Updated SplitNetwork to latest changes in networks. Added docs

bayesflow/summary_networks.py

@@ -198,7 +198,7 @@ class SplitNetwork(tf.keras.Model):
     of data to provide an individual network for each split of the data.
     """
 
-    def __init__(self, num_splits, split_data_configurator, network_type=InvariantNetwork, meta={}, **kwargs):
+    def __init__(self, num_splits, split_data_configurator, network_type=InvariantNetwork, network_kwargs={}, **kwargs):
         """Creates a composite network of `num_splits` sub-networks of type `network_type`, each with configuration
         specified by `meta`.
 
@@ -207,14 +207,22 @@ def __init__(self, num_splits, split_data_configurator, network_type=InvariantNe
         num_splits              : int
             The number if splits for the data, which will equal the number of sub-networks.
         split_data_configurator : callable
-            Function that takes the arguments `i` and `x` where `i` is the index of the network
-            and `x` are the inputs to the `SplitNetwork`. Should return the input for the corresponding network.
+            Function that takes the arguments `i` and `x` where `i` is the index of the
+            network and `x` are the inputs to the `SplitNetwork`. Should return the input
+            for the corresponding network.
 
-            For example, to achieve a network with is permutation-invariant both vertically (i.e., across rows)
-            and horizontally (i.e., across columns), one could to:
-            `def config(i, x):
-            TODO
+            For example, to achieve a network with is permutation-invariant both
+            vertically (i.e., across rows)  and horizontally (i.e., across columns), one could to:
+            `def split(i, x):
+                selector = tf.where(x[:,:,0]==i, 1.0, 0.0)
+                selected = x[:,:,1] * selector
+                split_x = tf.stack((selector, selected), axis=-1)
+                return split_x
             `
+            where `x[:,:,0]` contains an integer indicating which split the data
+            in `x[:,:,1]` belongs to. All values in `x[:,:,1]` that are not selected
+            are set to zero. The selector is passed along with the modified data,
+            indicating which rows belong to the split `i`.
         network_type            : callable, optional, default: `InvariantNetowk`
             Type of neural network to use.
         meta                    : dict, optional, default: {}
@@ -227,7 +235,7 @@ def __init__(self, num_splits, split_data_configurator, network_type=InvariantNe
 
         self.num_splits = num_splits
         self.split_data_configurator = split_data_configurator
-        self.networks = [network_type(meta) for _ in range(num_splits)]
+        self.networks = [network_type(**network_kwargs) for _ in range(num_splits)]
 
     def call(self, x):
         """Performs a forward pass through the subnetworks and concatenates their output.