tensorflow学习笔记（二）：tensor 变换
tensorflow学习笔记（二）：tensor 变换
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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，resi=∑jaj,iresi=∑jaj,iresi = \sum_{j} aj,i即（resi=∑a:,iresi=∑a:,iresi = \sum a:,i）， 如果是1的话，resi=∑jai,jresi=∑jai,jresi = \sum_{j} ai,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 si=−1si=−1s_i=-1 ，那么 返回值就是 input[b1:b1+s1,..., bi: ,...]
注意：input1:2 取不到input2
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 tensorEtc.
# '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.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], :, ..., :]tf.pad
tf.pad(tensor, paddings, mode="CONSTANT", name=None)tensor: 任意shape的tensor，维度Dnpaddings:[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.scatter_nd
scatter_nd(
indices,
updates,
shape,
name=None
)
# 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-2
# 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