[LAYER] add elementwise layer for logic operation

Author: zsdonghao

docs/modules/layers.rst

@@ -279,6 +279,7 @@ Layer list
    FlattenLayer
    ConcatLayer
    ReshapeLayer
+   ElementwiseLayer
    SlimNetsLayer
    PReluLayer
    MultiplexerLayer
@@ -432,6 +433,10 @@ Reshape layer
 
 .. autoclass:: ReshapeLayer
 
+Logic layer
+^^^^^^^^^^^^^
+.. autoclass:: ElementwiseLayer
+
 Merge TF-Slim
 ---------------
 

tensorlayer/layers.py

@@ -2542,6 +2542,57 @@ def __init__(
         self.all_drop = dict(layer.all_drop)
         self.all_layers.extend( [self.outputs] )
 
+## Logic layer
+class ElementwiseLayer(Layer):
+    """
+    The :class:`ElementwiseLayer` class combines multiple :class:`Layer` which have the same output shapes by a given elemwise-wise operation.
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    combine_fn : a TensorFlow elemwise-merge function
+        e.g. AND is ``tf.minimum`` ;  OR is ``tf.maximum`` ; ADD is ``tf.add`` ; MUL is ``tf.mul`` and so on.
+        See `TensorFlow Math API <https://www.tensorflow.org/versions/master/api_docs/python/math_ops.html#math>`_ .
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Examples
+    --------
+    - AND Logic
+    >>> net_0 = tl.layers.DenseLayer(net_0, n_units=500,
+    ...                        act = tf.nn.relu, name='net_0')
+    >>> net_1 = tl.layers.DenseLayer(net_1, n_units=500,
+    ...                        act = tf.nn.relu, name='net_1')
+    >>> net_com = tl.layers.ElementwiseLayer(layer = [net_0, net_1],
+    ...                         combine_fn = tf.minimum,
+    ...                         name = 'combine_layer')
+    """
+    def __init__(
+        self,
+        layer = [],
+        combine_fn = tf.minimum,
+        name ='elementwise_layer',
+    ):
+        Layer.__init__(self, name=name)
+
+        print("  tensorlayer:Instantiate ElementwiseLayer %s:  %s, %s" % (self.name, layer[0].outputs._shape, combine_fn.__name__))
+
+        self.outputs = layer[0].outputs
+        # print(self.outputs._shape, type(self.outputs._shape))
+        for l in layer[1:]:
+            assert str(self.outputs._shape) == str(l.outputs._shape), "Hint: the input shapes should be the same. %s != %s" %  (self.outputs._shape , str(l.outputs._shape))
+            self.outputs = combine_fn(self.outputs, l.outputs, name=name)
+
+        self.all_layers = list(layer[0].all_layers)
+        self.all_params = list(layer[0].all_params)
+        self.all_drop = dict(layer[0].all_drop)
+
+        for i in range(1, len(layer)):
+            self.all_layers.extend(list(layer[i].all_layers))
+            self.all_params.extend(list(layer[i].all_params))
+            self.all_drop.update(dict(layer[i].all_drop))
+
 ## TF-Slim layer
 class SlimNetsLayer(Layer):
     """