tensorflow【1】-Tensor

tensor tensor i.e., tf is the core data structure, which may be a scalar, vector, matrix, multi-dimensional arrays

 

Attributes

Data Format

tf.string, tf.float32, tf.int16, tf.int32, tf.complex64 (complex), tf.bool like

 

Shape data

1. The shape is a shape Tensor properties may be obtained directly, without session

2. The shape is not necessarily determined at compile time, it can be inferred by running

 

Order: Tensor dimension

Of the order of acquisition requires session

 

Examples

tf.ones = D1 ([. 3, 2 ])
 Print (d1.shape)          # (. 3, 2) Shape may be obtained directly, without the session 
N1 = tf.rank (D1)
 Print (N1)                # the Tensor ( "Rank: 0 ", shape = (), dtype = int32) not directly acquire the order requires the session 

D2 = tf.constant ([[[. 1,. 1,. 1], [2, 2, 2]], [[. 3,. 3, . 3], [. 4,. 4,. 4 ]]])
 Print (D2)            # the Tensor ( "Const: 0", Shape = (2, 2,. 3), DTYPE = Int32) 
N2 = tf.rank (D2) 

with TF the .session () AS sess: 
    Print (sess.run (N1))      # 2 tensor 
    Print (sess.run (n2))      # 3 rank tensor

 

tf There are several more special tensor

tf.constant constant

tf.Variable variable

tf.placeholder placeholder

 

constant

Note that:

1. The different types of operation is not constant

2. constant variables like the python as a direct assignment

def constant(value, dtype=None, shape=None, name="Const")

 

Examples

# ## single element 
D1 tf.constant = (. 1 ) 
D2 = tf.constant (2, DTYPE = tf.int32, name = ' int ' ) 
D3 = tf.constant (. 3., DTYPE = tf.float32, name = ' a float ' ) 
D4 = tf.add (D1, D2)
 # d5 of D1 + D3 = ### different types of operational data can not 
d6 = D1 + D2 

sess1 = tf.Session ()
 Print (sess1.run (D4))         # 3 
# Print (sess1.run (D5)) ### float32 does not match error of the type of the type Int32 
Print (sess1.run (d6))         # 3 
Print(type(d6))             # <class 'tensorflow.python.framework.ops.Tensor'>


### 矩阵
d1 = tf.constant([[1., 2.]])
d2 = tf.constant([[2.], [3.]])
d3 = tf.matmul(d1, d2)

## 常数赋值
d2 = d1

sess2 = tf.Session()
print(sess2.run(d3))        # [[8.]]
print(sess2.run(d2))        # [[1. 2.]]

 

 

 

References: