一、form numpy to tensor

C:\Users\hasee>ipython
Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)]
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IPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import tensorflow as tf
G:\Users\hasee\Anaconda3\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [2]: import numpy as np

In [3]: tf.convert_to_tensor(np.ones([2,3]))
2019-07-26 09:55:04.608429: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Out[3]:
<tf.Tensor: id=0, shape=(2, 3), dtype=float64, numpy=
array([[1., 1., 1.],
       [1., 1., 1.]])>

In [4]: tf.convert_to_tensor(np.zeros([2,3]))
Out[4]:
<tf.Tensor: id=2, shape=(2, 3), dtype=float64, numpy=
array([[0., 0., 0.],
       [0., 0., 0.]])>

In [5]: tf.convert_to_tensor([1,2])
Out[5]: <tf.Tensor: id=4, shape=(2,), dtype=int32, numpy=array([1, 2])>

In [6]: tf.convert_to_tensor([1,2.])
Out[6]: <tf.Tensor: id=6, shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>

In [7]: tf.convert_to_tensor([[1],[2.]])
Out[7]:
<tf.Tensor: id=8, shape=(2, 1), dtype=float32, numpy=
array([[1.],
       [2.]], dtype=float32)>

二、tf.zeros()注意到zeros()函数括号里面是shape（参数）。

In [8]: tf.zeros([])
Out[8]: <tf.Tensor: id=10, shape=(), dtype=float32, numpy=0.0>

In [9]: tf.zeros([1])
Out[9]: <tf.Tensor: id=14, shape=(1,), dtype=float32, numpy=array([0.], dtype=float32)>

In [10]: tf.zeros([2,2])
Out[10]:
<tf.Tensor: id=18, shape=(2, 2), dtype=float32, numpy=
array([[0., 0.],
       [0., 0.]], dtype=float32)>

In [11]: tf.zeros([2,3,3])
Out[11]:
<tf.Tensor: id=22, shape=(2, 3, 3), dtype=float32, numpy=
array([[[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]], dtype=float32)>

tf.zeros_like() 

a=tf.zeros([2,3,3])

In [13]: tf.zeros_like(a)
Out[13]:
<tf.Tensor: id=27, shape=(2, 3, 3), dtype=float32, numpy=
array([[[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]], dtype=float32)>

In [14]: tf.zeros(a.shape)
Out[14]:
<tf.Tensor: id=31, shape=(2, 3, 3), dtype=float32, numpy=
array([[[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]],

       [[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]]], dtype=float32)>

tf.ones()

In [15]: tf.ones(1)
Out[15]: <tf.Tensor: id=35, shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>

In [16]: tf.ones([])
Out[16]: <tf.Tensor: id=37, shape=(), dtype=float32, numpy=1.0>

In [17]: tf.ones([2])
Out[17]: <tf.Tensor: id=41, shape=(2,), dtype=float32, numpy=array([1., 1.], dtype=float32)>

In [18]: tf.ones([2,3])
Out[18]:
<tf.Tensor: id=45, shape=(2, 3), dtype=float32, numpy=
array([[1., 1., 1.],
       [1., 1., 1.]], dtype=float32)>

In [19]: tf.ones_like(a)
Out[19]:
<tf.Tensor: id=49, shape=(2, 3, 3), dtype=float32, numpy=
array([[[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]],

       [[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]]], dtype=float32)>

tf.fill()

In [20]: tf.fill([2,2],0)
Out[20]:
<tf.Tensor: id=53, shape=(2, 2), dtype=int32, numpy=
array([[0, 0],
       [0, 0]])>

In [21]: tf.fill([2,2],0.)
Out[21]:
<tf.Tensor: id=57, shape=(2, 2), dtype=float32, numpy=
array([[0., 0.],
       [0., 0.]], dtype=float32)>

In [22]: tf.fill([2,2],1)
Out[22]:
<tf.Tensor: id=61, shape=(2, 2), dtype=int32, numpy=
array([[1, 1],
       [1, 1]])>

In [23]: tf.fill([2,2],9)
Out[23]:
<tf.Tensor: id=65, shape=(2, 2), dtype=int32, numpy=
array([[9, 9],
       [9, 9]])>

normal(带有均值和方差的随机初始化)

1、正态分布

 tf.random.truncated_normal()推荐使用

In [24]: tf.random.normal([2,2],mean=1,stddev=1)
Out[24]:
<tf.Tensor: id=72, shape=(2, 2), dtype=float32, numpy=
array([[ 0.74498326,  1.2692506 ],
       [-0.186342  ,  2.4405448 ]], dtype=float32)>

In [25]: tf.random.normal([2,2])
Out[25]:
<tf.Tensor: id=79, shape=(2, 2), dtype=float32, numpy=
array([[-0.8279127 ,  0.4940451 ],
       [-0.5491317 ,  0.34313682]], dtype=float32)>

In [26]: tf.random.truncated_normal([2,2],mean=1,stddev=1)
Out[26]:
<tf.Tensor: id=86, shape=(2, 2), dtype=float32, numpy=
array([[ 0.6495031, -0.7680944],
       [ 0.5350886,  1.9835862]], dtype=float32)>

2、均匀分布

In [27]: tf.random.uniform([2,2],minval=0,maxval=1)
Out[27]:
<tf.Tensor: id=94, shape=(2, 2), dtype=float32, numpy=
array([[0.47823155, 0.5105146 ],
       [0.94305336, 0.24052179]], dtype=float32)>

In [28]: tf.random.uniform([2,2],minval=1,maxval=100)
Out[28]:
<tf.Tensor: id=102, shape=(2, 2), dtype=float32, numpy=
array([[63.2407  , 90.102486],
       [89.598114, 65.97715 ]], dtype=float32)>

应用 random permutation(随机打散)

In [29]: idx=tf.range(10)

In [30]: idx=tf.random.shuffle(idx)

In [31]: idx
Out[31]: <tf.Tensor: id=108, shape=(10,), dtype=int32, numpy=array([4, 5, 6, 3, 9, 2, 7, 0, 8, 1])>

 标量在deep learning中的呈现方式。

 out=tf.random.uniform([4,10])

In [34]: out
Out[34]:
<tf.Tensor: id=116, shape=(4, 10), dtype=float32, numpy=
array([[0.24594736, 0.1750027 , 0.38304508, 0.20767057, 0.6574384 ,
        0.95960414, 0.9286723 , 0.21315277, 0.38152838, 0.70844436],
       [0.6412004 , 0.15667486, 0.37563896, 0.36031973, 0.391747  ,
        0.68108964, 0.19414902, 0.00660384, 0.8796239 , 0.49293447],
       [0.5348973 , 0.0479815 , 0.9055302 , 0.7179408 , 0.06859863,
        0.35083127, 0.32901812, 0.6015886 , 0.7346724 , 0.40839958],
       [0.33565128, 0.34543478, 0.49530745, 0.2669555 , 0.7481148 ,
        0.22204006, 0.94577575, 0.9286548 , 0.7074944 , 0.7671062 ]],
      dtype=float32)>

In [35]: y=tf.range(4)

In [36]: y=tf.one_hot(y,depth=10)

In [37]: y
Out[37]:
<tf.Tensor: id=125, shape=(4, 10), dtype=float32, numpy=
array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]], dtype=float32)>

In [38]: loss=tf.keras.losses.mse(y,out)

In [39]: loss
Out[39]: <tf.Tensor: id=129, shape=(4,), dtype=float32, numpy=array([0.36974606, 0.3065079 , 0.2117265 , 0.44695395], dtype=float32)>

In [40]: loss=tf.reduce_mean(loss)

In [41]: loss
Out[41]: <tf.Tensor: id=132, shape=(), dtype=float32, numpy=0.3337336>