tf.split (API r1.3)

tf.split (API r1.3)

https://github.com/tensorflow/docs/blob/r1.3/site/en/api_docs/api_docs/python/tf/split.md
site/en/api_docs/api_docs/python/tf/split.md

split(
    value,
    num_or_size_splits,
    axis=0,
    num=None,
    name='split'
)

Defined in tensorflow/python/ops/array_ops.py.
See the guide: Tensor Transformations > Slicing and Joining

Splits a tensor into sub tensors.
将张量分割成子张量。

If num_or_size_splits is an integer type, num_split, then splits value along dimension axis into num_split smaller tensors. Requires that num_split evenly divides value.shape[axis].
如果 num_or_size_splits 是整数类型 (num_split)，则沿维度 axis 分割 value 成为 num_split 更小的张量。要求 num_split 均匀分配 value.shape[axis]。

If num_or_size_splits is not an integer type, it is presumed to be a Tensor size_splits, then splits value into len(size_splits) pieces. The shape of the i-th piece has the same size as the value except along dimension axis where the size is size_splits[i].
如果 num_or_size_splits 不是整数类型，则它被认为是一个张量 size_splits，然后将 value 分割成 len(size_splits) 块。第 i 部分的形状与 value 的大小相同，除了沿维度 axis 之外的大小 size_splits[i]。

For example:

# 'value' is a tensor with shape [5, 30]
# Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1
split0, split1, split2 = tf.split(value, [4, 15, 11], 1)
tf.shape(split0)  # [5, 4]
tf.shape(split1)  # [5, 15]
tf.shape(split2)  # [5, 11]
# Split 'value' into 3 tensors along dimension 1
split0, split1, split2 = tf.split(value, num_or_size_splits=3, axis=1)
tf.shape(split0)  # [5, 10]

1. Args

• value: The Tensor to split.
• num_or_size_splits: Either a 0-D integer Tensor indicating the number of splits along split_dim or a 1-D integer Tensor integer tensor containing the sizes of each output tensor along split_dim. If a scalar then it must evenly divide value.shape[axis]; otherwise the sum of sizes along the split dimension must match that of the value. (指示沿 split_dim 分割数量为 0-D 整数 Tensor 或包含沿 split_dim 每个输出张量大小的 1-D 整数 Tensor。如果为一个标量，那么它必须均匀分割 value.shape[axis]，否则沿分割维度的大小总和必须与该 value 相匹配。)
• axis: A 0-D int32 Tensor. The dimension along which to split. Must be in the range [-rank(value), rank(value)). Defaults to 0. (表示分割的尺寸，必须在 [-rank(value), rank(value)) 范围内，默认为 0。)
• num: Optional, used to specify the number of outputs when it cannot be inferred from the shape of size_splits. (可选的，用于指定无法从 size_splits 的形状推断出的输出数目。)
• name: A name for the operation (optional). (操作的名称 (可选)。)

如果 num_or_size_splits 传入的是一个整数，直接在 axis 维度上把张量平均切分成几个小张量。
如果 num_or_size_splits 传入的是一个向量 (向量各个元素的和要与原本这个维度的数值相等) 就根据这个向量有几个元素分为几项。

2. Returns

if num_or_size_splits is a scalar returns num_or_size_splits Tensor objects; if num_or_size_splits is a 1-D Tensor returns num_or_size_splits.get_shape[0] Tensor objects resulting from splitting value.
如果 num_or_size_splits 是标量，返回 num_or_size_splits Tensor对象。如果 num_or_size_splits 是一维张量，则返回由 value 分割产生的 num_or_size_splits.get_shape[0] Tensor 对象。

3. Raises

• ValueError: If num is unspecified and cannot be inferred. (如果 num 没有指定并且无法推断。)

4. Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys
import numpy as np
import tensorflow as tf

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
current_directory = os.path.dirname(os.path.abspath(__file__))

print(16 * "++--")
print("current_directory:", current_directory)
print(16 * "++--")

value = [[1, 2, 3, 4],
         [5, 6, 7, 8],
         [9, 10, 11, 12]]

split0, split1, split2 = tf.split(value, [1, 1, 1], 0)
split3, split4, split5 = tf.split(value, [1, 2, 1], 1)

with tf.Session() as sess:
    print("split0:\n", sess.run(split0))
    print('-' * 32)
    print("split1:\n", sess.run(split1))
    print('-' * 32)
    print("split2:\n", sess.run(split2))
    print('-' * 32)
    print("split3:\n", sess.run(split3))
    print('-' * 32)
    print("split4:\n", sess.run(split4))
    print('-' * 32)
    print("split5:\n", sess.run(split5))

/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
2019-08-20 08:15:08.428010: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-08-20 08:15:08.485819: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-20 08:15:08.486035: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 7.92GiB freeMemory: 3.80GiB
2019-08-20 08:15:08.486046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
split0:
 [[1 2 3 4]]
--------------------------------
split1:
 [[5 6 7 8]]
--------------------------------
split2:
 [[ 9 10 11 12]]
--------------------------------
split3:
 [[1]
 [5]
 [9]]
--------------------------------
split4:
 [[ 2  3]
 [ 6  7]
 [10 11]]
--------------------------------
split5:
 [[ 4]
 [ 8]
 [12]]

Process finished with exit code 0

5. Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys
import numpy as np
import tensorflow as tf

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
current_directory = os.path.dirname(os.path.abspath(__file__))

print(16 * "++--")
print("current_directory:", current_directory)
print(16 * "++--")

value = [[1, 2, 3, 4],
         [5, 6, 7, 8],
         [9, 10, 11, 12]]

split0, split1, split2 = tf.split(value, 3, axis=0)
split3, split4, split5, split6 = tf.split(value, num_or_size_splits=4, axis=1)

with tf.Session() as sess:
    print("split0:\n", sess.run(split0))
    print('-' * 32)
    print("split1:\n", sess.run(split1))
    print('-' * 32)
    print("split2:\n", sess.run(split2))
    print('-' * 32)
    print("split3:\n", sess.run(split3))
    print('-' * 32)
    print("split4:\n", sess.run(split4))
    print('-' * 32)
    print("split5:\n", sess.run(split5))
    print('-' * 32)
    print("split6:\n", sess.run(split6))

/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
2019-08-19 09:13:37.946792: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-08-19 09:13:38.027963: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-19 09:13:38.028232: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 7.92GiB freeMemory: 7.42GiB
2019-08-19 09:13:38.028256: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
split0:
 [[1 2 3 4]]
--------------------------------
split1:
 [[5 6 7 8]]
--------------------------------
split2:
 [[ 9 10 11 12]]
--------------------------------
split3:
 [[1]
 [5]
 [9]]
--------------------------------
split4:
 [[ 2]
 [ 6]
 [10]]
--------------------------------
split5:
 [[ 3]
 [ 7]
 [11]]
--------------------------------
split6:
 [[ 4]
 [ 8]
 [12]]

Process finished with exit code 0

6. Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys
import numpy as np
import tensorflow as tf

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
current_directory = os.path.dirname(os.path.abspath(__file__))

print(16 * "++--")
print("current_directory:", current_directory)
print(16 * "++--")

batch_size = 1
num_step = 6
num_input = 2

# x_anchor shape: (batch_size, num_step, num_input)
x_anchor = tf.constant([[[0, 1],
                         [2, 3],
                         [4, 5],
                         [6, 7],
                         [8, 9],
                         [10, 11]]], dtype=np.float32)

# Permute num_step and batch_size.
y_anchor = tf.transpose(x_anchor, perm=[1, 0, 2])

# (num_step * batch_size, num_input)
y_reshape = tf.reshape(y_anchor, [num_step * batch_size, num_input])

# Split data because rnn cell needs a list of inputs for the RNN inner loop num_step * (batch_size, num_input)
y_split = tf.split(y_reshape, num_step, 0)

with tf.Session() as sess:
    input_x_anchor = sess.run(x_anchor)
    print("type(input_x_anchor):", type(input_x_anchor))
    print("input_x_anchor.shape:", input_x_anchor.shape)
    print(8 * "++--")

    output_y_anchor = sess.run(y_anchor)
    print("type(output_y_anchor):", type(output_y_anchor))
    print("output_y_anchor.shape:", output_y_anchor.shape)
    print("output_y_anchor:\n", output_y_anchor)
    print(8 * "++--")

    output_y_reshape = sess.run(y_reshape)
    print("type(output_y_reshape):", type(output_y_reshape))
    print("output_y_reshape.shape:", output_y_reshape.shape)
    print("output_y_reshape:\n", output_y_reshape)
    print(8 * "++--")

    output_y_split = sess.run(y_split)
    print("type(output_y_split):", type(output_y_split))
    print("output_y_split:\n", output_y_split)
    print(8 * "++--")

    print("output_y_split[0-5]:")
    for step in range(num_step):
        print("type(output_y_split[%d]):%s" % (step, type(output_y_split[step])))
        print("output_y_split[%d]:\n" % (step), output_y_split[step])
    print(8 * "++--")

    print("output_y_split[0-5]:")
    for step in range(num_step):
        print("type([output_y_split[%d]]):%s" % (step, type([output_y_split[step]])))
        print("[output_y_split[%d]]:\n" % (step), [output_y_split[step]])
    print(8 * "++--")

/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
2019-08-19 10:39:35.983470: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-08-19 10:39:36.065438: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-19 10:39:36.065680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 7.92GiB freeMemory: 7.39GiB
2019-08-19 10:39:36.065692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
type(input_x_anchor): 
input_x_anchor.shape: (1, 6, 2)
++--++--++--++--++--++--++--++--
type(output_y_anchor): 
output_y_anchor.shape: (6, 1, 2)
output_y_anchor:
 [[[ 0.  1.]]

 [[ 2.  3.]]

 [[ 4.  5.]]

 [[ 6.  7.]]

 [[ 8.  9.]]

 [[10. 11.]]]
++--++--++--++--++--++--++--++--
type(output_y_reshape): 
output_y_reshape.shape: (6, 2)
output_y_reshape:
 [[ 0.  1.]
 [ 2.  3.]
 [ 4.  5.]
 [ 6.  7.]
 [ 8.  9.]
 [10. 11.]]
++--++--++--++--++--++--++--++--
type(output_y_split): 
output_y_split:
 [array([[0., 1.]], dtype=float32), array([[2., 3.]], dtype=float32), array([[4., 5.]], dtype=float32), array([[6., 7.]], dtype=float32), array([[8., 9.]], dtype=float32), array([[10., 11.]], dtype=float32)]
++--++--++--++--++--++--++--++--
output_y_split[0-5]:
type(output_y_split[0]):
output_y_split[0]:
 [[0. 1.]]
type(output_y_split[1]):
output_y_split[1]:
 [[2. 3.]]
type(output_y_split[2]):
output_y_split[2]:
 [[4. 5.]]
type(output_y_split[3]):
output_y_split[3]:
 [[6. 7.]]
type(output_y_split[4]):
output_y_split[4]:
 [[8. 9.]]
type(output_y_split[5]):
output_y_split[5]:
 [[10. 11.]]
++--++--++--++--++--++--++--++--
output_y_split[0-5]:
type([output_y_split[0]]):
[output_y_split[0]]:
 [array([[0., 1.]], dtype=float32)]
type([output_y_split[1]]):
[output_y_split[1]]:
 [array([[2., 3.]], dtype=float32)]
type([output_y_split[2]]):
[output_y_split[2]]:
 [array([[4., 5.]], dtype=float32)]
type([output_y_split[3]]):
[output_y_split[3]]:
 [array([[6., 7.]], dtype=float32)]
type([output_y_split[4]]):
[output_y_split[4]]:
 [array([[8., 9.]], dtype=float32)]
type([output_y_split[5]]):
[output_y_split[5]]:
 [array([[10., 11.]], dtype=float32)]
++--++--++--++--++--++--++--++--

Process finished with exit code 0

7. Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys
import numpy as np
import tensorflow as tf

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
current_directory = os.path.dirname(os.path.abspath(__file__))

print(16 * "++--")
print("current_directory:", current_directory)
print(16 * "++--")

batch_size = 2
num_step = 6
num_input = 2

# x_anchor shape: (batch_size, num_step, num_input)
x_anchor = tf.constant([[[0, 1],
                         [2, 3],
                         [4, 5],
                         [6, 7],
                         [8, 9],
                         [10, 11]],
                        [[12, 13],
                         [14, 15],
                         [16, 17],
                         [18, 19],
                         [20, 21],
                         [22, 23]]], dtype=np.float32)

# Permute num_step and batch_size.
y_anchor = tf.transpose(x_anchor, perm=[1, 0, 2])

# (num_step * batch_size, num_input)
y_reshape = tf.reshape(y_anchor, [num_step * batch_size, num_input])

# Split data because rnn cell needs a list of inputs for the RNN inner loop num_step * (batch_size, num_input)
y_split = tf.split(y_reshape, num_step, 0)

with tf.Session() as sess:
    input_x_anchor = sess.run(x_anchor)
    print("type(input_x_anchor):", type(input_x_anchor))
    print("input_x_anchor.shape:", input_x_anchor.shape)
    print(8 * "++--")

    output_y_anchor = sess.run(y_anchor)
    print("type(output_y_anchor):", type(output_y_anchor))
    print("output_y_anchor.shape:", output_y_anchor.shape)
    print("output_y_anchor:\n", output_y_anchor)
    print(8 * "++--")

    output_y_reshape = sess.run(y_reshape)
    print("type(output_y_reshape):", type(output_y_reshape))
    print("output_y_reshape.shape:", output_y_reshape.shape)
    print("output_y_reshape:\n", output_y_reshape)
    print(8 * "++--")

    output_y_split = sess.run(y_split)
    print("type(output_y_split):", type(output_y_split))
    print("output_y_split:\n", output_y_split)
    print(8 * "++--")

    print("output_y_split[0-5]:")
    for step in range(num_step):
        print("type(output_y_split[%d]):%s" % (step, type(output_y_split[step])))
        print("output_y_split[%d]:\n" % (step), output_y_split[step])
    print(8 * "++--")

    print("output_y_split[0-5]:")
    for step in range(num_step):
        print("type([output_y_split[%d]]):%s" % (step, type([output_y_split[step]])))
        print("[output_y_split[%d]]:\n" % (step), [output_y_split[step]])
    print(8 * "++--")

/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
2019-08-19 11:18:03.818784: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-08-19 11:18:03.896519: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:892] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-19 11:18:03.896755: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 7.92GiB freeMemory: 7.38GiB
2019-08-19 11:18:03.896766: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
type(input_x_anchor): 
input_x_anchor.shape: (2, 6, 2)
++--++--++--++--++--++--++--++--
type(output_y_anchor): 
output_y_anchor.shape: (6, 2, 2)
output_y_anchor:
 [[[ 0.  1.]
  [12. 13.]]

 [[ 2.  3.]
  [14. 15.]]

 [[ 4.  5.]
  [16. 17.]]

 [[ 6.  7.]
  [18. 19.]]

 [[ 8.  9.]
  [20. 21.]]

 [[10. 11.]
  [22. 23.]]]
++--++--++--++--++--++--++--++--
type(output_y_reshape): 
output_y_reshape.shape: (12, 2)
output_y_reshape:
 [[ 0.  1.]
 [12. 13.]
 [ 2.  3.]
 [14. 15.]
 [ 4.  5.]
 [16. 17.]
 [ 6.  7.]
 [18. 19.]
 [ 8.  9.]
 [20. 21.]
 [10. 11.]
 [22. 23.]]
++--++--++--++--++--++--++--++--
type(output_y_split): 
output_y_split:
 [array([[ 0.,  1.],
       [12., 13.]], dtype=float32), array([[ 2.,  3.],
       [14., 15.]], dtype=float32), array([[ 4.,  5.],
       [16., 17.]], dtype=float32), array([[ 6.,  7.],
       [18., 19.]], dtype=float32), array([[ 8.,  9.],
       [20., 21.]], dtype=float32), array([[10., 11.],
       [22., 23.]], dtype=float32)]
++--++--++--++--++--++--++--++--
output_y_split[0-5]:
type(output_y_split[0]):
output_y_split[0]:
 [[ 0.  1.]
 [12. 13.]]
type(output_y_split[1]):
output_y_split[1]:
 [[ 2.  3.]
 [14. 15.]]
type(output_y_split[2]):
output_y_split[2]:
 [[ 4.  5.]
 [16. 17.]]
type(output_y_split[3]):
output_y_split[3]:
 [[ 6.  7.]
 [18. 19.]]
type(output_y_split[4]):
output_y_split[4]:
 [[ 8.  9.]
 [20. 21.]]
type(output_y_split[5]):
output_y_split[5]:
 [[10. 11.]
 [22. 23.]]
++--++--++--++--++--++--++--++--
output_y_split[0-5]:
type([output_y_split[0]]):
[output_y_split[0]]:
 [array([[ 0.,  1.],
       [12., 13.]], dtype=float32)]
type([output_y_split[1]]):
[output_y_split[1]]:
 [array([[ 2.,  3.],
       [14., 15.]], dtype=float32)]
type([output_y_split[2]]):
[output_y_split[2]]:
 [array([[ 4.,  5.],
       [16., 17.]], dtype=float32)]
type([output_y_split[3]]):
[output_y_split[3]]:
 [array([[ 6.,  7.],
       [18., 19.]], dtype=float32)]
type([output_y_split[4]]):
[output_y_split[4]]:
 [array([[ 8.,  9.],
       [20., 21.]], dtype=float32)]
type([output_y_split[5]]):
[output_y_split[5]]:
 [array([[10., 11.],
       [22., 23.]], dtype=float32)]
++--++--++--++--++--++--++--++--

Process finished with exit code 0