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diff --git a/ot/gpu/cudamat/examples/nn_cudamat.py b/ot/gpu/cudamat/examples/nn_cudamat.py
deleted file mode 100644
index 7c56c7d..0000000
--- a/ot/gpu/cudamat/examples/nn_cudamat.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# This file shows how to implement a single hidden layer neural network for
-# performing binary classification on the GPU using cudamat.
-
-from __future__ import division
-import pdb
-import time
-import numpy as np
-import cudamat as cm
-from cudamat import learn as cl
-import util
-
-# initialize CUDA
-cm.cublas_init()
-
-# load data
-util.load('mnist49.dat', globals())
-
-# Put training data onto the GPU.
-dat_train = dat_train/255.
-dat_train = dat_train - (np.mean(dat_train, 1)+10**-8)[:, np.newaxis]
-dev_train = cm.CUDAMatrix(dat_train)
-dev_lbl = cm.CUDAMatrix(lbl_train)
-
-# training parameters
-epsilon = 0.01
-momentum = 0.9
-
-num_epochs = 30
-batch_size = 128
-num_batches = dat_train.shape[1]//batch_size
-
-# model parameters
-dim_in = dat_train.shape[0]
-dim_out = 1
-num_hid = 1024
-
-# initialize weights
-w_w1 = cm.CUDAMatrix(dim_in ** -0.5 * np.random.randn(dim_in, num_hid))
-w_b1 = cm.CUDAMatrix(np.zeros((num_hid, 1)))
-w_w2 = cm.CUDAMatrix(num_hid ** -0.5 * np.random.randn(num_hid, dim_out))
-w_b2 = cm.CUDAMatrix(np.zeros((dim_out, 1)))
-
-# initialize weight update matrices
-wu_w1 = cm.empty(w_w1.shape).assign(0)
-wu_b1 = cm.empty(w_b1.shape).assign(0)
-wu_w2 = cm.empty(w_w2.shape).assign(0)
-wu_b2 = cm.empty(w_b2.shape).assign(0)
-
-# initialize temporary storage
-h = cm.empty((num_hid, batch_size))
-out = cm.empty((dim_out, batch_size))
-delta = cm.empty((num_hid, batch_size))
-
-# Train neural network.
-start_time = time.time()
-for epoch in range(num_epochs):
-    print("Epoch %i" % (epoch + 1))
-    err = []
-
-    for batch in range(num_batches):
-        # get current minibatch
-        inp = dev_train.slice(batch*batch_size,(batch + 1)*batch_size)
-        target = dev_lbl.slice(batch*batch_size,(batch + 1)*batch_size)
-
-        # forward pass
-        cm.dot(w_w1.T, inp, target = h)
-
-        h.add_col_vec(w_b1)
-        h.apply_sigmoid()
-
-        cm.dot(w_w2.T, h, target = out)
-
-        out.add_col_vec(w_b2)
-        out.apply_sigmoid()
-
-        # back prop errors
-        out.subtract(target) # compute error
-
-        # gradients for w_w2 and w_b2
-        wu_w2.add_dot(h, out.T, beta = momentum)
-        wu_b2.add_sums(out, axis = 1, beta = momentum)
-
-        # compute delta
-        cm.dot(w_w2, out, target = delta)
-
-        # delta = delta * h * (1 - h)
-        cl.mult_by_sigmoid_deriv(delta, h)
-
-        # gradients for w_w1 and w_b1
-        wu_w1.add_dot(inp, delta.T, beta = momentum)
-        wu_b1.add_sums(delta, axis = 1, beta = momentum)
-
-        # update weights
-        w_w1.subtract_mult(wu_w1, epsilon/batch_size)
-        w_b1.subtract_mult(wu_b1, epsilon/batch_size)
-        w_w2.subtract_mult(wu_w2, epsilon/batch_size)
-        w_b2.subtract_mult(wu_b2, epsilon/batch_size)
-
-        # calculate error on current minibatch 
-        err.append(np.abs(out.asarray())>0.5)
-
-    print("Training misclassification rate: %f" % np.mean(err))
-    print("Time: %f" % (time.time() - start_time))
-
-# Evaluate neural network on test data.
-
-# Load test data onto the GPU.
-dat_test = dat_test/255.
-dat_test = dat_test - np.mean(dat_test, 1)[:, np.newaxis]
-dev_test = cm.CUDAMatrix(dat_test)
-dev_lbl = cm.CUDAMatrix(lbl_test)
-
-# Initalize temporary storage.
-h = cm.empty((num_hid, dat_test.shape[1]))
-out = cm.empty((dim_out, dat_test.shape[1]))
-
-# forward pass
-cm.dot(w_w1.T, dev_test, target = h)
-
-h.add_col_vec(w_b1)
-h.apply_sigmoid()
-
-cm.dot(w_w2.T, h, target = out)
-
-out.add_col_vec(w_b2)
-out.apply_sigmoid()
-
-# compute error
-out.subtract(dev_lbl)
-
-print("Testing misclassification rate: %f" % np.mean(np.abs(out.asarray())>0.5))
-
-cm.cublas_shutdown()