From 5d81faef4de70fc9f9ee93e201a1e27cb444a129 Mon Sep 17 00:00:00 2001
From: Aakash Kumar Nain <aakashnain@outlook.com>
Date: Thu, 11 May 2023 22:03:40 +0530
Subject: [PATCH] fix documentation in the random module (#141)

---
 keras_core/backend/jax/random.py        | 17 ++++++++++++++---
 keras_core/backend/tensorflow/random.py | 17 ++++++++++++++---
 2 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/keras_core/backend/jax/random.py b/keras_core/backend/jax/random.py
index 7c753c4bf..519e680af 100644
--- a/keras_core/backend/jax/random.py
+++ b/keras_core/backend/jax/random.py
@@ -7,7 +7,7 @@ from keras_core.backend.config import floatx
 
 
 def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
-    """Produce random number based on the normal distribution.
+    """Draw random samples from a normal (Gaussian) distribution.
 
     Args:
         shape: The shape of the random values to generate.
@@ -34,7 +34,14 @@ def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
 
 
 def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
-    """Produce random number based on the uniform distribution.
+    """Draw samples from a uniform distribution.
+
+    The generated values follow a uniform distribution in the range
+    `[minval, maxval)`. The lower bound `minval` is included in the range,
+    while the upper bound `maxval` is excluded.
+
+    For floats, the default range is `[0, 1)`.  For ints, at least `maxval`
+    must be specified explicitly.
 
     Args:
         shape: The shape of the random values to generate.
@@ -63,7 +70,11 @@ def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
 
 
 def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
-    """Produce random number based on the truncated normal distribution.
+    """Draw samples from a truncated normal distribution.
+
+    The values are drawn from a normal distribution with specified mean and
+    standard deviation, discarding and re-drawing any samples that are more
+    than two standard deviations from the mean.
 
     Args:
         shape: The shape of the random values to generate.
diff --git a/keras_core/backend/tensorflow/random.py b/keras_core/backend/tensorflow/random.py
index c0dea4268..0fe34c79f 100644
--- a/keras_core/backend/tensorflow/random.py
+++ b/keras_core/backend/tensorflow/random.py
@@ -12,7 +12,7 @@ def tf_draw_seed(seed):
 
 
 def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
-    """Produce random number based on the normal distribution.
+    """Draw random samples from a normal (Gaussian) distribution.
 
     Args:
         shape: The shape of the random values to generate.
@@ -40,7 +40,14 @@ def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
 
 
 def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
-    """Produce random number based on the uniform distribution.
+    """Draw samples from a uniform distribution.
+
+    The generated values follow a uniform distribution in the range
+    `[minval, maxval)`. The lower bound `minval` is included in the range,
+    while the upper bound `maxval` is excluded.
+
+    For floats, the default range is `[0, 1)`.  For ints, at least `maxval`
+    must be specified explicitly.
 
     Args:
         shape: The shape of the random values to generate.
@@ -73,7 +80,11 @@ def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
 
 
 def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
-    """Produce random number based on the truncated normal distribution.
+    """Draw samples from a truncated normal distribution.
+
+    The values are drawn from a normal distribution with specified mean and
+    standard deviation, discarding and re-drawing any samples that are more
+    than two standard deviations from the mean.
 
     Args:
         shape: The shape of the random values to generate.