Mercurial > pylearn
changeset 135:0d8e721cc63c
Fixed bugs in dataset to make test_mlp.py work
| author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
|---|---|
| date | Mon, 12 May 2008 14:30:21 -0400 |
| parents | 3f4e5c9bdc5e |
| children | ceae4de18981 |
| files | dataset.py learner.py test_mlp.py |
| diffstat | 3 files changed, 19 insertions(+), 12 deletions(-) [+] |
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--- a/dataset.py Fri May 09 17:38:57 2008 -0400 +++ b/dataset.py Mon May 12 14:30:21 2008 -0400 @@ -429,8 +429,8 @@ rows=None # or a slice if type(i) is slice: - if not i.start: i.start=0 - if not i.step: i.step=1 + if not i.start: i=slice(0,i.stop,i.step) + if not i.step: i=slice(i.start,i.stop,1) if i.step is 1: return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() rows = range(i.start,i.stop,i.step) @@ -497,7 +497,7 @@ dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of fields of the argument datasets. This only works if they all have the same length. """ - return HStackedDataSet(self,other) + return HStackedDataSet([self,other]) def __and__(self,other): """ @@ -505,7 +505,7 @@ (and whose length is the sum of the length of the argument datasets). This only works if they all have the same fields. """ - return VStackedDataSet(self,other) + return VStackedDataSet([self,other]) def hstack(datasets): """ @@ -1068,7 +1068,7 @@ def next(self): upper = self.current+minibatch_size cache_len = len(self.dataset.cached_examples) - if upper>=cache_len: # whole minibatch is not already in cache + if upper>cache_len: # whole minibatch is not already in cache # cache everything from current length to upper for example in self.dataset.source_dataset[cache_len:upper]: self.dataset.cached_examples.append(example)
--- a/learner.py Fri May 09 17:38:57 2008 -0400 +++ b/learner.py Mon May 12 14:30:21 2008 -0400 @@ -11,8 +11,9 @@ algorithms. A L{Learner} can be seen as a learning algorithm, a function that when - applied to training data returns a learned function, an object that - can be applied to other data and return some output data. + applied to training data returns a learned function (which is an object that + can be applied to other data and return some output data). + """ def __init__(self): @@ -51,7 +52,7 @@ return self.update(training_set,train_stats_collector) def use(self,input_dataset,output_fieldnames=None, - test_stats_collector=None,copy_inputs=True, + test_stats_collector=None,copy_inputs=False, put_stats_in_output_dataset=True, output_attributes=[]): """ @@ -85,11 +86,16 @@ If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) are also copied into the output dataset attributes. """ - minibatchwise_use_function = self.minibatchwiseUseFunction(input_dataset.fieldNames(), + input_fieldnames = input_dataset.fieldNames() + if not output_fieldnames: + output_fieldnames = self.defaultOutputFields(input_fieldnames) + + minibatchwise_use_function = self.minibatchwiseUseFunction(input_fieldnames, output_fieldnames, test_stats_collector) virtual_output_dataset = ApplyFunctionDataSet(input_dataset, minibatchwise_use_function, + output_fieldnames, True,DataSet.numpy_vstack, DataSet.numpy_hstack) # actually force the computation @@ -212,8 +218,6 @@ Implement minibatchwiseUseFunction by exploiting Theano compilation and the expression graph defined by a sub-class constructor. """ - if not output_fields: - output_fields = self.defaultOutputFields(input_fields) if stats_collector: stats_collector_inputs = stats_collector.input2UpdateAttributes() for attribute in stats_collector_inputs:
--- a/test_mlp.py Fri May 09 17:38:57 2008 -0400 +++ b/test_mlp.py Mon May 12 14:30:21 2008 -0400 @@ -11,7 +11,10 @@ {'input':slice(2),'target':2}) fprop=nnet(training_set) - print fprop(training_set) + output_ds = fprop(training_set) + + for fieldname in output_ds.fieldNames(): + print fieldname+"=",output_ds[fieldname] test0()