00-Baseline-Model-Evaluation.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"0-Baseline-Model-Evaluation.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyOJZRPDb0xLC4gW9O+wSSFx"},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"_uPkLIlOsBRT"},"source":["# Evaluating baseline models\n","* __Objective__: Evaluating the models described in the paper _Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation_ using same methods as descirbed in the paper to check the consistency of the results\n","* __Functionalities__: Allows evalauting baseline models \n","* __File Manager__: Google Drive\n","* __Runtime Type__: GPU\n","* __Notes__: To run this notebook, uncomment `line:268` and comment `line:269` in [this](https://github.com/utkarsh512/Ad-hominem-fallacies/blob/master/experiments/classification_experiments.py) script"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vuvQ2wQYou73","executionInfo":{"status":"ok","timestamp":1615640605405,"user_tz":-330,"elapsed":63199,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"ffcdd5eb-2f60-4b61-9433-5e7fb83bb01c"},"source":["# mounting Google Drive\n","from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_q4VXdX1o5p7","executionInfo":{"status":"ok","timestamp":1615645523004,"user_tz":-330,"elapsed":37277,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"e89ac442-5e11-49d4-bb9d-186afae55e2d"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/\n","rm -rf Ad-hominem-fallacies\n","git clone https://github.com/utkarsh512/Ad-hominem-fallacies.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'Ad-hominem-fallacies'...\n","remote: Enumerating objects: 46, done.\u001b[K\n","remote: Counting objects: 100% (46/46), done.\u001b[K\n","remote: Compressing objects: 100% (46/46), done.\u001b[K\n","remote: Total 152 (delta 25), reused 0 (delta 0), pack-reused 106\u001b[K\n","Receiving objects: 100% (152/152), 37.45 MiB | 10.90 MiB/s, done.\n","Resolving deltas: 100% (50/50), done.\n","Checking out files: 100% (77/77), done.\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8cH8F7I0pINZ","executionInfo":{"status":"ok","timestamp":1615645610358,"user_tz":-330,"elapsed":70637,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"e666f55b-843b-4c67-fce6-4aca02fa02b8"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","pip install virtualenv\n","virtualenv env --python=python3\n","source env/bin/activate\n","pip install lda scipy==1.1.0 nltk==3.2.5"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: virtualenv in /usr/local/lib/python3.7/dist-packages (20.4.2)\n","Requirement already satisfied: appdirs<2,>=1.4.3 in /usr/local/lib/python3.7/dist-packages (from virtualenv) (1.4.4)\n","Requirement already satisfied: distlib<1,>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from virtualenv) (0.3.1)\n","Requirement already satisfied: filelock<4,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from virtualenv) (3.0.12)\n","Requirement already satisfied: six<2,>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from virtualenv) (1.15.0)\n","Requirement already satisfied: importlib-metadata>=0.12; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from virtualenv) (3.7.0)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12; python_version < \"3.8\"->virtualenv) (3.7.4.3)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12; python_version < \"3.8\"->virtualenv) (3.4.1)\n","created virtual environment CPython3.7.10.final.0-64 in 6234ms\n","  creator CPython3Posix(dest=/content/gdrive/My Drive/Ad-hominem-fallacies/experiments/env, clear=False, no_vcs_ignore=False, global=False)\n","  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/.local/share/virtualenv)\n","    added seed packages: pip==21.0.1, setuptools==53.0.0, wheel==0.36.2\n","  activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator\n","Collecting lda\n","  Using cached lda-2.0.0-cp37-cp37m-manylinux1_x86_64.whl (351 kB)\n","Collecting scipy==1.1.0\n","  Using cached scipy-1.1.0-cp37-cp37m-manylinux1_x86_64.whl (31.2 MB)\n","Collecting nltk==3.2.5\n","  Using cached nltk-3.2.5-py3-none-any.whl\n","Collecting six\n","  Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)\n","Collecting numpy>=1.8.2\n","  Using cached numpy-1.20.1-cp37-cp37m-manylinux2010_x86_64.whl (15.3 MB)\n","Collecting pbr<4,>=0.6\n","  Using cached pbr-3.1.1-py2.py3-none-any.whl (99 kB)\n","Installing collected packages: six, pbr, numpy, scipy, nltk, lda\n","Successfully installed lda-2.0.0 nltk-3.2.5 numpy-1.20.1 pbr-3.1.1 scipy-1.1.0 six-1.15.0\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZoaT5qxTpQ-6","executionInfo":{"status":"ok","timestamp":1615645630874,"user_tz":-330,"elapsed":13328,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"d5e8fa93-d894-4690-93ff-af6cd47a29c4"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","wget https://public.ukp.informatik.tu-darmstadt.de/ih/RedditChangeMyView2017/en-top100k.embeddings.pkl.gz"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-03-13 14:26:57--  https://public.ukp.informatik.tu-darmstadt.de/ih/RedditChangeMyView2017/en-top100k.embeddings.pkl.gz\n","Resolving public.ukp.informatik.tu-darmstadt.de (public.ukp.informatik.tu-darmstadt.de)... 130.83.167.186\n","Connecting to public.ukp.informatik.tu-darmstadt.de (public.ukp.informatik.tu-darmstadt.de)|130.83.167.186|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 152970296 (146M) [application/octet-stream]\n","Saving to: ‘en-top100k.embeddings.pkl.gz’\n","\n","en-top100k.embeddin 100%[===================>] 145.88M  20.1MB/s    in 11s     \n","\n","2021-03-13 14:27:09 (13.1 MB/s) - ‘en-top100k.embeddings.pkl.gz’ saved [152970296/152970296]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yZfs6W5spwPw","executionInfo":{"status":"ok","timestamp":1615645679411,"user_tz":-330,"elapsed":36316,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"e88b40bc-88a7-4030-9355-1667aabc2dc4"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","tar -xvf sampled-threads-ah-delta-context3.tar.bz2 -C data/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sampled-threads-ah-delta-context3/\n","sampled-threads-ah-delta-context3/1213_delta_t1_dm10vxo.json\n","sampled-threads-ah-delta-context3/175_ah_t1_dlqrux0.json\n","sampled-threads-ah-delta-context3/647_ah_t1_cqy072s.json\n","sampled-threads-ah-delta-context3/261_ah_t1_cj87dst.json\n","sampled-threads-ah-delta-context3/134_delta_t1_dmnatwa.json\n","sampled-threads-ah-delta-context3/1070_ah_t1_di63b9y.json\n","sampled-threads-ah-delta-context3/846_ah_t1_cvyhbbh.json\n","sampled-threads-ah-delta-context3/575_delta_t1_cgjh9o2.json\n","sampled-threads-ah-delta-context3/284_ah_t1_d98wq0g.json\n","sampled-threads-ah-delta-context3/473_ah_t1_cjkayyc.json\n","sampled-threads-ah-delta-context3/871_delta_t1_djbfsuw.json\n","sampled-threads-ah-delta-context3/222_ah_t1_dlq9jlp.json\n","sampled-threads-ah-delta-context3/482_ah_t1_clrfkvu.json\n","sampled-threads-ah-delta-context3/1103_delta_t1_dmk47tv.json\n","sampled-threads-ah-delta-context3/821_ah_t1_cyb6qi4.json\n","sampled-threads-ah-delta-context3/157_delta_t1_cz0o1gz.json\n","sampled-threads-ah-delta-context3/280_ah_t1_d1gsg0i.json\n","sampled-threads-ah-delta-context3/1114_delta_t1_dkz6ij7.json\n","sampled-threads-ah-delta-context3/291_ah_t1_cwyfwds.json\n","sampled-threads-ah-delta-context3/551_delta_t1_db37o3u.json\n","sampled-threads-ah-delta-context3/308_ah_t1_d3s6bpa.json\n","sampled-threads-ah-delta-context3/686_delta_t1_cb9q9vz.json\n","sampled-threads-ah-delta-context3/64_ah_t1_dn7v1d2.json\n","sampled-threads-ah-delta-context3/792_ah_t1_dmfebte.json\n","sampled-threads-ah-delta-context3/865_ah_t1_cqyjpel.json\n","sampled-threads-ah-delta-context3/1117_ah_t1_d02r5f8.json\n","sampled-threads-ah-delta-context3/6_delta_t1_cu9omp7.json\n","sampled-threads-ah-delta-context3/315_ah_t1_d9vuw2p.json\n","sampled-threads-ah-delta-context3/1033_ah_t1_df80y7e.json\n","sampled-threads-ah-delta-context3/963_ah_t1_daw0128.json\n","sampled-threads-ah-delta-context3/195_delta_t1_crcwhyd.json\n","sampled-threads-ah-delta-context3/571_delta_t1_cc0e6s0.json\n","sampled-threads-ah-delta-context3/72_delta_t1_d17ilya.json\n","sampled-threads-ah-delta-context3/1089_delta_t1_dgadf90.json\n","sampled-threads-ah-delta-context3/564_delta_t1_cjwb0qb.json\n","sampled-threads-ah-delta-context3/408_ah_t1_dc201wj.json\n","sampled-threads-ah-delta-context3/86_ah_t1_d4m70l7.json\n","sampled-threads-ah-delta-context3/622_delta_t1_cki8rm6.json\n","sampled-threads-ah-delta-context3/157_ah_t1_cudvgcn.json\n","sampled-threads-ah-delta-context3/728_delta_t1_czikipe.json\n","sampled-threads-ah-delta-context3/1278_ah_t1_djte9xq.json\n","sampled-threads-ah-delta-context3/149_ah_t1_dcyi8ch.json\n","sampled-threads-ah-delta-context3/779_ah_t1_dl66hcq.json\n","sampled-threads-ah-delta-context3/506_ah_t1_d492xor.json\n","sampled-threads-ah-delta-context3/1277_ah_t1_dbt581e.json\n","sampled-threads-ah-delta-context3/343_ah_t1_djoctg3.json\n","sampled-threads-ah-delta-context3/1166_delta_t1_csrd67y.json\n","sampled-threads-ah-delta-context3/125_ah_t1_czgyvma.json\n","sampled-threads-ah-delta-context3/744_delta_t1_dlamoay.json\n","sampled-threads-ah-delta-context3/133_delta_t1_cpistdp.json\n","sampled-threads-ah-delta-context3/479_ah_t1_dk80cue.json\n","sampled-threads-ah-delta-context3/648_ah_t1_cko83bl.json\n","sampled-threads-ah-delta-context3/312_ah_t1_d2h9m8y.json\n","sampled-threads-ah-delta-context3/1203_ah_t1_dk13p85.json\n","sampled-threads-ah-delta-context3/457_ah_t1_dhdlxij.json\n","sampled-threads-ah-delta-context3/515_ah_t1_djpfbva.json\n","sampled-threads-ah-delta-context3/903_delta_t1_d8rl4e6.json\n","sampled-threads-ah-delta-context3/304_ah_t1_df2e66e.json\n","sampled-threads-ah-delta-context3/597_ah_t1_d76rmcd.json\n","sampled-threads-ah-delta-context3/224_delta_t1_cx2pp2o.json\n","sampled-threads-ah-delta-context3/47_delta_t1_crwte9c.json\n","sampled-threads-ah-delta-context3/228_ah_t1_d6q2dh8.json\n","sampled-threads-ah-delta-context3/659_delta_t1_chwzf6f.json\n","sampled-threads-ah-delta-context3/80_ah_t1_de92cak.json\n","sampled-threads-ah-delta-context3/872_delta_t1_ctsvyic.json\n","sampled-threads-ah-delta-context3/95_ah_t1_cokvmf9.json\n","sampled-threads-ah-delta-context3/914_delta_t1_d8lvw0x.json\n","sampled-threads-ah-delta-context3/355_ah_t1_d6txh2j.json\n","sampled-threads-ah-delta-context3/624_delta_t1_cvcpdrv.json\n","sampled-threads-ah-delta-context3/244_ah_t1_dheb883.json\n","sampled-threads-ah-delta-context3/84_ah_t1_ctj6vu5.json\n","sampled-threads-ah-delta-context3/293_ah_t1_d4svrgf.json\n","sampled-threads-ah-delta-context3/1286_delta_t1_d0coqrn.json\n","sampled-threads-ah-delta-context3/68_delta_t1_dcrd6ok.json\n","sampled-threads-ah-delta-context3/313_delta_t1_d9546fg.json\n","sampled-threads-ah-delta-context3/1219_ah_t1_d7udasc.json\n","sampled-threads-ah-delta-context3/512_ah_t1_dgam2z6.json\n","sampled-threads-ah-delta-context3/359_ah_t1_dm8a3y7.json\n","sampled-threads-ah-delta-context3/1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Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"923c6a7a-2069-43c9-f9d1-1dbf2e0d8a09"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","pip install lda\n","python classification_experiments.py --model cnn"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting lda\n","  Using cached https://files.pythonhosted.org/packages/d1/45/8bf6862a599649350280bf0020b0b23c4948304158702632f677dc967737/lda-2.0.0-cp37-cp37m-manylinux1_x86_64.whl\n","Requirement already satisfied: numpy<2.0,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from lda) (1.19.5)\n","Collecting pbr<4,>=0.6\n","  Using cached https://files.pythonhosted.org/packages/0c/5d/b077dbf309993d52c1d71e6bf6fe443a8029ea215135ebbe0b1b10e7aefc/pbr-3.1.1-py2.py3-none-any.whl\n","Installing collected packages: pbr, lda\n","Successfully installed lda-2.0.0 pbr-3.1.1\n","2021-03-13 13:13:00.977974: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","WARNING:tensorflow:From classification_experiments.py:76: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.config.list_physical_devices('GPU')` instead.\n","2021-03-13 13:13:02.677776: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:13:02.678756: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n","2021-03-13 13:13:02.739952: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:02.740544: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:13:02.740589: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:13:02.839802: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:13:02.839906: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:13:03.012979: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:13:03.062272: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:13:03.319888: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:13:03.351111: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:13:03.354741: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:13:03.354885: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:03.355557: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:03.358182: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:13:03.360062: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:13:07.755779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 13:13:07.755829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 13:13:07.755843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 13:13:07.818660: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.819388: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.819985: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.820498: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n","2021-03-13 13:13:07.820556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 13994 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","2021-03-13 13:13:07.825704: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:13:07.825820: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.826379: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:13:07.826430: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:13:07.826481: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:13:07.826504: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:13:07.826528: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:13:07.826553: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:13:07.826577: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:13:07.826599: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:13:07.826637: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:13:07.826711: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.827292: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.827828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:13:07.828118: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:13:07.828224: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.828754: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:13:07.828795: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:13:07.828831: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:13:07.828856: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:13:07.828879: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:13:07.828901: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:13:07.828925: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:13:07.828947: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:13:07.828968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:13:07.829035: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.829677: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.830175: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:13:07.830237: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 13:13:07.830254: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 13:13:07.830268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 13:13:07.830364: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.830920: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:13:07.831435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13994 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","Using GPU\n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Running fold 1/10\n","Max length in training data:  406\n","Compiling model\n","/content/gdrive/My Drive/Ad-hominem-fallacies/experiments/nnclassifiers.py:380: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","  result_x_numpy_array = numpy.array(result_x)\n","2021-03-13 13:13:14.757072: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_286\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 13:13:14.812572: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n","2021-03-13 13:13:14.814627: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz\n","Epoch 1/5\n","2021-03-13 13:13:17.683314: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:13:20.201936: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:13:20.290717: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","204/204 [==============================] - 54s 91ms/step - loss: 1.4602\n","Epoch 2/5\n","204/204 [==============================] - 18s 90ms/step - loss: 0.6521\n","Epoch 3/5\n","204/204 [==============================] - 19s 91ms/step - loss: 0.4674\n","Epoch 4/5\n","204/204 [==============================] - 19s 92ms/step - loss: 0.3662\n","Epoch 5/5\n","204/204 [==============================] - 19s 93ms/step - loss: 0.2897\n","2021-03-13 13:15:23.657872: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_2418\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 24ms/step\n","Evaluating after 1 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       283        71       354\n","      None        84       287       371\n","     [sum]       367       358       725\n","Accuracy: 0.7862 (95ppCI: 0.0298)  Macro F1: 0.7865\n","AH: P=0.7711/R=0.7994/F1=0.7850 None: P=0.8017/R=0.7736/F1=0.7874\n","Running fold 2/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:15:26.393576: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_2918\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 22s 94ms/step - loss: 1.4350\n","Epoch 2/5\n","204/204 [==============================] - 19s 95ms/step - loss: 0.6886\n","Epoch 3/5\n","204/204 [==============================] - 19s 95ms/step - loss: 0.4915\n","Epoch 4/5\n","204/204 [==============================] - 20s 96ms/step - loss: 0.3548\n","Epoch 5/5\n","204/204 [==============================] - 20s 96ms/step - loss: 0.3212\n","2021-03-13 13:17:06.029776: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_5050\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 1s 25ms/step\n","Evaluating after 2 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       556       164       720\n","      None       134       596       730\n","     [sum]       690       760      1450\n","Accuracy: 0.7945 (95ppCI: 0.0208)  Macro F1: 0.7947\n","AH: P=0.8058/R=0.7722/F1=0.7887 None: P=0.7842/R=0.8164/F1=0.8000\n","Running fold 3/10\n","Max length in training data:  404\n","Compiling model\n","2021-03-13 13:17:07.567263: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_5550\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 24s 100ms/step - loss: 1.4383\n","Epoch 2/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.6825\n","Epoch 3/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.4665\n","Epoch 4/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.3856\n","Epoch 5/5\n","204/204 [==============================] - 20s 100ms/step - loss: 0.3259\n","2021-03-13 13:18:52.092635: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_7682\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 3 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       870       228      1098\n","      None       206       871      1077\n","     [sum]      1076      1099      2175\n","Accuracy: 0.8005 (95ppCI: 0.0168)  Macro F1: 0.8005\n","AH: P=0.8086/R=0.7923/F1=0.8004 None: P=0.7925/R=0.8087/F1=0.8006\n","Running fold 4/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:18:54.618494: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_8182\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 98ms/step - loss: 1.4589\n","Epoch 2/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.7039\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4581\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3704\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3138\n","2021-03-13 13:20:37.384038: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_10314\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 4 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1111       335      1446\n","      None       241      1213      1454\n","     [sum]      1352      1548      2900\n","Accuracy: 0.8014 (95ppCI: 0.0145)  Macro F1: 0.8020\n","AH: P=0.8217/R=0.7683/F1=0.7941 None: P=0.7836/R=0.8343/F1=0.8081\n","Running fold 5/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:20:39.917445: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_10814\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 98ms/step - loss: 1.4278\n","Epoch 2/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.6879\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4775\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3917\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3151\n","2021-03-13 13:22:22.855888: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_12946\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 5 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1363       444      1807\n","      None       281      1537      1818\n","     [sum]      1644      1981      3625\n","Accuracy: 0.8000 (95ppCI: 0.0130)  Macro F1: 0.8012\n","AH: P=0.8291/R=0.7543/F1=0.7899 None: P=0.7759/R=0.8454/F1=0.8092\n","Running fold 6/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:22:25.363458: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_13446\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 98ms/step - loss: 1.3883\n","Epoch 2/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.6744\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4633\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4010\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3086\n","2021-03-13 13:24:08.117026: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_15578\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 6 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1635       541      2176\n","      None       316      1858      2174\n","     [sum]      1951      2399      4350\n","Accuracy: 0.8030 (95ppCI: 0.0118)  Macro F1: 0.8046\n","AH: P=0.8380/R=0.7514/F1=0.7923 None: P=0.7745/R=0.8546/F1=0.8126\n","Running fold 7/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:24:10.618067: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_16078\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 98ms/step - loss: 1.4080\n","Epoch 2/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.6826\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4954\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3898\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3070\n","2021-03-13 13:25:53.662470: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_18210\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 7 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1929       617      2546\n","      None       364      2165      2529\n","     [sum]      2293      2782      5075\n","Accuracy: 0.8067 (95ppCI: 0.0109)  Macro F1: 0.8083\n","AH: P=0.8413/R=0.7577/F1=0.7973 None: P=0.7782/R=0.8561/F1=0.8153\n","Running fold 8/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:25:56.189245: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_18710\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 98ms/step - loss: 1.4206\n","Epoch 2/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.6774\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4507\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3929\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3009\n","2021-03-13 13:27:39.080184: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_20842\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 25ms/step\n","Evaluating after 8 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2227       682      2909\n","      None       430      2461      2891\n","     [sum]      2657      3143      5800\n","Accuracy: 0.8083 (95ppCI: 0.0101)  Macro F1: 0.8095\n","AH: P=0.8382/R=0.7656/F1=0.8002 None: P=0.7830/R=0.8513/F1=0.8157\n","Running fold 9/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:27:41.569779: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_21342\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 99ms/step - loss: 1.4657\n","Epoch 2/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.6761\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4330\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3566\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3436\n","2021-03-13 13:29:24.642834: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_23474\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 1s 25ms/step\n","Evaluating after 9 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2523       741      3264\n","      None       513      2748      3261\n","     [sum]      3036      3489      6525\n","Accuracy: 0.8078 (95ppCI: 0.0096)  Macro F1: 0.8086\n","AH: P=0.8310/R=0.7730/F1=0.8010 None: P=0.7876/R=0.8427/F1=0.8142\n","Running fold 10/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:29:26.152617: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_23974\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 23s 101ms/step - loss: 1.4207\n","Epoch 2/5\n","204/204 [==============================] - 20s 99ms/step - loss: 0.6649\n","Epoch 3/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.4814\n","Epoch 4/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3839\n","Epoch 5/5\n","204/204 [==============================] - 20s 98ms/step - loss: 0.3158\n","2021-03-13 13:31:09.816118: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_26106\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 2s 32ms/step\n","Evaluating after 10 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2825       797      3622\n","      None       597      3023      3620\n","     [sum]      3422      3820      7242\n","Accuracy: 0.8075 (95ppCI: 0.0091)  Macro F1: 0.8080\n","AH: P=0.8255/R=0.7800/F1=0.8021 None: P=0.7914/R=0.8351/F1=0.8126\n","Final evaluation; reader.input_path_train was data/experiments/ah-classification1/exported-3621-sampled-positive-negative-ah-no-context.json\n","↓gold pr.→        AH      None     [sum]\n","        AH      2825       797      3622\n","      None       597      3023      3620\n","     [sum]      3422      3820      7242\n","Accuracy: 0.8075 (95ppCI: 0.0091)  Macro F1: 0.8080\n","AH: P=0.8255/R=0.7800/F1=0.8021 None: P=0.7914/R=0.8351/F1=0.8126\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzQ_M8vM3aen","executionInfo":{"status":"ok","timestamp":1615643235668,"user_tz":-330,"elapsed":881660,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"e3a5aee1-6ecd-49f6-ffe9-74c97bd7768f"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","pip install lda\n","python classification_experiments.py --model bilstm"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: lda in /usr/local/lib/python3.7/dist-packages (2.0.0)\n","Requirement already satisfied: numpy<2.0,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from lda) (1.19.5)\n","Requirement already satisfied: pbr<4,>=0.6 in /usr/local/lib/python3.7/dist-packages (from lda) (3.1.1)\n","2021-03-13 13:32:36.490056: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","WARNING:tensorflow:From classification_experiments.py:76: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.config.list_physical_devices('GPU')` instead.\n","2021-03-13 13:32:38.079147: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:32:38.079934: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n","2021-03-13 13:32:38.110810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.111365: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:32:38.111408: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:32:38.117491: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:32:38.117577: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:32:38.122103: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:32:38.122522: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:32:38.124406: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:32:38.125115: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:32:38.125320: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:32:38.125432: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.126039: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.126673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:32:38.126732: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:32:38.629348: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 13:32:38.629400: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 13:32:38.629420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 13:32:38.629600: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.630229: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.630789: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.631293: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n","2021-03-13 13:32:38.631344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 13994 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","2021-03-13 13:32:38.631902: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:32:38.632006: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.632531: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:32:38.632578: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:32:38.632633: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:32:38.632657: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:32:38.632677: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:32:38.632695: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:32:38.632724: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:32:38.632743: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:32:38.632762: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:32:38.632824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.633362: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.633854: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:32:38.634103: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 13:32:38.634198: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.634730: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 13:32:38.634770: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 13:32:38.634807: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:32:38.634827: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:32:38.634846: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 13:32:38.634864: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 13:32:38.634884: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 13:32:38.634902: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 13:32:38.634920: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 13:32:38.634978: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.635498: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.635980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 13:32:38.636011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 13:32:38.636026: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 13:32:38.636040: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 13:32:38.636135: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.636694: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 13:32:38.637188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13994 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","Using GPU\n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Warning: instance contains only a single token: \n","Running fold 1/10\n","Max length in training data:  406\n","Compiling model\n","/content/gdrive/My Drive/Ad-hominem-fallacies/experiments/nnclassifiers.py:380: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","  result_x_numpy_array = numpy.array(result_x)\n","2021-03-13 13:32:47.359202: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_7062\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 13:32:47.364840: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n","2021-03-13 13:32:47.365195: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz\n","Epoch 1/5\n","2021-03-13 13:32:58.402763: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 13:32:58.860113: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 13:32:59.064576: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","204/204 [==============================] - 27s 62ms/step - loss: 0.6834\n","Epoch 2/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.4503\n","Epoch 3/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.3019\n","Epoch 4/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.1850\n","Epoch 5/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.1283\n","2021-03-13 13:34:05.875032: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_28540\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 23ms/step\n","Evaluating after 1 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       289        65       354\n","      None       117       254       371\n","     [sum]       406       319       725\n","Accuracy: 0.7490 (95ppCI: 0.0316)  Macro F1: 0.7523\n","AH: P=0.7118/R=0.8164/F1=0.7605 None: P=0.7962/R=0.6846/F1=0.7362\n","Running fold 2/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:34:14.485038: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_42445\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 25s 64ms/step - loss: 0.6918\n","Epoch 2/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.4627\n","Epoch 3/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.3127\n","Epoch 4/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.1806\n","Epoch 5/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.1004\n","2021-03-13 13:35:31.526744: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_63923\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 25ms/step\n","Evaluating after 2 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       562       158       720\n","      None       192       538       730\n","     [sum]       754       696      1450\n","Accuracy: 0.7586 (95ppCI: 0.0220)  Macro F1: 0.7590\n","AH: P=0.7454/R=0.7806/F1=0.7626 None: P=0.7730/R=0.7370/F1=0.7546\n","Running fold 3/10\n","Max length in training data:  404\n","Compiling model\n","2021-03-13 13:35:40.723763: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_77828\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 25s 64ms/step - loss: 0.6750\n","Epoch 2/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.4890\n","Epoch 3/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.3240\n","Epoch 4/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.1890\n","Epoch 5/5\n","204/204 [==============================] - 13s 62ms/step - loss: 0.1038\n","2021-03-13 13:36:57.868518: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_99306\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 6s 25ms/step\n","Evaluating after 3 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH       859       239      1098\n","      None       258       819      1077\n","     [sum]      1117      1058      2175\n","Accuracy: 0.7715 (95ppCI: 0.0176)  Macro F1: 0.7715\n","AH: P=0.7690/R=0.7823/F1=0.7756 None: P=0.7741/R=0.7604/F1=0.7672\n","Running fold 4/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:37:06.852656: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_113211\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 25s 63ms/step - loss: 0.6902\n","Epoch 2/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.4619\n","Epoch 3/5\n","204/204 [==============================] - 13s 66ms/step - loss: 0.3091\n","Epoch 4/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.2040\n","Epoch 5/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.1158\n","2021-03-13 13:38:24.150978: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_134689\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 24ms/step\n","Evaluating after 4 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1157       289      1446\n","      None       370      1084      1454\n","     [sum]      1527      1373      2900\n","Accuracy: 0.7728 (95ppCI: 0.0153)  Macro F1: 0.7732\n","AH: P=0.7577/R=0.8001/F1=0.7783 None: P=0.7895/R=0.7455/F1=0.7669\n","Running fold 5/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:38:33.410457: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_148594\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 24s 64ms/step - loss: 0.6944\n","Epoch 2/5\n","204/204 [==============================] - 13s 62ms/step - loss: 0.4715\n","Epoch 3/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.3121\n","Epoch 4/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.2038\n","Epoch 5/5\n","204/204 [==============================] - 13s 65ms/step - loss: 0.1107\n","2021-03-13 13:39:49.651538: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_170072\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 25ms/step\n","Evaluating after 5 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1416       391      1807\n","      None       433      1385      1818\n","     [sum]      1849      1776      3625\n","Accuracy: 0.7727 (95ppCI: 0.0136)  Macro F1: 0.7728\n","AH: P=0.7658/R=0.7836/F1=0.7746 None: P=0.7798/R=0.7618/F1=0.7707\n","Running fold 6/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:39:58.605713: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_183977\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 25s 63ms/step - loss: 0.6744\n","Epoch 2/5\n","204/204 [==============================] - 13s 62ms/step - loss: 0.4501\n","Epoch 3/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.3041\n","Epoch 4/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.2129\n","Epoch 5/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.1077\n","2021-03-13 13:41:15.685762: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_205455\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 6s 25ms/step\n","Evaluating after 6 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1704       472      2176\n","      None       489      1685      2174\n","     [sum]      2193      2157      4350\n","Accuracy: 0.7791 (95ppCI: 0.0123)  Macro F1: 0.7791\n","AH: P=0.7770/R=0.7831/F1=0.7800 None: P=0.7812/R=0.7751/F1=0.7781\n","Running fold 7/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:41:24.860656: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_219360\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 26s 64ms/step - loss: 0.6720\n","Epoch 2/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.4469\n","Epoch 3/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.3338\n","Epoch 4/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.1993\n","Epoch 5/5\n","204/204 [==============================] - 13s 65ms/step - loss: 0.1245\n","2021-03-13 13:42:42.875906: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_240838\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 6s 24ms/step\n","Evaluating after 7 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      1991       555      2546\n","      None       561      1968      2529\n","     [sum]      2552      2523      5075\n","Accuracy: 0.7801 (95ppCI: 0.0114)  Macro F1: 0.7801\n","AH: P=0.7802/R=0.7820/F1=0.7811 None: P=0.7800/R=0.7782/F1=0.7791\n","Running fold 8/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:42:52.363026: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_254743\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 25s 63ms/step - loss: 0.6869\n","Epoch 2/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.4655\n","Epoch 3/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.3253\n","Epoch 4/5\n","204/204 [==============================] - 13s 65ms/step - loss: 0.1865\n","Epoch 5/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.1056\n","2021-03-13 13:44:09.839480: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_276221\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 27ms/step\n","Evaluating after 8 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2280       629      2909\n","      None       642      2249      2891\n","     [sum]      2922      2878      5800\n","Accuracy: 0.7809 (95ppCI: 0.0106)  Macro F1: 0.7809\n","AH: P=0.7803/R=0.7838/F1=0.7820 None: P=0.7814/R=0.7779/F1=0.7797\n","Running fold 9/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:44:19.093169: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_290126\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 26s 64ms/step - loss: 0.6846\n","Epoch 2/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.4668\n","Epoch 3/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.3338\n","Epoch 4/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.2030\n","Epoch 5/5\n","204/204 [==============================] - 13s 63ms/step - loss: 0.1185\n","2021-03-13 13:45:36.536058: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_311604\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 6s 26ms/step\n","Evaluating after 9 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2585       679      3264\n","      None       746      2515      3261\n","     [sum]      3331      3194      6525\n","Accuracy: 0.7816 (95ppCI: 0.0100)  Macro F1: 0.7817\n","AH: P=0.7760/R=0.7920/F1=0.7839 None: P=0.7874/R=0.7712/F1=0.7792\n","Running fold 10/10\n","Max length in training data:  406\n","Compiling model\n","2021-03-13 13:45:45.904797: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_325509\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","204/204 [==============================] - 26s 64ms/step - loss: 0.6813\n","Epoch 2/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.4763\n","Epoch 3/5\n","204/204 [==============================] - 13s 65ms/step - loss: 0.3034\n","Epoch 4/5\n","204/204 [==============================] - 13s 64ms/step - loss: 0.2040\n","Epoch 5/5\n","204/204 [==============================] - 13s 65ms/step - loss: 0.1074\n","2021-03-13 13:47:04.452667: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_346987\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","23/23 [==============================] - 5s 24ms/step\n","Evaluating after 10 folds\n","↓gold pr.→        AH      None     [sum]\n","        AH      2825       797      3622\n","      None       791      2829      3620\n","     [sum]      3616      3626      7242\n","Accuracy: 0.7807 (95ppCI: 0.0095)  Macro F1: 0.7807\n","AH: P=0.7812/R=0.7800/F1=0.7806 None: P=0.7802/R=0.7815/F1=0.7808\n","Final evaluation; reader.input_path_train was data/experiments/ah-classification1/exported-3621-sampled-positive-negative-ah-no-context.json\n","↓gold pr.→        AH      None     [sum]\n","        AH      2825       797      3622\n","      None       791      2829      3620\n","     [sum]      3616      3626      7242\n","Accuracy: 0.7807 (95ppCI: 0.0095)  Macro F1: 0.7807\n","AH: P=0.7812/R=0.7800/F1=0.7806 None: P=0.7802/R=0.7815/F1=0.7808\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"GahLy-de8W9c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1615646572046,"user_tz":-330,"elapsed":856222,"user":{"displayName":"Utkarsh Patel","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj4WnjwP-2YGoFk8O-jMTl4jmo7YjKJ6PEf7WDUbw=s64","userId":"14292413845157007490"}},"outputId":"549aed1f-5837-47fe-c80f-107bbf476765"},"source":["%%shell\n","cd /content/gdrive/'My Drive'/Ad-hominem-fallacies/experiments\n","pip install lda\n","python classification_experiments.py --model ssase"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: lda in /usr/local/lib/python3.7/dist-packages (2.0.0)\n","Requirement already satisfied: pbr<4,>=0.6 in /usr/local/lib/python3.7/dist-packages (from lda) (3.1.1)\n","Requirement already satisfied: numpy<2.0,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from lda) (1.19.5)\n","Collecting tensorflow\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/dc/e8c5e7983866fa4ef3fd619faa35f660b95b01a2ab62b3884f038ccab542/tensorflow-2.4.1-cp37-cp37m-manylinux2010_x86_64.whl (394.3MB)\n","\u001b[K     |████████████████████████████████| 394.3MB 39kB/s \n","\u001b[?25hCollecting tensorboard~=2.4\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/64/21/eebd23060763fedeefb78bc2b286e00fa1d8abda6f70efa2ee08c28af0d4/tensorboard-2.4.1-py3-none-any.whl (10.6MB)\n","\u001b[K     |████████████████████████████████| 10.6MB 53.5MB/s \n","\u001b[?25hRequirement already satisfied, skipping upgrade: h5py~=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (2.10.0)\n","Requirement already satisfied, skipping upgrade: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.36.2)\n","Requirement already satisfied, skipping upgrade: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.12.4)\n","Requirement already satisfied, skipping upgrade: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.12.1)\n","Requirement already satisfied, skipping upgrade: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.7.4.3)\n","Collecting gast==0.3.3\n","  Downloading https://files.pythonhosted.org/packages/d6/84/759f5dd23fec8ba71952d97bcc7e2c9d7d63bdc582421f3cd4be845f0c98/gast-0.3.3-py2.py3-none-any.whl\n","Requirement already satisfied, skipping upgrade: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.10.0)\n","Requirement already satisfied, skipping upgrade: grpcio~=1.32.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.32.0)\n","Requirement already satisfied, skipping upgrade: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.12)\n","Requirement already satisfied, skipping upgrade: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (3.3.0)\n","Requirement already satisfied, skipping upgrade: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.15.0)\n","Requirement already satisfied, skipping upgrade: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.19.5)\n","Requirement already satisfied, skipping upgrade: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.0)\n","Collecting tensorflow-estimator<2.5.0,>=2.4.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/7e/622d9849abf3afb81e482ffc170758742e392ee129ce1540611199a59237/tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462kB)\n","\u001b[K     |████████████████████████████████| 471kB 51.2MB/s \n","\u001b[?25hRequirement already satisfied, skipping upgrade: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.1.2)\n","Requirement already satisfied, skipping upgrade: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (1.6.3)\n","Requirement already satisfied, skipping upgrade: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow) (0.2.0)\n","Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (0.4.3)\n","Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (1.27.1)\n","Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (1.8.0)\n","Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (54.0.0)\n","Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (3.3.4)\n","Requirement already satisfied, skipping upgrade: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (2.23.0)\n","Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow) (1.0.1)\n","Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (1.3.0)\n","Requirement already satisfied, skipping upgrade: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.7.2)\n","Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.2.8)\n","Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (4.2.1)\n","Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow) (3.7.0)\n","Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2.10)\n","Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (2020.12.5)\n","Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (1.24.3)\n","Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow) (3.0.4)\n","Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow) (3.1.0)\n","Requirement already satisfied, skipping upgrade: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow) (0.4.8)\n","Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.4->tensorflow) (3.4.1)\n","Installing collected packages: tensorboard, gast, tensorflow-estimator, tensorflow\n","  Found existing installation: tensorboard 2.1.1\n","    Uninstalling tensorboard-2.1.1:\n","      Successfully uninstalled tensorboard-2.1.1\n","  Found existing installation: gast 0.2.2\n","    Uninstalling gast-0.2.2:\n","      Successfully uninstalled gast-0.2.2\n","  Found existing installation: tensorflow-estimator 2.1.0\n","    Uninstalling tensorflow-estimator-2.1.0:\n","      Successfully uninstalled tensorflow-estimator-2.1.0\n","  Found existing installation: tensorflow 2.1.0\n","    Uninstalling tensorflow-2.1.0:\n","      Successfully uninstalled tensorflow-2.1.0\n","Successfully installed gast-0.3.3 tensorboard-2.4.1 tensorflow-2.4.1 tensorflow-estimator-2.4.0\n","Collecting keras\n","  Downloading https://files.pythonhosted.org/packages/44/e1/dc0757b20b56c980b5553c1b5c4c32d378c7055ab7bfa92006801ad359ab/Keras-2.4.3-py2.py3-none-any.whl\n","Requirement already satisfied, skipping upgrade: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras) (1.4.1)\n","Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.7/dist-packages (from keras) (2.10.0)\n","Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras) (3.13)\n","Requirement already satisfied, skipping upgrade: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras) (1.19.5)\n","Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.7/dist-packages (from h5py->keras) (1.15.0)\n","Installing collected packages: keras\n","  Found existing installation: Keras 2.3.1\n","    Uninstalling Keras-2.3.1:\n","      Successfully uninstalled Keras-2.3.1\n","Successfully installed keras-2.4.3\n","2021-03-13 14:29:43.220230: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","WARNING:tensorflow:From classification_experiments.py:99: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.config.list_physical_devices('GPU')` instead.\n","2021-03-13 14:29:45.126366: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n","To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n","2021-03-13 14:29:45.126639: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 14:29:45.128666: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n","2021-03-13 14:29:45.157995: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.158563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 14:29:45.158616: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 14:29:45.174456: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 14:29:45.174529: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 14:29:45.176391: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 14:29:45.176778: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 14:29:45.180848: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 14:29:45.183198: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 14:29:45.183921: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 14:29:45.184032: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.184626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.185132: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 14:29:45.185255: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 14:29:45.782806: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 14:29:45.782853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 14:29:45.782865: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 14:29:45.783119: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.783733: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.784278: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.784775: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n","2021-03-13 14:29:45.784820: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 13968 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","2021-03-13 14:29:45.785425: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 14:29:45.785534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.786079: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 14:29:45.786123: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 14:29:45.786165: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 14:29:45.786185: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 14:29:45.786203: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 14:29:45.786221: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 14:29:45.786240: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 14:29:45.786258: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 14:29:45.786276: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 14:29:45.786338: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.786881: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.787366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 14:29:45.787723: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-03-13 14:29:45.787819: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.788349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n","coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n","2021-03-13 14:29:45.788388: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-03-13 14:29:45.788424: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 14:29:45.788445: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 14:29:45.788461: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-03-13 14:29:45.788480: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-03-13 14:29:45.788497: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-03-13 14:29:45.788515: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-03-13 14:29:45.788533: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-03-13 14:29:45.788593: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.789147: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.789650: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-03-13 14:29:45.789685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-03-13 14:29:45.789705: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-03-13 14:29:45.789715: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-03-13 14:29:45.789800: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.790335: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-03-13 14:29:45.790837: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13968 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n","Using GPU\n","Running fold 1/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten (Flatten)               (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten[0][0]                    \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","/content/gdrive/My Drive/Ad-hominem-fallacies/experiments/nnclassifiers.py:380: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n","  result_x_numpy_array = numpy.array(result_x)\n","2021-03-13 14:29:58.428623: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_3741\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:29:58.435889: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n","2021-03-13 14:29:58.436395: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz\n","Epoch 1/5\n","2021-03-13 14:30:05.622500: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-03-13 14:30:06.100804: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-03-13 14:30:06.327100: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","73/73 [==============================] - 26s 156ms/step - loss: 1.7576\n","Epoch 2/5\n","73/73 [==============================] - 11s 158ms/step - loss: 0.6780\n","Epoch 3/5\n","73/73 [==============================] - 11s 152ms/step - loss: 0.6260\n","Epoch 4/5\n","73/73 [==============================] - 11s 153ms/step - loss: 0.6066\n","Epoch 5/5\n","73/73 [==============================] - 11s 151ms/step - loss: 0.6362\n","2021-03-13 14:31:09.837126: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_15146\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 84ms/step\n","2021-03-13 14:31:13.545958: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_18796\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:31:17.203986: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 246205400 exceeds 10% of free system memory.\n","Collected words+weights saved to /tmp/visualization-context3/fold1.json\n","Evaluating after 1 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah        56        70       126\n","     delta        14       119       133\n","     [sum]        70       189       259\n","Accuracy: 0.6757 (95ppCI: 0.0570)  Macro F1: 0.6915\n","ah: P=0.8000/R=0.4444/F1=0.5714 delta: P=0.6296/R=0.8947/F1=0.7391\n","Running fold 2/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_2\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_1 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_1[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:31:20.754519: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_26131\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 20s 163ms/step - loss: 1.7027\n","Epoch 2/5\n","73/73 [==============================] - 12s 167ms/step - loss: 0.6550\n","Epoch 3/5\n","73/73 [==============================] - 12s 162ms/step - loss: 0.6154\n","Epoch 4/5\n","73/73 [==============================] - 12s 157ms/step - loss: 0.6005\n","Epoch 5/5\n","73/73 [==============================] - 12s 160ms/step - loss: 0.5629\n","2021-03-13 14:32:27.820878: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_37536\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 70ms/step\n","2021-03-13 14:32:31.979727: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_41186\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:32:35.416274: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 246205400 exceeds 10% of free system memory.\n","Collected words+weights saved to /tmp/visualization-context3/fold2.json\n","Evaluating after 2 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       170       105       275\n","     delta        49       194       243\n","     [sum]       219       299       518\n","Accuracy: 0.7027 (95ppCI: 0.0394)  Macro F1: 0.7104\n","ah: P=0.7763/R=0.6182/F1=0.6883 delta: P=0.6488/R=0.7984/F1=0.7159\n","Running fold 3/10\n","Max length in training data:  4514\n","Embeddings shape: (100004, 300)\n","Model: \"model_4\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_2 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_2[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:32:38.518897: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_48521\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 19s 154ms/step - loss: 2.0500\n","Epoch 2/5\n","73/73 [==============================] - 11s 153ms/step - loss: 0.6592\n","Epoch 3/5\n","73/73 [==============================] - 11s 152ms/step - loss: 0.6109\n","Epoch 4/5\n","73/73 [==============================] - 11s 154ms/step - loss: 0.6266\n","Epoch 5/5\n","73/73 [==============================] - 11s 155ms/step - loss: 0.5824\n","2021-03-13 14:33:42.830821: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_59926\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 100ms/step\n","2021-03-13 14:33:47.208105: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_63576\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:33:50.934746: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 233825200 exceeds 10% of free system memory.\n","Collected words+weights saved to /tmp/visualization-context3/fold3.json\n","Evaluating after 3 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       267       128       395\n","     delta        91       291       382\n","     [sum]       358       419       777\n","Accuracy: 0.7181 (95ppCI: 0.0316)  Macro F1: 0.7195\n","ah: P=0.7458/R=0.6759/F1=0.7092 delta: P=0.6945/R=0.7618/F1=0.7266\n","Running fold 4/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_6\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_3 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_3[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:33:53.891242: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_70911\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 20s 161ms/step - loss: 1.6979\n","Epoch 2/5\n","73/73 [==============================] - 12s 159ms/step - loss: 0.6483\n","Epoch 3/5\n","73/73 [==============================] - 11s 157ms/step - loss: 0.6488\n","Epoch 4/5\n","73/73 [==============================] - 11s 151ms/step - loss: 0.5707\n","Epoch 5/5\n","73/73 [==============================] - 12s 163ms/step - loss: 0.6426\n","2021-03-13 14:34:59.514074: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_82316\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 85ms/step\n","2021-03-13 14:35:03.823721: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_85966\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:35:07.370160: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 246205400 exceeds 10% of free system memory.\n","Collected words+weights saved to /tmp/visualization-context3/fold4.json\n","Evaluating after 4 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       366       158       524\n","     delta       132       380       512\n","     [sum]       498       538      1036\n","Accuracy: 0.7201 (95ppCI: 0.0273)  Macro F1: 0.7205\n","ah: P=0.7349/R=0.6985/F1=0.7162 delta: P=0.7063/R=0.7422/F1=0.7238\n","Running fold 5/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_8\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_4 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_4[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:35:10.358787: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_93301\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 19s 158ms/step - loss: 1.9518\n","Epoch 2/5\n","73/73 [==============================] - 11s 158ms/step - loss: 0.6870\n","Epoch 3/5\n","73/73 [==============================] - 11s 150ms/step - loss: 0.6239\n","Epoch 4/5\n","73/73 [==============================] - 12s 160ms/step - loss: 0.6447\n","Epoch 5/5\n","73/73 [==============================] - 12s 161ms/step - loss: 0.5857\n","2021-03-13 14:36:15.576714: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_104706\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 66ms/step\n","2021-03-13 14:36:19.684387: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_108356\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","2021-03-13 14:36:23.118387: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 246205400 exceeds 10% of free system memory.\n","Collected words+weights saved to /tmp/visualization-context3/fold5.json\n","Evaluating after 5 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       439       218       657\n","     delta       139       499       638\n","     [sum]       578       717      1295\n","Accuracy: 0.7243 (95ppCI: 0.0243)  Macro F1: 0.7264\n","ah: P=0.7595/R=0.6682/F1=0.7109 delta: P=0.6960/R=0.7821/F1=0.7365\n","Running fold 6/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_10\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_5 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_5[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:36:26.364825: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_115691\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 20s 165ms/step - loss: 2.0123\n","Epoch 2/5\n","73/73 [==============================] - 12s 163ms/step - loss: 0.6385\n","Epoch 3/5\n","73/73 [==============================] - 12s 162ms/step - loss: 0.6172\n","Epoch 4/5\n","73/73 [==============================] - 12s 163ms/step - loss: 0.6205\n","Epoch 5/5\n","73/73 [==============================] - 11s 156ms/step - loss: 0.5758\n","2021-03-13 14:37:33.541179: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_127096\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 79ms/step\n","2021-03-13 14:37:37.235188: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_130746\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Collected words+weights saved to /tmp/visualization-context3/fold6.json\n","Evaluating after 6 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       539       237       776\n","     delta       195       583       778\n","     [sum]       734       820      1554\n","Accuracy: 0.7220 (95ppCI: 0.0223)  Macro F1: 0.7223\n","ah: P=0.7343/R=0.6946/F1=0.7139 delta: P=0.7110/R=0.7494/F1=0.7297\n","Running fold 7/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_12\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_6 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_6[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:37:43.646644: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_138081\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 19s 150ms/step - loss: 1.9742\n","Epoch 2/5\n","73/73 [==============================] - 11s 157ms/step - loss: 0.6718\n","Epoch 3/5\n","73/73 [==============================] - 11s 155ms/step - loss: 0.6048\n","Epoch 4/5\n","73/73 [==============================] - 12s 160ms/step - loss: 0.5816\n","Epoch 5/5\n","73/73 [==============================] - 12s 159ms/step - loss: 0.5862\n","2021-03-13 14:38:49.364798: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_149486\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 85ms/step\n","2021-03-13 14:38:53.052219: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_153136\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Collected words+weights saved to /tmp/visualization-context3/fold7.json\n","Evaluating after 7 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       604       284       888\n","     delta       217       708       925\n","     [sum]       821       992      1813\n","Accuracy: 0.7237 (95ppCI: 0.0206)  Macro F1: 0.7237\n","ah: P=0.7357/R=0.6802/F1=0.7068 delta: P=0.7137/R=0.7654/F1=0.7387\n","Running fold 8/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_14\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_7 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_7[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:38:59.733910: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_160471\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 20s 157ms/step - loss: 1.8863\n","Epoch 2/5\n","73/73 [==============================] - 12s 162ms/step - loss: 0.6536\n","Epoch 3/5\n","73/73 [==============================] - 11s 157ms/step - loss: 0.6199\n","Epoch 4/5\n","73/73 [==============================] - 12s 165ms/step - loss: 0.6558\n","Epoch 5/5\n","73/73 [==============================] - 12s 159ms/step - loss: 0.5836\n","2021-03-13 14:40:07.259854: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_171876\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 104ms/step\n","2021-03-13 14:40:11.145306: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_175526\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Collected words+weights saved to /tmp/visualization-context3/fold8.json\n","Evaluating after 8 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       718       300      1018\n","     delta       277       777      1054\n","     [sum]       995      1077      2072\n","Accuracy: 0.7215 (95ppCI: 0.0193)  Macro F1: 0.7214\n","ah: P=0.7216/R=0.7053/F1=0.7134 delta: P=0.7214/R=0.7372/F1=0.7292\n","Running fold 9/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_16\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_8 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_8[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:40:17.970102: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_182861\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 20s 158ms/step - loss: 2.1359\n","Epoch 2/5\n","73/73 [==============================] - 12s 161ms/step - loss: 0.6531\n","Epoch 3/5\n","73/73 [==============================] - 12s 164ms/step - loss: 0.6358\n","Epoch 4/5\n","73/73 [==============================] - 11s 157ms/step - loss: 0.6392\n","Epoch 5/5\n","73/73 [==============================] - 11s 153ms/step - loss: 0.5807\n","2021-03-13 14:41:24.102215: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_194266\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","9/9 [==============================] - 4s 82ms/step\n","2021-03-13 14:41:27.733636: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_197916\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Collected words+weights saved to /tmp/visualization-context3/fold9.json\n","Evaluating after 9 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       827       327      1154\n","     delta       325       852      1177\n","     [sum]      1152      1179      2331\n","Accuracy: 0.7203 (95ppCI: 0.0182)  Macro F1: 0.7203\n","ah: P=0.7179/R=0.7166/F1=0.7173 delta: P=0.7226/R=0.7239/F1=0.7233\n","Running fold 10/10\n","Max length in training data:  4753\n","Embeddings shape: (100004, 300)\n","Model: \"model_18\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_sequence (InputLayer)     [(None, None)]       0                                            \n","__________________________________________________________________________________________________\n","embeddings (Embedding)          (None, None, 300)    30001200    input_sequence[0][0]             \n","__________________________________________________________________________________________________\n","BiLSTM (Bidirectional)          (None, None, 128)    186880      embeddings[0][0]                 \n","__________________________________________________________________________________________________\n","tanh_Ws1_HT (Dense)             (None, None, 300)    38400       BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","A_matrix (Dense)                (None, None, 50)     15000       tanh_Ws1_HT[0][0]                \n","__________________________________________________________________________________________________\n","A_softmax (Lambda)              (None, None, 50)     0           A_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","M_matrix (Lambda)               (None, 50, 128)      0           A_softmax[0][0]                  \n","                                                                 BiLSTM[0][0]                     \n","__________________________________________________________________________________________________\n","flatten_9 (Flatten)             (None, 6400)         0           M_matrix[0][0]                   \n","__________________________________________________________________________________________________\n","Output_dense (Dense)            (None, 2)            12802       flatten_9[0][0]                  \n","==================================================================================================\n","Total params: 30,254,282\n","Trainable params: 253,082\n","Non-trainable params: 30,001,200\n","__________________________________________________________________________________________________\n","Model compiled\n","2021-03-13 14:41:34.039943: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_205251\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Epoch 1/5\n","73/73 [==============================] - 19s 154ms/step - loss: 1.6998\n","Epoch 2/5\n","73/73 [==============================] - 12s 160ms/step - loss: 0.6179\n","Epoch 3/5\n","73/73 [==============================] - 12s 164ms/step - loss: 0.6255\n","Epoch 4/5\n","73/73 [==============================] - 11s 156ms/step - loss: 0.5878\n","Epoch 5/5\n","73/73 [==============================] - 12s 163ms/step - loss: 0.5712\n","2021-03-13 14:42:40.968149: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_216656\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","8/8 [==============================] - 3s 91ms/step\n","2021-03-13 14:42:44.583144: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:656] In AUTO-mode, and switching to DATA-based sharding, instead of FILE-based sharding as we cannot find appropriate reader dataset op(s) to shard. Error: Did not find a shardable source, walked to a node which is not a dataset: name: \"FlatMapDataset/_9\"\n","op: \"FlatMapDataset\"\n","input: \"PrefetchDataset/_8\"\n","attr {\n","  key: \"Targuments\"\n","  value {\n","    list {\n","    }\n","  }\n","}\n","attr {\n","  key: \"f\"\n","  value {\n","    func {\n","      name: \"__inference_Dataset_flat_map_slice_batch_indices_220305\"\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_shapes\"\n","  value {\n","    list {\n","      shape {\n","        dim {\n","          size: -1\n","        }\n","      }\n","    }\n","  }\n","}\n","attr {\n","  key: \"output_types\"\n","  value {\n","    list {\n","      type: DT_INT64\n","    }\n","  }\n","}\n",". Consider either turning off auto-sharding or switching the auto_shard_policy to DATA to shard this dataset. You can do this by creating a new `tf.data.Options()` object then setting `options.experimental_distribute.auto_shard_policy = AutoShardPolicy.DATA` before applying the options object to the dataset via `dataset.with_options(options)`.\n","Collected words+weights saved to /tmp/visualization-context3/fold10.json\n","Evaluating after 10 folds\n","↓gold pr.→        ah     delta     [sum]\n","        ah       932       359      1291\n","     delta       365       926      1291\n","     [sum]      1297      1285      2582\n","Accuracy: 0.7196 (95ppCI: 0.0173)  Macro F1: 0.7196\n","ah: P=0.7186/R=0.7219/F1=0.7202 delta: P=0.7206/R=0.7173/F1=0.7189\n","Final evaluation; reader.input_path_train was data/sampled-threads-ah-delta-context3\n","↓gold pr.→        ah     delta     [sum]\n","        ah       932       359      1291\n","     delta       365       926      1291\n","     [sum]      1297      1285      2582\n","Accuracy: 0.7196 (95ppCI: 0.0173)  Macro F1: 0.7196\n","ah: P=0.7186/R=0.7219/F1=0.7202 delta: P=0.7206/R=0.7173/F1=0.7189\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"UfGbpEH262w2"},"source":[""],"execution_count":null,"outputs":[]}]}