来源:tensorflow学习笔记(二):tensor 变换 矩阵操作

#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,res[i]=∑ja[j,i]即(res[i]=∑a[:,i]), 如果是1的话,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)

#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=−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.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], :, ..., :]