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0. 前言

本节实战tf.function auto_graph

普通函数转成tensorflow函数，优化，提高速度

函数签名与图结构

1. 代码部分

1. 导入模块

import matplotlib as mplimport matplotlib.pyplot as plt%matplotlib inlineimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import kerasprint(tf.__version__)print(sys.version_info)for module in mpl,np,pd,sklearn,tf,keras:print(module.__name__,module.__version__)

2. tf.function

说明：

普通Python语法写的函数，转换成tensorflow图，提高运行速度auto_graph 是tf.function所依赖的机制

2.1 普通函数

# tf.functin and autographdef scaled_elu(z,scale=1.0,alpha=1.0):# z >= 0 ? scale *z :scale * alpha * tf.nn.elu(z)is_positive = tf.greater_equal(z,0.0)return scale * tf.where(is_positive,z,alpha * tf.nn.elu(z))print(scaled_elu(tf.constant(-3.)))print(scaled_elu(tf.constant([-3.,-2.5])))

2.2 转成图结构

方法一：

# tf.function 转成图结构scaled_elu_tf = tf.function(scaled_elu)print(scaled_elu_tf(tf.constant(-3.)))print(scaled_elu_tf(tf.constant([-3.,-.5])))

2.2.1 测试

python_function ：tensorflow函数转成python函数

print(scaled_elu_tf.python_function is scaled_elu)

%timeit scaled_elu(tf.random.normal((1000,1000)))%timeit scaled_elu_tf(tf.random.normal((1000,1000)))

方法二：

@tf.functiondef converge_to_2(n_iters):total = tf.constant(0.)increment = tf.constant(1.)for _ in range(n_iters):total += incrementincrement /= 2.0return totalprint(converge_to_2(20))

2.2.2 tensorflow代码

展示tensorflow代码

def display_tf_code(func):code = tf.autograph.to_code(func)from IPython.display import display,Markdowndisplay(Markdown('```python\n{}\n```'.format(code)))

display_tf_code(scaled_elu)

display_tf_code(converge_to_2)

2.2.2 tf.Variable

注：变量声明在函数外

# tf.Variablevar = tf.Variable(0.)@tf.functiondef add_21():return var.assign_add(21) # +=print(add_21())

@tf.functiondef cube(z):return tf.pow(z,3)print(cube(tf.constant([1.,2.,3.])))print(cube(tf.constant([1 ,2 ,3])))

1. 限定类型

# 函数限定输入类型@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])))

input_signature =》 save_model

1. @tf.function py func -> tf graph

2. get_concrete_function -> add input signature -> SavedModel

# @tf.function py func -> tf graph# get_concrete_function -> add input signature -> SavedModelcube_func_int32 = cube.get_concrete_function(tf.TensorSpec([None],tf.int32))print(cube_func_int32)

print(cube_func_int32 is cube.get_concrete_function(tf.TensorSpec([5],tf.int32)))print(cube_func_int32 is cube.get_concrete_function(tf.constant([1,2,3])))

图

cube_func_int32.graph

# 操作cube_func_int32.graph.get_operations()

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

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

cube_func_int32.graph.get_operation_by_name('x')

cube_func_int32.graph.get_tensor_by_name('x:0')

cube_func_int32.graph.as_graph_def()

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