1 files changed, 0 insertions, 98 deletions
diff --git a/ot/gpu/cudamat/examples/rbm_cudamat.py b/ot/gpu/cudamat/examples/rbm_cudamat.py
deleted file mode 100644
index 3f6a900..0000000
--- a/ot/gpu/cudamat/examples/rbm_cudamat.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from __future__ import division
-import time
-import numpy as np
-import cudamat as cm
-import util
-
-# initialize CUDA
-cm.cublas_init()
-cm.CUDAMatrix.init_random(1)
-
-# load data
-util.load('mnist.dat', globals())
-dev_dat = cm.CUDAMatrix(cm.reformat(dat/255.))
-
-# training parameters
-epsilon = 0.1
-momentum = 0.9
-
-num_epochs = 30
-batch_size = 128
-num_batches = dat.shape[1]//batch_size
-
-# model parameters
-num_vis = dat.shape[0]
-num_hid = 4096
-
-# initialize weights
-w_vh = cm.CUDAMatrix(0.1 * np.random.randn(num_vis, num_hid))
-w_v = cm.CUDAMatrix(np.zeros((num_vis, 1)))
-w_h = cm.CUDAMatrix(-4.*np.ones((num_hid, 1)))
-
-# initialize weight updates
-wu_vh = cm.CUDAMatrix(np.zeros((num_vis, num_hid)))
-wu_v = cm.CUDAMatrix(np.zeros((num_vis, 1)))
-wu_h = cm.CUDAMatrix(np.zeros((num_hid, 1)))
-
-# initialize temporary storage
-v = cm.empty((num_vis, batch_size))
-h = cm.empty((num_hid, batch_size))
-r = cm.empty((num_hid, batch_size))
-
-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
-        v_true = dev_dat.slice(batch*batch_size,(batch + 1)*batch_size)
-        v.assign(v_true)
-
-        # apply momentum
-        wu_vh.mult(momentum)
-        wu_v.mult(momentum)
-        wu_h.mult(momentum)
-
-        # positive phase
-        cm.dot(w_vh.T, v, target = h)
-        h.add_col_vec(w_h)
-        h.apply_sigmoid()
-
-        wu_vh.add_dot(v, h.T)
-        wu_v.add_sums(v, axis = 1)
-        wu_h.add_sums(h, axis = 1)
-
-        # sample hiddens
-        r.fill_with_rand()
-        r.less_than(h, target = h)
-
-        # negative phase
-        cm.dot(w_vh, h, target = v)
-        v.add_col_vec(w_v)
-        v.apply_sigmoid()
-
-        cm.dot(w_vh.T, v, target = h)
-        h.add_col_vec(w_h)
-        h.apply_sigmoid()
-
-        wu_vh.subtract_dot(v, h.T)
-        wu_v.add_sums(v, axis = 1, mult = -1.)
-        wu_h.add_sums(h, axis = 1, mult = -1.)
-
-        # update weights
-        w_vh.add_mult(wu_vh, epsilon/batch_size)
-        w_v.add_mult(wu_v, epsilon/batch_size)
-        w_h.add_mult(wu_h, epsilon/batch_size)
-
-        # calculate reconstruction error
-        v.subtract(v_true)
-        err.append(v.euclid_norm()**2/(num_vis*batch_size))
-
-    print("Mean squared error: %f" % np.mean(err))
-    print("Time: %f" % (time.time() - start_time))
-
-w_vh.copy_to_host()
-util.save('weights.dat', 'w_vh', {'w_vh': w_vh.numpy_array})
-
-cm.cublas_shutdown()