def image_module_fn():
images = tf_v1.placeholder(dtype=tf.float32, shape=[None, 2, 4, 3])
sum_by_channels = tf.reduce_sum(images, [1, 2])
sum_all = tf.reduce_sum(images, [1, 2, 3])
native_module.add_signature(inputs=dict(images=images),
outputs=dict(default=sum_all,
sum_by_channels=sum_by_channels))
def image_module_fn():
images = tf.placeholder(dtype=tf.float32, shape=[None, 2, 4, 3])
sum_by_channels = tf.reduce_sum(images, [1, 2])
sum_all = tf.reduce_sum(images, [1, 2, 3])
native_module.add_signature(inputs=dict(images=images),
outputs=dict(default=sum_all,
sum_by_channels=sum_by_channels))
def image_module_fn_with_info():
images = tf_v1.placeholder(dtype=tf.float32, shape=[None, None, None, 3])
sum_all = tf.reduce_sum(images, [1, 2, 3])
native_module.add_signature(inputs=dict(images=images),
outputs=dict(default=sum_all))
image_module_info = image_util.ImageModuleInfo()
size = image_module_info.default_image_size
size.height, size.width = 2, 4
image_util.attach_image_module_info(image_module_info)
def batch_norm_module(training): x = tf.placeholder(tf.float32, shape=[None, 3]) y = tf.layers.batch_normalization(x, training=training) native_module.add_signature(inputs=x, outputs=y)
def multi_signature_module():
x = tf.placeholder(tf.float32, shape=[None])
native_module.add_signature("double", {"x": x}, {"y": 2*x})
z = tf.placeholder(tf.float32, shape=[None])
native_module.add_signature("square", {"z": z}, {"z_out": z*z})