def _sum_deterministic_policy(self, model_names, path):
net = core.Net('DeterministicPolicy')
C2.set_net(net)
output = 'ActionProbabilities'
workspace.FeedBlob(output, np.array([1.0]))
model_outputs = []
for model in model_names:
model_output = '{}_Output'.format(model)
workspace.FeedBlob(model_output, np.array([1.0], dtype=np.float32))
model_outputs.append(model_output)
max_action = C2.FlattenToVec(
C2.ArgMax(C2.Transpose(C2.Sum(*model_outputs)))
)
one_blob = C2.NextBlob('one')
workspace.FeedBlob(one_blob, np.array([1.0], dtype=np.float32))
C2.net().SparseToDense(
[
max_action,
one_blob,
model_outputs[0],
],
[output],
)
meta = PredictorExportMeta(
net,
[one_blob],
model_outputs,
[output],
)
save_to_db('minidb', path, meta)
def save_sum_deterministic_policy(model_names, path, db_type):
net = core.Net("DeterministicPolicy")
C2.set_net(net)
output = "ActionProbabilities"
workspace.FeedBlob(output, np.array([1.0]))
model_outputs = []
for model in model_names:
model_output = "{}_Output".format(model)
workspace.FeedBlob(model_output, np.array([[1.0]], dtype=np.float32))
model_outputs.append(model_output)
max_action = C2.FlattenToVec(C2.ArgMax(C2.Transpose(C2.Sum(*model_outputs))))
one_blob = C2.NextBlob("one")
workspace.FeedBlob(one_blob, np.array([1.0], dtype=np.float32))
C2.net().SparseToDense([max_action, one_blob, model_outputs[0]], [output])
meta = PredictorExportMeta(net, [one_blob], model_outputs, [output])
save_to_db(db_type, path, meta)
