Move get_source_inputs (keras-team#10415)

keras/engine/network.py

@@ -19,6 +19,7 @@
 from .. import backend as K
 from ..utils.io_utils import ask_to_proceed_with_overwrite
 from ..utils.layer_utils import print_summary as print_layer_summary
+from ..utils.layer_utils import get_source_inputs
 from ..utils.generic_utils import has_arg
 from ..utils.generic_utils import to_list
 from ..utils.generic_utils import object_list_uid
@@ -1270,49 +1271,6 @@ def summary(self, line_length=None, positions=None, print_fn=None):
                                    print_fn=print_fn)
 
 
-def get_source_inputs(tensor, layer=None, node_index=None):
-    """Returns the list of input tensors necessary to compute `tensor`.
-
-    Output will always be a list of tensors
-    (potentially with 1 element).
-
-    # Arguments
-        tensor: The tensor to start from.
-        layer: Origin layer of the tensor. Will be
-            determined via tensor._keras_history if not provided.
-        node_index: Origin node index of the tensor.
-
-    # Returns
-        List of input tensors.
-    """
-    if not hasattr(tensor, '_keras_history'):
-        return tensor
-
-    if layer is None or node_index:
-        layer, node_index, _ = tensor._keras_history
-    if not layer._inbound_nodes:
-        return [tensor]
-    else:
-        node = layer._inbound_nodes[node_index]
-        if not node.inbound_layers:
-            # Reached an Input layer, stop recursion.
-            return node.input_tensors
-        else:
-            source_tensors = []
-            for i in range(len(node.inbound_layers)):
-                x = node.input_tensors[i]
-                layer = node.inbound_layers[i]
-                node_index = node.node_indices[i]
-                previous_sources = get_source_inputs(x,
-                                                     layer,
-                                                     node_index)
-                # Avoid input redundancy.
-                for x in previous_sources:
-                    if x not in source_tensors:
-                        source_tensors.append(x)
-            return source_tensors
-
-
 def _make_node_key(layer_name, node_index):
     return layer_name + '_ib-' + str(node_index)
 

keras/utils/__init__.py

@@ -18,6 +18,7 @@
 from .generic_utils import deserialize_keras_object
 from .generic_utils import Progbar
 from .layer_utils import convert_all_kernels_in_model
+from .layer_utils import get_source_inputs
 from .layer_utils import print_summary
 from .vis_utils import plot_model
 from .np_utils import to_categorical

keras/utils/layer_utils.py

@@ -246,3 +246,46 @@ def convert_dense_weights_data_format(dense,
             ki = np.transpose(ki, (1, 2, 0))  # first -> last
         kernel[:, i] = np.reshape(ki, (np.prod(previous_feature_map_shape),))
     dense.set_weights([kernel, bias])
+
+
+def get_source_inputs(tensor, layer=None, node_index=None):
+    """Returns the list of input tensors necessary to compute `tensor`.
+
+    Output will always be a list of tensors
+    (potentially with 1 element).
+
+    # Arguments
+        tensor: The tensor to start from.
+        layer: Origin layer of the tensor. Will be
+            determined via tensor._keras_history if not provided.
+        node_index: Origin node index of the tensor.
+
+    # Returns
+        List of input tensors.
+    """
+    if not hasattr(tensor, '_keras_history'):
+        return tensor
+
+    if layer is None or node_index:
+        layer, node_index, _ = tensor._keras_history
+    if not layer._inbound_nodes:
+        return [tensor]
+    else:
+        node = layer._inbound_nodes[node_index]
+        if not node.inbound_layers:
+            # Reached an Input layer, stop recursion.
+            return node.input_tensors
+        else:
+            source_tensors = []
+            for i in range(len(node.inbound_layers)):
+                x = node.input_tensors[i]
+                layer = node.inbound_layers[i]
+                node_index = node.node_indices[i]
+                previous_sources = get_source_inputs(x,
+                                                     layer,
+                                                     node_index)
+                # Avoid input redundancy.
+                for x in previous_sources:
+                    if x not in source_tensors:
+                        source_tensors.append(x)
+            return source_tensors