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

@@ -392,6 +392,7 @@ class T_picklefunction(unittest.TestCase):
         old_default_mode = config.mode
         old_default_opt  = config.optimizer
         old_default_link = config.linker
+        try:
             try:
                 str_f = cPickle.dumps(f)
                 config.mode = 'Mode'

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

@@ -43,8 +43,11 @@ 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))
@@ -52,8 +55,12 @@ class T_sigmoid_opts(unittest.TestCase):
         # = - (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],

theano/tensor/tests/test_basic.py

@@ -250,6 +250,36 @@ SubInplaceTester = makeBroadcastTester(op = inplace.sub_inplace,
                                          grad = _grad_broadcast_binary_normal,
                                          inplace = True)
 
+MaximumTester = makeBroadcastTester(op = maximum,
+                                  expected = numpy.maximum,
+                                  good = _good_broadcast_binary_normal,
+                                  bad_build = _bad_build_broadcast_binary_normal,
+                                  bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                  grad = _grad_broadcast_binary_normal)
+
+MaximumInplaceTester = makeBroadcastTester(op = inplace.maximum_inplace,
+                                         expected = numpy.maximum,
+                                         good = _good_broadcast_binary_normal,
+                                         bad_build = _bad_build_broadcast_binary_normal,
+                                         bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                         grad = _grad_broadcast_binary_normal,
+                                         inplace = True)
+
+MinimumTester = makeBroadcastTester(op = minimum,
+                                  expected = numpy.minimum,
+                                  good = _good_broadcast_binary_normal,
+                                  bad_build = _bad_build_broadcast_binary_normal,
+                                  bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                  grad = _grad_broadcast_binary_normal)
+
+MinimumInplaceTester = makeBroadcastTester(op = inplace.minimum_inplace,
+                                         expected = numpy.minimum,
+                                         good = _good_broadcast_binary_normal,
+                                         bad_build = _bad_build_broadcast_binary_normal,
+                                         bad_runtime = _bad_runtime_broadcast_binary_normal,
+                                         grad = _grad_broadcast_binary_normal,
+                                         inplace = True)
+
 MulTester = makeBroadcastTester(op = mul,
                                   expected = lambda *inputs: reduce(lambda x, y: x * y, inputs),
                                   good = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),
@@ -654,6 +684,7 @@ class T_max_and_argmax(unittest.TestCase):
         _logger = logging.getLogger('theano.gof.opt')
         oldlevel = _logger.getEffectiveLevel()
         _logger.setLevel(logging.CRITICAL)
+        try:
             try:
                 eval_outputs(max_and_argmax(n,3))
                 assert False
@@ -665,6 +696,7 @@ class T_max_and_argmax(unittest.TestCase):
         n = as_tensor_variable(numpy.random.rand(2,3))
         old_stderr = sys.stderr
         sys.stderr = StringIO.StringIO()
+        try:
             try:
                 eval_outputs(max_and_argmax(n,-3))
                 assert False
@@ -725,6 +757,7 @@ class T_subtensor(unittest.TestCase):
         _logger = logging.getLogger('theano.gof.opt')
         oldlevel = _logger.getEffectiveLevel()
         _logger.setLevel(logging.CRITICAL)
+        try:
             try:
                 tval = eval_outputs([t])
                 assert 0
@@ -802,6 +835,7 @@ class T_subtensor(unittest.TestCase):
         _logger = logging.getLogger('theano.gof.opt')
         oldlevel = _logger.getEffectiveLevel()
         _logger.setLevel(logging.CRITICAL)
+        try:
             try:
                 tval = eval_outputs([t])
                 assert 0
@@ -815,6 +849,7 @@ class T_subtensor(unittest.TestCase):
         self.failUnless(isinstance(t.owner.op, Subtensor))
         old_stderr = sys.stderr
         sys.stderr = StringIO.StringIO()
+        try:
             try:
                 tval = eval_outputs([t])
             except Exception, e:
@@ -1496,6 +1531,7 @@ class t_dot(unittest.TestCase):
         _logger = logging.getLogger('theano.gof.opt')
         oldlevel = _logger.getEffectiveLevel()
         _logger.setLevel(logging.CRITICAL)
+        try:
             try:
                 tz = eval_outputs([z])
                 assert False # should have raised exception
@@ -2038,9 +2074,14 @@ class TestARange(unittest.TestCase):
     def test_infer_shape(self):
         start, stop, step = iscalars('start', 'stop', 'step')
         out = arange(start, stop, step)
-        f = function([start, stop, step], out.shape, mode=compile.mode.get_default_mode().excluding('fusion'))
-        assert len(f.maker.env.toposort())==12
-#ungly graph...        [DimShuffle{x}(step), DimShuffle{x}(start), DimShuffle{x}(stop), Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)](DimShuffle{x}.0, DimShuffle{x}.0), Elemwise{Cast{float64}}(Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{float64}}.0, DimShuffle{x}.0), Rebroadcast{0}(Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)](Rebroadcast{0}.0), Elemwise{Cast{int64}}(Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)].0), <theano.tensor.basic.Join object at 0x1fb1d10>(0, Elemwise{Cast{int64}}.0, [0]), MaxAndArgmax(<theano.tensor.basic.Join object at 0x1fb1d10>.0, [0]), MakeVector(max)]
+        mode = theano.config.mode
+        if mode == 'FAST_COMPILE':
+            mode = 'FAST_RUN'
+        mode = compile.mode.get_mode(mode).excluding('fusion')
+        f = function([start, stop, step], out.shape, mode=mode)
+        assert len(f.maker.env.toposort())==7
+#7 [Elemwise{sub,no_inplace}(stop, start), Elemwise{Cast{float64}}(Elemwise{sub,no_inplace}.0), Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{float64}}.0, step), Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{TrueDiv{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Cast{int64}}(Elemwise{Ceil{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{int64}}.0, 0), MakeVector(Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)].0)]
+
         assert out.dtype == start.type.dtype
         assert numpy.all(f(0,5,1) == len(numpy.arange(0,5,1)))
         assert numpy.all(f(2,11,4) == len(numpy.arange(2,11,4)))
@@ -2050,10 +2091,9 @@ class TestARange(unittest.TestCase):
         assert numpy.all(f(0,0,1) == len(numpy.arange(0,0,1)))
 
         out = arange(start, stop, 1)
-        f = function([start, stop], out.shape, mode=compile.mode.get_default_mode().excluding('fusion'))
-        assert len(f.maker.env.toposort())==8
-#ungly graph...        [DimShuffle{x}(start), DimShuffle{x}(stop), Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)](DimShuffle{x}.0, DimShuffle{x}.0), Rebroadcast{0}(Elemwise{Sub{output_types_preference=transfer_type{0}}}[(0, 0)].0), Elemwise{Cast{int64}}(Rebroadcast{0}.0), <theano.tensor.basic.Join object at 0x1fb1d10>(0, Elemwise{Cast{int64}}.0, [0]), MaxAndArgmax(<theano.tensor.basic.Join object at 0x1fb1d10>.0, [0]), MakeVector(max)]
-
+        f = function([start, stop], out.shape, mode=mode)
+        assert len(f.maker.env.toposort())==4
+#4 [Elemwise{sub,no_inplace}(stop, start), Elemwise{Cast{int64}}(Elemwise{sub,no_inplace}.0), Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)](Elemwise{Cast{int64}}.0, 0), MakeVector(Elemwise{Maximum{output_types_preference=transfer_type{0}}}[(0, 0)].0)]
         assert out.dtype == start.type.dtype
         assert numpy.all(f(0,5) == len(numpy.arange(0,5)))
         assert numpy.all(f(2,11) == len(numpy.arange(2,11)))
@@ -2063,7 +2103,7 @@ class TestARange(unittest.TestCase):
         assert numpy.all(f(0,0) == len(numpy.arange(0,0)))
 
         out = arange(0, stop, 1)
-        f = function([stop], out.shape, mode=compile.mode.get_default_mode().excluding('fusion'))
+        f = function([stop], out.shape, mode=mode)
         assert len(f.maker.env.toposort())==2
         #[Elemwise{Cast{int64}}(stop), MakeVector(Elemwise{Cast{int64}}.0)]
         
@@ -2447,7 +2487,7 @@ def test_autocast():
         ac = autocast_float_as('float32', 'float64')
         ac.__enter__()
         assert (dvector()+ 1.1).dtype == 'float64'
-        assert (fvector()+ 1.1).dtype == 'float64'
+        assert (fvector()+ 1.1).dtype == theano.config.floatX
         assert (fvector()+ 1.0).dtype == 'float32'
         try: #ghetto 2.4 version of with
             ac2 = autocast_float_as('float64')