如何使用张量索引访问tensorflow Tensor
中的次要元素,如下所示:
import tensorflow as tf
import numpy as np
# indexing in numpy [Working]
matrix = np.random.randint(0, 10, [100, 100])
indices = np.random.randint(0, 100, [1000, 100])
elements = matrix[indices[:, 0], indices[:, 1]]
# indexing in tensorflow [Not working]
tf_matrix = tf.constant(matrix, dtype=tf.int32)
tf_indices = tf.constant(indices, dtype=tf.int32)
tf_elements = tf_matrix[tf_indices[:, 0], tf_indices[:, 1]] # Error
session = tf.Session()
session.run(tf_elements)
我收到这些错误:
tensorflow.python.framework.errors_impl.InvalidArgumentError:Shape必须为1级,但对于'strided_slice_2'(op:'StridedSlice'),输入形状为[100,100],[2,1000],[2,1000] ,[2] . ValueError:Shape必须为1级,但对于'strided_slice_2'(op:'StridedSlice'),其输入形状为[100,100],[2,1000],[2,1000],[2] .
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