config = tf.ConfigProto(
device_count={'GPU': 1},
intra_op_parallelism_threads=1,
allow_soft_placement=True
)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.6
session = tf.Session(config=config)
keras.backend.set_session(session)
with session.as_default():
with session.graph.as_default():
predict_result = modell.predict([X1,X2])
