矩阵操作
#对于2-D
#所有的reduce_...，如果不加axis的话，都是对整个矩阵进行运算
tf.reduce_sum(a, 1） #对axis1
tf.reduce_mean(a,0) #每列均值
第二个参数是axis，如果为0的话，
r
e
s
[
i
]
=
∑
j
a
[
j
,
i
]
res[i] = \sum_{j} a[j,i]
res[i]=∑ja[j,i]即（
r
e
s
[
i
]
=
∑
a
[
:
,
i
]
res[i] = \sum a[:,i]
res[i]=∑a[:,i]）， 如果是1的话，
r
e
s
[
i
]
=
∑
j
a
[
i
,
j
]
res[i] = \sum_{j} a[i,j]
res[i]=∑ja[i,j]
NOTE:返回的都是行向量,（axis等于几，就是对那维操作,i.e.:沿着那维操作, 其它维度保留）
#关于concat，可以用来进行降维 3D->2D , 2D->1D
tf.concat(concat_dim, data)
#arr = np.zeros([2,3,4,5,6])
In [6]: arr2.shape
Out[6]: (2, 3, 4, 5)
In [7]: np.concatenate(arr2, 0).shape
Out[7]: (6, 4, 5) :(2*3, 4, 5)
In [9]: np.concatenate(arr2, 1).shape
Out[9]: (3, 8, 5) :(3, 2*4, 5)
#tf.concat()
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
# 将t1, t2进行concat，axis为0，等价于将shape=[2, 2, 3]的Tensor concat成
#shape=[4, 3]的tensor。在新生成的Tensor中tensor[:2,:]代表之前的t1
#tensor[2:,:]是之前的t2
tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
# 将t1, t2进行concat，axis为1，等价于将shape=[2, 2, 3]的Tensor concat成
#shape=[2, 6]的tensor。在新生成的Tensor中tensor[:,:3]代表之前的t1
#tensor[:,3:]是之前的t2
tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
concat是将list中的向量给连接起来，axis表示将那维的数据连接起来，而其他维的结构保持不变
#squeeze 降维 维度为1的降掉
tf.squeeze(arr, [])
降维， 将维度为1 的降掉
arr = tf.Variable(tf.truncated_normal([3,4,1,6,1], stddev=0.1))
arr2 = tf.squeeze(arr, [2,4])
arr3 = tf.squeeze(arr) #降掉所以是1的维
#split
tf.split(split_dim, num_split, value, name='split')
# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(1, 3, value)
tf.shape(split0) ==> [5, 10]
#embedding
mat = np.array([1,2,3,4,5,6,7,8,9]).reshape((3,-1))
ids = [[1,2], [0,1]]
res = tf.nn.embedding_lookup(mat, ids)
res.eval()
array([[[4, 5, 6],
[7, 8, 9]],
[[1, 2, 3],
[4, 5, 6]]])
#扩展维度，如果想用广播特性的话，经常会用到这个函数
# 't' is a tensor of shape [2]
#一次扩展一维
shape(tf.expand_dims(t, 0)) ==> [1, 2]
shape(tf.expand_dims(t, 1)) ==> [2, 1]
shape(tf.expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(tf.expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(tf.expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(tf.expand_dims(t2, 3)) ==> [2, 3, 5, 1]
tf.slice()
tf.slice(input_, begin, size, name=None)
先看例子
import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()
a = np.array([[1,2,3,4,5],[4,5,6,7,8],[9,10,11,12,13]])
tf.slice(a,[1,2],[-1,2]).eval()
#array([[ 6, 7],
# [11, 12]])
理解tf.slice()最好是从返回值上去理解，现在假设input的shape是[a1, a2, a3], begin的值是[b1, b2, b3],size的值是[s1, s2, s3],那么tf.slice()返回的值就是 input[b1:b1+s1, b2:b2+s2, b3:b3+s3]。
如果
s
i
=
−
1
s_i=-1
si=−1 ，那么 返回值就是 input[b1:b1+s1,..., bi: ,...]
注意：input[1:2] 取不到input[2]
tf.stack()
tf.stack(values, axis=0, name='stack')
将 a list of R 维的Tensor堆成 R+1维的Tensor。
Given a list of length N of tensors of shape (A, B, C);
if axis == 0 then the output tensor will have the shape (N, A, B, C)
这时 res[i,:,:,:] 就是原 list中的第 i 个 tensor
. if axis == 1 then the output tensor will have the shape (A, N, B, C).
这时 res[:,i,:,:] 就是原list中的第 i 个 tensor
Etc.
# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
tf.pad
tf.pad(tensor, paddings, mode="CONSTANT", name=None)
-
tensor: 任意shape的tensor，维度Dn -
paddings:[Dn, 2]的Tensor,Padding后tensor的某维上的长度变为padding[D,0]+tensor.dim_size(D)+padding[D,1] -
mode:CONSTANT表示填0,REFLECT表示反射填充,SYMMETRIC表示对称填充。
tf.gather()
tf.gather(params, indices, validate_indices=None, name=None)
indices must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape indices.shape + params.shape[1:]
# Scalar indices, 会降维
output[:, ..., :] = params[indices, :, ... :]
# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]
# Higher rank indices，会升维
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
"""
indices = [2, 0, 3]
output[0] <-- param[2]
output[1] <-- param[0]
output[2] <-- param[3]
indices = [[1, 2], [2, 3]]
output[0, 0] <-- param[1]
output[0, 1] <-- param[2]
output[1, 0] <-- param[2]
output[1, 1] <-- param[3]
"""
tf.gather_nd
tf.gather_nd(
params, indices, name=None, batch_dims=0
)
indices = [[1, 2], [2, 3]]
output[0] <-- param[1, 2]
output[1] <-- param[2, 3] . # 注意对比与 tf.gather之间的区别
indices = [[[1, 2]],
[[2, 3]]]
output[0, 0] <-- param[1, 2]
output[1, 0] <-- param[2, 3] . # 注意对比与 tf.gather之间的区别
tf.scatter_nd
scatter_nd(
indices,
updates,
shape,
name=None
)
"""
shape: 最终结果的shape
updates: 数据源
indices: 数据源相应未知的数据放到结果的什么位置
"""
# 文档中废话那么多，可以总结成两个式子
# res[*indice[i,j,..,z], ...] = updates[i,j,..,z,...]
# len([i,j,..,z]) = indice.rank-1. [i,j,...,z]表示原始未知。*indice[i,j,..,z] 表示目标位置！
# 在这里 rank 表示 几维， a=[1,2,3], a.rank=1, b = [[1,2], [2,3], [3,4]], b.rank=2
"""
indices = [ [1, 2, 3],
[2, 3, 4]
]
意味着
updates[0] --> res[1, 2, 3]
updates[1] --> res[2, 3, 4]
indices = [ [[1, 2, 3], [7, 8, 9]],
[[2, 3, 4], [9, 10, 11]]
]
意味着:
updates[0, 0] --> res[1, 2, 3]
updates[0, 1] --> res[7, 8, 9]
updates[1, 0] --> res[2, 3, 4]
updates[1, 1] --> res[9, 10, 11]
"""
