1 files changed, 0 insertions, 72 deletions
diff --git a/ot/gpu/cudamat/examples/rbm_numpy.py b/ot/gpu/cudamat/examples/rbm_numpy.py
deleted file mode 100644
index 1331566..0000000
--- a/ot/gpu/cudamat/examples/rbm_numpy.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from __future__ import division
-import time
-import numpy as np
-import util
-
-# load data
-util.load('mnist.dat', globals())
-dat = dat/255.
-
-# training parameters
-epsilon = 0.01
-momentum = 0.9
-
-num_epochs = 10
-batch_size = 64
-num_batches = dat.shape[1]//batch_size
-
-# model parameters
-num_vis = dat.shape[0]
-num_hid = 1024
-
-# initialize weights
-w_vh = 0.1 * np.random.randn(num_vis, num_hid)
-w_v = np.zeros((num_vis, 1))
-w_h = np.zeros((num_hid, 1))
-
-# initialize weight updates
-wu_vh = np.zeros((num_vis, num_hid))
-wu_v = np.zeros((num_vis, 1))
-wu_h = np.zeros((num_hid, 1))
-
-start_time = time.time()
-for epoch in range(num_epochs):
-    print("Epoch %i" % (epoch + 1))
-    err = []
-
-    for batch in range(num_batches):
-        v_true = dat[:, batch*batch_size:(batch + 1)*batch_size]
-        v = v_true
-
-        # apply momentum
-        wu_vh *= momentum
-        wu_v *= momentum
-        wu_h *= momentum
-
-        # positive phase
-        h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h)))
-
-        wu_vh += np.dot(v, h.T)
-        wu_v += v.sum(1)[:, np.newaxis]
-        wu_h += h.sum(1)[:, np.newaxis]
-
-        # sample hiddens
-        h = 1. * (h > np.random.rand(num_hid, batch_size))
-
-        # negative phase
-        v = 1. / (1 + np.exp(-(np.dot(w_vh, h) + w_v)))
-        h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h)))
-
-        wu_vh -= np.dot(v, h.T)
-        wu_v -= v.sum(1)[:, np.newaxis]
-        wu_h -= h.sum(1)[:, np.newaxis]
-
-        # update weights
-        w_vh += epsilon/batch_size * wu_vh
-        w_v += epsilon/batch_size * wu_v
-        w_h += epsilon/batch_size * wu_h
-
-        err.append(np.mean((v - v_true)**2))
-
-    print("Mean squared error: %f" % np.mean(err))
-    print("Time: %f" % (time.time() - start_time))