# 算術(shù)操作符：+ - * / % 
tf.add(x, y, name=None)  # 加法(支持 broadcasting)
tf.subtract(x, y, name=None) # 減法
tf.multiply(x, y, name=None) # 乘法
tf.divide(x, y, name=None)  # 浮點(diǎn)除法, 返回浮點(diǎn)數(shù)(python3 除法)
tf.mod(x, y, name=None)  # 取余
 
# 冪指對數(shù)操作符：^ ^2 ^0.5 e^ ln 
tf.pow(x, y, name=None)  # 冪次方
tf.square(x, name=None)  # 平方
tf.sqrt(x, name=None)   # 開根號，必須傳入浮點(diǎn)數(shù)或復(fù)數(shù)
tf.exp(x, name=None)   # 計算 e 的次方
tf.log(x, name=None)   # 以 e 為底，必須傳入浮點(diǎn)數(shù)或復(fù)數(shù)
 
# 取符號、負(fù)、倒數(shù)、絕對值、近似、兩數(shù)中較大/小的
tf.negative(x, name=None)  # 取負(fù)(y = -x).
tf.sign(x, name=None)   # 返回 x 的符號
tf.reciprocal(x, name=None) # 取倒數(shù)
tf.abs(x, name=None)   # 求絕對值
tf.round(x, name=None)   # 四舍五入
tf.ceil(x, name=None)   # 向上取整
tf.floor(x, name=None)   # 向下取整
tf.rint(x, name=None)   # 取最接近的整數(shù) 
tf.maximum(x, y, name=None) # 返回兩tensor中的最大值 (x > y ? x : y)
tf.minimum(x, y, name=None) # 返回兩tensor中的最小值 (x < y ? x : y)
 
# 三角函數(shù)和反三角函數(shù)
tf.cos(x, name=None) 
tf.sin(x, name=None) 
tf.tan(x, name=None) 
tf.acos(x, name=None)
tf.asin(x, name=None)
tf.atan(x, name=None) 
 
# 其它
tf.div(x, y, name=None) # python 2.7 除法, x/y-->int or x/float(y)-->float
tf.truediv(x, y, name=None) # python 3 除法, x/y-->float
tf.floordiv(x, y, name=None) # python 3 除法, x//y-->int
tf.realdiv(x, y, name=None)
tf.truncatediv(x, y, name=None)
tf.floor_div(x, y, name=None)
tf.truncatemod(x, y, name=None)
tf.floormod(x, y, name=None)
tf.cross(x, y, name=None)
tf.add_n(inputs, name=None) # inputs: A list of Tensor objects, each with same shape and type
tf.squared_difference(x, y, name=None) 

# 矩陣乘法(tensors of rank >= 2)
tf.matmul(a, b, transpose_a=False, transpose_b=False, adjoint_a=False, adjoint_b=False, a_is_sparse=False, b_is_sparse=False, name=None)
 
# 轉(zhuǎn)置，可以通過指定 perm=[1, 0] 來進(jìn)行軸變換
tf.transpose(a, perm=None, name='transpose')
 
# 在張量 a 的最后兩個維度上進(jìn)行轉(zhuǎn)置
tf.matrix_transpose(a, name='matrix_transpose')
# Matrix with two batch dimensions, x.shape is [1, 2, 3, 4]
# tf.matrix_transpose(x) is shape [1, 2, 4, 3]
 
# 求矩陣的跡
tf.trace(x, name=None)

# 計算方陣行列式的值
tf.matrix_determinant(input, name=None)

# 求解可逆方陣的逆，input 必須為浮點(diǎn)型或復(fù)數(shù)
tf.matrix_inverse(input, adjoint=None, name=None)

# 奇異值分解
tf.svd(tensor, full_matrices=False, compute_uv=True, name=None)
 
# QR 分解
tf.qr(input, full_matrices=None, name=None)
 
# 求張量的范數(shù)(默認(rèn)2)
tf.norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None)
 
# 構(gòu)建一個單位矩陣, 或者 batch 個矩陣，batch_shape 以 list 的形式傳入
tf.eye(num_rows, num_columns=None, batch_shape=None, dtype=tf.float32, name=None)
# Construct one identity matrix.
tf.eye(2)
==> [[1., 0.],
  [0., 1.]]
 
# Construct a batch of 3 identity matricies, each 2 x 2.
# batch_identity[i, :, :] is a 2 x 2 identity matrix, i = 0, 1, 2.
batch_identity = tf.eye(2, batch_shape=[3])
 
# Construct one 2 x 3 "identity" matrix
tf.eye(2, num_columns=3)
==> [[ 1., 0., 0.],
  [ 0., 1., 0.]]
 
# 構(gòu)建一個對角矩陣，rank = 2*rank(diagonal)
tf.diag(diagonal, name=None)
# '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]]

# 其它
tf.diag_part
tf.matrix_diag
tf.matrix_diag_part
tf.matrix_band_part
tf.matrix_set_diag
tf.cholesky
tf.cholesky_solve
tf.matrix_solve
tf.matrix_triangular_solve
tf.matrix_solve_ls
tf.self_adjoint_eig
tf.self_adjoint_eigvals

# 計算輸入 tensor 所有元素的和，或者計算指定的軸所有元素的和
tf.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None)
# '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]] # 維度不縮減
tf.reduce_sum(x, [0, 1]) ==> 6
 
# 計算輸入 tensor 所有元素的均值/最大值/最小值/積/邏輯與/或
# 或者計算指定的軸所有元素的均值/最大值/最小值/積/邏輯與/或(just like reduce_sum)
tf.reduce_mean(input_tensor, axis=None, keep_dims=False, name=None)
tf.reduce_max(input_tensor, axis=None, keep_dims=False, name=None)
tf.reduce_min(input_tensor, axis=None, keep_dims=False, name=None)
tf.reduce_prod(input_tensor, axis=None, keep_dims=False, name=None)
tf.reduce_all(input_tensor, axis=None, keep_dims=False, name=None) # 全部滿足條件
tf.reduce_any(input_tensor, axis=None, keep_dims=False, name=None) #至少有一個滿足條件

-------------------------------------------
# 分界線以上和 Numpy 中相應(yīng)的用法完全一致
-------------------------------------------
 
# inputs 為一 list, 計算 list 中所有元素的累計和，
# tf.add(x, y， name=None)只能計算兩個元素的和，此函數(shù)相當(dāng)于擴(kuò)展了其功能
tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None)

 
# Computes log(sum(exp(elements across dimensions of a tensor)))
tf.reduce_logsumexp(input_tensor, axis=None, keep_dims=False, name=None)
 
# Computes number of nonzero elements across dimensions of a tensor
tf.count_nonzero(input_tensor, axis=None, keep_dims=False, name=None)

# Compute the cumulative sum of the tensor x along axis
tf.cumsum(x, axis=0, exclusive=False, reverse=False, name=None)
# Eg:
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]
 
# Compute the cumulative product of the tensor x along axis
tf.cumprod(x, axis=0, exclusive=False, reverse=False, name=None)

# Computes the sum/mean/max/min/prod along segments of a tensor
tf.segment_sum(data, segment_ids, name=None)
# Eg:
m = tf.constant([5,1,7,2,3,4,1,3])
s_id = [0,0,0,1,2,2,3,3]
s.run(tf.segment_sum(m, segment_ids=s_id))
>array([13, 2, 7, 4], dtype=int32)
 
tf.segment_mean(data, segment_ids, name=None)
tf.segment_max(data, segment_ids, name=None)
tf.segment_min(data, segment_ids, name=None)
tf.segment_prod(data, segment_ids, name=None)
 
# 其它
tf.unsorted_segment_sum
tf.sparse_segment_sum
tf.sparse_segment_mean
tf.sparse_segment_sqrt_n

# 比較兩個 list 或者 string 的不同，并返回不同的值和索引
tf.setdiff1d(x, y, index_dtype=tf.int32, name=None) 
 
# 返回 x 中的唯一值所組成的tensor 和原 tensor 中元素在現(xiàn) tensor 中的索引
tf.unique(x, out_idx=None, name=None)
 
# x if condition else y, condition 為 bool 類型的，可用tf.equal()等來表示
# x 和 y 的形狀和數(shù)據(jù)類型必須一致
tf.where(condition, x=None, y=None, name=None) 
 
# 返回沿著坐標(biāo)軸方向的最大/最小值的索引
tf.argmax(input, axis=None, name=None, output_type=tf.int64)
tf.argmin(input, axis=None, name=None, output_type=tf.int64)
 
# x 的值當(dāng)作 y 的索引，range(len(x)) 索引當(dāng)作 y 的值
# y[x[i]] = i for i in [0, 1, ..., len(x) - 1]
tf.invert_permutation(x, name=None)
 
# 其它
tf.edit_distance