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changeset 564:e878003c3009

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merge
author desjagui@atchoum.iro.umontreal.ca
date Wed, 03 Dec 2008 17:24:37 -0500
parents 16f91ca016b1 (current diff) 4bcb7189ba21 (diff)
children 0a27ba2157b6
files pylearn/algorithms/logistic_regression.py
diffstat 10 files changed, 71 insertions(+), 50 deletions(-) [+]
line wrap: on
line diff
--- a/pylearn/algorithms/kernel_regression.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/kernel_regression.py	Wed Dec 03 17:24:37 2008 -0500
@@ -4,16 +4,16 @@
 
 from pylearn.learner import OfflineLearningAlgorithm
 from theano import tensor as T
-from nnet_ops import prepend_1_to_each_row
+from theano.tensor.nnet import prepend_1_to_each_row
 from theano.scalar import as_scalar
 from common.autoname import AutoName
 import theano
 import numpy
 
 # map a N-vector to a 1xN matrix
-row_vector = theano.elemwise.DimShuffle((False,),['x',0])
+row_vector = theano.tensor.DimShuffle((False,),['x',0])
 # map a N-vector to a Nx1 matrix
-col_vector = theano.elemwise.DimShuffle((False,),[0,'x'])
+col_vector = theano.tensor.DimShuffle((False,),[0,'x'])
 
 class KernelRegression(OfflineLearningAlgorithm):
     """
--- a/pylearn/algorithms/linear_regression.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/linear_regression.py	Wed Dec 03 17:24:37 2008 -0500
@@ -6,7 +6,7 @@
 
 from pylearn.learner import OfflineLearningAlgorithm,OnlineLearningAlgorithm
 from theano import tensor as T
-from nnet_ops import prepend_1_to_each_row
+from theano.tensor.nnet import prepend_1_to_each_row
 from theano.scalar import as_scalar
 from common.autoname import AutoName
 import theano
@@ -96,14 +96,14 @@
 
     __compiled = False
     @classmethod
-    def compile(cls,linker='c|py'):
+    def compile(cls, mode = "FAST_RUN"):
         if cls.__compiled:
             return
         def fn(input_vars,output_vars):
-            return staticmethod(theano.function(input_vars,output_vars, linker=linker))
+            return staticmethod(theano.function(input_vars, output_vars, mode=mode))
 
-        cls.compute_outputs = fn([cls.inputs,cls.theta],[cls.outputs])
-        cls.compute_errors = fn([cls.outputs,cls.targets],[cls.squared_errors])
+        cls.compute_outputs = fn([cls.inputs,cls.theta],cls.outputs)
+        cls.compute_errors = fn([cls.outputs,cls.targets],cls.squared_errors)
 
         cls.__compiled = True
 
@@ -115,17 +115,17 @@
     XtX = T.matrix() # (n_inputs+1) x (n_inputs+1)
     XtY = T.matrix() # (n_inputs+1) x n_outputs
     extended_input = prepend_1_to_each_row(P.inputs)
-    new_XtX = T.add_inplace(XtX,T.dot(extended_input.T,extended_input))
-    new_XtY = T.add_inplace(XtY,T.dot(extended_input.T,P.targets))
+    new_XtX = T.add(XtX,T.dot(extended_input.T,extended_input))
+    new_XtY = T.add(XtY,T.dot(extended_input.T,P.targets))
 
     __compiled = False
     
     @classmethod
-    def compile(cls,linker='c|py'):
+    def compile(cls, mode="FAST_RUN"):
         if cls.__compiled:
             return
         def fn(input_vars,output_vars):
-            return staticmethod(theano.function(input_vars,output_vars, linker=linker))
+            return staticmethod(theano.function(input_vars, output_vars, mode=mode))
 
         cls.update = fn([cls.XtX,cls.XtY,cls.P.inputs,cls.P.targets],[cls.new_XtX,cls.new_XtY])
 
--- a/pylearn/algorithms/logistic_regression.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/logistic_regression.py	Wed Dec 03 17:24:37 2008 -0500
@@ -153,12 +153,12 @@
     def __init__(self, input=None, targ=None, w=None, b=None, lr=None, regularize=False):
         super(LogReg2, self).__init__() #boilerplate
 
-        self.input = input if input is not None else T.matrix('input')
-        self.targ = targ if targ is not None else T.lcol()
+        self.input = module.Member(input) if input is not None else T.matrix('input')
+        self.targ = module.Member(targ) if targ is not None else T.lcol()
 
-        self.w = w if w is not None else module.Member(T.dmatrix())
-        self.b = b if b is not None else module.Member(T.dvector())
-        self.lr = lr if lr is not None else module.Member(T.dscalar())
+        self.w = module.Member(w) if w is not None else module.Member(T.dmatrix())
+        self.b = module.Member(b) if b is not None else module.Member(T.dvector())
+        self.lr = module.Member(lr) if lr is not None else module.Member(T.dscalar())
 
         self.params = [p for p in [self.w, self.b] if p.owner is None]
 
--- a/pylearn/algorithms/sandbox/_test_onehotop.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/sandbox/_test_onehotop.py	Wed Dec 03 17:24:37 2008 -0500
@@ -3,7 +3,7 @@
 import unittest
 from theano import compile
 from theano import gradient
-
+from theano import function
 from theano.tensor import as_tensor
 
 import random
@@ -14,8 +14,8 @@
         x = as_tensor([3, 2, 1])
         y = as_tensor(5)
         o = one_hot(x, y)
-        y = compile.eval_outputs([o])
-        self.failUnless(numpy.all(y == numpy.asarray([[0, 0, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0]])))
+        f = function([],o)
+        self.failUnless(numpy.all(f() == numpy.asarray([[0, 0, 0, 1, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0]])))
 
 if __name__ == '__main__':
     unittest.main()
--- a/pylearn/algorithms/tests/test_daa.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/tests/test_daa.py	Wed Dec 03 17:24:37 2008 -0500
@@ -28,7 +28,7 @@
             model.local_update[l]([[0, 1, 0, 1]])
             model.local_update[l]([[1, 0, 1, 0]])
 
-    for i in range(1):
+    for i in range(10):
         model.update([[0, 1, 0, 1]], [[1]])
         model.update([[1, 0, 1, 0]], [[0]])
     print model.classify([[0, 1, 0, 1]])
@@ -41,23 +41,31 @@
     daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(pylearn.algorithms.logistic_regression.Module_Nclass, 'pred')],
                          regularize = False)
 
-    model = daa.make([4, 20, 20, 20, 10],
+    model = daa.make([4] + [20] * ndaa + [10],
                      lr = 0.01,
                      mode = mode,
                      seed = 10)
 
-    model.layers[0].noise_level = 0.3
-    model.layers[1].noise_level = 0.3
-    model.layers[2].noise_level = 0.3
+    for l in range(ndaa): model.layers[l].noise_level = 0.3
 
-    for l in range(3):
+    instances = [([[0, 1, 0, 1]], [1]), ([[1, 0, 1, 0]], [0])]
+
+    for l in range(ndaa):
         for i in range(10):
-            model.local_update[l]([[0, 1, 0, 1]])
-            model.local_update[l]([[1, 0, 1, 0]])
+            for (input, output) in instances:
+                model.local_update[l](input)
 
-    for i in range(1):
-        model.update([[0, 1, 0, 1]], [1])
-        model.update([[1, 0, 1, 0]], [0])
+    for i in range(10):
+        for (input, output) in instances:
+#            model.update(input, output)
+            print "OLD:", 
+            print model.validate(input, output)
+            oldloss = model.update(input, output)
+            print oldloss
+            print "NEW:"
+            print model.validate(input, output)
+            print 
+
     print model.apply([[0, 1, 0, 1]])
     print model.apply([[1, 0, 1, 0]])
 
--- a/pylearn/algorithms/tests/test_linear_regression.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/algorithms/tests/test_linear_regression.py	Wed Dec 03 17:24:37 2008 -0500
@@ -21,5 +21,18 @@
         print 'mse = ',mse
         
 if __name__ == '__main__':
-    unittest.main()
-        
+    import sys
+
+    if len(sys.argv)==1:
+        unittest.main()
+    else:
+        assert sys.argv[1]=="--debug"
+        tests = []
+        for arg in sys.argv[2:]:
+            tests.append(arg)
+        if tests:
+            unittest.TestSuite(map(T_DataSet, tests)).debug()
+        else:
+            module = __import__("_test_linear_regression")
+            tests = unittest.TestLoader().loadTestsFromModule(module)
+            tests.debug()
--- a/pylearn/datasets/embeddings/parameters.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/datasets/embeddings/parameters.py	Wed Dec 03 17:24:37 2008 -0500
@@ -1,10 +1,10 @@
 """
 Locations of the embedding data files.
 """
-WEIGHTSFILE     = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/lm-weights.txt"
-VOCABFILE       = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/words.asc"
-#WEIGHTSFILE     = "/home/joseph/data/word_embeddings.collobert-and-weston/lm-weights.txt"
-#VOCABFILE       = "/home/joseph/data/word_embeddings.collobert-and-weston/words.asc"
+#WEIGHTSFILE     = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/lm-weights.txt"
+#VOCABFILE       = "/home/fringant2/lisa/data/word_embeddings.collobert-and-weston/words.asc"
+WEIGHTSFILE     = "/home/joseph/data/word_embeddings.collobert-and-weston/lm-weights.txt"
+VOCABFILE       = "/home/joseph/data/word_embeddings.collobert-and-weston/words.asc"
 NUMBER_OF_WORDS = 30000
 DIMENSIONS      = 50
 UNKNOWN         = "UNKNOWN"
--- a/pylearn/datasets/embeddings/process.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/datasets/embeddings/process.py	Wed Dec 03 17:24:37 2008 -0500
@@ -11,6 +11,12 @@
 __word_to_embedding = None
 __read = False
 
+def length():
+    """
+    @return: The length of embeddings
+    """
+    return len(__word_to_embedding[__words[0]])
+
 def word_to_embedding(w):
     read_embeddings()
     return __word_to_embedding[w]
@@ -39,29 +45,21 @@
         w = __words[i]
         __word_to_embedding[w] = l
     __read = True
+    for w in __word_to_embedding: assert len(__word_to_embedding[__words[0]]) == len(__word_to_embedding[w])
     sys.stderr.write("...done reading %s\n" % WEIGHTSFILE)
 
 import re
 numberre = re.compile("[0-9]")
-slashre = re.compile("\\\/")
 
-def preprocess_word(origw):
+def preprocess_word(w):
     """
     Convert a word so that it can be embedded directly.
     Returned the preprocessed sequence.
-    @note: Preprocessing is appropriate for Penn Treebank style documents.
+    @note: Perhaps run L{common.penntreebank.preprocess} on the word first.
     """
     read_embeddings()
-    if origw == "-LRB-": w = "("
-    elif origw == "-RRB-": w = ")"
-    elif origw == "-LCB-": w = "{"
-    elif origw == "-RCB-": w = "}"
-    elif origw == "-LSB-": w = "["
-    elif origw == "-RSB-": w = "]"
-    else:
-        w = origw
+    if w not in __word_to_embedding:
         w = string.lower(w)
-        w = slashre.sub("/", w)
         w = numberre.sub("NUMBER", w)
     if w not in __word_to_embedding:
 #        sys.stderr.write("Word not in vocabulary, using %s: %s (original %s)\n" % (UNKNOWN, w, origw))
--- a/pylearn/datasets/make_test_datasets.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/datasets/make_test_datasets.py	Wed Dec 03 17:24:37 2008 -0500
@@ -1,4 +1,4 @@
-import dataset
+from dataset import ArrayDataSet
 from shapeset.dset import Polygons
 from linear_regression import linear_predictor
 from kernel_regression import kernel_predictor
@@ -110,6 +110,7 @@
     #  testset = ArrayDataSet(inputs[n_examples/2:],{'input':slice(0,n_inputs)}) | \
     #            ArrayDataSet(targets[n_examples/2:],{'target':slice(0,n_targets)})
     data = hstack((inputs,targets))
+
     trainset = ArrayDataSet(data[0:n_train],
                             {'input':slice(0,n_inputs),'target':slice(n_inputs,n_inputs+n_targets)})
     testset = ArrayDataSet(data[n_train:],
--- a/pylearn/old_dataset/_test_dataset.py	Wed Dec 03 17:21:05 2008 -0500
+++ b/pylearn/old_dataset/_test_dataset.py	Wed Dec 03 17:24:37 2008 -0500
@@ -315,6 +315,7 @@
 #ds[i] returns the (i+1)-th example of the dataset.
     ds2=ds[5]
     assert isinstance(ds2,Example)
+    test_ds(ds,ds2,[5])
     assert have_raised("var['ds']["+str(len(ds))+"]",ds=ds)  # index not defined
     assert not have_raised("var['ds']["+str(len(ds)-1)+"]",ds=ds)
     del ds2