Tensorflow framework introduced

Tensorflow framework introduced

This article describes a mind map about the content

· Tensorflow overall structure and data flow diagram

• Structural Analysis

Construction of a drawing stage: definition data (the Tensor tensor) and operation (node OP)
a stage executes: invoke various resources, the defined data and operations running

• The structure of FIG.

FIG flow that contains a set of data between the computation unit represented tf.operation tf.Tensor team objects and computing unit.
Briefly Tensorflow = Tensor + Flow = data + action

1. The default map

By calling tf.get_default_graph()the visit, the operation will be added to the default map can be.
op, sess have a graph property, the default in a graph, we can add the suffix in its .graphdirect access to
columns such as: sess.graphYou can view the properties map directly sess

2. Custom map

Using tf.Graph()returns a tensor object is
then to create a new context manager through FIG.

Attach Code:

# coding=utf-8
import tensorflow as tf

def graph():
    a = tf.constant(1)
    b = tf.constant(2)
    c = tf.add(a, b)
    print(c)

# 方法一，查看默认图
    default_g = tf.get_default_graph()
    print("默认图的属性：\n", default_g)
# 方法二，直接查看
    print("a的图属性:\n", a.graph)
    print("b的图属性:\n", b.graph)
    # 开启会话
    with tf.compat.v1.Session() as sess:
        c_new = sess.run(c)
        print("c_new的值:\n", c_new)
        print("c_new的图属性:\n", sess.graph)

    # 自定义图：
    new_graph = tf.Graph()
    with new_graph.as_default():
        a_ng = tf.constant(1)
        b_ng = tf.constant(2)
        c_ng = tf.add(a, b)
        print("c_ng:\n", c_ng)

    return None
    
if __name__ == '__main__':
    graph()

Output:

Tensor("Add:0", shape=(), dtype=int32)
默认图的属性：
 <tensorflow.python.framework.ops.Graph object at 0x000001D9310E9188>
a的图属性:
 <tensorflow.python.framework.ops.Graph object at 0x000001D9310E9188>
b的图属性:
 <tensorflow.python.framework.ops.Graph object at 0x000001D9310E9188>
c_new的值:
 3
c_new的图属性:
 <tensorflow.python.framework.ops.Graph object at 0x000001D9310E9188>
c_ng:
 Tensor("Add_1:0", shape=(), dtype=int32)

· Session

A run TensorFlow operation class, there are two ways to open

• tf.Session: a complete program which
• tf.interactiveSession: for interactive context tensorflow

But generally we use the context manager to open the session.

• Session run () method to give

Provided using placeholder placeholder, run time using feed_dict specified parameters.
Attach Code:

import tensorflow as tf

def run():
    # 定义占位符
    a = tf.placeholder(tf.float32)
    b = tf.placeholder(tf.float32)
    c = tf.multiply(a, b)
    # 开启上下文管理器
    with tf.compat.v1.Session() as sess:
        c_new = sess.run(c, feed_dict={a: 3.0, b: 7.0})
        print ("c_new:\n", c_new)

    return None

if __name__ == '__main__':
    run()

· Tensor operations

• Properties and modify the properties

shape Shape:
modify a static shape: tensor.set_shape()
only in the case the shape is not completely fixed, static shape can be modified

# 没有完全固定下来的静态形状
a = tf.placeholder(dtype=tf.float32, shape=[None, None])
# 修改静态形状
a.set_shape([1, 2])

Modified dynamic shape: tf.reshape()
When creating dynamic tensor shape, must match the number of tensors

a = tf.placeholder(dtype=tf.float32, shape=[2, 3])
a_p = tf.reshape(a, [1, 2， 3]）
# 可以跨阶，但是不能改变张量的总数量2 * 3 = 1 * 2 * 3

Code demonstrates:

dtype type:
tf.cast(tensor, dtype)modifying tensor type

a = tf.placeholder(dtype=tf.float32, shape=[2, 3])
a_d = tf.cast(a, dtype=int32)

· Visualization of variables and model

When you define a certain model parameters, we use:

tf.Variable(initial_value = tf.random_normal(shape=[None, None]))

Because this is a variable, do not need to set their own values, we only need to fix its shape shape

• In a simple linear regression case, weights and bias is a pair of simple variable

weights = tf.Variable(initial_value=tf.random_nomal(shape=[1, 1]))
bias = tf.Variable(initial_value=tf.random_nomal(shape=[1, 1]))
y_predict = tf.matmul(x, weights) + bias

You must remember that after the variables used to initialize variables to be displayed:

init = tf.global_variables_initializer()
# 还要再会话中运行
sess.run(init)

Tensorflow Visualization

• Create an event file:

tf.summary.FileWriter("path", graph=sess.graph)

• Use tensorboard start event file

tensorboard --logdir=path