官术网_书友最值得收藏!

Constants

The constant valued tensors are created using the tf.constant() function that has the following signature:

tf.constant(
value,
dtype=None,
shape=None,
name='Const',
verify_shape=False
)

Let's look at the example code provided in the Jupyter Notebook with this book:

c1=tf.constant(5,name='x')
c2=tf.constant(6.0,name='y')
c3=tf.constant(7.0,tf.float32,name='z')

Let's look into the code in detail:

  • The first line defines a constant tensor c1, gives it value 5, and names it x.  
  • The second line defines a constant tensor c2, stores value 6.0, and names it y.
  • When we print these tensors, we see that the data types of c1 and c2 are automatically deduced by TensorFlow.
  • To specifically define a data type, we can use the dtype parameter or place the data type as the second argument. In the preceding code example, we define the data type as tf.float32 for c3.

Let's print the constants c1, c2, and c3:

print('c1 (x): ',c1)
print('c2 (y): ',c2)
print('c3 (z): ',c3)

When we print these constants, we get the following output:

c1 (x):  Tensor("x:0", shape=(), dtype=int32)
c2 (y): Tensor("y:0", shape=(), dtype=float32)
c3 (z): Tensor("z:0", shape=(), dtype=float32)

In order to print the values of these constants, we have to execute them in a TensorFlow session with the tfs.run() command:

print('run([c1,c2,c3]) : ',tfs.run([c1,c2,c3]))

We see the following output:

run([c1,c2,c3]) :  [5, 6.0, 7.0]
主站蜘蛛池模板: 静宁县| 涞水县| 收藏| 镇江市| 安国市| 化州市| 福安市| 边坝县| 长丰县| 平南县| 资讯 | 新竹市| 娱乐| 通化县| 古浪县| 武宁县| 长葛市| 轮台县| 吴川市| 屏山县| 南皮县| 桂东县| 深水埗区| 湖州市| 雅江县| 蓝田县| 松阳县| 宝应县| 东乌珠穆沁旗| 大兴区| 梁河县| 池州市| 钟祥市| 荆州市| 龙山县| 广水市| 丹江口市| 临澧县| 尉氏县| 泰和县| 延寿县|