Rebranding - Entity Extraction
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- .github/free_disk_space.sh +48 -0
- .github/pull_request_template.md +0 -23
- .github/workflows/ci-cd.yaml +108 -0
- .github/workflows/ci-cd.yml +0 -43
- .gitignore +3 -0
- .readthedocs.yaml +0 -29
- LICENSE +1 -1
- README.md +106 -34
- app.py +22 -0
- docs/Makefile +0 -34
- docs/make.bat +0 -35
- docs/source/conf.py +0 -96
- docs/source/faq.rst +0 -78
- docs/source/img/char-level-language-model.png +0 -0
- docs/source/img/hello-sampling.png +0 -0
- docs/source/img/hello-sound.png +0 -0
- docs/source/img/real-vs-sampling.png +0 -0
- docs/source/img/rnn-4-black-boxes-connected.drawio +0 -121
- docs/source/img/rnn-4-black-boxes-connected.png +0 -0
- docs/source/img/rnn-4-black-boxes.drawio +0 -94
- docs/source/img/rnn-4-black-boxes.png +0 -0
- docs/source/img/rnn-many-to-many-different-ltr.png +0 -0
- docs/source/img/rnn-many-to-many-same-ltr.png +0 -0
- docs/source/img/rnn-many-to-one-ltr.png +0 -0
- docs/source/img/rnn-multi-sequences.drawio +0 -250
- docs/source/img/rnn-multi-sequences.png +0 -0
- docs/source/img/rnn-one-to-many-ltr.png +0 -0
- docs/source/img/rnn-one-to-one-ltr.png +0 -0
- docs/source/img/rnn.drawio +0 -149
- docs/source/img/rnn.png +0 -0
- docs/source/img/sampling-sound-wave.gif +0 -0
- docs/source/img/sound-wave.png +0 -0
- docs/source/img/speech-processing.png +0 -0
- docs/source/index.rst +0 -46
- docs/source/intro/install.rst +0 -54
- docs/source/lamassu.rst +0 -9
- docs/source/requirements.txt +0 -7
- docs/source/rnn/rnn.rst +0 -612
- docs/source/speech/sampling.rst +0 -68
- lamassu-logo.png +0 -0
- lamassu/rnn/__init__.py +0 -0
- lamassu/rnn/example.py +0 -50
- lamassu/rnn/rnn.py +0 -114
- lamassu/speech/__init__.py +0 -0
- lamassu/speech/sampling.py +0 -16
- mlflow/HanLPner.py +57 -0
- {lamassu → mlflow}/__init__.py +0 -0
- mlflow/parser.py +84 -0
- mlflow/requirements.txt +4 -0
- mlflow/test_parser.py +25 -0
.github/free_disk_space.sh
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#!/usr/bin/env bash
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# The Azure provided machines typically have the following disk allocation:
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# Total space: 85GB
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# Allocated: 67 GB
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# Free: 17 GB
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# This script frees up 28 GB of disk space by deleting unneeded packages and
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# large directories.
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# The Flink end to end tests download and generate more than 17 GB of files,
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# causing unpredictable behavior and build failures.
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#
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echo "=============================================================================="
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echo "Freeing up disk space on CI system"
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echo "=============================================================================="
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echo "Listing 100 largest packages"
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dpkg-query -Wf '${Installed-Size}\t${Package}\n' | sort -n | tail -n 100
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df -h
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echo "Removing large packages"
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sudo apt-get remove -y '^ghc-8.*'
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sudo apt-get remove -y '^dotnet-.*'
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sudo apt-get remove -y '^llvm-.*'
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sudo apt-get remove -y 'php.*'
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sudo apt-get remove -y azure-cli google-cloud-sdk hhvm google-chrome-stable firefox powershell mono-devel
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sudo apt-get autoremove -y
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sudo apt-get clean
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df -h
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echo "Removing large directories"
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# deleting 15GB
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rm -rf /usr/share/dotnet/
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rm -rf /opt/hostedtoolcache
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df -h
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.github/pull_request_template.md
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Changelog
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---------
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### Added
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### Changed
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### Deprecated
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### Removed
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### Fixed
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### Security
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Checklist
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---------
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- [ ] Test
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- [ ] Self-review
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- [ ] Documentation
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- [ ] Version Bumped Manually*
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.github/workflows/ci-cd.yaml
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# Copyright Jiaqi Liu
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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| 14 |
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---
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| 15 |
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name: CI/CD
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on:
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pull_request:
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push:
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branches: [master]
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jobs:
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yml-md-style-and-link-checks:
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uses: QubitPi/hashistack/.github/workflows/yml-md-style-and-link-checks.yml@master
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unit-tests:
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| 27 |
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needs: yml-md-style-and-link-checks
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.10"]
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test: [
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{test-file: "mlflow/test_parser.py", requirements-file: "mlflow/requirements.txt"}
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]
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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| 40 |
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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| 43 |
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run: pip3 install -r ${{ matrix.test.requirements-file }}
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- name: Run all tests
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| 45 |
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run: python3 -m unittest ${{ matrix.test.test-file }}
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| 47 |
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mlflow-tests:
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| 48 |
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needs: unit-tests
|
| 49 |
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runs-on: ubuntu-latest
|
| 50 |
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strategy:
|
| 51 |
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fail-fast: false
|
| 52 |
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matrix:
|
| 53 |
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python-version: ["3.10"]
|
| 54 |
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steps:
|
| 55 |
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- uses: actions/checkout@v3
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| 56 |
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- name: Remove unnecessary files
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| 57 |
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run: .github/free_disk_space.sh
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| 58 |
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- name: Set up Python ${{ matrix.python-version }}
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| 59 |
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uses: actions/setup-python@v4
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| 60 |
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: pip3 install -r requirements.txt
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working-directory: mlflow
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- name: Build model
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run: python3 HanLPner.py
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working-directory: mlflow
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- name: Build Docker image
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run: mlflow models build-docker --name "entity-extraction"
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working-directory: mlflow
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- name: Run Container
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run: |
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cp parser.py models/HanLPner/
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export ML_MODEL_PATH=${{ github.workspace }}/mlflow/models/HanLPner
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docker run --rm \
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--memory=4000m \
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-p 8080:8080 \
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-v $ML_MODEL_PATH:/opt/ml/model \
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-e PYTHONPATH="/opt/ml/model:$PYTHONPATH" \
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-e GUNICORN_CMD_ARGS="--timeout 60 -k gevent --workers=1" \
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"entity-extraction" &
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working-directory: mlflow
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| 83 |
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- name: Wait until container is up
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| 84 |
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run: |
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npm install -g wait-on
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wait-on http://127.0.0.1:8080/ping
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- name: Get status code of a test request and verify it's 200
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| 88 |
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run: |
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status_code=$(curl -s -o /dev/null -w "%{http_code}" -X POST -H "Content-Type:application/json" --data '{"dataframe_split": {"columns":["text"], "data":[["我爱中国"], ["世界会变、科技会变，但「派昂」不会变，它不会向任何人低头，不会向任何困难低头，甚至不会向「时代」低头。「派昂」，永远引领对科技的热爱。只有那些不向梦想道路上的阻挠认输的人，才配得上与我们一起追逐梦想"]]}}' http://127.0.0.1:8080/invocations)
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if [ "$status_code" == 200 ]; then
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exit 0
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else
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echo "Integration test failed with a non-200 response from container"
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exit 1
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fi
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sync-to-huggingface-space:
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needs: unit-tests
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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with:
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fetch-depth: 0
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lfs: true
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| 105 |
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- name: Push to hub
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| 106 |
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run: git push https://QubitPi:[email protected]/spaces/QubitPi/lamassu master:main -f
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| 107 |
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env:
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| 108 |
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HF_TOKEN: ${{ secrets.HF_TOKEN }}
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.github/workflows/ci-cd.yml
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# Copyright Jiaqi Liu
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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-
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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---
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| 15 |
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name: CI/CD
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-
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"on":
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pull_request:
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push:
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branches:
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- master
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-
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jobs:
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yml-md-style-and-link-checks:
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uses: QubitPi/hashicorp-aws/.github/workflows/yml-md-style-and-link-checks.yml@master
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-
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release:
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name: Publish Lamassu To PyPI
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| 29 |
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runs-on: ubuntu-latest
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steps:
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| 31 |
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- uses: actions/checkout@v3
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| 32 |
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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| 36 |
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- name: Package up SDK
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| 37 |
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run: python setup.py sdist
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- name: Publish a Python distribution to PyPI
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| 39 |
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if: github.ref == 'refs/heads/master'
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| 40 |
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uses: pypa/gh-action-pypi-publish@release/v1
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| 41 |
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with:
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user: __token__
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password: ${{ secrets.PYPI_API_TOKEN }}
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.gitignore
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.idea/
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.DS_Store
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__pycache__
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.venv
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.idea/
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mlruns/
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models/
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.DS_Store
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__pycache__
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.readthedocs.yaml
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# Copyright Jiaqi Liu
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-
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
version: 2
|
| 15 |
-
|
| 16 |
-
build:
|
| 17 |
-
os: ubuntu-22.04
|
| 18 |
-
tools:
|
| 19 |
-
python: "3.11"
|
| 20 |
-
|
| 21 |
-
sphinx:
|
| 22 |
-
configuration: docs/source/conf.py
|
| 23 |
-
|
| 24 |
-
python:
|
| 25 |
-
install:
|
| 26 |
-
- method: pip
|
| 27 |
-
path: .
|
| 28 |
-
- requirements: requirements.txt
|
| 29 |
-
- requirements: docs/source/requirements.txt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
LICENSE
CHANGED
|
@@ -186,7 +186,7 @@
|
|
| 186 |
same "printed page" as the copyright notice for easier
|
| 187 |
identification within third-party archives.
|
| 188 |
|
| 189 |
-
Copyright
|
| 190 |
|
| 191 |
Licensed under the Apache License, Version 2.0 (the "License");
|
| 192 |
you may not use this file except in compliance with the License.
|
|
|
|
| 186 |
same "printed page" as the copyright notice for easier
|
| 187 |
identification within third-party archives.
|
| 188 |
|
| 189 |
+
Copyright 2024 Jiaqi Liu
|
| 190 |
|
| 191 |
Licensed under the Apache License, Version 2.0 (the "License");
|
| 192 |
you may not use this file except in compliance with the License.
|
README.md
CHANGED
|
@@ -1,50 +1,122 @@
|
|
| 1 |
-
|
|
|
|
|
|
|
|
|
|
|
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| 2 |
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| 3 |
-
|
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| 4 |
|
| 5 |
-
|
|
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|
| 6 |
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
[![PyPI][PyPI project badge]][PyPI project url]
|
| 10 |
-
[![GitHub Workflow Status][GitHub Workflow Status badge]][GitHub Workflow Status URL]
|
| 11 |
-
![Last Commit][GitHub Last Commit Badge]
|
| 12 |
-
[![Apache License badge]][Apache License URL]
|
| 13 |
|
| 14 |
-
Lamassu
|
| 15 |
-
=======
|
| 16 |
|
| 17 |
-
|
| 18 |
-
|
| 19 |
|
| 20 |
-
|
| 21 |
-
-------------
|
| 22 |
|
| 23 |
-
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
| 24 |
|
| 25 |
License
|
| 26 |
-------
|
| 27 |
|
| 28 |
-
The use and distribution terms for
|
| 29 |
-
[Apache License, Version 2.0][Apache License, Version 2.0].
|
| 30 |
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
<img align="center" width="50%" alt="License Illustration" src="https://github.com/QubitPi/QubitPi/blob/master/img/apache-2.png?raw=true">
|
| 34 |
-
</a>
|
| 35 |
-
</div>
|
| 36 |
|
| 37 |
-
[
|
| 38 |
-
[
|
| 39 |
-
[Apache License, Version 2.0]: http://www.apache.org/licenses/LICENSE-2.0.html
|
| 40 |
|
| 41 |
-
[
|
| 42 |
-
[
|
| 43 |
-
[GitHub Workflow Status URL]: https://github.com/QubitPi/lamassu/actions/workflows/ci-cd.yml
|
| 44 |
|
| 45 |
-
[
|
| 46 |
-
[
|
| 47 |
-
[
|
|
|
|
| 48 |
|
| 49 |
-
[
|
| 50 |
-
[Read the Docs URL]: https://lamassu.readthedocs.io/en/latest/
|
|
|
|
| 1 |
+
---
|
| 2 |
+
title: Lamassu
|
| 3 |
+
emoji: 🤗
|
| 4 |
+
colorFrom: gray
|
| 5 |
+
colorTo: red
|
| 6 |
+
sdk: gradio
|
| 7 |
+
sdk_version: 4.36.1
|
| 8 |
+
app_file: app.py
|
| 9 |
+
pinned: false
|
| 10 |
+
license: apache-2.0
|
| 11 |
+
---
|
| 12 |
|
| 13 |
+
[![Hugging Face space badge]][Hugging Face space URL]
|
| 14 |
+
[![Hugging Face sync status badge]][Hugging Face sync status URL]
|
| 15 |
+
[![MLflow badge]][MLflow URL]
|
| 16 |
+
[![MLflow build status badge]][MLflow build status URL]
|
| 17 |
+
[![Apache License Badge]][Apache License, Version 2.0]
|
| 18 |
|
| 19 |
+
Lamassu is a Named Entity Extraction service that is capable of running on [Hugging Face][Hugging Face space URL] and
|
| 20 |
+
MLflow managed environment. It is the service backing the [Nexus Graph](https://paion-data.github.io/nexusgraph.com/)
|
| 21 |
|
| 22 |
+
Hugging Face
|
| 23 |
+
------------
|
|
|
|
|
|
|
|
|
|
|
|
|
| 24 |
|
| 25 |
+
Lamassu is directly available on [Hugging Face space][Hugging Face space URL]. Please check it out.
|
|
|
|
| 26 |
|
| 27 |
+
MLflow
|
| 28 |
+
------
|
| 29 |
|
| 30 |
+
![Python Version Badge]
|
|
|
|
| 31 |
|
| 32 |
+
### Getting Source Code
|
| 33 |
+
|
| 34 |
+
```console
|
| 35 |
+
git clone [email protected]:QubitPi/lamassu.git
|
| 36 |
+
```
|
| 37 |
+
|
| 38 |
+
### Running Locally
|
| 39 |
+
|
| 40 |
+
Create virtual environment and install dependencies:
|
| 41 |
+
|
| 42 |
+
```console
|
| 43 |
+
cd lamassu/mlflow
|
| 44 |
+
python3 -m venv .venv
|
| 45 |
+
. .venv/bin/activate
|
| 46 |
+
pip3 install -r requirements.txt
|
| 47 |
+
```
|
| 48 |
+
|
| 49 |
+
Generate Model with
|
| 50 |
+
|
| 51 |
+
```console
|
| 52 |
+
python3 HanLPner.py
|
| 53 |
+
```
|
| 54 |
+
|
| 55 |
+
A model directory called "HanLPner" appears under `mlflow/models`. Then build Docker image
|
| 56 |
+
|
| 57 |
+
```console
|
| 58 |
+
mlflow models build-docker --name "entity-extraction"
|
| 59 |
+
```
|
| 60 |
+
|
| 61 |
+
and run container with
|
| 62 |
+
|
| 63 |
+
```console
|
| 64 |
+
cp parser.py models/HanLPner/
|
| 65 |
+
export ML_MODEL_PATH=/absolute/path/to/models/HanLPner
|
| 66 |
+
|
| 67 |
+
docker run --rm \
|
| 68 |
+
--memory=4000m \
|
| 69 |
+
-p 8080:8080 \
|
| 70 |
+
-v $ML_MODEL_PATH:/opt/ml/model \
|
| 71 |
+
-e PYTHONPATH="/opt/ml/model:$PYTHONPATH" \
|
| 72 |
+
-e GUNICORN_CMD_ARGS="--timeout 60 -k gevent --workers=1" \
|
| 73 |
+
"entity-extraction"
|
| 74 |
+
```
|
| 75 |
+
|
| 76 |
+
> [!TIP]
|
| 77 |
+
> If `docker.errors.DockerException: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))`
|
| 78 |
+
> error is seen, refer to
|
| 79 |
+
> https://forums.docker.com/t/docker-errors-dockerexception-error-while-fetching-server-api-version-connection-aborted-filenotfounderror-2-no-such-file-or-directory-error-in-python/135637/5
|
| 80 |
+
|
| 81 |
+
The container runs Gunicorn server inside to serve incoming requests
|
| 82 |
+
|
| 83 |
+
> [!WARNING]
|
| 84 |
+
> The number of gunicorn worker process MUST be **1** (`--workers=1`) to prevent multiple workers from downloading a
|
| 85 |
+
> HanLP pre-trained model to the same location, which results in runtime error in Docker container. In **native**
|
| 86 |
+
> environment, this error can be
|
| 87 |
+
>
|
| 88 |
+
> ```console
|
| 89 |
+
> OSError: [Errno 39] Directory not empty: '/root/.hanlp/mtl/close_tok_pos_ner_srl_dep_sdp_con_electra_small_20210304_135840'
|
| 90 |
+
> -> '/root/.hanlp/mtl/close_tok_pos_ner_srl_dep_sdp_con_electra_small_20210111_124159'
|
| 91 |
+
> ```
|
| 92 |
+
|
| 93 |
+
Example query:
|
| 94 |
+
|
| 95 |
+
```bash
|
| 96 |
+
curl -X POST -H "Content-Type:application/json" \
|
| 97 |
+
--data '{"dataframe_split": {"columns":["text"], "data":[["我爱中国"], ["世界会变、科技会变，但「派昂」不会变，它不会向任何人低头，不会向任何困难低头，甚至不会向「时代」低头。「派昂」，永远引领对科技的热爱。只有那些不向梦想道路上的阻挠认输的人，才配得上与我们一起追逐梦想"]]}}' \
|
| 98 |
+
http://127.0.0.1:8080/invocations
|
| 99 |
+
```
|
| 100 |
+
|
| 101 |
+
[Note the JSON schema of the `--data` value](https://stackoverflow.com/a/75104855)
|
| 102 |
|
| 103 |
License
|
| 104 |
-------
|
| 105 |
|
| 106 |
+
The use and distribution terms for [lamassu]() are covered by the [Apache License, Version 2.0].
|
|
|
|
| 107 |
|
| 108 |
+
[Apache License Badge]: https://img.shields.io/badge/Apache%202.0-F25910.svg?style=for-the-badge&logo=Apache&logoColor=white
|
| 109 |
+
[Apache License, Version 2.0]: https://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
|
|
|
|
|
|
| 110 |
|
| 111 |
+
[Hugging Face space badge]: https://img.shields.io/badge/Hugging%20Face%20Space-lamassu-FFD21E?style=for-the-badge&logo=huggingface&logoColor=white
|
| 112 |
+
[Hugging Face space URL]: https://huggingface.co/spaces/QubitPi/lamassu
|
|
|
|
| 113 |
|
| 114 |
+
[Hugging Face sync status badge]: https://img.shields.io/github/actions/workflow/status/QubitPi/lamassu/ci-cd.yaml?branch=master&style=for-the-badge&logo=github&logoColor=white&label=Hugging%20Face%20Sync%20Up
|
| 115 |
+
[Hugging Face sync status URL]: https://github.com/QubitPi/lamassu/actions/workflows/ci-cd.yaml
|
|
|
|
| 116 |
|
| 117 |
+
[MLflow badge]: https://img.shields.io/badge/MLflow%20Supported-0194E2?style=for-the-badge&logo=mlflow&logoColor=white
|
| 118 |
+
[MLflow URL]: https://mlflow.qubitpi.org/
|
| 119 |
+
[MLflow build status badge]: https://img.shields.io/github/actions/workflow/status/QubitPi/lamassu/ci-cd.yaml?branch=master&style=for-the-badge&logo=github&logoColor=white&label=MLflow%20Build
|
| 120 |
+
[MLflow build status URL]: https://github.com/QubitPi/lamassu/actions/workflows/ci-cd.yaml
|
| 121 |
|
| 122 |
+
[Python Version Badge]: https://img.shields.io/badge/Python-3.10-brightgreen?style=for-the-badge&logo=python&logoColor=white
|
|
|
app.py
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
|
| 3 |
+
import hanlp
|
| 4 |
+
from mlflow.parser import convert_to_knowledge_graph_spec
|
| 5 |
+
|
| 6 |
+
HanLP = hanlp.load(hanlp.pretrained.mtl.CLOSE_TOK_POS_NER_SRL_DEP_SDP_CON_ELECTRA_SMALL_ZH)
|
| 7 |
+
|
| 8 |
+
def inference(input):
|
| 9 |
+
return convert_to_knowledge_graph_spec(HanLP([input])["srl"])
|
| 10 |
+
|
| 11 |
+
app = gr.Interface(
|
| 12 |
+
fn=inference,
|
| 13 |
+
inputs="text",
|
| 14 |
+
outputs="json",
|
| 15 |
+
title="Named Entity Recognition",
|
| 16 |
+
description=("Turning text corpus into graph representation"),
|
| 17 |
+
examples=[
|
| 18 |
+
["我爱中国"],
|
| 19 |
+
["世界会变、科技会变，但「派昂」不会变，它不会向任何人低头，不会向任何困难低头，甚至不会向「时代」低头。「派昂」，永远引领对科技的热爱。只有那些不向梦想道路上的阻挠认输的人，才配得上与我们一起追逐梦想"]
|
| 20 |
+
],
|
| 21 |
+
)
|
| 22 |
+
app.launch()
|
docs/Makefile
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 1 |
-
# Copyright Jiaqi Liu
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
# Minimal makefile for Sphinx documentation
|
| 16 |
-
#
|
| 17 |
-
|
| 18 |
-
# You can set these variables from the command line, and also
|
| 19 |
-
# from the environment for the first two.
|
| 20 |
-
SPHINXOPTS ?=
|
| 21 |
-
SPHINXBUILD ?= sphinx-build
|
| 22 |
-
SOURCEDIR = source
|
| 23 |
-
BUILDDIR = build
|
| 24 |
-
|
| 25 |
-
# Put it first so that "make" without argument is like "make help".
|
| 26 |
-
help:
|
| 27 |
-
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
| 28 |
-
|
| 29 |
-
.PHONY: help Makefile
|
| 30 |
-
|
| 31 |
-
# Catch-all target: route all unknown targets to Sphinx using the new
|
| 32 |
-
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
| 33 |
-
%: Makefile
|
| 34 |
-
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
docs/make.bat
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
@ECHO OFF
|
| 2 |
-
|
| 3 |
-
pushd %~dp0
|
| 4 |
-
|
| 5 |
-
REM Command file for Sphinx documentation
|
| 6 |
-
|
| 7 |
-
if "%SPHINXBUILD%" == "" (
|
| 8 |
-
set SPHINXBUILD=sphinx-build
|
| 9 |
-
)
|
| 10 |
-
set SOURCEDIR=source
|
| 11 |
-
set BUILDDIR=build
|
| 12 |
-
|
| 13 |
-
if "%1" == "" goto help
|
| 14 |
-
|
| 15 |
-
%SPHINXBUILD% >NUL 2>NUL
|
| 16 |
-
if errorlevel 9009 (
|
| 17 |
-
echo.
|
| 18 |
-
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
| 19 |
-
echo.installed, then set the SPHINXBUILD environment variable to point
|
| 20 |
-
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
| 21 |
-
echo.may add the Sphinx directory to PATH.
|
| 22 |
-
echo.
|
| 23 |
-
echo.If you don't have Sphinx installed, grab it from
|
| 24 |
-
echo.http://sphinx-doc.org/
|
| 25 |
-
exit /b 1
|
| 26 |
-
)
|
| 27 |
-
|
| 28 |
-
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
| 29 |
-
goto end
|
| 30 |
-
|
| 31 |
-
:help
|
| 32 |
-
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
|
| 33 |
-
|
| 34 |
-
:end
|
| 35 |
-
popd
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docs/source/conf.py
DELETED
|
@@ -1,96 +0,0 @@
|
|
| 1 |
-
# Configuration file for the Sphinx documentation builder.
|
| 2 |
-
#
|
| 3 |
-
# This file only contains a selection of the most common options. For a full
|
| 4 |
-
# list see the documentation:
|
| 5 |
-
# https://www.sphinx-doc.org/en/master/usage/configuration.html
|
| 6 |
-
|
| 7 |
-
# -- Path setup --------------------------------------------------------------
|
| 8 |
-
|
| 9 |
-
# If extensions (or modules to document with autodoc) are in another directory,
|
| 10 |
-
# add these directories to sys.path here. If the directory is relative to the
|
| 11 |
-
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
| 12 |
-
#
|
| 13 |
-
import os
|
| 14 |
-
import sys
|
| 15 |
-
sys.path.insert(0, os.path.abspath('../../'))
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
# -- Project information -----------------------------------------------------
|
| 19 |
-
|
| 20 |
-
project = 'lamassu'
|
| 21 |
-
copyright = '2023, Jiaqi Liu'
|
| 22 |
-
author = 'Jiaqi Liu'
|
| 23 |
-
|
| 24 |
-
# The full version, including alpha/beta/rc tags
|
| 25 |
-
release = '0.1.0'
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
# -- General configuration ---------------------------------------------------
|
| 29 |
-
|
| 30 |
-
# Add any Sphinx extension module names here, as strings. They can be
|
| 31 |
-
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
| 32 |
-
# ones.
|
| 33 |
-
extensions = [
|
| 34 |
-
'sphinx.ext.autodoc',
|
| 35 |
-
'hoverxref.extension',
|
| 36 |
-
'notfound.extension',
|
| 37 |
-
'sphinx.ext.coverage',
|
| 38 |
-
'sphinx.ext.intersphinx',
|
| 39 |
-
'sphinx.ext.viewcode',
|
| 40 |
-
"sphinx.ext.graphviz",
|
| 41 |
-
"pyan.sphinx"
|
| 42 |
-
]
|
| 43 |
-
|
| 44 |
-
# add graphviz options
|
| 45 |
-
graphviz_output_format = "svg"
|
| 46 |
-
|
| 47 |
-
# Add any paths that contain templates here, relative to this directory.
|
| 48 |
-
templates_path = ['_templates']
|
| 49 |
-
|
| 50 |
-
# List of patterns, relative to source directory, that match files and
|
| 51 |
-
# directories to ignore when looking for source files.
|
| 52 |
-
# This pattern also affects html_static_path and html_extra_path.
|
| 53 |
-
exclude_patterns = []
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
# -- Options for HTML output -------------------------------------------------
|
| 57 |
-
|
| 58 |
-
# The theme to use for HTML and HTML Help pages. See the documentation for
|
| 59 |
-
# a list of builtin themes.
|
| 60 |
-
#
|
| 61 |
-
html_theme = 'sphinx_rtd_theme'
|
| 62 |
-
|
| 63 |
-
# Add any paths that contain custom static files (such as style sheets) here,
|
| 64 |
-
# relative to this directory. They are copied after the builtin static files,
|
| 65 |
-
# so a file named "default.css" will overwrite the builtin "default.css".
|
| 66 |
-
html_static_path = ['_static']
|
| 67 |
-
|
| 68 |
-
intersphinx_mapping = {
|
| 69 |
-
'attrs': ('https://www.attrs.org/en/stable/', None),
|
| 70 |
-
'coverage': ('https://coverage.readthedocs.io/en/stable', None),
|
| 71 |
-
'cryptography': ('https://cryptography.io/en/latest/', None),
|
| 72 |
-
'cssselect': ('https://cssselect.readthedocs.io/en/latest', None),
|
| 73 |
-
'itemloaders': ('https://itemloaders.readthedocs.io/en/latest/', None),
|
| 74 |
-
'pytest': ('https://docs.pytest.org/en/latest', None),
|
| 75 |
-
'python': ('https://docs.python.org/3', None),
|
| 76 |
-
'sphinx': ('https://www.sphinx-doc.org/en/master', None),
|
| 77 |
-
'tox': ('https://tox.wiki/en/latest/', None),
|
| 78 |
-
'twisted': ('https://docs.twisted.org/en/stable/', None),
|
| 79 |
-
'twistedapi': ('https://docs.twisted.org/en/stable/api/', None),
|
| 80 |
-
'w3lib': ('https://w3lib.readthedocs.io/en/latest', None),
|
| 81 |
-
}
|
| 82 |
-
intersphinx_disabled_reftypes = []
|
| 83 |
-
|
| 84 |
-
hoverxref_auto_ref = True
|
| 85 |
-
hoverxref_role_types = {
|
| 86 |
-
"class": "tooltip",
|
| 87 |
-
"command": "tooltip",
|
| 88 |
-
"confval": "tooltip",
|
| 89 |
-
"hoverxref": "tooltip",
|
| 90 |
-
"mod": "tooltip",
|
| 91 |
-
"ref": "tooltip",
|
| 92 |
-
"reqmeta": "tooltip",
|
| 93 |
-
"setting": "tooltip",
|
| 94 |
-
"signal": "tooltip",
|
| 95 |
-
}
|
| 96 |
-
hoverxref_roles = ['command', 'reqmeta', 'setting', 'signal']
|
|
|
|
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|
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|
|
|
docs/source/faq.rst
DELETED
|
@@ -1,78 +0,0 @@
|
|
| 1 |
-
.. _faq:
|
| 2 |
-
|
| 3 |
-
==========================
|
| 4 |
-
Frequently Asked Questions
|
| 5 |
-
==========================
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
Python Sphinx Autodoc Is Not Rendering on readthedocs
|
| 9 |
-
=====================================================
|
| 10 |
-
|
| 11 |
-
The project's dependencies are not specified on RTD, but instead have installed the dependencies locally. Visit the
|
| 12 |
-
project's Builds, click a build, and click "view raw"::
|
| 13 |
-
|
| 14 |
-
WARNING: autodoc: failed to import module 'rnn' from module 'lamassu'; the following exception was raised:
|
| 15 |
-
No module named 'matplotlib'
|
| 16 |
-
|
| 17 |
-
To remedy the situation, we must specify that the project's dependencies to be installed. See
|
| 18 |
-
`Specifying Dependencies <https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html>`_.
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
Generate Sphinx Documentation Locally
|
| 22 |
-
=====================================
|
| 23 |
-
|
| 24 |
-
This site is auto-generated using `Sphinx <https://www.sphinx-doc.org/en/master/>`_ with the following command in venv::
|
| 25 |
-
|
| 26 |
-
cd /path/to/lamassu/
|
| 27 |
-
python3 -m venv venv
|
| 28 |
-
source venv/bin/activate
|
| 29 |
-
pip3 install .
|
| 30 |
-
pip3 install -r docs/source/requirements.txt
|
| 31 |
-
sphinx-build -a -b html docs/source/ /path/to/html/output/dir
|
| 32 |
-
deactivate
|
| 33 |
-
|
| 34 |
-
.. NOTE::
|
| 35 |
-
The command above works for Linux/UNIX systems. Some commands will
|
| 36 |
-
`differ on Windows OS <https://realpython.com/python-virtual-environments-a-primer/>`_
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
Install Lamassu from Source Locally
|
| 40 |
-
===================================
|
| 41 |
-
|
| 42 |
-
We recommend creating a virtualenv for your application and activate it
|
| 43 |
-
|
| 44 |
-
Navigate to the ``lamassu`` root directory and run::
|
| 45 |
-
|
| 46 |
-
pip3 install -e .
|
| 47 |
-
|
| 48 |
-
For more general information, please refer to the
|
| 49 |
-
`Hitchhiker's Guide to Python <https://docs.python-guide.org/writing/structure/#structuring-your-project>`_: "Structuring Your Project".
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
"module 'collections' has no attribute 'Callable' Error When Running nosetests
|
| 53 |
-
==============================================================================
|
| 54 |
-
|
| 55 |
-
First, uninstall nose with the following command::
|
| 56 |
-
|
| 57 |
-
pip3 uninstall -y nose
|
| 58 |
-
|
| 59 |
-
Second, reinstall nose but with ``--nobinaries`` flag::
|
| 60 |
-
|
| 61 |
-
pip3 install -U nose --no-binary :all:
|
| 62 |
-
|
| 63 |
-
Why does this work? At the time of this writing the binary generated by nose was likely generated with a version of
|
| 64 |
-
Python 3.4 or older. This command forces to rebuild from source.
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
No module named 'pytest' while Running Test Directly in PyCharm
|
| 68 |
-
===============================================================
|
| 69 |
-
|
| 70 |
-
"Right-click" run a ``test_**.py`` file results in::
|
| 71 |
-
|
| 72 |
-
Traceback (most recent call last):
|
| 73 |
-
File "/Applications/PyCharm CE.app/Contents/plugins/python-ce/helpers/pycharm/_jb_pytest_runner.py", line 5, in <module>
|
| 74 |
-
import pytest
|
| 75 |
-
ModuleNotFoundError: No module named 'pytest'
|
| 76 |
-
|
| 77 |
-
The solution is going to '**Settings** -> **Tools** -> **Python Integrated Tools**' and scroll down to where it says
|
| 78 |
-
`pytest not found` and there is a **FIX** button. Clicking on it and apply the settings shall resolve the problem
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
docs/source/img/char-level-language-model.png
DELETED
|
Binary file (468 kB)
|
|
|
docs/source/img/hello-sampling.png
DELETED
|
Binary file (485 kB)
|
|
|
docs/source/img/hello-sound.png
DELETED
|
Binary file (161 kB)
|
|
|
docs/source/img/real-vs-sampling.png
DELETED
|
Binary file (133 kB)
|
|
|
docs/source/img/rnn-4-black-boxes-connected.drawio
DELETED
|
@@ -1,121 +0,0 @@
|
|
| 1 |
-
<mxfile host="app.diagrams.net" modified="2024-03-19T01:01:04.926Z" agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36" etag="OgWHmKqu6mVN4yDKoCwM" version="24.0.7" type="device">
|
| 2 |
-
<diagram name="Page-1" id="gxr7cFC-hZQY0lpAcxoR">
|
| 3 |
-
<mxGraphModel dx="816" dy="516" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
|
| 4 |
-
<root>
|
| 5 |
-
<mxCell id="0" />
|
| 6 |
-
<mxCell id="1" parent="0" />
|
| 7 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-21" value="<font size="1" data-font-src="https://fonts.googleapis.com/css?family=Italianno" face="Italianno" style="" color="#ffffff"><b style="font-size: 27px;">f</b></font>" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#000000;strokeColor=none;fontColor=#FF6666;" vertex="1" parent="1">
|
| 8 |
-
<mxGeometry x="40" y="320" width="60" height="60" as="geometry" />
|
| 9 |
-
</mxCell>
|
| 10 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-22" value="<font color="#ffffff" face="Italianno" style="font-size: 27px;">f</font>" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#000000;strokeColor=none;fontColor=#FF6666;" vertex="1" parent="1">
|
| 11 |
-
<mxGeometry x="180" y="320" width="60" height="60" as="geometry" />
|
| 12 |
-
</mxCell>
|
| 13 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-23" value="<span style="color: rgb(255, 255, 255); font-family: Italianno; font-size: 27px;">f</span>" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#000000;strokeColor=none;fontColor=#FF6666;" vertex="1" parent="1">
|
| 14 |
-
<mxGeometry x="320" y="320" width="60" height="60" as="geometry" />
|
| 15 |
-
</mxCell>
|
| 16 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-24" value="<span style="color: rgb(255, 255, 255); font-family: Italianno; font-size: 27px;">f</span>" style="rounded=1;whiteSpace=wrap;html=1;fillColor=#000000;strokeColor=none;fontColor=#FF6666;" vertex="1" parent="1">
|
| 17 |
-
<mxGeometry x="460" y="320" width="60" height="60" as="geometry" />
|
| 18 |
-
</mxCell>
|
| 19 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-25" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=0.5;exitY=0;exitDx=0;exitDy=0;strokeWidth=3;strokeColor=#6666FF;fontColor=#FF6666;" edge="1" parent="1">
|
| 20 |
-
<mxGeometry relative="1" as="geometry">
|
| 21 |
-
<mxPoint x="69.5" y="439" as="sourcePoint" />
|
| 22 |
-
<mxPoint x="69.5" y="389" as="targetPoint" />
|
| 23 |
-
</mxGeometry>
|
| 24 |
-
</mxCell>
|
| 25 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-26" value="<font face="Ubuntu" style="font-size: 20px;"><b>h</b></font>" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;fillColor=#00CC00;fontColor=#FFFFFF;" vertex="1" parent="1">
|
| 26 |
-
<mxGeometry x="45" y="450" width="50" height="50" as="geometry" />
|
| 27 |
-
</mxCell>
|
| 28 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-27" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=0.5;exitY=0;exitDx=0;exitDy=0;strokeWidth=3;strokeColor=#6666FF;fontColor=#FF6666;" edge="1" parent="1">
|
| 29 |
-
<mxGeometry relative="1" as="geometry">
|
| 30 |
-
<mxPoint x="69.5" y="310" as="sourcePoint" />
|
| 31 |
-
<mxPoint x="69.5" y="260" as="targetPoint" />
|
| 32 |
-
</mxGeometry>
|
| 33 |
-
</mxCell>
|
| 34 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-28" value="<font face="Ubuntu"><span style="font-size: 20px;"><b>e</b></span></font>" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;fillColor=#FF3399;fontColor=#FFFFFF;" vertex="1" parent="1">
|
| 35 |
-
<mxGeometry x="45" y="200" width="50" height="50" as="geometry" />
|
| 36 |
-
</mxCell>
|
| 37 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-29" value="<font face="Ubuntu"><span style="font-size: 20px;"><b>l</b></span></font>" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;fillColor=#FF3399;fontColor=#FFFFFF;" vertex="1" parent="1">
|
| 38 |
-
<mxGeometry x="185" y="200" width="50" height="50" as="geometry" />
|
| 39 |
-
</mxCell>
|
| 40 |
-
<mxCell id="o5WZRm4PuDRFcwwRBSCM-30" value="<font face="Ubuntu"><span style="font-size: 20px;"><b>l</b></span></font>" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;fillColor=#FF3399;fontColor=#FFFFFF;" vertex="1" parent="1">
|
| 41 |
-
<mxGeometry x="325" y="200" width="50" height="50" as="geometry" />
|
| 42 |
-
</mxCell>
|
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docs/source/index.rst
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|
|
| 1 |
-
=====================
|
| 2 |
-
Lamassu documentation
|
| 3 |
-
=====================
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
Getting help
|
| 7 |
-
============
|
| 8 |
-
|
| 9 |
-
Having trouble? We'd like to help!
|
| 10 |
-
|
| 11 |
-
* Try the :doc:`FAQ <faq>` -- it's got answers to some common questions.
|
| 12 |
-
* Looking for specific information? Try the :ref:`genindex` or :ref:`modindex`.
|
| 13 |
-
* Report bugs with lamassu in our `issue tracker`_.
|
| 14 |
-
* Join the Discord community `Lamassu Discord`_.
|
| 15 |
-
|
| 16 |
-
.. hint::
|
| 17 |
-
* Since methods with two underscores (`__`) cannot be tested due to the
|
| 18 |
-
`name mangling <https://qubitpi.github.io/cpython/tutorial/classes.html#private-variables>`_. Lamassu requires
|
| 19 |
-
all private methods and attributes to be prefixed with **single underscore prefix (`_`) only**
|
| 20 |
-
* The phrase "Chinese" used throughout this documentation referse to "**Simplified Chinese**", instead of
|
| 21 |
-
"Traditional Chinese"
|
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First Steps
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===========
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.. toctree::
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:caption: First steps
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:hidden:
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intro/install
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:doc:`intro/install`
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Get lamassu installed on your computer.
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Usage
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| 38 |
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=====
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.. toctree::
|
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:maxdepth: 100
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lamassu
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.. _issue tracker: https://github.com/QubitPi/lamassu/issues
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.. _Lamassu Discord: https://discord.com/widget?id=1208960229002317934&theme=dark
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docs/source/intro/install.rst
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.. _intro-install:
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==================
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Installation guide
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==================
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Supported Python versions
|
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=========================
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Lamassu has been tested with Python 3.10. It may work with older versions of Python but it is not guaranteed.
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| 14 |
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Installing Lamassu
|
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==================
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| 17 |
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If you are already familiar with installation of Python packages, we can install Lamassu and its dependencies from
|
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`PyPI <https://pypi.org/project/lamassu/>`_ with::
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| 20 |
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pip3 install lamassu
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| 22 |
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We strongly recommend that you install Lamassu in :ref:`a dedicated virtualenv <intro-using-virtualenv>`, to avoid
|
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conflicting with your system packages.
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| 25 |
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If you're using `Anaconda <https://docs.anaconda.com/anaconda/>`_ or
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`Miniconda <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>`_, please allow me to
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apologize because I hate those two, so we won't install the package from there.
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Installing from Source
|
| 31 |
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======================
|
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|
| 33 |
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When we want to apply a bug fix quicly by installing Lamassu locally, we can use::
|
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git clone https://github.com/QubitPi/lamassu.git
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cd lamassu
|
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pip3 install -e .
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| 40 |
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.. _intro-using-virtualenv:
|
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|
| 42 |
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Using a virtual environment (recommended)
|
| 43 |
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-----------------------------------------
|
| 44 |
-
|
| 45 |
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We recommend installing lamassu a virtual environment on all platforms.
|
| 46 |
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|
| 47 |
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Python packages can be installed either globally (a.k.a system wide), or in user-space. We do not recommend installing
|
| 48 |
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lamassu system wide. Instead, we recommend installing lamassu within a "virtual environment" (:mod:`venv`),
|
| 49 |
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which keep you from conflicting with already-installed Python system packages.
|
| 50 |
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|
| 51 |
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See :ref:`tut-venv` on how to create your virtual environment.
|
| 52 |
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|
| 53 |
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Once you have created a virtual environment, we can install lamassu inside it with ``pip3``, just like any other
|
| 54 |
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Python package.
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docs/source/lamassu.rst
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=======
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| 2 |
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Lamassu
|
| 3 |
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=======
|
| 4 |
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| 5 |
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.. toctree::
|
| 6 |
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:maxdepth: 100
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rnn/rnn
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speech/sampling.rst
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sphinx==5.0.2
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sphinx-hoverxref==1.1.1
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sphinx-notfound-page==0.8
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sphinx-rtd-theme==1.0.0
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pycodestyle
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requests
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pyan3
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================================================
|
| 2 |
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Introduction to Recurrent Neural Networks (RNNs)
|
| 3 |
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================================================
|
| 4 |
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|
| 5 |
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.. admonition:: Prerequisite
|
| 6 |
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|
| 7 |
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This article has the following prerequisites:
|
| 8 |
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|
| 9 |
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1. *Chapter 4 - Artificial Neural Networks* (p. 81) of `MACHINE LEARNING by Mitchell, Thom M. (1997)`_ Paperback
|
| 10 |
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2. *Deep Learning (Adaptive Computation and Machine Learning series), Ian Goodfellow*
|
| 11 |
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|
| 12 |
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.. contents:: Table of Contents
|
| 13 |
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:depth: 2
|
| 14 |
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|
| 15 |
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We all heard of this buz word "LLM" (Large Language Model). But let's put that aside for just a second and look at a
|
| 16 |
-
much simpler one called "character-level language model" where, for example, we input a prefix of a word such as
|
| 17 |
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"hell" and the model outputs a complete word "hello". That is, this language model predicts the next character of a
|
| 18 |
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character sequence
|
| 19 |
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|
| 20 |
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This is like a Math function where we have:
|
| 21 |
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|
| 22 |
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.. math::
|
| 23 |
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|
| 24 |
-
f(\text{“hell"}) = \text{“hello"}
|
| 25 |
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|
| 26 |
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.. NOTE::
|
| 27 |
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|
| 28 |
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We call inputs like "hell" as **sequence**
|
| 29 |
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|
| 30 |
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How do we obtain a function like this? One approach is to have 4 black boxes, each of which takes a single character as
|
| 31 |
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input and calculates an output:
|
| 32 |
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|
| 33 |
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.. figure:: ../img/rnn-4-black-boxes.png
|
| 34 |
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:align: center
|
| 35 |
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:width: 50%
|
| 36 |
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|
| 37 |
-
But one might have noticed that if the 3rd function (box) produces :math:`f(‘l') = ‘l'`, then why would the 4th function
|
| 38 |
-
(box), given the same input, gives a different output of 'o'? This suggest that we should take the "**history**" into
|
| 39 |
-
account. Instead of having :math:`f` depend on 1 parameter, we now have it take 2 parameters.
|
| 40 |
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|
| 41 |
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1: a character;
|
| 42 |
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2: a variable that summarizes the previous calculations:
|
| 43 |
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|
| 44 |
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.. figure:: ../img/rnn-4-black-boxes-connected.png
|
| 45 |
-
:align: center
|
| 46 |
-
:width: 50%
|
| 47 |
-
|
| 48 |
-
Now it makes much more sense with:
|
| 49 |
-
|
| 50 |
-
.. math::
|
| 51 |
-
|
| 52 |
-
f(\text{‘l'}, h_2) = \text{‘l'}
|
| 53 |
-
|
| 54 |
-
f(\text{‘l'}, h_3) = \text{‘o'}
|
| 55 |
-
|
| 56 |
-
But what if we want to predict a longer or shorter word? For example, how about predicting "cat" by "ca"? That's simple,
|
| 57 |
-
we will have 2 black boxes to do the work.
|
| 58 |
-
|
| 59 |
-
.. figure:: ../img/rnn-multi-sequences.png
|
| 60 |
-
:align: center
|
| 61 |
-
|
| 62 |
-
What if the function :math:`f` is not smart enough to produce the correct output everytime? We will simply collect a lot
|
| 63 |
-
of examples such as "cat" and "hello", and feed them into the boxes to train them until they can output correct
|
| 64 |
-
vocabulary like "cat" and "hello".
|
| 65 |
-
|
| 66 |
-
This is the idea behind RNN
|
| 67 |
-
|
| 68 |
-
- It's recurrent because the boxed function gets invoked repeatedly for each element of the sequence. In the case of our
|
| 69 |
-
character-level language model, element is a character such as "e" and sequence is a string like "hell"
|
| 70 |
-
|
| 71 |
-
.. figure:: ../img/rnn.png
|
| 72 |
-
:align: center
|
| 73 |
-
|
| 74 |
-
Each function :math:`f` is a network unit containing 2 perceptrons. One perceptron computes the "history" like
|
| 75 |
-
:math:`h_1`, :math:`h_2`, :math:`h_3`. Its formula is very similar to that of perceptron:
|
| 76 |
-
|
| 77 |
-
.. math::
|
| 78 |
-
|
| 79 |
-
h^{(t)} = g_1\left( W_{hh}h^{(t - 1)} + W_{xh}x^{(t)} + b_h \right)
|
| 80 |
-
|
| 81 |
-
where :math:`t` is the index of the "black boxes" shown above. In our example of "hell",
|
| 82 |
-
:math:`t \in \{ 1, 2, 3, 4 \}`
|
| 83 |
-
|
| 84 |
-
The other perceptron computes the output like 'e', 'l', 'l', 'o'. We call those value :math:`y` which is computed as
|
| 85 |
-
|
| 86 |
-
.. math::
|
| 87 |
-
|
| 88 |
-
o^{(t)} = g_2\left( W_{yh}h^{(t)} + b_o \right)
|
| 89 |
-
|
| 90 |
-
.. admonition:: What are :math:`g_1` and :math:`g_2`?
|
| 91 |
-
|
| 92 |
-
They are *activation functions* which are used to change the linear function in a perceptron to a non-linear
|
| 93 |
-
function. Please refer to `MACHINE LEARNING by Mitchell, Thom M. (1997)`_ Paperback (page 96) for why we bump it
|
| 94 |
-
to non-linear
|
| 95 |
-
|
| 96 |
-
A typical activation function for :math:`g_1` is :math:`tanh`:
|
| 97 |
-
|
| 98 |
-
.. math::
|
| 99 |
-
|
| 100 |
-
tanh(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}}
|
| 101 |
-
|
| 102 |
-
In practice, :math:`g_2` is constance, i.e. :math:`g_2 = 1`
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
Forward Propagation Equations for RNN
|
| 106 |
-
-------------------------------------
|
| 107 |
-
|
| 108 |
-
We now develop the forward propagation equations for the RNN. We assume the hyperbolic tangent activation function and
|
| 109 |
-
that the output is discrete, as if the RNN is used to predict words or characters. A natural way to represent discrete
|
| 110 |
-
variables is to regard the output :math:`\boldsymbol{o}` as giving the unnormalized log probabilities of each possible value of
|
| 111 |
-
the discrete variable. We can then apply the softmax (we will disucss softmax function in the next section) operation as
|
| 112 |
-
a post-processing step to obtain a vector :math:`\boldsymbol{\hat{y}}` of normalized probabilities over the output. Forward
|
| 113 |
-
propagation begins with a specification of the initial state :math:`\boldsymbol{h}^{(0)}`. Then, for each time step from
|
| 114 |
-
:math:`t = 1` to :math:`t = \tau`, we apply the following update equations:
|
| 115 |
-
|
| 116 |
-
.. math::
|
| 117 |
-
|
| 118 |
-
\color{green} \boxed{
|
| 119 |
-
\begin{gather*}
|
| 120 |
-
\boldsymbol{h}^{(t)} = \tanh\left( \boldsymbol{W_{hh}}h^{(t - 1)} + \boldsymbol{W_{xh}}x^{(t)} + \boldsymbol{b_h} \right) \\ \\
|
| 121 |
-
\boldsymbol{o}^{(t)} = \boldsymbol{W_{yh}}\boldsymbol{h}^{(t)} + \boldsymbol{b_o} \\ \\
|
| 122 |
-
\boldsymbol{\hat{y}} = softmax(\boldsymbol{o}^{(t)})
|
| 123 |
-
\end{gather*}
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
Note that this recurrent network maps an input sequence to an output sequence of the same length.
|
| 127 |
-
|
| 128 |
-
Loss Function of RNN
|
| 129 |
-
--------------------
|
| 130 |
-
|
| 131 |
-
According to the discussion of `MACHINE LEARNING by Mitchell, Thom M. (1997)`_, the key for training RNN or any neural
|
| 132 |
-
network is through "specifying a measure for the training error". We call this measure a *loss function*.
|
| 133 |
-
|
| 134 |
-
In RNN, the total loss for a given sequence of input :math:`\boldsymbol{x}` paired with a sequence of expected
|
| 135 |
-
:math:`\boldsymbol{y}` is the sum of the losses over all the time steps, i.e.
|
| 136 |
-
|
| 137 |
-
.. math::
|
| 138 |
-
|
| 139 |
-
\mathcal{L}\left( \{ \boldsymbol{x}^{(1)}, ..., \boldsymbol{x}^{(\tau)} \}, \{ \boldsymbol{y}^{(1)}, ..., \boldsymbol{y}^{(\tau)} \} \right) = \sum_t^{\tau} \mathcal{L}^{(t)} = \sum_t^{\tau}\log\boldsymbol{\hat{y}}^{(t)}
|
| 140 |
-
|
| 141 |
-
Why would we have :math:`\mathcal{L}^{(t)} = \log\boldsymbol{\hat{y}}^{(t)}`? We need to learn *Softmax Activation* first.
|
| 142 |
-
|
| 143 |
-
.. admonition:: Softmax Function by `Wikipedia <https://en.wikipedia.org/wiki/Softmax_function>`_
|
| 144 |
-
|
| 145 |
-
The softmax function takes as input a vector :math:`z` of :math:`K` real numbers, and normalizes it into a
|
| 146 |
-
probability distribution consisting of :math:`K` probabilities proportional to the exponentials of the input
|
| 147 |
-
numbers. That is, prior to applying softmax, some vector components could be negative, or greater than one; and
|
| 148 |
-
might not sum to 1; but after applying softmax, each component will be in the interval :math:`(0, 1)` and the
|
| 149 |
-
components will add up to 1, so that they can be interpreted as probabilities. Furthermore, the larger input
|
| 150 |
-
components will correspond to larger probabilities.
|
| 151 |
-
|
| 152 |
-
For a vector :math:`z` of :math:`K` real numbers, the the standard (unit) softmax function
|
| 153 |
-
:math:`\sigma: \mathbb{R}^K \mapsto (0, 1)^K`, where :math:`K \ge 1` is defined by
|
| 154 |
-
|
| 155 |
-
.. math::
|
| 156 |
-
|
| 157 |
-
\sigma(\boldsymbol{z})_i = \frac{e^{z_i}}{\sum_{j = 1}^Ke^{z_j}}
|
| 158 |
-
|
| 159 |
-
where :math:`i = 1, 2, ..., K` and :math:`\boldsymbol{x} = (x_1, x_2, ..., x_K) \in \mathbb{R}^K`
|
| 160 |
-
|
| 161 |
-
In the context of RNN,
|
| 162 |
-
|
| 163 |
-
.. math::
|
| 164 |
-
|
| 165 |
-
\sigma(\boldsymbol{o})_i = -\frac{e^{o_i}}{\sum_{j = 1}^ne^{o_j}}
|
| 166 |
-
|
| 167 |
-
where
|
| 168 |
-
|
| 169 |
-
- :math:`n` is the length of a sequence feed into the RNN
|
| 170 |
-
- :math:`o_i` is the output by perceptron unit `i`
|
| 171 |
-
- :math:`i = 1, 2, ..., n`,
|
| 172 |
-
- :math:`\boldsymbol{o} = (o_1, o_2, ..., o_n) \in \mathbb{R}^n`
|
| 173 |
-
|
| 174 |
-
The softmax function takes an N-dimensional vector of arbitrary real values and produces another N-dimensional vector
|
| 175 |
-
with real values in the range (0, 1) that add up to 1.0. It maps :math:`\mathbb{R}^N \rightarrow \mathbb{R}^N`
|
| 176 |
-
|
| 177 |
-
.. math::
|
| 178 |
-
|
| 179 |
-
\sigma(\boldsymbol{o}): \begin{pmatrix}o_1\\o_2\\\dots\\o_n\end{pmatrix} \rightarrow \begin{pmatrix}\sigma_1\\\sigma_2\\\dots\\\sigma_n\end{pmatrix}
|
| 180 |
-
|
| 181 |
-
This property of softmax function that it outputs a probability distribution makes it suitable for probabilistic
|
| 182 |
-
interpretation in classification tasks. Neural networks, however, are commonly trained under a log loss (or
|
| 183 |
-
cross-entropy) regime
|
| 184 |
-
|
| 185 |
-
We are going to compute the derivative of the softmax function because we will be using it for training our RNN model
|
| 186 |
-
shortly. But before diving in, it is important to keep in mind that Softmax is fundamentally a vector function. It takes
|
| 187 |
-
a vector as input and produces a vector as output; in other words, it has multiple inputs and multiple outputs.
|
| 188 |
-
Therefore, we cannot just ask for "the derivative of softmax"; We should instead specify:
|
| 189 |
-
|
| 190 |
-
1. Which component (output element) of softmax we're seeking to find the derivative of.
|
| 191 |
-
2. Since softmax has multiple inputs, with respect to which input element the partial derivative is computed.
|
| 192 |
-
|
| 193 |
-
What we're looking for is the partial derivatives of
|
| 194 |
-
|
| 195 |
-
.. math::
|
| 196 |
-
|
| 197 |
-
\frac{\partial \sigma_i}{\partial o_k} = \frac{\partial }{\partial o_k} \frac{e^{o_i}}{\sum_{j = 1}^ne^{o_j}}
|
| 198 |
-
|
| 199 |
-
|
| 200 |
-
:math:`\frac{\partial \sigma_i}{\partial o_k}` **is the partial derivative of the i-th output with respect with the
|
| 201 |
-
k-th input**.
|
| 202 |
-
|
| 203 |
-
We'll be using the quotient rule of derivatives. For :math:`h(x) = \frac{f(x)}{g(x)}` where both :math:`f` and :math:`g`
|
| 204 |
-
are differentiable and :math:`g(x) \ne 0`, The `quotient rule <https://en.wikipedia.org/wiki/Quotient_rule>`_ states
|
| 205 |
-
that the derivative of :math:`h(x)` is
|
| 206 |
-
|
| 207 |
-
.. math::
|
| 208 |
-
|
| 209 |
-
h'(x) = \frac{f'(x)g(x) - f(x)g'(x)}{g^2(x)}
|
| 210 |
-
|
| 211 |
-
In our case, we have
|
| 212 |
-
|
| 213 |
-
.. math::
|
| 214 |
-
|
| 215 |
-
f'(o_k) = \frac{\partial}{\partial o_k} e^{o_i} = \begin{cases}
|
| 216 |
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e^{o_k}, & \text{if}\ i = k \\
|
| 217 |
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0, & \text{otherwise}
|
| 218 |
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\end{cases}
|
| 219 |
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|
| 220 |
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.. math::
|
| 221 |
-
|
| 222 |
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g'(o_k) = \frac{\partial}{\partial o_k} \sum_{j = 1}^ne^{o_j} = \left( \frac{\partial e^{o_1}}{\partial o_k} + \frac{\partial e^{o_2}}{\partial o_k} + \dots + \frac{\partial e^{o_k}}{\partial o_k} + \dots + \frac{\partial e^{o_n}}{\partial o_k} \right) = \frac{\partial e^{o_k}}{\partial o_k} = e^{o_k}
|
| 223 |
-
|
| 224 |
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The rest of it becomes trivial then. When :math:`i = k`,
|
| 225 |
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|
| 226 |
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.. math::
|
| 227 |
-
|
| 228 |
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\frac{\partial \sigma_i}{\partial o_k} = \frac{e^{o_k} \sum_{j = 1}^ne^{o_j} - e^{o_k} e^{o_i}}{\left( \sum_{j = 1}^ne^{o_j} \right)^2}
|
| 229 |
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= \frac{e^{o_i} \sum_{j = 1}^ne^{o_j} - e^{o_i} e^{o_i}}{\left( \sum_{j = 1}^ne^{o_j} \right)^2}
|
| 230 |
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= \frac{e^{o_i}}{\sum_{j = 1}^ne^{o_j}} \frac{\sum_{j = 1}^ne^{o_j} - e^{o_i}}{\sum_{j = 1}^ne^{o_j}} \\
|
| 231 |
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|
| 232 |
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= \sigma_i\left( \frac{\sum_{j = 1}^ne^{o_j}}{\sum_{j = 1}^ne^{o_j}} - \frac{e^{o_i}}{\sum_{j = 1}^ne^{o_j}} \right)
|
| 233 |
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= \sigma_i \left( 1 - \sigma_i \right)
|
| 234 |
-
|
| 235 |
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When :math:`i \ne k`
|
| 236 |
-
|
| 237 |
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.. math::
|
| 238 |
-
|
| 239 |
-
\frac{\partial \sigma_i}{\partial o_k} = \frac{-e^{o_k} e^{o_i}}{\left( \sum_{j = 1}^ne^{o_j} \right)^2} = -\sigma_i\sigma_k
|
| 240 |
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|
| 241 |
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This concludes the derivative of the softmax function:
|
| 242 |
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|
| 243 |
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.. math::
|
| 244 |
-
|
| 245 |
-
\frac{\partial \sigma_i}{\partial o_k} = \begin{cases}
|
| 246 |
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\sigma_i \left( 1 - \sigma_i \right), & \text{if}\ i = k \\
|
| 247 |
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-\sigma_i\sigma_k, & \text{otherwise}
|
| 248 |
-
\end{cases}
|
| 249 |
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|
| 250 |
-
Cross-Entropy
|
| 251 |
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"""""""""""""
|
| 252 |
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|
| 253 |
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.. admonition:: Cross-Entropy `Wikipedia <https://en.wikipedia.org/wiki/Cross-entropy>`_
|
| 254 |
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|
| 255 |
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In information theory, the cross-entropy between two probability distributions :math:`p` and :math:`q` over the same
|
| 256 |
-
underlying set of events measures the average number of bits needed to identify an event drawn from the set if a
|
| 257 |
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coding scheme used for the set is optimized for an estimated probability distribution :math:`q`, rather than the
|
| 258 |
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true distribution :math:`p`
|
| 259 |
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|
| 260 |
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Confused? Let's put it in the context of Machine Learning.
|
| 261 |
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|
| 262 |
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Machine Learning sees the world based on probability. The "probability distribution" identifies the various tasks to
|
| 263 |
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learn. For example, a daily language such as English or Chinese, can be seen as a probability distribution. The
|
| 264 |
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probability of "name" followed by "is" is far greater than "are" as in "My name is Jack". We call such language
|
| 265 |
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distribution :math:`p`. The task of RNN (or Machine Learning in general) is to learn an approximated distribution of
|
| 266 |
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:math:`p`; we call this approximation :math:`q`
|
| 267 |
-
|
| 268 |
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"The average number of bits needed" is can be seen as the distance between :math:`p` and :math:`q` given an event. In
|
| 269 |
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analogy of language, this can be the *quantitative* measure of the deviation between a real language phrase
|
| 270 |
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"My name is Jack" and "My name are Jack".
|
| 271 |
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|
| 272 |
-
At this point, it is easy to image that, in the Machine Learning world, the cross entropy indicates the distance between
|
| 273 |
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what the model believes the output distribution should be and what the original distribution really is.
|
| 274 |
-
|
| 275 |
-
Now we have an intuitive understanding of cross entropy, let's formally define it.
|
| 276 |
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|
| 277 |
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The cross-entropy of the discrete probability distribution :math:`q` relative to a distribution :math:`p` over a given
|
| 278 |
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set is defined as
|
| 279 |
-
|
| 280 |
-
.. math::
|
| 281 |
-
|
| 282 |
-
H(p, q) = -\sum_x p(x)\log q(x)
|
| 283 |
-
|
| 284 |
-
In RNN, the probability distribution of :math:`q(x)` is exactly the softmax function we defined earlier:
|
| 285 |
-
|
| 286 |
-
.. math::
|
| 287 |
-
|
| 288 |
-
\mathcal{L} = -\sum_i p(i)\log\sigma(\boldsymbol{o})_i = -\sum_i \log\sigma(\boldsymbol{o})_i = -\log\boldsymbol{\hat{y}}^{(t)}
|
| 289 |
-
|
| 290 |
-
where
|
| 291 |
-
|
| 292 |
-
- :math:`\boldsymbol{o}` is the predicted sequence by RNN and :math:`o_i` is the i-th element of the predicted sequence
|
| 293 |
-
|
| 294 |
-
.. admonition:: What is the Mathematical form of :math:`p(i)` in RNN? Why would it become 1?
|
| 295 |
-
|
| 296 |
-
By definition, :math:`p(i)` is the *true* distribution whose exact functional form is unknown. In the language of
|
| 297 |
-
Approximation Theory, :math:`p(i)` is the function that RNN is trying to learn or approximate mathematically.
|
| 298 |
-
|
| 299 |
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Although the :math:`p(i)` makes the exact form of :math:`\mathcal{L}` unknown, computationally :math:`p(i)` is
|
| 300 |
-
perfectly defined in each training example. Taking our "hello" example:
|
| 301 |
-
|
| 302 |
-
.. figure:: ../img/char-level-language-model.png
|
| 303 |
-
:align: center
|
| 304 |
-
:width: 60%
|
| 305 |
-
|
| 306 |
-
The 4 probability distributions of :math:`q(x)` is "reflected" in the **output layer** of this example. They are
|
| 307 |
-
"reflecting" the probability distribution of :math:`q(x)` because they are only :math:`o` values and have not been
|
| 308 |
-
transformed to the :math:`\sigma` distribution yet. But in this case, we are 100% sure that the true probability
|
| 309 |
-
distribution :math:`p(i)` for the 4 outputs are
|
| 310 |
-
|
| 311 |
-
.. math::
|
| 312 |
-
|
| 313 |
-
\begin{pmatrix}0\\1\\0\\0\end{pmatrix}, \begin{pmatrix}0\\0\\1\\0\end{pmatrix}, \begin{pmatrix}0\\0\\1\\0\end{pmatrix}, \begin{pmatrix}0\\0\\0\\1\end{pmatrix}
|
| 314 |
-
|
| 315 |
-
respectively. *That is all we need for calculating the* :math:`\mathcal{L}`
|
| 316 |
-
|
| 317 |
-
Deriving Gradient Descent Weight Update Rule
|
| 318 |
-
--------------------------------------------
|
| 319 |
-
|
| 320 |
-
*Training a RNN model of is the same thing as searching for the optimal values for the following parameters of these two
|
| 321 |
-
perceptrons*:
|
| 322 |
-
|
| 323 |
-
1. :math:`W_{xh}`
|
| 324 |
-
2. :math:`W_{hh}`
|
| 325 |
-
3. :math:`W_{yh}`
|
| 326 |
-
4. :math:`b_h`
|
| 327 |
-
5. :math:`b_o`
|
| 328 |
-
|
| 329 |
-
By the Gradient Descent discussed in `MACHINE LEARNING by Mitchell, Thom M. (1997)`_ tells us we should derive the
|
| 330 |
-
weight updat rule by *taking partial derivatives with respect to all of the variables above*. Let's start with
|
| 331 |
-
:math:`W_{yh}`
|
| 332 |
-
|
| 333 |
-
`MACHINE LEARNING by Mitchell, Thom M. (1997)`_ has mentioned gradients and partial derivatives as being important for
|
| 334 |
-
an optimization algorithm to update, say, the model weights of a neural network to reach an optimal set of weights. The
|
| 335 |
-
use of partial derivatives permits each weight to be updated independently of the others, by calculating the gradient of
|
| 336 |
-
the error curve with respect to each weight in turn.
|
| 337 |
-
|
| 338 |
-
Many of the functions that we usually work with in machine learning are *multivariate*, *vector-valued* functions, which
|
| 339 |
-
means that they map multiple real inputs :math:`n` to multiple real outputs :math:`m`:
|
| 340 |
-
|
| 341 |
-
.. math::
|
| 342 |
-
|
| 343 |
-
f: \mathbb{R}^n \rightarrow \mathbb{R}^m
|
| 344 |
-
|
| 345 |
-
In training a neural network, the backpropagation algorithm is responsible for sharing back the error calculated at the
|
| 346 |
-
output layer among the neurons comprising the different hidden layers of the neural network, until it reaches the input.
|
| 347 |
-
|
| 348 |
-
If our RNN contains only 1 perceptron unit, the error is propagated back by, using the
|
| 349 |
-
`Chain Rule <https://en.wikipedia.org/wiki/Chain_rule>`_ of :math:`\frac{dz}{dx} = \frac{dz}{dy}\frac{dy}{dx}`:
|
| 350 |
-
|
| 351 |
-
.. math::
|
| 352 |
-
|
| 353 |
-
\frac{\partial \mathcal{L}}{\partial W} = \frac{\partial \mathcal{L}}{\partial o}\frac{\partial o}{\partial W}
|
| 354 |
-
|
| 355 |
-
Note that in the RNN mode, :math:`\mathcal{L}` is not a direct function of :math:`W`. Thus its first order derivative
|
| 356 |
-
cannot be computed unless we connect the :math:`\mathcal{L}` to :math:`o` first and then to :math:`W`, because both the
|
| 357 |
-
first order derivatives of :math:`\frac{\partial \mathcal{L}}{\partial o}` and :math:`\frac{\partial o}{\partial W}` are
|
| 358 |
-
defined by the model
|
| 359 |
-
|
| 360 |
-
It is more often the case that we'd have many connected perceptrons populating the network, each attributed a different
|
| 361 |
-
weight. Since this is the case for RNN, we can generalise multiple inputs and multiple outputs using the **Generalized
|
| 362 |
-
Chain Rule**:
|
| 363 |
-
|
| 364 |
-
Consider the case where :math:`x \in \mathbb{R}^m` and :math:`u \in \mathbb{R}^n`; an inner function, :math:`f`, maps
|
| 365 |
-
:math:`m` inputs to :math:`n` outputs, while an outer function, :math:`g`, receives :math:`n` inputs to produce an
|
| 366 |
-
output, :math:`h \in \mathbb{R}^k`. For :math:`i = 1, \dots, m` the generalized chain rule states:
|
| 367 |
-
|
| 368 |
-
.. math::
|
| 369 |
-
|
| 370 |
-
\frac{\partial h}{\partial x_i} = \frac{\partial h}{\partial u_1} \frac{\partial u_1}{\partial x_i} + \frac{\partial h}{\partial u_2} \frac{\partial u_2}{\partial x_i} + \dots + \frac{\partial h}{\partial u_n} \frac{\partial u_n}{\partial x_i} = \sum_{j = 1}^n \frac{\partial h}{\partial u_j} \frac{\partial u_j}{\partial x_i}
|
| 371 |
-
|
| 372 |
-
Therefore, the error propagation of Gradient Descent in RNN is
|
| 373 |
-
|
| 374 |
-
.. math::
|
| 375 |
-
|
| 376 |
-
\color{green} \boxed{
|
| 377 |
-
\begin{align}
|
| 378 |
-
\frac{\partial \mathcal{L}}{\partial W_{yh}} = \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial o_i^{(t)}} \frac{\partial o_i^{(t)}}{\partial W_{yh}} \\ \\
|
| 379 |
-
\frac{\partial \mathcal{L}}{\partial W_{hh}} = \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial h_i^{(t)}} \frac{\partial h_i^{(t)}}{\partial W_{hh}} \\ \\
|
| 380 |
-
\frac{\partial \mathcal{L}}{\partial W_{xh}} = \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial h_i^{(t)}} \frac{\partial h_i^{(t)}}{\partial W_{xh}}
|
| 381 |
-
\end{align}
|
| 382 |
-
}
|
| 383 |
-
|
| 384 |
-
where :math:`n` is the length of a RNN sequence and :math:`t` is the index of timestep
|
| 385 |
-
|
| 386 |
-
.. admonition:: :math:`\sum_{t = 1}^\tau`
|
| 387 |
-
|
| 388 |
-
We assume the error is the sum of all errors of each timestep, which is why we include the :math:`\sum_{t = 1}^\tau`
|
| 389 |
-
term
|
| 390 |
-
|
| 391 |
-
Let's look at :math:`\frac{\partial \mathcal{L}}{W_{yh}}` first
|
| 392 |
-
|
| 393 |
-
.. math::
|
| 394 |
-
|
| 395 |
-
\frac{\partial \mathcal{L}}{W_{yh}} = \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial o_i^{(t)}} \frac{\partial o_i^{(t)}}{\partial W_{yh}}
|
| 396 |
-
|
| 397 |
-
Since :math:`o_i = \left( W_{yh}h_i + b_o \right)`,
|
| 398 |
-
|
| 399 |
-
.. math::
|
| 400 |
-
|
| 401 |
-
\frac{\partial o_i}{W_{yh}} = \frac{\partial }{W_{yh}}\left( W_{yh}h_i + b_o \right) = h_i
|
| 402 |
-
|
| 403 |
-
For the :math:`\frac{\partial \mathcal{L}}{\partial o_i}` we shall recall from the earlier discussion on softmax
|
| 404 |
-
derivative that we cannot simply have
|
| 405 |
-
|
| 406 |
-
.. math::
|
| 407 |
-
|
| 408 |
-
\frac{\partial \mathcal{L}}{\partial o_i} = -\frac{\partial}{\partial o_i}\sum_i^np(i)\log\sigma_i
|
| 409 |
-
|
| 410 |
-
because we need to
|
| 411 |
-
|
| 412 |
-
1. specify which component (output element) we're seeking to find the derivative of
|
| 413 |
-
2. with respect to which input element the partial derivative is computed
|
| 414 |
-
|
| 415 |
-
Therefore:
|
| 416 |
-
|
| 417 |
-
.. math::
|
| 418 |
-
|
| 419 |
-
\frac{\partial \mathcal{L}}{\partial o_i} = -\frac{\partial}{\partial o_i}\sum_j^np(j)\log\sigma_j = -\sum_j^n\frac{\partial}{\partial o_i}p(j)\log\sigma_j = -\sum_j^np(j)\frac{\partial \log\sigma_j}{\partial o_i}
|
| 420 |
-
|
| 421 |
-
where :math:`n` is the number of timesteps (or the length of a sequence such as "hell")
|
| 422 |
-
|
| 423 |
-
Applying the chain rule again:
|
| 424 |
-
|
| 425 |
-
.. math::
|
| 426 |
-
|
| 427 |
-
-\sum_j^np(j)\frac{\partial \log\sigma_j}{\partial o_i} = -\sum_j^np(j)\frac{1}{\sigma_j}\frac{\partial\sigma_j}{\partial o_i}
|
| 428 |
-
|
| 429 |
-
Recall we have already derived that
|
| 430 |
-
|
| 431 |
-
.. math::
|
| 432 |
-
|
| 433 |
-
\frac{\partial \sigma_i}{\partial o_j} = \begin{cases}
|
| 434 |
-
\sigma_i \left( 1 - \sigma_i \right), & \text{if}\ i = j \\
|
| 435 |
-
-\sigma_i\sigma_j, & \text{otherwise}
|
| 436 |
-
\end{cases}
|
| 437 |
-
|
| 438 |
-
.. math::
|
| 439 |
-
|
| 440 |
-
-\sum_j^np(j)\frac{1}{\sigma_j}\frac{\partial\sigma_j}{\partial o_i} = -\sum_{i = j}^np(j)\frac{1}{\sigma_j}\frac{\partial\sigma_j}{\partial o_i} -\sum_{i \ne j}^np(j)\frac{1}{\sigma_j}\frac{\partial\sigma_j}{\partial o_i} = -p(i)(1 - \sigma_i) + \sum_{i \ne j}^np(j)\sigma_i
|
| 441 |
-
|
| 442 |
-
Observing that
|
| 443 |
-
|
| 444 |
-
.. math::
|
| 445 |
-
|
| 446 |
-
\sum_{j}^np(j) = 1
|
| 447 |
-
|
| 448 |
-
.. math::
|
| 449 |
-
|
| 450 |
-
-p(i)(1 - \sigma_i) + \sum_{i \ne j}^np(j)\sigma_i = -p(i) + p(i)\sigma_i + \sum_{i \ne j}^np(j)\sigma_i = \sigma_i - p(i)
|
| 451 |
-
|
| 452 |
-
.. math::
|
| 453 |
-
|
| 454 |
-
\color{green} \boxed{\frac{\partial \mathcal{L}}{\partial o_i} = \sigma_i - p(i)}
|
| 455 |
-
|
| 456 |
-
.. math::
|
| 457 |
-
|
| 458 |
-
\color{green} \boxed{ \frac{\partial \mathcal{L}}{\partial W_{yh}} = \sum_{t = 1}^\tau \sum_i^n\left[ \sigma_i - p(i) \right] h_i = \sum_{t = 1}^\tau \left( \boldsymbol{\sigma} - \boldsymbol{p} \right) \boldsymbol{h}^{(t)} }
|
| 459 |
-
|
| 460 |
-
.. math::
|
| 461 |
-
|
| 462 |
-
\frac{\partial \mathcal{L}}{b_o} = \sum_{t = 1}^\tau \sum_i^n\frac{\partial \mathcal{L}}{\partial o_i^{(t)}}\frac{\partial o_i^{(t)}}{\partial b_o^{(t)}} = \sum_{t = 1}^\tau \sum_i^n\left[ \sigma_i - p(i) \right] \times 1
|
| 463 |
-
|
| 464 |
-
.. math::
|
| 465 |
-
|
| 466 |
-
\color{green} \boxed{ \frac{\partial \mathcal{L}}{\partial b_o} = \sum_{t = 1}^\tau \sum_i^n\left[ \sigma_i - p(i) \right] = \sum_{t = 1}^\tau \boldsymbol{\sigma} - \boldsymbol{p} }
|
| 467 |
-
|
| 468 |
-
We have at this point derived backpropagating rule for :math:`W_{yh}` and :math:`b_o`:
|
| 469 |
-
|
| 470 |
-
1. :math:`W_{xh}`
|
| 471 |
-
2. :math:`W_{hh}`
|
| 472 |
-
3. ✅ :math:`W_{yh}`
|
| 473 |
-
4. :math:`b_h`
|
| 474 |
-
5. ✅ :math:`b_o`
|
| 475 |
-
|
| 476 |
-
Now let's look at :math:`\frac{\partial \mathcal{L}}{\partial W_{hh}}`:
|
| 477 |
-
|
| 478 |
-
Recall from *Deep Learning*, section 6.5.2, p. 207 that the vector notation of
|
| 479 |
-
:math:`\frac{\partial z}{\partial x_i} = \sum_j \frac{\partial z}{\partial y_j}\frac{\partial y_j}{\partial x_i}` is
|
| 480 |
-
|
| 481 |
-
.. math::
|
| 482 |
-
|
| 483 |
-
\nabla_{\boldsymbol{x}}z = \left( \frac{\partial \boldsymbol{y}}{\partial \boldsymbol{x}} \right)^\intercal \nabla_{\boldsymbol{y}}z
|
| 484 |
-
|
| 485 |
-
This gives us a start with:
|
| 486 |
-
|
| 487 |
-
.. math::
|
| 488 |
-
|
| 489 |
-
\begin{align}
|
| 490 |
-
\frac{\partial \mathcal{L}}{\partial W_{hh}} &= \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial h_i^{(t)}} \frac{\partial h_i^{(t)}}{\partial W_{hh}} \\
|
| 491 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \nabla_{\boldsymbol{W_{hh}}}\boldsymbol{h}^{(t)} \\
|
| 492 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\boldsymbol{h}^{(t)} \\
|
| 493 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \\
|
| 494 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 495 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t - 1)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t - 1)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 496 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t - 1)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t - 1)}}{\partial \boldsymbol{h}^{(t)}}\frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t)}}\frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 497 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t - 1)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t - 1)}}{\partial \boldsymbol{h}^{(t)}}\frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}}\frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 498 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t - 1)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t - 1)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 499 |
-
& = \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{hh}}} \right)^\intercal \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \frac{\partial \mathcal{L}}{\partial \boldsymbol{h}^{(t)}} \\
|
| 500 |
-
& = \sum_{t = 1}^\tau diag\left[ 1 - \left(\boldsymbol{h}^{(t)}\right)^2 \right] \boldsymbol{h}^{(t - 1)} \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \\
|
| 501 |
-
& = \sum_{t = 1}^\tau diag\left[ 1 - \left(\boldsymbol{h}^{(t)}\right)^2 \right] \left( \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \right) {\boldsymbol{h}^{(t - 1)}}^\intercal
|
| 502 |
-
\end{align}
|
| 503 |
-
|
| 504 |
-
.. math::
|
| 505 |
-
|
| 506 |
-
\color{green} \boxed{ \frac{\partial \mathcal{L}}{\partial W_{hh}} = \sum_{t = 1}^\tau diag\left[ 1 - \left(\boldsymbol{h}^{(t)}\right)^2 \right] \left( \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \right) {\boldsymbol{h}^{(t - 1)}}^\intercal }
|
| 507 |
-
|
| 508 |
-
The equation above leaves us with a term :math:`\nabla_{\boldsymbol{h}^{(t)}}\mathcal{L}`, which we calculate next. Note
|
| 509 |
-
that the back propagation on :math:`\boldsymbol{h}^{(t)}` has source from both :math:`\boldsymbol{o}^{(t)}` and
|
| 510 |
-
:math:`\boldsymbol{h}^{(t + 1)}`. It's gradient, therefore, is given by
|
| 511 |
-
|
| 512 |
-
.. math::
|
| 513 |
-
|
| 514 |
-
\begin{align}
|
| 515 |
-
\nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} &= \left( \frac{\partial \boldsymbol{o}^{(t)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \nabla_{\boldsymbol{o}^{(t)}}\mathcal{L} + \left( \frac{\partial \boldsymbol{h}^{(t + 1)}}{\partial \boldsymbol{h}^{(t)}} \right)^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \\
|
| 516 |
-
&= \left( \boldsymbol{W_{yh}} \right)^\intercal \nabla_{\boldsymbol{o}^{(t)}}\mathcal{L} + \left( diag\left[ 1 - (\boldsymbol{h}^{(t + 1)})^2 \right] \boldsymbol{W_{hh}} \right)^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \\
|
| 517 |
-
&= \left( \boldsymbol{W_{yh}} \right)^\intercal \nabla_{\boldsymbol{o}^{(t)}}\mathcal{L}+ \boldsymbol{W_{hh}}^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \left( diag\left[ 1 - (\boldsymbol{h}^{(t + 1)})^2 \right] \right)
|
| 518 |
-
\end{align}
|
| 519 |
-
|
| 520 |
-
.. math::
|
| 521 |
-
|
| 522 |
-
\color{green} \boxed{ \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} = \left( \boldsymbol{W_{yh}} \right)^\intercal \nabla_{\boldsymbol{o}^{(t)}}\mathcal{L} + \boldsymbol{W_{hh}}^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \left( diag\left[ 1 - (\boldsymbol{h}^{(t + 1)})^2 \right] \right) }
|
| 523 |
-
|
| 524 |
-
Note that the 2nd term
|
| 525 |
-
:math:`\boldsymbol{W_{xh}}^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \left( diag\left[ 1 - (\boldsymbol{h}^{(t + 1)})^2 \right] \right)`
|
| 526 |
-
is zero at first iteration propagating back because for the last-layer (unrolled) of RNN , there's no gradient update
|
| 527 |
-
flow from the next hidden state.
|
| 528 |
-
|
| 529 |
-
So far we have derived backpropagating rule for :math:`W_{hh}`
|
| 530 |
-
|
| 531 |
-
1. :math:`W_{xh}`
|
| 532 |
-
2. ✅ :math:`W_{hh}`
|
| 533 |
-
3. ✅ :math:`W_{yh}`
|
| 534 |
-
4. :math:`b_h`
|
| 535 |
-
5. ✅ :math:`b_o`
|
| 536 |
-
|
| 537 |
-
Let's tackle the remaining :math:`\frac{\partial \mathcal{L}}{\partial W_{xh}}` and :math:`b_h`:
|
| 538 |
-
|
| 539 |
-
.. math::
|
| 540 |
-
|
| 541 |
-
\begin{align}
|
| 542 |
-
\frac{\partial \mathcal{L}}{\partial W_{xh}} &= \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial h_i^{(t)}} \frac{\partial h_i^{(t)}}{\partial W_{xh}} \\
|
| 543 |
-
&= \sum_{t = 1}^\tau \left( \frac{\partial \boldsymbol{h}^{(t)}}{\partial \boldsymbol{W_{xh}}} \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \\
|
| 544 |
-
&= \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \boldsymbol{x}^{(t)} \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \\
|
| 545 |
-
&= \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \left( \boldsymbol{x}^{(t)} \right)
|
| 546 |
-
\end{align}
|
| 547 |
-
|
| 548 |
-
.. math::
|
| 549 |
-
|
| 550 |
-
\color{green} \boxed{ \frac{\partial \mathcal{L}}{\partial W_{xh}} = \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \left( \boldsymbol{x}^{(t)} \right) }
|
| 551 |
-
|
| 552 |
-
.. math::
|
| 553 |
-
|
| 554 |
-
\begin{align}
|
| 555 |
-
\frac{\partial \mathcal{L}}{\partial b_h} &= \sum_{t = 1}^\tau \sum_{i = 1}^n \frac{\partial \mathcal{L}}{\partial h_i^{(t)}} \frac{\partial h_i^{(t)}}{\partial b_h^{(t)}} \\
|
| 556 |
-
&= \sum_{t = 1}^\tau \left( \frac{\partial h_i^{(t)}}{\partial b_h^{(t)}} \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \\
|
| 557 |
-
&= \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L}
|
| 558 |
-
\end{align}
|
| 559 |
-
|
| 560 |
-
.. math::
|
| 561 |
-
|
| 562 |
-
\color{green} \boxed{ \frac{\partial \mathcal{L}}{\partial b_h} = \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} }
|
| 563 |
-
|
| 564 |
-
This concludes our propagation rules for training RNN:
|
| 565 |
-
|
| 566 |
-
.. math::
|
| 567 |
-
|
| 568 |
-
\color{green} \boxed{
|
| 569 |
-
\begin{gather*}
|
| 570 |
-
\frac{\partial \mathcal{L}}{\partial W_{xh}} = \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \left( \boldsymbol{x}^{(t)} \right) \\ \\
|
| 571 |
-
\frac{\partial \mathcal{L}}{\partial W_{hh}} = \sum_{t = 1}^\tau diag\left[ 1 - \left(\boldsymbol{h}^{(t)}\right)^2 \right] \left( \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \right) {\boldsymbol{h}^{(t - 1)}}^\intercal \\ \\
|
| 572 |
-
\frac{\partial \mathcal{L}}{\partial W_{yh}} = \sum_{t = 1}^\tau \left( \boldsymbol{\sigma} - \boldsymbol{p} \right) \boldsymbol{h}^{(t)} \\ \\
|
| 573 |
-
\frac{\partial \mathcal{L}}{\partial b_h} = \sum_{t = 1}^\tau \left( diag\left[ 1 - (\boldsymbol{h}^{(t)})^2 \right] \right)^\intercal \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} \\ \\
|
| 574 |
-
\frac{\partial \mathcal{L}}{\partial b_o} =\sum_{t = 1}^\tau \boldsymbol{\sigma} - \boldsymbol{p}
|
| 575 |
-
\end{gather*}
|
| 576 |
-
}
|
| 577 |
-
|
| 578 |
-
where
|
| 579 |
-
|
| 580 |
-
.. math::
|
| 581 |
-
|
| 582 |
-
\color{green} \boxed{ \nabla_{\boldsymbol{h}^{(t)}}\mathcal{L} = \left( \boldsymbol{W_{yh}} \right)^\intercal \nabla_{\boldsymbol{o}^{(t)}}\mathcal{L}+ \boldsymbol{W_{hh}}^\intercal \nabla_{\boldsymbol{h}^{(t + 1)}}\mathcal{L} \left( diag\left[ 1 - (\boldsymbol{h}^{(t + 1)})^2 \right] \right) }
|
| 583 |
-
|
| 584 |
-
Computational Gradient Descent Weight Update Rule
|
| 585 |
-
-------------------------------------------------
|
| 586 |
-
|
| 587 |
-
What does the propagation rules above look like in Python?
|
| 588 |
-
|
| 589 |
-
Example
|
| 590 |
-
-------
|
| 591 |
-
|
| 592 |
-
`Pride and Prejudice by Jane Austen <https://www.gutenberg.org/ebooks/1342>`_
|
| 593 |
-
|
| 594 |
-
|
| 595 |
-
.. code-block:: python
|
| 596 |
-
|
| 597 |
-
|
| 598 |
-
|
| 599 |
-
|
| 600 |
-
|
| 601 |
-
|
| 602 |
-
|
| 603 |
-
|
| 604 |
-
|
| 605 |
-
.. _`exploding gradient`: https://qubitpi.github.io/stanford-cs231n.github.io/rnn/#vanilla-rnn-gradient-flow--vanishing-gradient-problem
|
| 606 |
-
|
| 607 |
-
.. _`MACHINE LEARNING by Mitchell, Thom M. (1997)`: https://a.co/d/bjmsEOg
|
| 608 |
-
|
| 609 |
-
.. _`loss function`: https://qubitpi.github.io/stanford-cs231n.github.io/neural-networks-2/#losses
|
| 610 |
-
.. _`LSTM Formulation`: https://qubitpi.github.io/stanford-cs231n.github.io/rnn/#lstm-formulation
|
| 611 |
-
|
| 612 |
-
.. _`Vanilla RNN Gradient Flow & Vanishing Gradient Problem`: https://qubitpi.github.io/stanford-cs231n.github.io/rnn/#vanilla-rnn-gradient-flow--vanishing-gradient-problem
|
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docs/source/speech/sampling.rst
DELETED
|
@@ -1,68 +0,0 @@
|
|
| 1 |
-
===============================
|
| 2 |
-
Speech Recognition with Lamassu
|
| 3 |
-
===============================
|
| 4 |
-
|
| 5 |
-
.. contents:: Table of Contents
|
| 6 |
-
:depth: 2
|
| 7 |
-
|
| 8 |
-
Speech recognition will become a primary way that we interact with computers.
|
| 9 |
-
|
| 10 |
-
One might guess that we could simply feed sound recordings into a neural network and train it to produce text:
|
| 11 |
-
|
| 12 |
-
.. figure:: ../img/speech-processing.png
|
| 13 |
-
:align: center
|
| 14 |
-
|
| 15 |
-
That's the holy grail of speech recognition with deep learning, but we aren't quite there yet. The big problem is that
|
| 16 |
-
speech varies in speed. One person might say "hello!" very quickly and another person might say
|
| 17 |
-
"heeeelllllllllllllooooo!" very slowly, producing a much longer sound file with much more data. Both sounds should be
|
| 18 |
-
recognized as exactly the same text - "hello!" Automatically aligning audio files of various lengths to a fixed-length
|
| 19 |
-
piece of text turns out to be pretty hard. To work around this, we have to use some special tricks and extra precessing.
|
| 20 |
-
|
| 21 |
-
Turning Sounds into Bits
|
| 22 |
-
========================
|
| 23 |
-
|
| 24 |
-
The first step in speech recognition is obvious — we need to feed sound waves into a computer. Sound is transmitted as
|
| 25 |
-
waves. A sound clip of someone saying "Hello" looks like
|
| 26 |
-
|
| 27 |
-
.. figure:: ../img/hello-sound.png
|
| 28 |
-
:align: center
|
| 29 |
-
|
| 30 |
-
Sound waves are one-dimensional. At every moment in time, they have a single value based on the height of the wave.
|
| 31 |
-
Let's zoom in on one tiny part of the sound wave and take a look:
|
| 32 |
-
|
| 33 |
-
.. figure:: ../img/sound-wave.png
|
| 34 |
-
:align: center
|
| 35 |
-
|
| 36 |
-
To turn this sound wave into numbers, we just record of the height of the wave at equally-spaced points:
|
| 37 |
-
|
| 38 |
-
.. figure:: ../img/sampling-sound-wave.gif
|
| 39 |
-
:align: center
|
| 40 |
-
|
| 41 |
-
This is called *sampling*. We are taking a reading thousands of times a second and recording a number representing the
|
| 42 |
-
height of the sound wave at that point in time. That's basically all an uncompressed .wav audio file is.
|
| 43 |
-
|
| 44 |
-
"CD Quality" audio is sampled at 44.1khz (44,100 readings per second). But for speech recognition, a sampling rate of
|
| 45 |
-
16khz (16,000 samples per second) is enough to cover the frequency range of human speech.
|
| 46 |
-
|
| 47 |
-
Lets sample our "Hello" sound wave 16,000 times per second. Here's the first 100 samples:
|
| 48 |
-
|
| 49 |
-
.. figure:: ../img/hello-sampling.png
|
| 50 |
-
:align: center
|
| 51 |
-
|
| 52 |
-
.. note:: Can digital samples perfectly recreate the original analog sound wave? What about those gaps?
|
| 53 |
-
|
| 54 |
-
You might be thinking that sampling is only creating a rough approximation of the original sound wave because it's
|
| 55 |
-
only taking occasional readings. There's gaps in between our readings so we must be losing data, right?
|
| 56 |
-
|
| 57 |
-
.. figure:: ../img/real-vs-sampling.png
|
| 58 |
-
:align: center
|
| 59 |
-
|
| 60 |
-
But thanks to the `Nyquist theorem`_, we know that we can use math to perfectly reconstruct the original sound wave
|
| 61 |
-
from the spaced-out samples — as long as we sample at least twice as fast as the highest frequency we want to record.
|
| 62 |
-
|
| 63 |
-
.. automodule:: lamassu.speech.sampling
|
| 64 |
-
:members:
|
| 65 |
-
:undoc-members:
|
| 66 |
-
:show-inheritance:
|
| 67 |
-
|
| 68 |
-
.. _`Nyquist theorem`: https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem
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lamassu-logo.png
DELETED
|
Binary file (14.3 kB)
|
|
|
lamassu/rnn/__init__.py
DELETED
|
File without changes
|
lamassu/rnn/example.py
DELETED
|
@@ -1,50 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
|
| 3 |
-
from lamassu.rnn.rnn import Config
|
| 4 |
-
from lamassu.rnn.rnn import RecurrentNeuralNetwork
|
| 5 |
-
|
| 6 |
-
if __name__ == "__main__":
|
| 7 |
-
num_hidden_perceptrons= 100
|
| 8 |
-
seq_length = 25
|
| 9 |
-
learning_rate = 1e-1
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
data = open('pride-and-prejudice.txt', 'r').read()
|
| 13 |
-
char_set = list(set(data))
|
| 14 |
-
num_chars, num_unique_chars = len(data), len(char_set)
|
| 15 |
-
char_to_idx = { ch:i for i,ch in enumerate(char_set) }
|
| 16 |
-
idx_to_char = { i:ch for i,ch in enumerate(char_set) }
|
| 17 |
-
|
| 18 |
-
rnn = RecurrentNeuralNetwork(
|
| 19 |
-
Config(
|
| 20 |
-
num_hidden_perceptrons=num_hidden_perceptrons,
|
| 21 |
-
input_size=num_unique_chars,
|
| 22 |
-
learning_rate=learning_rate
|
| 23 |
-
)
|
| 24 |
-
)
|
| 25 |
-
|
| 26 |
-
num_iter, pointer = 0, 0
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
while True:
|
| 30 |
-
if pointer + seq_length + 1 >= len(data) or num_iter == 0:
|
| 31 |
-
prev_history = np.zeros((num_hidden_perceptrons, 1))
|
| 32 |
-
pointer = 0
|
| 33 |
-
input = [char_to_idx[c] for c in data[pointer: pointer + seq_length]]
|
| 34 |
-
target = [char_to_idx[c] for c in data[pointer + 1: pointer + seq_length + 1]]
|
| 35 |
-
|
| 36 |
-
if num_iter % 100 == 0: # inference after every 100 trainings
|
| 37 |
-
inferenced_idxes = rnn.inference(prev_history, input[0])
|
| 38 |
-
inferenced = ''.join(idx_to_char[idx] for idx in inferenced_idxes)
|
| 39 |
-
print("============ inference ============")
|
| 40 |
-
print(inferenced)
|
| 41 |
-
|
| 42 |
-
history, q, x, loss = rnn.forward_pass(input, target, prev_history)
|
| 43 |
-
|
| 44 |
-
if num_iter % 100 == 0:
|
| 45 |
-
print("loss: {}".format(loss))
|
| 46 |
-
|
| 47 |
-
prev_history = rnn.back_propagation(input, target, history, q, x)
|
| 48 |
-
|
| 49 |
-
pointer += seq_length
|
| 50 |
-
num_iter += 1
|
|
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|
lamassu/rnn/rnn.py
DELETED
|
@@ -1,114 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
from math import exp
|
| 3 |
-
from dataclasses import dataclass
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
np.random.seed(0)
|
| 7 |
-
|
| 8 |
-
@dataclass
|
| 9 |
-
class Config():
|
| 10 |
-
num_hidden_perceptrons: int
|
| 11 |
-
input_size: int
|
| 12 |
-
learning_rate: float
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class RecurrentNeuralNetwork(object):
|
| 16 |
-
"""
|
| 17 |
-
Architecture is single-hidden-layer
|
| 18 |
-
"""
|
| 19 |
-
|
| 20 |
-
def __init__(self, config: Config):
|
| 21 |
-
self.config = config
|
| 22 |
-
|
| 23 |
-
self.W_xh = np.random.randn(config.num_hidden_perceptrons, config.input_size)
|
| 24 |
-
self.W_hh = np.random.randn(config.num_hidden_perceptrons, config.num_hidden_perceptrons)
|
| 25 |
-
self.W_yh = np.random.randn(config.input_size, config.num_hidden_perceptrons)
|
| 26 |
-
|
| 27 |
-
self.b_h = np.zeros((config.num_hidden_perceptrons, 1))
|
| 28 |
-
self.b_o = np.zeros((config.input_size, 1))
|
| 29 |
-
|
| 30 |
-
def forward_pass(self, input, target, prev_history):
|
| 31 |
-
"""
|
| 32 |
-
|
| 33 |
-
:param input: The input vector; each element is an index
|
| 34 |
-
:return:
|
| 35 |
-
"""
|
| 36 |
-
|
| 37 |
-
history, x, o, q, loss = {}, {}, {}, {}, 0
|
| 38 |
-
history[-1] = np.copy(prev_history)
|
| 39 |
-
|
| 40 |
-
for t in range(len(input)):
|
| 41 |
-
x[t] = np.zeros((self.config.input_size, 1))
|
| 42 |
-
x[t][input[t]] = 1
|
| 43 |
-
|
| 44 |
-
if t == 0:
|
| 45 |
-
np.dot(self.W_hh, history[t - 1])
|
| 46 |
-
np.dot(self.W_xh, x[t])
|
| 47 |
-
|
| 48 |
-
history[t] = np.tanh(
|
| 49 |
-
np.dot(self.W_hh, history[t - 1]) + np.dot(self.W_xh, x[t]) + self.b_h
|
| 50 |
-
)
|
| 51 |
-
o[t] = np.dot(self.W_yh, history[t]) + self.b_o
|
| 52 |
-
q[t] = np.exp(o[t]) / np.sum(np.exp(o[t]))
|
| 53 |
-
loss += -np.log(q[t][target, 0])
|
| 54 |
-
|
| 55 |
-
return history, q, x, loss
|
| 56 |
-
|
| 57 |
-
def back_propagation(self, input, target, history, q, x):
|
| 58 |
-
gradient_loss_over_W_xh = np.zeros_like(self.W_xh)
|
| 59 |
-
gradient_loss_over_W_hh = np.zeros_like(self.W_hh)
|
| 60 |
-
gradient_loss_over_W_yh = np.zeros_like(self.W_yh)
|
| 61 |
-
|
| 62 |
-
gradient_loss_over_b_h = np.zeros_like(self.b_h)
|
| 63 |
-
gradient_loss_over_b_y = np.zeros_like(self.b_o)
|
| 64 |
-
|
| 65 |
-
gradient_loss_over_next_h = np.zeros_like(history[0])
|
| 66 |
-
|
| 67 |
-
for t in reversed(range(len(input))):
|
| 68 |
-
gradient_loss_over_o = np.copy(q[t])
|
| 69 |
-
gradient_loss_over_o[target[t]] -= 1
|
| 70 |
-
|
| 71 |
-
gradient_loss_over_W_yh += np.dot(gradient_loss_over_o, history[t].T)
|
| 72 |
-
gradient_loss_over_b_y += gradient_loss_over_o #
|
| 73 |
-
|
| 74 |
-
gradient_loss_over_h = np.dot(self.W_yh.T, gradient_loss_over_o) + gradient_loss_over_next_h
|
| 75 |
-
diag_times_gradient_loss_over_h = (1 - history[t] * history[t]) * gradient_loss_over_h
|
| 76 |
-
|
| 77 |
-
gradient_loss_over_b_h += diag_times_gradient_loss_over_h #
|
| 78 |
-
|
| 79 |
-
gradient_loss_over_W_xh += np.dot(diag_times_gradient_loss_over_h, x[t].T) #
|
| 80 |
-
gradient_loss_over_W_hh += np.dot(diag_times_gradient_loss_over_h, history[t - 1].T) #
|
| 81 |
-
|
| 82 |
-
gradient_loss_over_next_h = np.dot(self.W_hh.T, diag_times_gradient_loss_over_h)
|
| 83 |
-
|
| 84 |
-
for gradient in [gradient_loss_over_W_xh, gradient_loss_over_W_hh, gradient_loss_over_W_yh, gradient_loss_over_b_h, gradient_loss_over_b_y]:
|
| 85 |
-
np.clip(gradient, -5, 5, out=gradient) # avoid exploding gradients
|
| 86 |
-
|
| 87 |
-
# update weights
|
| 88 |
-
for param, gradient in zip(
|
| 89 |
-
[self.W_xh, self.W_hh, self.W_yh, self.b_h, self.b_o],
|
| 90 |
-
[gradient_loss_over_W_xh, gradient_loss_over_W_hh, gradient_loss_over_W_yh, gradient_loss_over_b_h, gradient_loss_over_b_y]):
|
| 91 |
-
param += -self.config.learning_rate * gradient
|
| 92 |
-
|
| 93 |
-
return history[len(input) - 1]
|
| 94 |
-
|
| 95 |
-
def inference(self, history, seed_idx):
|
| 96 |
-
x = np.zeros((self.config.input_size, 1))
|
| 97 |
-
x[seed_idx] = 1
|
| 98 |
-
idxes = []
|
| 99 |
-
|
| 100 |
-
for timestep in range(200):
|
| 101 |
-
history = np.tanh(np.dot(self.W_xh, x) + np.dot(self.W_hh, history) + self.b_h)
|
| 102 |
-
o = np.dot(self.W_yh, history) + self.b_o
|
| 103 |
-
p = np.exp(o) / np.sum(np.exp(o))
|
| 104 |
-
|
| 105 |
-
next_idx = self._inference_single(p.ravel())
|
| 106 |
-
|
| 107 |
-
x[next_idx] = 1
|
| 108 |
-
idxes.append(next_idx)
|
| 109 |
-
|
| 110 |
-
return idxes
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
def _inference_single(self, probability_distribution):
|
| 114 |
-
return np.random.choice(range(self.config.input_size), p=probability_distribution)
|
|
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|
lamassu/speech/__init__.py
DELETED
|
File without changes
|
lamassu/speech/sampling.py
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
import wave
|
| 2 |
-
|
| 3 |
-
import numpy as np
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def sample_wav(file_path: str):
|
| 7 |
-
"""
|
| 8 |
-
Sampling a .wav file
|
| 9 |
-
|
| 10 |
-
:param file_path: The absolute path to the .wav file to be sampled
|
| 11 |
-
|
| 12 |
-
:return: an array of sampled points
|
| 13 |
-
"""
|
| 14 |
-
with wave.open(file_path, "rb") as f:
|
| 15 |
-
frames = f.readframes(f.getnframes())
|
| 16 |
-
return np.frombuffer(frames, dtype=np.int16)
|
|
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|
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|
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|
|
|
|
|
|
mlflow/HanLPner.py
ADDED
|
@@ -0,0 +1,57 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
| 1 |
+
import hanlp
|
| 2 |
+
import mlflow.pyfunc
|
| 3 |
+
import pandas
|
| 4 |
+
from parser import convert_to_knowledge_graph_spec
|
| 5 |
+
|
| 6 |
+
|
| 7 |
+
class HanLPner(mlflow.pyfunc.PythonModel):
|
| 8 |
+
|
| 9 |
+
def __init__(self):
|
| 10 |
+
self.HanLP = None
|
| 11 |
+
|
| 12 |
+
def load_context(self, context):
|
| 13 |
+
HanLP = hanlp.load(hanlp.pretrained.mtl.CLOSE_TOK_POS_NER_SRL_DEP_SDP_CON_ELECTRA_SMALL_ZH)
|
| 14 |
+
self.HanLP = HanLP
|
| 15 |
+
|
| 16 |
+
def predict(self, context, model_input):
|
| 17 |
+
texts = []
|
| 18 |
+
for _, row in model_input.iterrows():
|
| 19 |
+
texts.append(row["text"])
|
| 20 |
+
|
| 21 |
+
return pandas.Series(convert_to_knowledge_graph_spec(self.HanLP(texts)["srl"]))
|
| 22 |
+
|
| 23 |
+
if __name__ == '__main__':
|
| 24 |
+
conda_env = {
|
| 25 |
+
'channels': ['defaults'],
|
| 26 |
+
'dependencies': [
|
| 27 |
+
'python=3.10.7',
|
| 28 |
+
'pip',
|
| 29 |
+
{
|
| 30 |
+
'pip': [
|
| 31 |
+
'mlflow',
|
| 32 |
+
'mlflow-skinny',
|
| 33 |
+
'mlflow[extras]',
|
| 34 |
+
'pandas=={}'.format(pandas.__version__),
|
| 35 |
+
'hanlp[amr, fasttext, full, tf]'
|
| 36 |
+
],
|
| 37 |
+
},
|
| 38 |
+
],
|
| 39 |
+
'name': 'HanLPner'
|
| 40 |
+
}
|
| 41 |
+
|
| 42 |
+
# Save the MLflow Model
|
| 43 |
+
mlflow_pyfunc_model_path = "models/HanLPner"
|
| 44 |
+
mlflow.pyfunc.save_model(path=mlflow_pyfunc_model_path, python_model=HanLPner(), conda_env=conda_env)
|
| 45 |
+
|
| 46 |
+
loaded_model = mlflow.pyfunc.load_model(mlflow_pyfunc_model_path)
|
| 47 |
+
|
| 48 |
+
test_data = pandas.DataFrame(
|
| 49 |
+
{
|
| 50 |
+
"text": [
|
| 51 |
+
"我爱中国"
|
| 52 |
+
]
|
| 53 |
+
}
|
| 54 |
+
)
|
| 55 |
+
|
| 56 |
+
test_predictions = loaded_model.predict(test_data)
|
| 57 |
+
print(test_predictions.to_markdown())
|
{lamassu → mlflow}/__init__.py
RENAMED
|
File without changes
|
mlflow/parser.py
ADDED
|
@@ -0,0 +1,84 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import random
|
| 2 |
+
import string
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
def _random_id():
|
| 6 |
+
return "n" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(5)).lower()
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
def _construct_knowledge_graph_spec_node(extrapolated_entity: str):
|
| 10 |
+
return {
|
| 11 |
+
"id": _random_id(),
|
| 12 |
+
"fields": {
|
| 13 |
+
"name": extrapolated_entity,
|
| 14 |
+
"type": "entity"
|
| 15 |
+
}
|
| 16 |
+
}
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def _construct_knowledge_graph_spec_link(source: str, target: str, extrapolated_relationship: str):
|
| 20 |
+
return {
|
| 21 |
+
"id": _random_id(),
|
| 22 |
+
"source": source,
|
| 23 |
+
"target": target,
|
| 24 |
+
"fields": {
|
| 25 |
+
"type": extrapolated_relationship
|
| 26 |
+
}
|
| 27 |
+
}
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
def convert_to_knowledge_graph_spec(model_results):
|
| 31 |
+
nodes = []
|
| 32 |
+
links = []
|
| 33 |
+
|
| 34 |
+
node_name_to_id_map = {}
|
| 35 |
+
link_set = set()
|
| 36 |
+
for srl_results in model_results:
|
| 37 |
+
for srl_result in srl_results:
|
| 38 |
+
subject = None
|
| 39 |
+
verb = None
|
| 40 |
+
object = None
|
| 41 |
+
|
| 42 |
+
for tuple in srl_result:
|
| 43 |
+
if tuple[1] == "ARG0":
|
| 44 |
+
subject = tuple
|
| 45 |
+
if tuple[1] == "PRED":
|
| 46 |
+
verb = tuple
|
| 47 |
+
if tuple[1] == "ARG1":
|
| 48 |
+
object = tuple
|
| 49 |
+
|
| 50 |
+
if subject and verb and object:
|
| 51 |
+
source_node = _construct_knowledge_graph_spec_node(subject[0])
|
| 52 |
+
target_node = _construct_knowledge_graph_spec_node(object[0])
|
| 53 |
+
|
| 54 |
+
source_node_id = source_node["id"]
|
| 55 |
+
source_node_name = source_node["fields"]["name"]
|
| 56 |
+
target_node_id = target_node["id"]
|
| 57 |
+
target_node_name = target_node["fields"]["name"]
|
| 58 |
+
|
| 59 |
+
if source_node_name not in node_name_to_id_map.keys():
|
| 60 |
+
node_name_to_id_map[source_node_name] = source_node_id
|
| 61 |
+
nodes.append(source_node)
|
| 62 |
+
if target_node_name not in node_name_to_id_map.keys():
|
| 63 |
+
node_name_to_id_map[target_node_name] = target_node_id
|
| 64 |
+
nodes.append(target_node)
|
| 65 |
+
|
| 66 |
+
link: str = source_node_name + target_node_name + verb[0]
|
| 67 |
+
if link not in link_set:
|
| 68 |
+
links.append(
|
| 69 |
+
_construct_knowledge_graph_spec_link(
|
| 70 |
+
node_name_to_id_map[source_node_name],
|
| 71 |
+
node_name_to_id_map[target_node_name],
|
| 72 |
+
verb[0]
|
| 73 |
+
)
|
| 74 |
+
)
|
| 75 |
+
link_set.add(link)
|
| 76 |
+
|
| 77 |
+
subject = None
|
| 78 |
+
verb = None
|
| 79 |
+
object = None
|
| 80 |
+
|
| 81 |
+
return {
|
| 82 |
+
"nodes": nodes,
|
| 83 |
+
"links": links
|
| 84 |
+
}
|
mlflow/requirements.txt
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
mlflow
|
| 2 |
+
mlflow-skinny
|
| 3 |
+
mlflow[extras]
|
| 4 |
+
hanlp[amr,fasttext,full,tf]
|
mlflow/test_parser.py
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import unittest
|
| 2 |
+
|
| 3 |
+
from mlflow.parser import convert_to_knowledge_graph_spec
|
| 4 |
+
|
| 5 |
+
|
| 6 |
+
class TestParser(unittest.TestCase):
|
| 7 |
+
|
| 8 |
+
def test_parser(self):
|
| 9 |
+
model_results: list = [
|
| 10 |
+
[
|
| 11 |
+
[['我', 'ARG0', 0, 1], ['爱', 'PRED', 1, 2], ['中国', 'ARG1', 2, 3]]
|
| 12 |
+
]
|
| 13 |
+
]
|
| 14 |
+
|
| 15 |
+
expected_nodes = [
|
| 16 |
+
'我',
|
| 17 |
+
'中国'
|
| 18 |
+
]
|
| 19 |
+
|
| 20 |
+
expected_links = ['爱']
|
| 21 |
+
|
| 22 |
+
assert [node["fields"]["name"] for node in
|
| 23 |
+
convert_to_knowledge_graph_spec(model_results)["nodes"]] == expected_nodes
|
| 24 |
+
assert [node["fields"]["type"] for node in
|
| 25 |
+
convert_to_knowledge_graph_spec(model_results)["links"]] == expected_links
|