--- a/deep/convolutional_dae/scdae.py	Thu Mar 25 12:20:27 2010 -0400
+++ b/deep/convolutional_dae/scdae.py	Fri Mar 26 17:24:17 2010 -0400
@@ -7,6 +7,7 @@
 import theano.tensor as T
 
 from itertools import izip
+from ift6266.utils.seriestables import *
 
 class cdae(LayerStack):
     def __init__(self, filter_size, num_filt, num_in, subsampling, corruption,
@@ -68,6 +69,9 @@
     
     n = scdae_net((1,)+img_size, batch_size, filter_sizes, num_filters, subs,
                   noise, mlp_sizes, out_size, dtype, batch_size)
+
+    n.save('start.net')
+
     x = T.fmatrix('x')
     y = T.ivector('y')
     
@@ -110,12 +114,12 @@
 
     trainf = select_f2(trainf_opt, trainf_reg, batch_size)
     evalf = select_f2(evalf_opt, evalf_reg, batch_size)
-    return pretrain_funcs, trainf, evalf
+    return pretrain_funcs, trainf, evalf, n
 
-def do_pretrain(pretrain_funcs, pretrain_epochs):
-    for f in pretrain_funcs:
-        for i in xrange(pretrain_epochs):
-            f()
+def do_pretrain(pretrain_funcs, pretrain_epochs, serie):
+    for layer, f in enumerate(pretrain_funcs):
+        for epoch in xrange(pretrain_epochs):
+            serie.append((layer, epoch), f())
 
 def massage_funcs(train_it, dset, batch_size, pretrain_funcs, trainf, evalf):
     def pretrain_f(f):
@@ -156,16 +160,52 @@
         for e in itf(*args, **kwargs):
             yield e
 
+def create_series():
+    import tables
+
+    series = {}
+    h5f = tables.openFile('series.h5', 'w')
+
+    series['recons_error'] = AccumulatorSeriesWrapper(
+        base_series=ErrorSeries(error_name='reconstruction_error',
+                                table_name='reconstruction_error',
+                                hdf5_file=h5f,
+                                index_names=('layer', 'epoch'),
+                                title="Reconstruction error (mse)")
+        reduce_every=100)
+        
+    series['training_err'] = AccumulatorSeriesWrapper(
+        base_series=ErrorSeries(error_name='training_error',
+                                table_name='training_error'
+                                hdf5_file=h5f,
+                                index_names=('iter',),
+                                titles='Training error (nll)')
+        reduce_every=100)
+
+    series['valid_err'] = ErrorSeries(error_name='valid_error',
+                                         table_name='valid_error'
+                                         hdf5_file=h5f,
+                                         index_names=('iter',),
+                                         titles='Validation error (class)')
+
+    series['test_err'] = ErrorSeries(error_name='test_error',
+                                         table_name='test_error'
+                                         hdf5_file=h5f,
+                                         index_names=('iter',),
+                                         titles='Test error (class)')
+
 def run_exp(state, channel):
     from ift6266 import datasets
     from sgd_opt import sgd_opt
     import sys, time
 
-    channel.save()
-
     # params: bsize, pretrain_lr, train_lr, nfilts1, nfilts2, nftils3, nfilts4
     #         pretrain_rounds
 
+    pylearn.version.record_versions(state, [theano,ift6266,pylearn])
+    # TODO: maybe record pynnet version?
+    channel.save()
+
     dset = dataset.nist_all()
 
     nfilts = []
@@ -182,7 +222,7 @@
     subs = [(2,2)]*len(nfilts)
     noise = [state.noise]*len(nfilts)
 
-    pretrain_funcs, trainf, evalf = build_funcs(
+    pretrain_funcs, trainf, evalf, net = build_funcs(
         img_size=(32, 32),
         batch_size=state.bsize,
         filter_sizes=fsizes,
@@ -198,11 +238,13 @@
     pretrain_fs, train, valid, test = massage_funcs(
         state.bsize, dset, pretrain_funcs, trainf, evalf)
 
-    do_pretrain(pretrain_fs, state.pretrain_rounds)
+    series = create_series()
+
+    do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error'])
 
     sgd_opt(train, valid, test, training_epochs=100000, patience=10000,
             patience_increase=2., improvement_threshold=0.995,
-            validation_frequency=2500)
+            validation_frequency=2500, series=series, net=net)
 
 if __name__ == '__main__':
     from ift6266 import datasets
@@ -212,7 +254,7 @@
     batch_size = 100
     dset = datasets.mnist()
 
-    pretrain_funcs, trainf, evalf = build_funcs(
+    pretrain_funcs, trainf, evalf, net = build_funcs(
         img_size = (28, 28),
         batch_size=batch_size, filter_sizes=[(5,5), (3,3)],
         num_filters=[4, 4], subs=[(2,2), (2,2)], noise=[0.2, 0.2],
@@ -227,7 +269,7 @@
     print "pretraining ...",
     sys.stdout.flush()
     start = time.time()
-    do_pretrain(pretrain_fs, 2500)
+    do_pretrain(pretrain_fs, 2500, DummySeries())
     end = time.time()
     print "done (in", end-start, "s)"