索引4D数组时的Tensorflow错误：ValueError：形状必须相等，但是1和0

Question

我正在修改Tensorflow中的一个简单CNN，并且当我索引4d数组时，出现此错误。 我可再现的例子是：

from __future__ import print_function 
import pdb 
import numpy as np 
import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 

def weight_variable(shape): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

def conv2d(x, W, stride=1): 
    return tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding='SAME') 

def max_pool_2d(x, k=10): 
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], 
               strides=[1, k, k, 1], padding='SAME') 


indices = np.array([[0, 1], [5, 2],[300, 400]]).astype(np.int32) 

input_updatable = weight_variable(shape=[1, 1200, 600, 100]) 

# Convolutional layer 1 
W_conv1 = weight_variable([5, 5, 100, 100]) 
b_conv1 = bias_variable([100]) 

h_conv1 = tf.nn.relu(conv2d(input_updatable, W_conv1) + b_conv1) 
h_pool1 = max_pool_2d(h_conv1) 

# Convolutional layer 2 
W_conv2 = weight_variable([5, 5, 100, 100]) 
b_conv2 = bias_variable([100]) 

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
h_pool2 = max_pool_2d(h_conv2) 

#extract vectoris based on input 
l1_vecs = input_updatable[0, indices[:, 0], indices[:, 1], :] 



# Training steps 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 

    max_steps = 1000 
    for step in range(max_steps): 
     l1 = sess.run(l1_vecs) 
     pdb.set_trace() 

此代码引发以下错误：

l1_vecs = input_updatable[0, indices[:, 0], indices[:, 1], :] 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 722, in _SliceHelperVar 
    return _SliceHelper(var._AsTensor(), slice_spec, var) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 480, in _SliceHelper 
    stack(begin), stack(end), stack(strides)) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 824, in stack 
    return gen_array_ops._pack(values, axis=axis, name=name) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2041, in _pack 
    result = _op_def_lib.apply_op("Pack", values=values, axis=axis, name=name) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op 
    op_def=op_def) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2329, in create_op 
    set_shapes_for_outputs(ret) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1717, in set_shapes_for_outputs 
    shapes = shape_func(op) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1667, in call_with_requiring 
    return call_cpp_shape_fn(op, require_shape_fn=True) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn 
    debug_python_shape_fn, require_shape_fn) 
    File "/home/arahimi/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl 
    raise ValueError(err.message) 
ValueError: Shapes must be equal rank, but are 1 and 0 
     From merging shape 2 with other shapes. for 'strided_slice/stack_1' (op: 'Pack') with input shapes: [], [3], [3], []. 

需要注意的是，当我提取input_updatable值有：

ip = sess.run(input_updatable) 

然后我就可以建立索引使用：

l1_vecs = input_updatable[0, indices[:, 0], indices[:, 1], :] 

我不确定是什么原因。

Answer 1

如果你有一个像在Tensorflow以下变量：

input_updatable = weight_variable(shape=[1, 1200, 600, 100]) 

，你有指标，有大小NX2二维数组索引input_updatable到输出，在numpy的一个NX100阵列，你可以通过做：

input_updatable[0, Indices[:, 0], Indices[:, 1], :] 

我想你也可以在Theano中做到这一点。但Tensorflow不支持高级索引，因此您需要使用tf.gather_nd（）。

您首先需要通过添加列，所有行的2D指数转换成3D：

# create a zero column to index into the first dimension of input_updatable 
zz = np.zeros(shape=(Indices.shape[0], 1), dtype=np.int32) 
#then attach this vector to 2d matrix Indices (Nx2) to create a 3d (Nx3) matrix where the first column is zero. 
Indices = np.hstack((zz, Indices)) 
#then use gather_nd 
output = tf.gather_nd(input_updatable, Indices) 

的将输出的是NX100矩阵。