我通过占位符输入输入数据，如下所示：
input = tf.placeholder(tf.float32, [None, batch_size])
inputs = tf.unstack(input, axis=0)
显然这与ValueError: Cannot infer num from shape (?, 32).
如何解开input沿轴 0 的堆叠？
您无法沿未知维度拆开这样的张量。文档：
通过沿轴维度对它进行切片，将 num 张量从 value 中解压缩。如果未指定 num（默认值），则从值的形状推断。如果 value.shape[axis] 未知，则会引发 ValueError。
你希望这如何运作？对于不同的维度值，图表将完全不同。
tf.dynamic_partition在这种情况下，即使像这样的解决方法也无济于事，因为它的参数需要是一个整数，在图构建阶段就已经知道了。
如果您知道未知维度的上限，您可以尝试：
import tensorflow as tf
import numpy as np
data = np.random.randn(10, 8)
tensor = tf.placeholder(tf.float32, [10, 8])
rets = tf.unstack(tensor, axis=0)
tensor2 = tf.placeholder(tf.float32, [None, 8])
dyn_shape = tf.shape(tensor2)
b_op = dyn_shape[0]
partition = tf.range(b_op)
some_large_number = 20
rets2 = tf.dynamic_partition(tensor2, partition, some_large_number)
with tf.Session() as sess:
print sess.run(rets, {tensor:data})
print sess.run(rets2, {tensor2:data})
给出输出：
[array([-0.60014623, -0.0812249 , 1.5079778 , -0.45486602, -1.3389106 ,
-1.3552084 , 2.3415568 , 0.24747756], dtype=float32), array([-0.2079824 , 0.33814394, 0.8470432 , -1.3832365 , -0.01087348,
-0.13608357, 0.89929885, -1.2724507 ], dtype=float32), array([ 0.36865985, 0.45177847, -1.1189924 , 1.2984366 , -0.67447174,
2.3120618 , 0.91252357, -0.13333966], dtype=float32), array([ 1.0067816 , 1.2311213 , 0.03433327, -0.09440815, 0.01012954,
-2.0957463 , -0.49972147, -0.30406335], dtype=float32), array([-0.5904513 , 0.49920034, -1.5793694 , 1.3227024 , -0.93950355,
-0.03706869, -0.1222709 , 2.0227952 ], dtype=float32), array([-0.06153346, -0.7300583 , 1.7760276 , 0.13010012, -1.7523713 ,
-0.52992773, 1.367956 , 0.48238465], dtype=float32), array([ 1.2311738 , -0.72093534, -0.28476417, -1.1963955 , 0.60491234,
0.35766497, -0.4614565 , 1.0839593 ], dtype=float32), array([ 1.0952466 , -2.5115075 , 1.6301945 , 0.20886853, 0.8650316 ,
-0.56956375, 0.08775095, -1.4105127 ], dtype=float32), array([ 1.3576531 , 0.5293029 , 0.60603464, -0.41250053, 1.0304515 ,
0.71655554, -1.2762316 , -1.1565298 ], dtype=float32), array([-0.26633576, 1.5087231 , -0.0391343 , 0.40856156, -0.6008501 ,
0.3730529 , 0.28835198, 0.20331612], dtype=float32)]
[array([[-0.60014623, -0.0812249 , 1.5079778 , -0.45486602, -1.3389106 ,
-1.3552084 , 2.3415568 , 0.24747756]], dtype=float32), array([[-0.2079824 , 0.33814394, 0.8470432 , -1.3832365 , -0.01087348,
-0.13608357, 0.89929885, -1.2724507 ]], dtype=float32), array([[ 0.36865985, 0.45177847, -1.1189924 , 1.2984366 , -0.67447174,
2.3120618 , 0.91252357, -0.13333966]], dtype=float32), array([[ 1.0067816 , 1.2311213 , 0.03433327, -0.09440815, 0.01012954,
-2.0957463 , -0.49972147, -0.30406335]], dtype=float32), array([[-0.5904513 , 0.49920034, -1.5793694 , 1.3227024 , -0.93950355,
-0.03706869, -0.1222709 , 2.0227952 ]], dtype=float32), array([[-0.06153346, -0.7300583 , 1.7760276 , 0.13010012, -1.7523713 ,
-0.52992773, 1.367956 , 0.48238465]], dtype=float32), array([[ 1.2311738 , -0.72093534, -0.28476417, -1.1963955 , 0.60491234,
0.35766497, -0.4614565 , 1.0839593 ]], dtype=float32), array([[ 1.0952466 , -2.5115075 , 1.6301945 , 0.20886853, 0.8650316 ,
-0.56956375, 0.08775095, -1.4105127 ]], dtype=float32), array([[ 1.3576531 , 0.5293029 , 0.60603464, -0.41250053, 1.0304515 ,
0.71655554, -1.2762316 , -1.1565298 ]], dtype=float32), array([[-0.26633576, 1.5087231 , -0.0391343 , 0.40856156, -0.6008501 ,
0.3730529 , 0.28835198, 0.20331612]], dtype=float32),
array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32), array([], shape=(0, 8), dtype=float32)]
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