Mercurial > pylearn
changeset 388:98ca97cc9910
Debugging simple AA
| author | Joseph Turian <turian@gmail.com> |
|---|---|
| date | Tue, 08 Jul 2008 17:41:26 -0400 |
| parents | dace8b9743af |
| children | ec8aadb6694d |
| files | simple_autoassociator.py/README.txt simple_autoassociator.py/globals.py simple_autoassociator.py/graph.py simple_autoassociator.py/main.py simple_autoassociator.py/model.py sparse_random_autoassociator/README.txt |
| diffstat | 6 files changed, 21 insertions(+), 7 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/simple_autoassociator.py/README.txt Tue Jul 08 17:41:26 2008 -0400 @@ -0,0 +1,1 @@ +This may be buggy. -jpt
--- a/simple_autoassociator.py/globals.py Tue Jul 08 17:08:23 2008 -0400 +++ b/simple_autoassociator.py/globals.py Tue Jul 08 17:41:26 2008 -0400 @@ -3,8 +3,10 @@ """ #INPUT_DIMENSION = 1000 +#INPUT_DIMENSION = 100 INPUT_DIMENSION = 10 -HIDDEN_DIMENSION = 20 -LEARNING_RATE = 0.1 +#HIDDEN_DIMENSION = 20 +HIDDEN_DIMENSION = 4 +LEARNING_RATE = 0.01 LR = LEARNING_RATE SEED = 666
--- a/simple_autoassociator.py/graph.py Tue Jul 08 17:08:23 2008 -0400 +++ b/simple_autoassociator.py/graph.py Tue Jul 08 17:41:26 2008 -0400 @@ -14,12 +14,13 @@ h = sigmoid(dot(x, w1) + b1) y = sigmoid(dot(h, w2) + b2) -loss = t.sum(binary_crossentropy(y, x)) +loss_unsummed = binary_crossentropy(y, x) +loss = t.sum(loss_unsummed) (gw1, gb1, gw2, gb2) = t.grad(loss, [w1, b1, w2, b2]) import theano.compile inputs = [x, w1, b1, w2, b2] -outputs = [y, loss, gw1, gb1, gw2, gb2] +outputs = [y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2] trainfn = theano.compile.function(inputs, outputs)
--- a/simple_autoassociator.py/main.py Tue Jul 08 17:08:23 2008 -0400 +++ b/simple_autoassociator.py/main.py Tue Jul 08 17:41:26 2008 -0400 @@ -18,7 +18,7 @@ nonzero_instances = [] nonzero_instances.append({1: 0.1, 5: 0.5, 9: 1}) nonzero_instances.append({2: 0.3, 5: 0.5, 8: 0.8}) -nonzero_instances.append({1: 0.2, 2: 0.3, 5: 0.5}) +#nonzero_instances.append({1: 0.2, 2: 0.3, 5: 0.5}) import model model = model.Model()
--- a/simple_autoassociator.py/model.py Tue Jul 08 17:08:23 2008 -0400 +++ b/simple_autoassociator.py/model.py Tue Jul 08 17:41:26 2008 -0400 @@ -28,11 +28,17 @@ for idx in instance.keys(): x[idx] = instance[idx] - (y, loss, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) + (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) print print "instance:", instance + print "x:", x print "OLD y:", y + print "NEW loss (unsummed):", loss_unsummed print "OLD total loss:", loss + print "gw1:", gw1 + print "gb1:", gb1 + print "gw2:", gw2 + print "gb2:", gb2 # SGD update self.parameters.w1 -= LR * gw1 @@ -41,6 +47,9 @@ self.parameters.b2 -= LR * gb2 # Recompute the loss, to make sure it's descreasing - (y, loss, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) + (y, h, loss, loss_unsummed, gw1, gb1, gw2, gb2) = trainfn(x, self.parameters.w1, self.parameters.b1, self.parameters.w2, self.parameters.b2) print "NEW y:", y + print "NEW loss (unsummed):", loss_unsummed print "NEW total loss:", loss + print h + print self.parameters