added nnet_ops to sandbox

sandbox/_test_nnet_ops.py

+
+import unittest
+import theano
+import theano._test_tensor as TT
+import numpy
+
+from nnet_ops import *
+
+class T_sigmoid(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(9999)
+    def test_elemwise(self):
+        TT.verify_grad(self, sigmoid, [numpy.random.rand(3,4)])
+
+class T_softplus(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(9999)
+    def test_elemwise(self):
+        TT.verify_grad(self, softplus, [numpy.random.rand(3,4)])
+
+class T_Softmax(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(9999)
+    def test0(self):
+        class Dummy(object):
+            def make_node(self, a):
+                return [softmax(a)[:,0]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+    def test1(self):
+        class Dummy(object):
+            def make_node(self, a):
+                return [softmax(a)[:,1]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+    def test2(self):
+        class Dummy(object):
+            def make_node(self, a):
+                return [softmax(a)[:,2]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+    def test3(self):
+        class Dummy(object):
+            def make_node(self, a):
+                return [softmax(a)[:,3]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+
+
+class T_SoftmaxWithBias(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(9999)
+    def test0(self):
+        class Dummy(object):
+            def make_node(self, a, b):
+                return [softmax_with_bias(a, b)[:,0]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
+            numpy.random.rand(4)])
+    def test1(self):
+        class Dummy(object):
+            def make_node(self, a, b):
+                return [softmax_with_bias(a, b)[:,1]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
+            numpy.random.rand(4)])
+    def test2(self):
+        class Dummy(object):
+            def make_node(self, a, b):
+                return [softmax_with_bias(a, b)[:,2]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
+            numpy.random.rand(4)])
+    def test3(self):
+        class Dummy(object):
+            def make_node(self, a, b):
+                return [softmax_with_bias(a, b)[:,3]]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
+            numpy.random.rand(4)])
+
+class T_CrossentropySoftmax1Hot(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(9999)
+    def test0(self):
+        y_idx = [0,1,3]
+        class Dummy(object):
+            def make_node(self, a,b):
+                return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0:1]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4),
+            numpy.random.rand(4)])
+
+    def test1(self):
+        y_idx = [0,1,3]
+        class Dummy(object):
+            def make_node(self, a):
+                return crossentropy_softmax_1hot(a, y_idx)[0:1]
+        TT.verify_grad(self, Dummy(), [numpy.random.rand(3,4)])
+
+class T_prepend(unittest.TestCase):
+    def test0(self):
+        """basic functionality"""
+        x=tensor.matrix('x')
+        y=Prepend_scalar_constant_to_each_row(4.)(x)
+        f=theano.function([x],[y])
+        m=numpy.random.rand(3,5)
+        my = f(m)
+        self.failUnless(my.shape == (3, 6), my.shape)
+        self.failUnless(numpy.all( my[:,0] == 4.0))
+
+
+class T_prepend(unittest.TestCase):
+    def test0(self):
+        """basic functionality"""
+        x=tensor.matrix('x')
+        y=Prepend_scalar_to_each_row()(5.,x)
+        f=theano.function([x],[y])
+        m=numpy.ones((3,5),dtype="float32")
+        my = f(m)
+        self.failUnless(str(my.dtype) == 'float64')
+        self.failUnless(my.shape == (3, 6))
+        self.failUnless(numpy.all(my[:,0] == 5.0))
+
+class T_solve(unittest.TestCase):
+    def setUp(self):
+        self.rng = numpy.random.RandomState(666)
+
+    def test0(self):
+        A=self.rng.randn(5,5)
+        b=numpy.array(range(5),dtype=float)
+        x=numpy.linalg.solve(A,b)
+        Ax = numpy.dot(A,x)
+        are = theano.gradient.numeric_grad.abs_rel_err(Ax, b)
+        self.failUnless(numpy.all(are < 1.0e-5), (are, Ax, b))
+        #print A,b
+        #print numpy.dot(A,x)
+
+
+if __name__ == '__main__':
+    unittest.main()