Tensorflow 快速入门-常用操作

末蓝、 2024-04-17 17:28 78阅读 0赞

### 加载TensorFlow ###

import tensorflow as tf

### 输出版本号 ###

print("Tensorflow Version: {}".format(tf.__version__))

Tensorflow Version: 1.14.0

### 进行两个矩阵的运算 ###

# 来进行两个矩阵的运算
    w = tf.Variable([[0.5, 1.0]])
    x = tf.Variable([[2.0], [1.0]])
    y = tf.matmul(w, x)
    y

<tf.Tensor 'MatMul:0' shape=(1, 1) dtype=float32>

### 变量值显示 ###

init_op = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init_op)
        print(y.eval())

[[2.]]

### 初试化矩阵 ###

# float32
    tf.zeros([3, 4], tf.int32)
    xx = tf.Variable([[1, 2, 3], [4, 5, 6]])
    tf.zeros_like(xx)
    tf.ones_like(xx)

<tf.Tensor 'ones_like:0' shape=(2, 3) dtype=int32>

### 常量显示 ###

tensor = tf.constant([1,2,3,4,5])
    tensor = tf.constant(-1.0, shape=[2, 3])
    tensor = tf.linspace(10.0, 15.0,5 ,name="linespace")
    tensor = tf.range(10, 18, 2)
    with tf.Session() as sess:
        sess.run(init_op)
        print(tensor.eval())

[10 12 14 16]

### 打乱数组排序，随机赋值 ###

norm = tf.random_normal([2, 3], mean=-1, stddev=4)
    
    # Shuffle the first dimension of a tensor
    c = tf.constant([[1, 2], [3, 4], [5, 6]])
    shuff = tf.random_shuffle(c)
    
    # Each time we run these ops, different results are generated
    sess = tf.Session()
    print(sess.run(norm))
    print(sess.run(shuff))

[[-4.6861687 -0.6405117 -2.5583053]
     [ 6.6142516 -3.3452137 -6.293489 ]]
    [[3 4]
     [5 6]
     [1 2]]

### 加1操作 ###

state = tf.Variable(0)
    new_value = tf.add(state, tf.constant(1))
    update = tf.assign(state, new_value)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        print(sess.run(state))
        for _ in range(3):
            sess.run(update)
            print(sess.run(state))

0
    1
    2
    3

### 模型存储 ###

# tf.train.Saver
    w = tf.Variable([[0.5, 1.0]])
    x = tf.Variable([[2.0], [1.0]])
    y = tf.matmul(w, x)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(init_op)
    # DO SOME WORK WITH THE MODEL
    # Save the variable to disk.
        save_path = saver.save(0, "E://private-project//test")
        print("Model saved in file: ", save_path)

Model saved in file:  E://private-project//test

### numpy 和 tensorflow转换 ###

import numpy as np
    a = np.zeros((3, 3))
    ta = tf.convert_to_tensor(a)
    with tf.Session() as sess:
        print(sess.run(ta))

[[0. 0. 0.]
     [0. 0. 0.]
     [0. 0. 0.]]

### placeholder ###

input1 = tf.placeholder(tf.float32)
    input2 = tf.placeholder(tf.float32)
    output = tf.multiply(input1, input2)
    with tf.Session() as sess:
        print(sess.run([output], feed_dict={ input1:[7.], input2:[2.]}))

[array([14.], dtype=float32)]

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Tips:如果有啥不懂的地方，可以去B站找相关视频观看：[☞这里][Link 1]

[Link 1]: https://www.bilibili.com/video/av55403478/?p=12&t=650