Tensorflow 2.0 基础API

1.导入

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import os
import sklearn
import sys
import time
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl,np,pd,sklearn,tf,keras:
    print(module.__name__, module.__version__)

2. @tf.constant

    t = tf.constant([[1.,2.,3.],[4.,5.,6.]])
    # index
    print(t)
    print(t[:,1:])
    print(t[..., 1])
    print(t+10)
    print(tf.square(t))
    print(t @ tf.transpose(t))
    
    # numpy conversion
    print(t.numpy())
    print(np.square(t))
    np_t = np.array([[1.,2.,3.],[4.,5.,6.]])
    print(tf.constant(np_t))
    
    # Scalars 0维
    t = tf.constant(2.781)
    print(t.numpy())
    print(t.shape)
    
    # string
    t = tf.constant("cafe")
    print(t)
    print(tf.strings.length(t))
    print(tf.strings.length(t,unit="UTF8_CHAR"))
    print(tf.strings.unicode_decode(t,"UTF-8"))
    
    # string array
    t = tf.constant(["cafe","coffee","咖啡"])
    print(tf.strings.length(t,unit = "UTF8_CHAR"))
    r = tf.strings.unicode_decode(t,"UTF-8")
    print(r) 
    
    # RaggedTensor是不完整的n维矩阵
    # ragged tensor
    r = tf.ragged.constant([[11,12],[21,22,32],[],[41]])
    #op
    print(r)
    print(r[1])
    print(r[1:2])
    
    r2 = tf.ragged.constant([[51,52],[],[71]])
    print(tf.concat([r,r2],axis = 0))
    
    r3 = tf.ragged.constant([[13,14],[21,32,43],[],[33]])
    print(tf.concat([r,r3],axis = 1))
    
    
    #raged tensor->tensor
    # 0在正向值后边
    print(r.to_tensor())  
    
    # sparse tensor  ：indices必须排好序,否则调用不了to_dense
    # 0随意位置（稀疏矩阵）
    s = tf.SparseTensor(indices = [[0,1],[1,0],[2,3]],
                       values = [1.,2.,3.],
                       dense_shape=[3,4])
    print(s)
    print(tf.sparse.to_dense(s))
    
    
    
    s2 = s*2.0
    print(s2)
    
    try:
        s3 = s+1
    except TypeError as ex:
        print(ex)
        
    s4 = tf.constant([[10.,20,],
                     [30.,40],
                     [50.,60],
                     [70.,80]])
    print(tf.sparse.sparse_dense_matmul(s,s4))
    
    # sparse tensor
    # 不排序
    s5 = tf.SparseTensor(indices = [[0,2],[0,1],[2,3]],
                       values = [1.,2.,3.],
                       dense_shape=[3,4])
    print(s5)
    s6 = tf.sparse.reorder(s5)
    print(tf.sparse.to_dense(s6))
    
    
    
    # Variables
    v = tf.Variable([[1.,2.,3.],[4.,5.,6.]])
    print(v)
    print(v.value())
    print(v.numpy())
    
    
    # assign value   可对变量重新赋值
    v.assign(2*v)
    print(v.numpy())
    v[0,1].assign(42)
    print(v.numpy())
    v[1]..assign([7.,8.,9.])
    print(v.numpy())
    
    try:
        v[1]=[7.,8.,9.]
    except TypeError as ex:
        print(ex)