Adds Random Unit Tests (#174)

* Add torch backend - random

* Add Random Unit Test

* Adds Random Unit Test

* Random Test

* Adds Random Test

keras_core/layers/convolutional/conv_test.py

@@ -473,4 +473,4 @@ class ConvCorrectnessTest(testing.TestCase, parameterized.TestCase):
         outputs = layer(inputs)
 
         expected = tf_keras_layer(inputs)
-        self.assertAllClose(outputs, expected)
+        self.assertAllClose(outputs, expected, rtol=1e-5)

keras_core/random/random.py

@@ -24,7 +24,9 @@ def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
             across multiple calls, use as seed an instance
             of `keras_core.random.SeedGenerator`.
     """
-    return normal(shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed)
+    return backend.random.normal(
+        shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
+    )
 
 
 @keras_core_export("keras_core.random.uniform")

keras_core/random/random_test.py

@@ -0,0 +1,63 @@
+import numpy as np
+from absl.testing import parameterized
+
+from keras_core import testing
+from keras_core.operations import numpy as knp
+from keras_core.random import random
+
+
+class RandomTest(testing.TestCase, parameterized.TestCase):
+    @parameterized.parameters(
+        {"seed": 10, "shape": (5,), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3, 4), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 3},
+    )
+    def test_normal(self, seed, shape, mean, stddev):
+        np.random.seed(seed)
+        np_res = np.random.normal(loc=mean, scale=stddev, size=shape)
+        res = random.normal(shape, mean=mean, stddev=stddev, seed=seed)
+        self.assertEqual(res.shape, shape)
+        self.assertEqual(res.shape, np_res.shape)
+
+    @parameterized.parameters(
+        {"seed": 10, "shape": (5,), "minval": 0, "maxval": 1},
+        {"seed": 10, "shape": (2, 3), "minval": 0, "maxval": 1},
+        {"seed": 10, "shape": (2, 3, 4), "minval": 0, "maxval": 2},
+        {"seed": 10, "shape": (2, 3), "minval": -1, "maxval": 1},
+        {"seed": 10, "shape": (2, 3), "minval": 1, "maxval": 3},
+    )
+    def test_uniform(self, seed, shape, minval, maxval):
+        np.random.seed(seed)
+        np_res = np.random.uniform(low=minval, high=maxval, size=shape)
+        res = random.uniform(shape, minval=minval, maxval=maxval, seed=seed)
+        self.assertEqual(res.shape, shape)
+        self.assertEqual(res.shape, np_res.shape)
+        self.assertLessEqual(knp.max(res), maxval)
+        self.assertGreaterEqual(knp.max(res), minval)
+
+    @parameterized.parameters(
+        {"seed": 10, "shape": (5,), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3, 4), "mean": 0, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 1},
+        {"seed": 10, "shape": (2, 3), "mean": 10, "stddev": 3},
+    )
+    def test_truncated_normal(self, seed, shape, mean, stddev):
+        np.random.seed(seed)
+        np_res = np.random.normal(loc=mean, scale=stddev, size=shape)
+        res = random.truncated_normal(
+            shape, mean=mean, stddev=stddev, seed=seed
+        )
+        self.assertEqual(res.shape, shape)
+        self.assertEqual(res.shape, np_res.shape)
+        self.assertLessEqual(knp.max(res), mean + 2 * stddev)
+        self.assertGreaterEqual(knp.max(res), mean - 2 * stddev)
+
+    def test_dropout(self):
+        x = knp.ones((3, 5))
+        self.assertAllClose(random.dropout(x, rate=0, seed=0), x)
+        x_res = random.dropout(x, rate=0.8, seed=0)
+        self.assertGreater(knp.max(x_res), knp.max(x))
+        self.assertGreater(knp.sum(x_res == 0), 2)

requirements.txt

@@ -1,5 +1,7 @@
 tensorflow
-torch
+# TODO: Use Torch CPU
+# Remove after resolving Cuda version differences with TF
+torch>=2.0.1+cpu
 jax[cpu]
 namex
 black>=22