convenience reapply

agentnet/utils/clone.py

@@ -138,3 +138,24 @@ def clone_network(original_network, bottom_layers=None,
 
 
     return network_clone
+
+
+def reapply(layer_or_layers, new_bottom, share_params=True, name_prefix=None):
+    """
+    Applies a part of lasagne network to a new place. Wraps clone_network
+    :param layer_or_layers: layers to be re-applied
+    :param new_bottom: a dict {old_layer:new_layer} that defines which layers should be substituted by which other layers
+    :param share_params: if True, cloned network will use same shared variables for weights.
+        Otherwise new shared variables will be created and set to original NN values.
+        WARNING! shared weights must be accessible via lasagne.layers.get_all_params with no flags
+        If you want custom other parameters to be shared, agentnet.utils.clone_network
+    :param name_prefix: if not None, adds this prefix to all the layers and params of the cloned network
+
+    :return: a new layer or layers that represent re-applying layer_or_layers to new_bottom
+    """
+    assert isinstance(new_bottom,dict)
+    for layer in lasagne.layers.get_all_layers(layer_or_layers,list(new_bottom.keys())):
+        if isinstance(layer,lasagne.layers.InputLayer):
+            assert layer in new_bottom, "must explicitly provide all new_bottom for each branch of original network. " \
+                                        "Assert caused by {}. For dirty hacks, use clone_network.".format(layer.name or layer)
+    return clone_network(layer_or_layers, new_bottom, share_params=share_params, share_inputs=False, name_prefix=name_prefix)