tensorflow2.0的函数签名与图结构(推荐)

@tf.function(input_signature=[tf.TensorSpec([None], tf.int32, name='x')])
def cube(z): #实现输入的立方
 return tf.pow(z, 3)
try:
 print(cube(tf.constant([1., 2., 3.])))
except ValueError as ex:
 print(ex)
print(cube(tf.constant([1, 2, 3])))

@tf.function(input_signature=[tf.TensorSpec([None], tf.int32, name='x')])
def cube(z):
 return tf.pow(z, 3)
 
try:
 print(cube(tf.constant([1., 2., 3.])))
except ValueError as ex:
 print(ex)
 
print(cube(tf.constant([1, 2, 3])))
 
# @tf.function py func -> tf graph
# get_concrete_function -> add input signature -> SavedModel
 
cube_func_int32 = cube.get_concrete_function(
 tf.TensorSpec([None], tf.int32)) #tensorflow的类型
print(cube_func_int32)

#看用新参数获得的对象与原来的对象是否一样
print(cube_func_int32 is cube.get_concrete_function(
 tf.TensorSpec([5], tf.int32))) #输入大小为5
print(cube_func_int32 is cube.get_concrete_function(
 tf.constant([1, 2, 3]))) #传具体数据

[<tf.Operation 'x' type=Placeholder>,
 <tf.Operation 'Pow/y' type=Const>,
 <tf.Operation 'Pow' type=Pow>,
 <tf.Operation 'Identity' type=Identity>]

pow_op = cube_func_int32.graph.get_operations()[2]
print(pow_op)

print(list(pow_op.inputs))
print(list(pow_op.outputs))

node {
 name: "x"
 op: "Placeholder"
 attr {
 key: "_user_specified_name"
 value {
 s: "x"
 }
 }
 attr {
 key: "dtype"
 value {
 type: DT_INT32
 }
 }
 attr {
 key: "shape"
 value {
 shape {
 dim {
  size: -1
 }
 }
 }
 }
}
node {
 name: "Pow/y"
 op: "Const"
 attr {
 key: "dtype"
 value {
 type: DT_INT32
 }
 }
 attr {
 key: "value"
 value {
 tensor {
 dtype: DT_INT32
 tensor_shape {
 }
 int_val: 3
 }
 }
 }
}
node {
 name: "Pow"
 op: "Pow"
 input: "x"
 input: "Pow/y"
 attr {
 key: "T"
 value {
 type: DT_INT32
 }
 }
}
node {
 name: "Identity"
 op: "Identity"
 input: "Pow"
 attr {
 key: "T"
 value {
 type: DT_INT32
 }
 }
}
versions {
 producer: 119
}