8000 Activation layers inherit rank from source by dguest · Pull Request #99 · lwtnn/lwtnn · GitHub
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Activation layers inherit rank from source #99

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Merged
merged 1 commit into from
Oct 4, 2019
Merged

Activation layers inherit rank from source #99

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26 changes: 24 additions & 2 deletions converters/kerasfunc2json.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,9 @@ def _run():
input_layer_arch = arch['config']['input_layers']
nodes = _build_node_list(node_dict, input_layer_arch)

# deal with the nodes that need a type based on their source
_resolve_inheriting_types(nodes)

vars_per_input = _get_vars_per_input(input_layer_arch, node_dict)
out_dict = {
'layers': layers, 'nodes': nodes,
Expand All @@ -61,6 +64,21 @@ def _run():
}
print(json.dumps(out_dict, indent=2, sort_keys=True))

def _resolve_inheriting_types(nodes):
"""
Sometimes we're not sure what the type of node it is when we parse
the graph. In these cases we have to inhrit from the source node.
"""
sequence_types = {'sequence', 'input_sequences','time_distributed'}
for node in nodes:
if node['type'] == 'INHERIT_FROM_SOURCE':
assert len(node['sources']) == 1
parent_type = nodes[node['sources'][0]]['type']
if parent_type in sequence_types:
node['type'] = 'time_distributed'
else:
node['type'] = 'feed_forward'

def _check_version(arch):
if arch["class_name"] != "Model":
sys.exit("this is not a graph, try using keras2json")
Expand Down Expand Up @@ -297,7 +315,9 @@ def _build_layer(output_layers, node_key, h5, node_dict, layer_dict):
'concatenate': 'concatenate', # <- v2
'inputlayer': 'input',
'dense': 'feed_forward',
'activation': 'feed_forward',
# we don't know what type of node based on the activation type
# instead we tell the node to inherit from its source
'activation': 'INHERIT_FROM_SOURCE',
'lstm': 'sequence',
'gru': 'sequence',
'sum': 'sum',
Expand All @@ -312,6 +332,8 @@ def _build_node_list(node_dict, input_layer_arch):
no effort is made to sort this list in any way, but the ordering
is important because each node contains indices for other nodes
"""
nodes_with_layers = {
'feed_forward', 'sequence', 'time_distributed', 'INHERIT_FROM_SOURCE'}
node_list = []
input_map = {}
for kname, kid, ks in input_layer_arch:
Expand All @@ -331,7 +353,7 @@ def _build_node_list(node_dict, input_layer_arch):
out_node['size'] = node.n_outputs
if node.dims > 1:
out_node['type'] = 'input_sequence'
elif node_type in ['feed_forward', 'sequence', 'time_distributed']:
elif node_type in nodes_with_layers:
out_node['layer_index'] = node.layer_number
node_list.append(out_node)
return node_list
Expand Down
0