n - 2;
+				printf("... ");
+			}
+			printf("%3d ", a[i]);
+		}
+		printf("]\n\n");
+	}
+	void printFloatArray(int n, float *a, bool abridged = false) {
+		printf("    [ ");
+		for (int i = 0; i < n; i++) {
+			if (abridged && i + 2 == 15 && n > 16) {
+				i = n - 2;
+				printf("... ");
+			}
+			printf("%3f ", a[i]);
+		}
+		printf("]\n\n");
+	}
+
+
+
+	// Kernel for Gradient update on Weights
+	__global__ void kernUpdateWeights(int N, float *dev_dw, float *dev_w, float LR) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_w[tid] += -LR * dev_dw[tid];
+		}
+	}
+
+	// Kernel for derivative of sigmoid
+	__global__ void kernGradSigmoid(int N, int H, float *dev_hLayer) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N*H) {
+			dev_hLayer[tid] = dev_hLayer[tid] * (1 - dev_hLayer[tid]);
+		}
+	}
+
+	// Matrix Transpose
+	__global__ void kernMatrixTranspose(int N, int C, float *matrix, float *matrix_T) {
+
+		int row = blockIdx.y * blockDim.y + threadIdx.y;
+		int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (col < C && row < N) {
+			matrix_T[C*row + col] = matrix[N*col + row];
+		}
+	}
+
+	// Divide by N
+	__global__ void kernDivNdscores(int N, int C, float *dev_dscores) {
+
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+		if (tid < N*C) {
+			dev_dscores[tid] /= N;
+		}
+	}
+
+	// Compute dscores gradient
+	__global__ void kernSetdscores(int N, int C, float *dev_dscores, int *dev_gtruth) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_dscores[tid*C + dev_gtruth[tid]] -= 1;
+		}
+	}
+
+	// compute predictions
+	__global__ void kernPredsN(int N, int C, float* dev_oLayer, int* dev_gtruth, int* dev_preds, float * dev_preds_probab) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_preds[tid] = dev_oLayer[tid*C + dev_gtruth[tid]] > 0.5 ? dev_gtruth[tid] : (dev_gtruth[tid]==0 ? 1:0) ;
+			dev_preds_probab[tid] = dev_oLayer[tid*C + dev_gtruth[tid]];
+		}
+	}
+
+	// compute loss per example
+	__global__ void kernLossPerN(int N, int C, float* dev_oLayer, int* dev_gtruth, float* dev_losses) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			dev_losses[tid] = (float)(-log(dev_oLayer[tid*C + dev_gtruth[tid]]));
+		}
+	}
+
+	// kernel to compute exp softmax
+	__global__ void kernSoftmax(int N, int C, float* scores) {
+		int tid = threadIdx.x + blockIdx.x * blockDim.x;
+
+		if (tid < N) {
+			float sums = 0.0;
+
+			for (int i = 0; i < C; i++) {
+				sums += exp(scores[tid*C + i]);
+			}
+
+			for (int i = 0; i < C; i++) {
+				scores[tid*C + i] = exp(scores[tid*C + i]) / sums;
+			}
+		}
+	}
+
+	// kern for sigmoid // f(x) = 1/(1 + e^-x).
+	__global__ void kernSigmoid(int N, float *idata) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			idata[tid] = 1.0 / (1.0 + exp(-idata[tid]));
+		}
+	}
+
+	// kern for element wise product 
+	__global__ void kernElementProduct(int N, float *matrixA, float* matrixB, float* matrixC) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			matrixC[tid] = matrixA[tid] * matrixB[tid];
+		}
+	}
+
+
+	// kernel to to matmul // A mxn // B nxk // C mxk
+	__global__ void kernMatrixMultiply(const float *dev_A, const float *dev_B, float *dev_C, int m, int n, int k) {
+
+		int row = blockIdx.y * blockDim.y + threadIdx.y;
+		int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+		float sum = 0;
+		if (col < k && row < m)
+		{
+			for (int i = 0; i < n; i++) {
+				sum += dev_A[row * n + i] * dev_B[i * k + col];
+			}
+			dev_C[row * k + col] = sum;
+		}
+	}
+
+	// Dumb reduction
+	__global__ void kernReduction(int N, float *dev_losses, float *dev_LossAvg) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+		float sum = 0.0;
+		if (tid == 0) {
+			for (int i = 0; i < N; i++) {
+				sum += dev_losses[i];
+			}
+			dev_LossAvg[0] = sum/N;
+		}
+
+	}
+
+	// Ele wise addition A = A+B
+		__global__ void kernAddition(int N, float *dev_A, float *dev_B) {
+
+		int tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+		if (tid < N) {
+			dev_A[tid] += dev_B[tid];
+		}
+
+	}
+
+	void trainMLP(int N, int D, int H, int C, float *idata, int *preds, int *gtruth, int epochs, float *lossAvgPerEpoch, const float LR, unsigned long seed) {
+
+		timer().startGpuTimer();
+
+		// N = number of examples
+		// D = dim of each example 
+		// H = Hidden layer nodes 
+		// C = number of classes
+
+		// NETWORK DEFITION_____________
+		// Compute f1		= W1*X1 + b1
+		// Compute X2		= Sig(f1)
+		// Compute Scroes S = W2*X2 + b2
+		// Compute Probab P = Softmax(S)
+		// Compute Loss   L = CEntropy(P)
+
+		//================================================================
+		//======================INITIALIZATIONS===========================
+		//================================================================
+
+		printf("\nN = %d \n", N);
+		printf("D = %d \n", D);
+		printf("H = %d \n", H);
+		printf("C = %d \n", C);
+
+		// Allocate input layer
+		cudaMalloc((void**)&dev_iLayer, N*D * sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_iLayer failed!");
+		cudaMemcpy(dev_iLayer, idata, N*D * sizeof(float), cudaMemcpyHostToDevice);
+		checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_iLayer failed!");
+
+		// Allocate hidden layer
+		cudaMalloc((void**)&dev_hLayer, N*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer failed!");
+
+		// Allocate output layer
+		cudaMalloc((void**)&dev_oLayer, N*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_oLayer failed!");
+
+
+		// Allocate losses holder
+		cudaMalloc((void**)&dev_losses, N * sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_losses failed!");
+		cudaMalloc((void**)&dev_LossAvg, 1* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_LossAvg failed!");
+
+
+		// Allocate gtruth and preds
+		cudaMalloc((void**)&dev_gtruth, N*sizeof(int));
+		checkCUDAErrorFn("cudaMalloc dev_gtruth failed!");
+		cudaMemcpy(dev_gtruth, gtruth, N * sizeof(int), cudaMemcpyHostToDevice);
+		checkCUDAErrorFn("cudaMemcpyToSymbol from gtruth to dev_gtruth failed!");
+
+		cudaMalloc((void**)&dev_preds, N * sizeof(int));
+		checkCUDAErrorFn("cudaMalloc dev_preds failed!");
+
+		cudaMalloc((void**)&dev_preds_probab, N * sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_preds_probab failed!");
+
+		// Allocate Weights
+		cudaMalloc((void**)&dev_w1, D*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_w1 failed!");
+
+		cudaMalloc((void**)&dev_w2, C*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_w2 failed!");
+
+
+		// Allocate Derivatives
+		cudaMalloc((void**)&dev_dw1, D*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_w1 failed!");
+
+		cudaMalloc((void**)&dev_dw2, H*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_w2 failed!");
+
+		cudaMalloc((void**)&dev_dscores, N*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_dscores failed!");
+
+		cudaMalloc((void**)&dev_dscores_2, N*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_dscores_2 failed!");
+
+		
+		// Allocate transposes
+		cudaMalloc((void**)&dev_hLayer_T, N*H * sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_iLayer_T, N*D * sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_w2_T, C*H*sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_w2_T failed!");
+		
+		/*
+		//Allocate biases
+		cudaMalloc((void**)&dev_b1, N*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_b2, N*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_db1, N*H* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");
+
+		cudaMalloc((void**)&dev_db2, N*C* sizeof(float));
+		checkCUDAErrorFn("cudaMalloc dev_hLayer_T failed!");*/
+
+		//==============================
+		// Initialise Weights and Biases
+		//==============================
+		cudaMalloc((void**)&devState, H*D* sizeof(curandState));
+
+		kernInitCurand <<<((D*H + blockSize - 1) / blockSize), blockSize >>> (devState, D*H, seed); //XOR = 99
+		checkCUDAErrorFn("KernInitCurand failed!");
+		KernGenRand <<<((D*H + blockSize - 1) / blockSize), blockSize >>> (devState, D*H, dev_w1);
+		checkCUDAErrorFn("KernGenRand dev_w1 failed!");
+
+		kernInitCurand <<<((H*C + blockSize - 1) / blockSize), blockSize >> > (devState, H*C, seed+1);//XOR = 999
+		checkCUDAErrorFn("KernInitCurand failed!");
+		KernGenRand <<<((H*C + blockSize - 1) / blockSize), blockSize >> > (devState, H*C, dev_w2);
+		checkCUDAErrorFn("KernGenRand dev_w1 failed!");
+
+		//kernInitCurand << <((N*C + blockSize - 1) / blockSize), blockSize >> > (devState, N*C, seed+2);//XOR = 9
+		//checkCUDAErrorFn("KernInitCurand failed!");
+		//KernGenRand << <((N*C  + blockSize - 1) / blockSize), blockSize >> > (devState, N*C, dev_b2);
+		//checkCUDAErrorFn("KernGenRand dev_w1 failed!");
+
+		//kernInitCurand << <((N*H + blockSize - 1) / blockSize), blockSize >> > (devState, N*H, seed+3);//XOR = 9999
+		//checkCUDAErrorFn("KernInitCurand failed!");
+		//KernGenRand << <((N*H + blockSize - 1) / blockSize), blockSize >> > (devState, N*H, dev_b1);
+		//checkCUDAErrorFn("KernGenRand dev_w1 failed!");
+		
+		/*
+		float *rand = new float[D*H];
+		cudaMemcpy(rand, dev_w1, D*H* sizeof(float), cudaMemcpyDeviceToHost);
+		checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_w1 to rand failed!");
+		printf("Post random inits dev_w1 - \n");
+		printFloatArray(D*H, rand, true);
+
+		float *rand2 = new float[H*C];
+		cudaMemcpy(rand2, dev_w2, H*C * sizeof(float), cudaMemcpyDeviceToHost);
+		checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_w2 to rand failed!");
+		printf("Post random inits dev_w2 - \n");
+		printFloatArray(H*C, rand2, true);*/
+
+
+		float *tmp = new float[N*D];
+		float *tmp2 = new float[N*D];
+		float *tmp3 = new float[N*D];
+		float *lossesN = new float[N];
+
+		printf("\nInput DATA\n");
+		printFloatArray(N*D, idata, true);
+		dim3 dimBlock(blockWidth, blockWidth);
+		dim3 dimGrid;
+
+		//================================================================
+		//======================TRAINING LOOP=============================
+		//================================================================
+
+		for (int ep = 0; ep < epochs; ep++) {
+
+			//================================================================
+			//========================= FORWARD ==============================
+
+			// STEP 1
+			// f1 = W1*X1 (Matrix Mul)
+			//=================================
+			// dev_hLayer = dev_iLayer * dev_w1 
+			//   NxH      =    NxD         DxH 
+
+
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_iLayer, dev_w1, dev_hLayer, N, D, H);
+			//kernAddition <<< ((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_hLayer,dev_b1);
+			//checkCUDAErrorFn("kernAddition failed!");
+
+			// Copy back to cpu
+			cudaMemcpy(tmp, dev_hLayer, N*H*sizeof(float), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_hLayer to tmp failed!");
+			printf("Post matmul [f1 = dev_iLayer*dev_w1]\n");
+			printFloatArray(N*H, tmp, true);
+			
+			// STEP 2
+			// X2         = Sigmoid(f1) 
+			//================================
+			// dev_hLayer = sigmoid(dev_hLayer)
+			//   NxH     =    NxH 
+			kernSigmoid << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_hLayer);
+
+
+			// Copy back to cpu
+			//cudaMemcpy(tmp, dev_hLayer, N*H*sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_arrayA to odata failed!");
+			//printf("Post sigmoid [X2 = Sigmoid(f1) ]\n");
+			//printFloatArray(N*H, tmp, true);
+
+			// STEP 3
+			// Scores S = W2*X2 (Matrix Mul)
+			//================================
+			// dev_oLayer = dev_hLayer*dev_w2 
+			//   NxC      =    NxH         HxC
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_hLayer, dev_w2, dev_oLayer, N, H, C);
+			checkCUDAErrorFn("kernMatrixMultiply failed!");
+			//kernAddition << < ((N*C + blockSize - 1) / blockSize), blockSize >> > (N*C, dev_oLayer, dev_b2);
+			//checkCUDAErrorFn("kernAddition failed!");
+
+			// Copy back to cpu
+			//cudaMemcpy(tmp3, dev_oLayer, N*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_arrayA to odata failed!");
+			//printf("Post SCORES =W2*x2\n");
+			//printFloatArray(N*C, tmp3, true);
+
+			// STEP 4
+			// P = Softmax(S) 
+			//===============
+			// dev_smaxDen = Sum_Over_classses(dev_olayer)
+			// dev_olayer = dev_olayer/Sum_Over_classses
+			//   NxC      =    NxC         1
+			kernSoftmax << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_oLayer);
+			checkCUDAErrorFn("kernSoftmax failed!");
+
+			// Copy back to cpu
+			//cudaMemcpy(tmp, dev_oLayer, N*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_oLayer to tmp failed!");
+			//printf("Post Softmax  [dev_olayer = exp(dev_olayer)/Sum_Over_classses]\n");
+			//printFloatArray(N*C, tmp, true);
+
+
+			// STEP 5
+			// Compute Losses | Cross Entropy Loss
+			//==================================
+			// Compute Loss   L = CEntropy(P)
+			kernLossPerN <<<((N + blockSize - 1) / blockSize), blockSize >>>(N, C, dev_oLayer, dev_gtruth, dev_losses);
+			checkCUDAErrorFn("kernLossPerN  failed!");
+
+			// Copy back to cpu
+			//cudaMemcpy(lossesN, dev_losses, N * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_losses to lossesN failed!");
+			//printf("Post dev_losses [Loss = CEntropy(P)]\n");
+			//printFloatArray(N, lossesN, true);
+
+
+			// Predictions
+			kernPredsN << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_oLayer, dev_gtruth, dev_preds, dev_preds_probab);
+			cudaMemcpy(preds, dev_preds, N*sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyDeviceToHost from dev_preds to preds failed!");
+			
+			cudaMemcpy(tmp2, dev_preds_probab, N*sizeof(float), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyDeviceToHost from dev_preds_probab to tmp2 failed!");
+
+			printf("Predictions\n");
+			printArray(N, preds, true);
+			printFloatArray(N, tmp2, true);
+
+
+			// STEP 5.2
+			// Compute Avg of Losses
+			//==================================
+			// Dumb Reduction
+			
+			kernReduction << <((N + blockSize - 1) / blockSize), blockSize >> > (N, dev_losses, dev_LossAvg);
+			// Copy back to cpu
+			cudaMemcpy(lossAvgPerEpoch + ep, dev_LossAvg, sizeof(float), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_LossAvg to lossAvgPerEpoch failed!");
+			
+			printf("Epoch: %d | LossAvg %3f \n", ep, lossAvgPerEpoch[ep]);
+
+
+			//=================================================================
+			//========================= BACKPROP ==============================
+
+			// STEP 1 : Gradient wrt w_ji
+			// dW_ji = Probs_k - [1](gth == k) dev_dscores; 
+			cudaMemcpy(dev_dscores, dev_oLayer, N*C* sizeof(float), cudaMemcpyDeviceToDevice);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from probabs to dev_dscores failed!");
+
+			kernSetdscores << <((N + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_dscores, dev_gtruth);
+			checkCUDAErrorFn("kernSetdscores failed!");
+
+			// Copy back to cpu
+			//cudaMemcpy(tmp, dev_dscores, N*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol [kernSetdscores] from dev_dscores to tmp failed!");
+			//printf("Post setting loss at positions dev_dscores \n");
+			//printFloatArray(N*C, tmp, true);
+
+			kernDivNdscores << <((N*C + blockSize - 1) / blockSize), blockSize >> > (N, C, dev_dscores);
+			checkCUDAErrorFn("kernDivNdscores failed!");
+
+			//cudaMemcpy(tmp, dev_dscores, N*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("cudaMemcpyFromSymbol [kernSetdscores] from dev_dscores to tmp failed!");
+			//printf("Post div by N -> setting loss at positions-> dev_dscores \n");
+			//printFloatArray(N*C, tmp, true);
+
+
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (N, H, dev_hLayer, dev_hLayer_T);
+
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (C + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_hLayer_T, dev_dscores, dev_dw2, H, N, C);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dw2 failed!");
+
+			
+			//===========================
+			// STEP 2 : Gradient wrt w_kj
+			//===========================
+
+			// Transpose Wji (W2)
+			dimGrid.x = (C + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (H + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (H, C, dev_w2, dev_w2_T);
+
+			// Transpose Input Data
+			dimGrid.x = (D + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixTranspose << <dimGrid, dimBlock >> > (N, D, dev_iLayer, dev_iLayer_T);
+
+			// Mul dev_dscores * dev_w1_T == dev_dscores_2
+			//             NxC          CxH             NxH
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (N + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_dscores, dev_w2_T, dev_dscores_2, N, C, H);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dscores_2 failed!");
+			
+			// compute sig gradient on dev_hlayer N*H [IN PLACE]
+			kernGradSigmoid << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N, H, dev_hLayer);
+			checkCUDAErrorFn("kernGradSigmoid failed!");
+
+			//Element wise mul dev_dscores_2 [INPLACE] = dev_dscores_2 . dev_hlayer[sig gradient] 
+			kernElementProduct << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_dscores_2, dev_hLayer, dev_dscores_2);
+			checkCUDAErrorFn("kernElementProduct failed!");
+
+			// matrix Mul final with Xi_T
+			dimGrid.x = (H + dimBlock.x - 1) / dimBlock.x;
+			dimGrid.y = (D + dimBlock.y - 1) / dimBlock.y;
+			kernMatrixMultiply << <dimGrid, dimBlock >> > (dev_iLayer_T, dev_dscores_2, dev_dw1, D, N, H);
+			checkCUDAErrorFn("kernMatrixMultiply for dev_dw1 failed!");
+
+
+			//=================================================================
+			//========================= Update Weights=========================
+
+			// Update weights1
+			kernUpdateWeights << <((D*H + blockSize - 1) / blockSize), blockSize >> > (D*H, dev_dw1, dev_w1, LR);
+			checkCUDAErrorFn("kernUpdateWeights dev_w1 failed!");
+
+			// InitUpdate weights2
+			kernUpdateWeights << <((H*C + blockSize - 1) / blockSize), blockSize >> > (H*C, dev_dw2, dev_w2, LR);
+			checkCUDAErrorFn("kernUpdateWeights dev_w2 failed!");
+
+			// Update biases1
+			//kernUpdateWeights << <((N*H + blockSize - 1) / blockSize), blockSize >> > (N*H, dev_db1, dev_dscores_2, LR);
+			//checkCUDAErrorFn("kernUpdateWeights dev_db1 failed!");
+
+			// InitUpdate biases2
+			//kernUpdateWeights << <((N*C + blockSize - 1) / blockSize), blockSize >> > (N*C, dev_db2, dev_dscores, LR);
+			//checkCUDAErrorFn("kernUpdateWeights dev_db2 failed!");
+
+			// COntinue to next epoch 
+			//cudaMemcpy(tmp2, dev_w1, D*H * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("dev_w1 memcopy failed!");
+			//printf("w_kj \n");
+			//printFloatArray(D*H, tmp2, true);
+			//cudaMemcpy(tmp2, dev_dw1, D*H * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("dev_dw1 memcopy failed!");
+			//printf("Dw_kj \n");
+			//printFloatArray(D*H, tmp2, true);
+			
+			//cudaMemcpy(tmp2, dev_w2, H*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("dev_w2 memcopy failed!");
+			//printf("w_ji \n");
+			//printFloatArray(H*C, tmp2, true);
+			//cudaMemcpy(tmp2, dev_dw2, H*C * sizeof(float), cudaMemcpyDeviceToHost);
+			//checkCUDAErrorFn("dev_dw2 memcopy failed!");
+			//printf("Dw_ji \n");
+			//printFloatArray(H*C, tmp2, true);
+
+
+			//printf("\n-----------------------------------------------------\n\n");
+		}
+
+
+		printf("Finished training.\n");
+		printf("losses:\n");
+		printFloatArray(epochs, lossAvgPerEpoch, true);
+
+		//====================
+		// CleanUp
+		//====================
+		cudaFree(dev_iLayer);
+		cudaFree(dev_hLayer);
+		cudaFree(dev_oLayer);
+
+		cudaFree(dev_losses);
+		
+		cudaFree(dev_gtruth);
+		cudaFree(dev_preds);
+		cudaFree(dev_preds_probab);
+
+		cudaFree(dev_w1);
+		cudaFree(dev_w2);
+
+		/*
+		cudaFree(dev_b1);
+		cudaFree(dev_b2);
+		cudaFree(dev_db1);
+		cudaFree(dev_db2);
+		*/
+
+		cudaFree(dev_dw2);
+		cudaFree(dev_dw1);
+
+		cudaFree(dev_dscores);
+		cudaFree(dev_dscores_2);
+
+		cudaFree(dev_hLayer_T);
+		cudaFree(dev_iLayer_T);
+
+		delete(tmp);
+		delete(tmp2);
+		delete(tmp3);
+
+		timer().endGpuTimer();
+	}
+}
diff --git a/Project2-Character-Recognition/character_recognition/mlp.h b/Project2-Character-Recognition/character_recognition/mlp.h
index 2096228..1facd5e 100644
--- a/Project2-Character-Recognition/character_recognition/mlp.h
+++ b/Project2-Character-Recognition/character_recognition/mlp.h
@@ -3,7 +3,15 @@
 #include "common.h"
 
 namespace CharacterRecognition {
-    Common::PerformanceTimer& timer();
+	Common::PerformanceTimer& timer();
+	// TODO: implement required elements for MLP sections 1 and 2 here
 
-    // TODO: implement required elements for MLP sections 1 and 2 here
+	// MLP section 1 and 2 Character Reader
+	//void initMLP(int N, int P, int iDim, int hDim, int oDim);
+	//void readData(int N, int P, int iDim, int hDim, int oDim);
+		//void testMLP(int N, int P, int iDim, int hDim, int oDim);
+
+	void trainMLP(int N, int D, int H, int C, double *idata, int *preds, int *gtruth, int epochs, double *losses, 
+		const double LR, double *w1, double *w2, unsigned long seed );
 }
+
diff --git a/Project2-Character-Recognition/img/CharRecLoss.PNG b/Project2-Character-Recognition/img/CharRecLoss.PNG
new file mode 100644
index 0000000..c668d54
Binary files /dev/null and b/Project2-Character-Recognition/img/CharRecLoss.PNG differ
diff --git a/Project2-Character-Recognition/img/CharRecStats.PNG b/Project2-Character-Recognition/img/CharRecStats.PNG
new file mode 100644
index 0000000..31f1691
Binary files /dev/null and b/Project2-Character-Recognition/img/CharRecStats.PNG differ
diff --git a/Project2-Character-Recognition/img/MLPmine.PNG b/Project2-Character-Recognition/img/MLPmine.PNG
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new file mode 100644
index 0000000..7cd1d1d
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diff --git a/Project2-Character-Recognition/img/XorStats.PNG b/Project2-Character-Recognition/img/XorStats.PNG
new file mode 100644
index 0000000..2956065
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diff --git a/Project2-Character-Recognition/img/title.png b/Project2-Character-Recognition/img/title.png
new file mode 100644
index 0000000..f171bfc
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diff --git a/Project2-Character-Recognition/img/xor-table.png b/Project2-Character-Recognition/img/xor-table.png
new file mode 100644
index 0000000..1794587
Binary files /dev/null and b/Project2-Character-Recognition/img/xor-table.png differ
diff --git a/Project2-Character-Recognition/src/main.cpp b/Project2-Character-Recognition/src/main.cpp
index 11dd534..0badaaa 100644
--- a/Project2-Character-Recognition/src/main.cpp
+++ b/Project2-Character-Recognition/src/main.cpp
@@ -1,8 +1,8 @@
 /**
  * @file      main.cpp
  * @brief     Stream compaction test program
- * @authors   Kai Ninomiya
- * @date      2015
+ * @authors   Chhavi Sharma
+ * @date      2019
  * @copyright University of Pennsylvania
  */
 
@@ -11,142 +11,226 @@
 #include <character_recognition/common.h>
 #include "testing_helpers.hpp"
 
-const int SIZE = 1 << 8; // feel free to change the size of array
-const int NPOT = SIZE - 3; // Non-Power-Of-Two
-int *a = new int[SIZE];
-int *b = new int[SIZE];
-int *c = new int[SIZE];
+#include <iomanip>
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+
+unsigned long seed = time(0);
+#define XOR 1
+#define CR 1
+
+int N ;  
+int D ;    
+int H ;     
+int C ;     
+double LR;
+int epochs;
 
 int main(int argc, char* argv[]) {
-    // Scan tests
-
-    printf("\n");
-    printf("****************\n");
-    printf("** SCAN TESTS **\n");
-    printf("****************\n");
-
-    genArray(SIZE - 1, a, 50);  // Leave a 0 at the end to test that edge case
-    a[SIZE - 1] = 0;
-    printArray(SIZE, a, true);
-
-    // initialize b using StreamCompaction::CPU::scan you implement
-    // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct.
-    // At first all cases passed because b && c are all zeroes.
-    zeroArray(SIZE, b);
-    printDesc("cpu scan, power-of-two");
-    StreamCompaction::CPU::scan(SIZE, b, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(SIZE, b, true);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu scan, non-power-of-two");
-    StreamCompaction::CPU::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(NPOT, b, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("naive scan, power-of-two");
-    StreamCompaction::Naive::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-	/* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan
-	onesArray(SIZE, c);
-	printDesc("1s array for finding bugs");
-	StreamCompaction::Naive::scan(SIZE, c, a);
-	printArray(SIZE, c, true); */
-
-    zeroArray(SIZE, c);
-    printDesc("naive scan, non-power-of-two");
-    StreamCompaction::Naive::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient scan, power-of-two");
-    StreamCompaction::Efficient::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient scan, non-power-of-two");
-    StreamCompaction::Efficient::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(NPOT, c, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("thrust scan, power-of-two");
-    StreamCompaction::Thrust::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("thrust scan, non-power-of-two");
-    StreamCompaction::Thrust::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(NPOT, c, true);
-    printCmpResult(NPOT, b, c);
-
-    printf("\n");
-    printf("*****************************\n");
-    printf("** STREAM COMPACTION TESTS **\n");
-    printf("*****************************\n");
-
-    // Compaction tests
-
-    genArray(SIZE - 1, a, 4);  // Leave a 0 at the end to test that edge case
-    a[SIZE - 1] = 0;
-    printArray(SIZE, a, true);
-
-    int count, expectedCount, expectedNPOT;
-
-    // initialize b using StreamCompaction::CPU::compactWithoutScan you implement
-    // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct.
-    zeroArray(SIZE, b);
-    printDesc("cpu compact without scan, power-of-two");
-    count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    expectedCount = count;
-    printArray(count, b, true);
-    printCmpLenResult(count, expectedCount, b, b);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu compact without scan, non-power-of-two");
-    count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    expectedNPOT = count;
-    printArray(count, c, true);
-    printCmpLenResult(count, expectedNPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu compact with scan");
-    count = StreamCompaction::CPU::compactWithScan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(count, c, true);
-    printCmpLenResult(count, expectedCount, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient compact, power-of-two");
-    count = StreamCompaction::Efficient::compact(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(count, c, true);
-    printCmpLenResult(count, expectedCount, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient compact, non-power-of-two");
-    count = StreamCompaction::Efficient::compact(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(count, c, true);
-    printCmpLenResult(count, expectedNPOT, b, c);
-
-    system("pause"); // stop Win32 console from closing on exit
-	delete[] a;
-	delete[] b;
-	delete[] c;
+
+	printf("****************\n");
+	printf("***MLP TESTS***\n");
+	printf("****************\n");
+
+	//==========================================================================================
+	//================================= XOR GATE NEURAL NET ====================================
+	//==========================================================================================
+	if (XOR == true) {
+		N = 4;     // Number of examples
+		D = 2;     //Feature length per example
+		H = 4;     // Number of Hidden unit
+		C = 2;     // NN number of classes
+		LR = 0.5;
+		epochs = 1001;
+
+		double	*losses = new double[epochs];
+		double	*idata = new double[N*D];
+		int		* preds = new int[N];
+		int		* gtruth = new int[N];
+		
+		double	*w1 = new double[D*H];
+		double	*w2 = new double[H*C];
+
+		printf("Launch XOR Training\n");
+		// XOR input data set 2*4
+		idata[0] = 0.0;
+		idata[1] = 0.0;
+		idata[2] = 0.0;
+		idata[3] = 1.0;
+		idata[4] = 1.0;
+		idata[5] = 0.0;
+		idata[6] = 1.0;
+		idata[7] = 1.0;
+
+		// XOR ground truth data set 4
+		gtruth[0] = 0;
+		gtruth[1] = 1;
+		gtruth[2] = 1;
+		gtruth[3] = 0;
+
+		CharacterRecognition::trainMLP(N, D, H, C, idata, preds, gtruth, epochs, losses, LR, w1, w2, seed);
+		printf("\nCompleted XOR Training\n");
+		// STORE LOSSES
+		std::ofstream myfile("xor_losses.txt");
+		if (myfile.is_open())
+		{
+			for (int i = 0; i < epochs; i++) {
+				myfile << std::fixed << std::setprecision(8) << losses[i] << '\n';
+			}
+			myfile.close();
+			printf("Wrote loss to file\n");
+		}
+
+		// STORE WEIGHTS
+		std::ofstream weight1_file("xor_W1_DxH_"+std::to_string(D)+"_x_"+std::to_string(H)+".txt");
+		if (weight1_file.is_open())
+		{
+			for (int i = 0; i < D*H; i++) {
+				weight1_file << std::fixed << std::setprecision(8) << w1[i] << '\n';
+			}
+			weight1_file.close();
+			printf("Wrote w1 to file\n");
+		}
+		std::ofstream weight2_file("xor_W2_HxC_"+std::to_string(H)+"_x_"+std::to_string(C)+".txt");
+		if (weight2_file.is_open())
+		{
+			for (int i = 0; i < C*H; i++) {
+				weight2_file << std::fixed << std::setprecision(8) << w2[i] << '\n';
+			}
+			weight2_file.close();
+			printf("Wrote w2 to file\n");
+		}
+
+		delete(losses);
+		delete(idata);
+		delete(preds);
+		delete(gtruth);
+		delete(w1);
+		delete(w2);
+	}
+	//==========================================================================================
+	//================================= CHARACTER RECOGNITION ==================================
+	//==========================================================================================
+	if (CR == true) {
+		// Char Recognition
+		N = 52;			// Number of examples
+		D = 10201;		// Feature length per example
+		H = 10;			// Number of Hidden unit
+		C = 52;			// NN number of classes
+		LR = 0.5;		// Learning Rate
+		epochs = 5001;  // Epochs
+
+		double *CR_losses = new double[epochs];
+		double *CR_idata = new double[N*D];
+		int * CR_preds = new int[N];
+		int * CR_gtruth = new int[N];
+
+		double	*w1 = new double[D*H];
+		double	*w2 = new double[H*C];
+
+		printf("\n\nLaunch CharRec Training\n");
+		// Data loading
+		printf("Loading data...\n");
+		int data_sz = 0;
+		int x = 0;
+
+		std::string line;
+		int *id = new int[N*D];
+		for (int i = 1; i <= 52; i++) {
+			std::string fname;
+			if (i < 10) {
+				fname = "C:\\Users\\chhavis\\cis565\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\0" + std::to_string(i) + "info.txt";
+			}
+			else {
+				fname = "C:\\Users\\chhavis\\cis565\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\" + std::to_string(i) + "info.txt";
+			}
+			std::ifstream myfile(fname);
+			std::stringstream sstream;
+			std::stringstream sstream2;
+			std::stringstream sstream3;
+
+			//std::cout<<fname<<std::endl;
+			// Reading file
+			if (myfile.is_open())
+			{
+
+				// Read line 1
+				getline(myfile, line);
+				sstream << line;
+				sstream >> CR_gtruth[i - 1];
+				CR_gtruth[i - 1] -= 1;
+				//printf(" gtruth = %d |", CR_gtruth[i - 1]);
+
+				// Read line 2 // Data Size
+				getline(myfile, line);
+				sstream2 << line;
+				sstream2 >> data_sz;
+				//printf("data_sz = %d \n", data_sz);
+
+				// Read line 3 Pixel values
+				getline(myfile, line);
+				sstream3 << line;
+				for (int j = 0; j < data_sz; j++) {
+					sstream3 >> id[(i - 1) * 10201 + j];
+				}
+
+				myfile.close();
+			}
+			else {
+				printf("Unable to open file.\n");;
+			}
+		}
+
+		// Normalize Data
+		for (int i = 0; i < N*D; i++) {
+			CR_idata[i] = id[i] / 255.0;
+			//printf("\t %lf ", CR_idata[i]);
+		}
+		delete(id);
+
+		CharacterRecognition::trainMLP(N, D, H, C, CR_idata, CR_preds, CR_gtruth, epochs, CR_losses, LR, w1, w2, seed);
+		printf("\nCompleted CharRec Training\n");
+
+		// STORE LOSSES
+		std::ofstream myfile1("cr_losses.txt");
+		if (myfile1.is_open())
+		{
+			for (int i = 0; i < epochs; i++) {
+				myfile1 << std::fixed << std::setprecision(8) << CR_losses[i] << '\n';
+			}
+			myfile1.close();
+			printf("Wrote loss to file\n");
+		}
+
+		// STORE WEIGHTS
+		std::ofstream weight1file("cr_W1_DxH_"+std::to_string(D)+"_x_" + std::to_string(H)+".txt");
+		if (myfile1.is_open())
+		{
+			for (int i = 0; i < D*H; i++) {
+				weight1file << std::fixed << std::setprecision(8) << w1[i] << '\n';
+			}
+			weight1file.close();
+			printf("Wrote w1 to file\n");
+		}
+		std::ofstream weight2file("cr_W2_HxC_" + std::to_string(H) + "_x_" + std::to_string(C)+".txt");
+		if (weight2file.is_open())
+		{
+			for (int i = 0; i < C*H; i++) {
+				weight2file << std::fixed << std::setprecision(8) << w2[i] << '\n';
+			}
+			weight2file.close();
+			printf("Wrote w2 to file\n");
+		}
+
+		delete(CR_losses);
+		delete(CR_idata);
+		delete(CR_preds);
+		delete(CR_gtruth);
+		delete(w1);
+		delete(w2);
+	}
+
+	return 0;
 }
diff --git a/Project2-Character-Recognition/src/main.cpp_back b/Project2-Character-Recognition/src/main.cpp_back
new file mode 100644
index 0000000..abcacb9
--- /dev/null
+++ b/Project2-Character-Recognition/src/main.cpp_back
@@ -0,0 +1,158 @@
+/**
+ * @file      main.cpp
+ * @brief     Stream compaction test program
+ * @authors   Kai Ninomiya
+ * @date      2015
+ * @copyright University of Pennsylvania
+ */
+
+#include <cstdio>
+#include <character_recognition/mlp.h>
+#include <character_recognition/common.h>
+#include "testing_helpers.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+using namespace std;
+unsigned long seed = time(0);
+//====CONFIG Neural Network for XOR ================
+//==================================================
+
+
+// XOR
+const int N = 4;     // Number of examples
+const int D = 3;     //Feature length per example
+const int H = 4;     // Number of Hidden unit
+const int C = 2;     // NN number of classes
+const float LR = 0.1;
+const int epochs = 1000;
+
+/*
+//====CONFIG Neural Network for Char Recognition ===
+//==================================================
+const int N = 52;     // Number of examples
+const int D = 10201;  // Feature length per example
+const int H = 10;     // Number of Hidden unit
+const int C = 52;     // NN number of classes
+const float LR = 0.01;
+const int epochs = 5000;
+*/
+
+
+int main(int argc, char* argv[]) {
+
+	float *losses = new float[epochs];
+	float *idata = new float[N*D];
+	int * preds = new int[N];
+	int * gtruth = new int[N];
+
+    printf("\n");
+    printf("****************\n");
+    printf("***MLP TESTS***\n");
+    printf("****************\n");
+
+	printf("Launch XOR Training\n");
+	// XOR input dtat set 2 * 4
+	/*
+	idata[0] = 0;
+	idata[1] = 0;
+	idata[2] = 0;
+	idata[3] = 1;
+	idata[4] = 1;
+	idata[5] = 0;
+	idata[6] = 1;
+	idata[7] = 1;
+	*/
+	
+	// with bias
+	idata[0] = 0;
+	idata[1] = 0;
+	idata[2] = 1;
+	idata[3] = 0;
+	idata[4] = 1;
+	idata[5] = 1;
+	idata[6] = 1;
+	idata[7] = 0;
+	idata[8] = 1;
+	idata[9] = 1;
+	idata[10] = 1;
+	idata[11] = 1;
+
+
+	// XOR input dtat set 2 * 4
+	gtruth[0] = 0;
+	gtruth[1] = 1;
+	gtruth[2] = 1;
+	gtruth[3] = 0;
+
+	CharacterRecognition::trainMLP(N, D, H, C, idata, preds, gtruth, epochs, losses, LR, seed);
+	printf("\nCompleted XOR Training\n");
+	
+	/*
+	// Data loading
+	printf("Trying to load data.\n");
+	int data_sz = 0;
+	int x = 0;
+	
+	string line;
+	int *id = new int[N*D];
+	for (int i = 1; i <= 52; i++) {
+		std::string fname;
+		if (i < 10) {
+			fname="C:\\Users\\chhavis\\cis565\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\0" + std::to_string(i) + "info.txt";
+		}
+		else {
+			fname = "C:\\Users\\chhavis\\cis565\\Project2-Number-Algorithms\\Project2-Character-Recognition\\data-set\\" + std::to_string(i) + "info.txt";
+		}
+		std::ifstream myfile(fname);
+		std::stringstream sstream;
+		std::stringstream sstream2;
+		std::stringstream sstream3;
+
+		//std::cout<<fname<<std::endl;
+		// Reading file
+		if (myfile.is_open())
+		{
+
+			// Read line 1
+			getline(myfile, line);
+			sstream << line;
+			sstream >> gtruth[i-1];
+			gtruth[i-1] -= 1;
+			printf(" gtruth = %d |", gtruth[i-1]);
+
+			// Read line 2 // Data Size
+			getline(myfile, line);
+			sstream2 << line;
+			sstream2 >> data_sz;
+			//printf("data_sz = %d \n", data_sz);
+
+			// Read line 3 Pixel values
+			getline(myfile, line);
+			sstream3 << line;
+			for (int j = 0; j < data_sz; j++){
+				sstream3 >> id[(i-1)*10201 + j];
+			}
+				
+			myfile.close();
+		}
+		else {
+			printf("Unable to open file.\n");;
+		}
+	}
+	
+	// Normalize Data
+	for (int i = 0; i < N*D; i++) {
+		idata[i] = id[i]/255.0;
+		//printf("\t %lf ", idata[i]);
+	}
+	delete(id);
+
+	CharacterRecognition::trainMLP(N, D, H, C, idata, preds, gtruth, epochs, losses, LR, seed);
+	printf("\nCompleted CharRec Training\n");
+	*/
+
+	return 0;
+}
diff --git a/Project2-Stream-Compaction/README.md b/Project2-Stream-Compaction/README.md
index 0e38ddb..b5fe356 100644
--- a/Project2-Stream-Compaction/README.md
+++ b/Project2-Stream-Compaction/README.md
@@ -1,14 +1,149 @@
-CUDA Stream Compaction
-======================
+## Project 2 Part 1 - CUDA Stream Compaction
+**University of Pennsylvania
+CIS 565: GPU Programming and Architecture**
 
-**University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2**
+* Author: Chhavi Sharma ([LinkedIn](https://www.linkedin.com/in/chhavi275/))
+* Tested on: Windows 10, Intel Core(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, 
+             NVIDIA Quadro P1000 4GB (MOORE100B-06)
 
-* (TODO) YOUR NAME HERE
-  * (TODO) [LinkedIn](), [personal website](), [twitter](), etc.
-* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)
+### Index
 
-### (TODO: Your README)
+- [Introduction](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Stream-Compaction/README.md#introduciton)
+- [Algorithms](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Stream-Compaction/README.md#algorithms)
+- [Questions and Performance Analysis](https://github.com/chhavisharma/Project2-Number-Algorithms/blob/master/Project2-Stream-Compaction/README.md#questions-and-performance-analysis)
 
-Include analysis, etc. (Remember, this is public, so don't put
-anything here that you don't want to share with the world.)
 
+### Introduciton
+
+In this project, we implement GPU based Stream Compaction in CUDA. To aid stream compaction, we also implement various versions of the *Scan* (*Prefix Sum*) algorithm, such as CPU, GPU Naive and GPU Work Efficient versions.
+algorithm
+
+Stream compaction, also known as stream filtering or selection, usually produces a smaller output array which contains only the wanted elements from the input array based on some criteria for further processing, while preserving order. For our implementation, We will attemp to remove '0's from an array of ints.
+
+
+### Algorithms
+
+#### 1: CPU Scan & Stream Compaction
+ 
+ We implement stream compaction in two ways:
+ 
+ - CPU based stream compaction: 
+   - Loop over the input data array
+      - Copy non-zero elements to output array
+      - count copies to track size
+   ```
+   compactWithoutScan(N, Odata, Idata)
+     if n > 0
+       int size=0;
+       for i in Idata
+           if (idata[i] != 0) 
+              Odata[counter] = Idata[i]
+              size+=1
+       return size
+   ```
+   
+ - CPU based stream compaction with CPU based scan: 
+   - Compute *Indicator Array* of the input data size that is 1 for non zero elements, an 0 otherwise.
+   - Compute *Scan* over indicator Array to get another array. This gives us write indices for the valid elements in the output array. It also gives us the total valid elelemts.
+   - *Scatter* data, read from the input array where Indiacator Array is 1, write to the outut array at index given by the scan array. That is:
+  ```
+      - map
+        goal: map our original data array (integer, Light Ray, etc) to a bool array
+        input  [1 5 0 1 2 0 3]
+        output [1 1 0 1 1 0 1]
+    - scan
+        take the output of last step as input
+        input   [1 1 0 1 1 0 1]
+        output  [0 1 2 2 3 4 4]
+    - scatter
+        preserve non-zero elements and compact them into a new array
+        original array  [1 5 0 1 2 0 3]
+        mapped array    [1 1 0 1 1 0 1]
+        scanned array   [0 1 2 2 3 4 4]
+        output          [1 5 1 2 3]
+  ```
+  
+-Scan : The goal is to produce a prefic sum of the input array.
+    ```
+    input  [1 1 0 1 1 0 1]
+    output [0 1 2 2 3 4 4]
+    ```  
+
+
+#### 2: Naive GPU Scan
+We can naively parallelize the scan algorithm on the GPU to reduce the loop to ```log2(n)``` iterations. At the first iteraction, n-1 threads add a pair of values and store it in the next array, but as iteractions progress, the number of additions come down to 'O(1)'. Thus this scan has a runtime of 'log2(n)' where as the CPU sequential scan has the runtime of 'O(n)'. The number of additions in this scenario increase to ```O(n*log2(n))```.
+
+
+![Naive Parallel Scan](img/NaiveScan.png)
+
+#### 3: Work-Efficient GPU Scan
+
+We can further parallelize the scan algorithm to bring down the number of addition operations to ```O(n)``` and make it *Work Efficient*. This is done by implementing the scan algorithm using a Balanced Binary Tree and perfroming the UpSweep and DownSweep algorithm. During Upsweep, we start from the tree's leaf nodes and compute partial sums upto the root. These operations are in place. 
+
+![UpSweep](img/UpSweepScan.png)
+
+Finally in the downsweep, starting from the Root node, we perfom the following steps to get the preorder sum.
+
+![DownSweep](img/DownSweepScan.png)
+
+#### 4: Work-Efficient Stream Compaction
+Work efficient stram compaction is nothing but the stream compaction algorithm explained above that uses the work-efficient scan. 
+    ```
+     compactWithScan(n, odata, idata) 
+        Compute indicator array (Parallely)
+        Compute scan (Work efficiently)
+        Scatter 
+    ```
+#### 5: Using Thrust's Implementation
+We also experiemented with CUDA's `thrust::exclusive_scan(first, last, result)` function to compute scan and compare performance.
+
+
+### Questions and Performance Analysis
+
+  * **BlockSize Optimization for each Implementation**
+  We compare the rumtime of GPU Naive scan and and the work efficient naive scan with the number of threads per block to pick  the most optimal configuration for furhter tests.
+
+   *Block Size v/s Runtime*
+  ![](img/BlockSize_vs_Runtime.png)
+
+  From the above plot, we find that BlockSize 128 was the most optimal for both Naive and Work Efficient Scan.
+
+  * **Compare all of these GPU Scan implementations (Naive, Work-Efficient, and Thrust) to the serial CPU version of Scan. Plot a graph of the comparison (with array size on the independent axis).**
+
+     The following plots show the runtime comparasion of all the above scan implementations. 
+     
+     SCAN with increasing data size: Lower Runtime -> Faster Algorithm
+
+    ![](img/Scan1.png)    
+    ![](img/Scan2.png)    
+
+
+     SCAN with increasing data size and nonPowersOf2: Lower Runtime -> Faster Algorithm
+
+    ![](img/Scan1NP.png)    
+    ![](img/Scan2NP.png)   
+    
+  * **Write a brief explanation of the phenomena you see here.Can you find the performance bottlenecks? Is it memory I/O? Computation? Is it different for each implementation?**
+
+  **Scan Comparasion**
+  Varying the data from '2^10' to '2^28', we observe the following:-
+  - The CPU implementation of Scan is realtively faster than all the GPU implementations when the data size is small. This is becuase the compute time is not able to hide the other kernel overheads such as allocating threads and multiple kernel launches. 
+  - As the data size grows, serial compute scan on the CPU grows in time. The naive GPU scan and Work efficint scan are faster. 
+  - For really large data sizes, CPU scan runs faster than Naive GPU scan, which could be becuause the 'n* log2(n)' additional compute in the naive GPU scan overpowers the parallelization. 
+  - Work efficient scan perfroms the best on large data sizes, as comapred to Naive nad CPU scan, becuase of compute 'log(n)' and time 'log(n)' optimization. 
+  - Thrust scan perfroms far better than any other implementation for large data sizes. Thrust maybe optimizing the memory I/O and kernel launching overheads further as comapred to our implementation of Work-efficient scan where we launch a kernel every time in a for loop for upstream and downstream. 
+    
+    
+  **Compaction Comparasion**
+
+  ![](img/SC1.png) 
+    
+  ![](img/SC2.png) 
+
+  Varying the data from '2^10' to '2^28', we observe the following:-
+  - Internstingly, stream compction without scan on the cpu outperforms stream compaction with scan in terms of computation time. The drawback of stream compaction without scan is that it is not in place and uses double the memory of input size. 
+  
+
+  * **Paste the output of the test program **
+
+  ![](img/BlockSz-128-DataSz-20.PNG)
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+19 | size 524288 | 0.282624 (CUDA Measured)
+20 | size 1048576 | 0.499712 (CUDA Measured)
+21 | size 2097152 | 0.545792 (CUDA Measured)
+22 | size 4194304 | 0.939808 (CUDA Measured)
+23 | size 8388608 | 1.44179 (CUDA Measured)
+24 | size 16777216 | 2.50982 (CUDA Measured)
+25 | size 33554432 | 4.66563 (CUDA Measured)
+26 | size 67108864 | 8.83098 (CUDA Measured)
+27 | size 134217728 | 17.2687 (CUDA Measured)
diff --git a/Project2-Stream-Compaction/build/ThrustScan_powOf2.txt b/Project2-Stream-Compaction/build/ThrustScan_powOf2.txt
new file mode 100644
index 0000000..171e56e
--- /dev/null
+++ b/Project2-Stream-Compaction/build/ThrustScan_powOf2.txt
@@ -0,0 +1,18 @@
+10 | size 1024 | 0.057536 (CUDA Measured)
+11 | size 2048 | 0.098432 (CUDA Measured)
+12 | size 4096 | 0.054528 (CUDA Measured)
+13 | size 8192 | 0.102432 (CUDA Measured)
+14 | size 16384 | 0.070112 (CUDA Measured)
+15 | size 32768 | 0.090112 (CUDA Measured)
+16 | size 65536 | 0.089184 (CUDA Measured)
+17 | size 131072 | 0.08608 (CUDA Measured)
+18 | size 262144 | 0.256 (CUDA Measured)
+19 | size 524288 | 0.380928 (CUDA Measured)
+20 | size 1048576 | 0.49024 (CUDA Measured)
+21 | size 2097152 | 0.546112 (CUDA Measured)
+22 | size 4194304 | 0.878592 (CUDA Measured)
+23 | size 8388608 | 1.31277 (CUDA Measured)
+24 | size 16777216 | 2.76662 (CUDA Measured)
+25 | size 33554432 | 4.59968 (CUDA Measured)
+26 | size 67108864 | 8.79779 (CUDA Measured)
+27 | size 134217728 | 17.3084 (CUDA Measured)
diff --git a/Project2-Stream-Compaction/build/WorkEff_Scan.txt b/Project2-Stream-Compaction/build/WorkEff_Scan.txt
new file mode 100644
index 0000000..c5aa6b8
--- /dev/null
+++ b/Project2-Stream-Compaction/build/WorkEff_Scan.txt
@@ -0,0 +1,18 @@
+10 | size 1024  StreamCompaction::Efficient::scan Poweof2  1024 | 0.144384 (CUDA Measured)
+11 | size 2048  StreamCompaction::Efficient::scan Poweof2  2048 | 0.149504 (CUDA Measured)
+12 | size 4096  StreamCompaction::Efficient::scan Poweof2  4096 | 0.120096 (CUDA Measured)
+13 | size 8192  StreamCompaction::Efficient::scan Poweof2  8192 | 0.144224 (CUDA Measured)
+14 | size 16384  StreamCompaction::Efficient::scan Poweof2  16384 | 0.178176 (CUDA Measured)
+15 | size 32768  StreamCompaction::Efficient::scan Poweof2  32768 | 0.2072 (CUDA Measured)
+16 | size 65536  StreamCompaction::Efficient::scan Poweof2  65536 | 0.316352 (CUDA Measured)
+17 | size 131072  StreamCompaction::Efficient::scan Poweof2  131072 | 0.634336 (CUDA Measured)
+18 | size 262144  StreamCompaction::Efficient::scan Poweof2  262144 | 0.91856 (CUDA Measured)
+19 | size 524288  StreamCompaction::Efficient::scan Poweof2  524288 | 1.89235 (CUDA Measured)
+20 | size 1048576  StreamCompaction::Efficient::scan Poweof2  1048576 | 3.70922 (CUDA Measured)
+21 | size 2097152  StreamCompaction::Efficient::scan Poweof2  2097152 | 7.4793 (CUDA Measured)
+22 | size 4194304  StreamCompaction::Efficient::scan Poweof2  4194304 | 15.842 (CUDA Measured)
+23 | size 8388608  StreamCompaction::Efficient::scan Poweof2  8388608 | 28.4611 (CUDA Measured)
+24 | size 16777216  StreamCompaction::Efficient::scan Poweof2  16777216 | 56.9283 (CUDA Measured)
+25 | size 33554432  StreamCompaction::Efficient::scan Poweof2  33554432 | 115.696 (CUDA Measured)
+26 | size 67108864  StreamCompaction::Efficient::scan Poweof2  67108864 | 238.946 (CUDA Measured)
+27 | size 134217728  StreamCompaction::Efficient::scan Poweof2  134217728 | 493.241 (CUDA Measured)
diff --git a/Project2-Stream-Compaction/build/WorkEff_Scan_NP.txt b/Project2-Stream-Compaction/build/WorkEff_Scan_NP.txt
new file mode 100644
index 0000000..474e038
--- /dev/null
+++ b/Project2-Stream-Compaction/build/WorkEff_Scan_NP.txt
@@ -0,0 +1,18 @@
+10 | size 1024  StreamCompaction::Efficient::scan NonPoweof2  1024 | 0.161792 (CUDA Measured)
+11 | size 2048  StreamCompaction::Efficient::scan NonPoweof2  2048 | 0.132384 (CUDA Measured)
+12 | size 4096  StreamCompaction::Efficient::scan NonPoweof2  4096 | 0.132928 (CUDA Measured)
+13 | size 8192  StreamCompaction::Efficient::scan NonPoweof2  8192 | 0.181408 (CUDA Measured)
+14 | size 16384  StreamCompaction::Efficient::scan NonPoweof2  16384 | 0.219008 (CUDA Measured)
+15 | size 32768  StreamCompaction::Efficient::scan NonPoweof2  32768 | 0.222848 (CUDA Measured)
+16 | size 65536  StreamCompaction::Efficient::scan NonPoweof2  65536 | 0.296288 (CUDA Measured)
+17 | size 131072  StreamCompaction::Efficient::scan NonPoweof2  131072 | 0.504832 (CUDA Measured)
+18 | size 262144  StreamCompaction::Efficient::scan NonPoweof2  262144 | 0.892832 (CUDA Measured)
+19 | size 524288  StreamCompaction::Efficient::scan NonPoweof2  524288 | 2.23437 (CUDA Measured)
+20 | size 1048576  StreamCompaction::Efficient::scan NonPoweof2  1048576 | 3.67309 (CUDA Measured)
+21 | size 2097152  StreamCompaction::Efficient::scan NonPoweof2  2097152 | 7.51827 (CUDA Measured)
+22 | size 4194304  StreamCompaction::Efficient::scan NonPoweof2  4194304 | 14.0038 (CUDA Measured)
+23 | size 8388608  StreamCompaction::Efficient::scan NonPoweof2  8388608 | 28.6353 (CUDA Measured)
+24 | size 16777216  StreamCompaction::Efficient::scan NonPoweof2  16777216 | 56.4352 (CUDA Measured)
+25 | size 33554432  StreamCompaction::Efficient::scan NonPoweof2  33554432 | 115.666 (CUDA Measured)
+26 | size 67108864  StreamCompaction::Efficient::scan NonPoweof2  67108864 | 239.354 (CUDA Measured)
+27 | size 134217728  StreamCompaction::Efficient::scan NonPoweof2  134217728 | 493.4 (CUDA Measured)
diff --git a/Project2-Stream-Compaction/img/BlockSize_vs_Runtime.png b/Project2-Stream-Compaction/img/BlockSize_vs_Runtime.png
new file mode 100644
index 0000000..bbd12d9
Binary files /dev/null and b/Project2-Stream-Compaction/img/BlockSize_vs_Runtime.png differ
diff --git a/Project2-Stream-Compaction/img/BlockSz-128-DataSz-20.PNG b/Project2-Stream-Compaction/img/BlockSz-128-DataSz-20.PNG
new file mode 100644
index 0000000..c980577
Binary files /dev/null and b/Project2-Stream-Compaction/img/BlockSz-128-DataSz-20.PNG differ
diff --git a/Project2-Stream-Compaction/img/DownSweepScan.png b/Project2-Stream-Compaction/img/DownSweepScan.png
new file mode 100644
index 0000000..a8ecb33
Binary files /dev/null and b/Project2-Stream-Compaction/img/DownSweepScan.png differ
diff --git a/Project2-Stream-Compaction/img/NaiveScan.png b/Project2-Stream-Compaction/img/NaiveScan.png
new file mode 100644
index 0000000..55f53ca
Binary files /dev/null and b/Project2-Stream-Compaction/img/NaiveScan.png differ
diff --git a/Project2-Stream-Compaction/img/SC1.png b/Project2-Stream-Compaction/img/SC1.png
new file mode 100644
index 0000000..b73a929
Binary files /dev/null and b/Project2-Stream-Compaction/img/SC1.png differ
diff --git a/Project2-Stream-Compaction/img/SC2.png b/Project2-Stream-Compaction/img/SC2.png
new file mode 100644
index 0000000..cdb2eab
Binary files /dev/null and b/Project2-Stream-Compaction/img/SC2.png differ
diff --git a/Project2-Stream-Compaction/img/Scan1.png b/Project2-Stream-Compaction/img/Scan1.png
new file mode 100644
index 0000000..f0d07ad
Binary files /dev/null and b/Project2-Stream-Compaction/img/Scan1.png differ
diff --git a/Project2-Stream-Compaction/img/Scan1NP.png b/Project2-Stream-Compaction/img/Scan1NP.png
new file mode 100644
index 0000000..07d3ab6
Binary files /dev/null and b/Project2-Stream-Compaction/img/Scan1NP.png differ
diff --git a/Project2-Stream-Compaction/img/Scan2.png b/Project2-Stream-Compaction/img/Scan2.png
new file mode 100644
index 0000000..f010255
Binary files /dev/null and b/Project2-Stream-Compaction/img/Scan2.png differ
diff --git a/Project2-Stream-Compaction/img/Scan2NP.png b/Project2-Stream-Compaction/img/Scan2NP.png
new file mode 100644
index 0000000..e663e7d
Binary files /dev/null and b/Project2-Stream-Compaction/img/Scan2NP.png differ
diff --git a/Project2-Stream-Compaction/img/UpSweepScan.png b/Project2-Stream-Compaction/img/UpSweepScan.png
new file mode 100644
index 0000000..7ca9b14
Binary files /dev/null and b/Project2-Stream-Compaction/img/UpSweepScan.png differ
diff --git a/Project2-Stream-Compaction/src/main.cpp b/Project2-Stream-Compaction/src/main.cpp
index d016553..bc83133 100644
--- a/Project2-Stream-Compaction/src/main.cpp
+++ b/Project2-Stream-Compaction/src/main.cpp
@@ -13,139 +13,205 @@
 #include <stream_compaction/thrust.h>
 #include "testing_helpers.hpp"
 
-const int SIZE = 1 << 8; // feel free to change the size of array
-const int NPOT = SIZE - 3; // Non-Power-Of-Two
-int *a = new int[SIZE];
-int *b = new int[SIZE];
-int *c = new int[SIZE];
+#include <iostream>
+#include <fstream>
+using namespace std;
 
-int main(int argc, char* argv[]) {
-    // Scan tests
+//const int SIZE = 1 << 20; // feel free to change the size of array
+//const int NPOT = SIZE - 3; // Non-Power-Of-Two
+//int *a = new int[SIZE];
+//int *b = new int[SIZE];
+//int *c = new int[SIZE];
 
-    printf("\n");
-    printf("****************\n");
-    printf("** SCAN TESTS **\n");
-    printf("****************\n");
-
-    genArray(SIZE - 1, a, 50);  // Leave a 0 at the end to test that edge case
-    a[SIZE - 1] = 0;
-    printArray(SIZE, a, true);
-
-    // initialize b using StreamCompaction::CPU::scan you implement
-    // We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct.
-    // At first all cases passed because b && c are all zeroes.
-    zeroArray(SIZE, b);
-    printDesc("cpu scan, power-of-two");
-    StreamCompaction::CPU::scan(SIZE, b, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(SIZE, b, true);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu scan, non-power-of-two");
-    StreamCompaction::CPU::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(NPOT, b, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("naive scan, power-of-two");
-    StreamCompaction::Naive::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-	/* For bug-finding only: Array of 1s to help find bugs in stream compaction or scan
-	onesArray(SIZE, c);
-	printDesc("1s array for finding bugs");
-	StreamCompaction::Naive::scan(SIZE, c, a);
-	printArray(SIZE, c, true); */
-
-    zeroArray(SIZE, c);
-    printDesc("naive scan, non-power-of-two");
-    StreamCompaction::Naive::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient scan, power-of-two");
-    StreamCompaction::Efficient::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient scan, non-power-of-two");
-    StreamCompaction::Efficient::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(NPOT, c, true);
-    printCmpResult(NPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("thrust scan, power-of-two");
-    StreamCompaction::Thrust::scan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(SIZE, c, true);
-    printCmpResult(SIZE, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("thrust scan, non-power-of-two");
-    StreamCompaction::Thrust::scan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(NPOT, c, true);
-    printCmpResult(NPOT, b, c);
+int SIZE ; 
+int NPOT ;
+int *a ;
+int *b ;
+int *c ;
+
+
+int main(int argc, char* argv[]) {
+	// Scan tests
+
+	printf("\n");
+	printf("****************\n");
+	printf("** SCAN TESTS **\n");
+	printf("****************\n");
+
+	//ofstream outputFile1("Naive_Scan.txt");
+	//ofstream outputFile11("Naive_Scan_NP.txt");
+	//ofstream outputFile2("WorkEff_Scan.txt");
+	//ofstream outputFile22("WorkEff_Scan_NP.txt");
+
+
+	for (int sz = 20; sz < 21; sz++) {
+
+		//int SIZE = 1 << 20; // feel free to change the size of array
+		SIZE = 1 << sz;
+		NPOT = SIZE - 3; // Non-Power-Of-Two
+		a = new int[SIZE];
+		b = new int[SIZE];
+		c = new int[SIZE];
+
+		genArray(SIZE - 1, a, 50);  // Leave a 0 at the end to test that edge case
+		a[SIZE - 1] = 0;
+		printArray(SIZE, a, true);
+		
+		// CPU Scans ==================================================================================================
+
+		// initialize b using StreamCompaction::CPU::scan you implement
+		// We use b for further comparison. Make sure your StreamCompaction::CPU::scan is correct.
+		// At first all cases passed because b && c are all zeroes.
+
+		zeroArray(SIZE, b);
+		printDesc("cpu scan, power-of-two");
+		StreamCompaction::CPU::scan(SIZE, b, a);
+		printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
+		//outputFile1 << sz << " | size " << SIZE << " | " << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " " << "(std::chrono Measured)" << endl;
+		printArray(SIZE, b, true);
+
+		zeroArray(SIZE, c);
+		printDesc("cpu scan, non-power-of-two");
+		StreamCompaction::CPU::scan(NPOT, c, a);
+		printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
+		//outputFile2 << sz << " | size " << SIZE << " | " << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " " << "(std::chrono Measured)" << endl;
+		printArray(NPOT, b, true);
+		printCmpResult(NPOT, b, c);
+
+
+		// GPU naive Scan ===========================================================================================
+		
+		zeroArray(SIZE, c);
+		printDesc("GPU naive scan, power-of-two");
+		StreamCompaction::Naive::scan(SIZE, c, a);
+		printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile1 << sz << " | size " << SIZE << "  StreamCompaction::Naive::scan Poweof2  " << SIZE << " | " << StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation() << " (CUDA Measured)" << endl;
+		//printArray(SIZE, c, true);
+		printCmpResult(SIZE, b, c);
+
+		// For bug-finding only: Array of 1s to help find bugs in stream compaction or scan
+		//onesArray(SIZE, c);
+		//printDesc("1s array for finding bugs");
+		//StreamCompaction::Naive::scan(SIZE, c, a);
+		//printArray(SIZE, c, true); 
+
+		zeroArray(SIZE, c);
+		printDesc("GPU naive scan, non-power-of-two");
+		StreamCompaction::Naive::scan(NPOT, c, a);
+		printElapsedTime(StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile11 << sz << " | size " << SIZE << "  StreamCompaction::Naive::scan NonPoweof2  " << SIZE << " | " << StreamCompaction::Naive::timer().getGpuElapsedTimeForPreviousOperation() << " (CUDA Measured)" << endl;
+		//printArray(SIZE, c, true);
+		printCmpResult(NPOT, b, c);
+
+		// GPU Work Eff Scan ===========================================================================================
+
+		zeroArray(SIZE, c);
+		printDesc("work-efficient scan, power-of-two");
+		StreamCompaction::Efficient::scan(SIZE, c, a);
+		printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile2 << sz << " | size " << SIZE <<"  StreamCompaction::Efficient::scan Poweof2  " << SIZE << " | " << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " (CUDA Measured)" << endl;
+		printCmpResult(SIZE, b, c);
+
+		zeroArray(SIZE, c);
+		printDesc("work-efficient scan, non-power-of-two");
+		StreamCompaction::Efficient::scan(NPOT, c, a);
+		printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile22<< sz << " | size " << SIZE << "  StreamCompaction::Efficient::scan NonPoweof2  " << SIZE << " | " << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " (CUDA Measured)" << endl;
+		//printArray(NPOT, c, true);
+		printCmpResult(NPOT, b, c);
+
+		// GPU Thrust Scan ===========================================================================================
+
+		zeroArray(SIZE, c);
+		printDesc("thrust scan, power-of-two");
+		StreamCompaction::Thrust::scan(SIZE, c, a);
+		printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile1 << sz << " | size " << SIZE << " | " << StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation() << " " << "(CUDA Measured)" << endl;
+		//printArray(SIZE, c, true);
+		printCmpResult(SIZE, b, c);
+
+		zeroArray(SIZE, c);
+		printDesc("thrust scan, non-power-of-two");
+		StreamCompaction::Thrust::scan(NPOT, c, a);
+		printElapsedTime(StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile2 << sz << " | size " << SIZE << " | " << StreamCompaction::Thrust::timer().getGpuElapsedTimeForPreviousOperation() << " " << "(CUDA Measured)" << endl;
+		//printArray(NPOT, c, true);
+		printCmpResult(NPOT, b, c);
+
+	}
 
     printf("\n");
     printf("*****************************\n");
     printf("** STREAM COMPACTION TESTS **\n");
     printf("*****************************\n");
 
-    // Compaction tests
-
-    genArray(SIZE - 1, a, 4);  // Leave a 0 at the end to test that edge case
-    a[SIZE - 1] = 0;
-    printArray(SIZE, a, true);
-
-    int count, expectedCount, expectedNPOT;
-
-    // initialize b using StreamCompaction::CPU::compactWithoutScan you implement
-    // We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct.
-    zeroArray(SIZE, b);
-    printDesc("cpu compact without scan, power-of-two");
-    count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    expectedCount = count;
-    printArray(count, b, true);
-    printCmpLenResult(count, expectedCount, b, b);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu compact without scan, non-power-of-two");
-    count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    expectedNPOT = count;
-    printArray(count, c, true);
-    printCmpLenResult(count, expectedNPOT, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("cpu compact with scan");
-    count = StreamCompaction::CPU::compactWithScan(SIZE, c, a);
-    printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
-    printArray(count, c, true);
-    printCmpLenResult(count, expectedCount, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient compact, power-of-two");
-    count = StreamCompaction::Efficient::compact(SIZE, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(count, c, true);
-    printCmpLenResult(count, expectedCount, b, c);
-
-    zeroArray(SIZE, c);
-    printDesc("work-efficient compact, non-power-of-two");
-    count = StreamCompaction::Efficient::compact(NPOT, c, a);
-    printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
-    //printArray(count, c, true);
-    printCmpLenResult(count, expectedNPOT, b, c);
+	//ofstream outputFile1("SC_CPU.txt");
+	//ofstream outputFile11("SC_CPU_NP.txt");
+	//ofstream outputFile2("SC_CPU_withScan.txt");
+	//ofstream outputFile3("SC_WorkEff.txt");
+	//ofstream outputFile33("SC_WorkEff_NP.txt");
+
+	for (int sz = 20; sz < 21; sz++) {
+
+		//int SIZE = 1 << 20; // feel free to change the size of array
+		SIZE = 1 << sz;
+		NPOT = SIZE - 3; // Non-Power-Of-Two
+		a = new int[SIZE];
+		b = new int[SIZE];
+		c = new int[SIZE];
+
+		// Compaction tests
+
+		genArray(SIZE - 1, a, 4);  // Leave a 0 at the end to test that edge case
+		a[SIZE - 1] = 0;
+		printArray(SIZE, a, true);
+
+		int count, expectedCount, expectedNPOT;
+
+		// initialize b using StreamCompaction::CPU::compactWithoutScan you implement
+		// We use b for further comparison. Make sure your StreamCompaction::CPU::compactWithoutScan is correct.
+		zeroArray(SIZE, b);
+		printDesc("cpu compact without scan, power-of-two");
+		count = StreamCompaction::CPU::compactWithoutScan(SIZE, b, a);
+		printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
+		//outputFile1 << sz << " " << SIZE << " " << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " " << "(std::chrono Measured)" << endl;
+		expectedCount = count;
+		printArray(count, b, true);
+		printCmpLenResult(count, expectedCount, b, b);
+
+		zeroArray(SIZE, c);
+		printDesc("cpu compact without scan, non-power-of-two");
+		count = StreamCompaction::CPU::compactWithoutScan(NPOT, c, a);
+		printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
+		//outputFile11 << sz << " " << SIZE << " " << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " " << "(std::chrono Measured)" << endl;
+		expectedNPOT = count;
+		printArray(count, c, true);
+		printCmpLenResult(count, expectedNPOT, b, c);
+
+		zeroArray(SIZE, c);
+		printDesc("cpu compact with scan");
+		count = StreamCompaction::CPU::compactWithScan(SIZE, c, a);
+		printElapsedTime(StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation(), "(std::chrono Measured)");
+		//outputFile2 << sz << " " << SIZE << " " << StreamCompaction::CPU::timer().getCpuElapsedTimeForPreviousOperation() << " " << "(std::chrono Measured)" << endl;
+		printArray(count, c, true);
+		printCmpLenResult(count, expectedCount, b, c);
+
+		zeroArray(SIZE, c);
+		printDesc("work-efficient compact, power-of-two");
+		count = StreamCompaction::Efficient::compact(SIZE, c, a);
+		printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile3 << sz << " " << SIZE << " " << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " " << "(CUDA Measured)" << endl;
+		//printArray(count, c, true);
+		printCmpLenResult(count, expectedCount, b, c);
+
+		zeroArray(SIZE, c);
+		printDesc("work-efficient compact, non-power-of-two");
+		count = StreamCompaction::Efficient::compact(NPOT, c, a);
+		printElapsedTime(StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation(), "(CUDA Measured)");
+		//outputFile33 << sz << " " << SIZE << " " << StreamCompaction::Efficient::timer().getGpuElapsedTimeForPreviousOperation() << " " << "(CUDA Measured)" << endl;
+		//printArray(count, c, true);
+		printCmpLenResult(count, expectedNPOT, b, c);
+	}
 
     system("pause"); // stop Win32 console from closing on exit
 	delete[] a;
diff --git a/Project2-Stream-Compaction/src/testing_helpers.hpp b/Project2-Stream-Compaction/src/testing_helpers.hpp
index b28a8d2..daa629f 100644
--- a/Project2-Stream-Compaction/src/testing_helpers.hpp
+++ b/Project2-Stream-Compaction/src/testing_helpers.hpp
@@ -73,4 +73,5 @@ template<typename T>
 void printElapsedTime(T time, std::string note = "")
 {
 	std::cout << "   elapsed time: " << time << "ms    " << note << std::endl;
-}
\ No newline at end of file
+}
+
diff --git a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt
index cdbef77..4bb0dc2 100644
--- a/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt
+++ b/Project2-Stream-Compaction/stream_compaction/CMakeLists.txt
@@ -13,5 +13,5 @@ set(SOURCE_FILES
 
 cuda_add_library(stream_compaction
     ${SOURCE_FILES}
-    OPTIONS -arch=sm_20
+    OPTIONS -arch=sm_61
     )
diff --git a/Project2-Stream-Compaction/stream_compaction/common.cu b/Project2-Stream-Compaction/stream_compaction/common.cu
index 2ed6d63..ded83d0 100644
--- a/Project2-Stream-Compaction/stream_compaction/common.cu
+++ b/Project2-Stream-Compaction/stream_compaction/common.cu
@@ -24,7 +24,16 @@ namespace StreamCompaction {
          */
         __global__ void kernMapToBoolean(int n, int *bools, const int *idata) {
             // TODO
-        }
+			int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+			if (tid >= n) return;
+			
+			if(idata[tid]!=0){
+				bools[tid] = 1;
+			}
+			else {
+				bools[tid] = 0;
+			}
+		}
 
         /**
          * Performs scatter on an array. That is, for each element in idata,
@@ -33,6 +42,12 @@ namespace StreamCompaction {
         __global__ void kernScatter(int n, int *odata,
                 const int *idata, const int *bools, const int *indices) {
             // TODO
+			int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+			if (tid >= n) return;
+
+			if (bools[tid] == 1) {
+				odata[indices[tid]] = idata[tid];
+			}
         }
 
     }
diff --git a/Project2-Stream-Compaction/stream_compaction/common.h b/Project2-Stream-Compaction/stream_compaction/common.h
index 996997e..2938bc0 100644
--- a/Project2-Stream-Compaction/stream_compaction/common.h
+++ b/Project2-Stream-Compaction/stream_compaction/common.h
@@ -12,6 +12,7 @@
 
 #define FILENAME (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
 #define checkCUDAError(msg) checkCUDAErrorFn(msg, FILENAME, __LINE__)
+#define blockSize 128
 
 /**
  * Check for CUDA errors; print and exit if there was a problem.
diff --git a/Project2-Stream-Compaction/stream_compaction/cpu.cu b/Project2-Stream-Compaction/stream_compaction/cpu.cu
index a2d3e6c..a5c30b1 100644
--- a/Project2-Stream-Compaction/stream_compaction/cpu.cu
+++ b/Project2-Stream-Compaction/stream_compaction/cpu.cu
@@ -18,9 +18,22 @@ namespace StreamCompaction {
          * (Optional) For better understanding before starting moving to GPU, you can simulate your GPU scan in this function first.
          */
         void scan(int n, int *odata, const int *idata) {
-	        timer().startCpuTimer();
+			bool tmp=true;
+			try { 
+				timer().startCpuTimer();
+			}
+			catch (const std::runtime_error& e) {
+				tmp = false;
+			}
+			
             // TODO
-	        timer().endCpuTimer();
+			if (n > 0) {
+				odata[0] = 0;
+				for (int i = 0; i < n-1; i++) {
+					odata[i+1] = idata[i] + odata[i];
+				}
+			}
+			if(tmp ==true) timer().endCpuTimer();
         }
 
         /**
@@ -29,9 +42,19 @@ namespace StreamCompaction {
          * @returns the number of elements remaining after compaction.
          */
         int compactWithoutScan(int n, int *odata, const int *idata) {
-	        timer().startCpuTimer();
             // TODO
-	        timer().endCpuTimer();
+			if (n > 0) {
+				timer().startCpuTimer();
+				int counter = 0;
+				for (int i = 0; i < n; i++) {
+					if (idata[i] != 0) {
+						odata[counter] = idata[i];
+						counter+=1;
+					}
+				}
+				timer().endCpuTimer();
+				return counter;
+			}
             return -1;
         }
 
@@ -41,9 +64,37 @@ namespace StreamCompaction {
          * @returns the number of elements remaining after compaction.
          */
         int compactWithScan(int n, int *odata, const int *idata) {
-	        timer().startCpuTimer();
 	        // TODO
-	        timer().endCpuTimer();
+			if (n > 0) {
+				timer().startCpuTimer();
+
+				int * indicator = new int[n];
+				int * scanIndex = new int[n];
+				int tmp = 0;
+
+				// Compute indicator array
+				for (int i = 0; i < n; i++) {
+					if (idata[i] != 0) {
+						indicator[i] = 1;
+					}
+					else {
+						indicator[i] = 0;
+					}
+				}
+
+				// Compute scan
+				scan(n, scanIndex, indicator);
+
+				//Scatter
+				for (int i = 0; i < n; i++) {
+					if (indicator[i] == 1) {
+						odata[scanIndex[i]] = idata[i];
+						tmp = scanIndex[i];
+					}
+				}
+				timer().endCpuTimer();
+				return tmp+1;
+			}
             return -1;
         }
     }
diff --git a/Project2-Stream-Compaction/stream_compaction/efficient.cu b/Project2-Stream-Compaction/stream_compaction/efficient.cu
index 2db346e..d6640d5 100644
--- a/Project2-Stream-Compaction/stream_compaction/efficient.cu
+++ b/Project2-Stream-Compaction/stream_compaction/efficient.cu
@@ -12,15 +12,134 @@ namespace StreamCompaction {
             return timer;
         }
 
+		int *dev_arrayA;
+		int *dev_arrayB;
+
+		int *dev_bools;
+		int *dev_boolScans;
+
+		int *dev_idata;
+		int *dev_odata;
+		
+		int * dev_indices;
+			   		 
+		void printArray(int n, int *a, bool abridged = false) {
+			printf("    [ ");
+			for (int i = 0; i < n; i++) {
+				if (abridged && i + 2 == 15 && n > 16) {
+					i = n - 2;
+					printf("... ");
+				}
+				printf("%3d ", a[i]);
+			}
+			printf("]\n");
+		}
+
+
+		__global__ void kernEffScanUpSweep(int N, int pow2d, int pow2d1, int* arrA) {
+			int k = (blockIdx.x * blockDim.x) + threadIdx.x;
+			if (k >= N) return;
+
+			if ((k % pow2d1) == 0 && (k + pow2d1 - 1)<N && (k + pow2d - 1)<N ){
+				arrA[k + pow2d1 - 1] += arrA[k + pow2d - 1];
+			}
+		}
+
+		__global__ void kernEffScanDownSweep(int N, int pow2d, int pow2d1, int* arrA) {
+			int k = (blockIdx.x * blockDim.x) + threadIdx.x;
+			if (k >= N) return;
+			
+			int tmp = 0;
+			
+			if ((k % pow2d1) == 0 && (k + pow2d1 - 1) < N && (k + pow2d - 1) < N) {
+				tmp = arrA[k + pow2d -1];
+				arrA[k + pow2d - 1] = arrA[k + pow2d1 - 1];
+				arrA[k + pow2d1 - 1] += tmp;
+			}
+		}
+
+		__global__ void kernInitZero(int N, int* array) {
+			
+			int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
+			
+			if (tid < N) {
+				array[tid] = 0;
+			}
+		}
+
         /**
          * Performs prefix-sum (aka scan) on idata, storing the result into odata.
          */
         void scan(int n, int *odata, const int *idata) {
-            timer().startGpuTimer();
+
             // TODO
-            timer().endGpuTimer();
+			int n_new = n;
+
+			//check for non-2powerN
+			if (1 << ilog2ceil(n) != n)
+				n_new = (1 << ilog2ceil(n));
+
+			int fullBlocksPerGrid((n_new + blockSize - 1) / blockSize);
+
+			cudaMalloc((void**)&dev_arrayA, n_new * sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_arrayA failed!");
+
+			//Initialize to Zero
+			kernInitZero <<<fullBlocksPerGrid, blockSize >>> (n_new, dev_arrayA);
+			checkCUDAErrorFn("kernInitZero failed!");
+
+			// Fill dev_arrayA with idata
+			cudaMemcpy(dev_arrayA, idata, n * sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_arrayA failed!");
+
+			bool tmp = true;
+			try {
+				timer().startGpuTimer();
+				//printf("IN WEScan timer started!\n");
+			}
+			catch (const std::runtime_error& e) {
+				tmp = false;
+			}
+
+			// Upstream
+			int pow2d1 = 0;
+			int pow2d = 0;
+			for (int d = 0; d <= ilog2ceil(n_new)-1; d++) {
+				pow2d = 1 << (d);
+				pow2d1 = 1 << (d+1);
+				kernEffScanUpSweep << <fullBlocksPerGrid, blockSize >> > (n_new, pow2d, pow2d1, dev_arrayA);
+				checkCUDAErrorFn("kernEffScanUpSweep failed!");		
+			}
+
+			// Downstream
+			int *zero = new int[1];
+			zero[0] = 0;
+			cudaMemcpy(dev_arrayA + n_new-1, zero, 1*sizeof(int), cudaMemcpyHostToDevice);
+
+			for (int d = ilog2ceil(n_new)-1; d >= 0; d--) {
+				pow2d = 1 << (d);
+				pow2d1 = 1 << (d + 1);
+				kernEffScanDownSweep << <fullBlocksPerGrid, blockSize >> > (n_new, pow2d, pow2d1, dev_arrayA);
+				checkCUDAErrorFn("kernGenerateRandomPosArray failed!");
+			}
+
+			if (tmp == true) { 
+				timer().endGpuTimer();
+				//printf("IN WEScan timer ended!\n");
+			}
+
+			// Copy back to cpu
+			cudaMemcpy(odata, dev_arrayA, n*sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_arrayA to odata failed!");
+			
+			//printf("BBT Scan Final Computed : \n");
+			//printArray(n, odata, true);
+
+			cudaFree(dev_arrayA);
+			return;
         }
 
+
         /**
          * Performs stream compaction on idata, storing the result into odata.
          * All zeroes are discarded.
@@ -31,10 +150,68 @@ namespace StreamCompaction {
          * @returns      The number of elements remaining after compaction.
          */
         int compact(int n, int *odata, const int *idata) {
-            timer().startGpuTimer();
+           
             // TODO
-            timer().endGpuTimer();
-            return -1;
+			int * indices = new int[n];
+			int * bools = new int[n];
+			int fullBlocksPerGrid((n + blockSize - 1) / blockSize);
+
+			cudaMalloc((void**)&dev_bools, n * sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_bools failed!");
+
+			cudaMalloc((void**)&dev_idata, n*sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_arrayA failed!");
+
+			cudaMemcpy(dev_idata, idata, n*sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_arrayA failed!");
+			
+			timer().startGpuTimer();
+			
+			//Compute bools
+			Common::kernMapToBoolean<<<fullBlocksPerGrid, blockSize >>>(n, dev_bools, dev_idata);
+			checkCUDAErrorFn("kernMapToBoolean failed!");
+
+			cudaMemcpy(bools, dev_bools, n*sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from bools to dev_bools failed!");
+			
+			//compute scans
+			scan(n, indices, bools);
+
+			cudaMalloc((void**)&dev_indices, n*sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_indices failed!");
+
+			cudaMemcpy(dev_indices, indices, n*sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from indices to dev_indices failed!");
+
+			cudaMalloc((void**)&dev_odata, n*sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_indices failed!");
+
+			cudaMemcpy(dev_odata, odata, n*sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from indices to dev_indices failed!");
+
+			//scatter
+			Common::kernScatter<<<fullBlocksPerGrid, blockSize >>>(n, dev_odata, dev_idata, dev_bools, dev_indices);
+			checkCUDAErrorFn("kernScatter failed!");
+			
+			timer().endGpuTimer();
+
+			// Copy back to cpu
+			cudaMemcpy(odata, dev_odata, n*sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_odata to odata failed!");
+
+			//printf("GPU Compaction : \n");
+			//printArray(indices[n - 1], odata, true);
+
+			cudaFree(dev_bools);
+			cudaFree(dev_idata);
+			cudaFree(dev_indices);
+			cudaFree(dev_odata);
+			if (idata[n - 1] != 0) {
+				return indices[n - 1] + 1;
+			}
+			else {
+				return indices[n - 1];
+			}
         }
     }
 }
diff --git a/Project2-Stream-Compaction/stream_compaction/naive.cu b/Project2-Stream-Compaction/stream_compaction/naive.cu
index 4308876..49b8259 100644
--- a/Project2-Stream-Compaction/stream_compaction/naive.cu
+++ b/Project2-Stream-Compaction/stream_compaction/naive.cu
@@ -3,6 +3,7 @@
 #include "common.h"
 #include "naive.h"
 
+
 namespace StreamCompaction {
     namespace Naive {
         using StreamCompaction::Common::PerformanceTimer;
@@ -11,15 +12,95 @@ namespace StreamCompaction {
             static PerformanceTimer timer;
             return timer;
         }
+
+		int *dev_arrayA; 
+		int *dev_arrayB;
+
+		void printArray(int n, int *a, bool abridged = false) {
+			printf("    [ ");
+			for (int i = 0; i < n; i++) {
+				if (abridged && i + 2 == 15 && n > 16) {
+					i = n - 2;
+					printf("... ");
+				}
+				printf("%3d ", a[i]);
+			}
+			printf("]\n");
+		}
+
         // TODO: __global__
+		__global__ void kernPrefixSumScanArray(int N, int pow2d1, int* arrA, int*arrB) {
+			int k = (blockIdx.x * blockDim.x) + threadIdx.x;
+			if (k >= N) return;
+			
+			if (k >= pow2d1) {
+				arrB[k] = arrA[k - (pow2d1)] + arrA[k];			
+			}
+		}
 
-        /**
+		__global__ void kernExclusiveShiftArray(int N,  int* arrA, int*arrB) {
+			int k = (blockIdx.x * blockDim.x) + threadIdx.x;
+			
+			if (k >= N) return;
+
+			if (k == 0) {
+				arrA[0] = 0;
+			}
+			else {
+				arrA[k] = arrB[k-1];
+			}
+		}
+
+		/**
          * Performs prefix-sum (aka scan) on idata, storing the result into odata.
          */
         void scan(int n, int *odata, const int *idata) {
-            timer().startGpuTimer();
-            // TODO
-            timer().endGpuTimer();
-        }
+            
+			int fullBlocksPerGrid((n + blockSize - 1) / blockSize);
+			
+			cudaMalloc((void**)&dev_arrayA, n*sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_arrayA failed!");
+
+			cudaMalloc((void**)&dev_arrayB, n*sizeof(int));
+			checkCUDAErrorFn("cudaMalloc dev_arrayB failed!");
+			
+			// Fill dev_arrayA with idata
+			cudaMemcpy(dev_arrayA, idata, n*sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_arrayA failed!");
+			
+			// Fill dev_arrayB with idata
+			cudaMemcpy(dev_arrayB, idata, n*sizeof(int), cudaMemcpyHostToDevice);
+			checkCUDAErrorFn("cudaMemcpyToSymbol from idata to dev_arrayB failed!");
+
+			timer().startGpuTimer();
+
+			// Call Scan Kernel
+			int pow2d1 = 0;
+
+			for (int d = 1; d <= ilog2ceil(n); d++) {
+				pow2d1 = 1 << (d - 1);
+				kernPrefixSumScanArray<<<fullBlocksPerGrid, blockSize>>>(n, pow2d1, dev_arrayA, dev_arrayB);
+				checkCUDAErrorFn("kernGenerateRandomPosArray failed!");
+				
+				//Copy
+				cudaMemcpy(dev_arrayA, dev_arrayB, n*sizeof(int), cudaMemcpyDeviceToDevice);
+			}
+
+			kernExclusiveShiftArray <<<fullBlocksPerGrid, blockSize >>> (n, dev_arrayA, dev_arrayB);
+			checkCUDAErrorFn("kernExclusiveShiftArray failed!");
+
+			timer().endGpuTimer();
+
+			// Fill dev_arrayA with idata
+			cudaMemcpy(odata, dev_arrayA, n*sizeof(int), cudaMemcpyDeviceToHost);
+			checkCUDAErrorFn("cudaMemcpyFromSymbol from dev_arrayA to odata failed!");
+
+			//printf("Final Computed after shifting: \n");
+			//printArray(n, odata, true);
+			//printf("Computed: \n");
+
+			cudaFree(dev_arrayA);
+			cudaFree(dev_arrayB);
+		}
     }
 }
diff --git a/Project2-Stream-Compaction/stream_compaction/thrust.cu b/Project2-Stream-Compaction/stream_compaction/thrust.cu
index 1def45e..e5310b3 100644
--- a/Project2-Stream-Compaction/stream_compaction/thrust.cu
+++ b/Project2-Stream-Compaction/stream_compaction/thrust.cu
@@ -17,12 +17,28 @@ namespace StreamCompaction {
         /**
          * Performs prefix-sum (aka scan) on idata, storing the result into odata.
          */
+
         void scan(int n, int *odata, const int *idata) {
-            timer().startGpuTimer();
+            
             // TODO use `thrust::exclusive_scan`
             // example: for device_vectors dv_in and dv_out:
             // thrust::exclusive_scan(dv_in.begin(), dv_in.end(), dv_out.begin());
-            timer().endGpuTimer();
+
+			thrust::host_vector<int>host_idata(idata, idata+n);
+			thrust::host_vector<int>host_odata(odata, odata+n);
+			checkCUDAErrorFn("thrust::host_vector host_odata or host_idata failed!");
+			printf("Created Thrust pointers \n");
+
+			thrust::device_vector<int> device_idata = host_idata;
+			thrust::device_vector<int> device_odata = host_odata;
+			checkCUDAErrorFn("thrust::device_vector device_idata or device_odata failed!");
+
+			timer().startGpuTimer();
+			thrust::exclusive_scan(device_idata.begin(), device_idata.end(), device_odata.begin());
+			timer().endGpuTimer();
+
+			// Copy back to cpu
+			thrust::copy(device_odata.begin(), device_odata.end(), odata);
         }
     }
 }
diff --git a/README.md b/README.md
index 3a0b2fe..8ade9d8 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,14 @@
-CUDA Number Algorithms
-======================
+## Project 2 - CUDA Number Algorithms
+**University of Pennsylvania
+CIS 565: GPU Programming and Architecture**
 
-**University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2**
+* Author: Chhavi Sharma ([LinkedIn](https://www.linkedin.com/in/chhavi275/))
+* Tested on: Windows 10, Intel Core(R) Core(TM) i7-6700 CPU @ 3.40GHz 16GB, 
+             NVIDIA Quadro P1000 4GB (MOORE100B-06)
 
-* (TODO) YOUR NAME HERE
-  * (TODO) [LinkedIn](), [personal website](), [twitter](), etc.
-* Tested on: (TODO) Windows 22, i7-2222 @ 2.22GHz 22GB, GTX 222 222MB (Moore 2222 Lab)
-
-### (TODO: Your README)
-
-Link to the readmes of the other two subprojects.
-
-Add anything else you think is relevant up to this point.
-(Remember, this is public, so don't put anything here that you don't want to share with the world.)
+### Index
+This project consists of two subcomponents:
+- [Stream-Compaction](Project2-Stream-Compaction/README.md)
+- [Character-Recognition](Project2-Character-Recognition/README.md)
 
+Please see the individual sub-folders for detials.
diff --git a/stats.xlsx b/stats.xlsx
new file mode 100644
index 0000000..f95dc91
Binary files /dev/null and b/stats.xlsx differ
