代码用法链接

￡神魔★判官ぃ 2023-01-02 04:21 225阅读 0赞

[keras用法][keras]

models.  
搭建模型 ------ 配置训练方法 ------ 执行训练过程 ------ 统计参数数目  
Sequential() compile(). fit() summary()

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tf 的 shape  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZV9fNw_size_16_color_FFFFFF_t_70]

aaa = tf.random_uniform([2,3],minval=-0.1,maxval = 0.1)
    print(aaa.shape)      # --- 1
    
    with tf.Session() as sess:
        print(aaa.get_shape())     # --- 2
    
    输出：
    (2, 3)      # --- 1
    (2, 3)      # --- 2

numpy 的shape:  
![在这里插入图片描述][20210128133308642.png]

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[nn.embedding()][nn.embedding]

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tf.placeholder()

a=tf.placeholder(tf.int32, shape=[2,2])
    
    with tf.Session() as sess:
         print(sess.run(a,feed_dict={
        a:[[2,4],[3,3]]}))
     
    输出：
    [[2 4]
     [3 3]]

bb = tf.placeholder(tf.int32, shape=[2, ])
    cc = tf.placeholder(tf.int32, shape=[2])
    with tf.Session() as sess:
        print(sess.run(b, feed_dict={
        b: [2,2]  }))
        print(sess.run(c, feed_dict={
        c: [2, 2] }))
    
    输出：
    bb =  [2 2]
    cc =  [2 2]

b = tf.placeholder(tf.int32, shape=[None, None])
    c = tf.placeholder(tf.int32, shape=[2,])
    d = tf.placeholder(tf.float32, shape=[None,1])
    e = tf.placeholder(tf.float32, shape=[1, None])
    
    print(b.shape)   # (?, ?)
    print(c.shape)   # (2,)
    print(d.shape)   # (?, 1)
    print(e.shape)   # (1, ?)
    
    
    with tf.Session() as sess:
        print(sess.run(b,feed_dict={
        b:[[4,4,2],[3,5,6],[5,6,7]]}))
        print(sess.run(c,feed_dict={
        c:[42,33] }))
        print(sess.run(d,feed_dict={
        d:[[42],[4],[6]] }))
        print(sess.run(e, feed_dict={
        e:[[42,4,5,6]] }))
    
    输出：
    b = [[4 4 2]
         [3 5 6]
         [5 6 7]]
    c = [42 33]
    d = [[42.]
         [ 4.]
         [ 6.]]
    e = [[42.  4.  5.  6.]]

--------------------

矩阵None的用法：对应维度加一维  
在 ndarray 和 tensor 类型使用，list 不适用（会报错）

b=np.array([[3,4,5,6],[7,8,9,10],[11,2,3,33]])
    print(b)                  # --- 1        
    print(b[:,])              # --- 1          
    print(b[:,None])          # --- 2              
    
    print(b.shape)                # (3, 4)
    print(b[:,None].shape)        # (3, 1, 4)
    print(b[None,:].shape)        # (1, 3, 4)    
    
    输出：
    # b 和 b[:,]        # --- 1
    [[ 3  4  5  6]
     [ 7  8  9 10]
     [11  2  3 33]]
     
    # b[:,None]         # --- 2
    [[[ 3  4  5  6]]
    
     [[ 7  8  9 10]]
    
     [[11  2  3 33]]]

[keras]: https://blog.csdn.net/yunfeather/article/details/106461462
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2xlZV9fNw_size_16_color_FFFFFF_t_70]: /images/20221120/5edac2a2c7df4e43bbb2b001bc5c5f68.png
[20210128133308642.png]: /images/20221120/69e6cd3dfe3a4f1982cf8e95d99a8080.png
[nn.embedding]: https://www.cnblogs.com/lindaxin/p/7991436.html