def import_keras_model(model, network_name):
"""
Import a keras model into conx.
"""
from .network import Network
import inspect
import conx
network = Network(network_name)
network.model = model
conx_layers = {
name: layer
for (name, layer) in inspect.getmembers(conx.layers, inspect.isclass)
}
# First, make all of the conx layers:
for layer in model.layers:
clayer_class = conx_layers[layer.__class__.__name__ + "Layer"]
if clayer_class.__name__ == "InputLayerLayer":
clayer = conx.layers.InputLayer(layer.name, None)
#clayer.make_input_layer_k = lambda layer=layer: layer
clayer.shape = None
clayer.params["batch_shape"] = layer.get_config(
)["batch_input_shape"]
#clayer.params = layer.get_config()
clayer.k = clayer.make_input_layer_k()
clayer.keras_layer = clayer.k
else:
clayer = clayer_class(**layer.get_config())
clayer.k = layer
clayer.keras_layer = layer
network.add(clayer)
# Next, connect them up:
for layer_from in model.layers:
for node in layer.outbound_nodes:
network.connect(layer_from, node.outbound_layer.name)
print("connecting:", layer_from, node.outbound_layer.name)
# Connect them all up, and set input banks:
network.connect()
for clayer in network.layers:
clayer.input_names = network.input_bank_order
# Finally, make the internal models:
for clayer in network.layers:
## FIXME: the appropriate inputs:
if clayer.kind() != "input":
clayer.model = keras.models.Model(
inputs=model.layers[0].input,
outputs=clayer.keras_layer.output)
return network
