算术操作
TensorFlow的算术操作如下：
操作 描述
tf.add(x, y, name=None) 求和
tf.sub(x, y, name=None) 减法
tf.mul(x, y, name=None) 乘法
tf.div(x, y, name=None) 除法
tf.mod(x, y, name=None) 取模
tf.abs(x, name=None) 求绝对值
tf.neg(x, name=None) 取负 (y = -x).
tf.sign(x, name=None) 返回符号 y = sign(x) = -1 if x < 0; 0 if x == 0; 1 if x > 0.
tf.inv(x, name=None) 取反
tf.square(x, name=None) 计算平方 (y = x * x = x^2).
tf.round(x, name=None) 舍入最接近的整数
# ‘a’ is [0.9, 2.5, 2.3, -4.4]
tf.round(a) ==> [ 1.0, 3.0, 2.0, -4.0 ]
tf.sqrt(x, name=None) 开根号 (y = sqrt{x} = x^{1/2}).
tf.pow(x, y, name=None) 幂次方
# tensor ‘x’ is [[2, 2], [3, 3]]
# tensor ‘y’ is [[8, 16], [2, 3]]
tf.pow(x, y) ==> [[256, 65536], [9, 27]]
tf.exp(x, name=None) 计算e的次方
tf.log(x, name=None) 计算log，一个输入计算e的ln，两输入以第二输入为底
tf.maximum(x, y, name=None) 返回最大值 (x > y ? x : y)
tf.minimum(x, y, name=None) 返回最小值 (x < y ? x : y)
tf.cos(x, name=None) 三角函数cosine
tf.sin(x, name=None) 三角函数sine
tf.tan(x, name=None) 三角函数tan
tf.atan(x, name=None) 三角函数ctan
张量转换
数据类型转换Casting
操作 描述
tf.string_to_number
(string_tensor, out_type=None, name=None) 字符串转为数字
tf.to_double(x, name=’ToDouble’) 转为64位浮点类型–float64
tf.to_float(x, name=’ToFloat’) 转为32位浮点类型–float32
tf.to_int32(x, name=’ToInt32’) 转为32位整型–int32
tf.to_int64(x, name=’ToInt64’) 转为64位整型–int64
tf.cast(x, dtype, name=None) 将x或者x.values转换为dtype
# tensor a is [1.8, 2.2], dtype=tf.float
tf.cast(a, tf.int32) ==> [1, 2] # dtype=tf.int32
形状操作Shapes and Shaping
操作 描述
tf.shape(input, name=None) 返回数据的shape
tf.size(input, name=None) 返回数据的元素数量
# ‘t’ is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12
tf.rank(input, name=None) 返回tensor的rank
注意：此rank不同于矩阵的rank，
tensor的rank表示一个tensor需要的索引数目来
唯一表示任何一个元素也就是通常
所说的 “order”, “degree”或”ndims”
#’t’ is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
# shape of tensor ‘t’ is [2, 2, 3]
rank(t) ==> 3
tf.reshape(tensor, shape, name=None) 改变tensor的形状
tf.expand_dims(input, dim, name=None) 插入维度1进入一个tensor中
#该操作要求-1-input.dims()
# ‘t’ is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
切片与合并（Slicing and Joining）
操作 描述
tf.slice(input_, begin, size, name=None) 对tensor进行切片操作
其中size[i] = input.dim_size(i) - begin[i]
input is [[[1, 1, 1], [2, 2, 2]],[[3, 3, 3], [4, 4, 4]],
[[5, 5, 5], [6, 6, 6]]]
tf.slice(input, [1, 0, 0], [1, 1, 3]) ==> [[[3, 3, 3]]]
tf.slice(input, [1, 0, 0], [1, 2, 3]) ==>
[[[3, 3, 3],
[4, 4, 4]]]
tf.slice(input, [1, 0, 0], [2, 1, 3]) ==>
[[[3, 3, 3]],
[[5, 5, 5]]]
tf.split(split_dim, num_split, value, name=’split’) 沿着某一维度将tensor分离为num_split tensors
# ‘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]
tf.concat(concat_dim, values, name=’concat’) 沿着某一维度连结tensor
t1 = [[1, 2, 3], [4, 5, 6]]
t2 = [[7, 8, 9], [10, 11, 12]]
tf.concat(0, [t1, t2]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tf.concat(1, [t1, t2]) ==> [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
如果想沿着tensor一新轴连结打包,那么可以：
tf.concat(axis, [tf.expand_dims(t, axis) for t in tensors])
tf.pack(tensors, axis=axis) 等同于tf.concat(concat_dim, values, name=’concat’)
tf.reverse(tensor, dims, name=None) 沿着某维度进行序列反转
其中dim为列表，元素为bool型，
size等于rank(tensor)，tensor ‘t’ is
[[[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]]]
tensor ‘t’ shape is [1, 2, 3, 4]
‘dims’ is [False, False, False, True]
reverse(t, dims) ==>
[[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
tf.transpose(a, perm=None, name=’transpose’) 调换tensor的维度顺序
按照列表perm的维度排列调换tensor顺序，
如为定义，则perm为(n-1…0)
‘x’ is [[1 2 3],[4 5 6]]
tf.transpose(x) ==> [[1 4], [2 5],[3 6]]
Equivalently
tf.transpose(x, perm=[1, 0]) ==> [[1 4],[2 5], [3 6]]
矩阵相关运算
操作 描述
tf.diag(diagonal, name=None) 返回一个给定对角值的对角tensor
‘diagonal’ is [1, 2, 3, 4]
tf.diag(diagonal) ==>
[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
tf.diag_part(input, name=None) 功能与上面相反
tf.trace(x, name=None) 求一个2维tensor足迹，即对角值diagonal之和
tf.transpose(a, perm=None, name=’transpose’) 调换tensor的维度顺序
tf.matmul(a, b, transpose_a=False, transpose_b=False, 矩阵相乘
a_is_sparse=False, b_is_sparse=False, name=None)
tf.matrix_inverse(input, adjoint=None, name=None) 求方阵的逆矩阵
归约计算
操作 描述
tf.reduce_sum(input_tensor, reduction_indices=None 计算输入tensor按照reduction_indices指定的轴进行求和
‘x’ is [[1, 1, 1],[1, 1, 1]]
tf.reduce_sum(x) ==> 6
tf.reduce_sum(x, 0) ==> [2, 2, 2]
tf.reduce_sum(x, 1) ==> [3, 3]
tf.reduce_sum(x, 1, keep_dims=True) ==> [[3], [3]]
tf.reduce_sum(x, [0, 1]) ==> 6
tf.reduce_min(input_tensor, reduction_indices=None) 求tensor中最小值
tf.reduce_max(input_tensor, reduction_indices=None) 求tensor中最大值
tf.reduce_mean(input_tensor, reduction_indices=None) 求tensor中平均值
tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None) 求累积和
tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c]
tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b]
tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c]
tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0]