Create jaccard_test.py

tests/keras_contrib/losses/jaccard_test.py

@@ -0,0 +1,31 @@
+from keras_contrib.losses import jaccard_distance
+import numpy as np
+
+def test_jaccard_distance():
+    # all_right, almost_right, half_right, all_wrong
+    y_true = np.array([[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0],
+                       [0, 0, 1., 0.]])
+    y_pred = np.array([[0, 0, 1, 0], [0, 0, 0.9, 0], [0, 0, 0.1, 0],
+                       [1, 1, 0.1, 1.]])
+
+    r = jaccard_distance(
+        K.variable(y_true),
+        K.variable(y_pred), )
+    all_right, almost_right, half_right, all_wrong = K.eval(r)
+    assert r.shape == (4, )
+    assert all_right == 0, 'should converge on zero'
+    assert all_right < almost_right
+    assert almost_right < half_right
+    assert half_right < all_wrong
+
+def test_jaccard_distance_shapes_3d():
+    y_a = K.variable(np.random.random((5, 6, 7)))
+    y_b = K.variable(np.random.random((5, 6, 7)))
+    objective_output = jaccard_distance(y_a, y_b)
+    assert K.eval(objective_output).shape == (5, 6)
+
+def test_jaccard_distance_shapes_2d():
+    y_a = K.variable(np.random.random((6, 7)))
+    y_b = K.variable(np.random.random((6, 7)))
+    objective_output = jaccard_distance(y_a, y_b)
+    assert K.eval(objective_output).shape == (6, )