Merge

doc/library/tensor/basic.txt

@@ -601,7 +601,7 @@ dimensions, see :meth:`_tensor_py_operators.dimshuffle`
 
     Create a matrix by filling the shape of `a` with `b`
 
-.. function:: eye(n, m = None, k = 0, dtype='float64')
+.. function:: eye(n, m = None, k = 0, dtype=theano.config.floatX)
 
     :param n: number of rows in output (value or theano scalar)
     :param m: number of columns in output (value or theano scalar)
@@ -628,6 +628,7 @@ Reductions
     :Parameter: *x* - symbolic Tensor (or compatible)
     :Parameter: *axis* - axis along which to compute the maximum
     :Returns: the maximum value along a given axis
+    :note: see maximum for elemwise max
 
     If axis=None, then axis is assumed to be ndim(x)-1
 
@@ -636,6 +637,7 @@ Reductions
     :Parameter: *x* - symbolic Tensor (or compatible)
     :Parameter: *axis* - axis along which to compute the minimum
     :Returns: the minimum value along a given axis
+    :note: see miminum for elemwise min
 
     if axis=None, then axis is assumed to be ndim(x)-1
 
@@ -920,6 +922,14 @@ Mathematical
 
     Returns a variable representing the exponential of a, ie e^a.
 
+.. function:: maximum(a,b)
+
+   Returns a variable representing the maximum element by element of a and b
+
+.. function:: minimum(a,b)
+
+   Returns a variable representing the minimum element by element of a and b
+
 .. function:: neg(a)
 
     Returns a variable representing the negation of `a` (also ``-a``).

theano/compile/tests/test_function_module.py

@@ -393,16 +393,17 @@ class T_picklefunction(unittest.TestCase):
         old_default_opt  = config.optimizer
         old_default_link = config.linker
         try:
-            str_f = cPickle.dumps(f)
-            config.mode = 'Mode'
-            config.linker = 'py'
-            config.optimizer = 'None'
-            g = cPickle.loads(str_f)
-            #print g.maker.mode
-            #print compile.mode.default_mode
-        except NotImplementedError, e:
-            if e[0].startswith('DebugMode is not pickl'):
-                g = 'ok'
+            try:
+                str_f = cPickle.dumps(f)
+                config.mode = 'Mode'
+                config.linker = 'py'
+                config.optimizer = 'None'
+                g = cPickle.loads(str_f)
+                #print g.maker.mode
+                #print compile.mode.default_mode
+            except NotImplementedError, e:
+                if e[0].startswith('DebugMode is not pickl'):
+                    g = 'ok'
         finally:
             config.mode = old_default_mode
             config.optimizer = old_default_opt

theano/gof/link.py

@@ -46,7 +46,7 @@ def raise_with_op(op, exc_info = None):
     except AttributeError:
         trace = ()
     exc_value.__thunk_trace__ = trace
-    exc_value.args = exc_value.args + (op, )
+    exc_value.args = exc_value.args + (op, ) + ('Sequence id of Apply node='+str(op.env.toposort().index(op)),)
     raise exc_type, exc_value, exc_trace
 
 

theano/sandbox/test_theano_object.py

@@ -7,7 +7,9 @@ def run(TF):
         if TF and RUN_TESTS:
             print 'running test', f.__name__
             f()
-        return f if RUN_TESTS else None
+        if RUN_TESTS:
+            return f
+        else: return None
     return deco
 
 

theano/scalar/basic.py

@@ -726,6 +726,42 @@ invert = Invert()
 # Arithmetic
 ##############
 
+class Maximum(BinaryScalarOp):
+    commutative = True
+    associative = True
+    def impl(self, *inputs):
+        return max(inputs)
+    def c_code(self, node, name, (x,y), (z, ), sub):
+        return "%(z)s = ((%(y)s)>(%(x)s)? (%(y)s):(%(x)s));" %locals()
+        
+    def grad(self, (x, y), (gz, )):
+      gx, gy = None, None
+      if x.type in grad_types:
+        gx = eq(maximum(x,y),  x)*gz
+      if y.type in grad_types:
+        gy = eq(maximum(x,y),  y)*gz
+      return (gx,gy)
+
+maximum = Maximum(upcast_out, name = 'maximum')
+
+class Minimum(BinaryScalarOp):
+    commutative = True
+    associative = True
+    def impl(self, *inputs):
+        return min(inputs)
+    def c_code(self, node, name, (x,y), (z, ), sub):
+        return "%(z)s = ((%(y)s)<(%(x)s)? (%(y)s):(%(x)s));" %locals()
+        
+    def grad(self, (x, y), (gz, )):
+      gx, gy = None, None
+      if x.type in grad_types:
+        gx = eq(minimum(x,y),  x)*gz
+      if y.type in grad_types:
+        gy = eq(minimum(x,y),  y)*gz
+      return (gx,gy)
+
+minimum = Minimum(upcast_out, name = 'minimum')
+
 class Add(ScalarOp):
     identity = 0
     commutative = True

theano/tensor/basic.py

@@ -1703,7 +1703,7 @@ def zeros_like(model):
     return fill(model, constant(0.0, dtype=model.type.dtype))
 
 class Eye(gof.Op):
-    def __init__(self, dtype='float64'):
+    def __init__(self, dtype=config.floatX):
         self.dtype = dtype
     def make_node(self,n,m,k):
         n = as_tensor_variable(n)
@@ -1724,7 +1724,7 @@ class Eye(gof.Op):
         return hash(self.dtype) ^ hash(type(self))
 
 
-def eye(n, m=None, k = 0, dtype = 'float64'):
+def eye(n, m=None, k = 0, dtype = config.floatX):
     if m == None:
         m = n
     localop = Eye(dtype)
@@ -2029,6 +2029,17 @@ setdefault = default # legacy
 ##########################
 # Arithmetics
 ##########################
+@_scal_elemwise
+def maximum(x,y):
+    """elemwise maximum. See max for the maximum in one tensor
+    """
+    # see decorator for function body
+
+@_scal_elemwise
+def minimum(x,y):
+    """elemwise minimum. See min for the minimum in one tensor
+    """
+    # see decorator for function body
 
 def div_proxy(x, y):
     """Proxy for either true_div or int_div, depending on types of x, y.
@@ -3172,9 +3183,10 @@ class ARange(Op):
             if is_constant_value(start, 0):
                 return [(cast(stop, 'int64'),)]
             else:
-                return [(theano.tensor.max([cast(stop-start, 'int64'),0]),)]
+                return [(maximum(cast(stop-start, 'int64'),0),)]
         else:
-            return [(theano.tensor.max([cast(ceil(cast((stop-start),'float64')/step),'int64'),0]),)]
+            return [(maximum(cast(ceil(cast((stop-start),'float64')
+                                                     /step),'int64'),0),)]
 
     def perform(self, node, (start, stop, step), (out,)):
         start = start.item()

theano/tensor/inplace.py

@@ -168,6 +168,14 @@ fill_inplace = second_inplace
 pprint.assign(fill_inplace, printing.FunctionPrinter('fill='))
 
 
+@_scal_inplace
+def maximum_inplace(a, b):
+    """elementwise addition (inplace on `a`)"""
+
+@_scal_inplace
+def minimum_inplace(a, b):
+    """elementwise addition (inplace on `a`)"""
+
 @_scal_inplace
 def add_inplace(a, b):
     """elementwise addition (inplace on `a`)"""

theano/tensor/nnet/tests/test_sigm.py

@@ -43,17 +43,24 @@ class T_sigmoid_opts(unittest.TestCase):
         # tests inv_1_plus_exp with neg
         f = theano.function([x], T.fill(x,-1.0) / (1+T.exp(-x)), mode=m)
         #theano.printing.debugprint(f)
-        assert [node.op for node in f.maker.env.toposort()] == [sigmoid, 
-                T.inplace.neg_inplace]
-
+        assert len(f.maker.env.toposort())==1
+        assert str(f.maker.env.toposort()[0].op)=='Elemwise{Composite{scalar_sigmoid,neg}}'
+        #without fusion
+        #assert [node.op for node in f.maker.env.toposort()] == [sigmoid,
+        #        T.inplace.neg_inplace]
+        
         # tests double inv_1_plus_exp with neg
         # (-1)(exp(x)) / (1+exp(x))(1+exp(-x))
         # = (-1)/(1+exp(-x)) * exp(x)/(1+exp(x))
         # = - (sigm(x) * sigm(x))
         f = theano.function([x], (T.fill(x,-1.0)*T.exp(x)) / ((1+T.exp(x))*(1+T.exp(-x))), mode=m)
         theano.printing.debugprint(f)
-        assert [node.op for node in f.maker.env.toposort()] == [sigmoid, 
-                T.mul, T.inplace.neg_inplace]
+        assert len(f.maker.env.toposort())==2
+        assert f.maker.env.toposort()[0].op == sigmoid
+        assert str(f.maker.env.toposort()[1].op)=='Elemwise{Composite{mul,neg}}'
+        #without fusion
+        #assert [node.op for node in f.maker.env.toposort()] == [sigmoid, 
+        #        T.mul, T.inplace.neg_inplace]
 
     def test_1msigmoid(self):
         if not register_local_1msigmoid:

theano/tensor/sharedvar.py

-
+import traceback
 import numpy
 import theano.tensor.basic
 from basic import TensorType, _tensor_py_operators

theano/tensor/tests/mlp_test.py

@@ -9,6 +9,7 @@ import numpy
 
 import theano
 import theano.tensor as T
+from theano.gof.python25 import any
 
 def gen_data():
 
@@ -301,7 +302,7 @@ def test_mlp():
                 y:train_set_y[index*batch_size:(index+1)*batch_size]},
             mode=mode)
     for i in train_model.maker.env.toposort(): print i
-    theano.printing.pydotprint(train_model)
+    #theano.printing.pydotprint(train_model)
 
     assert any( [isinstance(i.op,T.nnet.CrossentropySoftmax1HotWithBiasDx) for i in train_model.maker.env.toposort()])
     train_model =theano.function( inputs = [index],