tensorflow2.0---笔记2 tensor高阶操作

tensor的合并与分割

合并

tf.concat([a, b], axis = )
注：
（1）不创建新的维度；
（2）在哪个维度合并，哪个维度数量增加；其他维度必须相同。

a = tf.ones([4, 35, 8])
b = tf.ones([2, 35, 8])
c = tf.ones([4, 35, 8])

d = tf.concat([a, b], axis=0)
print(d.shape)  # (6, 35, 8)

e = tf.concat([a, c], axis=-1)
print(e.shape)  # (4, 35, 16)

tf.stack([a, b], axis = )
注意，会创建新的维度!

a = tf.ones([4, 35, 8])
b = tf.ones([4, 35, 8])

c = tf.stack([a, b], axis=0)
print(c.shape)

d = tf.stack([a, b], axis=-1)
print(d.shape)  # (4, 35, 8, 2)

a、b的原始维度必须完全相同，否则报错：

a = tf.ones([4, 35, 8])
b = tf.ones([2, 35, 8])

c = tf.stack([a, b], axis=0)
print(c.shape)
#  ensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes of all inputs must match: values[0].shape = [4,35,8] != values[1].shape = [2,35,8] [Op:Pack] name: stack

分割

tf.unstack(a, axis = )
注意，返回一个list，list里面的内容是tensor

a = tf.ones([4, 35, 8])
b = tf.ones([4, 35, 8])
c = tf.stack([a, b], axis=0)
print(c.shape)  # (2, 4, 35, 8)

d = tf.unstack(c, axis=0)
print(type(d))  # 
print(d[0].shape)  # (4, 35, 8)

e = tf.unstack(c, axis=-1)
print(len(e)) # 8
print(e[0].shape)  # (2, 4, 35)   范围e[0]~e[7]

tf.split(a, axis = , num_or_size_splits = )

a = tf.ones([4, 35, 8])
b = tf.ones([4, 35, 8])
c = tf.stack([a, b], axis=0)
print(c.shape)  # (2, 4, 35, 8)

d = tf.split(c, axis=3, num_or_size_splits=2)  # 分成两份
print(type(d))  # 
print(len(d))  # 2
print(d[0].shape)  # (2, 4, 35, 4)

e = tf.split(c, axis=3, num_or_size_splits=[2, 2, 4])
print(e[0].shape)  # (2, 4, 35, 2)
print(e[2].shape)  # (2, 4, 35, 4)

数据统计

向量的范数：

tf.norm (范数)

a = tf.ones([2, 2])
print(tf.norm(a)) # tf.Tensor(2.0, shape=(), dtype=float32)  
# 不指定几范数和轴，默认二范数
print(tf.sqrt(tf.reduce_sum(tf.square(a))))