TensorFlow 合并与分割

Reference


concat

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a = tf.range(12)
b = tf.range(12, 36)
a1 = tf.reshape(a, [2, 6])
b1 = tf.reshape(b, [4, 6])
t = tf.concat([a1, b1], axis=0)
print(a) # tf.Tensor([ 0 1 2 3 4 5 6 7 8 9 10 11], shape=(12,), dtype=int32)
print(b) # tf.Tensor([12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35], shape=(24,), dtype=int32)
print(a1) # tf.Tensor(
# [[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]], shape=(2, 6), dtype=int32)
print(b1) # tf.Tensor(
# [[12 13 14 15 16 17]
# [18 19 20 21 22 23]
# [24 25 26 27 28 29]
# [30 31 32 33 34 35]], shape=(4, 6), dtype=int32)
print(t) # tf.Tensor(
# [[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]
# [12 13 14 15 16 17]
# [18 19 20 21 22 23]
# [24 25 26 27 28 29]
# [30 31 32 33 34 35]], shape=(6, 6), dtype=int32)

stack

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a = tf.range(12)
c = tf.range(12, 24)
a1 = tf.reshape(a, [2, 6])
c1 = tf.reshape(c, [2, 6])
t = tf.stack([a1, c1], axis=0)
print(a) # tf.Tensor([ 0 1 2 3 4 5 6 7 8 9 10 11], shape=(12,), dtype=int32)
print(c) # tf.Tensor([12 13 14 15 16 17 18 19 20 21 22 23], shape=(12,), dtype=int32)
print(a1) # tf.Tensor(
# [[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]], shape=(2, 6), dtype=int32)
print(c1) # tf.Tensor(
# [[12 13 14 15 16 17]
# [18 19 20 21 22 23]], shape=(2, 6), dtype=int32)
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]]
#
# [[12 13 14 15 16 17]
# [18 19 20 21 22 23]]], shape=(2, 2, 6), dtype=int32)

unstack

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a = tf.range(12)
c = tf.range(12, 24)
a1 = tf.reshape(a, [2, 6])
c1 = tf.reshape(c, [2, 6])
t = tf.stack([a1, c1], axis=0)
t1 = tf.unstack(t, axis=0)
t2 = tf.unstack(t, axis=2)
print(a) # tf.Tensor([ 0 1 2 3 4 5 6 7 8 9 10 11], shape=(12,), dtype=int32)
print(c) # tf.Tensor([12 13 14 15 16 17 18 19 20 21 22 23], shape=(12,), dtype=int32)
print(a1) # tf.Tensor(
# [[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]], shape=(2, 6), dtype=int32)
print(c1) # tf.Tensor(
# [[12 13 14 15 16 17]
# [18 19 20 21 22 23]], shape=(2, 6), dtype=int32)
print(t) # tf.Tensor(
# [[[ 0 1 2 3 4 5]
# [ 6 7 8 9 10 11]]
#
# [[12 13 14 15 16 17]
# [18 19 20 21 22 23]]], shape=(2, 2, 6), dtype=int32)
print(t1) # [<tf.Tensor: id=13, shape=(2, 6), dtype=int32, numpy=
# array([[ 0, 1, 2, 3, 4, 5],
# [ 6, 7, 8, 9, 10, 11]])>, <tf.Tensor: id=14, shape=(2, 6), dtype=int32, numpy=
# array([[12, 13, 14, 15, 16, 17],
# [18, 19, 20, 21, 22, 23]])>]
print(t2) # [<tf.Tensor: id=15, shape=(2, 2), dtype=int32, numpy=
# array([[ 0, 6],
# [12, 18]])>, <tf.Tensor: id=16, shape=(2, 2), dtype=int32, numpy=
# array([[ 1, 7],
# [13, 19]])>, <tf.Tensor: id=17, shape=(2, 2), dtype=int32, numpy=
# array([[ 2, 8],
# [14, 20]])>, <tf.Tensor: id=18, shape=(2, 2), dtype=int32, numpy=
# array([[ 3, 9],
# [15, 21]])>, <tf.Tensor: id=19, shape=(2, 2), dtype=int32, numpy=
# array([[ 4, 10],
# [16, 22]])>, <tf.Tensor: id=20, shape=(2, 2), dtype=int32, numpy=
# array([[ 5, 11],
# [17, 23]])>]

split

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arr = tf.range(24)
t = tf.reshape(arr, [2, 3, 4])
t0 = tf.unstack(t, axis=2)
t1 = tf.split(t, axis=2, num_or_size_splits=2)
t2 = tf.split(t, axis=2, num_or_size_splits=[1, 2, 1])
print(t) # tf.Tensor(
# [[[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
#
# [[12 13 14 15]
# [16 17 18 19]
# [20 21 22 23]]], shape=(2, 3, 4), dtype=int32)
print(t0) # [<tf.Tensor: id=6, shape=(2, 3), dtype=int32, numpy=
# array([[ 0, 4, 8],
# [12, 16, 20]])>, <tf.Tensor: id=7, shape=(2, 3), dtype=int32, numpy=
# array([[ 1, 5, 9],
# [13, 17, 21]])>, <tf.Tensor: id=8, shape=(2, 3), dtype=int32, numpy=
# array([[ 2, 6, 10],
# [14, 18, 22]])>, <tf.Tensor: id=9, shape=(2, 3), dtype=int32, numpy=
# array([[ 3, 7, 11],
# [15, 19, 23]])>]
print(t1) # [<tf.Tensor: id=12, shape=(2, 3, 2), dtype=int32, numpy=
# array([[[ 0, 1],
# [ 4, 5],
# [ 8, 9]],
#
# [[12, 13],
# [16, 17],
# [20, 21]]])>, <tf.Tensor: id=13, shape=(2, 3, 2), dtype=int32, numpy=
# array([[[ 2, 3],
# [ 6, 7],
# [10, 11]],
#
# [[14, 15],
# [18, 19],
# [22, 23]]])>]
print(t2) # [<tf.Tensor: id=16, shape=(2, 3, 1), dtype=int32, numpy=
# array([[[ 0],
# [ 4],
# [ 8]],
#
# [[12],
# [16],
# [20]]])>, <tf.Tensor: id=17, shape=(2, 3, 2), dtype=int32, numpy=
# array([[[ 1, 2],
# [ 5, 6],
# [ 9, 10]],
#
# [[13, 14],
# [17, 18],
# [21, 22]]])>, <tf.Tensor: id=18, shape=(2, 3, 1), dtype=int32, numpy=
# array([[[ 3],
# [ 7],
# [11]],
#
# [[15],
# [19],
# [23]]])>]

本文标题:TensorFlow 合并与分割

文章作者:魏超

发布时间:2019年05月21日 - 17:05

最后更新:2019年05月21日 - 17:05

原始链接:http://www.weichao.io/2019/05/21/TensorFlow-合并与分割/

许可协议: 署名-非商业性使用-禁止演绎 4.0 国际 转载请保留原文链接及作者。

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