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changeset 818:f4729745bb58

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backporting to 2.4
author dumitru@deepnets.mtv.corp.google.com
date Wed, 02 Sep 2009 14:22:02 -0700
parents c61d775f4f95
children 7dfecf11cbf4
files pylearn/algorithms/logistic_regression.py pylearn/algorithms/regressor.py pylearn/algorithms/stacker.py pylearn/algorithms/tests/test_daa.py pylearn/datasets/MNIST.py pylearn/datasets/config.py pylearn/datasets/smallNorb.py pylearn/io/filetensor.py
diffstat 8 files changed, 145 insertions(+), 28 deletions(-) [+]
line wrap: on
line diff
--- a/pylearn/algorithms/logistic_regression.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/algorithms/logistic_regression.py	Wed Sep 02 14:22:02 2009 -0700
@@ -33,18 +33,57 @@
         self.n_in = n_in
         self.n_out = n_out
 
-        self.input = input if input is not None else T.matrix()
-        self.target = target if target is not None else T.lvector()
+        if input is not None:
+          self.input = input
+        else:
+          self.input = T.matrix()
 
-        self.w = w if w is not None else (T.dmatrix())
-        self.b = b if b is not None else (T.dvector())
+        if target is not None:
+          self.target = target
+        else:
+          self.target = T.lvector()
+
+        #backport
+        #self.input = input if input is not None else T.matrix()
+        #self.target = target if target is not None else T.lvector()
+
+        if w is not None:
+          self.w = w
+        else:
+          self.w = (T.dmatrix())
 
+        if b is not None:
+          self.b = b
+        else:
+          self.b = (T.dvector())
+  
+        #backport
+        #self.w = w if w is not None else (T.dmatrix())
+        #self.b = b if b is not None else (T.dvector())
+
+        self.params = []
+        for p in [self.w, self.b]:
+          if p.owner is None:
+            self.params += [p]
+
+        #backport
         #the params of the model are the ones we fit to the data
-        self.params = [p for p in [self.w, self.b] if p.owner is None]
+        #self.params = [p for p in [self.w, self.b] if p.owner is None]
         
+        if l2 is not None:
+          self.l2 = l2
+        else:
+          self.l2 = (T.dscalar())
+
+        if l1 is not None:
+          self.l1 = l1
+        else:
+          self.l1 = (T.dscalar())
+
+        #backport
         #the hyper-parameters of the model are not fit to the data
-        self.l2 = l2 if l2 is not None else (T.dscalar())
-        self.l1 = l1 if l1 is not None else (T.dscalar())
+        #self.l2 = l2 if l2 is not None else (T.dscalar())
+        #self.l1 = l1 if l1 is not None else (T.dscalar())
 
         #here we actually build the model
         self.linear_output = T.dot(self.input, self.w) + self.b
@@ -163,14 +202,46 @@
     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()
+        if input is not None:
+          self.input = (input)
+        else:
+          self.input = T.matrix('input')
+        
+        if targ is not None:
+          self.targ = (targ)
+        else:
+          self.targ = T.lcol()
+
+        #self.input = (input) if input is not None else T.matrix('input')
+        #self.targ = (targ) if targ is not None else T.lcol()
+
+        if w is not None:
+          self.w = (w)
+        else:
+          self.w = (T.dmatrix())
 
-        self.w = (w) if w is not None else (T.dmatrix())
-        self.b = (b) if b is not None else (T.dvector())
-        self.lr = (lr) if lr is not None else (T.dscalar())
+        if b is not None:
+          self.b = (b)
+        else:
+          self.b = (T.dvector())
+
+        if lr is not None:
+          self.lr = (lr)
+        else:
+          self.lr = (T.scalar())
 
-        self.params = [p for p in [self.w, self.b] if p.owner is None]
+        #backport
+        #self.w = (w) if w is not None else (T.dmatrix())
+        #self.b = (b) if b is not None else (T.dvector())
+        #self.lr = (lr) if lr is not None else (T.dscalar())
+
+        self.params = []
+        for p in [self.w, self.b]:
+          if p.owner is None:
+            self.params += [p]
+
+        #backport
+        #self.params = [p for p in [self.w, self.b] if p.owner is None]
 
         output = nnet.sigmoid(T.dot(self.x, self.w) + self.b)
         xent = -self.targ * T.log(output) - (1.0 - self.targ) * T.log(1.0 - output)
@@ -251,11 +322,23 @@
     def __init__(self, n_in=None, n_out=None, w=None, b=None):
         super(LogRegNew, self).__init__() #boilerplate
 
+        if w is not None:
+          self.w = w
+        else:
+          self.w = (T.dmatrix())
+
+        if b is not None:
+          self.b = b
+        else:
+          self.b = (T.dvector())
+
+
         self.n_in = n_in
         self.n_out = n_out
 
-        self.w = w if w is not None else (T.dmatrix())
-        self.b = b if b is not None else (T.dvector())
+        #backport
+        #self.w = w if w is not None else (T.dmatrix())
+        #self.b = b if b is not None else (T.dvector())
 
     def _instance_initialize(self, obj):
         obj.w = N.zeros((self.n_in, self.n_out))
--- a/pylearn/algorithms/regressor.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/algorithms/regressor.py	Wed Sep 02 14:22:02 2009 -0700
@@ -13,8 +13,18 @@
         self.regularize = regularize
 
         # ACQUIRE/MAKE INPUT AND TARGET
-        self.input = input if input else T.matrix('input')
-        self.target = target if target else T.matrix('target')
+        if input:
+          self.input = input
+        else:
+          self.target = target
+
+        if target:
+          self.target = target
+        else:
+          self.target = T.dmatrix('target')
+        #backport
+        #self.input = input if input else T.matrix('input')
+        #self.target = target if target else T.matrix('target')
 
         # HYPER-PARAMETERS
         self.lr = T.scalar()
--- a/pylearn/algorithms/stacker.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/algorithms/stacker.py	Wed Sep 02 14:22:02 2009 -0700
@@ -69,7 +69,14 @@
             if isinstance(method, theano.Method) and not hasattr(self, name):
                 if not isinstance(method.inputs, (list,dict)):
                     method.inputs = [method.inputs]
-                inputs = [self.input if x is ll.input else x for x in method.inputs]
+                inputs = []
+                for x in method.inputs:
+                  if x is ll.input:
+                    inputs += [self.input]
+                  else:
+                    inputs += [x]
+                #backport
+                #inputs = [self.input if x is ll.input else x for x in method.inputs]
                 m = theano.Method(inputs, method.outputs, method.updates)
                 setattr(self, name, m)
 
--- a/pylearn/algorithms/tests/test_daa.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/algorithms/tests/test_daa.py	Wed Sep 02 14:22:02 2009 -0700
@@ -6,8 +6,9 @@
 import time
 
 import pylearn.algorithms.logistic_regression
+from theano.compile.mode import default_mode
 
-def test_train_daa(mode = theano.Mode('c|py', 'fast_run')):
+def test_train_daa(mode = default_mode):
 
     ndaa = 3
     daa = models.Stacker([(models.SigmoidXEDenoisingAA, 'hidden')] * ndaa + [(models.BinRegressor, 'output')],
--- a/pylearn/datasets/MNIST.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/datasets/MNIST.py	Wed Sep 02 14:22:02 2009 -0700
@@ -6,9 +6,9 @@
 import os
 import numpy
 
-from ..io.pmat import PMat
-from .config import data_root # config
-from .dataset import Dataset
+from pylearn.io.pmat import PMat
+from pylearn.datasets.config import data_root # config
+from pylearn.datasets.dataset import Dataset
 
 def head(n=10, path=None):
     """Load the first MNIST examples.
@@ -18,7 +18,8 @@
     is the label of the i'th row of x.
     
     """
-    path = os.path.join(data_root(), 'mnist','mnist_all.pmat') if path is None else path
+    if path is None:
+      path = os.path.join(data_root(), 'mnist','mnist_all.pmat')
 
     dat = PMat(fname=path)
 
--- a/pylearn/datasets/config.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/datasets/config.py	Wed Sep 02 14:22:02 2009 -0700
@@ -11,7 +11,11 @@
     if os.getenv(key) is None:
         print >> sys.stderr, "WARNING: Environment variable", key,
         print >> sys.stderr, "is not set. Using default of", default
-    return default if os.getenv(key) is None else os.getenv(key)
+    if os.getenv(key) is None:
+      return default
+    else:
+      return os.getenv(key)
+    #return default if os.getenv(key) is None else os.getenv(key)
 
 def data_root():
     return env_get('PYLEARN_DATA_ROOT', os.getenv('HOME')+'/data', 'DBPATH')
--- a/pylearn/datasets/smallNorb.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/datasets/smallNorb.py	Wed Sep 02 14:22:02 2009 -0700
@@ -1,7 +1,7 @@
 import os
 import numpy
-from ..io.filetensor import read
-from .config import data_root
+from pylearn.io.filetensor import read
+from pylearn.datasets.config import data_root
 
 #Path = '/u/bergstrj/pub/data/smallnorb'
 #Path = '/home/fringant2/lisa/louradoj/data/smallnorb'
--- a/pylearn/io/filetensor.py	Thu Jul 30 18:09:17 2009 -0400
+++ b/pylearn/io/filetensor.py	Wed Sep 02 14:22:02 2009 -0700
@@ -129,8 +129,19 @@
         self.magic_t, self.elsize, self.ndim, self.dim, self.dim_size = _read_header(f,debug)
         self.f_start = f.tell()
 
-        self.readshape = tuple(self.dim[self.ndim-rank:]) if rank <= self.ndim else tuple(self.dim)
-        padding = tuple() if rank <= self.ndim else (1,) * (rank - self.ndim)
+        if rank <= self.ndim:
+          self.readshape = tuple(self.dim[self.ndim-rank:])
+        else:
+          self.readshape = tuple(self.dim)
+
+        #self.readshape = tuple(self.dim[self.ndim-rank:]) if rank <= self.ndim else tuple(self.dim)
+
+        if rank <= self.ndim:
+          padding = tuple()
+        else:
+          padding = (1,) * (rank - self.ndim)
+
+        #padding = tuple() if rank <= self.ndim else (1,) * (rank - self.ndim)
         self.returnshape = padding + self.readshape
         self.readsize = _prod(self.readshape)
         if debug: print 'READ PARAM', self.readshape, self.returnshape, self.readsize