Help on train2.py

[YashBangera7]

Can someone please tell me how to run train2.py ?
I have already trained train1.py and tested it using eval1.py have a case generated as TIMIT_ACTUAL1 .
I do not understand what to use as the arguments for train2

[Huishou]

Hi，If you work in Pycharm， just check the train2.py. Do you have any problem with running it?

[YashBangera7]

Hi，If you work in Pycharm， just check the train2.py. Do you have any problem with running it?

Yeah . Actually I was running it on windows 10 . Succesfully got off running train1.py on NVIDIA GTX 1050 Ti and unable to run the train2.py . No idea about the -case arguments and how to run it . It would be really helpful if you could suggest me the procedure. Running it through anaconda prompt on the gpu

[YashBangera7]

The entire command is as follows :
(dlwin36) C:\Users\Yash\Downloads\Deep Voice\deep-voice-conversion-master>python train2.py -case TIMIT_ACTUAL1 -gpu 0
case: TIMIT_ACTUAL1, logdir1: cases/TIMIT_ACTUAL1/train1, logdir2: cases/TIMIT_ACTUAL1/train2
�[32m[0130 22:20:11 @logger.py:108]�[0m �[5m�[31mWRN�[0m Log directory cases/TIMIT_ACTUAL1/train2 exists! Use 'd' to delete it.
�[32m[0130 22:20:11 @logger.py:111]�[0m �[5m�[31mWRN�[0m If you're resuming from a previous run, you can choose to keep it.
Press any other key to exit.
Select Action: k (keep) / d (delete) / q (quit):d
�[32m[0130 22:20:13 @logger.py:73]�[0m Argv: train2.py -case TIMIT_ACTUAL1 -gpu 0
�[32m[0130 22:20:13 @parallel.py:176]�[0m �[5m�[31mWRN�[0m MultiProcessPrefetchData does support windows. However, windows requires more strict picklability on processes, which may lead of failure on some of the code.
�[32m[0130 22:20:13 @parallel.py:186]�[0m [MultiProcessPrefetchData] Will fork a dataflow more than one times. This assumes the datapoints are i.i.d.
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\effects.py:486: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return y[full_index], np.asarray([start, end])
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\util\utils.py:343: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return data[slices]
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\effects.py:486: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return y[full_index], np.asarray([start, end])
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\util\utils.py:343: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return data[slices]
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\effects.py:486: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return y[full_index], np.asarray([start, end])
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\util\utils.py:343: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return data[slices]
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\effects.py:486: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return y[full_index], np.asarray([start, end])
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\util\utils.py:343: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return data[slices]
�[32m[0130 22:22:35 @sesscreate.py:38]�[0m �[5m�[31mWRN�[0m User-provided custom session config may not work due to TF bugs. See https://github.com/tensorpack/tensorpack/issues/497 for workarounds.
�[32m[0130 22:22:35 @config.py:165]�[0m �[5m�[31mWRN�[0m TrainConfig.nr_tower was deprecated! Set the number of GPUs on the trainer instead!
�[32m[0130 22:22:35 @config.py:166]�[0m �[5m�[31mWRN�[0m See https://github.com/tensorpack/tensorpack/issues/458 for more information.
�[32m[0130 22:22:35 @develop.py:96]�[0m �[5m�[31mWRN�[0m [Deprecated] ModelDescBase._get_inputs() interface will be deprecated after 30 Mar. Use inputs() instead!
�[32m[0130 22:22:35 @input_source.py:220]�[0m Setting up the queue 'QueueInput/input_queue' for CPU prefetching ...
�[32m[0130 22:22:35 @trainers.py:52]�[0m Building graph for a single training tower ...
�[32m[0130 22:22:35 @develop.py:96]�[0m �[5m�[31mWRN�[0m [Deprecated] ModelDescBase._build_graph() interface will be deprecated after 30 Mar. Use build_graph() instead!
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\effects.py:486: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return y[full_index], np.asarray([start, end])
C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\librosa\util\utils.py:343: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use arr[tuple(seq)] instead of arr[seq]. In the future this will be interpreted as an array index, arr[np.array(seq)], which will result either in an error or a different result.
return data[slices]
�[32m[0130 22:22:46 @develop.py:96]�[0m �[5m�[31mWRN�[0m [Deprecated] get_cost() and self.cost will be deprecated after 30 Mar. Return the cost tensor directly in build_graph() instead!
�[32m[0130 22:22:46 @develop.py:96]�[0m �[5m�[31mWRN�[0m [Deprecated] ModelDescBase._get_optimizer() interface will be deprecated after 30 Mar. Use optimizer() instead!
�[32m[0130 22:23:11 @model_utils.py:64]�[0m �[36mTrainable Variables:
�[0mname shape dim

---

net1/prenet/dense1/kernel:0 [60, 128] 7680
net1/prenet/dense1/bias:0 [128] 128
net1/prenet/dense2/kernel:0 [128, 64] 8192
net1/prenet/dense2/bias:0 [64] 64
net1/cbhg/conv1d_banks/num_1/conv1d/conv1d/kernel:0 [1, 64, 64] 4096
net1/cbhg/conv1d_banks/num_1/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_1/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_2/conv1d/conv1d/kernel:0 [2, 64, 64] 8192
net1/cbhg/conv1d_banks/num_2/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_2/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_3/conv1d/conv1d/kernel:0 [3, 64, 64] 12288
net1/cbhg/conv1d_banks/num_3/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_3/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_4/conv1d/conv1d/kernel:0 [4, 64, 64] 16384
net1/cbhg/conv1d_banks/num_4/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_4/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_5/conv1d/conv1d/kernel:0 [5, 64, 64] 20480
net1/cbhg/conv1d_banks/num_5/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_5/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_6/conv1d/conv1d/kernel:0 [6, 64, 64] 24576
net1/cbhg/conv1d_banks/num_6/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_6/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_7/conv1d/conv1d/kernel:0 [7, 64, 64] 28672
net1/cbhg/conv1d_banks/num_7/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_7/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_banks/num_8/conv1d/conv1d/kernel:0 [8, 64, 64] 32768
net1/cbhg/conv1d_banks/num_8/normalize/beta:0 [64] 64
net1/cbhg/conv1d_banks/num_8/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_1/conv1d/kernel:0 [3, 512, 64] 98304
net1/cbhg/normalize/beta:0 [64] 64
net1/cbhg/normalize/gamma:0 [64] 64
net1/cbhg/conv1d_2/conv1d/kernel:0 [3, 64, 64] 12288
net1/cbhg/highwaynet_0/dense1/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_0/dense1/bias:0 [64] 64
net1/cbhg/highwaynet_0/dense2/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_0/dense2/bias:0 [64] 64
net1/cbhg/highwaynet_1/dense1/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_1/dense1/bias:0 [64] 64
net1/cbhg/highwaynet_1/dense2/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_1/dense2/bias:0 [64] 64
net1/cbhg/highwaynet_2/dense1/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_2/dense1/bias:0 [64] 64
net1/cbhg/highwaynet_2/dense2/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_2/dense2/bias:0 [64] 64
net1/cbhg/highwaynet_3/dense1/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_3/dense1/bias:0 [64] 64
net1/cbhg/highwaynet_3/dense2/kernel:0 [64, 64] 4096
net1/cbhg/highwaynet_3/dense2/bias:0 [64] 64
net1/cbhg/gru/bidirectional_rnn/fw/gru_cell/gates/kernel:0 [128, 128] 16384
net1/cbhg/gru/bidirectional_rnn/fw/gru_cell/gates/bias:0 [128] 128
net1/cbhg/gru/bidirectional_rnn/fw/gru_cell/candidate/kernel:0 [128, 64] 8192
net1/cbhg/gru/bidirectional_rnn/fw/gru_cell/candidate/bias:0 [64] 64
net1/cbhg/gru/bidirectional_rnn/bw/gru_cell/gates/kernel:0 [128, 128] 16384
net1/cbhg/gru/bidirectional_rnn/bw/gru_cell/gates/bias:0 [128] 128
net1/cbhg/gru/bidirectional_rnn/bw/gru_cell/candidate/kernel:0 [128, 64] 8192
net1/cbhg/gru/bidirectional_rnn/bw/gru_cell/candidate/bias:0 [64] 64
net1/dense/kernel:0 [128, 61] 7808
net1/dense/bias:0 [61] 61
net2/prenet/dense1/kernel:0 [61, 256] 15616
net2/prenet/dense1/bias:0 [256] 256
net2/prenet/dense2/kernel:0 [256, 128] 32768
net2/prenet/dense2/bias:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_1/conv1d/conv1d/kernel:0 [1, 128, 128] 16384
net2/cbhg_mel/conv1d_banks/num_1/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_1/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_2/conv1d/conv1d/kernel:0 [2, 128, 128] 32768
net2/cbhg_mel/conv1d_banks/num_2/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_2/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_3/conv1d/conv1d/kernel:0 [3, 128, 128] 49152
net2/cbhg_mel/conv1d_banks/num_3/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_3/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_4/conv1d/conv1d/kernel:0 [4, 128, 128] 65536
net2/cbhg_mel/conv1d_banks/num_4/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_4/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_5/conv1d/conv1d/kernel:0 [5, 128, 128] 81920
net2/cbhg_mel/conv1d_banks/num_5/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_5/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_6/conv1d/conv1d/kernel:0 [6, 128, 128] 98304
net2/cbhg_mel/conv1d_banks/num_6/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_6/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_7/conv1d/conv1d/kernel:0 [7, 128, 128] 114688
net2/cbhg_mel/conv1d_banks/num_7/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_7/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_8/conv1d/conv1d/kernel:0 [8, 128, 128] 131072
net2/cbhg_mel/conv1d_banks/num_8/normalize/beta:0 [128] 128
net2/cbhg_mel/conv1d_banks/num_8/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_1/conv1d/kernel:0 [3, 1024, 128] 393216
net2/cbhg_mel/normalize/beta:0 [128] 128
net2/cbhg_mel/normalize/gamma:0 [128] 128
net2/cbhg_mel/conv1d_2/conv1d/kernel:0 [3, 128, 128] 49152
net2/cbhg_mel/highwaynet_0/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_0/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_0/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_0/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_1/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_1/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_1/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_1/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_2/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_2/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_2/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_2/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_3/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_3/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_3/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_3/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_4/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_4/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_4/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_4/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_5/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_5/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_5/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_5/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_6/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_6/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_6/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_6/dense2/bias:0 [128] 128
net2/cbhg_mel/highwaynet_7/dense1/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_7/dense1/bias:0 [128] 128
net2/cbhg_mel/highwaynet_7/dense2/kernel:0 [128, 128] 16384
net2/cbhg_mel/highwaynet_7/dense2/bias:0 [128] 128
net2/cbhg_mel/gru/bidirectional_rnn/fw/gru_cell/gates/kernel:0 [256, 256] 65536
net2/cbhg_mel/gru/bidirectional_rnn/fw/gru_cell/gates/bias:0 [256] 256
net2/cbhg_mel/gru/bidirectional_rnn/fw/gru_cell/candidate/kernel:0 [256, 128] 32768
net2/cbhg_mel/gru/bidirectional_rnn/fw/gru_cell/candidate/bias:0 [128] 128
net2/cbhg_mel/gru/bidirectional_rnn/bw/gru_cell/gates/kernel:0 [256, 256] 65536
net2/cbhg_mel/gru/bidirectional_rnn/bw/gru_cell/gates/bias:0 [256] 256
net2/cbhg_mel/gru/bidirectional_rnn/bw/gru_cell/candidate/kernel:0 [256, 128] 32768
net2/cbhg_mel/gru/bidirectional_rnn/bw/gru_cell/candidate/bias:0 [128] 128
net2/pred_mel/kernel:0 [256, 90] 23040
net2/pred_mel/bias:0 [90] 90
net2/dense/kernel:0 [90, 128] 11520
net2/dense/bias:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_1/conv1d/conv1d/kernel:0 [1, 128, 128] 16384
net2/cbhg_linear/conv1d_banks/num_1/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_1/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_2/conv1d/conv1d/kernel:0 [2, 128, 128] 32768
net2/cbhg_linear/conv1d_banks/num_2/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_2/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_3/conv1d/conv1d/kernel:0 [3, 128, 128] 49152
net2/cbhg_linear/conv1d_banks/num_3/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_3/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_4/conv1d/conv1d/kernel:0 [4, 128, 128] 65536
net2/cbhg_linear/conv1d_banks/num_4/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_4/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_5/conv1d/conv1d/kernel:0 [5, 128, 128] 81920
net2/cbhg_linear/conv1d_banks/num_5/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_5/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_6/conv1d/conv1d/kernel:0 [6, 128, 128] 98304
net2/cbhg_linear/conv1d_banks/num_6/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_6/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_7/conv1d/conv1d/kernel:0 [7, 128, 128] 114688
net2/cbhg_linear/conv1d_banks/num_7/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_7/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_8/conv1d/conv1d/kernel:0 [8, 128, 128] 131072
net2/cbhg_linear/conv1d_banks/num_8/normalize/beta:0 [128] 128
net2/cbhg_linear/conv1d_banks/num_8/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_1/conv1d/kernel:0 [3, 1024, 128] 393216
net2/cbhg_linear/normalize/beta:0 [128] 128
net2/cbhg_linear/normalize/gamma:0 [128] 128
net2/cbhg_linear/conv1d_2/conv1d/kernel:0 [3, 128, 128] 49152
net2/cbhg_linear/highwaynet_0/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_0/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_0/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_0/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_1/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_1/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_1/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_1/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_2/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_2/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_2/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_2/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_3/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_3/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_3/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_3/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_4/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_4/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_4/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_4/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_5/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_5/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_5/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_5/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_6/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_6/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_6/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_6/dense2/bias:0 [128] 128
net2/cbhg_linear/highwaynet_7/dense1/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_7/dense1/bias:0 [128] 128
net2/cbhg_linear/highwaynet_7/dense2/kernel:0 [128, 128] 16384
net2/cbhg_linear/highwaynet_7/dense2/bias:0 [128] 128
net2/cbhg_linear/gru/bidirectional_rnn/fw/gru_cell/gates/kernel:0 [256, 256] 65536
net2/cbhg_linear/gru/bidirectional_rnn/fw/gru_cell/gates/bias:0 [256] 256
net2/cbhg_linear/gru/bidirectional_rnn/fw/gru_cell/candidate/kernel:0 [256, 128] 32768
net2/cbhg_linear/gru/bidirectional_rnn/fw/gru_cell/candidate/bias:0 [128] 128
net2/cbhg_linear/gru/bidirectional_rnn/bw/gru_cell/gates/kernel:0 [256, 256] 65536
net2/cbhg_linear/gru/bidirectional_rnn/bw/gru_cell/gates/bias:0 [256] 256
net2/cbhg_linear/gru/bidirectional_rnn/bw/gru_cell/candidate/kernel:0 [256, 128] 32768
net2/cbhg_linear/gru/bidirectional_rnn/bw/gru_cell/candidate/bias:0 [128] 128
net2/pred_spec/kernel:0 [256, 569] 145664
net2/pred_spec/bias:0 [569] 569�[36m
Total #vars=204, #params=3587856, size=13.69MB�[0m
�[32m[0130 22:23:11 @base.py:209]�[0m Setup callbacks graph ...
�[32m[0130 22:23:12 @summary.py:38]�[0m Maintain moving average summary of 0 tensors in collection MOVING_SUMMARY_OPS.
�[32m[0130 22:23:12 @summary.py:75]�[0m Summarizing collection 'summaries' of size 2.
�[32m[0130 22:23:19 @common.py:147]�[0m �[4m�[5m�[31mERR�[0m Cannot batch data. Perhaps they are of inconsistent shape?
Traceback (most recent call last):
File "C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\tensorpack\dataflow\common.py", line 145, in _aggregate_batch
np.asarray([x[k] for x in data_holder], dtype=tp))
File "C:\Users\Yash\anaconda\envs\dlwin36\lib\site-packages\numpy\core\numeric.py", line 501, in asarray
return array(a, dtype, copy=False, order=order)
MemoryError
�[32m[0130 22:23:19 @common.py:150]�[0m �[4m�[5m�[31mERR�[0m Shape of all arrays to be batched: [(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569),
(334, 569)]

[YashBangera7]

I believe it had something to do with the set CUDA_VISIBLE_DEVICES=0 keyword .