Merge pull request #295 from delallea/fix_argmax_test

theano/tensor/tests/test_basic.py

@@ -6,6 +6,9 @@ import sys
 import unittest
 import warnings
 from copy import copy, deepcopy
+# Import builtin min to be able to use it after importing the tensor version.
+import __builtin__
+builtin_min = __builtin__.min
 
 from nose.plugins.skip import SkipTest
 import numpy
@@ -1562,6 +1565,31 @@ class T_max_and_argmax(unittest.TestCase):
         data = rand(2, 3)
         n = as_tensor_variable(data)
 
+        def safe_verify_grad(func, data):
+            """
+            Wrapper around 'verify_grad' that picks a proper value for epsilon.
+
+            This is needed because 'verify_grad' may fail when its epsilon is
+            too large, due to the fact the argmax is not continuous.
+            We make sure epsilon is less than the minimum absolute value found
+            in the matrix of pairwise differences between all elements in the
+            data. This way, the argmax will not change when adding epsilon.
+            """
+            # 'data' is a one-element list.
+            data_tensor, = data
+            # Flatten it into a 1D vector.
+            data_vector = data_tensor.flatten()
+            # Compute pairwise absolute differences.
+            diff = numpy.abs(data_vector.reshape((-1, 1)) - data_vector)
+            # Alter the diagonal to avoid a zero minimum.
+            for i in xrange(len(diff)):
+                diff[i, i] = 1
+            # Find an appropriate epsilon.
+            eps = builtin_min(numeric_grad.type_eps[config.floatX],
+                              diff.min() / 2)
+            # Run gradient verification.
+            utt.verify_grad(func, data, eps=eps)
+
         def check_grad_max(data, max_grad_data, axis=None):
             """
             Why this is needed? verify_grad is not enough?
@@ -1583,10 +1611,10 @@ class T_max_and_argmax(unittest.TestCase):
 
         for axis in (-1, 0, 1, None):
             for j in xrange(2):
-                utt.verify_grad(lambda v: max_and_argmax(v, axis=axis)[j],
+                safe_verify_grad(lambda v: max_and_argmax(v, axis=axis)[j],
                                 [data])
                 if axis != 1:
-                    utt.verify_grad(lambda v: max_and_argmax(v.flatten(),
+                    safe_verify_grad(lambda v: max_and_argmax(v.flatten(),
                                                              axis=axis)[j],
                                     [data])
             if axis in (0, None):
@@ -1599,16 +1627,15 @@ class T_max_and_argmax(unittest.TestCase):
         data = rand(3, 4, 5)
 
         for i in [0, 1, 2]:
-            utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
-            utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
+            safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
+            safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
 
         # Test 4d inner dimensions
-        # Use float64 as otherwise the test does not pass.
-        data = rand(2, 3, 4, 5).astype("float64")
+        data = rand(2, 3, 4, 5)
 
         for i in [0, 1, 2, 3]:
-            utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
-            utt.verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
+            safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[0], [data])
+            safe_verify_grad(lambda v: max_and_argmax(v, axis=[i])[1], [data])
 
 
 class T_argmin_argmax(unittest.TestCase):