Add name arg to MobileNet V1 and V2 (#19712)

Followup from #19695 and 2b7120b.

keras/src/applications/mobilenet.py

@@ -30,6 +30,7 @@ def MobileNet(
     pooling=None,
     classes=1000,
     classifier_activation="softmax",
+    name=None,
 ):
     """Instantiates the MobileNet architecture.
 
@@ -101,6 +102,7 @@ def MobileNet(
             Set `classifier_activation=None` to return the logits of the "top"
             layer. When loading pretrained weights, `classifier_activation`
             can only be `None` or `"softmax"`.
+        name: String, the name of the model.
 
     Returns:
         A model instance.
@@ -237,7 +239,9 @@ def MobileNet(
         inputs = img_input
 
     # Create model.
-    model = Functional(inputs, x, name=f"mobilenet_{alpha:0.2f}_{rows}")
+    if name is None:
+        name = f"mobilenet_{alpha:0.2f}_{rows}"
+    model = Functional(inputs, x, name=name)
 
     # Load weights.
     if weights == "imagenet":

keras/src/applications/mobilenet_v2.py

@@ -28,6 +28,7 @@ def MobileNetV2(
     pooling=None,
     classes=1000,
     classifier_activation="softmax",
+    name=None,
 ):
     """Instantiates the MobileNetV2 architecture.
 
@@ -103,6 +104,7 @@ def MobileNetV2(
             Set `classifier_activation=None` to return the logits of the "top"
             layer. When loading pretrained weights, `classifier_activation`
             can only be `None` or `"softmax"`.
+        name: String, the name of the model.
 
     Returns:
         A model instance.
@@ -359,7 +361,9 @@ def MobileNetV2(
         inputs = img_input
 
     # Create model.
-    model = Functional(inputs, x, name=f"mobilenetv2_{alpha:0.2f}_{rows}")
+    if name is None:
+        name = f"mobilenetv2_{alpha:0.2f}_{rows}"
+    model = Functional(inputs, x, name=name)
 
     # Load weights.
     if weights == "imagenet":

keras/src/applications/mobilenet_v3.py

@@ -135,6 +135,7 @@
         be `None` or `"softmax"`.
     include_preprocessing: Boolean, whether to include the preprocessing
         layer (`Rescaling`) at the bottom of the network. Defaults to `True`.
+    name: String, the name of the model.
 
 Call arguments:
     inputs: A floating point `numpy.array` or backend-native tensor,