# HG changeset patch
# User Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
# Date 1211559774 14400
# Node ID df3fae88ab46499b702338e76c63da51d93d02a1
# Parent  44dd9b6448c56a0a7540f446ef9b2228a384eb0f
small debugging

diff -r 44dd9b6448c5 -r df3fae88ab46 dataset.py
--- a/dataset.py	Thu May 22 19:08:46 2008 -0400
+++ b/dataset.py	Fri May 23 12:22:54 2008 -0400
@@ -245,8 +245,7 @@
             if n_batches is not None:
                 ds_nbatches = min(n_batches,ds_nbatches)
             if fieldnames:
-                if not dataset.hasFields(*fieldnames):
-                    raise ValueError('field not present', fieldnames)
+                assert dataset.hasFields(*fieldnames)
             else:
                 self.fieldnames=dataset.fieldNames()
             self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size,
@@ -970,16 +969,7 @@
         for fieldname, fieldcolumns in self.fields_columns.items():
             if type(fieldcolumns) is int:
                 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1]
-
-                if 0:
-                    #I changed this because it didn't make sense to me,
-                    # and it made it more difficult to write my learner.
-                    # If it breaks stuff, let's talk about it.
-                    # - James 22/05/2008
-                    self.fields_columns[fieldname]=[fieldcolumns]
-                else:
-                    self.fields_columns[fieldname]=fieldcolumns
-
+                self.fields_columns[fieldname]=[fieldcolumns]
             elif type(fieldcolumns) is slice:
                 start,step=None,None
                 if not fieldcolumns.start:
diff -r 44dd9b6448c5 -r df3fae88ab46 denoising_aa.py
--- a/denoising_aa.py	Thu May 22 19:08:46 2008 -0400
+++ b/denoising_aa.py	Fri May 23 12:22:54 2008 -0400
@@ -31,6 +31,7 @@
 
 def squash_affine_formula(squash_function=sigmoid):
     """
+    Simply does: squash_function(b + xW)
     By convention prefix the parameters by _
     """
     class SquashAffineFormula(Formulas):
@@ -53,7 +54,7 @@
     class ProbabilisticClassifierLossFormula(Formulas):
         a = t.matrix() # of dimensions minibatch_size x n_classes, pre-softmax output
         target_class = t.ivector() # dimension (minibatch_size)
-        nll, probability_predictions = crossentropy_softmax_1hot(a, target_class)
+        nll, probability_predictions = crossentropy_softmax_1hot(a, target_class) # defined in nnet_ops.py
     return ProbabilisticClassifierLossFormula()
 
 def binomial_cross_entropy_formula():
@@ -64,6 +65,8 @@
         # using the identity softplus(a) - softplus(-a) = a,
         # we obtain that q log(p) + (1-q) log(1-p) = q a - softplus(a)
         nll = -t.sum(q*a - softplus(-a))
+    # next line was missing... hope it's all correct above
+    return BinomialCrossEntropyFormula()
 
 def squash_affine_autoencoder_formula(hidden_squash=t.tanh,
                                       reconstruction_squash=sigmoid,
@@ -102,9 +105,33 @@
         self.denoising_autoencoder_formula = corruption_formula + autoencoder.rename(x='corrupted_x')
         
     def __call__(self, training_set=None):
+        """ Allocate and optionnaly train a model"""
         model = DenoisingAutoEncoderModel(self)
         if training_set:
-            print 'what do I do if training set????'
+            print 'DenoisingAutoEncoder(): what do I do if training_set????'
+            # copied from mlp_factory_approach:
+            if len(trainset) == sys.maxint:
+                raise NotImplementedError('Learning from infinite streams is not supported')
+            nval = int(self.validation_portion * len(trainset))
+            nmin = len(trainset) - nval
+            assert nmin >= 0
+            minset = trainset[:nmin] #real training set for minimizing loss
+            valset = trainset[nmin:] #validation set for early stopping
+            best = model
+            for stp in self.early_stopper():
+                model.update(
+                    minset.minibatches([input, target], minibatch_size=min(32,
+                        len(trainset))))
+                #print 'mlp.__call__(), we did an update'
+                if stp.set_score:
+                    stp.score = model(valset, ['loss_01'])
+                    if (stp.score < stp.best_score):
+                        best = copy.copy(model)
+            model = best
+            # end of the copy from mlp_factory_approach
+ 
+        return model
+
             
     def compile(self, inputs, outputs):
         return theano.function(inputs,outputs,unpack_single=False,linker=self.linker)
diff -r 44dd9b6448c5 -r df3fae88ab46 mlp_factory_approach.py
--- a/mlp_factory_approach.py	Thu May 22 19:08:46 2008 -0400
+++ b/mlp_factory_approach.py	Fri May 23 12:22:54 2008 -0400
@@ -4,7 +4,7 @@
 import theano
 from theano import tensor as t
 
-import dataset, nnet_ops, stopper
+from pylearn import dataset, nnet_ops, stopper
 
 
 def _randshape(*shape): 
@@ -31,18 +31,19 @@
         """Update this model from more training data."""
         params = self.params
         #TODO: why should we have to unpack target like this?
+        # tbm : creates problem...
         for input, target in input_target:
             rval= self.update_fn(input, target[:,0], *params)
             #print rval[0]
 
-    def __call__(self, testset, fieldnames=['output_class']):
+    def __call__(self, testset, fieldnames=['output_class'],input='input',target='target'):
         """Apply this model (as a function) to new data"""
         #TODO: cache fn between calls
-        assert 'input' == testset.fieldNames()[0]
+        assert input == testset.fieldNames()[0] # why first one???
         assert len(testset.fieldNames()) <= 2
         v = self.algo.v
         outputs = [getattr(v, name) for name in fieldnames]
-        inputs = [v.input] + ([v.target] if 'target' in testset else [])
+        inputs = [v.input] + ([v.target] if target in testset else [])
         inputs.extend(v.params)
         theano_fn = _cache(self._fn_cache, (tuple(inputs), tuple(outputs)),
                 lambda: self.algo._fn(inputs, outputs))
diff -r 44dd9b6448c5 -r df3fae88ab46 nnet_ops.py
--- a/nnet_ops.py	Thu May 22 19:08:46 2008 -0400
+++ b/nnet_ops.py	Fri May 23 12:22:54 2008 -0400
@@ -44,7 +44,7 @@
         return ScalarSoftplus.static_impl(x)
     def grad(self, (x,), (gz,)):
         return [gz * scalar_sigmoid(x)]
-    def c_code(self, node, name, (x,), (z,), sub):
+    def c_code(self, name, node, (x,), (z,), sub):
         if node.inputs[0].type in [scalar.float32, scalar.float64]:
             return """%(z)s =
                 %(x)s < -30.0 
diff -r 44dd9b6448c5 -r df3fae88ab46 test_dataset.py
--- a/test_dataset.py	Thu May 22 19:08:46 2008 -0400
+++ b/test_dataset.py	Fri May 23 12:22:54 2008 -0400
@@ -491,5 +491,4 @@
     test_ArrayDataSet()
     test_CachedDataSet()
     test_ApplyFunctionDataSet()
-
 #test pmat.py