def print_all_node_names(model_file, is_BrainScript=True):
loaded_model = load_model(model_file)
if is_BrainScript:
loaded_model = combine([loaded_model.outputs[0]])
node_list = graph.depth_first_search(loaded_model, lambda x: x.is_output)
print("printing node information in the format")
print("node name (tensor shape)")
for node in node_list:
print(node.name, node.shape)
def print_all_node_names(model_file, is_BrainScript=True):
loaded_model = load_model(model_file)
if is_BrainScript:
loaded_model = combine([loaded_model.outputs[0]])
node_list = graph.depth_first_search(loaded_model,
lambda x: isinstance(x, Function))
print("printing node information in the format")
for node in node_list:
print("Node name:", node.name)
for out in node.outputs:
print("Output name and shape:", out.name, out.shape)
def _nodes_to_debug(model):
from cntk.graph import depth_first_search
def node_filter(x):
if hasattr(x, 'op_name') and x.op_name in ['NoOp']:
return False
else:
return True
nodes = set(depth_first_search(model, lambda x: True))
uf_nodes = [n for n in nodes if hasattr(n, 'op_name')
and n.op_name == 'UserFunction']
already_covered = [n.inputs[0].owner if n.inputs[0].is_output else
n.inputs[0] for n in uf_nodes]
to_remove = [n.uid for n in (already_covered + uf_nodes)]
return [n for n in nodes if n.uid not in to_remove]
def _nodes_to_debug(model):
from cntk.graph import depth_first_search
def node_filter(x):
if hasattr(x, 'op_name') and x.op_name in ['NoOp']:
return False
else:
return True
nodes = set(depth_first_search(model, lambda x: True))
uf_nodes = [
n for n in nodes
if hasattr(n, 'op_name') and n.op_name == 'UserFunction'
]
already_covered = [
n.inputs[0].owner if n.inputs[0].is_output else n.inputs[0]
for n in uf_nodes
]
to_remove = [n.uid for n in (already_covered + uf_nodes)]
return [n for n in nodes if n.uid not in to_remove]