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)