def _make_saved_model() -> SavedModel: with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as session: input_x = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 256]) input_y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 256]) weight = tf.Variable(initial_value=numpy.arange(256, dtype=numpy.float32), dtype=tf.float32) output_z = input_x + input_y + weight session.run(weight.initializer) return SavedModel(inputs=[Input(name='x', tensor=input_x, data_format=DataFormat.CHANNELS_FIRST), Input(name='y', tensor=input_y, data_format=DataFormat.CHANNELS_LAST)], outputs=[Output(name='z', tensor=output_z)], session=session)
def _make_saved_model() -> SavedModel: with tf.Graph().as_default(), tf.compat.v1.Session().as_default() as session: input_x = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 2, 3, 4], name='x') input_y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 2, 3, 4], name='y') weight = tf.Variable(initial_value=4.2, dtype=tf.float32) output_z = tf.multiply(input_x + input_y, weight, name='z') session.run(weight.initializer) return SavedModel(inputs=[Input(name='x', tensor=input_x, data_format=None), Input(name='y', tensor=input_y, data_format=None)], outputs=[Output(name='z', tensor=output_z)], session=session)
def _make_saved_model() -> SavedModel: with eager_context.graph_mode(), tf.Graph().as_default( ), tf.compat.v1.Session().as_default() as session: input_x = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4]) input_y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 4]) weight = tf.Variable(initial_value=[2.0, 3.0, 4.0, 5.0], dtype=tf.float32) output_z = input_x + input_y + weight session.run(weight.initializer) return SavedModel(inputs=[ Input(name='x', tensor=input_x), Input(name='y', tensor=input_y) ], outputs=[Output(name='z', tensor=output_z)], session=session)