深度学习之tensorflow框架（下）

 def tensor_demo():
     """
     张量的演示
     :return:
     """
     tensor1 = tf.constant(4.0)
     tensor2 = tf.constant([1, 2, 3, 4])
     linear_squares = tf.constant([[4], [9], [16], [25]], dtype=tf.int32)
     print("tensor1:\n", tensor1)
     print("tensor2:\n", tensor2)
     print("linear_squares:\n", linear_squares)

     # 生成常用张量
     tensor3 = tf.zeros(shape=(3, 4))
     print("tensor3:\n", tensor3)
     tensor4 = tf.ones(shape=(2, 3, 4))
     print("tensor4:\n", tensor4)
     tensor5 = tf.random_normal(shape=(2, 3), mean=1.75, stddev=0.2)
     print("tensor5:\n", tensor5)

     with tf.compat.v1.Session() as sess:
         print("tensor3_value:\n", tensor3.eval())
         print("tensor4_value:\n", tensor4.eval())
         print("tensor4_value:\n", tensor5.eval())

     return None

 def tensoredit_demo():
     """
     张量类型的修改
     :return:
     """
     linear_squares = tf.constant([[4], [9], [16], [25]], dtype=tf.int32)
     print("linear_squares_before:\n", linear_squares)

     l_cast = tf.cast(linear_squares, dtype=tf.float32)
     print("linear_squares_after:\n", linear_squares)
     print("l_cast:\n", l_cast)
     return None

 def editstaticshape_demo():
     """
     更新/改变静态形状
     :return:
     """
     a = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, None])
     b = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, 10])
     c = tf.compat.v1.placeholder(dtype=tf.float32, shape=[3, 2])
     print("a:\n", a)
     print("b:\n", b)
     print("c:\n", c)

     # 更新形状未确定的部分
     a.set_shape([2, 3])
     b.set_shape([2, 10])
     print("a:\n", a)
     print("b:\n", b)

     return None;

 def editshape_demo():
     """
     更新/改变动态形状
     不会改变原始的tensor
     返回新的改变类型后的tensor
     :return:
     """
     a = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, None])
     print("a:\n", a)
     a.set_shape([2, 3])
     print("a_setShape:\n", a)
     # 元素个数没有变，还是2*3*1=6个
     a_reshape = tf.reshape(a,shape=[2,3,1])
     print("a_reshape:\n", a_reshape)
     print("a:\n", a)

     return None;

 def variable_demo():
     """
     变量的演示
     变量需要显式初始化，才能运行值
     :return:
     """
     # 创建变量
     # 使用命名空间可以使图的结构更加清晰
     with tf.variable_scope("myscope"):
         a = tf.Variable(initial_value=50)
         b = tf.Variable(initial_value=40)
     with tf.variable_scope("yourscope"):
         c= tf.add(a,b)
     print("a:\n",a)
     print("b:\n",b)
     print("c:\n",c)

     # 初始化变量
     init = tf.global_variables_initializer()

     # 开启会话
     with tf.Session() as sess:
         sess.run(init)
         a_value,b_value,c_value=sess.run([a,b,c])
         print("a_value:\n",a_value)
         print("b_value:\n",b_value)
         print("c_value:\n",c_value)

     return None