TensorFlow学习笔记（一）TensorFlow基础
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| import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
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数据类型
数值类型
标量在 TensorFlow 是如何创建的
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a = 1.2
aa = tf.constant(1.2)
type(a), type(aa), tf.is_tensor(aa)
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(float, tensorflow.python.framework.ops.EagerTensor, True)
如果要使用 TensorFlow 提供的功能函数， 须通过 TensorFlow 规定的方式去创建张量，而不能使用 Python 语言的标准变量创建方式。
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| x = tf.constant([1,2.,3.3])
x
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<tf.Tensor: id=1, shape=(3,), dtype=float32, numpy=array([1. , 2. , 3.3], dtype=float32)>
array([1. , 2. , 3.3], dtype=float32)
与标量不同，向量的定义须通过 List 容器传给 tf.constant()函数。
创建一个元素的向量：
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a = tf.constant([1.2])
a, a.shape
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(<tf.Tensor: id=2, shape=(1,), dtype=float32, numpy=array([1.2], dtype=float32)>,
TensorShape([1]))
创建 3 个元素的向量：
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a = tf.constant([1,2, 3.])
a, a.shape
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(<tf.Tensor: id=3, shape=(3,), dtype=float32, numpy=array([1., 2., 3.], dtype=float32)>,
TensorShape([3]))
定义矩阵
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a = tf.constant([[1,2],[3,4]])
a, a.shape
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(<tf.Tensor: id=4, shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
[3, 4]], dtype=int32)>,
TensorShape([2, 2]))
三维张量可以定义为：
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tf.constant([[[1,2],[3,4]],[[5,6],[7,8]]])
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<tf.Tensor: id=5, shape=(2, 2, 2), dtype=int32, numpy=
array([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]], dtype=int32)>
通过传入字符串对象即可创建字符串类型的张量
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a = tf.constant('Hello, Deep Learning.')
a
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<tf.Tensor: id=6, shape=(), dtype=string, numpy=b'Hello, Deep Learning.'>
字符串类型
通过传入字符串对象即可创建字符串类型的张量
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a = tf.constant('Hello, Deep Learning.')
a
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<tf.Tensor: id=7, shape=(), dtype=string, numpy=b'Hello, Deep Learning.'>
在 tf.strings 模块中，提供了常见的字符串类型的工具函数，如小写化 lower()、 拼接
join()、 长度 length()、 切分 split()等。
<tf.Tensor: id=8, shape=(), dtype=string, numpy=b'hello, deep learning.'>
布尔类型
布尔类型的张量只需要传入 Python 语言的布尔类型数据，转换成 TensorFlow 内部布尔型即可。
<tf.Tensor: id=9, shape=(), dtype=bool, numpy=True>
创建布尔类型的向量
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tf.constant([True, False])
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<tf.Tensor: id=10, shape=(2,), dtype=bool, numpy=array([ True, False])>
需要注意的是， TensorFlow 的布尔类型和 Python 语言的布尔类型并不等价，不能通用
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a = tf.constant(True)
print(a is True)
print(a == True)
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False
tf.Tensor(True, shape=(), dtype=bool)
数值精度
在创建张量时，可以指定张量的保存精度
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tf.constant(123456789, dtype=tf.int16)
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<tf.Tensor: id=14, shape=(), dtype=int16, numpy=-13035>
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| tf.constant(123456789, dtype=tf.int32)
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<tf.Tensor: id=15, shape=(), dtype=int32, numpy=123456789>
对于浮点数， 高精度的张量可以表示更精准的数据，例如采用 tf.float32 精度保存π时，实际保存的数据为 3.1415927
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| import numpy as np
np.pi
tf.constant(np.pi, dtype=tf.float32)
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<tf.Tensor: id=16, shape=(), dtype=float32, numpy=3.1415927>
如果采用 tf.float64 精度保存π，则能获得更高的精度
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| tf.constant(np.pi, dtype=tf.float64)
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<tf.Tensor: id=17, shape=(), dtype=float64, numpy=3.141592653589793>
读取精度
通过访问张量的 dtype 成员属性可以判断张量的保存精度
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| a = tf.constant(np.pi, dtype=tf.float16)
print('before:',a.dtype)
if a.dtype != tf.float32:
a = tf.cast(a,tf.float32)
print('after :',a.dtype)
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before: <dtype: 'float16'>
after : <dtype: 'float32'>
类型转换
系统的每个模块使用的数据类型、 数值精度可能各不相同， 对于不符合要求的张量的类型及精度， 需要通过 tf.cast 函数进行转换
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a = tf.constant(np.pi, dtype=tf.float16)
tf.cast(a, tf.double)
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<tf.Tensor: id=21, shape=(), dtype=float64, numpy=3.140625>
进行类型转换时，需要保证转换操作的合法性， 例如将高精度的张量转换为低精度的张量时，可能发生数据溢出隐患：
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| a = tf.constant(123456789, dtype=tf.int32)
tf.cast(a, tf.int16)
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<tf.Tensor: id=23, shape=(), dtype=int16, numpy=-13035>
布尔类型与整型之间相互转换也是合法的， 是比较常见的操作
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| a = tf.constant([True, False])
tf.cast(a, tf.int32)
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<tf.Tensor: id=25, shape=(2,), dtype=int32, numpy=array([1, 0], dtype=int32)>
一般默认 0 表示 False， 1 表示 True，在 TensorFlow 中，将非 0 数字都视为 True，
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| a = tf.constant([-1, 0, 1, 2])
tf.cast(a, tf.bool)
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<tf.Tensor: id=27, shape=(4,), dtype=bool, numpy=array([ True, False, True, True])>
待优化张量
TensorFlow 增加了一种专门的数据类型来支持梯度信息的记录： tf.Variable。 tf.Variable 类型在普通的张量类型基础上添加了 name， trainable 等属性来支持计算图的构建。
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a = tf.constant([-1, 0, 1, 2])
aa = tf.Variable(a)
aa.name, aa.trainable
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('Variable:0', True)
name 属性用于命名计算图中的变量，这套命名体系是 TensorFlow 内部维护的， 一般不需要用户关注 name 属性；
trainable属性表征当前张量是否需要被优化，创建 Variable 对象时是默认启用优化标志，可以设置trainable=False 来设置张量不需要优化。
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tf.Variable([[1,2],[3,4]])
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<tf.Variable 'Variable:0' shape=(2, 2) dtype=int32, numpy=
array([[1, 2],
[3, 4]], dtype=int32)>
创建张量
从数组、列表对象创建
通过 tf.convert_to_tensor 函数可以创建新 Tensor，并将保存在 Python List 对象或者Numpy Array 对象中的数据导入到新 Tensor 中。
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tf.convert_to_tensor([1,2.])
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<tf.Tensor: id=44, shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
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tf.convert_to_tensor(np.array([[1,2.],[3,4]]))
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<tf.Tensor: id=45, shape=(2, 2), dtype=float64, numpy=
array([[1., 2.],
[3., 4.]])>
创建全0或全1张量
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tf.zeros([]),tf.ones([])
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(<tf.Tensor: id=46, shape=(), dtype=float32, numpy=0.0>,
<tf.Tensor: id=47, shape=(), dtype=float32, numpy=1.0>)
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tf.zeros([1]),tf.ones([1])
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(<tf.Tensor: id=50, shape=(1,), dtype=float32, numpy=array([0.], dtype=float32)>,
<tf.Tensor: id=53, shape=(1,), dtype=float32, numpy=array([1.], dtype=float32)>)
创建全 0 的矩阵
<tf.Tensor: id=56, shape=(2, 2), dtype=float32, numpy=
array([[0., 0.],
[0., 0.]], dtype=float32)>
创建全 1 的矩阵
<tf.Tensor: id=59, shape=(3, 2), dtype=float32, numpy=
array([[1., 1.],
[1., 1.],
[1., 1.]], dtype=float32)>
通过 tf.zeros_like, tf.ones_like 可以方便地新建与某个张量 shape 一致， 且内容为全 0 或全 1 的张量。
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a = tf.ones([2,3])
tf.zeros_like(a)
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<tf.Tensor: id=63, shape=(2, 3), dtype=float32, numpy=
array([[0., 0., 0.],
[0., 0., 0.]], dtype=float32)>
创建与张量A形状一样的全 1 张量
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a = tf.zeros([3,2])
tf.ones_like(a)
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<tf.Tensor: id=69, shape=(3, 2), dtype=float32, numpy=
array([[1., 1.],
[1., 1.],
[1., 1.]], dtype=float32)>
创建自定义数值张量
通过 tf.fill(shape, value)可以创建全为自定义数值 value 的张量，形状由 shape 参数指定。
<tf.Tensor: id=72, shape=(), dtype=int32, numpy=-1>
<tf.Tensor: id=75, shape=(1,), dtype=int32, numpy=array([-1], dtype=int32)>
<tf.Tensor: id=78, shape=(2, 2), dtype=int32, numpy=
array([[99, 99],
[99, 99]], dtype=int32)>
创建已知分布的张量
通过 tf.random.normal(shape, mean=0.0, stddev=1.0)可以创建形状为 shape，均值为mean，标准差为 stddev 的正态分布$\mathcal{N}(mean, stddev^2)$。
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tf.random.normal([2,2])
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<tf.Tensor: id=84, shape=(2, 2), dtype=float32, numpy=
array([[ 0.8372936 , -0.00487547],
[ 0.5917305 , 0.9924748 ]], dtype=float32)>
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tf.random.normal([2,2], mean=1,stddev=2)
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<tf.Tensor: id=90, shape=(2, 2), dtype=float32, numpy=
array([[1.6426632 , 0.9099915 ],
[1.7133203 , 0.14123482]], dtype=float32)>
通过 tf.random.uniform(shape, minval=0, maxval=None, dtype=tf.float32)可以创建采样自[minval, maxval)区间的均匀分布的张量
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tf.random.uniform([3,2])
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<tf.Tensor: id=97, shape=(3, 2), dtype=float32, numpy=
array([[0.80524087, 0.5057876 ],
[0.5653434 , 0.21946168],
[0.48825264, 0.09415054]], dtype=float32)>
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tf.random.uniform([2,2],maxval=10)
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<tf.Tensor: id=104, shape=(2, 2), dtype=float32, numpy=
array([[8.02882 , 9.814098 ],
[5.9886417, 1.3643861]], dtype=float32)>
如果需要均匀采样整形类型的数据，必须指定采样区间的最大值 maxval 参数，同时指定数据类型为 tf.int*型
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tf.random.uniform([2,2],maxval=100,dtype=tf.int32)
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<tf.Tensor: id=108, shape=(2, 2), dtype=int32, numpy=
array([[ 5, 91],
[33, 20]], dtype=int32)>
创建序列
tf.range(limit, delta=1)可以创建[0, limit)之间，步长为 delta 的整型序列，不包含 limit 本身。
<tf.Tensor: id=112, shape=(10,), dtype=int32, numpy=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)>
<tf.Tensor: id=116, shape=(5,), dtype=int32, numpy=array([0, 2, 4, 6, 8], dtype=int32)>
<tf.Tensor: id=120, shape=(5,), dtype=int32, numpy=array([1, 3, 5, 7, 9], dtype=int32)>
张量的典型应用
标量
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out = tf.random.uniform([4,10])
y = tf.constant([2,3,2,0])
y = tf.one_hot(y, depth=10)
loss = tf.keras.losses.mse(y, out)
loss = tf.reduce_mean(loss)
print(loss)
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tf.Tensor(0.26203847, shape=(), dtype=float32)
- tf.reduce_mean()函数用于计算张量tensor沿着指定的数轴（tensor的某一维度）上的的平均值，主要用作降维或者计算tensor（图像）的平均值。
向量
考虑 2 个输出节点的网络层， 我们创建长度为 2 的偏置向量b，并累加在每个输出节点上：
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z = tf.random.normal([4,2])
print(z)
b = tf.zeros([2])
print(b)
z = z + b
z
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tf.Tensor(
[[ 0.8107377 1.2481661 ]
[-0.9203342 -0.55204725]
[ 0.944986 0.00977302]
[ 0.65324616 0.9092525 ]], shape=(4, 2), dtype=float32)
tf.Tensor([0. 0.], shape=(2,), dtype=float32)
<tf.Tensor: id=432714, shape=(4, 2), dtype=float32, numpy=
array([[ 0.8107377 , 1.2481661 ],
[-0.9203342 , -0.55204725],
[ 0.944986 , 0.00977302],
[ 0.65324616, 0.9092525 ]], dtype=float32)>
创建输入节点数为 4，输出节点数为 3 的线性层网络，那么它的偏置向量 b 的长度应为 3
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fc = tf.keras.layers.Dense(3)
fc.build(input_shape=(2,4))
fc.bias
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<tf.Variable 'bias:0' shape=(3,) dtype=float32, numpy=array([0., 0., 0.], dtype=float32)>
矩阵
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x = tf.random.normal([2,4])
w = tf.ones([4,3])
b = tf.zeros([3])
o = x@w+b
o
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<tf.Tensor: id=184, shape=(2, 3), dtype=float32, numpy=
array([[-5.028141 , -5.028141 , -5.028141 ],
[ 0.67261326, 0.67261326, 0.67261326]], dtype=float32)>
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fc = tf.keras.layers.Dense(3)
fc.build(input_shape=(2,4))
fc.kernel
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<tf.Variable 'kernel:0' shape=(4, 3) dtype=float32, numpy=
array([[ 0.5571135 , 0.40619254, 0.7768836 ],
[-0.61082566, -0.13341528, -0.90817606],
[-0.16371965, -0.00938004, 0.6606846 ],
[ 0.38958526, -0.87978166, -0.36103284]], dtype=float32)>
三维张量
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(x_train,y_train),(x_test,y_test)=keras.datasets.imdb.load_data(num_words=10000)
x_train = keras.preprocessing.sequence.pad_sequences(x_train,maxlen=80)
print(x_train[0:2])
x_train.shape
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/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:129: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:130: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
[[ 15 256 4 2 7 3766 5 723 36 71 43 530 476 26
400 317 46 7 4 2 1029 13 104 88 4 381 15 297
98 32 2071 56 26 141 6 194 7486 18 4 226 22 21
134 476 26 480 5 144 30 5535 18 51 36 28 224 92
25 104 4 226 65 16 38 1334 88 12 16 283 5 16
4472 113 103 32 15 16 5345 19 178 32]
[ 125 68 2 6853 15 349 165 4362 98 5 4 228 9 43
2 1157 15 299 120 5 120 174 11 220 175 136 50 9
4373 228 8255 5 2 656 245 2350 5 4 9837 131 152 491
18 2 32 7464 1212 14 9 6 371 78 22 625 64 1382
9 8 168 145 23 4 1690 15 16 4 1355 5 28 6
52 154 462 33 89 78 285 16 145 95]]
(25000, 80)
可以看到 x_train 张量的 shape 为[25000,80]，其中 25000 表示句子个数， 80 表示每个句子共 80 个单词，每个单词使用数字编码方式表示。
我们通过 layers.Embedding 层将数字编码的单词转换为长度为 100 个词向量：
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embedding = tf.keras.layers.Embedding(10000, 100)
out = embedding(x_train)
out.shape
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TensorShape([25000, 80, 100])
可以看到，经过 Embedding 层编码后，句子张量的 shape 变为[25000,80,100]，其中 100 表示每个单词编码为长度是 100 的向量。
四维张量
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x = tf.random.normal([4,32,32,3])
layer = layers.Conv2D(16, kernel_size=3)
out = layer(x)
out.shape
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TensorShape([4, 30, 30, 16])
TensorShape([3, 3, 3, 16])
索引与切片
索引
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x = tf.random.normal([4,32,32,3])
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<tf.Tensor: id=265, shape=(32, 32, 3), dtype=float32, numpy=
array([[[ 2.2041936 , -1.9026781 , 0.8702505 ],
[-1.2282028 , -0.33232537, 0.40958533],
[ 0.11558069, -0.95446974, -1.5603778 ],
...,
[ 1.8689036 , 1.3471965 , 0.46157768],
[-0.04014067, 0.8095603 , 1.0308311 ],
[-0.2001917 , -1.0876633 , -0.35982683]],
[[-0.6193978 , -1.1049955 , -0.06628878],
[ 0.5612249 , 1.5542006 , 0.6287516 ],
[ 0.34846973, 0.44159728, 0.8838649 ],
...,
[-0.7220847 , 0.67017406, 0.1659171 ],
[ 0.17958985, -0.65319884, 0.39171842],
[ 0.8067303 , 0.43496 , 0.2798552 ]],
[[-1.163977 , -0.06057478, -0.4857398 ],
[ 1.3414443 , -0.6038178 , -0.23302878],
[-2.0975337 , 0.94285005, -0.27974698],
...,
[-0.5631729 , 1.0614241 , -0.3096405 ],
[-0.9624238 , 1.3738877 , -1.8948269 ],
[ 1.132725 , -0.20089822, -1.7373965 ]],
...,
[[-0.14071971, -0.5568062 , 0.01075767],
[-1.7140628 , 1.3289738 , -0.8903278 ],
[-1.0916421 , -0.3162519 , -1.249703 ],
...,
[ 1.325685 , 1.5440601 , -0.4913852 ],
[-1.3840119 , 0.23958059, -0.20719068],
[ 0.877472 , 1.3066201 , -1.4298698 ]],
[[ 0.3794225 , 0.8216657 , -0.3639167 ],
[-1.4976484 , -1.0524081 , -1.302156 ],
[ 0.26988387, 0.34318095, 0.06246407],
...,
[ 2.7228684 , -0.2831678 , -1.0059422 ],
[-0.7020755 , -1.4222299 , 0.9356876 ],
[ 0.4152088 , -0.04397644, -0.73320246]],
[[ 0.65700305, -1.7467034 , -1.5898855 ],
[ 1.1514107 , -1.0907453 , -0.5877316 ],
[ 0.86260825, -0.59653807, 0.0976033 ],
...,
[-0.04578071, -1.2980894 , 0.9463795 ],
[-0.09251038, 0.25678882, -0.1819165 ],
[-0.36038232, -0.53460985, 1.2337509 ]]], dtype=float32)>
<tf.Tensor: id=273, shape=(32, 3), dtype=float32, numpy=
array([[-0.6193978 , -1.1049955 , -0.06628878],
[ 0.5612249 , 1.5542006 , 0.6287516 ],
[ 0.34846973, 0.44159728, 0.8838649 ],
[-0.66014725, -0.29447266, -0.8719525 ],
[-0.53212637, 0.6360704 , 0.02135803],
[ 0.40355667, 0.14078747, -0.39829007],
[-1.3842081 , 0.04412093, -0.91313547],
[-0.37355164, -2.0390503 , -0.50824887],
[-0.7682212 , 1.4448624 , -0.37302288],
[ 0.13697726, 0.57252467, -1.0642116 ],
[-0.17128809, 0.7596571 , 0.37190843],
[-0.8967074 , -0.18937345, -0.5372808 ],
[ 0.33156198, -0.66581064, -0.21653776],
[-0.11285859, -2.4033732 , 0.0636418 ],
[-0.31247538, -0.8419992 , 0.4025044 ],
[ 1.2428769 , 0.34773824, 0.8888833 ],
[-1.5594406 , -0.0539138 , 0.7797568 ],
[-0.5584576 , 0.44812298, -0.26227227],
[-0.4017965 , -1.6668578 , -2.0081973 ],
[ 1.7921695 , 1.1685921 , -0.537693 ],
[-0.16341975, -0.42829806, 0.09798718],
[ 0.49063244, -0.19753823, 0.28310525],
[ 0.73069364, 0.33411032, 0.06241602],
[ 0.1417386 , 0.46909812, 0.90380406],
[-0.32593566, -0.98549616, 0.36107165],
[ 1.5818663 , -0.362372 , 1.0220544 ],
[ 0.26198712, -1.6119221 , 0.07946812],
[ 1.1173558 , -0.677369 , 0.9825754 ],
[ 1.2875233 , 0.2511964 , 0.9508616 ],
[-0.7220847 , 0.67017406, 0.1659171 ],
[ 0.17958985, -0.65319884, 0.39171842],
[ 0.8067303 , 0.43496 , 0.2798552 ]], dtype=float32)>
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1
2
3
4
5
|
x = tf.range(9)
x[8:0:-1]
|
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<tf.Tensor: id=329, shape=(9,), dtype=int32, numpy=array([8, 7, 6, 5, 4, 3, 2, 1, 0], dtype=int32)>
<tf.Tensor: id=333, shape=(5,), dtype=int32, numpy=array([8, 6, 4, 2, 0], dtype=int32)>
读取每张图片的所有通道，其中行按着逆序隔行采样，列按着逆序隔行采样
1
2
3
| x = tf.random.normal([4,32,32,3])
x[0,::-2,::-2]
|
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[-3.68833989e-01, 6.17409274e-02, 2.55871916e+00, ...,
-7.19225705e-02, -1.25733685e+00, 6.05888307e-01]]],
dtype=float32)>
<tf.Tensor: id=351, shape=(2, 32, 32, 2), dtype=float32, numpy=
array([[[[ 1.1490967 , 1.7380066 ],
[-0.04064592, 0.48029 ],
[-0.5781278 , -1.291669 ],
...,
[ 0.1551372 , 0.8534301 ],
[ 0.760451 , 0.587535 ],
[ 0.9857314 , 0.1369431 ]],
[[-1.1358613 , -0.06066316],
[-0.11467516, -2.3852282 ],
[ 0.16320415, 0.01811434],
...,
[-0.5132677 , 1.1296953 ],
[-1.1824567 , 0.7329599 ],
[-0.13755238, 0.16607961]],
[[ 0.9946899 , 0.48675606],
[ 0.5301358 , -1.0126823 ],
[ 0.3250262 , -0.6064818 ],
...,
[-1.9235115 , -0.41639256],
[-1.8274072 , -0.5375008 ],
[ 0.16437471, -0.4204572 ]],
...,
[[-0.15271704, 0.02707502],
[-0.592184 , 0.02546674],
[-1.482662 , -1.4665922 ],
...,
[-0.3486502 , -1.5472578 ],
[ 0.48329976, -2.0207098 ],
[-1.3078482 , -0.68001777]],
[[-1.2671758 , 0.03466341],
[-0.5013425 , 0.1263919 ],
[ 0.0909223 , 0.29931667],
...,
[ 0.02439718, 0.5069986 ],
[ 0.5131848 , -0.6002897 ],
[ 0.6985006 , -1.3119441 ]],
[[-0.88683474, -0.14877406],
[-0.09816919, -0.6858855 ],
[ 0.7178166 , 0.44352156],
...,
[ 0.5970503 , -2.1432486 ],
[ 0.12252079, -0.15961307],
[-0.15289137, -0.26840857]]],
[[[ 0.5272119 , 0.6869629 ],
[ 0.51094985, 0.2770362 ],
[ 0.6687934 , -1.4204 ],
…,
[ 1.017234 , 0.35187325],
[-0.92099065, -0.585941 ],
[ 1.555217 , -0.6104895 ]],
[[ 0.5970455 , 0.7830326 ],
[-1.1216139 , 0.16928901],
[ 1.0129018 , 0.71436375],
...,
[-0.1120592 , 0.37095946],
[ 1.6332924 , 0.4852164 ],
[ 0.9036849 , 0.84450924]],
[[-0.37046513, -0.4693162 ],
[ 1.3525819 , -0.66847706],
[ 0.01917811, -0.40561342],
...,
[ 0.79178476, 1.6169451 ],
[-0.5384039 , -2.6904156 ],
[ 1.1943746 , 0.15126795]],
...,
[[-0.02663194, -0.42372993],
[ 0.4801877 , -1.6053843 ],
[-0.06814828, 0.39376357],
...,
[-0.08337818, -0.56289715],
[-2.1539602 , 0.7823069 ],
[-0.10082836, 0.64499325]],
[[-0.5733437 , 0.8600085 ],
[ 1.2716624 , 1.4874613 ],
[-0.05363007, -1.5294101 ],
...,
[ 0.6653092 , 0.31750998],
[ 1.0214761 , 0.22179288],
[ 0.78608286, -1.4824792 ]],
[[-0.65061414, -0.6899978 ],
[ 0.80076987, -1.4741213 ],
[-0.36097565, -0.48046836],
...,
[ 0.3290608 , -1.5610422 ],
[-0.72132474, 0.18023647],
[-1.7177783 , -0.53801376]]]], dtype=float32)>
<tf.Tensor: id=359, shape=(4, 32, 32, 2), dtype=float32, numpy=
array([[[[-1.30701756e+00, 1.14909673e+00],
[ 3.61870110e-01, -4.06459235e-02],
[ 6.26487672e-01, -5.78127801e-01],
...,
[ 1.38576820e-01, 1.55137196e-01],
[-1.29257798e+00, 7.60451019e-01],
[-3.25585663e-01, 9.85731423e-01]],
[[-2.72432625e-01, -1.13586128e+00],
[-4.51551750e-02, -1.14675157e-01],
[-4.10570800e-01, 1.63204148e-01],
...,
[ 1.00940740e+00, -5.13267696e-01],
[-1.04479229e+00, -1.18245673e+00],
[-9.39358115e-01, -1.37552381e-01]],
[[-1.14408398e+00, 9.94689882e-01],
[-2.45610863e-01, 5.30135810e-01],
[ 3.69893968e-01, 3.25026214e-01],
...,
[ 1.47702467e+00, -1.92351151e+00],
[-8.77718687e-01, -1.82740724e+00],
[-1.90951622e+00, 1.64374709e-01]],
...,
[[-8.38505983e-01, -1.52717039e-01],
[ 1.60369647e+00, -5.92184007e-01],
[ 2.45542109e-01, -1.48266196e+00],
...,
[ 3.65186512e-01, -3.48650187e-01],
[-6.50465429e-01, 4.83299762e-01],
[-1.05812716e+00, -1.30784822e+00]],
[[ 7.95438468e-01, -1.26717579e+00],
[ 9.37338114e-01, -5.01342475e-01],
[-1.69611961e-01, 9.09223035e-02],
...,
[ 1.64364791e+00, 2.43971795e-02],
[ 1.96424723e-01, 5.13184786e-01],
[ 8.26264262e-01, 6.98500574e-01]],
[[ 2.59421289e-01, -8.86834741e-01],
[-1.79539633e+00, -9.81691927e-02],
[ 3.78742844e-01, 7.17816591e-01],
...,
[-1.74235809e+00, 5.97050309e-01],
[ 6.53830469e-01, 1.22520790e-01],
[ 1.32819211e+00, -1.52891368e-01]]],
[[[-5.31493947e-02, 5.27211905e-01],
[-8.06730747e-01, 5.10949850e-01],
[ 1.84080076e+00, 6.68793380e-01],
…,
[ 1.31973469e+00, 1.01723397e+00],
[ 4.49128337e-02, -9.20990646e-01],
[-1.32044387e+00, 1.55521703e+00]],
[[ 7.68865108e-01, 5.97045481e-01],
[-7.52771422e-02, -1.12161386e+00],
[ 1.21640265e+00, 1.01290178e+00],
...,
[ 1.01818316e-01, -1.12059198e-01],
[ 1.12015426e+00, 1.63329244e+00],
[ 1.95406660e-01, 9.03684914e-01]],
[[-3.43454480e-02, -3.70465130e-01],
[-3.47994983e-01, 1.35258186e+00],
[ 1.07138467e+00, 1.91781148e-02],
...,
[ 9.40576553e-01, 7.91784763e-01],
[ 5.54417372e-01, -5.38403928e-01],
[-1.44541347e+00, 1.19437456e+00]],
...,
[[ 1.11028528e+00, -2.66319364e-02],
[-1.03816831e+00, 4.80187714e-01],
[ 5.60190491e-02, -6.81482777e-02],
...,
[ 4.46985304e-01, -8.33781809e-02],
[-1.76779434e-01, -2.15396023e+00],
[-1.36233258e+00, -1.00828364e-01]],
[[ 1.25010625e-01, -5.73343694e-01],
[ 4.23534930e-01, 1.27166235e+00],
[ 6.20880544e-01, -5.36300726e-02],
...,
[-4.97313976e-01, 6.65309191e-01],
[-6.49542287e-02, 1.02147615e+00],
[ 1.87847123e-01, 7.86082864e-01]],
[[-8.89460385e-01, -6.50614142e-01],
[ 6.55708909e-01, 8.00769866e-01],
[ 1.00335670e+00, -3.60975653e-01],
...,
[-7.29620278e-01, 3.29060793e-01],
[ 2.53696367e-02, -7.21324742e-01],
[-4.38493162e-01, -1.71777833e+00]]],
[[[-5.11693060e-01, -5.47546387e-01],
[-2.56009412e+00, -1.24894366e-01],
[-1.66868377e+00, -6.13053203e-01],
…,
[-3.40102255e-01, -4.76122051e-01],
[-2.68808216e-01, -6.67316198e-01],
[ 1.95494068e+00, -5.32188356e-01]],
[[-6.79937303e-01, 7.25843370e-01],
[ 7.51152635e-01, 1.25086391e+00],
[ 1.31343961e+00, 6.61642969e-01],
...,
[ 3.19355845e-01, -1.11920547e+00],
[-4.93650079e-01, 8.22943971e-02],
[ 1.77995250e-01, 8.71762872e-01]],
[[ 5.79456747e-01, 6.10169657e-02],
[-3.90781134e-01, 8.98746789e-01],
[-1.64386973e-01, -1.89981267e-01],
...,
[ 1.72087538e+00, 1.32393092e-03],
[-1.03725746e-01, 7.66479552e-01],
[ 8.60096216e-01, 4.74087834e-01]],
...,
[[ 2.98859119e-01, 1.36904991e+00],
[-1.31470454e+00, 1.88162339e+00],
[ 3.38255256e-01, -1.29588962e+00],
...,
[ 4.79147226e-01, 2.02118421e+00],
[ 3.93357724e-01, 2.84831226e-01],
[-1.07760859e+00, -8.29148889e-01]],
[[-7.26247966e-01, 3.66007835e-01],
[ 6.38583839e-01, 5.39520979e-01],
[ 2.58788407e-01, -1.21468163e+00],
...,
[ 3.30879092e-01, 1.26315391e+00],
[-9.85577762e-01, -1.57071245e+00],
[ 1.34247553e+00, 3.33765388e-01]],
[[ 3.28157872e-01, 3.69738698e-01],
[-4.69663978e-01, -3.00485075e-01],
[ 8.08599889e-01, 3.49693507e-01],
...,
[ 1.20650291e-01, -1.00170338e+00],
[-1.25450063e+00, -8.53059292e-01],
[-5.60456105e-02, -1.43128681e+00]]],
[[[ 6.02736592e-01, 9.08448339e-01],
[ 1.45205522e+00, -7.05780163e-02],
[ 1.12210441e+00, -5.45533061e-01],
…,
[-1.61648536e+00, 1.39675033e+00],
[ 3.89932483e-01, -9.83740449e-01],
[-3.43187571e-01, 4.93973970e-01]],
[[-4.33481991e-01, -2.29770586e-01],
[ 4.20535475e-01, -1.70520768e-01],
[ 1.40664136e+00, 4.63991873e-02],
...,
[ 3.30364525e-01, 1.25932079e-02],
[-5.44138372e-01, 2.69956380e-01],
[ 5.51277101e-01, -2.21568316e-01]],
[[-2.39359438e-01, -1.71562707e+00],
[ 8.63479078e-02, -2.53337473e-01],
[-5.11896372e-01, 1.14060119e-01],
...,
[-7.51873851e-01, 1.60762429e+00],
[-1.85268188e+00, -3.74208689e-01],
[-4.49496716e-01, 1.31152779e-01]],
...,
[[-1.24805138e-01, 6.35229349e-01],
[ 1.62983191e+00, -3.34331602e-01],
[-3.98483366e-01, -2.70434052e-01],
...,
[ 2.51731694e-01, -4.81671304e-01],
[ 1.65011346e+00, -1.03246319e+00],
[-1.56109953e+00, 1.72697484e+00]],
[[ 3.73855352e-01, -3.85653168e-01],
[-1.18297446e+00, -3.87742639e-01],
[-5.74579597e-01, -7.38137007e-01],
...,
[ 5.06586790e-01, -4.67593260e-02],
[ 4.67046916e-01, 8.60109150e-01],
[-8.88322115e-01, 4.53103155e-01]],
[[-1.47322047e+00, -3.68833989e-01],
[ 3.80937368e-01, 6.17409274e-02],
[-1.07242978e+00, 2.55871916e+00],
...,
[ 1.02848232e+00, -7.19225705e-02],
[-1.13464808e+00, -1.25733685e+00],
[-6.02429748e-01, 6.05888307e-01]]]], dtype=float32)>
维度变换
改变视图
我们通过 tf.range()模拟生成一个向量数据，并通过 tf.reshape 视图改变函数产生不同的视图
1
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4
5
|
x=tf.range(96)
x=tf.reshape(x,[2,4,4,3])
x
|
<tf.Tensor: id=365, shape=(2, 4, 4, 3), dtype=int32, numpy=
array([[[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17],
[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29],
[30, 31, 32],
[33, 34, 35]],
[[36, 37, 38],
[39, 40, 41],
[42, 43, 44],
[45, 46, 47]]],
[[[48, 49, 50],
[51, 52, 53],
[54, 55, 56],
[57, 58, 59]],
[[60, 61, 62],
[63, 64, 65],
[66, 67, 68],
[69, 70, 71]],
[[72, 73, 74],
[75, 76, 77],
[78, 79, 80],
[81, 82, 83]],
[[84, 85, 86],
[87, 88, 89],
[90, 91, 92],
[93, 94, 95]]]], dtype=int32)>
(4, TensorShape([2, 4, 4, 3]))
通过 tf.reshape(x, new_shape)，可以将张量的视图任意地合法改变
<tf.Tensor: id=373, shape=(2, 48), dtype=int32, numpy=
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]],
dtype=int32)>
<tf.Tensor: id=375, shape=(2, 4, 12), dtype=int32, numpy=
array([[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],
[36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]],
[[48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71],
[72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83],
[84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95]]], dtype=int32)>
<tf.Tensor: id=377, shape=(2, 16, 3), dtype=int32, numpy=
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17],
[18, 19, 20],
[21, 22, 23],
[24, 25, 26],
[27, 28, 29],
[30, 31, 32],
[33, 34, 35],
[36, 37, 38],
[39, 40, 41],
[42, 43, 44],
[45, 46, 47]],
[[48, 49, 50],
[51, 52, 53],
[54, 55, 56],
[57, 58, 59],
[60, 61, 62],
[63, 64, 65],
[66, 67, 68],
[69, 70, 71],
[72, 73, 74],
[75, 76, 77],
[78, 79, 80],
[81, 82, 83],
[84, 85, 86],
[87, 88, 89],
[90, 91, 92],
[93, 94, 95]]], dtype=int32)>
增、删维度
1
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3
|
x = tf.random.uniform([28,28],maxval=10,dtype=tf.int32)
x
|
<tf.Tensor: id=381, shape=(28, 28), dtype=int32, numpy=
array([[4, 1, 1, 5, 4, 1, 3, 5, 4, 0, 8, 8, 0, 1, 8, 6, 4, 9, 5, 1, 8, 0,
1, 3, 3, 5, 0, 4],
[5, 9, 2, 1, 8, 6, 3, 8, 6, 3, 6, 4, 7, 7, 5, 9, 2, 8, 4, 6, 6, 4,
9, 0, 5, 9, 9, 0],
[8, 0, 0, 1, 4, 2, 5, 8, 9, 3, 5, 7, 0, 1, 3, 6, 2, 0, 4, 7, 7, 5,
8, 2, 7, 8, 6, 0],
[9, 6, 4, 8, 5, 5, 7, 1, 2, 8, 6, 9, 5, 3, 3, 6, 5, 9, 4, 4, 1, 0,
5, 9, 3, 7, 1, 6],
[5, 8, 7, 4, 6, 5, 4, 5, 7, 5, 1, 3, 2, 2, 9, 0, 9, 5, 3, 3, 4, 9,
5, 1, 7, 0, 4, 6],
[9, 2, 6, 7, 5, 7, 9, 3, 1, 8, 2, 0, 0, 8, 2, 7, 2, 2, 1, 1, 7, 1,
9, 5, 2, 2, 6, 4],
[6, 4, 2, 2, 7, 2, 8, 0, 1, 5, 9, 5, 0, 8, 0, 3, 8, 6, 3, 7, 0, 5,
8, 1, 6, 1, 5, 4],
[3, 9, 2, 4, 1, 8, 1, 5, 7, 0, 0, 2, 9, 0, 5, 0, 5, 1, 7, 0, 5, 0,
1, 3, 2, 6, 3, 8],
[2, 9, 2, 6, 0, 4, 8, 7, 7, 4, 0, 3, 0, 9, 1, 6, 1, 8, 5, 2, 0, 6,
4, 0, 7, 5, 5, 9],
[4, 6, 8, 6, 5, 5, 8, 8, 2, 5, 1, 7, 0, 7, 7, 2, 3, 2, 5, 3, 3, 4,
4, 1, 2, 4, 7, 1],
[8, 3, 0, 5, 0, 4, 4, 0, 2, 1, 3, 0, 8, 8, 3, 0, 5, 8, 6, 4, 3, 2,
1, 4, 2, 4, 9, 5],
[4, 3, 1, 4, 7, 0, 4, 9, 3, 2, 5, 9, 2, 4, 1, 5, 5, 8, 0, 5, 0, 7,
0, 1, 0, 0, 2, 6],
[2, 4, 9, 9, 4, 2, 0, 0, 2, 5, 6, 0, 0, 9, 7, 3, 6, 2, 7, 3, 8, 8,
7, 2, 9, 9, 7, 3],
[2, 8, 8, 8, 5, 7, 7, 9, 1, 8, 6, 5, 4, 8, 4, 4, 4, 5, 6, 5, 8, 2,
5, 1, 1, 3, 5, 9],
[2, 3, 8, 5, 2, 1, 6, 9, 5, 9, 0, 5, 7, 5, 7, 8, 8, 0, 9, 9, 3, 0,
4, 3, 3, 3, 4, 5],
[9, 6, 3, 8, 8, 3, 6, 0, 3, 4, 1, 1, 2, 9, 8, 0, 5, 3, 0, 7, 0, 9,
2, 0, 8, 1, 1, 9],
[4, 8, 7, 0, 3, 6, 1, 7, 7, 9, 0, 1, 4, 6, 7, 0, 9, 5, 2, 2, 6, 5,
5, 0, 3, 1, 1, 7],
[9, 2, 4, 6, 0, 5, 8, 2, 2, 7, 7, 9, 1, 1, 9, 5, 5, 8, 0, 3, 8, 4,
2, 7, 0, 4, 2, 7],
[9, 2, 3, 7, 7, 6, 3, 7, 4, 6, 4, 8, 4, 9, 3, 3, 2, 4, 8, 4, 7, 6,
6, 2, 0, 7, 1, 9],
[6, 1, 2, 0, 2, 0, 0, 0, 5, 8, 7, 6, 9, 7, 9, 0, 6, 6, 6, 5, 3, 1,
3, 2, 3, 2, 3, 4],
[7, 4, 8, 9, 8, 3, 4, 0, 8, 0, 5, 2, 0, 3, 9, 8, 3, 8, 4, 2, 5, 3,
6, 1, 9, 8, 6, 5],
[6, 3, 1, 6, 4, 1, 8, 1, 6, 7, 2, 7, 1, 2, 8, 1, 4, 5, 0, 0, 4, 9,
9, 4, 6, 9, 7, 5],
[4, 0, 1, 1, 8, 7, 0, 8, 1, 2, 8, 6, 2, 1, 4, 6, 9, 2, 6, 9, 4, 0,
9, 0, 7, 9, 8, 4],
[2, 8, 3, 1, 9, 6, 1, 0, 0, 4, 5, 7, 1, 2, 3, 5, 9, 4, 7, 9, 5, 5,
8, 5, 0, 0, 5, 8],
[4, 6, 7, 6, 4, 1, 6, 8, 4, 2, 4, 5, 6, 1, 6, 6, 4, 2, 1, 1, 2, 6,
8, 3, 0, 0, 4, 0],
[6, 3, 3, 6, 8, 4, 6, 3, 6, 3, 8, 9, 7, 2, 2, 9, 0, 5, 7, 7, 2, 6,
3, 4, 6, 9, 4, 2],
[2, 7, 0, 8, 7, 0, 7, 8, 2, 2, 8, 3, 9, 6, 3, 0, 0, 5, 5, 7, 3, 9,
4, 7, 4, 4, 5, 0],
[3, 5, 7, 5, 4, 6, 8, 5, 9, 4, 7, 1, 6, 8, 0, 3, 1, 5, 2, 0, 3, 5,
9, 7, 6, 3, 3, 1]], dtype=int32)>
通过 tf.expand_dims(x, axis)可在指定的 axis 轴前可以插入一个新的维度
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x = tf.expand_dims(x,axis=2)
x
|
<tf.Tensor: id=383, shape=(28, 28, 1), dtype=int32, numpy=
array([[[4],
[1],
[1],
[5],
[4],
[1],
[3],
[5],
[4],
[0],
[8],
[8],
[0],
[1],
[8],
[6],
[4],
[9],
[5],
[1],
[8],
[0],
[1],
[3],
[3],
[5],
[0],
[4]],
[[5],
[9],
[2],
[1],
[8],
[6],
[3],
[8],
[6],
[3],
[6],
[4],
[7],
[7],
[5],
[9],
[2],
[8],
[4],
[6],
[6],
[4],
[9],
[0],
[5],
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1
2
3
|
x = tf.random.uniform([28,28],maxval=10,dtype=tf.int32)
x
|
<tf.Tensor: id=432726, shape=(28, 28), dtype=int32, numpy=
array([[5, 3, 3, 6, 9, 3, 0, 4, 1, 6, 3, 8, 7, 7, 5, 4, 0, 0, 8, 5, 2, 3,
6, 4, 4, 8, 8, 3],
[8, 4, 7, 9, 3, 6, 1, 7, 5, 9, 4, 9, 7, 4, 4, 8, 4, 9, 7, 9, 6, 1,
3, 5, 0, 2, 4, 2],
[3, 5, 5, 5, 7, 9, 5, 3, 6, 0, 7, 8, 0, 0, 4, 7, 8, 7, 4, 1, 9, 9,
2, 6, 6, 2, 0, 3],
[3, 5, 5, 2, 4, 0, 2, 5, 0, 2, 3, 2, 1, 0, 1, 4, 5, 3, 1, 9, 5, 3,
0, 5, 8, 1, 9, 4],
[6, 0, 1, 4, 0, 0, 7, 7, 0, 4, 3, 3, 3, 5, 2, 3, 5, 6, 4, 6, 4, 1,
3, 2, 8, 5, 8, 4],
[1, 2, 9, 3, 2, 3, 4, 4, 0, 0, 6, 6, 4, 0, 2, 7, 2, 2, 4, 2, 6, 0,
4, 0, 5, 5, 4, 5],
[3, 2, 6, 7, 7, 1, 0, 3, 3, 5, 5, 7, 8, 1, 6, 5, 5, 4, 3, 2, 9, 0,
0, 3, 7, 0, 0, 0],
[7, 7, 0, 7, 9, 8, 4, 5, 3, 7, 3, 3, 6, 1, 4, 8, 9, 2, 2, 8, 3, 1,
0, 9, 3, 3, 2, 4],
[9, 8, 8, 1, 4, 9, 3, 0, 5, 9, 6, 6, 8, 1, 4, 9, 8, 9, 8, 9, 3, 8,
7, 5, 8, 0, 3, 6],
[0, 8, 3, 1, 2, 1, 9, 4, 5, 0, 7, 1, 3, 5, 4, 4, 7, 3, 6, 5, 5, 6,
2, 0, 6, 1, 4, 8],
[4, 2, 6, 1, 7, 5, 6, 2, 4, 0, 9, 8, 0, 0, 0, 6, 8, 2, 3, 0, 0, 5,
6, 6, 9, 8, 4, 9],
[7, 3, 2, 5, 2, 5, 4, 3, 6, 4, 9, 2, 1, 7, 6, 4, 4, 5, 7, 7, 1, 6,
4, 0, 3, 6, 4, 8],
[1, 7, 6, 0, 4, 8, 0, 3, 0, 2, 0, 7, 0, 5, 6, 5, 3, 1, 4, 3, 8, 8,
8, 6, 7, 3, 1, 7],
[0, 8, 0, 5, 5, 2, 2, 5, 2, 1, 4, 4, 1, 4, 9, 4, 4, 1, 7, 1, 7, 7,
4, 1, 8, 2, 3, 2],
[5, 3, 5, 6, 6, 0, 9, 2, 9, 2, 8, 2, 1, 4, 0, 4, 7, 3, 0, 3, 8, 1,
5, 7, 5, 4, 6, 6],
[4, 7, 0, 1, 9, 6, 1, 1, 2, 1, 8, 2, 2, 9, 4, 7, 5, 1, 2, 4, 7, 5,
7, 6, 6, 4, 1, 5],
[4, 5, 7, 8, 2, 0, 5, 3, 4, 6, 3, 4, 5, 4, 9, 3, 6, 0, 2, 7, 1, 0,
1, 7, 2, 4, 4, 0],
[5, 3, 9, 1, 2, 4, 4, 8, 8, 2, 2, 1, 6, 4, 5, 2, 5, 0, 0, 1, 6, 4,
5, 9, 5, 8, 9, 5],
[6, 1, 1, 8, 9, 6, 8, 9, 9, 8, 2, 0, 9, 7, 9, 0, 9, 7, 0, 5, 3, 8,
0, 9, 1, 8, 9, 4],
[9, 1, 7, 3, 3, 7, 8, 3, 2, 2, 6, 3, 2, 1, 0, 5, 8, 2, 8, 4, 5, 5,
2, 9, 9, 6, 8, 3],
[0, 3, 8, 2, 5, 1, 6, 3, 5, 0, 2, 3, 9, 9, 4, 6, 1, 9, 3, 8, 7, 8,
8, 7, 2, 4, 4, 8],
[6, 7, 8, 6, 6, 6, 5, 2, 1, 8, 4, 7, 8, 9, 9, 4, 5, 2, 4, 7, 8, 5,
9, 3, 0, 0, 9, 1],
[7, 8, 4, 9, 4, 8, 9, 3, 5, 4, 8, 3, 7, 9, 2, 0, 2, 3, 8, 5, 6, 0,
4, 3, 6, 1, 6, 3],
[2, 4, 3, 9, 0, 2, 5, 9, 0, 0, 0, 3, 1, 2, 4, 3, 1, 4, 5, 7, 4, 3,
9, 6, 9, 3, 6, 6],
[3, 8, 6, 4, 0, 0, 3, 8, 1, 1, 5, 4, 8, 4, 8, 3, 3, 1, 1, 9, 6, 4,
9, 7, 6, 7, 3, 7],
[4, 3, 7, 5, 3, 4, 0, 4, 8, 2, 5, 1, 2, 8, 2, 6, 4, 7, 7, 6, 3, 5,
9, 6, 4, 2, 5, 9],
[4, 9, 2, 7, 8, 6, 1, 1, 9, 4, 1, 3, 1, 8, 6, 5, 3, 0, 2, 2, 3, 0,
3, 6, 9, 1, 2, 4],
[0, 7, 3, 6, 4, 2, 3, 7, 5, 0, 9, 5, 5, 2, 7, 5, 0, 3, 6, 8, 7, 3,
3, 6, 1, 2, 3, 6]], dtype=int32)>
1
2
| x = tf.expand_dims(x,axis=0)
x
|
<tf.Tensor: id=432728, shape=(1, 28, 28), dtype=int32, numpy=
array([[[5, 3, 3, 6, 9, 3, 0, 4, 1, 6, 3, 8, 7, 7, 5, 4, 0, 0, 8, 5, 2,
3, 6, 4, 4, 8, 8, 3],
[8, 4, 7, 9, 3, 6, 1, 7, 5, 9, 4, 9, 7, 4, 4, 8, 4, 9, 7, 9, 6,
1, 3, 5, 0, 2, 4, 2],
[3, 5, 5, 5, 7, 9, 5, 3, 6, 0, 7, 8, 0, 0, 4, 7, 8, 7, 4, 1, 9,
9, 2, 6, 6, 2, 0, 3],
[3, 5, 5, 2, 4, 0, 2, 5, 0, 2, 3, 2, 1, 0, 1, 4, 5, 3, 1, 9, 5,
3, 0, 5, 8, 1, 9, 4],
[6, 0, 1, 4, 0, 0, 7, 7, 0, 4, 3, 3, 3, 5, 2, 3, 5, 6, 4, 6, 4,
1, 3, 2, 8, 5, 8, 4],
[1, 2, 9, 3, 2, 3, 4, 4, 0, 0, 6, 6, 4, 0, 2, 7, 2, 2, 4, 2, 6,
0, 4, 0, 5, 5, 4, 5],
[3, 2, 6, 7, 7, 1, 0, 3, 3, 5, 5, 7, 8, 1, 6, 5, 5, 4, 3, 2, 9,
0, 0, 3, 7, 0, 0, 0],
[7, 7, 0, 7, 9, 8, 4, 5, 3, 7, 3, 3, 6, 1, 4, 8, 9, 2, 2, 8, 3,
1, 0, 9, 3, 3, 2, 4],
[9, 8, 8, 1, 4, 9, 3, 0, 5, 9, 6, 6, 8, 1, 4, 9, 8, 9, 8, 9, 3,
8, 7, 5, 8, 0, 3, 6],
[0, 8, 3, 1, 2, 1, 9, 4, 5, 0, 7, 1, 3, 5, 4, 4, 7, 3, 6, 5, 5,
6, 2, 0, 6, 1, 4, 8],
[4, 2, 6, 1, 7, 5, 6, 2, 4, 0, 9, 8, 0, 0, 0, 6, 8, 2, 3, 0, 0,
5, 6, 6, 9, 8, 4, 9],
[7, 3, 2, 5, 2, 5, 4, 3, 6, 4, 9, 2, 1, 7, 6, 4, 4, 5, 7, 7, 1,
6, 4, 0, 3, 6, 4, 8],
[1, 7, 6, 0, 4, 8, 0, 3, 0, 2, 0, 7, 0, 5, 6, 5, 3, 1, 4, 3, 8,
8, 8, 6, 7, 3, 1, 7],
[0, 8, 0, 5, 5, 2, 2, 5, 2, 1, 4, 4, 1, 4, 9, 4, 4, 1, 7, 1, 7,
7, 4, 1, 8, 2, 3, 2],
[5, 3, 5, 6, 6, 0, 9, 2, 9, 2, 8, 2, 1, 4, 0, 4, 7, 3, 0, 3, 8,
1, 5, 7, 5, 4, 6, 6],
[4, 7, 0, 1, 9, 6, 1, 1, 2, 1, 8, 2, 2, 9, 4, 7, 5, 1, 2, 4, 7,
5, 7, 6, 6, 4, 1, 5],
[4, 5, 7, 8, 2, 0, 5, 3, 4, 6, 3, 4, 5, 4, 9, 3, 6, 0, 2, 7, 1,
0, 1, 7, 2, 4, 4, 0],
[5, 3, 9, 1, 2, 4, 4, 8, 8, 2, 2, 1, 6, 4, 5, 2, 5, 0, 0, 1, 6,
4, 5, 9, 5, 8, 9, 5],
[6, 1, 1, 8, 9, 6, 8, 9, 9, 8, 2, 0, 9, 7, 9, 0, 9, 7, 0, 5, 3,
8, 0, 9, 1, 8, 9, 4],
[9, 1, 7, 3, 3, 7, 8, 3, 2, 2, 6, 3, 2, 1, 0, 5, 8, 2, 8, 4, 5,
5, 2, 9, 9, 6, 8, 3],
[0, 3, 8, 2, 5, 1, 6, 3, 5, 0, 2, 3, 9, 9, 4, 6, 1, 9, 3, 8, 7,
8, 8, 7, 2, 4, 4, 8],
[6, 7, 8, 6, 6, 6, 5, 2, 1, 8, 4, 7, 8, 9, 9, 4, 5, 2, 4, 7, 8,
5, 9, 3, 0, 0, 9, 1],
[7, 8, 4, 9, 4, 8, 9, 3, 5, 4, 8, 3, 7, 9, 2, 0, 2, 3, 8, 5, 6,
0, 4, 3, 6, 1, 6, 3],
[2, 4, 3, 9, 0, 2, 5, 9, 0, 0, 0, 3, 1, 2, 4, 3, 1, 4, 5, 7, 4,
3, 9, 6, 9, 3, 6, 6],
[3, 8, 6, 4, 0, 0, 3, 8, 1, 1, 5, 4, 8, 4, 8, 3, 3, 1, 1, 9, 6,
4, 9, 7, 6, 7, 3, 7],
[4, 3, 7, 5, 3, 4, 0, 4, 8, 2, 5, 1, 2, 8, 2, 6, 4, 7, 7, 6, 3,
5, 9, 6, 4, 2, 5, 9],
[4, 9, 2, 7, 8, 6, 1, 1, 9, 4, 1, 3, 1, 8, 6, 5, 3, 0, 2, 2, 3,
0, 3, 6, 9, 1, 2, 4],
[0, 7, 3, 6, 4, 2, 3, 7, 5, 0, 9, 5, 5, 2, 7, 5, 0, 3, 6, 8, 7,
3, 3, 6, 1, 2, 3, 6]]], dtype=int32)>
1
2
| x = tf.squeeze(x, axis=0)
x
|
<tf.Tensor: id=432729, shape=(28, 28), dtype=int32, numpy=
array([[5, 3, 3, 6, 9, 3, 0, 4, 1, 6, 3, 8, 7, 7, 5, 4, 0, 0, 8, 5, 2, 3,
6, 4, 4, 8, 8, 3],
[8, 4, 7, 9, 3, 6, 1, 7, 5, 9, 4, 9, 7, 4, 4, 8, 4, 9, 7, 9, 6, 1,
3, 5, 0, 2, 4, 2],
[3, 5, 5, 5, 7, 9, 5, 3, 6, 0, 7, 8, 0, 0, 4, 7, 8, 7, 4, 1, 9, 9,
2, 6, 6, 2, 0, 3],
[3, 5, 5, 2, 4, 0, 2, 5, 0, 2, 3, 2, 1, 0, 1, 4, 5, 3, 1, 9, 5, 3,
0, 5, 8, 1, 9, 4],
[6, 0, 1, 4, 0, 0, 7, 7, 0, 4, 3, 3, 3, 5, 2, 3, 5, 6, 4, 6, 4, 1,
3, 2, 8, 5, 8, 4],
[1, 2, 9, 3, 2, 3, 4, 4, 0, 0, 6, 6, 4, 0, 2, 7, 2, 2, 4, 2, 6, 0,
4, 0, 5, 5, 4, 5],
[3, 2, 6, 7, 7, 1, 0, 3, 3, 5, 5, 7, 8, 1, 6, 5, 5, 4, 3, 2, 9, 0,
0, 3, 7, 0, 0, 0],
[7, 7, 0, 7, 9, 8, 4, 5, 3, 7, 3, 3, 6, 1, 4, 8, 9, 2, 2, 8, 3, 1,
0, 9, 3, 3, 2, 4],
[9, 8, 8, 1, 4, 9, 3, 0, 5, 9, 6, 6, 8, 1, 4, 9, 8, 9, 8, 9, 3, 8,
7, 5, 8, 0, 3, 6],
[0, 8, 3, 1, 2, 1, 9, 4, 5, 0, 7, 1, 3, 5, 4, 4, 7, 3, 6, 5, 5, 6,
2, 0, 6, 1, 4, 8],
[4, 2, 6, 1, 7, 5, 6, 2, 4, 0, 9, 8, 0, 0, 0, 6, 8, 2, 3, 0, 0, 5,
6, 6, 9, 8, 4, 9],
[7, 3, 2, 5, 2, 5, 4, 3, 6, 4, 9, 2, 1, 7, 6, 4, 4, 5, 7, 7, 1, 6,
4, 0, 3, 6, 4, 8],
[1, 7, 6, 0, 4, 8, 0, 3, 0, 2, 0, 7, 0, 5, 6, 5, 3, 1, 4, 3, 8, 8,
8, 6, 7, 3, 1, 7],
[0, 8, 0, 5, 5, 2, 2, 5, 2, 1, 4, 4, 1, 4, 9, 4, 4, 1, 7, 1, 7, 7,
4, 1, 8, 2, 3, 2],
[5, 3, 5, 6, 6, 0, 9, 2, 9, 2, 8, 2, 1, 4, 0, 4, 7, 3, 0, 3, 8, 1,
5, 7, 5, 4, 6, 6],
[4, 7, 0, 1, 9, 6, 1, 1, 2, 1, 8, 2, 2, 9, 4, 7, 5, 1, 2, 4, 7, 5,
7, 6, 6, 4, 1, 5],
[4, 5, 7, 8, 2, 0, 5, 3, 4, 6, 3, 4, 5, 4, 9, 3, 6, 0, 2, 7, 1, 0,
1, 7, 2, 4, 4, 0],
[5, 3, 9, 1, 2, 4, 4, 8, 8, 2, 2, 1, 6, 4, 5, 2, 5, 0, 0, 1, 6, 4,
5, 9, 5, 8, 9, 5],
[6, 1, 1, 8, 9, 6, 8, 9, 9, 8, 2, 0, 9, 7, 9, 0, 9, 7, 0, 5, 3, 8,
0, 9, 1, 8, 9, 4],
[9, 1, 7, 3, 3, 7, 8, 3, 2, 2, 6, 3, 2, 1, 0, 5, 8, 2, 8, 4, 5, 5,
2, 9, 9, 6, 8, 3],
[0, 3, 8, 2, 5, 1, 6, 3, 5, 0, 2, 3, 9, 9, 4, 6, 1, 9, 3, 8, 7, 8,
8, 7, 2, 4, 4, 8],
[6, 7, 8, 6, 6, 6, 5, 2, 1, 8, 4, 7, 8, 9, 9, 4, 5, 2, 4, 7, 8, 5,
9, 3, 0, 0, 9, 1],
[7, 8, 4, 9, 4, 8, 9, 3, 5, 4, 8, 3, 7, 9, 2, 0, 2, 3, 8, 5, 6, 0,
4, 3, 6, 1, 6, 3],
[2, 4, 3, 9, 0, 2, 5, 9, 0, 0, 0, 3, 1, 2, 4, 3, 1, 4, 5, 7, 4, 3,
9, 6, 9, 3, 6, 6],
[3, 8, 6, 4, 0, 0, 3, 8, 1, 1, 5, 4, 8, 4, 8, 3, 3, 1, 1, 9, 6, 4,
9, 7, 6, 7, 3, 7],
[4, 3, 7, 5, 3, 4, 0, 4, 8, 2, 5, 1, 2, 8, 2, 6, 4, 7, 7, 6, 3, 5,
9, 6, 4, 2, 5, 9],
[4, 9, 2, 7, 8, 6, 1, 1, 9, 4, 1, 3, 1, 8, 6, 5, 3, 0, 2, 2, 3, 0,
3, 6, 9, 1, 2, 4],
[0, 7, 3, 6, 4, 2, 3, 7, 5, 0, 9, 5, 5, 2, 7, 5, 0, 3, 6, 8, 7, 3,
3, 6, 1, 2, 3, 6]], dtype=int32)>
1
2
| x = tf.random.uniform([1,28,28,1],maxval=10,dtype=tf.int32)
tf.squeeze(x)
|
<tf.Tensor: id=391, shape=(28, 28), dtype=int32, numpy=
array([[3, 5, 3, 9, 7, 0, 0, 8, 3, 1, 4, 8, 5, 7, 8, 6, 9, 4, 1, 1, 5, 8,
6, 2, 8, 3, 5, 3],
[4, 8, 9, 7, 6, 0, 8, 7, 8, 3, 1, 3, 5, 9, 3, 6, 6, 2, 3, 1, 7, 6,
9, 6, 2, 7, 4, 2],
[5, 1, 2, 0, 3, 7, 5, 0, 7, 4, 7, 7, 5, 8, 9, 2, 2, 6, 7, 3, 8, 9,
4, 1, 6, 5, 4, 7],
[2, 5, 3, 4, 4, 7, 5, 5, 1, 1, 7, 0, 9, 8, 4, 3, 8, 6, 9, 3, 3, 2,
1, 2, 4, 4, 4, 7],
[9, 2, 3, 0, 3, 5, 4, 5, 8, 7, 0, 8, 6, 4, 9, 7, 1, 8, 3, 6, 5, 7,
0, 4, 4, 2, 6, 9],
[9, 3, 4, 4, 6, 8, 1, 7, 0, 8, 6, 0, 0, 2, 8, 3, 5, 0, 6, 6, 8, 4,
8, 9, 4, 0, 9, 4],
[3, 8, 5, 9, 4, 5, 1, 8, 5, 3, 5, 9, 7, 8, 9, 2, 8, 8, 5, 5, 5, 9,
1, 9, 3, 4, 4, 8],
[9, 5, 9, 4, 2, 0, 8, 1, 4, 2, 0, 3, 6, 9, 7, 6, 0, 5, 8, 9, 0, 8,
0, 0, 3, 1, 1, 7],
[4, 6, 9, 0, 6, 6, 7, 6, 2, 3, 1, 7, 8, 7, 8, 5, 2, 5, 4, 5, 1, 9,
9, 6, 6, 4, 4, 8],
[1, 4, 2, 6, 7, 8, 4, 9, 2, 7, 8, 8, 0, 7, 0, 3, 8, 2, 3, 1, 9, 2,
7, 9, 1, 1, 6, 7],
[0, 1, 7, 6, 4, 1, 4, 3, 0, 0, 7, 4, 7, 2, 6, 1, 3, 1, 8, 9, 1, 5,
7, 3, 4, 3, 4, 6],
[7, 7, 7, 3, 6, 6, 3, 6, 2, 8, 0, 3, 5, 5, 9, 1, 5, 0, 1, 8, 3, 9,
7, 6, 7, 8, 0, 9],
[3, 3, 9, 2, 4, 8, 1, 8, 8, 7, 5, 7, 4, 0, 1, 8, 5, 2, 9, 1, 1, 5,
7, 5, 4, 0, 5, 5],
[7, 9, 7, 1, 7, 7, 1, 5, 7, 1, 8, 3, 0, 5, 1, 9, 4, 0, 2, 4, 4, 4,
5, 1, 8, 0, 2, 8],
[8, 6, 4, 6, 5, 3, 3, 6, 7, 6, 1, 9, 0, 3, 6, 3, 9, 3, 0, 0, 4, 2,
5, 5, 7, 1, 2, 0],
[6, 7, 0, 4, 3, 2, 7, 8, 4, 4, 5, 8, 5, 0, 0, 4, 3, 4, 4, 9, 6, 6,
8, 8, 4, 9, 8, 7],
[1, 3, 5, 7, 6, 0, 2, 2, 1, 9, 8, 6, 6, 6, 0, 3, 6, 8, 9, 4, 0, 4,
4, 0, 8, 0, 8, 9],
[4, 6, 1, 4, 4, 8, 9, 7, 6, 8, 7, 9, 0, 8, 8, 3, 0, 5, 9, 8, 6, 6,
9, 6, 5, 1, 0, 9],
[0, 3, 1, 4, 2, 1, 2, 7, 6, 2, 1, 3, 0, 6, 6, 0, 7, 9, 5, 7, 7, 9,
7, 6, 9, 9, 2, 7],
[2, 8, 2, 1, 4, 4, 8, 8, 0, 3, 4, 6, 8, 2, 4, 5, 8, 3, 7, 5, 1, 6,
7, 5, 6, 3, 1, 2],
[4, 0, 7, 4, 0, 8, 3, 4, 9, 0, 0, 8, 9, 1, 1, 9, 7, 8, 9, 1, 9, 2,
0, 7, 3, 6, 6, 2],
[0, 4, 0, 9, 8, 3, 2, 5, 9, 1, 0, 2, 7, 9, 9, 7, 4, 5, 0, 0, 2, 7,
7, 2, 1, 7, 5, 3],
[9, 6, 3, 2, 6, 3, 1, 5, 1, 6, 6, 8, 9, 8, 3, 9, 6, 2, 8, 2, 3, 5,
9, 6, 8, 0, 9, 5],
[0, 3, 4, 7, 3, 5, 5, 0, 7, 3, 7, 7, 2, 1, 8, 4, 9, 7, 9, 1, 2, 5,
9, 7, 7, 7, 8, 0],
[6, 7, 3, 1, 2, 6, 4, 8, 5, 5, 4, 3, 7, 5, 4, 4, 1, 9, 6, 7, 6, 6,
5, 2, 4, 0, 3, 3],
[8, 8, 4, 5, 9, 3, 2, 7, 6, 5, 8, 4, 5, 4, 8, 3, 4, 6, 7, 3, 3, 4,
9, 8, 0, 4, 1, 2],
[5, 5, 9, 3, 6, 7, 4, 5, 2, 3, 4, 8, 0, 5, 3, 4, 1, 0, 3, 7, 6, 9,
3, 8, 9, 4, 9, 8],
[1, 4, 2, 1, 9, 3, 4, 7, 8, 1, 9, 3, 5, 8, 9, 4, 8, 3, 6, 9, 2, 1,
7, 7, 4, 4, 9, 3]], dtype=int32)>
交换维度
1
2
| x = tf.random.uniform([1,2,3,4])
print(x)
|
tf.Tensor(
[[[[0.5282526 0.3555627 0.41090894 0.47944117]
[0.06685734 0.73899055 0.274917 0.786981 ]
[0.5963073 0.47864938 0.4129647 0.9002305 ]]
[[0.70865 0.46636987 0.76260746 0.23017025]
[0.2235589 0.3718114 0.8150687 0.30672145]
[0.78165174 0.63648796 0.61503696 0.35355854]]]], shape=(1, 2, 3, 4), dtype=float32)
1
2
|
tf.transpose(x,perm=[0,3,1,2])
|
<tf.Tensor: id=432771, shape=(1, 4, 2, 3), dtype=float32, numpy=
array([[[[0.5282526 , 0.06685734, 0.5963073 ],
[0.70865 , 0.2235589 , 0.78165174]],
[[0.3555627 , 0.73899055, 0.47864938],
[0.46636987, 0.3718114 , 0.63648796]],
[[0.41090894, 0.274917 , 0.4129647 ],
[0.76260746, 0.8150687 , 0.61503696]],
[[0.47944117, 0.786981 , 0.9002305 ],
[0.23017025, 0.30672145, 0.35355854]]]], dtype=float32)>
复制数据
1
2
3
4
5
6
|
b = tf.constant([1,2])
print(b)
b = tf.expand_dims(b, axis=0)
b
|
tf.Tensor([1 2], shape=(2,), dtype=int32)
<tf.Tensor: id=432780, shape=(1, 2), dtype=int32, numpy=array([[1, 2]], dtype=int32)>
1
2
3
|
b = tf.tile(b, multiples=[2,1])
b
|
<tf.Tensor: id=412, shape=(2, 2), dtype=int32, numpy=
array([[1, 2],
[1, 2]], dtype=int32)>
1
2
3
4
| x = tf.range(4)
x=tf.reshape(x,[2,2])
x
|
<tf.Tensor: id=432787, shape=(2, 2), dtype=int32, numpy=
array([[0, 1],
[2, 3]], dtype=int32)>
1
2
3
|
x = tf.tile(x,multiples=[1,2])
x
|
<tf.Tensor: id=432789, shape=(2, 4), dtype=int32, numpy=
array([[0, 1, 0, 1],
[2, 3, 2, 3]], dtype=int32)>
1
2
3
|
x = tf.tile(x,multiples=[2,1])
x
|
<tf.Tensor: id=432791, shape=(4, 4), dtype=int32, numpy=
array([[0, 1, 0, 1],
[2, 3, 2, 3],
[0, 1, 0, 1],
[2, 3, 2, 3]], dtype=int32)>
Broadcasting
1
2
3
4
|
A = tf.random.normal([32,1])
tf.broadcast_to(A, [2,32,32,3])
|
<tf.Tensor: id=430, shape=(2, 32, 32, 3), dtype=float32, numpy=
array([[[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
...,
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]]],
[[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
…,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
...,
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]],
[[ 0.04447514, 0.04447514, 0.04447514],
[-0.8540972 , -0.8540972 , -0.8540972 ],
[ 0.30159432, 0.30159432, 0.30159432],
...,
[-0.84129137, -0.84129137, -0.84129137],
[ 0.58230823, 0.58230823, 0.58230823],
[ 0.1573652 , 0.1573652 , 0.1573652 ]]]], dtype=float32)>
1
2
3
4
5
6
| A = tf.random.normal([32,2])
try:
tf.broadcast_to(A, [2,32,32,4])
except Exception as e:
print(e)
|
Incompatible shapes: [32,2] vs. [2,32,32,4] [Op:BroadcastTo]
数学运算
加、减、乘、除运算
1
2
3
4
| a = tf.range(5)
b = tf.constant(2)
a//b
|
<tf.Tensor: id=443, shape=(5,), dtype=int32, numpy=array([0, 0, 1, 1, 2], dtype=int32)>
<tf.Tensor: id=444, shape=(5,), dtype=int32, numpy=array([0, 1, 0, 1, 0], dtype=int32)>
乘方运算
1
2
3
| x = tf.range(4)
tf.pow(x,3)
|
<tf.Tensor: id=450, shape=(4,), dtype=int32, numpy=array([ 0, 1, 8, 27], dtype=int32)>
<tf.Tensor: id=452, shape=(4,), dtype=int32, numpy=array([0, 1, 4, 9], dtype=int32)>
1
2
3
| x=tf.constant([1.,4.,9.])
x**(0.5)
|
<tf.Tensor: id=455, shape=(3,), dtype=float32, numpy=array([1., 2., 3.], dtype=float32)>
1
2
3
4
5
6
7
8
| x = tf.range(5)
print(x)
x = tf.cast(x, dtype=tf.float32)
print(x)
x = tf.square(x)
print(x)
|
tf.Tensor([0 1 2 3 4], shape=(5,), dtype=int32)
tf.Tensor([0. 1. 2. 3. 4.], shape=(5,), dtype=float32)
tf.Tensor([ 0. 1. 4. 9. 16.], shape=(5,), dtype=float32)
<tf.Tensor: id=432798, shape=(5,), dtype=float32, numpy=
array([0. , 0.99999994, 1.9999999 , 2.9999998 , 4. ],
dtype=float32)>
指数和对数运算
1
2
3
| x = tf.constant([1.,2.,3.])
2**x
|
<tf.Tensor: id=465, shape=(3,), dtype=float32, numpy=array([2., 4., 8.], dtype=float32)>
<tf.Tensor: id=467, shape=(), dtype=float32, numpy=2.7182817>
1
2
3
| x = tf.exp(3.)
tf.math.log(x)
|
<tf.Tensor: id=470, shape=(), dtype=float32, numpy=3.0>
1
2
3
4
| x = tf.constant([1.,2.])
x = 10**x
tf.math.log(x)/tf.math.log(10.)
|
<tf.Tensor: id=477, shape=(2,), dtype=float32, numpy=array([1., 2.], dtype=float32)>
矩阵相乘运算
1
2
3
4
| a = tf.random.normal([4,3,28,32])
b = tf.random.normal([4,3,32,2])
print(a@b)
|
tf.Tensor(
[[[[ 2.93815994e+00 1.80159616e+00]
[-4.95495558e+00 -2.65059781e+00]
[ 6.36351776e+00 8.27180672e+00]
[-2.50184441e+00 -3.22499895e+00]
[-6.89737129e+00 6.43845701e+00]
[ 4.72115231e+00 -7.39528358e-01]
[-5.87444019e+00 -6.75603390e+00]
[ 8.61762238e+00 4.49309635e+00]
[ 3.18081021e+00 -1.84904563e+00]
[-5.71209073e-01 1.76863492e+00]
[-8.77548409e+00 -2.09427929e+00]
[ 6.11399221e+00 3.75506377e+00]
[-5.72681367e-01 -5.56786919e+00]
[ 1.03334942e+01 -6.21349716e+00]
[ 2.05781221e+00 -2.48031449e+00]
[-1.05474174e-01 1.17951145e+01]
[-8.32847595e+00 8.04420090e+00]
[ 1.10347319e+01 -7.15183640e+00]
[-1.10890408e+01 2.06656051e+00]
[ 2.04201794e+00 -1.72195137e-01]
[ 4.16003466e+00 2.92319274e+00]
[ 1.00829735e+01 3.50188327e+00]
[ 1.60061455e+01 -3.23914313e+00]
[-1.34949207e+00 -2.27372718e+00]
[ 1.16594486e+01 -1.30499089e+00]
[ 2.90008926e+00 6.59213543e+00]
[-3.04731274e+00 -1.17982030e-01]
[-6.25353050e+00 -1.59929824e+00]]
[[ 2.35922217e+00 -1.44711876e+00]
[-2.82181549e+00 -4.16362000e+00]
[-4.13206530e+00 1.96330786e-01]
[-1.13723636e+00 -1.90036798e+00]
[-1.42907238e+00 4.24102306e-01]
[ 1.01430655e+01 2.54081345e+00]
[-4.05478477e+00 -9.29689407e+00]
[ 1.36705666e+01 1.40576875e+00]
[-5.09379244e+00 4.66089153e+00]
[ 6.25803471e-02 -2.86052656e+00]
[-1.12946069e+00 -4.28373003e+00]
[ 5.32545996e+00 1.97562897e+00]
[ 7.16341162e+00 6.10791969e+00]
[-4.77171421e+00 -1.75261497e+00]
[-1.43213348e+01 5.44925928e+00]
[-2.13357735e+00 -2.74817157e+00]
[-6.38115454e+00 6.48117113e+00]
[-1.21313601e+01 -1.16765201e+00]
[-1.98863697e+00 1.22314978e+01]
[ 3.63174462e+00 5.20076323e+00]
[-1.05080090e+01 4.47047186e+00]
[ 8.52560043e+00 -3.26042938e+00]
[ 1.86961699e+00 1.04149675e+00]
[ 3.27967310e+00 4.52322531e+00]
[-1.08596125e+01 4.40047550e+00]
[ 3.30025196e+00 -3.57261777e-01]
[-4.17899323e+00 -5.29293346e+00]
[-6.29359818e+00 2.55025506e-01]]
[[ 2.79792500e+00 -1.14968262e+01]
[ 6.42120302e-01 8.60604167e-01]
[ 8.26789284e+00 9.11268139e+00]
[-1.24864876e+00 -1.29506755e+00]
[ 1.83019781e+00 1.32512970e+01]
[-4.38226223e+00 2.93613434e+00]
[-1.01948481e+01 -2.50259852e+00]
[-6.08818817e+00 5.71516156e-01]
[ 8.14604282e-01 8.74936581e-01]
[-9.27050591e-01 1.68381357e+00]
[-2.39078522e+00 -4.39953446e-01]
[-1.79722738e+00 2.44799304e+00]
[-6.19097829e-01 4.20792866e+00]
[-3.59187007e+00 2.05337834e+00]
[-2.02478099e+00 3.92844319e+00]
[-2.78609324e+00 -1.00785866e+01]
[ 1.35041237e-01 9.82832527e+00]
[ 3.77985573e+00 -2.92683578e+00]
[ 2.50951290e+00 -1.10158062e+00]
[-2.69217896e+00 7.27837420e+00]
[ 4.59399509e+00 -4.81438732e+00]
[ 9.01638508e+00 5.12754726e+00]
[ 5.19506645e+00 -2.35464978e+00]
[ 2.05791235e-01 3.24537897e+00]
[-6.17561936e-02 1.22012386e+01]
[ 8.34735334e-01 2.56306553e+00]
[-8.42908740e-01 -4.72223663e+00]
[ 7.59096265e-01 -8.70975971e-01]]]
[[[-1.25730896e+01 1.73365784e+00]
[-8.16483736e-01 -3.12521791e+00]
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| a = tf.random.normal([4,28,32])
b = tf.random.normal([32,16])
tf.matmul(a,b)
|
<tf.Tensor: id=503, shape=(4, 28, 16), dtype=float32, numpy=
array([[[ -2.7598646 , 7.0569715 , -2.0019226 , ..., -1.2552259 ,
-5.3215303 , -1.2467324 ],
[ -3.5755327 , -3.8002384 , -12.492091 , ..., 6.249779 ,
-3.7607257 , -2.4373896 ],
[ 1.3191882 , -4.4746413 , 2.4289536 , ..., -1.8787553 ,
-0.10033526, -1.4797553 ],
...,
[ -2.0344322 , 5.086643 , -7.6664243 , ..., -3.0846074 ,
-8.448284 , -1.7322202 ],
[ 0.57995903, -3.7676647 , -3.6173913 , ..., 7.0568666 ,
2.3793366 , 3.498049 ],
[ 0.5311534 , 3.0278618 , 3.9090858 , ..., 4.8734083 ,
-6.3130484 , -3.7237642 ]],
[[ 4.89592 , -2.8002422 , -0.7761507 , ..., 9.516954 ,
11.723758 , -2.5442433 ],
[ -0.47682646, -13.358232 , -11.200428 , ..., -0.46236166,
-2.9554985 , -1.4849511 ],
[ 11.57021 , -8.973025 , 2.9124722 , ..., -5.2608457 ,
1.2784045 , -5.1128254 ],
...,
[ 2.318095 , 2.843607 , 4.602457 , ..., 5.6242056 ,
6.1018414 , -5.076501 ],
[ -4.3738413 , -5.2155914 , 8.190216 , ..., -3.7748199 ,
-4.86178 , 2.7263112 ],
[ -1.4741284 , 0.5153975 , -2.7228315 , ..., -0.1337083 ,
-8.092061 , -3.1821835 ]],
[[ 0.28089905, 9.784105 , 2.9840403 , ..., 0.33226973,
-0.6826554 , -4.040504 ],
[ -5.8831253 , 12.158736 , -7.0445533 , ..., 2.2380865 ,
-8.451615 , 3.1144416 ],
[-12.613248 , -10.317265 , -5.9143896 , ..., -2.8576682 ,
-10.0681925 , 6.5913053 ],
...,
[ 5.002187 , 0.69802207, 4.616313 , ..., 1.8524637 ,
1.6469531 , 1.4813223 ],
[ -1.0592954 , -1.9839575 , 6.1675334 , ..., -3.4000485 ,
9.097794 , 3.3264492 ],
[ -2.3593228 , -6.8569756 , -14.06582 , ..., -9.968381 ,
9.856624 , 5.7211127 ]],
[[ 0.79352164, -12.076045 , 4.5146046 , ..., -0.5590708 ,
-0.44884235, 4.5407653 ],
[ 3.3152225 , -1.2491262 , 8.590666 , ..., 0.24038552,
12.144938 , 11.659479 ],
[ -0.8445607 , 6.594575 , -2.8742118 , ..., -2.4811752 ,
3.3992496 , 4.638756 ],
...,
[ -8.9465065 , -6.0752807 , -4.039912 , ..., -0.8335391 ,
-3.9777448 , 3.659201 ],
[ 4.1183553 , 3.9281585 , -4.132287 , ..., -7.197991 ,
5.790247 , -8.167656 ],
[ -4.423063 , -20.040358 , -9.854829 , ..., -3.0150864 ,
-5.763957 , 4.594075 ]]], dtype=float32)>
前向传播实战
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| import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras.datasets as datasets
plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = ['STKaiti']
plt.rcParams['axes.unicode_minus'] = False
|
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| def load_data():
(x, y), (x_val, y_val) = datasets.mnist.load_data()
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
y = tf.convert_to_tensor(y, dtype=tf.int32)
y = tf.one_hot(y, depth=10)
x = tf.reshape(x, (-1, 28 * 28))
train_dataset = tf.data.Dataset.from_tensor_slices((x, y))
train_dataset = train_dataset.batch(200)
return train_dataset
|
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| def init_paramaters():
w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
b1 = tf.Variable(tf.zeros([256]))
w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
b2 = tf.Variable(tf.zeros([128]))
w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1))
b3 = tf.Variable(tf.zeros([10]))
return w1, b1, w2, b2, w3, b3
|
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| def train_epoch(epoch, train_dataset, w1, b1, w2, b2, w3, b3, lr=0.001):
for step, (x, y) in enumerate(train_dataset):
with tf.GradientTape() as tape:
h1 = x @ w1 + tf.broadcast_to(b1, (x.shape[0], 256))
h1 = tf.nn.relu(h1)
h2 = h1 @ w2 + b2
h2 = tf.nn.relu(h2)
out = h2 @ w3 + b3
loss = tf.square(y - out)
loss = tf.reduce_mean(loss)
grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
w1.assign_sub(lr * grads[0])
b1.assign_sub(lr * grads[1])
w2.assign_sub(lr * grads[2])
b2.assign_sub(lr * grads[3])
w3.assign_sub(lr * grads[4])
b3.assign_sub(lr * grads[5])
return loss.numpy()
|
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| def train(epochs):
losses = []
train_dataset = load_data()
w1, b1, w2, b2, w3, b3 = init_paramaters()
for epoch in range(epochs):
loss = train_epoch(epoch, train_dataset, w1, b1, w2, b2, w3, b3, lr=0.001)
print('epoch:', epoch, 'loss:', loss)
losses.append(loss)
x = [i for i in range(0, epochs)]
plt.plot(x, losses, color='blue', marker='s', label='训练')
plt.xlabel('Epoch')
plt.ylabel('MSE')
plt.legend()
plt.show()
|
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 2s 0us/step
epoch: 0 loss: 0.16654462
epoch: 1 loss: 0.14800379
epoch: 2 loss: 0.13541555
epoch: 3 loss: 0.12577298
epoch: 4 loss: 0.11817748
epoch: 5 loss: 0.11203371
epoch: 6 loss: 0.1069127
epoch: 7 loss: 0.10258315
epoch: 8 loss: 0.09884895
epoch: 9 loss: 0.095569395
epoch: 10 loss: 0.092678
epoch: 11 loss: 0.09010928
epoch: 12 loss: 0.0878074
epoch: 13 loss: 0.08572935
epoch: 14 loss: 0.08384038
epoch: 15 loss: 0.0821046
epoch: 16 loss: 0.08050328
epoch: 17 loss: 0.079019025
epoch: 18 loss: 0.07763501
findfont: Font family ['STKaiti'] not found. Falling back to DejaVu Sans.
epoch: 19 loss: 0.07634819
/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 35757 missing from current font.
font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:240: RuntimeWarning: Glyph 32451 missing from current font.
font.set_text(s, 0.0, flags=flags)
/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 35757 missing from current font.
font.set_text(s, 0, flags=flags)
/Users/maqi/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:203: RuntimeWarning: Glyph 32451 missing from current font.
font.set_text(s, 0, flags=flags)
