我就废话不多说了,大家还是直接看代码吧~
In [1]: import os In [2]: os.environ["CUDA_VISIBLE_DEVICES"] = "0" In [3]: import tensorflow as tf In [4]:sess =tf.Session() In [5]: input = tf.constant([[[1,2,3],[4,5,6],[7,8,9]],[[10,11,12],[13,14,15],[1 ...: 6,17,18]]]) In [6]: input.get_shape() Out[6]: TensorShape([Dimension(2), Dimension(3), Dimension(3)]) In [7]: input_2 = input[:,:,2] In [8]: print(sess.run(input_2)) [[ 3 6 9] [12 15 18]] In [9]: input_2 = input[:,:,0:2] In [10]: print(sess.run(input_2)) [[[ 1 2] [ 4 5] [ 7 8]] [[10 11] [13 14] [16 17]]] In [11]: input = tf.constant([[[[1,2,3],[4,5,6],[7,8,9]],[[10,11,12],[13,14,15], ...: [16,17,18]]]]) In [12]: input.get_shape() Out[12]: TensorShape([Dimension(1), Dimension(2), Dimension(3), Dimension(3)]) In [13]: input_2 = input[:,:,2] In [14]: print(sess.run(input_2)) [[[ 7 8 9] [16 17 18]]] In [15]: input_2 = input[:,:,:,2] In [16]: print(sess.run(input_2)) [[[ 3 6 9] [12 15 18]]]
补充知识:TensorFlow 训练过程中获取某个Tensor值;只有conv1和bn1存在NAN
1. 在训练过程中,获取某个参数Tensor的值:
获取所有Tensor的name:
[tensor.name for tensor in tf.get_default_graph().as_graph_def().node]
根据name获得Tensor:
bn_gamma = sess.graph.get_tensor_by_name('bn1_audio/batch_normalization/beta:0')
sess.run(), print
2. 只有conv1的filter, bias和bn1的gamma为nan:
由于训练数据中存在nan.
bn1后的max pooling层输出全为0 (∵bn1输出有0), 导致后续参数和输出看起来正常, 但是不会更新.
以上这篇Tensorflow--取tensorf指定列的操作方式就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。