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『TensorFlow』函数查询列表_张量属性调整

2024-09-25 18:51:39   221人阅读

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
# ‘t’ is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]
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
# tensor ‘t’ is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor ‘t’ has shape [9]
reshape(t, [3, 3]) ==>
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
#如果shape有元素[-1],表示在该维度打平至一维
# -1 将自动推导得为 9:
reshape(t, [2, -1]) ==>
[[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
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] <= dim <= input.dims()

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]
该操作要求 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n]
#’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])
等同于tf.pack(tensors, axis=axis)
tf.pack(values, axis=0, name=’pack’) 将一系列rank-R的tensor打包为一个rank-(R+1)的tensor
# ‘x’ is [1, 4], ‘y’ is [2, 5], ‘z’ is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]
# 沿着第一维pack
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
等价于tf.pack([x, y, z]) = np.asarray([x, y, z])
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]]
tf.gather(params, indices, validate_indices=None, name=None) 合并索引indices所指示params中的切片
技术分享
tf.one_hot
(indices, depth, on_value=http://www.mamicode.com/None, off_value=None,
axis=None, dtype=None, name=None)		 indices = [0, 2, -1, 1]
depth = 3
on_value = http://www.mamicode.com/5.0
off_value = http://www.mamicode.com/0.0
axis = -1
#Then output is [4 x 3]:
output =
[5.0 0.0 0.0] // one_hot(0)
[0.0 0.0 5.0] // one_hot(2)
[0.0 0.0 0.0] // one_hot(-1)
[0.0 5.0 0.0] // one_hot(1)

分割(Segmentation)

 

操作描述
tf.segment_sum(data, segment_ids, name=None) 根据segment_ids的分段计算各个片段的和
其中segment_ids为一个size与data第一维相同的tensor
其中id为int型数据，最大id不大于size
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
tf.segment_sum(c, tf.constant([0, 0, 1]))
==>[[0 0 0 0]
[5 6 7 8]]
上面例子分为[0,1]两id,对相同id的data相应数据进行求和,
并放入结果的相应id中，
且segment_ids只升不降
tf.segment_prod(data, segment_ids, name=None) 根据segment_ids的分段计算各个片段的积
tf.segment_min(data, segment_ids, name=None) 根据segment_ids的分段计算各个片段的最小值
tf.segment_max(data, segment_ids, name=None) 根据segment_ids的分段计算各个片段的最大值
tf.segment_mean(data, segment_ids, name=None) 根据segment_ids的分段计算各个片段的平均值
tf.unsorted_segment_sum(data, segment_ids,
num_segments, name=None)		 与tf.segment_sum函数类似，
不同在于segment_ids中id顺序可以是无序的
tf.sparse_segment_sum(data, indices,
segment_ids, name=None)		 输入进行稀疏分割求和
c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
# Select two rows, one segment.
tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
==> [[0 0 0 0]]
对原data的indices为[0,1]位置的进行分割，
并按照segment_ids的分组进行求和

『TensorFlow』函数查询列表_张量属性调整


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