1.tf,floor
tf.floor(
x,
name=None
)
将x向下取整
2.tf.ceil
tf.ceil(
x,
name=None
)
将x向上取整
3.tf.stack
tf.stack(
values,
axis=0,
name='stack'
)
将values按照axis轴进行合并,例如:
x = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z]) # [[1, 4], [2, 5], [3, 6]] (Pack along first dim.)
tf.stack([x, y, z], axis=1) # [[1, 2, 3], [4, 5, 6]]
4.tf.cast
tf.cast(
x,
dtype,
name=None
)
将x转为dtype类型,例如
x = tf.constant([1.8, 2.2], dtype=tf.float32)
tf.cast(x, tf.int32) # [1, 2], dtype=tf.int32
5.tf.pad
tf.pad(
tensor,
paddings,
mode='CONSTANT',
name=None,
constant_values=0
)
将tensor按照padding进行边缘扩充,例如
t = tf.constant([[1, 2, 3], [4, 5, 6]])
paddings = tf.constant([[1, 1,], [2, 2]])
# 'constant_values' is 0.
# rank of 't' is 2.
tf.pad(t, paddings, "CONSTANT") # [[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 1, 2, 3, 0, 0],
# [0, 0, 4, 5, 6, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]
tf.pad(t, paddings, "REFLECT") # [[6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1],
# [6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1]]
tf.pad(t, paddings, "SYMMETRIC") # [[2, 1, 1, 2, 3, 3, 2],
# [2, 1, 1, 2, 3, 3, 2],
# [5, 4, 4, 5, 6, 6, 5],
# [5, 4, 4, 5, 6, 6, 5]]
6.tf.reduce_max
tf.reduce_max(
input_tensor,
axis=None,
keepdims=None,
name=None,
reduction_indices=None,
keep_dims=None
)
根据第
axis维度进行比较大小,例如
a = tf.constant([1,2,3,4,5,6,7,8,9]) a = tf.reshape(a,(3,3)) b = tf.reduce_max(a,0)#,keep_dims=True) c = tf.reduce_max(a,0,keep_dims=True) sess = tf.Session() print(sess.run(a)) print(sess.run(b)) print(sess.run(c))[[1 2 3]
[4 5 6]
[7 8 9]]
[7 8 9]
[[7 8 9]]