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December 26, 2019 06:47
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Returnn - asr_local_attention
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| RETURNN starting up, version 20191217.234858--git-09b41c6f-dirty, date/time 2019-12-26-06-45-58 (UTC+0000), pid 20215, cwd /home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention, Python /home/ubuntu/tf1.13/bin/python3 | |
| RETURNN command line options: ['local-heuristic.argmax.win05.exp3.ctc.config'] | |
| Hostname: ip-10-1-21-241 | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| _np_qint8 = np.dtype([("qint8", np.int8, 1)]) | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| _np_quint8 = np.dtype([("quint8", np.uint8, 1)]) | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| _np_qint16 = np.dtype([("qint16", np.int16, 1)]) | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| _np_quint16 = np.dtype([("quint16", np.uint16, 1)]) | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| _np_qint32 = np.dtype([("qint32", np.int32, 1)]) | |
| /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'. | |
| np_resource = np.dtype([("resource", np.ubyte, 1)]) | |
| TensorFlow: 1.13.1 (b'v1.13.1-0-g6612da8951') (<site-package> in /home/ubuntu/tf1.13/lib/python3.6/site-packages/tensorflow) | |
| Setup TF inter and intra global thread pools, num_threads None, session opts {'log_device_placement': False, 'device_count': {'GPU': 0}}. | |
| 2019-12-26 06:45:59.202047: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA | |
| 2019-12-26 06:45:59.425681: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | |
| 2019-12-26 06:45:59.429258: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | |
| 2019-12-26 06:45:59.430831: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55e4d8dd5850 executing computations on platform CUDA. Devices: | |
| 2019-12-26 06:45:59.430864: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): Tesla K80, Compute Capability 3.7 | |
| 2019-12-26 06:45:59.430881: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (1): Tesla K80, Compute Capability 3.7 | |
| 2019-12-26 06:45:59.450542: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300090000 Hz | |
| 2019-12-26 06:45:59.452625: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55e4d9498d60 executing computations on platform Host. Devices: | |
| 2019-12-26 06:45:59.452659: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined> | |
| 2019-12-26 06:45:59.452768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: | |
| 2019-12-26 06:45:59.452791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] | |
| CUDA_VISIBLE_DEVICES is set to '1,2'. | |
| Collecting TensorFlow device list... | |
| 2019-12-26 06:45:59.455892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: | |
| name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235 | |
| pciBusID: 0000:00:18.0 | |
| totalMemory: 11.17GiB freeMemory: 446.06MiB | |
| 2019-12-26 06:45:59.456040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 1 with properties: | |
| name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235 | |
| pciBusID: 0000:00:19.0 | |
| totalMemory: 11.17GiB freeMemory: 446.06MiB | |
| 2019-12-26 06:45:59.456482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1 | |
| 2019-12-26 06:45:59.459962: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: | |
| 2019-12-26 06:45:59.459989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 1 | |
| 2019-12-26 06:45:59.460007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N Y | |
| 2019-12-26 06:45:59.460016: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1: Y N | |
| 2019-12-26 06:45:59.460292: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 221 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:18.0, compute capability: 3.7) | |
| 2019-12-26 06:45:59.463035: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:1 with 221 MB memory) -> physical GPU (device: 1, name: Tesla K80, pci bus id: 0000:00:19.0, compute capability: 3.7) | |
| Local devices available to TensorFlow: | |
| 1/6: name: "/device:CPU:0" | |
| device_type: "CPU" | |
| memory_limit: 268435456 | |
| locality { | |
| } | |
| incarnation: 13382603103062640486 | |
| 2/6: name: "/device:XLA_GPU:0" | |
| device_type: "XLA_GPU" | |
| memory_limit: 17179869184 | |
| locality { | |
| } | |
| incarnation: 13903808377023195374 | |
| physical_device_desc: "device: XLA_GPU device" | |
| 3/6: name: "/device:XLA_GPU:1" | |
| device_type: "XLA_GPU" | |
| memory_limit: 17179869184 | |
| locality { | |
| } | |
| incarnation: 10467231846590301634 | |
| physical_device_desc: "device: XLA_GPU device" | |
| 4/6: name: "/device:XLA_CPU:0" | |
| device_type: "XLA_CPU" | |
| memory_limit: 17179869184 | |
| locality { | |
| } | |
| incarnation: 2300646191363693310 | |
| physical_device_desc: "device: XLA_CPU device" | |
| 5/6: name: "/device:GPU:0" | |
| device_type: "GPU" | |
| memory_limit: 231800832 | |
| locality { | |
| bus_id: 1 | |
| links { | |
| link { | |
| device_id: 1 | |
| type: "StreamExecutor" | |
| strength: 1 | |
| } | |
| } | |
| } | |
| incarnation: 15532672733655798053 | |
| physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:18.0, compute capability: 3.7" | |
| 6/6: name: "/device:GPU:1" | |
| device_type: "GPU" | |
| memory_limit: 231800832 | |
| locality { | |
| bus_id: 1 | |
| links { | |
| link { | |
| type: "StreamExecutor" | |
| strength: 1 | |
| } | |
| } | |
| } | |
| incarnation: 4180441292150800506 | |
| physical_device_desc: "device: 1, name: Tesla K80, pci bus id: 0000:00:19.0, compute capability: 3.7" | |
| Using gpu device 1: Tesla K80 | |
| Using gpu device 2: Tesla K80 | |
| <LibriSpeechCorpus 'train' epoch=1>, epoch 1. Old mean seq len (transcription) is 183.267376, new is 63.708029, requested max is 75.000000. Old num seqs is 6575, new num seqs is 822. | |
| <LibriSpeechCorpus 'train' epoch=1>, epoch 1. Old num seqs 14063, new num seqs 822. | |
| <LibriSpeechCorpus 'train' epoch=1>, epoch 1. Old mean seq len (transcription) is 183.267376, new is 63.708029, requested max is 75.000000. Old num seqs is 6575, new num seqs is 822. | |
| <LibriSpeechCorpus 'train' epoch=1>, epoch 1. Old num seqs 14063, new num seqs 822. | |
| Train data: | |
| input: 40 x 1 | |
| output: {'classes': [10025, 1], 'raw': {'dtype': 'string', 'shape': ()}, 'data': [40, 2]} | |
| LibriSpeechCorpus, sequences: 822, frames: unknown | |
| Dev data: | |
| LibriSpeechCorpus, sequences: 3000, frames: unknown | |
| Learning-rate-control: file newbob.data does not exist yet | |
| Update config key 'max_seq_length' for epoch 1: {'classes': 75} -> {'classes': 60} | |
| Setup tf.Session with options {'log_device_placement': False, 'device_count': {'GPU': 1}} ... | |
| 2019-12-26 06:46:05.591177: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0, 1 | |
| 2019-12-26 06:46:05.591272: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: | |
| 2019-12-26 06:46:05.591293: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 1 | |
| 2019-12-26 06:46:05.591310: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N Y | |
| 2019-12-26 06:46:05.591325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 1: Y N | |
| 2019-12-26 06:46:05.591489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 221 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:18.0, compute capability: 3.7) | |
| layer root/'data' output: Data(name='data', shape=(None, 40), batch_shape_meta=[B,T|'time:var:extern_data:data',F|40]) | |
| layer root/'source' output: Data(name='source_output', shape=(None, 40), batch_shape_meta=[B,T|'time:var:extern_data:data',F|40]) | |
| layer root/'lstm0_fw' output: Data(name='lstm0_fw_output', shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'time:var:extern_data:data',B,F|1024]) | |
| layer root/'lstm0_bw' output: Data(name='lstm0_bw_output', shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'time:var:extern_data:data',B,F|1024]) | |
| layer root/'lstm0_pool' output: Data(name='lstm0_pool_output', shape=(None, 2048), batch_shape_meta=[B,T|?,F|2048]) | |
| layer root/'lstm5_fw' output: Data(name='lstm5_fw_output', shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) | |
| layer root/'lstm5_bw' output: Data(name='lstm5_bw_output', shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) | |
| layer root/'encoder' output: Data(name='encoder_output', shape=(None, 2048), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|2048]) | |
| layer root/'ctc' output: Data(name='ctc_output', shape=(None, 10026), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|10026]) | |
| layer root/'enc_ctx' output: Data(name='enc_ctx_output', shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) | |
| layer root/'inv_fertility' output: Data(name='inv_fertility_output', shape=(None, 1), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1]) | |
| layer root/'enc_value' output: Data(name='enc_value_output', shape=(None, 1, 2048), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,1,F|2048]) | |
| <_SubnetworkRecCell of None>: exception constructing template network (for deps and data shapes) | |
| Most recent construction stack: | |
| <_TemplateLayer(EvalLayer)(:template:eval) 'output/p_t_in' out_type=Data(shape=(), time_dim_axis=None, batch_shape_meta=[B]) (construction stack 'att_weights')>, kwargs: | |
| {'eval': 'tf.squeeze(tf.argmax(source(0), axis=1, output_type=tf.int32), ' | |
| 'axis=1)', | |
| 'name': 'p_t_in', | |
| 'network': <TFNetwork 'root/output:rec-subnet' parent_net=<TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>, | |
| 'out_type': {'batch_dim_axis': 0, 'dtype': 'float32', 'shape': ()}, | |
| 'sources': [<_TemplateLayer(SoftmaxOverSpatialLayer)(:prev:softmax_over_spatial) 'output/prev:att_weights' out_type=Data(shape=(1, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|1,T|'spatial:0:lstm0_pool']) (construction stack None)>]} | |
| Template network so far: | |
| {'accum_att_weights': <_TemplateLayer(EvalLayer)(:template:eval) 'output/accum_att_weights' out_type=Data(shape=(None, 1), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1]) (construction stack 'weight_feedback')>, | |
| 'att': <_TemplateLayer(MergeDimsLayer)(:template:merge_dims) 'output/att' out_type=Data(shape=(2048,), time_dim_axis=None, batch_shape_meta=[B,F|2048]) (construction stack 's')>, | |
| 'att0': <_TemplateLayer(GenericAttentionLayer)(:template:generic_attention) 'output/att0' out_type=Data(shape=(1, 2048), time_dim_axis=None, batch_shape_meta=[B,1,F|2048]) (construction stack 'att')>, | |
| 'att_weights': <_TemplateLayer(SoftmaxOverSpatialLayer)(:template:softmax_over_spatial) 'output/att_weights' out_type=Data(shape=(1, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|1,T|'spatial:0:lstm0_pool']) (construction stack 'att0')>, | |
| 'end': <_TemplateLayer(CompareLayer)(:template:compare) 'output/end' out_type=Data(shape=(), dtype='bool', sparse=True, dim=2, time_dim_axis=None, batch_shape_meta=[B]) (construction stack None)>, | |
| 'energy': <_TemplateLayer(LinearLayer)(:template:linear) 'output/energy' out_type=Data(shape=(None, 1), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1]) (construction stack 'energy_reinterpreted')>, | |
| 'energy_in': <_TemplateLayer(CombineLayer)(:template:combine) 'output/energy_in' out_type=Data(shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) (construction stack 'energy_tanh')>, | |
| 'energy_reinterpreted': <_TemplateLayer(ReinterpretDataLayer)(:template:reinterpret_data) 'output/energy_reinterpreted' out_type=Data(shape=(None, 1), batch_shape_meta=[B,T|'spatial:0:lstm0_pool',F|1]) (construction stack 'att_weights')>, | |
| 'energy_tanh': <_TemplateLayer(ActivationLayer)(:template:activation) 'output/energy_tanh' out_type=Data(shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) (construction stack 'energy')>, | |
| 'output': <_TemplateLayer(ChoiceLayer)(:template:choice) 'output/output' out_type=Data(shape=(), dtype='int32', sparse=True, dim=10025, time_dim_axis=None, batch_shape_meta=[B]) (construction stack None)>, | |
| 'output_prob': <_TemplateLayer(SoftmaxLayer)(:template:softmax) 'output/output_prob' out_type=Data(shape=(10025,), time_dim_axis=None, batch_shape_meta=[B,F|10025]) (construction stack None)>, | |
| 'p_t_in': <_TemplateLayer(EvalLayer)(:template:eval) 'output/p_t_in' out_type=Data(shape=(), time_dim_axis=None, batch_shape_meta=[B]) (construction stack 'att_weights')>, | |
| 'readout': <_TemplateLayer(ReduceOutLayer)(:template:reduce_out) 'output/readout' out_type=Data(shape=(500,), time_dim_axis=None, batch_shape_meta=[B,F|500]) (construction stack 'output_prob')>, | |
| 'readout_in': <_TemplateLayer(LinearLayer)(:template:linear) 'output/readout_in' out_type=Data(shape=(1000,), time_dim_axis=None, batch_shape_meta=[B,F|1000]) (construction stack 'readout')>, | |
| 's': <_TemplateLayer(RnnCellLayer)(:template:rnn_cell) 'output/s' out_type=Data(shape=(1000,), time_dim_axis=None, batch_shape_meta=[B,F|1000]) (construction stack 'readout_in')>, | |
| 's_transformed': <_TemplateLayer(LinearLayer)(:template:linear) 'output/s_transformed' out_type=Data(shape=(1024,), time_dim_axis=None, batch_shape_meta=[B,F|1024]) (construction stack 'energy_in')>, | |
| 'target_embed': <_TemplateLayer(LinearLayer)(:template:linear) 'output/target_embed' out_type=Data(shape=(621,), time_dim_axis=None, batch_shape_meta=[B,F|621]) (construction stack 's')>, | |
| 'weight_feedback': <_TemplateLayer(LinearLayer)(:template:linear) 'output/weight_feedback' out_type=Data(shape=(None, 1024), batch_dim_axis=1, batch_shape_meta=[T|'spatial:0:lstm0_pool',B,F|1024]) (construction stack 'energy_in')>} | |
| Collected (unique) exceptions during template construction: | |
| (Note that many of these can be ignored, or are expected.) | |
| EXCEPTION | |
| NetworkConstructionDependencyLoopException: Error: There is a dependency loop on layer 'accum_att_weights'. | |
| Construction stack (most recent first): | |
| accum_att_weights | |
| weight_feedback | |
| energy_in | |
| energy_tanh | |
| energy | |
| energy_reinterpreted | |
| att_weights | |
| att0 | |
| att | |
| s | |
| readout_in | |
| readout | |
| output_prob | |
| EXCEPTION | |
| CannotHandleUndefinedSourcesException: 's_transformed': cannot handle undefined sources without defined out_type. | |
| {'activation': None, | |
| 'loss': None, | |
| 'n_out': 1024, | |
| 'network': <TFNetwork 'root/output:rec-subnet' parent_net=<TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>, | |
| 'size_target': None, | |
| 'sources': [None], | |
| 'target': None, | |
| 'with_bias': False} | |
| Exception creating layer root/'output' of class RecLayer with opts: | |
| {'_target_layers': {}, | |
| 'cheating': False, | |
| 'max_seq_len': <tf.Tensor 'max_seq_len_encoder:0' shape=() dtype=int32>, | |
| 'n_out': <class 'Util.NotSpecified'>, | |
| 'name': 'output', | |
| 'network': <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>, | |
| 'sources': [], | |
| 'target': 'classes', | |
| 'unit': {'accum_att_weights': {'class': 'eval', | |
| 'eval': 'source(0) + source(1) * source(2) * ' | |
| '0.5', | |
| 'from': ['prev:accum_att_weights', | |
| 'att_weights', | |
| 'base:inv_fertility'], | |
| 'out_type': {'dim': 1, 'shape': (None, 1)}}, | |
| 'att': {'axes': 'except_batch', | |
| 'class': 'merge_dims', | |
| 'from': ['att0']}, | |
| 'att0': {'base': 'base:enc_value', | |
| 'class': 'generic_attention', | |
| 'weights': 'att_weights'}, | |
| 'att_weights': {'class': 'softmax_over_spatial', | |
| 'from': ['energy_reinterpreted'], | |
| 'window_size': 5, | |
| 'window_start': 'p_t_in'}, | |
| 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, | |
| 'energy': {'activation': None, | |
| 'class': 'linear', | |
| 'from': ['energy_tanh'], | |
| 'n_out': 1, | |
| 'with_bias': False}, | |
| 'energy_in': {'class': 'combine', | |
| 'from': ['base:enc_ctx', | |
| 'weight_feedback', | |
| 's_transformed'], | |
| 'kind': 'add', | |
| 'n_out': 1024}, | |
| 'energy_reinterpreted': {'class': 'reinterpret_data', | |
| 'enforce_batch_major': True, | |
| 'from': 'energy', | |
| 'trainable': False}, | |
| 'energy_tanh': {'activation': 'tanh', | |
| 'class': 'activation', | |
| 'from': ['energy_in']}, | |
| 'output': {'beam_size': 12, | |
| 'cheating': False, | |
| 'class': 'choice', | |
| 'from': ['output_prob'], | |
| 'initial_output': 0, | |
| 'target': 'classes'}, | |
| 'output_prob': {'class': 'softmax', | |
| 'dropout': 0.3, | |
| 'from': ['readout'], | |
| 'loss': 'ce', | |
| 'loss_only_on_non_search': True, | |
| 'loss_opts': {'label_smoothing': 0}, | |
| 'target': 'classes'}, | |
| 'p_t': {'class': 'eval', | |
| 'eval': 'tf.to_float(source(0))', | |
| 'from': 'p_t_in'}, | |
| 'p_t_in': {'class': 'eval', | |
| 'eval': 'tf.squeeze(tf.argmax(source(0), axis=1, ' | |
| 'output_type=tf.int32), axis=1)', | |
| 'from': 'prev:att_weights', | |
| 'out_type': {'batch_dim_axis': 0, | |
| 'dtype': 'float32', | |
| 'shape': ()}}, | |
| 'readout': {'class': 'reduce_out', | |
| 'from': ['readout_in'], | |
| 'mode': 'max', | |
| 'num_pieces': 2}, | |
| 'readout_in': {'activation': None, | |
| 'class': 'linear', | |
| 'from': ['s', 'prev:target_embed', 'att'], | |
| 'n_out': 1000}, | |
| 's': {'class': 'rnn_cell', | |
| 'from': ['prev:target_embed', 'prev:att'], | |
| 'n_out': 1000, | |
| 'unit': 'LSTMBlock'}, | |
| 's_transformed': {'activation': None, | |
| 'class': 'linear', | |
| 'from': ['s'], | |
| 'n_out': 1024, | |
| 'with_bias': False}, | |
| 'target_embed': {'activation': None, | |
| 'class': 'linear', | |
| 'from': ['output'], | |
| 'initial_output': 0, | |
| 'n_out': 621, | |
| 'with_bias': False}, | |
| 'weight_feedback': {'activation': None, | |
| 'class': 'linear', | |
| 'from': ['prev:accum_att_weights'], | |
| 'n_out': 1024, | |
| 'with_bias': False}}} | |
| Unhandled exception <class 'AssertionError'> in thread <_MainThread(MainThread, started 140613301045056)>, proc 20215. | |
| Thread current, main, <_MainThread(MainThread, started 140613301045056)>: | |
| (Excluded thread.) | |
| That were all threads. | |
| EXCEPTION | |
| Traceback (most recent call last): | |
| File "./returnn/rnn.py", line 654, in <module> | |
| line: main(sys.argv) | |
| locals: | |
| main = <local> <function main at 0x7fe30c132158> | |
| sys = <local> <module 'sys' (built-in)> | |
| sys.argv = <local> ['./returnn/rnn.py', 'local-heuristic.argmax.win05.exp3.ctc.config'], _[0]: {len = 16} | |
| File "./returnn/rnn.py", line 642, in main | |
| line: execute_main_task() | |
| locals: | |
| execute_main_task = <global> <function execute_main_task at 0x7fe30c132048> | |
| File "./returnn/rnn.py", line 451, in execute_main_task | |
| line: engine.init_train_from_config(config, train_data, dev_data, eval_data) | |
| locals: | |
| engine = <global> <TFEngine.Engine object at 0x7fe04dbe8748> | |
| engine.init_train_from_config = <global> <bound method Engine.init_train_from_config of <TFEngine.Engine object at 0x7fe04dbe8748>> | |
| config = <global> <Config.Config object at 0x7fe3147c69e8> | |
| train_data = <global> <LibriSpeechCorpus 'train' epoch=1> | |
| dev_data = <global> <LibriSpeechCorpus 'dev' epoch=1> | |
| eval_data = <global> None | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFEngine.py", line 891, in init_train_from_config | |
| line: self.init_network_from_config(config) | |
| locals: | |
| self = <local> <TFEngine.Engine object at 0x7fe04dbe8748> | |
| self.init_network_from_config = <local> <bound method Engine.init_network_from_config of <TFEngine.Engine object at 0x7fe04dbe8748>> | |
| config = <local> <Config.Config object at 0x7fe3147c69e8> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFEngine.py", line 934, in init_network_from_config | |
| line: self._init_network(net_desc=net_dict, epoch=self.epoch) | |
| locals: | |
| self = <local> <TFEngine.Engine object at 0x7fe04dbe8748> | |
| self._init_network = <local> <bound method Engine._init_network of <TFEngine.Engine object at 0x7fe04dbe8748>> | |
| net_desc = <not found> | |
| net_dict = <local> {'source': {'class': 'eval', 'eval': 'tf.clip_by_value(source(0), -3.0, 3.0)'}, 'lstm0_fw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': 1, 'from': ['source']}, 'lstm0_bw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': -1, 'from': ['source']}, 'lstm0_p..., len = 14 | |
| epoch = <local> None | |
| self.epoch = <local> 1 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFEngine.py", line 1081, in _init_network | |
| line: self.network, self.updater = self.create_network( | |
| config=self.config, | |
| rnd_seed=net_random_seed, | |
| train_flag=train_flag, eval_flag=self.use_eval_flag, search_flag=self.use_search_flag, | |
| initial_learning_rate=getattr(self, "initial_learning_rate", None), | |
| net_dict=net_desc) | |
| locals: | |
| self = <local> <TFEngine.Engine object at 0x7fe04dbe8748> | |
| self.network = <local> None | |
| self.updater = <local> None | |
| self.create_network = <local> <bound method Engine.create_network of <class 'TFEngine.Engine'>> | |
| config = <not found> | |
| self.config = <local> <Config.Config object at 0x7fe3147c69e8> | |
| rnd_seed = <not found> | |
| net_random_seed = <local> 1 | |
| train_flag = <local> <tf.Tensor 'globals/train_flag:0' shape=() dtype=bool> | |
| eval_flag = <not found> | |
| self.use_eval_flag = <local> True | |
| search_flag = <not found> | |
| self.use_search_flag = <local> False | |
| initial_learning_rate = <not found> | |
| getattr = <builtin> <built-in function getattr> | |
| net_dict = <not found> | |
| net_desc = <local> {'source': {'class': 'eval', 'eval': 'tf.clip_by_value(source(0), -3.0, 3.0)'}, 'lstm0_fw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': 1, 'from': ['source']}, 'lstm0_bw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': -1, 'from': ['source']}, 'lstm0_p..., len = 14 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFEngine.py", line 1113, in create_network | |
| line: network.construct_from_dict(net_dict) | |
| locals: | |
| network = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| network.construct_from_dict = <local> <bound method TFNetwork.construct_from_dict of <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| net_dict = <local> {'source': {'class': 'eval', 'eval': 'tf.clip_by_value(source(0), -3.0, 3.0)'}, 'lstm0_fw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': 1, 'from': ['source']}, 'lstm0_bw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': -1, 'from': ['source']}, 'lstm0_p..., len = 14 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 460, in construct_from_dict | |
| line: self.construct_layer(net_dict, name) | |
| locals: | |
| self = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| self.construct_layer = <local> <bound method TFNetwork.construct_layer of <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| net_dict = <local> {'source': {'class': 'eval', 'eval': 'tf.clip_by_value(source(0), -3.0, 3.0)'}, 'lstm0_fw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': 1, 'from': ['source']}, 'lstm0_bw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': -1, 'from': ['source']}, 'lstm0_p..., len = 14 | |
| name = <local> 'decision', len = 8 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 652, in construct_layer | |
| line: layer_class.transform_config_dict(layer_desc, network=self, get_layer=get_layer) | |
| locals: | |
| layer_class = <local> <class 'TFNetworkRecLayer.DecideLayer'> | |
| layer_class.transform_config_dict = <local> <bound method BaseChoiceLayer.transform_config_dict of <class 'TFNetworkRecLayer.DecideLayer'>> | |
| layer_desc = <local> {'loss': 'edit_distance', 'target': 'classes', 'loss_only_on_non_search': False} | |
| network = <not found> | |
| self = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| get_layer = <local> <function TFNetwork.construct_layer.<locals>.get_layer at 0x7fe3147289d8> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 4089, in transform_config_dict | |
| line: super(BaseChoiceLayer, cls).transform_config_dict(d, network=network, get_layer=get_layer) | |
| locals: | |
| super = <builtin> <class 'super'> | |
| BaseChoiceLayer = <global> <class 'TFNetworkRecLayer.BaseChoiceLayer'> | |
| cls = <local> <class 'TFNetworkRecLayer.DecideLayer'> | |
| transform_config_dict = <not found> | |
| d = <local> {'loss': 'edit_distance', 'target': 'classes', 'loss_only_on_non_search': False} | |
| network = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| get_layer = <local> <function TFNetwork.construct_layer.<locals>.get_layer at 0x7fe3147289d8> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkLayer.py", line 448, in transform_config_dict | |
| line: for src_name in src_names | |
| locals: | |
| src_name = <not found> | |
| src_names = <local> ['output'], _[0]: {len = 6} | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkLayer.py", line 449, in <listcomp> | |
| line: d["sources"] = [ | |
| get_layer(src_name) | |
| for src_name in src_names | |
| if not src_name == "none"] | |
| locals: | |
| d = <not found> | |
| get_layer = <local> <function TFNetwork.construct_layer.<locals>.get_layer at 0x7fe3147289d8> | |
| src_name = <local> 'output', len = 6 | |
| src_names = <not found> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 607, in get_layer | |
| line: return self.construct_layer(net_dict=net_dict, name=src_name) # set get_layer to wrap construct_layer | |
| locals: | |
| self = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| self.construct_layer = <local> <bound method TFNetwork.construct_layer of <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| net_dict = <local> {'source': {'class': 'eval', 'eval': 'tf.clip_by_value(source(0), -3.0, 3.0)'}, 'lstm0_fw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': 1, 'from': ['source']}, 'lstm0_bw': {'class': 'rec', 'unit': 'nativelstm2', 'n_out': 1024, 'direction': -1, 'from': ['source']}, 'lstm0_p..., len = 14 | |
| name = <not found> | |
| src_name = <local> 'output', len = 6 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 655, in construct_layer | |
| line: return add_layer(name=name, layer_class=layer_class, **layer_desc) | |
| locals: | |
| add_layer = <local> <bound method TFNetwork.add_layer of <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| name = <local> 'output', len = 6 | |
| layer_class = <local> <class 'TFNetworkRecLayer.RecLayer'> | |
| layer_desc = <local> {'cheating': False, 'unit': {'output': {'class': 'choice', 'target': 'classes', 'beam_size': 12, 'cheating': False, 'from': ['output_prob'], 'initial_output': 0}, 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, 'target_embed': {'class': 'linear', 'activation': None, 'with_bias': Fals..., len = 7 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 760, in add_layer | |
| line: layer = self._create_layer(name=name, layer_class=layer_class, **layer_desc) | |
| locals: | |
| layer = <not found> | |
| self = <local> <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| self._create_layer = <local> <bound method TFNetwork._create_layer of <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| name = <local> 'output', len = 6 | |
| layer_class = <local> <class 'TFNetworkRecLayer.RecLayer'> | |
| layer_desc = <local> {'cheating': False, 'unit': {'output': {'class': 'choice', 'target': 'classes', 'beam_size': 12, 'cheating': False, 'from': ['output_prob'], 'initial_output': 0}, 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, 'target_embed': {'class': 'linear', 'activation': None, 'with_bias': Fals..., len = 7 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 701, in _create_layer | |
| line: layer_desc["output"] = layer_class.get_out_data_from_opts(**layer_desc) | |
| locals: | |
| layer_desc = <local> {'cheating': False, 'unit': {'output': {'class': 'choice', 'target': 'classes', 'beam_size': 12, 'cheating': False, 'from': ['output_prob'], 'initial_output': 0}, 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, 'target_embed': {'class': 'linear', 'activation': None, 'with_bias': Fals..., len = 9 | |
| layer_class = <local> <class 'TFNetworkRecLayer.RecLayer'> | |
| layer_class.get_out_data_from_opts = <local> <bound method RecLayer.get_out_data_from_opts of <class 'TFNetworkRecLayer.RecLayer'>> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 362, in get_out_data_from_opts | |
| line: subnet = _SubnetworkRecCell( | |
| parent_net=kwargs["network"], net_dict=unit, source_data=source_data, rec_layer_name=kwargs["name"]) | |
| locals: | |
| subnet = <not found> | |
| _SubnetworkRecCell = <global> <class 'TFNetworkRecLayer._SubnetworkRecCell'> | |
| parent_net = <not found> | |
| kwargs = <local> {'cheating': False, 'target': 'classes', 'max_seq_len': <tf.Tensor 'max_seq_len_encoder:0' shape=() dtype=int32>, 'n_out': <class 'Util.NotSpecified'>, '_target_layers': {}, 'name': 'output', 'network': <TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>}, len = 7 | |
| net_dict = <not found> | |
| unit = <local> {'output': {'class': 'choice', 'target': 'classes', 'beam_size': 12, 'cheating': False, 'from': ['output_prob'], 'initial_output': 0}, 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, 'target_embed': {'class': 'linear', 'activation': None, 'with_bias': False, 'from': ['output'], 'n_ou..., len = 19 | |
| source_data = <local> None | |
| rec_layer_name = <not found> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 917, in __init__ | |
| line: self._construct_template() | |
| locals: | |
| self = <local> <_SubnetworkRecCell of None> | |
| self._construct_template = <local> <bound method _SubnetworkRecCell._construct_template of <_SubnetworkRecCell of None>> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 1229, in _construct_template | |
| line: direct_get_layer.construct(layer.name) | |
| locals: | |
| direct_get_layer = <local> <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 1, parents None) | |
| direct_get_layer.construct = <local> <bound method _SubnetworkRecCell._construct_template.<locals>.GetLayer.construct of <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 1, parents None)> | |
| layer = <local> <_TemplateLayer(EvalLayer)(:template:eval) 'output/p_t_in' out_type=Data(shape=(), time_dim_axis=None, batch_shape_meta=[B]) (construction stack 'att_weights')> | |
| layer.name = <local> 'p_t_in', len = 6 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 1021, in construct | |
| line: self.__call__(layer_name_) | |
| locals: | |
| self = <local> <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 1, parents None) | |
| self.__call__ = <local> <bound method _SubnetworkRecCell._construct_template.<locals>.GetLayer.__call__ of <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 1, parents None)> | |
| layer_name_ = <local> 'p_t_in', len = 6 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 1188, in __call__ | |
| line: self.net.construct_layer( | |
| net_dict=self.net_dict, name=name, | |
| get_layer=default_get_layer, add_layer=default_get_layer.add_templated_layer) | |
| locals: | |
| self = <local> <_SubnetworkRecCell of None> | |
| self.net = <local> <TFNetwork 'root/output:rec-subnet' parent_net=<TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> | |
| self.net.construct_layer = <local> <bound method TFNetwork.construct_layer of <TFNetwork 'root/output:rec-subnet' parent_net=<TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>>> | |
| net_dict = <not found> | |
| self.net_dict = <local> {'output': {'class': 'choice', 'target': 'classes', 'beam_size': 12, 'cheating': False, 'from': ['output_prob'], 'initial_output': 0}, 'end': {'class': 'compare', 'from': ['output'], 'value': 0}, 'target_embed': {'class': 'linear', 'activation': None, 'with_bias': False, 'from': ['output'], 'n_ou..., len = 19 | |
| name = <local> 'p_t_in', len = 6 | |
| get_layer = <not found> | |
| default_get_layer = <local> <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 0, parents 'p_t_in') | |
| add_layer = <not found> | |
| default_get_layer.add_templated_layer = <local> <bound method _SubnetworkRecCell._construct_template.<locals>.GetLayer.add_templated_layer of <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 0, parents 'p_t_in')> | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetwork.py", line 655, in construct_layer | |
| line: return add_layer(name=name, layer_class=layer_class, **layer_desc) | |
| locals: | |
| add_layer = <local> <bound method _SubnetworkRecCell._construct_template.<locals>.GetLayer.add_templated_layer of <RecLayer construct template GetLayer>(safe False, allow_construct_in_call_nrs None, allow_uninitialized_template False, count 0, parents 'p_t_in')> | |
| name = <local> 'p_t_in', len = 6 | |
| layer_class = <local> <class 'TFNetworkLayer.EvalLayer'> | |
| layer_desc = <local> {'eval': 'tf.squeeze(tf.argmax(source(0), axis=1, output_type=tf.int32), axis=1)', 'out_type': {'shape': (), 'batch_dim_axis': 0, 'dtype': 'float32'}, 'sources': [<_TemplateLayer(SoftmaxOverSpatialLayer)(:prev:softmax_over_spatial) 'output/prev:att_weights' out_type=Data(shape=(1, None), time_dim... | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkRecLayer.py", line 1041, in add_templated_layer | |
| line: output = layer_class.get_out_data_from_opts(**layer_desc) | |
| locals: | |
| output = <not found> | |
| layer_class = <local> <class 'TFNetworkLayer.EvalLayer'> | |
| layer_class.get_out_data_from_opts = <local> <bound method CombineLayer.get_out_data_from_opts of <class 'TFNetworkLayer.EvalLayer'>> | |
| layer_desc = <local> {'eval': 'tf.squeeze(tf.argmax(source(0), axis=1, output_type=tf.int32), axis=1)', 'out_type': {'shape': (), 'batch_dim_axis': 0, 'dtype': 'float32'}, 'sources': [<_TemplateLayer(SoftmaxOverSpatialLayer)(:prev:softmax_over_spatial) 'output/prev:att_weights' out_type=Data(shape=(1, None), time_dim... | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkLayer.py", line 6069, in get_out_data_from_opts | |
| line: return super(CombineLayer, cls).get_out_data_from_opts(n_out=n_out, out_type=out_type_, sources=sources, **kwargs) | |
| locals: | |
| super = <builtin> <class 'super'> | |
| CombineLayer = <global> <class 'TFNetworkLayer.CombineLayer'> | |
| cls = <local> <class 'TFNetworkLayer.EvalLayer'> | |
| get_out_data_from_opts = <not found> | |
| n_out = <local> <class 'Util.NotSpecified'> | |
| out_type = <local> {'shape': (), 'batch_dim_axis': 0, 'dtype': 'float32'} | |
| out_type_ = <local> {'name': 'p_t_in_output', 'shape': (), 'dtype': 'float32', 'sparse': False, 'dim': 1, 'batch_dim_axis': 0, 'time_dim_axis': 2, 'feature_dim_axis': 1}, len = 8 | |
| sources = <local> [<_TemplateLayer(SoftmaxOverSpatialLayer)(:prev:softmax_over_spatial) 'output/prev:att_weights' out_type=Data(shape=(1, None), time_dim_axis=2, feature_dim_axis=1, batch_shape_meta=[B,F|1,T|'spatial:0:lstm0_pool']) (construction stack None)>] | |
| kwargs = <local> {'eval': 'tf.squeeze(tf.argmax(source(0), axis=1, output_type=tf.int32), axis=1)', 'name': 'p_t_in', 'network': <TFNetwork 'root/output:rec-subnet' parent_net=<TFNetwork 'root' train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=bool>> train=<tf.Tensor 'globals/train_flag:0' shape=() dtype=boo... | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkLayer.py", line 227, in get_out_data_from_opts | |
| line: return cls._base_get_out_data_from_opts(**kwargs) | |
| locals: | |
| cls = <local> <class 'TFNetworkLayer.EvalLayer'> | |
| cls._base_get_out_data_from_opts = <local> <bound method LayerBase._base_get_out_data_from_opts of <class 'TFNetworkLayer.EvalLayer'>> | |
| kwargs = <local> {'n_out': <class 'Util.NotSpecified'>, 'out_type': {'name': 'p_t_in_output', 'shape': (), 'dtype': 'float32', 'sparse': False, 'dim': 1, 'batch_dim_axis': 0, 'time_dim_axis': 2, 'feature_dim_axis': 1}, 'sources': [<_TemplateLayer(SoftmaxOverSpatialLayer)(:prev:softmax_over_spatial) 'output/prev:a..., len = 6 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFNetworkLayer.py", line 324, in _base_get_out_data_from_opts | |
| line: output = Data(**out_type) | |
| locals: | |
| output = <not found> | |
| Data = <global> <class 'TFUtil.Data'> | |
| out_type = <local> {'name': 'p_t_in_output', 'shape': (), 'dtype': 'float32', 'sparse': False, 'dim': 1, 'batch_dim_axis': 0, 'time_dim_axis': 2, 'feature_dim_axis': 1, 'beam': None}, len = 9 | |
| File "/home/ubuntu/rwth-i6/returnn-experiments/2018-asr-attention/librispeech/full-setup-attention/returnn/TFUtil.py", line 559, in __init__ | |
| line: assert 0 <= feature_dim_axis < self.batch_ndim | |
| locals: | |
| feature_dim_axis = <local> 1 | |
| self = <local> !AttributeError: 'Data' object has no attribute 'time_dim_axis' | |
| self.batch_ndim = <local> 1 | |
| AssertionError |
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