Update tf.math and tf.linalg op names in Customization basics tensors and operations tutorial

Author: 8bitmp3

site/en/tutorials/customization/basics.ipynb

@@ -106,7 +106,7 @@
       "source": [
         "## Tensors\n",
         "\n",
-        "A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor`s can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`s. These operations automatically convert built-in Python types. For example:\n"
+        "A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor`s can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.linalg.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`s. These operations automatically convert built-in Python types. For example:\n"
       ]
     },
     {
@@ -118,13 +118,13 @@
       },
       "outputs": [],
       "source": [
-        "print(tf.add(1, 2))\n",
-        "print(tf.add([1, 2], [3, 4]))\n",
-        "print(tf.square(5))\n",
-        "print(tf.reduce_sum([1, 2, 3]))\n",
+        "print(tf.math.add(1, 2))\n",
+        "print(tf.math.add([1, 2], [3, 4]))\n",
+        "print(tf.math.square(5))\n",
+        "print(tf.math.reduce_sum([1, 2, 3]))\n",
         "\n",
         "# Operator overloading is also supported\n",
-        "print(tf.square(2) + tf.square(3))"
+        "print(tf.math.square(2) + tf.math.square(3))"
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "x = tf.matmul([[1]], [[2, 3]])\n",
+        "x = tf.linalg.matmul([[1]], [[2, 3]])\n",
         "print(x)\n",
         "print(x.shape)\n",
         "print(x.dtype)"
@@ -191,11 +191,11 @@
         "ndarray = np.ones([3, 3])\n",
         "\n",
         "print(\"TensorFlow operations convert numpy arrays to Tensors automatically\")\n",
-        "tensor = tf.multiply(ndarray, 42)\n",
+        "tensor = tf.math.multiply(ndarray, 42)\n",
         "print(tensor)\n",
         "\n",
         "\n",
-        "print(\"And NumPy operations convert Tensors to numpy arrays automatically\")\n",
+        "print(\"And NumPy operations convert Tensors to NumPy arrays automatically\")\n",
         "print(np.add(tensor, 1))\n",
         "\n",
         "print(\"The .numpy() method explicitly converts a Tensor to a numpy array\")\n",
@@ -268,7 +268,7 @@
         "def time_matmul(x):\n",
         "  start = time.time()\n",
         "  for loop in range(10):\n",
-        "    tf.matmul(x, x)\n",
+        "    tf.linalg.matmul(x, x)\n",
         "\n",
         "  result = time.time()-start\n",
         "\n",
@@ -354,7 +354,7 @@
       },
       "outputs": [],
       "source": [
-        "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n",
+        "ds_tensors = ds_tensors.map(tf.math.square).shuffle(2).batch(2)\n",
         "\n",
         "ds_file = ds_file.batch(2)"
       ]