Add more merging layers (keras-team#71)

* add base merge layer

* format docstrings

* add  layer

* add test cases for  layer

* Add import for  layer

* fix build function

* add dynamic and static tests

* fix pytest import

* fix pytest decorator

* remove batch size from dynamic shape test

* fix keras reference

* refactor test class

* fix tf tests, and linting issues

* add subtract layer

* add tests for subtract layer

* fix linting issues

* add average layer

* add maximum layer

* dd minimum layer

* add multiply layer

* add tests for average, minimum, maximum, and multiply layers

keras_core/layers/__init__.py

@@ -6,6 +6,14 @@
 from keras_core.layers.layer import Layer
 from keras_core.layers.merging.add import Add
 from keras_core.layers.merging.add import add
+from keras_core.layers.merging.average import Average
+from keras_core.layers.merging.average import average
+from keras_core.layers.merging.maximum import Maximum
+from keras_core.layers.merging.maximum import maximum
+from keras_core.layers.merging.minimum import Minimum
+from keras_core.layers.merging.minimum import minimum
+from keras_core.layers.merging.multiply import Multiply
+from keras_core.layers.merging.multiply import multiply
 from keras_core.layers.merging.subtract import Subtract
 from keras_core.layers.merging.subtract import subtract
 from keras_core.layers.pooling.average_pooling1d import AveragePooling1D

keras_core/layers/merging/average.py

@@ -0,0 +1,69 @@
+from keras_core.api_export import keras_core_export
+from keras_core.layers.merging.base_merge import Merge
+
+
+@keras_core_export("keras_core.layers.Average")
+class Average(Merge):
+    """Averages a list of inputs element-wise..
+
+    It takes as input a list of tensors, all of the same shape,
+    and returns a single tensor (also of the same shape).
+
+    Examples:
+
+    >>> input_shape = (2, 3, 4)
+    >>> x1 = np.random.rand(*input_shape)
+    >>> x2 = np.random.rand(*input_shape)
+    >>> y = keras_core.layers.Average()([x1, x2])
+
+    Usage in a Keras model:
+
+    >>> input1 = keras_core.layers.Input(shape=(16,))
+    >>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
+    >>> input2 = keras_core.layers.Input(shape=(32,))
+    >>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
+    >>> # equivalent to `y = keras_core.layers.average([x1, x2])`
+    >>> y = keras_core.layers.Average()([x1, x2])
+    >>> out = keras_core.layers.Dense(4)(y)
+    >>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
+
+    """
+
+    def _merge_function(self, inputs):
+        output = inputs[0]
+        for i in range(1, len(inputs)):
+            output += inputs[i]
+        return output / len(inputs)
+
+
+@keras_core_export("keras_core.layers.average")
+def average(inputs, **kwargs):
+    """Functional interface to the `keras_core.layers.Average` layer.
+
+    Args:
+        inputs: A list of input tensors , all of the same shape.
+        **kwargs: Standard layer keyword arguments.
+
+    Returns:
+        A tensor as the element-wise product of the inputs with the same
+        shape as the inputs.
+
+    Examples:
+
+    >>> input_shape = (2, 3, 4)
+    >>> x1 = np.random.rand(*input_shape)
+    >>> x2 = np.random.rand(*input_shape)
+    >>> y = keras_core.layers.average([x1, x2])
+
+    Usage in a Keras model:
+
+    >>> input1 = keras_core.layers.Input(shape=(16,))
+    >>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
+    >>> input2 = keras_core.layers.Input(shape=(32,))
+    >>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
+    >>> y = keras_core.layers.average([x1, x2])
+    >>> out = keras_core.layers.Dense(4)(y)
+    >>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
+
+    """
+    return Average(**kwargs)(inputs)

keras_core/layers/merging/maximum.py

@@ -0,0 +1,70 @@
+from keras_core import operations as ops
+from keras_core.api_export import keras_core_export
+from keras_core.layers.merging.base_merge import Merge
+
+
+@keras_core_export("keras_core.layers.Maximum")
+class Maximum(Merge):
+    """Computes element-wise maximum on a list of inputs.
+
+    It takes as input a list of tensors, all of the same shape,
+    and returns a single tensor (also of the same shape).
+
+    Examples:
+
+    >>> input_shape = (2, 3, 4)
+    >>> x1 = np.random.rand(*input_shape)
+    >>> x2 = np.random.rand(*input_shape)
+    >>> y = keras_core.layers.Maximum()([x1, x2])
+
+    Usage in a Keras model:
+
+    >>> input1 = keras_core.layers.Input(shape=(16,))
+    >>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
+    >>> input2 = keras_core.layers.Input(shape=(32,))
+    >>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
+    >>> # equivalent to `y = keras_core.layers.maximum([x1, x2])`
+    >>> y = keras_core.layers.Maximum()([x1, x2])
+    >>> out = keras_core.layers.Dense(4)(y)
+    >>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
+
+    """
+
+    def _merge_function(self, inputs):
+        output = inputs[0]
+        for i in range(1, len(inputs)):
+            output = ops.maximum(output, inputs[i])
+        return output
+
+
+@keras_core_export("keras_core.layers.maximum")
+def maximum(inputs, **kwargs):
+    """Functional interface to the `keras_core.layers.Maximum` layer.
+
+    Args:
+        inputs: A list of input tensors , all of the same shape.
+        **kwargs: Standard layer keyword arguments.
+
+    Returns:
+        A tensor as the element-wise product of the inputs with the same
+        shape as the inputs.
+
+    Examples:
+
+    >>> input_shape = (2, 3, 4)
+    >>> x1 = np.random.rand(*input_shape)
+    >>> x2 = np.random.rand(*input_shape)
+    >>> y = keras_core.layers.maximum([x1, x2])
+
+    Usage in a Keras model:
+
+    >>> input1 = keras_core.layers.Input(shape=(16,))
+    >>> x1 = keras_core.layers.Dense(8, activation='relu')(input1)
+    >>> input2 = keras_core.layers.Input(shape=(32,))
+    >>> x2 = keras_core.layers.Dense(8, activation='relu')(input2)
+    >>> y = keras_core.layers.maximum([x1, x2])
+    >>> out = keras_core.layers.Dense(4)(y)
+    >>> model = keras_core.models.Model(inputs=[input1, input2], outputs=out)
+
+    """
+    return Maximum(**kwargs)(inputs)