Add minimum merge layer (keras-team#7758)

keras/layers/merge.py

@@ -290,6 +290,21 @@ def _merge_function(self, inputs):
         return output
 
 
+class Minimum(_Merge):
+    """Layer that computes the minimum (element-wise) 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).
+    """
+
+    def _merge_function(self, inputs):
+        output = inputs[0]
+        for i in range(1, len(inputs)):
+            output = K.minimum(output, inputs[i])
+        return output
+
+
 class Concatenate(_Merge):
     """Layer that concatenates a list of inputs.
 
@@ -586,6 +601,19 @@ def maximum(inputs, **kwargs):
     return Maximum(**kwargs)(inputs)
 
 
+def minimum(inputs, **kwargs):
+    """Functional interface to the `Minimum` layer.
+
+    # Arguments
+        inputs: A list of input tensors (at least 2).
+        **kwargs: Standard layer keyword arguments.
+
+    # Returns
+        A tensor, the element-wise minimum of the inputs.
+    """
+    return Minimum(**kwargs)(inputs)
+
+
 def concatenate(inputs, axis=-1, **kwargs):
     """Functional interface to the `Concatenate` layer.
 

tests/keras/layers/merge_test.py

@@ -138,6 +138,25 @@ def test_merge_maximum():
     assert_allclose(out, np.maximum(x1, x2), atol=1e-4)
 
 
+@keras_test
+def test_merge_minimum():
+    i1 = layers.Input(shape=(4, 5))
+    i2 = layers.Input(shape=(4, 5))
+    o = layers.minimum([i1, i2])
+    assert o._keras_shape == (None, 4, 5)
+    model = models.Model([i1, i2], o)
+
+    max_layer = layers.Minimum()
+    o2 = max_layer([i1, i2])
+    assert max_layer.output_shape == (None, 4, 5)
+
+    x1 = np.random.random((2, 4, 5))
+    x2 = np.random.random((2, 4, 5))
+    out = model.predict([x1, x2])
+    assert out.shape == (2, 4, 5)
+    assert_allclose(out, np.minimum(x1, x2), atol=1e-4)
+
+
 @keras_test
 def test_merge_concatenate():
     i1 = layers.Input(shape=(None, 5))