tensorflow学习笔记（二）：tensor 变换

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axis1tf.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->1Dtf.concat(concat_dim, data)#arr = np.zeros([2,3,4,5,6])In [6]: arr2.shapeOut[6]: (2, 3, 4, 5)In [7]: np.concatenate(arr2, 0).shapeOut[7]: (6, 4, 5)   :(2*3, 4, 5)In [9]: np.concatenate(arr2, 1).shapeOut[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t2tf.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t2tf.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#splittf.split(split_dim, num_split, value, name='split')# 'value' is a tensor with shape [5, 30]# Split 'value' into 3 tensors along dimension 1split0, split1, split2 = tf.split(1, 3, value)tf.shape(split0) ==> [5, 10]#embeddingmat = 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 tfimport numpy as npsess = 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 indicesoutput[i, :, ..., :] = params[indices[i], :, ... :]# Higher rank indices，会升维output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]