From 16f51f971607efab2c73958d207c582b389406c8 Mon Sep 17 00:00:00 2001
From: Leo gautheron <gautheron@iv-cm-359.creatis.insa-lyon.fr>
Date: Thu, 20 Apr 2017 12:12:15 +0200
Subject: sinkhorn GPU implementation

---
 ot/gpu/cudamat/examples/rbm_numpy.py | 72 ++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 ot/gpu/cudamat/examples/rbm_numpy.py

(limited to 'ot/gpu/cudamat/examples/rbm_numpy.py')

diff --git a/ot/gpu/cudamat/examples/rbm_numpy.py b/ot/gpu/cudamat/examples/rbm_numpy.py
new file mode 100644
index 0000000..1331566
--- /dev/null
+++ b/ot/gpu/cudamat/examples/rbm_numpy.py
@@ -0,0 +1,72 @@
+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))
-- 
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