Fix SKLearnClassifier and SKLearnRegressor examples (#21385)

* fix imports and make_classification call

* Update examples

* Fix transformer example

* Remove unnecessary lists and add a clarifying comment

* Revert transformer changes

* use linear activation for regression

Author: timovdk

keras/src/wrappers/sklearn_wrapper.py

@@ -235,7 +235,8 @@ class SKLearnClassifier(ClassifierMixin, SKLBase):
     scikit-learn model.
 
     ``` python
-    from keras.src.layers import Dense, Input, Model
+    from keras.layers import Dense, Input
+    from keras.models import Model
 
     def dynamic_model(X, y, loss, layers=[10]):
         # Creates a basic MLP model dynamically choosing the input and
@@ -248,7 +249,7 @@ def dynamic_model(X, y, loss, layers=[10]):
             hidden = Dense(layer_size, activation="relu")(hidden)
 
         n_outputs = y.shape[1] if len(y.shape) > 1 else 1
-        out = [Dense(n_outputs, activation="softmax")(hidden)]
+        out = Dense(n_outputs, activation="softmax")(hidden)
         model = Model(inp, out)
         model.compile(loss=loss, optimizer="rmsprop")
 
@@ -262,7 +263,7 @@ def dynamic_model(X, y, loss, layers=[10]):
     from sklearn.datasets import make_classification
     from keras.wrappers import SKLearnClassifier
 
-    X, y = make_classification(n_samples=1000, n_features=10, n_classes=3)
+    X, y = make_classification(n_samples=1000, n_features=10)
     est = SKLearnClassifier(
         model=dynamic_model,
         model_kwargs={
@@ -346,7 +347,8 @@ class SKLearnRegressor(RegressorMixin, SKLBase):
     scikit-learn model.
 
     ``` python
-    from keras.src.layers import Dense, Input, Model
+    from keras.layers import Dense, Input
+    from keras.models import Model
 
     def dynamic_model(X, y, loss, layers=[10]):
         # Creates a basic MLP model dynamically choosing the input and
@@ -359,7 +361,7 @@ def dynamic_model(X, y, loss, layers=[10]):
             hidden = Dense(layer_size, activation="relu")(hidden)
 
         n_outputs = y.shape[1] if len(y.shape) > 1 else 1
-        out = [Dense(n_outputs, activation="softmax")(hidden)]
+        out = Dense(n_outputs)(hidden)
         model = Model(inp, out)
         model.compile(loss=loss, optimizer="rmsprop")