Add Activation layer.

keras_core/layers/__init__.py

@@ -1,3 +1,4 @@
+from keras_core.layers.core.activation import Activation
 from keras_core.layers.core.dense import Dense
 from keras_core.layers.core.input_layer import Input
 from keras_core.layers.core.input_layer import InputLayer

keras_core/layers/core/activation.py

@@ -0,0 +1,39 @@
+from keras_core import activations
+from keras_core.api_export import keras_core_export
+from keras_core.layers.layer import Layer
+
+
+@keras_core_export("keras_core.layers.Activation")
+class Activation(Layer):
+    """Applies an activation function to an output.
+
+    Args:
+        activation: Activation function. It could be
+            a callable, or the name of an activation
+            from the `keras_core.activations` namespace.
+
+    Example:
+
+    >>> layer = keras_core.layers.Activation('relu')
+    >>> layer([-3.0, -1.0, 0.0, 2.0])
+    [0.0, 0.0, 0.0, 2.0]
+    >>> layer = keras_core.layers.Activation(keras_core.activations.relu)
+    >>> layer([-3.0, -1.0, 0.0, 2.0])
+    [0.0, 0.0, 0.0, 2.0]
+    """
+
+    def __init__(self, activation, **kwargs):
+        super().__init__(**kwargs)
+        self.supports_masking = True
+        self.activation = activations.get(activation)
+
+    def call(self, inputs):
+        return self.activation(inputs)
+
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
+    def get_config(self):
+        config = {"activation": activations.serialize(self.activation)}
+        base_config = super().get_config()
+        return {**base_config, **config}

keras_core/layers/core/activation_test.py

@@ -0,0 +1,33 @@
+from keras_core import activations
+from keras_core import layers
+from keras_core import testing
+
+
+class ActivationTest(testing.TestCase):
+    def test_dense_basics(self):
+        self.run_layer_test(
+            layers.Activation,
+            init_kwargs={
+                "activation": "relu",
+            },
+            input_shape=(2, 3),
+            expected_output_shape=(2, 3),
+            expected_num_trainable_weights=0,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            expected_num_losses=0,
+            supports_masking=True,
+        )
+        self.run_layer_test(
+            layers.Activation,
+            init_kwargs={
+                "activation": activations.gelu,
+            },
+            input_shape=(2, 2),
+            expected_output_shape=(2, 2),
+            expected_num_trainable_weights=0,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            expected_num_losses=0,
+            supports_masking=True,
+        )

keras_core/layers/core/dense.py

@@ -19,9 +19,9 @@ class Dense(Layer):
         activity_regularizer=None,
         kernel_constraint=None,
         bias_constraint=None,
-        name=None,
+        **kwargs,
     ):
-        super().__init__(name=name)
+        super().__init__(**kwargs)
         self.units = units
         self.activation = activations.get(activation)
         self.use_bias = use_bias
@@ -57,6 +57,11 @@ class Dense(Layer):
             x = x + self.bias
         return self.activation(x)
 
+    def compute_output_shape(self, input_shape):
+        output_shape = list(input_shape)
+        output_shape[-1] = self.units
+        return tuple(output_shape)
+
     def get_config(self):
         base_config = super().get_config()
         config = {

keras_core/layers/core/dense_test.py

@@ -1,14 +1,14 @@
 import numpy as np
 
+from keras_core import layers
 from keras_core import testing
-from keras_core.layers.core.dense import Dense
 
 
 class DenseTest(testing.TestCase):
     def test_dense_basics(self):
         # 2D case, no bias.
         self.run_layer_test(
-            Dense,
+            layers.Dense,
             init_kwargs={
                 "units": 4,
                 "activation": "relu",
@@ -26,7 +26,7 @@ class DenseTest(testing.TestCase):
         )
         # 3D case, some regularizers.
         self.run_layer_test(
-            Dense,
+            layers.Dense,
             init_kwargs={
                 "units": 5,
                 "activation": "sigmoid",
@@ -43,7 +43,7 @@ class DenseTest(testing.TestCase):
         )
 
     def test_dense_correctness(self):
-        layer = Dense(units=2, activation="relu")
+        layer = layers.Dense(units=2, activation="relu")
         layer.build((1, 2))
         layer.set_weights(
             [