From 6ab1fc72697dacac9167dfa601a5345d8ca99a1a Mon Sep 17 00:00:00 2001 From: celinehuang Date: Tue, 21 May 2019 12:23:04 -0400 Subject: [PATCH 01/24] Initial Commit --- apps/dash-live-model-training/.gitignore | 5 + apps/dash-live-model-training/CONTRIBUTING.md | 92 + apps/dash-live-model-training/LICENSE.md | 21 + apps/dash-live-model-training/Procfile | 1 + apps/dash-live-model-training/README.md | 96 + apps/dash-live-model-training/app.py | 382 ++ apps/dash-live-model-training/assets/base.css | 435 ++ apps/dash-live-model-training/assets/font.css | 112 + .../assets/live-model-training.css | 7 + .../assets/normalize.css | 202 + apps/dash-live-model-training/config.py | 60 + .../custom_styles.css | 7 + apps/dash-live-model-training/demo.md | 34 + .../demo_run_logs/cifar_cnn_run_log.csv | 4000 +++++++++++++++++ .../demo_run_logs/cifar_softmax_run_log.csv | 4000 +++++++++++++++++ .../demo_run_logs/fashion_cnn_run_log.csv | 2000 +++++++++ .../demo_run_logs/fashion_softmax_run_log.csv | 2000 +++++++++ .../demo_run_logs/mnist_cnn_run_log.csv | 1999 ++++++++ .../demo_run_logs/mnist_softmax_run_log.csv | 1999 ++++++++ apps/dash-live-model-training/demo_utils.py | 125 + .../examples/cifar_deep_modified.py | 212 + .../examples/cifar_softmax_modified.py | 129 + .../examples/mnist_deep_modified.py | 177 + .../examples/mnist_softmax_modified.py | 108 + .../images/animated1.gif | Bin 0 -> 1629390 bytes .../images/flowchart.png | Bin 0 -> 29204 bytes .../images/screenshot1.png | Bin 0 -> 194712 bytes .../images/screenshot2.png | Bin 0 -> 288767 bytes .../dash-live-model-training/requirements.txt | 11 + apps/dash-live-model-training/runtime.txt | 1 + apps/dash-live-model-training/tfutils.py | 67 + 31 files changed, 18282 insertions(+) create mode 100644 apps/dash-live-model-training/.gitignore create mode 100644 apps/dash-live-model-training/CONTRIBUTING.md create mode 100644 apps/dash-live-model-training/LICENSE.md create mode 100644 apps/dash-live-model-training/Procfile create mode 100644 apps/dash-live-model-training/README.md create mode 100644 apps/dash-live-model-training/app.py create mode 100644 apps/dash-live-model-training/assets/base.css create mode 100644 apps/dash-live-model-training/assets/font.css create mode 100644 apps/dash-live-model-training/assets/live-model-training.css create mode 100644 apps/dash-live-model-training/assets/normalize.css create mode 100644 apps/dash-live-model-training/config.py create mode 100644 apps/dash-live-model-training/custom_styles.css create mode 100644 apps/dash-live-model-training/demo.md create mode 100644 apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv create mode 100644 apps/dash-live-model-training/demo_run_logs/cifar_softmax_run_log.csv create mode 100644 apps/dash-live-model-training/demo_run_logs/fashion_cnn_run_log.csv create mode 100644 apps/dash-live-model-training/demo_run_logs/fashion_softmax_run_log.csv create mode 100644 apps/dash-live-model-training/demo_run_logs/mnist_cnn_run_log.csv create mode 100644 apps/dash-live-model-training/demo_run_logs/mnist_softmax_run_log.csv create mode 100644 apps/dash-live-model-training/demo_utils.py create mode 100644 apps/dash-live-model-training/examples/cifar_deep_modified.py create mode 100644 apps/dash-live-model-training/examples/cifar_softmax_modified.py create mode 100644 apps/dash-live-model-training/examples/mnist_deep_modified.py create mode 100644 apps/dash-live-model-training/examples/mnist_softmax_modified.py create mode 100644 apps/dash-live-model-training/images/animated1.gif create mode 100644 apps/dash-live-model-training/images/flowchart.png create mode 100644 apps/dash-live-model-training/images/screenshot1.png create mode 100644 apps/dash-live-model-training/images/screenshot2.png create mode 100644 apps/dash-live-model-training/requirements.txt create mode 100644 apps/dash-live-model-training/runtime.txt create mode 100644 apps/dash-live-model-training/tfutils.py diff --git a/apps/dash-live-model-training/.gitignore b/apps/dash-live-model-training/.gitignore new file mode 100644 index 000000000..e0cc3cf8b --- /dev/null +++ b/apps/dash-live-model-training/.gitignore @@ -0,0 +1,5 @@ +.idea/* +run_log.csv +*.pyc +cifar10/.idea/* +*.gz diff --git a/apps/dash-live-model-training/CONTRIBUTING.md b/apps/dash-live-model-training/CONTRIBUTING.md new file mode 100644 index 000000000..f9cb6a8b0 --- /dev/null +++ b/apps/dash-live-model-training/CONTRIBUTING.md @@ -0,0 +1,92 @@ +# Contributing + +When contributing to this repository, please first discuss the change you wish to make via issue, +email, or any other method with the owners of this repository before making a change. + +Please note we have a code of conduct, please follow it in all your interactions with the project. + +## Pull Request Process + +1. Ensure any install or build dependencies are removed before the end of the layer when doing a + build. +2. Update the README.md with details of changes to the interface, this includes new environment + variables, exposed ports, useful file locations and container parameters. +3. Increase the version numbers in any examples files and the README.md to the new version that this + Pull Request would represent. The versioning scheme we use is [SemVer](http://semver.org/). +4. You may merge the Pull Request in once you have the sign-off of two other developers, or if you + do not have permission to do that, you may request the second reviewer to merge it for you. + +## Code of Conduct + +### Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, gender identity and expression, level of experience, +nationality, personal appearance, race, religion, or sexual identity and +orientation. + +### Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +### Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +### Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +### Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at [INSERT EMAIL ADDRESS]. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +### Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at [http://contributor-covenant.org/version/1/4][version] + +[homepage]: http://contributor-covenant.org +[version]: http://contributor-covenant.org/version/1/4/ \ No newline at end of file diff --git a/apps/dash-live-model-training/LICENSE.md b/apps/dash-live-model-training/LICENSE.md new file mode 100644 index 000000000..c91737e78 --- /dev/null +++ b/apps/dash-live-model-training/LICENSE.md @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2018 plotly + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/apps/dash-live-model-training/Procfile b/apps/dash-live-model-training/Procfile new file mode 100644 index 000000000..776a73151 --- /dev/null +++ b/apps/dash-live-model-training/Procfile @@ -0,0 +1 @@ +web: gunicorn --pythonpath apps/dash-live-model-training app:server \ No newline at end of file diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md new file mode 100644 index 000000000..b94df641f --- /dev/null +++ b/apps/dash-live-model-training/README.md @@ -0,0 +1,96 @@ +# Dash Live Model Training Viewer + +![GitHub](https://img.shields.io/github/license/mashape/apistatus.svg) +[![GitHub stars](https://img.shields.io/github/stars/plotly/dash-live-model-training.svg)](https://github.com/plotly/dash-live-model-training/stargazers) + + +This is a demo of the Dash interactive Python framework developed by [Plotly](https://plot.ly/). + +Dash abstracts away all of the technologies and protocols required to build an interactive web-based application and is a simple and effective way to bind a user interface around your Python code. To learn more check out our [documentation](https://plot.ly/dash). + +Try out the [demo app here](https://dash-live-model-training.plot.ly/). + +![animated1](images/animated1.gif) + +## Getting Started + +### Using the demo +To use the demo, simply choose the model and dataset for which you want to replay the training, __using the two dropdown menus at the top of the page__. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. + +### Running the app locally + +First create a virtual environment with conda or venv inside a temp folder, then activate it. + +``` +virtualenv dash-live-model-training-venv + +# Windows +dash-live-model-training-venv\Scripts\activate +# Or Linux +source venv/bin/activate +``` + +Clone the git repo, then install the requirements with pip +``` +git clone https://github.com/plotly/dash-live-model-training.git +cd dash-live-model-training +pip install -r requirements.txt +``` + +Run the app +``` +python app.py +``` + +### How to use the app + +The demo app shows how the viewer works by simulating the training process of a few basic models. If you want to use the full app with your own model, follow theses steps: + +1. Import the helper functions, `add_eval()` and `write_data()` from `tfutils.py`. +2. Use `add_eval()` to add the accuracy and cross-entropy operations in your tensorflow graph, if they are not already present. It takes as input `y_`, the Tensor containing the true target, aka labels, and `y`, which contains the predicted targets, aka logits. It will return two variables, accuracy and cross_entropy. +3. Create a feed dictionary ([read more about it here](https://www.tensorflow.org/versions/r1.0/programmers_guide/reading_data)) for both your training and validation batch. +4. At every step, after running the session once, call `write_data()` to write the data in the log file. Use the feed dicts, _accuracy_ and _cross_entropy_ generated in the previous steps as input. If the output log file is renamed, update the _LOGFILE_ variable inside `app.py` as well to reflect the changes. +5. Run `app.py`, and open the given link. + +Make sure that you correctly clone the repo with all the required libraries. You also need the latest version of Tensorflow and Sci-kit Learn. + +## About the app +### What does the app do? +For the majority of Deep Learning models, it is extremely helpful to keep track of the accuracy and loss as it is training. At the moment, the best application to do that is [Tensorboard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), which is a collection of visualization tools (metrics plots, image examples, graph representation, weight histogram, etc.) useful to debug and monitor the training of your model. + +_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your __Tensorflow model__ during training. It complements the Tensorboard by offering the following: +* __Real-time visualization__: The app is designed to visualize your metrics as they are updated inside your model. +* __Small and Lightweight__: The viewer loads a small number of important visualization, so that it loads and runs quickly. +* __Simple to use__: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. +* __Easy to modify__: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. +* __Plotly Graphs and Dash Integration__: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. + +![flowchart](images/flowchart.png) + +At the moment, the logging only works for iterative Tensorflow models. We are planning to extend it for PyTorch. You are encouraged to port the logging function (which is a simple csv logging) to Keras, Tensorflow's high-level API, MXNet, etc. + +## Built With + +* [Dash](https://dash.plot.ly/) - Main server and interactive components +* [Plotly Python](https://plot.ly/python/) - Used to create the interactive plots + +## Contributing + +Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us. + +## Authors + +* **Xing Han Lu** - *Initial Work* - [@xhlulu](https://github.com/xhlulu) + +See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project. + +## License + +This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details + +## Acknowledgments +## Screenshots + +![screenshot1](images/screenshot1.png) + +![screenshot2](images/screenshot2.png) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py new file mode 100644 index 000000000..7580ceb32 --- /dev/null +++ b/apps/dash-live-model-training/app.py @@ -0,0 +1,382 @@ +import dash +import dash_core_components as dcc +import dash_html_components as html +import pandas as pd +import plotly.graph_objs as go +from dash.dependencies import Input, Output, State +from plotly import tools + +from demo_utils import demo_components, demo_callbacks, demo_explanation + +LOGFILE = 'examples/run_log.csv' + +app = dash.Dash(__name__) +server = app.server + +demo_mode = True + +def div_graph(name): + """Generates an html Div containing graph and control options for smoothing and display, given the name""" + return html.Div([ + html.Div( + id=f'div-{name}-graph', + className="ten columns" + ), + + html.Div([ + html.Div([ + html.P("Smoothing:", style={'font-weight': 'bold', 'margin-bottom': '0px'}), + + dcc.Checklist( + options=[ + {'label': ' Training', 'value': 'train'}, + {'label': ' Validation', 'value': 'val'} + ], + values=[], + id=f'checklist-smoothing-options-{name}' + ) + ], + style={'margin-top': '10px'} + ), + + html.Div([ + dcc.Slider( + min=0, + max=1, + step=0.05, + marks={i / 5: i / 5 for i in range(0, 6)}, + value=0.6, + updatemode='drag', + id=f'slider-smoothing-{name}' + ) + ], + style={'margin-bottom': '40px'} + ), + + html.Div([ + html.P("Plot Display mode:", style={'font-weight': 'bold', 'margin-bottom': '0px'}), + + dcc.RadioItems( + options=[ + {'label': ' Overlapping', 'value': 'overlap'}, + {'label': ' Separate (Vertical)', 'value': 'separate_vertical'}, + {'label': ' Separate (Horizontal)', 'value': 'separate_horizontal'} + ], + value='overlap', + id=f'radio-display-mode-{name}' + ), + + html.Div(id=f'div-current-{name}-value') + ]), + ], + className="two columns" + ), + ], + className="row" + ) + + +app.layout = html.Div([ + # Banner display + html.Div([ + html.H2( + 'Live Model Training Viewer', + id='title' + ), + html.Img( + src="https://s3-us-west-1.amazonaws.com/plotly-tutorials/logo/new-branding/dash-logo-by-plotly-stripe-inverted.png" + ) + ], + className="banner" + ), + + # Body + html.Div([ + # Extract the demo components if we are in demo mode + *demo_components(demo_mode), + + html.Div([ + dcc.Dropdown( + id='dropdown-interval-control', + options=[ + {'label': 'No Updates', 'value': 'no'}, + {'label': 'Slow Updates', 'value': 'slow'}, + {'label': 'Regular Updates', 'value': 'regular'}, + {'label': 'Fast Updates', 'value': 'fast'} + ], + value='regular', + className='ten columns', + clearable=False, + searchable=False + ), + + html.Div( + id="div-step-display", + className="two columns" + ) + ], + id='div-interval-control', + className='row' + ), + + dcc.Interval( + id="interval-log-update", + n_intervals=0 + ), + + # Hidden Div Storing JSON-serialized dataframe of run log + html.Div(id='run-log-storage', style={'display': 'none'}), + + # The html divs storing the graphs and display parameters + div_graph('accuracy'), + div_graph('cross-entropy'), + + # Explanation for the demo version of the app + demo_explanation(demo_mode) + ], + className="container" + ) +]) + + +def update_graph(graph_id, + graph_title, + y_train_index, + y_val_index, + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + yaxis_title): + """ + :param graph_id: ID for Dash callbacks + :param graph_title: Displayed on layout + :param y_train_index: name of column index for y train we want to retrieve + :param y_val_index: name of column index for y val we want to retrieve + :param run_log_json: the json file containing the data + :param display_mode: 'separate' or 'overlap' + :param checklist_smoothing_options: 'train' or 'val' + :param slider_smoothing: value between 0 and 1, at interval of 0.05 + :return: dcc Graph object containing the updated figures + """ + + def smooth(scalars, weight=0.6): + last = scalars[0] + smoothed = list() + for point in scalars: + smoothed_val = last * weight + (1 - weight) * point + smoothed.append(smoothed_val) + last = smoothed_val + return smoothed + + if run_log_json: # exists + layout = go.Layout( + title=graph_title, + margin=go.Margin(l=50, r=50, b=50, t=50), + yaxis={'title': yaxis_title} + ) + + run_log_df = pd.read_json(run_log_json, orient='split') + + step = run_log_df['step'] + y_train = run_log_df[y_train_index] + y_val = run_log_df[y_val_index] + + # Apply Smoothing if needed + if 'train' in checklist_smoothing_options: + y_train = smooth(y_train, weight=slider_smoothing) + + if 'val' in checklist_smoothing_options: + y_val = smooth(y_val, weight=slider_smoothing) + + trace_train = go.Scatter( + x=step, + y=y_train, + mode='lines', + name='Training' + ) + + trace_val = go.Scatter( + x=step, + y=y_val, + mode='lines', + name='Validation' + ) + + if display_mode == 'separate_vertical': + figure = tools.make_subplots(rows=2, + cols=1, + print_grid=False, + shared_yaxes=True) + + figure.append_trace(trace_train, 1, 1) + figure.append_trace(trace_val, 2, 1) + + figure['layout'].update(title=layout.title, + margin=layout.margin, + scene={'domain': {'x': (0., 0.5), 'y': (0.5,1)}}) + + elif display_mode == 'separate_horizontal': + figure = tools.make_subplots(rows=1, + cols=2, + shared_yaxes=True, + print_grid=False) + + figure.append_trace(trace_train, 1, 1) + figure.append_trace(trace_val, 1, 2) + + figure['layout'].update(title=layout.title, + margin=layout.margin) + + elif display_mode == 'overlap': + figure = go.Figure( + data=[trace_train, trace_val], + layout=layout + ) + + else: + figure = None + + return dcc.Graph(figure=figure, id=graph_id) + + return dcc.Graph(id=graph_id) + + +demo_callbacks(app, demo_mode) + + +@app.callback(Output('interval-log-update', 'interval'), + [Input('dropdown-interval-control', 'value')]) +def update_interval_log_update(interval_rate): + if interval_rate == 'fast': + return 500 + + elif interval_rate == 'regular': + return 1000 + + elif interval_rate == 'slow': + return 5 * 1000 + + # Refreshes every 24 hours + elif interval_rate == 'no': + return 24 * 60 * 60 * 1000 + + +if not demo_mode: + @app.callback(Output('run-log-storage', 'children'), + [Input('interval-log-update', 'n_intervals')]) + def get_run_log(_): + names = ['step', 'train accuracy', 'val accuracy', 'train cross entropy', 'val cross entropy'] + + try: + run_log_df = pd.read_csv(LOGFILE, names=names) + json = run_log_df.to_json(orient='split') + except FileNotFoundError as error: + print(error) + print("Please verify if the csv file generated by your model is placed in the correct directory.") + return None + + return json + + +@app.callback(Output('div-step-display', 'children'), + [Input('run-log-storage', 'children')]) +def update_div_step_display(run_log_json): + if run_log_json: + run_log_df = pd.read_json(run_log_json, orient='split') + return html.H6(f"Step: {run_log_df['step'].iloc[-1]}", style={'margin-top': '3px'}) + + +@app.callback(Output('div-accuracy-graph', 'children'), + [Input('run-log-storage', 'children'), + Input('radio-display-mode-accuracy', 'value'), + Input('checklist-smoothing-options-accuracy', 'values'), + Input('slider-smoothing-accuracy', 'value')]) +def update_accuracy_graph(run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing): + graph = update_graph('accuracy-graph', + 'Prediction Accuracy', + 'train accuracy', + 'val accuracy', + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + 'Accuracy') + + try: + if display_mode in ['separate_horizontal', 'overlap']: + graph.figure.layout.yaxis['range'] = [0, 1] + else: + graph.figure.layout.yaxis1['range'] = [0, 1] + graph.figure.layout.yaxis2['range'] = [0, 1] + + except AttributeError: + pass + + return [graph] + + +@app.callback(Output('div-cross-entropy-graph', 'children'), + [Input('run-log-storage', 'children'), + Input('radio-display-mode-cross-entropy', 'value'), + Input('checklist-smoothing-options-cross-entropy', 'values'), + Input('slider-smoothing-cross-entropy', 'value')]) +def update_cross_entropy_graph(run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing): + graph = update_graph('cross-entropy-graph', + 'Cross Entropy Loss', + 'train cross entropy', + 'val cross entropy', + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + 'Loss') + return [graph] + + +@app.callback(Output('div-current-accuracy-value', 'children'), + [Input('run-log-storage', 'children')]) +def update_div_current_accuracy_value(run_log_json): + if run_log_json: + run_log_df = pd.read_json(run_log_json, orient='split') + return [ + html.P( + "Current Accuracy:", + style={ + 'font-weight': 'bold', + 'margin-top': '15px', + 'margin-bottom': '0px' + } + ), + html.Div(f"Training: {run_log_df['train accuracy'].iloc[-1]:.4f}"), + html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}") + ] + + +@app.callback(Output('div-current-cross-entropy-value', 'children'), + [Input('run-log-storage', 'children')]) +def update_div_current_cross_entropy_value(run_log_json): + if run_log_json: + run_log_df = pd.read_json(run_log_json, orient='split') + return [ + html.P( + "Current Loss:", + style={ + 'font-weight': 'bold', + 'margin-top': '15px', + 'margin-bottom': '0px' + } + ), + html.Div(f"Training: {run_log_df['train cross entropy'].iloc[-1]:.4f}"), + html.Div(f"Validation: {run_log_df['val cross entropy'].iloc[-1]:.4f}") + ] + +# Running the server +if __name__ == '__main__': + app.run_server(debug=True) diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css new file mode 100644 index 000000000..bc20f16d1 --- /dev/null +++ b/apps/dash-live-model-training/assets/base.css @@ -0,0 +1,435 @@ +/* +Gist: https://gist.github.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/ +Production: https://cdn.rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash_template.css +Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash_template.css +*/ + + +/* Table of contents +–––––––––––––––––––––––––––––––––––––––––––––––––– +- Banner +- Grid +- Base Styles +- Typography +- Links +- Buttons +- Forms +- Lists +- Code +- Tables +- Spacing +- Utilities +- Clearing +- Media Queries +*/ + + + +/* Banner +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +.banner { + height: 75px; + background-color: #414073; + border-radius: 5px 5px 2px 2px; + box-shadow: rgb(240, 240, 240) 5px 5px 5px 0px; + padding-top: 0px; + padding-left: 0px; + padding-right: 0px; + width: 90%; + margin-left: auto; + margin-right: auto; +} + +.banner h2{ + color: white; + margin-left: 1.5%; + display: inline-block; + font-family: 'Open Sans', sans-serif; + font-size:4rem; +line-height:1; +} + +.banner Img{ + position: relative; + float: right; + right: 1.5%; + height: 75px; +} + + +/* Grid +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +.container { + position: relative; + background-color: white; + border-radius: 2px 2px 5px 5px; + font-size: 1.5rem; + box-shadow: rgb(240, 240, 240) 5px 5px 5px 0px; + border: thin solid rgb(240, 240, 240); + margin-left: auto; + margin-right: auto; + color: #302F54; + padding: 8px; + width: 90%; + max-width: none; + box-sizing: border-box; +} + +.column, +.columns { + width: 100%; + float: left; + box-sizing: border-box; } + + +/* For devices larger than 550px */ +@media (min-width: 550px) { + .column, + .columns { + margin-left: 0.5%; } + .column:first-child, + .columns:first-child { + margin-left: 0; } + + .one.column, + .one.columns { width: 8%; } + .two.columns { width: 16.25%; } + .three.columns { width: 22%; } + .four.columns { width: 33%; } + .five.columns { width: 39.3333333333%; } + .six.columns { width: 49.75%; } + .seven.columns { width: 56.6666666667%; } + .eight.columns { width: 66.5%; } + .nine.columns { width: 74.0%; } + .ten.columns { width: 82.6666666667%; } + .eleven.columns { width: 91.5%; } + .twelve.columns { width: 100%; margin-left: 0; } + + .one-third.column { width: 30.6666666667%; } + .two-thirds.column { width: 65.3333333333%; } + + .one-half.column { width: 48%; } + + /* Offsets */ + .offset-by-one.column, + .offset-by-one.columns { margin-left: 8.66666666667%; } + .offset-by-two.column, + .offset-by-two.columns { margin-left: 17.3333333333%; } + .offset-by-three.column, + .offset-by-three.columns { margin-left: 26%; } + .offset-by-four.column, + .offset-by-four.columns { margin-left: 34.6666666667%; } + .offset-by-five.column, + .offset-by-five.columns { margin-left: 43.3333333333%; } + .offset-by-six.column, + .offset-by-six.columns { margin-left: 52%; } + .offset-by-seven.column, + .offset-by-seven.columns { margin-left: 60.6666666667%; } + .offset-by-eight.column, + .offset-by-eight.columns { margin-left: 69.3333333333%; } + .offset-by-nine.column, + .offset-by-nine.columns { margin-left: 78.0%; } + .offset-by-ten.column, + .offset-by-ten.columns { margin-left: 86.6666666667%; } + .offset-by-eleven.column, + .offset-by-eleven.columns { margin-left: 95.3333333333%; } + + .offset-by-one-third.column, + .offset-by-one-third.columns { margin-left: 34.6666666667%; } + .offset-by-two-thirds.column, + .offset-by-two-thirds.columns { margin-left: 69.3333333333%; } + + .offset-by-one-half.column, + .offset-by-one-half.columns { margin-left: 52%; } + +} + + +/* Base Styles +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +/* NOTE +html is set to 62.5% so that all the REM measurements throughout Skeleton +are based on 10px sizing. So basically 1.5rem = 15px :) */ +html { + font-size: 62.5%; } +body { + font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */ + line-height: 1.6; + font-weight: 400; + font-family: 'Roboto', sans-serif; + color: #222; } + + +/* Typography +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +h1, h2, h3, h4, h5, h6 { + margin-top: 0; + margin-bottom: 0; + font-weight: 300; } +h1 { font-size: 4.5rem; line-height: 1.2; letter-spacing: -.1rem; margin-bottom: 2rem; } +h2 { font-size: 3.6rem; line-height: 1.25; letter-spacing: -.1rem; margin-bottom: 1.8rem; margin-top: 1.8rem;} +h3 { font-size: 3.0rem; line-height: 1.3; letter-spacing: -.1rem; margin-bottom: 1.5rem; margin-top: 1.5rem;} +h4 { font-size: 2.6rem; line-height: 1.35; letter-spacing: -.08rem; margin-bottom: 1.2rem; margin-top: 1.2rem;} +h5 { font-size: 2.2rem; line-height: 1.5; letter-spacing: -.05rem; margin-bottom: 0.6rem; margin-top: 0.6rem;} +h6 { font-size: 2.0rem; line-height: 1.6; letter-spacing: 0; margin-bottom: 0.75rem; margin-top: 0.75rem;} + +p { + margin-top: 0; } + + +/* Blockquotes +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +blockquote { + border-left: 4px lightgrey solid; + padding-left: 1rem; + margin-top: 2rem; + margin-bottom: 2rem; + margin-left: 0rem; +} + + +/* Links +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +a { + color: #1EAEDB; } +a:hover { + color: #0FA0CE; } + + +/* Buttons +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +.button, +button, +input[type="submit"], +input[type="reset"], +input[type="button"] { + display: inline-block; + height: 38px; + padding: 0 30px; + color: #555; + text-align: center; + font-size: 11px; + font-weight: 600; + line-height: 38px; + letter-spacing: .1rem; + text-transform: uppercase; + text-decoration: none; + white-space: nowrap; + background-color: transparent; + border-radius: 4px; + border: 1px solid #bbb; + cursor: pointer; + box-sizing: border-box; } +.button:hover, +button:hover, +input[type="submit"]:hover, +input[type="reset"]:hover, +input[type="button"]:hover, +.button:focus, +button:focus, +input[type="submit"]:focus, +input[type="reset"]:focus, +input[type="button"]:focus { + color: #333; + border-color: #888; + outline: 0; } +.button.button-primary, +button.button-primary, +input[type="submit"].button-primary, +input[type="reset"].button-primary, +input[type="button"].button-primary { + color: #FFF; + background-color: #33C3F0; + border-color: #33C3F0; } +.button.button-primary:hover, +button.button-primary:hover, +input[type="submit"].button-primary:hover, +input[type="reset"].button-primary:hover, +input[type="button"].button-primary:hover, +.button.button-primary:focus, +button.button-primary:focus, +input[type="submit"].button-primary:focus, +input[type="reset"].button-primary:focus, +input[type="button"].button-primary:focus { + color: #FFF; + background-color: #1EAEDB; + border-color: #1EAEDB; } + + +/* Forms +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +input[type="email"], +input[type="number"], +input[type="search"], +input[type="text"], +input[type="tel"], +input[type="url"], +input[type="password"], +textarea, +select { + height: 38px; + padding: 6px 10px; /* The 6px vertically centers text on FF, ignored by Webkit */ + background-color: #fff; + border: 1px solid #D1D1D1; + border-radius: 4px; + box-shadow: none; + box-sizing: border-box; + font-family: inherit; + font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/} +/* Removes awkward default styles on some inputs for iOS */ +input[type="email"], +input[type="number"], +input[type="search"], +input[type="text"], +input[type="tel"], +input[type="url"], +input[type="password"], +textarea { + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; } +textarea { + min-height: 65px; + padding-top: 6px; + padding-bottom: 6px; } +input[type="email"]:focus, +input[type="number"]:focus, +input[type="search"]:focus, +input[type="text"]:focus, +input[type="tel"]:focus, +input[type="url"]:focus, +input[type="password"]:focus, +textarea:focus, +select:focus { + border: 1px solid #33C3F0; + outline: 0; } +label, +legend { + display: block; + margin-bottom: 0px; } +fieldset { + padding: 0; + border-width: 0; } +input[type="checkbox"], +input[type="radio"] { + display: inline; } +label > .label-body { + display: inline-block; + margin-left: .5rem; + font-weight: normal; } + + +/* Lists +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +ul { + list-style: circle inside; } +ol { + list-style: decimal inside; } +ol, ul { + padding-left: 0; + margin-top: 0; } +ul ul, +ul ol, +ol ol, +ol ul { + margin: 1.5rem 0 1.5rem 3rem; + font-size: 90%; } +li { + margin-bottom: 1rem; } + + +/* Tables +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +th, +td { + padding: 12px 15px; + text-align: left; + border-bottom: 1px solid #E1E1E1; } +th:first-child, +td:first-child { + padding-left: 0; } +th:last-child, +td:last-child { + padding-right: 0; } + + +/* Spacing +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +button, +.button { + margin-bottom: 0rem; } +input, +textarea, +select, +fieldset { + margin-bottom: 0rem; } +pre, +dl, +figure, +table, +form { + margin-bottom: 0rem; } +p, +ul, +ol { + margin-bottom: 0.75rem; } + +/* Utilities +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +.u-full-width { + width: 100%; + box-sizing: border-box; } +.u-max-full-width { + max-width: 100%; + box-sizing: border-box; } +.u-pull-right { + float: right; } +.u-pull-left { + float: left; } + + +/* Misc +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +hr { + margin-top: 3rem; + margin-bottom: 3.5rem; + border-width: 0; + border-top: 1px solid #E1E1E1; } + + +/* Clearing +–––––––––––––––––––––––––––––––––––––––––––––––––– */ + +/* Self Clearing Goodness */ +.container:after, +.row:after, +.u-cf { + content: ""; + display: table; + clear: both; } + + +/* Media Queries +–––––––––––––––––––––––––––––––––––––––––––––––––– */ +/* +Note: The best way to structure the use of media queries is to create the queries +near the relevant code. For example, if you wanted to change the styles for buttons +on small devices, paste the mobile query code up in the buttons section and style it +there. +*/ + + +/* Larger than mobile */ +@media (min-width: 400px) {} + +/* Larger than phablet (also point when grid becomes active) */ +@media (min-width: 550px) {} + +/* Larger than tablet */ +@media (min-width: 750px) {} + +/* Larger than desktop */ +@media (min-width: 1000px) {} + +/* Larger than Desktop HD */ +@media (min-width: 1200px) {} \ No newline at end of file diff --git a/apps/dash-live-model-training/assets/font.css b/apps/dash-live-model-training/assets/font.css new file mode 100644 index 000000000..cab8e83e4 --- /dev/null +++ b/apps/dash-live-model-training/assets/font.css @@ -0,0 +1,112 @@ +/* cyrillic-ext */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWJ0bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} +/* cyrillic */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFUZ0bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} +/* greek-ext */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWZ0bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+1F00-1FFF; +} +/* greek */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFVp0bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+0370-03FF; +} +/* vietnamese */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWp0bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB; +} +/* latin-ext */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFW50bf8pkAp6a.woff2) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} +/* latin */ +@font-face { + font-family: 'Open Sans'; + font-style: normal; + font-weight: 400; + src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFVZ0bf8pkAg.woff2) format('woff2'); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} +/* cyrillic-ext */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu72xKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; +} +/* cyrillic */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu5mxKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; +} +/* greek-ext */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7mxKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+1F00-1FFF; +} +/* greek */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu4WxKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+0370-03FF; +} +/* vietnamese */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7WxKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB; +} +/* latin-ext */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7GxKKTU1Kvnz.woff2) format('woff2'); + unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; +} +/* latin */ +@font-face { + font-family: 'Roboto'; + font-style: normal; + font-weight: 400; + src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu4mxKKTU1Kg.woff2) format('woff2'); + unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; +} \ No newline at end of file diff --git a/apps/dash-live-model-training/assets/live-model-training.css b/apps/dash-live-model-training/assets/live-model-training.css new file mode 100644 index 000000000..dfcf0545a --- /dev/null +++ b/apps/dash-live-model-training/assets/live-model-training.css @@ -0,0 +1,7 @@ +.markdown img { + width: 100%; +} + +._dash-undo-redo { + display: none; +} \ No newline at end of file diff --git a/apps/dash-live-model-training/assets/normalize.css b/apps/dash-live-model-training/assets/normalize.css new file mode 100644 index 000000000..dc79fbffd --- /dev/null +++ b/apps/dash-live-model-training/assets/normalize.css @@ -0,0 +1,202 @@ +/*! normalize.css v7.0.0 | MIT License | github.com/necolas/normalize.css */ +html { + line-height: 1.15; + -ms-text-size-adjust: 100%; + -webkit-text-size-adjust: 100% +} + +body { + margin: 0 +} + +article, aside, footer, header, nav, section { + display: block +} + +h1 { + font-size: 2em; + margin: .67em 0 +} + +figcaption, figure, main { + display: block +} + +figure { + margin: 1em 40px +} + +hr { + box-sizing: content-box; + height: 0; + overflow: visible +} + +pre { + font-family: monospace, monospace; + font-size: 1em +} + +a { + background-color: transparent; + -webkit-text-decoration-skip: objects +} + +abbr[title] { + border-bottom: none; + text-decoration: underline; + text-decoration: underline dotted +} + +b, strong { + font-weight: inherit +} + +b, strong { + font-weight: bolder +} + +code, kbd, samp { + font-family: monospace, monospace; + font-size: 1em +} + +dfn { + font-style: italic +} + +mark { + background-color: #ff0; + color: #000 +} + +small { + font-size: 80% +} + +sub, sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline +} + +sub { + bottom: -.25em +} + +sup { + top: -.5em +} + +audio, video { + display: inline-block +} + +audio:not([controls]) { + display: none; + height: 0 +} + +img { + border-style: none +} + +svg:not(:root) { + overflow: hidden +} + +button, input, optgroup, select, textarea { + font-family: sans-serif; + font-size: 100%; + line-height: 1.15; + margin: 0 +} + +button, input { + overflow: visible +} + +button, select { + text-transform: none +} + +[type=reset], [type=submit], button, html [type=button] { + -webkit-appearance: button +} + +[type=button]::-moz-focus-inner, [type=reset]::-moz-focus-inner, [type=submit]::-moz-focus-inner, button::-moz-focus-inner { + border-style: none; + padding: 0 +} + +[type=button]:-moz-focusring, [type=reset]:-moz-focusring, [type=submit]:-moz-focusring, button:-moz-focusring { + outline: 1px dotted ButtonText +} + +fieldset { + padding: .35em .75em .625em +} + +legend { + box-sizing: border-box; + color: inherit; + display: table; + max-width: 100%; + padding: 0; + white-space: normal +} + +progress { + display: inline-block; + vertical-align: baseline +} + +textarea { + overflow: auto +} + +[type=checkbox], [type=radio] { + box-sizing: border-box; + padding: 0 +} + +[type=number]::-webkit-inner-spin-button, [type=number]::-webkit-outer-spin-button { + height: auto +} + +[type=search] { + -webkit-appearance: textfield; + outline-offset: -2px +} + +[type=search]::-webkit-search-cancel-button, [type=search]::-webkit-search-decoration { + -webkit-appearance: none +} + +::-webkit-file-upload-button { + -webkit-appearance: button; + font: inherit +} + +details, menu { + display: block +} + +summary { + display: list-item +} + +canvas { + display: inline-block +} + +template { + display: none +} + +[hidden] { + display: none +} + +/*# sourceMappingURL=normalize.min.css.map */ diff --git a/apps/dash-live-model-training/config.py b/apps/dash-live-model-training/config.py new file mode 100644 index 000000000..91f34aa75 --- /dev/null +++ b/apps/dash-live-model-training/config.py @@ -0,0 +1,60 @@ +import os + +# Replace with the name of your Dash app +# This will end up being part of the URL of your deployed app, +# so it can't contain any spaces, capitalizations, or special characters +# +# This name MUST match the name that you specified in the +# Dash App Manager +DASH_APP_NAME = 'dash-live-model-training' + +# Set to 'private' if you want to add a login screen to your app +# You can choose who can view the app in your list of files +# at /organize. +# Set to 'public' if you want your app to be accessible to +# anyone who has access to your Plotly server on your network without +# a login screen. +# Set to 'secret' if you want to add a login screen, but allow it +# to be bypassed by using a secret "share_key" parameter. +DASH_APP_PRIVACY = 'public' + +# Dash On-Premise is configured with either "Path based routing" +# or "Domain based routing" +# Ask your server administrator which version was set up. +# If a separate subdomain was created, +# then set this to `False`. If it was not, set this to 'True'. +# Path based routing is the default option and most On-Premise +# users use this option. +PATH_BASED_ROUTING = True + +# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # +# This section only needs to be filled out # +# if DASH_APP_PRIVACY is set to 'private' or 'secret' # +# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # + +# Fill in with your Plotly On-Premise username +os.environ['PLOTLY_USERNAME'] = 'plotly-username' + +# Fill in with your Plotly On-Premise API key +# See /settings/api to generate a key +# If you have already created a key and saved it on your own machine +# (from the Plotly-Python library instructions at https://plot.ly/python/getting-started) +# then you can view that key in your ~/.plotly/.config file +# or inside a Python session with these commands: +# import plotly +# print(plotly.tools.get_config_file()) +os.environ['PLOTLY_API_KEY'] = 'your-plotly-api-key' + +# Fill in with your Plotly On-Premise domain +os.environ['PLOTLY_DOMAIN'] = 'https://your-plotly-domain.com' +os.environ['PLOTLY_API_DOMAIN'] = os.environ['PLOTLY_DOMAIN'] + +# Fill in with the domain of your Dash subdomain. +# This matches the domain of the Dash App Manager +PLOTLY_DASH_DOMAIN='https://your-dash-manager-plotly-domain.com' + +# Keep as True if your SSL certificates are valid. +# If you are just trialing Plotly On-Premise with self signed certificates, +# then you can set this to False. Note that self-signed certificates are not +# safe for production. +os.environ['PLOTLY_SSL_VERIFICATION'] = 'True' diff --git a/apps/dash-live-model-training/custom_styles.css b/apps/dash-live-model-training/custom_styles.css new file mode 100644 index 000000000..dfcf0545a --- /dev/null +++ b/apps/dash-live-model-training/custom_styles.css @@ -0,0 +1,7 @@ +.markdown img { + width: 100%; +} + +._dash-undo-redo { + display: none; +} \ No newline at end of file diff --git a/apps/dash-live-model-training/demo.md b/apps/dash-live-model-training/demo.md new file mode 100644 index 000000000..cef58d9d6 --- /dev/null +++ b/apps/dash-live-model-training/demo.md @@ -0,0 +1,34 @@ +## Getting Started with the Demo + +The purpose of this demo is to show what can be done with the Viewer. Therefore, the trainings shown above are displayed using pre-generated data. To try with your own data, please visit the [project repo](https://github.com/plotly/dash-live-model-training). + +To use the demo, simply choose the model and dataset for which you want to replay the training, __using the two dropdown menus at the top of the page__. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. + +At the moment, the following datasets have been pre-generated: +* __CIFAR10:__ 50,000 RGB images of size 32x32. It contains 10 common objects. [Link](https://www.cs.toronto.edu/~kriz/cifar.html) +* __MNIST:__ 60,000 grayscale images of size 28x28. It contains handwritten digits from 0 to 9. [Link](http://yann.lecun.com/exdb/mnist/) +* __Fashion MNIST:__ 60,000 grayscale images of size 28x28. It contains 10 commonly found fashion items. [Link](https://github.com/zalandoresearch/fashion-mnist) + +## What am I looking at? +The two plots, updated at the interval of your choice, shows two important metrics for training Machine Learning classifiers, i.e. Accuracy and Cross Entropy Loss. Because we feed small mini-batches of 50 examples for every iteration, you will see a lot of variation along the y-axis, which can be made smoother using the sliders. + +Accuracy is the fraction of labels correctly classified inside the mini-batch currently used to train/validate the model. In our case, given that each dataset has 10 different labels, an accuracy of 0.1 is equivalent to a random guess, and an accuracy of 1 means the model correctly predicted every single label. + +Cross Entropy Loss is the value that you are trying to minimize with your model. It basically indicates how far off our model is from predicting the correct label every time. It is described more in depth in the [Tensorflow Tutorial](https://www.tensorflow.org/versions/r1.0/get_started/mnist/beginners#training). + +## What does the app do? +For the majority of Deep Learning models, it is extremely helpful to keep track of the accuracy and loss as it is training. At the moment, the best application to do that is [Tensorboard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), which is a collection of visualization tools (metrics plots, image examples, graph representation, weight histogram, etc.) useful to debug and monitor the training of your model. + +_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your __Tensorflow model__ during training. It complements the Tensorboard by offering the following: +* __Real-time visualization__: The app is designed to visualize your metrics as they are updated inside your model. +* __Small and Lightweight__: The viewer loads a small number of important visualization, so that it loads and runs quickly. +* __Simple to use__: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. +* __Easy to modify__: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. +* __Plotly Graphs and Dash Integration__: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. + +Here is a flowchart of the process from model training to visualization: +![flowchart](https://raw.githubusercontent.com/plotly/dash-live-model-training/master/images/flowchart.png) + +At the moment, the logging only works for iterative Tensorflow models. We are planning to extend it for PyTorch. You are encouraged to port the logging function (which is a simple csv logging) to Keras, Tensorflow's high-level API, MXNet, etc. + +You can find the code for the app, and examples of using the app in the [repository](https://github.com/plotly/dash-live-model-training). \ No newline at end of file diff --git a/apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv b/apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv new file mode 100644 index 000000000..ca80637d9 --- /dev/null +++ b/apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv @@ -0,0 +1,4000 @@ +5,0.1,0.08,5.8956494,4.8241897 +10,0.08,0.08,4.5714474,5.037195 +15,0.12,0.08,4.354046,4.217165 +20,0.04,0.06,4.091554,3.52815 +25,0.16,0.08,3.6563296,3.8780494 +30,0.02,0.1,3.2807555,3.3940895 +35,0.12,0.12,2.6690378,3.3186572 +40,0.22,0.1,3.0429263,3.0123975 +45,0.16,0.1,3.217795,2.5819886 +50,0.12,0.08,2.9977787,2.4542398 +55,0.08,0.12,2.69982,2.6936789 +60,0.12,0.2,2.5832047,2.6222985 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a/apps/dash-live-model-training/demo_utils.py b/apps/dash-live-model-training/demo_utils.py new file mode 100644 index 000000000..5d2bef2bf --- /dev/null +++ b/apps/dash-live-model-training/demo_utils.py @@ -0,0 +1,125 @@ +import dash_core_components as dcc +import dash_html_components as html +import pandas as pd +from dash.dependencies import Input, Output, State + + +def demo_explanation(demo_mode): + if demo_mode: + # Markdown files + with open('demo.md', 'r') as file: + demo_md = file.read() + + return html.Div( + html.Div([ + dcc.Markdown(demo_md, className='markdown'), + ], + className='ten columns' + ), + className='row', + style={ + 'margin': '20px' + } + ) + + +def demo_components(demo_mode): + if demo_mode: + return [ + # Hidden Div that will store the result of simulating a model run + html.Div(id='storage-simulated-run', style={'display': 'none'}), + + # Increment the simulation step count at a fixed time interval + dcc.Interval( + id='interval-simulated-step', + interval=125, # Updates every 100 milliseconds, i.e. every step takes 25 ms + n_intervals=0 + ), + + html.Div(className="row", style={'margin-bottom':'8px'}, children=[ + html.Div(className="ten columns", children=[ + html.Div(className="six columns", children=dcc.Dropdown( + id='dropdown-demo-dataset', + options=[ + {'label': 'CIFAR 10', 'value': 'cifar'}, + {'label': 'MNIST', 'value': 'mnist'}, + {'label': 'Fashion MNIST', 'value': 'fashion'} + ], + placeholder="Select a demo dataset", + searchable=False + )), + + html.Div(className="six columns", children=dcc.Dropdown( + id='dropdown-simulation-model', + options=[ + {'label': '1-Layer Neural Net', 'value': 'softmax'}, + {'label': 'Simple Conv Net', 'value': 'cnn'} + ], + placeholder="Select Model to Simulate", + searchable=False + )) + ]), + + html.Div(id="div-total-step-count", className="two columns") + ]) + ] + + else: + return [] + + +def demo_callbacks(app, demo_mode): + if demo_mode: + @app.server.before_first_request + def load_demo_run_logs(): + global data_dict, demo_md + + names = ['step', 'train accuracy', 'val accuracy', 'train cross entropy', 'val cross entropy'] + + data_dict = { + 'softmax': { + 'cifar': pd.read_csv('demo_run_logs/cifar_softmax_run_log.csv', names=names), + 'mnist': pd.read_csv('demo_run_logs/mnist_softmax_run_log.csv', names=names), + 'fashion': pd.read_csv('demo_run_logs/fashion_softmax_run_log.csv', names=names) + }, + + 'cnn': { + 'cifar': pd.read_csv('demo_run_logs/cifar_cnn_run_log.csv', names=names), + 'mnist': pd.read_csv('demo_run_logs/mnist_cnn_run_log.csv', names=names), + 'fashion': pd.read_csv('demo_run_logs/fashion_cnn_run_log.csv', names=names) + } + } + + @app.callback(Output('storage-simulated-run', 'children'), + [Input('interval-simulated-step', 'n_intervals')], + [State('dropdown-demo-dataset', 'value'), + State('dropdown-simulation-model', 'value')]) + def simulate_run(n_intervals, demo_dataset, simulation_model): + if simulation_model and demo_dataset and n_intervals > 0: + step = n_intervals * 5 + run_logs = data_dict[simulation_model][demo_dataset] + + run_below_steps = run_logs[run_logs['step'] <= step] + json = run_below_steps.to_json(orient='split') + + return json + + @app.callback(Output('interval-simulated-step', 'n_intervals'), + [Input('dropdown-demo-dataset', 'value'), + Input('dropdown-simulation-model', 'value')]) + def reset_interval_simulated_step(*_): + return 0 + + @app.callback(Output('run-log-storage', 'children'), + [Input('interval-log-update', 'n_intervals')], + [State('storage-simulated-run', 'children')]) + def get_run_log(_, simulated_run): + if simulate_run: + return simulated_run + + @app.callback(Output('div-total-step-count', 'children'), + [Input('dropdown-demo-dataset', 'value')]) + def total_step_count(dataset_name): + if dataset_name is not None: + dataset = data_dict['softmax'][dataset_name] + return html.H6(f"Total Steps: {dataset['step'].iloc[-1]}", style={'margin-top': '3px'}) diff --git a/apps/dash-live-model-training/examples/cifar_deep_modified.py b/apps/dash-live-model-training/examples/cifar_deep_modified.py new file mode 100644 index 000000000..fdd2af283 --- /dev/null +++ b/apps/dash-live-model-training/examples/cifar_deep_modified.py @@ -0,0 +1,212 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""A deep CIFAR10 classifier using convolutional layers. + +Code taken from the official tensorflow guide: +https://www.tensorflow.org/get_started/mnist/pros + +The architecture comes from Keras CIFAR10 CNN example: +https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py + + +""" +# Disable linter warnings to maintain consistency with tutorial. +# pylint: disable=invalid-name +# pylint: disable=g-bad-import-order + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + +# Modified Import +import numpy as np +from sklearn.model_selection import train_test_split +from skimage.transform import rescale +from skimage import color +from tfutils import write_data +from sklearn.preprocessing import OneHotEncoder + +FLAGS = None + + +def deepnn(x): + """deepnn builds the graph for a deep net for classifying digits. + + Args: + x: an input tensor with the dimensions (N_examples, 784), where 784 is the + number of pixels in a standard MNIST image. + + Returns: + A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values + equal to the logits of classifying the digit into one of 10 classes (the + digits 0-9). keep_prob is a scalar placeholder for the probability of + dropout. + """ + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is three here, since images are + # rgb -- it would be 1 for a grayscale image, 4 for RGBA, etc. + x_image = tf.reshape(x, [-1, 32, 32, 3]) + + # Convolutional layers 1 and 2 - maps 3-color image to 32 feature maps. + W_conv1 = weight_variable([3, 3, 3, 32]) # 3x3 filters + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + + W_conv2 = weight_variable([3, 3, 32, 32]) + b_conv2 = bias_variable([32]) + h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2) + + # Pooling layer - downsamples by 2X. + h_pool2 = max_pool_2x2(h_conv2) + + # Dropout + h_pool2_drop = tf.nn.dropout(h_pool2, 0.75) + + # Convolutional layers 3 and 4 - maps 32 feature maps to 64. + W_conv3 = weight_variable([3, 3, 32, 64]) # 3x3 filters + b_conv3 = bias_variable([64]) + h_conv3 = tf.nn.relu(conv2d(h_pool2_drop, W_conv3) + b_conv3) + + W_conv4 = weight_variable([3, 3, 64, 64]) # 3x3 filters + b_conv4 = bias_variable([64]) + h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4) + + # Second pooling layer. + h_pool4 = max_pool_2x2(h_conv4) + + # Dropout + h_pool4_drop = tf.nn.dropout(h_pool4, 0.75) + + # Fully connected layer 1 -- after 2 round of downsampling, our 32x32 image + # is down to 8x8x64 feature maps -- maps this to 512 features. + W_fc1 = weight_variable([8 * 8 * 64, 512]) + b_fc1 = bias_variable([512]) + + h_pool4_flat = tf.reshape(h_pool4_drop, [-1, 8*8*64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1) + + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + + # Map the 512 features to 10 classes, one for each digit + W_fc2 = weight_variable([512, 10]) + b_fc2 = bias_variable([10]) + + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob + + +def conv2d(x, W): + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + + +def max_pool_2x2(x): + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], padding='SAME') + + +def weight_variable(shape): + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) + + +def bias_variable(shape): + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) + + +def main(_): + # Import data + print("Starting to generate CIFAR10 images.") + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() + x_train = np.moveaxis(x_train, 1, 3) / 255. # Normalize values + x_train_vec = x_train.reshape(50000, -1) + + x_test = np.moveaxis(x_test, 1, 3) / 255. # Normalize values + x_test_vec = x_test.reshape(10000, -1) + + X_train, X_val, y_train, y_val = train_test_split(x_train_vec, y_train, test_size=0.1, random_state=42) + print("Finished generating CIFAR10 images.") + + # Create the model + x = tf.placeholder(tf.float32, [None, 32*32*3]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv)) + train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # RMS is used in keras example, Adam is better + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + with tf.Session() as sess: + y_train = OneHotEncoder(sparse=False).fit_transform(y_train) + y_val = OneHotEncoder(sparse=False).fit_transform(y_val) + + sess.run(tf.global_variables_initializer()) + for i in range(20001): + start_train = i * 50 % y_train.shape[0] + end_train = start_train + 50 + + start_val = i * 50 % y_val.shape[0] + end_val = start_val + 50 + + batch = (X_train[start_train:end_train], y_train[start_train:end_train]) + batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) + + feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} + feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i + ) + + if i % 100 == 0: + train_accuracy = accuracy.eval(feed_dict={ + x: batch[0], y_: batch[1], keep_prob: 1.0}) + print('step %d, training accuracy %g' % (i, train_accuracy)) + train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + + print('test accuracy %g' % accuracy.eval(feed_dict={ + x: x_test_vec, y_: y_test, keep_prob: 1.0})) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/cifar_softmax_modified.py b/apps/dash-live-model-training/examples/cifar_softmax_modified.py new file mode 100644 index 000000000..0ab80012b --- /dev/null +++ b/apps/dash-live-model-training/examples/cifar_softmax_modified.py @@ -0,0 +1,129 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""A very simple CIFAR10 classifier. + +This code was modified from the MNIST beginner tutorial found here: +https://www.tensorflow.org/get_started/mnist/beginners + +Accuracy on test set is 26.74%. Simple ConvNet models can achieve over 70% accuracy: +https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py + +Whether the low accuracy is caused by an error or the simplicity of the classifier is unknown. It is encouraged to +report errors within this code. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + +# Custom Imports +import numpy as np +from sklearn.model_selection import train_test_split +from skimage.transform import rescale +from skimage import color +from tfutils import add_eval, write_data + +FLAGS = None + + +def main(_): + # Import data + print("Starting to generate CIFAR10 images.") + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() + x_train = np.moveaxis(x_train, 1, 3) / 255. # Normalize values + x_train_vec = x_train.reshape(50000, -1) + + y_train = np.squeeze(y_train) + y_test = np.squeeze(y_test) + + x_test = np.moveaxis(x_test, 1, 3) / 255. # Normalize values + x_test_vec = x_test.reshape(10000, -1) + + X_train, X_val, y_train, y_val = train_test_split(x_train_vec, y_train, test_size=0.1, random_state=42) + print("Finished generating CIFAR10 images.") + + # Create the model + x = tf.placeholder(tf.float32, [None, 3*32*32]) + W = tf.Variable(tf.zeros([3*32*32, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.int64, [None]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) + + # Add accuracy and cross entropy to the graph using util function + accuracy, cross_entropy = add_eval(y, y_) + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for i in range(20001): + start_train = i * 100 % y_train.shape[0] + end_train = start_train + 100 + + start_val = i * 100 % y_val.shape[0] + end_val = start_val + 100 + + batch = (X_train[start_train:end_train], y_train[start_train:end_train]) + batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) + + feed_dict_train = {x: batch[0], y_: batch[1]} + feed_dict_val = {x: batch_val[0], y_: batch_val[1]} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i + ) + sess.run(train_step, feed_dict={x: batch[0], y_: batch[1]}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), y_) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run( + accuracy, feed_dict={ + x: x_test_vec, + y_: y_test + })) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--data_dir', + type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/mnist_deep_modified.py b/apps/dash-live-model-training/examples/mnist_deep_modified.py new file mode 100644 index 000000000..438a36996 --- /dev/null +++ b/apps/dash-live-model-training/examples/mnist_deep_modified.py @@ -0,0 +1,177 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +"""A deep MNIST classifier using convolutional layers. + +See extensive documentation at +https://www.tensorflow.org/get_started/mnist/pros +""" +# Disable linter warnings to maintain consistency with tutorial. +# pylint: disable=invalid-name +# pylint: disable=g-bad-import-order + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + +# Modified Import +from tfutils import write_data + +FLAGS = None +DATA = "MNIST" + +def deepnn(x): + """deepnn builds the graph for a deep net for classifying digits. + + Args: + x: an input tensor with the dimensions (N_examples, 784), where 784 is the + number of pixels in a standard MNIST image. + + Returns: + A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values + equal to the logits of classifying the digit into one of 10 classes (the + digits 0-9). keep_prob is a scalar placeholder for the probability of + dropout. + """ + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is only one here, since images are + # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. + x_image = tf.reshape(x, [-1, 28, 28, 1]) + + # First convolutional layer - maps one grayscale image to 32 feature maps. + W_conv1 = weight_variable([5, 5, 1, 32]) + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + + # Pooling layer - downsamples by 2X. + h_pool1 = max_pool_2x2(h_conv1) + + # Second convolutional layer -- maps 32 feature maps to 64. + W_conv2 = weight_variable([5, 5, 32, 64]) + b_conv2 = bias_variable([64]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) + + # Second pooling layer. + h_pool2 = max_pool_2x2(h_conv2) + + # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image + # is down to 7x7x64 feature maps -- maps this to 1024 features. + W_fc1 = weight_variable([7 * 7 * 64, 1024]) + b_fc1 = bias_variable([1024]) + + h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) + + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + + # Map the 1024 features to 10 classes, one for each digit + W_fc2 = weight_variable([1024, 10]) + b_fc2 = bias_variable([10]) + + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob + + +def conv2d(x, W): + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + + +def max_pool_2x2(x): + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], padding='SAME') + + +def weight_variable(shape): + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) + + +def bias_variable(shape): + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) + + +def main(_): + # Import data + if DATA == "MNIST": + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + elif DATA == "FASHION": + mnist = input_data.read_data_sets('data/fashion', + source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/', + one_hot=True) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) + train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for i in range(10001): + batch = mnist.train.next_batch(50) + + ################################## MODIFIED CODE BELOW ################################## + batch_val = mnist.validation.next_batch(50) + feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} + feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i + ) + ################################## MODIFIED CODE ABOVE ################################## + + if i % 100 == 0: + train_accuracy = accuracy.eval(feed_dict={ + x: batch[0], y_: batch[1], keep_prob: 1.0}) + print('step %d, training accuracy %g' % (i, train_accuracy)) + train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + + print('test accuracy %g' % accuracy.eval(feed_dict={ + x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/mnist_softmax_modified.py b/apps/dash-live-model-training/examples/mnist_softmax_modified.py new file mode 100644 index 000000000..24d818509 --- /dev/null +++ b/apps/dash-live-model-training/examples/mnist_softmax_modified.py @@ -0,0 +1,108 @@ +# Copyright 2015 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""A very simple MNIST classifier. + +See extensive documentation at +https://www.tensorflow.org/get_started/mnist/beginners +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import sys + +import tensorflow as tf +from tensorflow.examples.tutorials.mnist import input_data + +from tfutils import add_eval, write_data + +FLAGS = None +DATA = "MNIST" + +def main(_): + # Import data + if DATA == "MNIST": + mnist = input_data.read_data_sets(FLAGS.data_dir) + elif DATA == "FASHION": + mnist = input_data.read_data_sets('data/fashion', + source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/') + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + W = tf.Variable(tf.zeros([784, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.int64, [None]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) + + ################################## MODIFIED CODE BELOW ################################## + accuracy, cross_entropy = add_eval(y, y_) + ################################## MODIFIED CODE ABOVE ################################## + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for i in range(10001): + batch_xs, batch_ys = mnist.train.next_batch(100) + + ################################## MODIFIED CODE BELOW ################################## + batch = mnist.train.next_batch(100) + batch_val = mnist.validation.next_batch(100) + feed_dict_train = {x: batch[0], y_: batch[1]} + feed_dict_val = {x: batch_val[0], y_: batch_val[1]} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i + ) + ################################## MODIFIED CODE ABOVE ################################## + + sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), y_) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run( + accuracy, feed_dict={ + x: mnist.test.images, + y_: mnist.test.labels + })) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + '--data_dir', + type=str, + default='/tmp/tensorflow/mnist/input_data', + help='Directory for storing input data') + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/images/animated1.gif b/apps/dash-live-model-training/images/animated1.gif new file mode 100644 index 0000000000000000000000000000000000000000..bbb42c3536444c9c0151a4f11b265cad20809ffa GIT binary patch literal 1629390 zcmV(!K;^$jNk%w1VOa%@19t!bA^!_bMO0HmK~P09E-(WD0000X`2+wf0000i00000 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z69jenH}2h{-3FA2-IGii;jDo=KAkENXZ#i2 zfzGm?1-+CS>e1%Uwfe2kF|1#^0?A#4DiR?oY02J^vOzL!^^Z%^B>GO}O$~SSky*Sq z+WE6J_1Xwb>m3ID&CX;|>Acqkiofixj}Kk1!fTE%QO&UGQ{cn1Na-}Hg*A__LcY+2* za;vqJ_+GKBEpk$D@ccRltgml8{xn()rv;r)HcmM&x($HjSn_o^N6Ns&vy*)&F(SHW zT5gk6w~vifCgR3TrG}yKf2fuW=PCHtb-67zUT4f sp)L&{|0~M>{|5i4>;Dx+Jrg?-6!a&Y4 Date: Wed, 22 May 2019 13:08:41 -0400 Subject: [PATCH 02/24] changed basic layout, updated README --- apps/dash-live-model-training/.gitignore | 2 + apps/dash-live-model-training/README.md | 36 +- apps/dash-live-model-training/app.py | 428 +++++++++--------- apps/dash-live-model-training/assets/base.css | 399 ++++++++++------ .../assets/custom_styles.css | 17 + .../assets/dash-by-plotly-logo.png | Bin 0 -> 5734 bytes .../assets/dash-logo-stripe.png | Bin 0 -> 3839 bytes apps/dash-live-model-training/config.py | 60 --- .../custom_styles.css | 7 - apps/dash-live-model-training/demo.md | 32 +- apps/dash-live-model-training/demo_utils.py | 173 ++++--- .../examples/cifar_deep_modified.py | 255 ++++++----- .../examples/cifar_softmax_modified.py | 163 ++++--- .../examples/mnist_deep_modified.py | 214 ++++----- .../examples/mnist_softmax_modified.py | 138 +++--- .../dash-live-model-training/requirements.txt | 14 +- apps/dash-live-model-training/tfutils.py | 38 +- 17 files changed, 1073 insertions(+), 903 deletions(-) create mode 100644 apps/dash-live-model-training/assets/custom_styles.css create mode 100644 apps/dash-live-model-training/assets/dash-by-plotly-logo.png create mode 100644 apps/dash-live-model-training/assets/dash-logo-stripe.png delete mode 100644 apps/dash-live-model-training/config.py delete mode 100644 apps/dash-live-model-training/custom_styles.css diff --git a/apps/dash-live-model-training/.gitignore b/apps/dash-live-model-training/.gitignore index e0cc3cf8b..c98b9ad60 100644 --- a/apps/dash-live-model-training/.gitignore +++ b/apps/dash-live-model-training/.gitignore @@ -3,3 +3,5 @@ run_log.csv *.pyc cifar10/.idea/* *.gz +.vscode +venv diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md index b94df641f..61aeb4a7b 100644 --- a/apps/dash-live-model-training/README.md +++ b/apps/dash-live-model-training/README.md @@ -3,7 +3,6 @@ ![GitHub](https://img.shields.io/github/license/mashape/apistatus.svg) [![GitHub stars](https://img.shields.io/github/stars/plotly/dash-live-model-training.svg)](https://github.com/plotly/dash-live-model-training/stargazers) - This is a demo of the Dash interactive Python framework developed by [Plotly](https://plot.ly/). Dash abstracts away all of the technologies and protocols required to build an interactive web-based application and is a simple and effective way to bind a user interface around your Python code. To learn more check out our [documentation](https://plot.ly/dash). @@ -15,7 +14,8 @@ Try out the [demo app here](https://dash-live-model-training.plot.ly/). ## Getting Started ### Using the demo -To use the demo, simply choose the model and dataset for which you want to replay the training, __using the two dropdown menus at the top of the page__. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. + +To use the demo, simply choose the model and dataset for which you want to replay the training, **using the two dropdown menus at the top of the page**. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. ### Running the app locally @@ -31,13 +31,16 @@ source venv/bin/activate ``` Clone the git repo, then install the requirements with pip + ``` -git clone https://github.com/plotly/dash-live-model-training.git +git clone https://github.com/plotly/dash-sample-apps.git +cd apps cd dash-live-model-training pip install -r requirements.txt ``` Run the app + ``` python app.py ``` @@ -46,8 +49,8 @@ python app.py The demo app shows how the viewer works by simulating the training process of a few basic models. If you want to use the full app with your own model, follow theses steps: -1. Import the helper functions, `add_eval()` and `write_data()` from `tfutils.py`. -2. Use `add_eval()` to add the accuracy and cross-entropy operations in your tensorflow graph, if they are not already present. It takes as input `y_`, the Tensor containing the true target, aka labels, and `y`, which contains the predicted targets, aka logits. It will return two variables, accuracy and cross_entropy. +1. Import the helper functions, `add_eval()` and `write_data()` from `tfutils.py`. +2. Use `add_eval()` to add the accuracy and cross-entropy operations in your tensorflow graph, if they are not already present. It takes as input `y_`, the Tensor containing the true target, aka labels, and `y`, which contains the predicted targets, aka logits. It will return two variables, accuracy and cross_entropy. 3. Create a feed dictionary ([read more about it here](https://www.tensorflow.org/versions/r1.0/programmers_guide/reading_data)) for both your training and validation batch. 4. At every step, after running the session once, call `write_data()` to write the data in the log file. Use the feed dicts, _accuracy_ and _cross_entropy_ generated in the previous steps as input. If the output log file is renamed, update the _LOGFILE_ variable inside `app.py` as well to reflect the changes. 5. Run `app.py`, and open the given link. @@ -55,15 +58,18 @@ The demo app shows how the viewer works by simulating the training process of a Make sure that you correctly clone the repo with all the required libraries. You also need the latest version of Tensorflow and Sci-kit Learn. ## About the app + ### What does the app do? + For the majority of Deep Learning models, it is extremely helpful to keep track of the accuracy and loss as it is training. At the moment, the best application to do that is [Tensorboard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), which is a collection of visualization tools (metrics plots, image examples, graph representation, weight histogram, etc.) useful to debug and monitor the training of your model. -_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your __Tensorflow model__ during training. It complements the Tensorboard by offering the following: -* __Real-time visualization__: The app is designed to visualize your metrics as they are updated inside your model. -* __Small and Lightweight__: The viewer loads a small number of important visualization, so that it loads and runs quickly. -* __Simple to use__: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. -* __Easy to modify__: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. -* __Plotly Graphs and Dash Integration__: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. +_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your **Tensorflow model** during training. It complements the Tensorboard by offering the following: + +- **Real-time visualization**: The app is designed to visualize your metrics as they are updated inside your model. +- **Small and Lightweight**: The viewer loads a small number of important visualization, so that it loads and runs quickly. +- **Simple to use**: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. +- **Easy to modify**: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. +- **Plotly Graphs and Dash Integration**: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. ![flowchart](images/flowchart.png) @@ -71,8 +77,8 @@ At the moment, the logging only works for iterative Tensorflow models. We are pl ## Built With -* [Dash](https://dash.plot.ly/) - Main server and interactive components -* [Plotly Python](https://plot.ly/python/) - Used to create the interactive plots +- [Dash](https://dash.plot.ly/) - Main server and interactive components +- [Plotly Python](https://plot.ly/python/) - Used to create the interactive plots ## Contributing @@ -80,7 +86,8 @@ Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduc ## Authors -* **Xing Han Lu** - *Initial Work* - [@xhlulu](https://github.com/xhlulu) +- **Celine Huang** [@celinehuang](https://github.com/celinehuang) +- **Xing Han Lu** - _Initial Work_ - [@xhlulu](https://github.com/xhlulu) See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project. @@ -89,6 +96,7 @@ See also the list of [contributors](https://github.com/your/project/contributors This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details ## Acknowledgments + ## Screenshots ![screenshot1](images/screenshot1.png) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 7580ceb32..85d70201f 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -1,4 +1,5 @@ import dash +import pathlib import dash_core_components as dcc import dash_html_components as html import pandas as pd @@ -8,146 +9,136 @@ from demo_utils import demo_components, demo_callbacks, demo_explanation -LOGFILE = 'examples/run_log.csv' +# get relative data folder +PATH = pathlib.Path(__file__).parent +DATA_PATH = PATH.joinpath("data").resolve() + +LOGFILE = "examples/run_log.csv" app = dash.Dash(__name__) server = app.server demo_mode = True + def div_graph(name): """Generates an html Div containing graph and control options for smoothing and display, given the name""" - return html.Div([ - html.Div( - id=f'div-{name}-graph', - className="ten columns" - ), - - html.Div([ + return html.Div(className="row", children=[ + html.Div(className="two columns", children=[ html.Div([ - html.P("Smoothing:", style={'font-weight': 'bold', 'margin-bottom': '0px'}), - + html.P( + "Smoothing:", + style={"font-weight": "bold", + "margin-bottom": "0px"}, + ), dcc.Checklist( options=[ - {'label': ' Training', 'value': 'train'}, - {'label': ' Validation', 'value': 'val'} + {"label": " Training", "value": "train"}, + {"label": " Validation", "value": "val"}, ], values=[], - id=f'checklist-smoothing-options-{name}' - ) + id=f"checklist-smoothing-options-{name}", + ), ], - style={'margin-top': '10px'} + style={"margin-top": "10px"}, ), - html.Div([ dcc.Slider( min=0, max=1, step=0.05, - marks={i / 5: i / 5 for i in range(0, 6)}, + marks={i / 5: str(i / 5) for i in range(0, 6)}, value=0.6, - updatemode='drag', - id=f'slider-smoothing-{name}' + updatemode="drag", + id=f"slider-smoothing-{name}", ) - ], - style={'margin-bottom': '40px'} - ), - + ], style={"margin-bottom": "40px"} ), html.Div([ - html.P("Plot Display mode:", style={'font-weight': 'bold', 'margin-bottom': '0px'}), - + html.P( + "Plot Display mode:", + style={"font-weight": "bold", + "margin-bottom": "0px"}, + ), dcc.RadioItems( options=[ - {'label': ' Overlapping', 'value': 'overlap'}, - {'label': ' Separate (Vertical)', 'value': 'separate_vertical'}, - {'label': ' Separate (Horizontal)', 'value': 'separate_horizontal'} + {"label": " Overlapping", "value": "overlap"}, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, ], - value='overlap', - id=f'radio-display-mode-{name}' + value="overlap", + id=f"radio-display-mode-{name}", ), - - html.Div(id=f'div-current-{name}-value') + html.Div(id=f"div-current-{name}-value"), ]), - ], - className="two columns" - ), - ], - className="row" - ) + ]), + html.Div(id=f"div-{name}-graph", className="ten columns"), + ]) app.layout = html.Div([ # Banner display html.Div([ - html.H2( - 'Live Model Training Viewer', - id='title' - ), - html.Img( - src="https://s3-us-west-1.amazonaws.com/plotly-tutorials/logo/new-branding/dash-logo-by-plotly-stripe-inverted.png" - ) - ], - className="banner" + html.H2("Live Model Training Viewer", id="title"), + html.Button("Learn More", id="learn-more-button"), + html.Img(src=app.get_asset_url("dash-by-plotly-logo.png"))], + className="banner", ), - # Body html.Div([ # Extract the demo components if we are in demo mode *demo_components(demo_mode), - html.Div([ - dcc.Dropdown( - id='dropdown-interval-control', - options=[ - {'label': 'No Updates', 'value': 'no'}, - {'label': 'Slow Updates', 'value': 'slow'}, - {'label': 'Regular Updates', 'value': 'regular'}, - {'label': 'Fast Updates', 'value': 'fast'} - ], - value='regular', - className='ten columns', - clearable=False, - searchable=False - ), - - html.Div( - id="div-step-display", - className="two columns" - ) - ], - id='div-interval-control', - className='row' - ), - - dcc.Interval( - id="interval-log-update", - n_intervals=0 + html.Div( + className="row", id="div-interval-control", + children=[ + html.Div(id="div-step-display", className="two columns", style={"float":"right"}), + dcc.Dropdown( + id="dropdown-interval-control", + options=[ + {"label": "No Updates", "value": "no"}, + {"label": "Slow Updates", "value": "slow"}, + {"label": "Regular Updates", "value": "regular"}, + {"label": "Fast Updates", "value": "fast"}, + ], + value="regular", + className="ten columns", + clearable=False, + searchable=False, + )] ), + dcc.Interval(id="interval-log-update", n_intervals=0), # Hidden Div Storing JSON-serialized dataframe of run log - html.Div(id='run-log-storage', style={'display': 'none'}), - + html.Div(id="run-log-storage", style={"display": "none"}), # The html divs storing the graphs and display parameters - div_graph('accuracy'), - div_graph('cross-entropy'), - + div_graph("accuracy", style={"color":"#6b6b6b"}), + div_graph("cross-entropy", style={"color":"#6b6b6b"}), # Explanation for the demo version of the app - demo_explanation(demo_mode) + demo_explanation(demo_mode), ], - className="container" - ) -]) - - -def update_graph(graph_id, - graph_title, - y_train_index, - y_val_index, - run_log_json, - display_mode, - checklist_smoothing_options, - slider_smoothing, - yaxis_title): + className="container", + )] +) + + + +def update_graph( + graph_id, + graph_title, + y_train_index, + y_val_index, + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + yaxis_title, +): """ :param graph_id: ID for Dash callbacks :param graph_title: Displayed on layout @@ -172,67 +163,55 @@ def smooth(scalars, weight=0.6): if run_log_json: # exists layout = go.Layout( title=graph_title, - margin=go.Margin(l=50, r=50, b=50, t=50), - yaxis={'title': yaxis_title} + margin=go.layout.Margin(l=50, r=50, b=50, t=50), + yaxis={"title": yaxis_title}, ) - run_log_df = pd.read_json(run_log_json, orient='split') + run_log_df = pd.read_json(run_log_json, orient="split") - step = run_log_df['step'] + step = run_log_df["step"] y_train = run_log_df[y_train_index] y_val = run_log_df[y_val_index] # Apply Smoothing if needed - if 'train' in checklist_smoothing_options: + if "train" in checklist_smoothing_options: y_train = smooth(y_train, weight=slider_smoothing) - if 'val' in checklist_smoothing_options: + if "val" in checklist_smoothing_options: y_val = smooth(y_val, weight=slider_smoothing) trace_train = go.Scatter( - x=step, - y=y_train, - mode='lines', - name='Training' - ) + x=step, y=y_train, mode="lines", name="Training") trace_val = go.Scatter( - x=step, - y=y_val, - mode='lines', - name='Validation' - ) + x=step, y=y_val, mode="lines", name="Validation") - if display_mode == 'separate_vertical': - figure = tools.make_subplots(rows=2, - cols=1, - print_grid=False, - shared_yaxes=True) + if display_mode == "separate_vertical": + figure = tools.make_subplots( + rows=2, cols=1, print_grid=False, shared_yaxes=True + ) figure.append_trace(trace_train, 1, 1) figure.append_trace(trace_val, 2, 1) - figure['layout'].update(title=layout.title, - margin=layout.margin, - scene={'domain': {'x': (0., 0.5), 'y': (0.5,1)}}) + figure["layout"].update( + title=layout.title, + margin=layout.margin, + scene={"domain": {"x": (0.0, 0.5), "y": (0.5, 1)}}, + ) - elif display_mode == 'separate_horizontal': - figure = tools.make_subplots(rows=1, - cols=2, - shared_yaxes=True, - print_grid=False) + elif display_mode == "separate_horizontal": + figure = tools.make_subplots( + rows=1, cols=2, shared_yaxes=True, print_grid=False + ) figure.append_trace(trace_train, 1, 1) figure.append_trace(trace_val, 1, 2) - figure['layout'].update(title=layout.title, - margin=layout.margin) + figure["layout"].update(title=layout.title, margin=layout.margin) - elif display_mode == 'overlap': - figure = go.Figure( - data=[trace_train, trace_val], - layout=layout - ) + elif display_mode == "overlap": + figure = go.Figure(data=[trace_train, trace_val], layout=layout) else: figure = None @@ -245,73 +224,95 @@ def smooth(scalars, weight=0.6): demo_callbacks(app, demo_mode) -@app.callback(Output('interval-log-update', 'interval'), - [Input('dropdown-interval-control', 'value')]) +@app.callback( + Output("interval-log-update", "interval"), + [Input("dropdown-interval-control", "value")], +) def update_interval_log_update(interval_rate): - if interval_rate == 'fast': + if interval_rate == "fast": return 500 - elif interval_rate == 'regular': + elif interval_rate == "regular": return 1000 - elif interval_rate == 'slow': + elif interval_rate == "slow": return 5 * 1000 # Refreshes every 24 hours - elif interval_rate == 'no': + elif interval_rate == "no": return 24 * 60 * 60 * 1000 if not demo_mode: - @app.callback(Output('run-log-storage', 'children'), - [Input('interval-log-update', 'n_intervals')]) + + @app.callback( + Output("run-log-storage", "children"), + [Input("interval-log-update", "n_intervals")], + ) def get_run_log(_): - names = ['step', 'train accuracy', 'val accuracy', 'train cross entropy', 'val cross entropy'] + names = [ + "step", + "train accuracy", + "val accuracy", + "train cross entropy", + "val cross entropy", + ] try: - run_log_df = pd.read_csv(LOGFILE, names=names) - json = run_log_df.to_json(orient='split') + run_log_df = pd.read_csv(DATA_PATH.joinpath(LOGFILE), names=names) + json = run_log_df.to_json(orient="split") except FileNotFoundError as error: print(error) - print("Please verify if the csv file generated by your model is placed in the correct directory.") + print( + "Please verify if the csv file generated by your model is placed in the correct directory." + ) return None return json -@app.callback(Output('div-step-display', 'children'), - [Input('run-log-storage', 'children')]) +@app.callback( + Output("div-step-display", + "children"), [Input("run-log-storage", "children")] +) def update_div_step_display(run_log_json): if run_log_json: - run_log_df = pd.read_json(run_log_json, orient='split') - return html.H6(f"Step: {run_log_df['step'].iloc[-1]}", style={'margin-top': '3px'}) - - -@app.callback(Output('div-accuracy-graph', 'children'), - [Input('run-log-storage', 'children'), - Input('radio-display-mode-accuracy', 'value'), - Input('checklist-smoothing-options-accuracy', 'values'), - Input('slider-smoothing-accuracy', 'value')]) -def update_accuracy_graph(run_log_json, - display_mode, - checklist_smoothing_options, - slider_smoothing): - graph = update_graph('accuracy-graph', - 'Prediction Accuracy', - 'train accuracy', - 'val accuracy', - run_log_json, - display_mode, - checklist_smoothing_options, - slider_smoothing, - 'Accuracy') + run_log_df = pd.read_json(run_log_json, orient="split") + return html.H6( + f"Step: {run_log_df['step'].iloc[-1]}", style={"margin-top": "3px"} + ) + + +@app.callback( + Output("div-accuracy-graph", "children"), + [ + Input("run-log-storage", "children"), + Input("radio-display-mode-accuracy", "value"), + Input("checklist-smoothing-options-accuracy", "values"), + Input("slider-smoothing-accuracy", "value"), + ], +) +def update_accuracy_graph( + run_log_json, display_mode, checklist_smoothing_options, slider_smoothing +): + graph = update_graph( + "accuracy-graph", + "Prediction Accuracy", + "train accuracy", + "val accuracy", + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + "Accuracy", + ) try: - if display_mode in ['separate_horizontal', 'overlap']: - graph.figure.layout.yaxis['range'] = [0, 1] + if display_mode in ["separate_horizontal", "overlap"]: + graph.figure.layout.yaxis["range"] = [0, 1] else: - graph.figure.layout.yaxis1['range'] = [0, 1] - graph.figure.layout.yaxis2['range'] = [0, 1] + graph.figure.layout.yaxis1["range"] = [0, 1] + graph.figure.layout.yaxis2["range"] = [0, 1] except AttributeError: pass @@ -319,64 +320,77 @@ def update_accuracy_graph(run_log_json, return [graph] -@app.callback(Output('div-cross-entropy-graph', 'children'), - [Input('run-log-storage', 'children'), - Input('radio-display-mode-cross-entropy', 'value'), - Input('checklist-smoothing-options-cross-entropy', 'values'), - Input('slider-smoothing-cross-entropy', 'value')]) -def update_cross_entropy_graph(run_log_json, - display_mode, - checklist_smoothing_options, - slider_smoothing): - graph = update_graph('cross-entropy-graph', - 'Cross Entropy Loss', - 'train cross entropy', - 'val cross entropy', - run_log_json, - display_mode, - checklist_smoothing_options, - slider_smoothing, - 'Loss') +@app.callback( + Output("div-cross-entropy-graph", "children"), + [ + Input("run-log-storage", "children"), + Input("radio-display-mode-cross-entropy", "value"), + Input("checklist-smoothing-options-cross-entropy", "values"), + Input("slider-smoothing-cross-entropy", "value"), + ], +) +def update_cross_entropy_graph( + run_log_json, display_mode, checklist_smoothing_options, slider_smoothing +): + graph = update_graph( + "cross-entropy-graph", + "Cross Entropy Loss", + "train cross entropy", + "val cross entropy", + run_log_json, + display_mode, + checklist_smoothing_options, + slider_smoothing, + "Loss", + ) return [graph] -@app.callback(Output('div-current-accuracy-value', 'children'), - [Input('run-log-storage', 'children')]) +@app.callback( + Output("div-current-accuracy-value", "children"), + [Input("run-log-storage", "children")], +) def update_div_current_accuracy_value(run_log_json): if run_log_json: - run_log_df = pd.read_json(run_log_json, orient='split') + run_log_df = pd.read_json(run_log_json, orient="split") return [ html.P( "Current Accuracy:", style={ - 'font-weight': 'bold', - 'margin-top': '15px', - 'margin-bottom': '0px' - } + "font-weight": "bold", + "margin-top": "15px", + "margin-bottom": "0px", + }, ), html.Div(f"Training: {run_log_df['train accuracy'].iloc[-1]:.4f}"), - html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}") + html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}"), ] -@app.callback(Output('div-current-cross-entropy-value', 'children'), - [Input('run-log-storage', 'children')]) +@app.callback( + Output("div-current-cross-entropy-value", "children"), + [Input("run-log-storage", "children")], +) def update_div_current_cross_entropy_value(run_log_json): if run_log_json: - run_log_df = pd.read_json(run_log_json, orient='split') + run_log_df = pd.read_json(run_log_json, orient="split") return [ html.P( "Current Loss:", style={ - 'font-weight': 'bold', - 'margin-top': '15px', - 'margin-bottom': '0px' - } + "font-weight": "bold", + "margin-top": "15px", + "margin-bottom": "0px", + }, ), - html.Div(f"Training: {run_log_df['train cross entropy'].iloc[-1]:.4f}"), - html.Div(f"Validation: {run_log_df['val cross entropy'].iloc[-1]:.4f}") + html.Div( + f"Training: {run_log_df['train cross entropy'].iloc[-1]:.4f}"), + html.Div( + f"Validation: {run_log_df['val cross entropy'].iloc[-1]:.4f}"), ] + # Running the server -if __name__ == '__main__': +if __name__ == "__main__": app.run_server(debug=True) + diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index bc20f16d1..a8b5ec3e2 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -4,7 +4,6 @@ Production: https://cdn.rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/d Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash_template.css */ - /* Table of contents –––––––––––––––––––––––––––––––––––––––––––––––––– - Banner @@ -23,40 +22,39 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash - Media Queries */ - - /* Banner –––––––––––––––––––––––––––––––––––––––––––––––––– */ .banner { - height: 75px; - background-color: #414073; - border-radius: 5px 5px 2px 2px; - box-shadow: rgb(240, 240, 240) 5px 5px 5px 0px; - padding-top: 0px; - padding-left: 0px; - padding-right: 0px; - width: 90%; - margin-left: auto; - margin-right: auto; + height: 75px; + background-color: #191e27; + /* border-radius: 5px 5px 2px 2px; */ + padding-top: 0px; + padding-left: 0px; + padding-right: 0px; + /* width: 90%; */ + margin-left: auto; + margin-right: auto; } -.banner h2{ - color: white; - margin-left: 1.5%; - display: inline-block; - font-family: 'Open Sans', sans-serif; - font-size:4rem; -line-height:1; +.banner h2 { + /* color: white; */ + color: #e6ecf4; + margin-left: 1.5%; + display: inline-block; + /* font-family: "Raleway"; */ + font-family: "Raleway Medium", sans-serif; + /* font-size: 4rem; */ + /* line-height: 1; */ + font-size: 30px; } -.banner Img{ - position: relative; - float: right; - right: 1.5%; - height: 75px; +.banner img { + position: relative; + float: right; + right: 1.5%; + height: 75px; } - /* Grid –––––––––––––––––––––––––––––––––––––––––––––––––– */ .container { @@ -64,11 +62,11 @@ line-height:1; background-color: white; border-radius: 2px 2px 5px 5px; font-size: 1.5rem; - box-shadow: rgb(240, 240, 240) 5px 5px 5px 0px; border: thin solid rgb(240, 240, 240); margin-left: auto; margin-right: auto; - color: #302F54; + color: #302f54; + margin-top: 30px; padding: 8px; width: 90%; max-width: none; @@ -79,103 +77,204 @@ line-height:1; .columns { width: 100%; float: left; - box-sizing: border-box; } - + box-sizing: border-box; +} /* For devices larger than 550px */ @media (min-width: 550px) { .column, .columns { - margin-left: 0.5%; } + margin-left: 0.5%; + } .column:first-child, .columns:first-child { - margin-left: 0; } + margin-left: 0; + } .one.column, - .one.columns { width: 8%; } - .two.columns { width: 16.25%; } - .three.columns { width: 22%; } - .four.columns { width: 33%; } - .five.columns { width: 39.3333333333%; } - .six.columns { width: 49.75%; } - .seven.columns { width: 56.6666666667%; } - .eight.columns { width: 66.5%; } - .nine.columns { width: 74.0%; } - .ten.columns { width: 82.6666666667%; } - .eleven.columns { width: 91.5%; } - .twelve.columns { width: 100%; margin-left: 0; } - - .one-third.column { width: 30.6666666667%; } - .two-thirds.column { width: 65.3333333333%; } - - .one-half.column { width: 48%; } + .one.columns { + width: 8%; + } + .two.columns { + width: 16.25%; + } + .three.columns { + width: 22%; + } + .four.columns { + width: 33%; + } + .five.columns { + width: 39.3333333333%; + } + .six.columns { + width: 49.75%; + } + .seven.columns { + width: 56.6666666667%; + } + .eight.columns { + width: 66.5%; + } + .nine.columns { + width: 74%; + } + .ten.columns { + width: 82.6666666667%; + } + .eleven.columns { + width: 91.5%; + } + .twelve.columns { + width: 100%; + margin-left: 0; + } + + .one-third.column { + width: 30.6666666667%; + } + .two-thirds.column { + width: 65.3333333333%; + } + + .one-half.column { + width: 48%; + } /* Offsets */ .offset-by-one.column, - .offset-by-one.columns { margin-left: 8.66666666667%; } + .offset-by-one.columns { + margin-left: 8.66666666667%; + } .offset-by-two.column, - .offset-by-two.columns { margin-left: 17.3333333333%; } + .offset-by-two.columns { + margin-left: 17.3333333333%; + } .offset-by-three.column, - .offset-by-three.columns { margin-left: 26%; } + .offset-by-three.columns { + margin-left: 26%; + } .offset-by-four.column, - .offset-by-four.columns { margin-left: 34.6666666667%; } + .offset-by-four.columns { + margin-left: 34.6666666667%; + } .offset-by-five.column, - .offset-by-five.columns { margin-left: 43.3333333333%; } + .offset-by-five.columns { + margin-left: 43.3333333333%; + } .offset-by-six.column, - .offset-by-six.columns { margin-left: 52%; } + .offset-by-six.columns { + margin-left: 52%; + } .offset-by-seven.column, - .offset-by-seven.columns { margin-left: 60.6666666667%; } + .offset-by-seven.columns { + margin-left: 60.6666666667%; + } .offset-by-eight.column, - .offset-by-eight.columns { margin-left: 69.3333333333%; } + .offset-by-eight.columns { + margin-left: 69.3333333333%; + } .offset-by-nine.column, - .offset-by-nine.columns { margin-left: 78.0%; } + .offset-by-nine.columns { + margin-left: 78%; + } .offset-by-ten.column, - .offset-by-ten.columns { margin-left: 86.6666666667%; } + .offset-by-ten.columns { + margin-left: 86.6666666667%; + } .offset-by-eleven.column, - .offset-by-eleven.columns { margin-left: 95.3333333333%; } + .offset-by-eleven.columns { + margin-left: 95.3333333333%; + } .offset-by-one-third.column, - .offset-by-one-third.columns { margin-left: 34.6666666667%; } + .offset-by-one-third.columns { + margin-left: 34.6666666667%; + } .offset-by-two-thirds.column, - .offset-by-two-thirds.columns { margin-left: 69.3333333333%; } + .offset-by-two-thirds.columns { + margin-left: 69.3333333333%; + } .offset-by-one-half.column, - .offset-by-one-half.columns { margin-left: 52%; } - + .offset-by-one-half.columns { + margin-left: 52%; + } } - /* Base Styles –––––––––––––––––––––––––––––––––––––––––––––––––– */ /* NOTE html is set to 62.5% so that all the REM measurements throughout Skeleton are based on 10px sizing. So basically 1.5rem = 15px :) */ html { - font-size: 62.5%; } + font-size: 62.5%; +} body { font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */ line-height: 1.6; font-weight: 400; - font-family: 'Roboto', sans-serif; - color: #222; } - + font-family: "Roboto", sans-serif; + color: #222; +} /* Typography –––––––––––––––––––––––––––––––––––––––––––––––––– */ -h1, h2, h3, h4, h5, h6 { +h1, +h2, +h3, +h4, +h5, +h6 { margin-top: 0; margin-bottom: 0; - font-weight: 300; } -h1 { font-size: 4.5rem; line-height: 1.2; letter-spacing: -.1rem; margin-bottom: 2rem; } -h2 { font-size: 3.6rem; line-height: 1.25; letter-spacing: -.1rem; margin-bottom: 1.8rem; margin-top: 1.8rem;} -h3 { font-size: 3.0rem; line-height: 1.3; letter-spacing: -.1rem; margin-bottom: 1.5rem; margin-top: 1.5rem;} -h4 { font-size: 2.6rem; line-height: 1.35; letter-spacing: -.08rem; margin-bottom: 1.2rem; margin-top: 1.2rem;} -h5 { font-size: 2.2rem; line-height: 1.5; letter-spacing: -.05rem; margin-bottom: 0.6rem; margin-top: 0.6rem;} -h6 { font-size: 2.0rem; line-height: 1.6; letter-spacing: 0; margin-bottom: 0.75rem; margin-top: 0.75rem;} + font-weight: 300; +} +h1 { + font-size: 4.5rem; + line-height: 1.2; + letter-spacing: -0.1rem; + margin-bottom: 2rem; +} +h2 { + font-size: 3.6rem; + line-height: 1.25; + letter-spacing: -0.1rem; + margin-bottom: 1.8rem; + margin-top: 1.8rem; +} +h3 { + font-size: 3rem; + line-height: 1.3; + letter-spacing: -0.1rem; + margin-bottom: 1.5rem; + margin-top: 1.5rem; +} +h4 { + font-size: 2.6rem; + line-height: 1.35; + letter-spacing: -0.08rem; + margin-bottom: 1.2rem; + margin-top: 1.2rem; +} +h5 { + font-size: 2.2rem; + line-height: 1.5; + letter-spacing: -0.05rem; + margin-bottom: 0.6rem; + margin-top: 0.6rem; +} +h6 { + font-size: 2rem; + line-height: 1.6; + letter-spacing: 0; + margin-bottom: 0.75rem; + margin-top: 0.75rem; +} p { - margin-top: 0; } - + margin-top: 0; +} /* Blockquotes –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -187,14 +286,14 @@ blockquote { margin-left: 0rem; } - /* Links –––––––––––––––––––––––––––––––––––––––––––––––––– */ a { - color: #1EAEDB; } + color: #1eaedb; +} a:hover { - color: #0FA0CE; } - + color: #0fa0ce; +} /* Buttons –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -211,7 +310,7 @@ input[type="button"] { font-size: 11px; font-weight: 600; line-height: 38px; - letter-spacing: .1rem; + letter-spacing: 0.1rem; text-transform: uppercase; text-decoration: none; white-space: nowrap; @@ -219,7 +318,8 @@ input[type="button"] { border-radius: 4px; border: 1px solid #bbb; cursor: pointer; - box-sizing: border-box; } + box-sizing: border-box; +} .button:hover, button:hover, input[type="submit"]:hover, @@ -232,15 +332,17 @@ input[type="reset"]:focus, input[type="button"]:focus { color: #333; border-color: #888; - outline: 0; } + outline: 0; +} .button.button-primary, button.button-primary, input[type="submit"].button-primary, input[type="reset"].button-primary, input[type="button"].button-primary { - color: #FFF; - background-color: #33C3F0; - border-color: #33C3F0; } + color: #fff; + background-color: #33c3f0; + border-color: #33c3f0; +} .button.button-primary:hover, button.button-primary:hover, input[type="submit"].button-primary:hover, @@ -251,10 +353,10 @@ button.button-primary:focus, input[type="submit"].button-primary:focus, input[type="reset"].button-primary:focus, input[type="button"].button-primary:focus { - color: #FFF; - background-color: #1EAEDB; - border-color: #1EAEDB; } - + color: #fff; + background-color: #1eaedb; + border-color: #1eaedb; +} /* Forms –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -270,12 +372,13 @@ select { height: 38px; padding: 6px 10px; /* The 6px vertically centers text on FF, ignored by Webkit */ background-color: #fff; - border: 1px solid #D1D1D1; + border: 1px solid #d1d1d1; border-radius: 4px; box-shadow: none; box-sizing: border-box; font-family: inherit; - font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/} + font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/ +} /* Removes awkward default styles on some inputs for iOS */ input[type="email"], input[type="number"], @@ -286,12 +389,14 @@ input[type="url"], input[type="password"], textarea { -webkit-appearance: none; - -moz-appearance: none; - appearance: none; } + -moz-appearance: none; + appearance: none; +} textarea { min-height: 65px; padding-top: 6px; - padding-bottom: 6px; } + padding-bottom: 6px; +} input[type="email"]:focus, input[type="number"]:focus, input[type="search"]:focus, @@ -301,42 +406,51 @@ input[type="url"]:focus, input[type="password"]:focus, textarea:focus, select:focus { - border: 1px solid #33C3F0; - outline: 0; } + border: 1px solid #33c3f0; + outline: 0; +} label, legend { display: block; - margin-bottom: 0px; } + margin-bottom: 0px; +} fieldset { padding: 0; - border-width: 0; } + border-width: 0; +} input[type="checkbox"], input[type="radio"] { - display: inline; } + display: inline; +} label > .label-body { display: inline-block; - margin-left: .5rem; - font-weight: normal; } - + margin-left: 0.5rem; + font-weight: normal; +} /* Lists –––––––––––––––––––––––––––––––––––––––––––––––––– */ ul { - list-style: circle inside; } + list-style: circle inside; +} ol { - list-style: decimal inside; } -ol, ul { + list-style: decimal inside; +} +ol, +ul { padding-left: 0; - margin-top: 0; } + margin-top: 0; +} ul ul, ul ol, ol ol, ol ul { margin: 1.5rem 0 1.5rem 3rem; - font-size: 90%; } + font-size: 90%; +} li { - margin-bottom: 1rem; } - + margin-bottom: 1rem; +} /* Tables –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -344,49 +458,58 @@ th, td { padding: 12px 15px; text-align: left; - border-bottom: 1px solid #E1E1E1; } + border-bottom: 1px solid #e1e1e1; +} th:first-child, td:first-child { - padding-left: 0; } + padding-left: 0; +} th:last-child, td:last-child { - padding-right: 0; } - + padding-right: 0; +} /* Spacing –––––––––––––––––––––––––––––––––––––––––––––––––– */ button, .button { - margin-bottom: 0rem; } + margin-bottom: 0rem; +} input, textarea, select, fieldset { - margin-bottom: 0rem; } + margin-bottom: 0rem; +} pre, dl, figure, table, form { - margin-bottom: 0rem; } + margin-bottom: 0rem; +} p, ul, ol { - margin-bottom: 0.75rem; } + margin-bottom: 0.75rem; +} /* Utilities –––––––––––––––––––––––––––––––––––––––––––––––––– */ .u-full-width { width: 100%; - box-sizing: border-box; } + box-sizing: border-box; +} .u-max-full-width { max-width: 100%; - box-sizing: border-box; } + box-sizing: border-box; +} .u-pull-right { - float: right; } + float: right; +} .u-pull-left { - float: left; } - + float: left; +} /* Misc –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -394,8 +517,8 @@ hr { margin-top: 3rem; margin-bottom: 3.5rem; border-width: 0; - border-top: 1px solid #E1E1E1; } - + border-top: 1px solid #e1e1e1; +} /* Clearing –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -406,8 +529,8 @@ hr { .u-cf { content: ""; display: table; - clear: both; } - + clear: both; +} /* Media Queries –––––––––––––––––––––––––––––––––––––––––––––––––– */ @@ -418,18 +541,22 @@ on small devices, paste the mobile query code up in the buttons section and styl there. */ - /* Larger than mobile */ -@media (min-width: 400px) {} +@media (min-width: 400px) { +} /* Larger than phablet (also point when grid becomes active) */ -@media (min-width: 550px) {} +@media (min-width: 550px) { +} /* Larger than tablet */ -@media (min-width: 750px) {} +@media (min-width: 750px) { +} /* Larger than desktop */ -@media (min-width: 1000px) {} +@media (min-width: 1000px) { +} /* Larger than Desktop HD */ -@media (min-width: 1200px) {} \ No newline at end of file +@media (min-width: 1200px) { +} diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css new file mode 100644 index 000000000..8268b7baf --- /dev/null +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -0,0 +1,17 @@ +.markdown img { + width: 100%; 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If it was not, set this to 'True'. -# Path based routing is the default option and most On-Premise -# users use this option. -PATH_BASED_ROUTING = True - -# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # -# This section only needs to be filled out # -# if DASH_APP_PRIVACY is set to 'private' or 'secret' # -# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # - -# Fill in with your Plotly On-Premise username -os.environ['PLOTLY_USERNAME'] = 'plotly-username' - -# Fill in with your Plotly On-Premise API key -# See /settings/api to generate a key -# If you have already created a key and saved it on your own machine -# (from the Plotly-Python library instructions at https://plot.ly/python/getting-started) -# then you can view that key in your ~/.plotly/.config file -# or inside a Python session with these commands: -# import plotly -# print(plotly.tools.get_config_file()) -os.environ['PLOTLY_API_KEY'] = 'your-plotly-api-key' - -# Fill in with your Plotly On-Premise domain -os.environ['PLOTLY_DOMAIN'] = 'https://your-plotly-domain.com' -os.environ['PLOTLY_API_DOMAIN'] = os.environ['PLOTLY_DOMAIN'] - -# Fill in with the domain of your Dash subdomain. -# This matches the domain of the Dash App Manager -PLOTLY_DASH_DOMAIN='https://your-dash-manager-plotly-domain.com' - -# Keep as True if your SSL certificates are valid. -# If you are just trialing Plotly On-Premise with self signed certificates, -# then you can set this to False. Note that self-signed certificates are not -# safe for production. -os.environ['PLOTLY_SSL_VERIFICATION'] = 'True' diff --git a/apps/dash-live-model-training/custom_styles.css b/apps/dash-live-model-training/custom_styles.css deleted file mode 100644 index dfcf0545a..000000000 --- a/apps/dash-live-model-training/custom_styles.css +++ /dev/null @@ -1,7 +0,0 @@ -.markdown img { - width: 100%; -} - -._dash-undo-redo { - display: none; -} \ No newline at end of file diff --git a/apps/dash-live-model-training/demo.md b/apps/dash-live-model-training/demo.md index cef58d9d6..72b7d5521 100644 --- a/apps/dash-live-model-training/demo.md +++ b/apps/dash-live-model-training/demo.md @@ -1,34 +1,38 @@ ## Getting Started with the Demo -The purpose of this demo is to show what can be done with the Viewer. Therefore, the trainings shown above are displayed using pre-generated data. To try with your own data, please visit the [project repo](https://github.com/plotly/dash-live-model-training). +The purpose of this demo is to show what can be done with the Viewer. Therefore, the trainings shown above are displayed using pre-generated data. To try with your own data, please visit the [project repo](https://github.com/plotly/dash-sample-apps/tree/master/apps/dash-live-model-training). -To use the demo, simply choose the model and dataset for which you want to replay the training, __using the two dropdown menus at the top of the page__. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. +To use the demo, simply choose the model and dataset for which you want to replay the training, **using the two dropdown menus at the top of the page**. For every dataset, we trained a simple 1-layer Neural Network, and a small Convolutional Neural Network that were taken from the official [Tensorflow](https://www.tensorflow.org/tutorials/layers) and [Keras](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) tutorials. At the moment, the following datasets have been pre-generated: -* __CIFAR10:__ 50,000 RGB images of size 32x32. It contains 10 common objects. [Link](https://www.cs.toronto.edu/~kriz/cifar.html) -* __MNIST:__ 60,000 grayscale images of size 28x28. It contains handwritten digits from 0 to 9. [Link](http://yann.lecun.com/exdb/mnist/) -* __Fashion MNIST:__ 60,000 grayscale images of size 28x28. It contains 10 commonly found fashion items. [Link](https://github.com/zalandoresearch/fashion-mnist) + +- **CIFAR10:** 50,000 RGB images of size 32x32. It contains 10 common objects. [Link](https://www.cs.toronto.edu/~kriz/cifar.html) +- **MNIST:** 60,000 grayscale images of size 28x28. It contains handwritten digits from 0 to 9. [Link](http://yann.lecun.com/exdb/mnist/) +- **Fashion MNIST:** 60,000 grayscale images of size 28x28. It contains 10 commonly found fashion items. [Link](https://github.com/zalandoresearch/fashion-mnist) ## What am I looking at? -The two plots, updated at the interval of your choice, shows two important metrics for training Machine Learning classifiers, i.e. Accuracy and Cross Entropy Loss. Because we feed small mini-batches of 50 examples for every iteration, you will see a lot of variation along the y-axis, which can be made smoother using the sliders. - + +The two plots, updated at the interval of your choice, shows two important metrics for training Machine Learning classifiers, i.e. Accuracy and Cross Entropy Loss. Because we feed small mini-batches of 50 examples for every iteration, you will see a lot of variation along the y-axis, which can be made smoother using the sliders. + Accuracy is the fraction of labels correctly classified inside the mini-batch currently used to train/validate the model. In our case, given that each dataset has 10 different labels, an accuracy of 0.1 is equivalent to a random guess, and an accuracy of 1 means the model correctly predicted every single label. Cross Entropy Loss is the value that you are trying to minimize with your model. It basically indicates how far off our model is from predicting the correct label every time. It is described more in depth in the [Tensorflow Tutorial](https://www.tensorflow.org/versions/r1.0/get_started/mnist/beginners#training). ## What does the app do? + For the majority of Deep Learning models, it is extremely helpful to keep track of the accuracy and loss as it is training. At the moment, the best application to do that is [Tensorboard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), which is a collection of visualization tools (metrics plots, image examples, graph representation, weight histogram, etc.) useful to debug and monitor the training of your model. -_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your __Tensorflow model__ during training. It complements the Tensorboard by offering the following: -* __Real-time visualization__: The app is designed to visualize your metrics as they are updated inside your model. -* __Small and Lightweight__: The viewer loads a small number of important visualization, so that it loads and runs quickly. -* __Simple to use__: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. -* __Easy to modify__: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. -* __Plotly Graphs and Dash Integration__: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. +_Dash's Live Model Training Viewer_ is a compact visualization app that monitors core metrics of your **Tensorflow model** during training. It complements the Tensorboard by offering the following: + +- **Real-time visualization**: The app is designed to visualize your metrics as they are updated inside your model. +- **Small and Lightweight**: The viewer loads a small number of important visualization, so that it loads and runs quickly. +- **Simple to use**: For simpler tensorflow models, all you need to do is to call `add_eval` to add the accuracy and cross entropy operations in the graph, and generate a log of the metrics using `write_data`. Both functions are inside `tfutils.py`, and examples are included in the `examples` directory. +- **Easy to modify**: The app is stored inside one module, and is written in under 400 lines. You can quickly modify and improve the app without breaking anything. +- **Plotly Graphs and Dash Integration**: Easily integrate the app into more complex Dash Apps, and includes all the tools found in Plotly graphs. Here is a flowchart of the process from model training to visualization: ![flowchart](https://raw.githubusercontent.com/plotly/dash-live-model-training/master/images/flowchart.png) At the moment, the logging only works for iterative Tensorflow models. We are planning to extend it for PyTorch. You are encouraged to port the logging function (which is a simple csv logging) to Keras, Tensorflow's high-level API, MXNet, etc. -You can find the code for the app, and examples of using the app in the [repository](https://github.com/plotly/dash-live-model-training). \ No newline at end of file +You can find the code for the app, and examples of using the app in the [repository](https://github.com/plotly/dash-live-model-training). diff --git a/apps/dash-live-model-training/demo_utils.py b/apps/dash-live-model-training/demo_utils.py index 5d2bef2bf..bf7bd8b64 100644 --- a/apps/dash-live-model-training/demo_utils.py +++ b/apps/dash-live-model-training/demo_utils.py @@ -7,19 +7,15 @@ def demo_explanation(demo_mode): if demo_mode: # Markdown files - with open('demo.md', 'r') as file: + with open("demo.md", "r") as file: demo_md = file.read() return html.Div( - html.Div([ - dcc.Markdown(demo_md, className='markdown'), - ], - className='ten columns' + html.Div( + [dcc.Markdown(demo_md, className="markdown")], className="ten columns" ), - className='row', - style={ - 'margin': '20px' - } + className="row", + style={"margin": "20px"}, ) @@ -27,41 +23,53 @@ def demo_components(demo_mode): if demo_mode: return [ # Hidden Div that will store the result of simulating a model run - html.Div(id='storage-simulated-run', style={'display': 'none'}), - + html.Div(id="storage-simulated-run", style={"display": "none"}), # Increment the simulation step count at a fixed time interval dcc.Interval( - id='interval-simulated-step', + id="interval-simulated-step", interval=125, # Updates every 100 milliseconds, i.e. every step takes 25 ms - n_intervals=0 + n_intervals=0, ), - - html.Div(className="row", style={'margin-bottom':'8px'}, children=[ - html.Div(className="ten columns", children=[ - html.Div(className="six columns", children=dcc.Dropdown( - id='dropdown-demo-dataset', - options=[ - {'label': 'CIFAR 10', 'value': 'cifar'}, - {'label': 'MNIST', 'value': 'mnist'}, - {'label': 'Fashion MNIST', 'value': 'fashion'} + html.Div( + className="row", + style={"margin-bottom": "8px"}, + children=[ + html.Div( + className="ten columns", + children=[ + html.Div( + className="six columns", + children=dcc.Dropdown( + id="dropdown-demo-dataset", + options=[ + {"label": "CIFAR 10", "value": "cifar"}, + {"label": "MNIST", "value": "mnist"}, + {"label": "Fashion MNIST", "value": "fashion"}, + ], + placeholder="Select a demo dataset", + searchable=False, + ), + ), + html.Div( + className="six columns", + children=dcc.Dropdown( + id="dropdown-simulation-model", + options=[ + { + "label": "1-Layer Neural Net", + "value": "softmax", + }, + {"label": "Simple Conv Net", "value": "cnn"}, + ], + placeholder="Select Model to Simulate", + searchable=False, + ), + ), ], - placeholder="Select a demo dataset", - searchable=False - )), - - html.Div(className="six columns", children=dcc.Dropdown( - id='dropdown-simulation-model', - options=[ - {'label': '1-Layer Neural Net', 'value': 'softmax'}, - {'label': 'Simple Conv Net', 'value': 'cnn'} - ], - placeholder="Select Model to Simulate", - searchable=False - )) - ]), - - html.Div(id="div-total-step-count", className="two columns") - ]) + ), + html.Div(id="div-total-step-count", className="two columns"), + ], + ), ] else: @@ -70,56 +78,89 @@ def demo_components(demo_mode): def demo_callbacks(app, demo_mode): if demo_mode: + @app.server.before_first_request def load_demo_run_logs(): global data_dict, demo_md - names = ['step', 'train accuracy', 'val accuracy', 'train cross entropy', 'val cross entropy'] + names = [ + "step", + "train accuracy", + "val accuracy", + "train cross entropy", + "val cross entropy", + ] data_dict = { - 'softmax': { - 'cifar': pd.read_csv('demo_run_logs/cifar_softmax_run_log.csv', names=names), - 'mnist': pd.read_csv('demo_run_logs/mnist_softmax_run_log.csv', names=names), - 'fashion': pd.read_csv('demo_run_logs/fashion_softmax_run_log.csv', names=names) + "softmax": { + "cifar": pd.read_csv( + "demo_run_logs/cifar_softmax_run_log.csv", names=names + ), + "mnist": pd.read_csv( + "demo_run_logs/mnist_softmax_run_log.csv", names=names + ), + "fashion": pd.read_csv( + "demo_run_logs/fashion_softmax_run_log.csv", names=names + ), + }, + "cnn": { + "cifar": pd.read_csv( + "demo_run_logs/cifar_cnn_run_log.csv", names=names + ), + "mnist": pd.read_csv( + "demo_run_logs/mnist_cnn_run_log.csv", names=names + ), + "fashion": pd.read_csv( + "demo_run_logs/fashion_cnn_run_log.csv", names=names + ), }, - - 'cnn': { - 'cifar': pd.read_csv('demo_run_logs/cifar_cnn_run_log.csv', names=names), - 'mnist': pd.read_csv('demo_run_logs/mnist_cnn_run_log.csv', names=names), - 'fashion': pd.read_csv('demo_run_logs/fashion_cnn_run_log.csv', names=names) - } } - @app.callback(Output('storage-simulated-run', 'children'), - [Input('interval-simulated-step', 'n_intervals')], - [State('dropdown-demo-dataset', 'value'), - State('dropdown-simulation-model', 'value')]) + @app.callback( + Output("storage-simulated-run", "children"), + [Input("interval-simulated-step", "n_intervals")], + [ + State("dropdown-demo-dataset", "value"), + State("dropdown-simulation-model", "value"), + ], + ) def simulate_run(n_intervals, demo_dataset, simulation_model): if simulation_model and demo_dataset and n_intervals > 0: step = n_intervals * 5 run_logs = data_dict[simulation_model][demo_dataset] - run_below_steps = run_logs[run_logs['step'] <= step] - json = run_below_steps.to_json(orient='split') + run_below_steps = run_logs[run_logs["step"] <= step] + json = run_below_steps.to_json(orient="split") return json - @app.callback(Output('interval-simulated-step', 'n_intervals'), - [Input('dropdown-demo-dataset', 'value'), - Input('dropdown-simulation-model', 'value')]) + @app.callback( + Output("interval-simulated-step", "n_intervals"), + [ + Input("dropdown-demo-dataset", "value"), + Input("dropdown-simulation-model", "value"), + ], + ) def reset_interval_simulated_step(*_): return 0 - @app.callback(Output('run-log-storage', 'children'), - [Input('interval-log-update', 'n_intervals')], - [State('storage-simulated-run', 'children')]) + @app.callback( + Output("run-log-storage", "children"), + [Input("interval-log-update", "n_intervals")], + [State("storage-simulated-run", "children")], + ) def get_run_log(_, simulated_run): if simulate_run: return simulated_run - @app.callback(Output('div-total-step-count', 'children'), - [Input('dropdown-demo-dataset', 'value')]) + @app.callback( + Output("div-total-step-count", "children"), + [Input("dropdown-demo-dataset", "value")], + ) def total_step_count(dataset_name): if dataset_name is not None: - dataset = data_dict['softmax'][dataset_name] - return html.H6(f"Total Steps: {dataset['step'].iloc[-1]}", style={'margin-top': '3px'}) + dataset = data_dict["softmax"][dataset_name] + return html.H6( + f"Total Steps: {dataset['step'].iloc[-1]}", + style={"margin-top": "3px"}, + ) diff --git a/apps/dash-live-model-training/examples/cifar_deep_modified.py b/apps/dash-live-model-training/examples/cifar_deep_modified.py index fdd2af283..3da1abb72 100644 --- a/apps/dash-live-model-training/examples/cifar_deep_modified.py +++ b/apps/dash-live-model-training/examples/cifar_deep_modified.py @@ -49,7 +49,7 @@ def deepnn(x): - """deepnn builds the graph for a deep net for classifying digits. + """deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the @@ -61,152 +61,163 @@ def deepnn(x): digits 0-9). keep_prob is a scalar placeholder for the probability of dropout. """ - # Reshape to use within a convolutional neural net. - # Last dimension is for "features" - there is three here, since images are - # rgb -- it would be 1 for a grayscale image, 4 for RGBA, etc. - x_image = tf.reshape(x, [-1, 32, 32, 3]) + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is three here, since images are + # rgb -- it would be 1 for a grayscale image, 4 for RGBA, etc. + x_image = tf.reshape(x, [-1, 32, 32, 3]) - # Convolutional layers 1 and 2 - maps 3-color image to 32 feature maps. - W_conv1 = weight_variable([3, 3, 3, 32]) # 3x3 filters - b_conv1 = bias_variable([32]) - h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + # Convolutional layers 1 and 2 - maps 3-color image to 32 feature maps. + W_conv1 = weight_variable([3, 3, 3, 32]) # 3x3 filters + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) - W_conv2 = weight_variable([3, 3, 32, 32]) - b_conv2 = bias_variable([32]) - h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2) + W_conv2 = weight_variable([3, 3, 32, 32]) + b_conv2 = bias_variable([32]) + h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2) - # Pooling layer - downsamples by 2X. - h_pool2 = max_pool_2x2(h_conv2) + # Pooling layer - downsamples by 2X. + h_pool2 = max_pool_2x2(h_conv2) - # Dropout - h_pool2_drop = tf.nn.dropout(h_pool2, 0.75) + # Dropout + h_pool2_drop = tf.nn.dropout(h_pool2, 0.75) - # Convolutional layers 3 and 4 - maps 32 feature maps to 64. - W_conv3 = weight_variable([3, 3, 32, 64]) # 3x3 filters - b_conv3 = bias_variable([64]) - h_conv3 = tf.nn.relu(conv2d(h_pool2_drop, W_conv3) + b_conv3) + # Convolutional layers 3 and 4 - maps 32 feature maps to 64. + W_conv3 = weight_variable([3, 3, 32, 64]) # 3x3 filters + b_conv3 = bias_variable([64]) + h_conv3 = tf.nn.relu(conv2d(h_pool2_drop, W_conv3) + b_conv3) - W_conv4 = weight_variable([3, 3, 64, 64]) # 3x3 filters - b_conv4 = bias_variable([64]) - h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4) + W_conv4 = weight_variable([3, 3, 64, 64]) # 3x3 filters + b_conv4 = bias_variable([64]) + h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4) - # Second pooling layer. - h_pool4 = max_pool_2x2(h_conv4) + # Second pooling layer. + h_pool4 = max_pool_2x2(h_conv4) - # Dropout - h_pool4_drop = tf.nn.dropout(h_pool4, 0.75) + # Dropout + h_pool4_drop = tf.nn.dropout(h_pool4, 0.75) - # Fully connected layer 1 -- after 2 round of downsampling, our 32x32 image - # is down to 8x8x64 feature maps -- maps this to 512 features. - W_fc1 = weight_variable([8 * 8 * 64, 512]) - b_fc1 = bias_variable([512]) + # Fully connected layer 1 -- after 2 round of downsampling, our 32x32 image + # is down to 8x8x64 feature maps -- maps this to 512 features. + W_fc1 = weight_variable([8 * 8 * 64, 512]) + b_fc1 = bias_variable([512]) - h_pool4_flat = tf.reshape(h_pool4_drop, [-1, 8*8*64]) - h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1) + h_pool4_flat = tf.reshape(h_pool4_drop, [-1, 8 * 8 * 64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat, W_fc1) + b_fc1) - # Dropout - controls the complexity of the model, prevents co-adaptation of - # features. - keep_prob = tf.placeholder(tf.float32) - h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) - # Map the 512 features to 10 classes, one for each digit - W_fc2 = weight_variable([512, 10]) - b_fc2 = bias_variable([10]) + # Map the 512 features to 10 classes, one for each digit + W_fc2 = weight_variable([512, 10]) + b_fc2 = bias_variable([10]) - y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 - return y_conv, keep_prob + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob def conv2d(x, W): - """conv2d returns a 2d convolution layer with full stride.""" - return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME") def max_pool_2x2(x): - """max_pool_2x2 downsamples a feature map by 2X.""" - return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], - strides=[1, 2, 2, 1], padding='SAME') + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") def weight_variable(shape): - """weight_variable generates a weight variable of a given shape.""" - initial = tf.truncated_normal(shape, stddev=0.1) - return tf.Variable(initial) + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) def bias_variable(shape): - """bias_variable generates a bias variable of a given shape.""" - initial = tf.constant(0.1, shape=shape) - return tf.Variable(initial) + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) def main(_): - # Import data - print("Starting to generate CIFAR10 images.") - (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() - x_train = np.moveaxis(x_train, 1, 3) / 255. # Normalize values - x_train_vec = x_train.reshape(50000, -1) - - x_test = np.moveaxis(x_test, 1, 3) / 255. # Normalize values - x_test_vec = x_test.reshape(10000, -1) - - X_train, X_val, y_train, y_val = train_test_split(x_train_vec, y_train, test_size=0.1, random_state=42) - print("Finished generating CIFAR10 images.") - - # Create the model - x = tf.placeholder(tf.float32, [None, 32*32*3]) - - # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) - - # Build the graph for the deep net - y_conv, keep_prob = deepnn(x) - - cross_entropy = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv)) - train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # RMS is used in keras example, Adam is better - correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - - with tf.Session() as sess: - y_train = OneHotEncoder(sparse=False).fit_transform(y_train) - y_val = OneHotEncoder(sparse=False).fit_transform(y_val) - - sess.run(tf.global_variables_initializer()) - for i in range(20001): - start_train = i * 50 % y_train.shape[0] - end_train = start_train + 50 - - start_val = i * 50 % y_val.shape[0] - end_val = start_val + 50 - - batch = (X_train[start_train:end_train], y_train[start_train:end_train]) - batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) - - feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} - feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} - # Writes data into run log csv file - write_data( - accuracy=accuracy, - cross_entropy=cross_entropy, - feed_dict_train=feed_dict_train, - feed_dict_val=feed_dict_val, - step=i - ) - - if i % 100 == 0: - train_accuracy = accuracy.eval(feed_dict={ - x: batch[0], y_: batch[1], keep_prob: 1.0}) - print('step %d, training accuracy %g' % (i, train_accuracy)) - train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) - - print('test accuracy %g' % accuracy.eval(feed_dict={ - x: x_test_vec, y_: y_test, keep_prob: 1.0})) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', type=str, - default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + # Import data + print("Starting to generate CIFAR10 images.") + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() + x_train = np.moveaxis(x_train, 1, 3) / 255.0 # Normalize values + x_train_vec = x_train.reshape(50000, -1) + + x_test = np.moveaxis(x_test, 1, 3) / 255.0 # Normalize values + x_test_vec = x_test.reshape(10000, -1) + + X_train, X_val, y_train, y_val = train_test_split( + x_train_vec, y_train, test_size=0.1, random_state=42 + ) + print("Finished generating CIFAR10 images.") + + # Create the model + x = tf.placeholder(tf.float32, [None, 32 * 32 * 3]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y_conv) + ) + train_step = tf.train.AdamOptimizer(1e-4).minimize( + cross_entropy + ) # RMS is used in keras example, Adam is better + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + with tf.Session() as sess: + y_train = OneHotEncoder(sparse=False).fit_transform(y_train) + y_val = OneHotEncoder(sparse=False).fit_transform(y_val) + + sess.run(tf.global_variables_initializer()) + for i in range(20001): + start_train = i * 50 % y_train.shape[0] + end_train = start_train + 50 + + start_val = i * 50 % y_val.shape[0] + end_val = start_val + 50 + + batch = (X_train[start_train:end_train], y_train[start_train:end_train]) + batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) + + feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} + feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i, + ) + + if i % 100 == 0: + train_accuracy = accuracy.eval( + feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0} + ) + print("step %d, training accuracy %g" % (i, train_accuracy)) + train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + + print( + "test accuracy %g" + % accuracy.eval(feed_dict={x: x_test_vec, y_: y_test, keep_prob: 1.0}) + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data_dir", + type=str, + default="/tmp/tensorflow/mnist/input_data", + help="Directory for storing input data", + ) + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/cifar_softmax_modified.py b/apps/dash-live-model-training/examples/cifar_softmax_modified.py index 0ab80012b..ae724e9ee 100644 --- a/apps/dash-live-model-training/examples/cifar_softmax_modified.py +++ b/apps/dash-live-model-training/examples/cifar_softmax_modified.py @@ -44,86 +44,85 @@ def main(_): - # Import data - print("Starting to generate CIFAR10 images.") - (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() - x_train = np.moveaxis(x_train, 1, 3) / 255. # Normalize values - x_train_vec = x_train.reshape(50000, -1) - - y_train = np.squeeze(y_train) - y_test = np.squeeze(y_test) - - x_test = np.moveaxis(x_test, 1, 3) / 255. # Normalize values - x_test_vec = x_test.reshape(10000, -1) - - X_train, X_val, y_train, y_val = train_test_split(x_train_vec, y_train, test_size=0.1, random_state=42) - print("Finished generating CIFAR10 images.") - - # Create the model - x = tf.placeholder(tf.float32, [None, 3*32*32]) - W = tf.Variable(tf.zeros([3*32*32, 10])) - b = tf.Variable(tf.zeros([10])) - y = tf.matmul(x, W) + b - - # Define loss and optimizer - y_ = tf.placeholder(tf.int64, [None]) - - # The raw formulation of cross-entropy, - # - # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), - # reduction_indices=[1])) - # - # can be numerically unstable. - # - # So here we use tf.losses.sparse_softmax_cross_entropy on the raw - # outputs of 'y', and then average across the batch. - cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) - train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) - - # Add accuracy and cross entropy to the graph using util function - accuracy, cross_entropy = add_eval(y, y_) - - sess = tf.InteractiveSession() - tf.global_variables_initializer().run() - # Train - for i in range(20001): - start_train = i * 100 % y_train.shape[0] - end_train = start_train + 100 - - start_val = i * 100 % y_val.shape[0] - end_val = start_val + 100 - - batch = (X_train[start_train:end_train], y_train[start_train:end_train]) - batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) - - feed_dict_train = {x: batch[0], y_: batch[1]} - feed_dict_val = {x: batch_val[0], y_: batch_val[1]} - # Writes data into run log csv file - write_data( - accuracy=accuracy, - cross_entropy=cross_entropy, - feed_dict_train=feed_dict_train, - feed_dict_val=feed_dict_val, - step=i + # Import data + print("Starting to generate CIFAR10 images.") + (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() + x_train = np.moveaxis(x_train, 1, 3) / 255.0 # Normalize values + x_train_vec = x_train.reshape(50000, -1) + + y_train = np.squeeze(y_train) + y_test = np.squeeze(y_test) + + x_test = np.moveaxis(x_test, 1, 3) / 255.0 # Normalize values + x_test_vec = x_test.reshape(10000, -1) + + X_train, X_val, y_train, y_val = train_test_split( + x_train_vec, y_train, test_size=0.1, random_state=42 + ) + print("Finished generating CIFAR10 images.") + + # Create the model + x = tf.placeholder(tf.float32, [None, 3 * 32 * 32]) + W = tf.Variable(tf.zeros([3 * 32 * 32, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.int64, [None]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) + + # Add accuracy and cross entropy to the graph using util function + accuracy, cross_entropy = add_eval(y, y_) + + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for i in range(20001): + start_train = i * 100 % y_train.shape[0] + end_train = start_train + 100 + + start_val = i * 100 % y_val.shape[0] + end_val = start_val + 100 + + batch = (X_train[start_train:end_train], y_train[start_train:end_train]) + batch_val = (X_val[start_val:end_val], y_val[start_val:end_val]) + + feed_dict_train = {x: batch[0], y_: batch[1]} + feed_dict_val = {x: batch_val[0], y_: batch_val[1]} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i, + ) + sess.run(train_step, feed_dict={x: batch[0], y_: batch[1]}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), y_) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run(accuracy, feed_dict={x: x_test_vec, y_: y_test})) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data_dir", + type=str, + default="/tmp/tensorflow/mnist/input_data", + help="Directory for storing input data", ) - sess.run(train_step, feed_dict={x: batch[0], y_: batch[1]}) - - # Test trained model - correct_prediction = tf.equal(tf.argmax(y, 1), y_) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print(sess.run( - accuracy, feed_dict={ - x: x_test_vec, - y_: y_test - })) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument( - '--data_dir', - type=str, - default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/mnist_deep_modified.py b/apps/dash-live-model-training/examples/mnist_deep_modified.py index 438a36996..31fe47170 100644 --- a/apps/dash-live-model-training/examples/mnist_deep_modified.py +++ b/apps/dash-live-model-training/examples/mnist_deep_modified.py @@ -38,8 +38,9 @@ FLAGS = None DATA = "MNIST" + def deepnn(x): - """deepnn builds the graph for a deep net for classifying digits. + """deepnn builds the graph for a deep net for classifying digits. Args: x: an input tensor with the dimensions (N_examples, 784), where 784 is the @@ -51,127 +52,138 @@ def deepnn(x): digits 0-9). keep_prob is a scalar placeholder for the probability of dropout. """ - # Reshape to use within a convolutional neural net. - # Last dimension is for "features" - there is only one here, since images are - # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. - x_image = tf.reshape(x, [-1, 28, 28, 1]) + # Reshape to use within a convolutional neural net. + # Last dimension is for "features" - there is only one here, since images are + # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. + x_image = tf.reshape(x, [-1, 28, 28, 1]) - # First convolutional layer - maps one grayscale image to 32 feature maps. - W_conv1 = weight_variable([5, 5, 1, 32]) - b_conv1 = bias_variable([32]) - h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) + # First convolutional layer - maps one grayscale image to 32 feature maps. + W_conv1 = weight_variable([5, 5, 1, 32]) + b_conv1 = bias_variable([32]) + h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) - # Pooling layer - downsamples by 2X. - h_pool1 = max_pool_2x2(h_conv1) + # Pooling layer - downsamples by 2X. + h_pool1 = max_pool_2x2(h_conv1) - # Second convolutional layer -- maps 32 feature maps to 64. - W_conv2 = weight_variable([5, 5, 32, 64]) - b_conv2 = bias_variable([64]) - h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) + # Second convolutional layer -- maps 32 feature maps to 64. + W_conv2 = weight_variable([5, 5, 32, 64]) + b_conv2 = bias_variable([64]) + h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) - # Second pooling layer. - h_pool2 = max_pool_2x2(h_conv2) + # Second pooling layer. + h_pool2 = max_pool_2x2(h_conv2) - # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image - # is down to 7x7x64 feature maps -- maps this to 1024 features. - W_fc1 = weight_variable([7 * 7 * 64, 1024]) - b_fc1 = bias_variable([1024]) + # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image + # is down to 7x7x64 feature maps -- maps this to 1024 features. + W_fc1 = weight_variable([7 * 7 * 64, 1024]) + b_fc1 = bias_variable([1024]) - h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) - h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) + h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64]) + h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) - # Dropout - controls the complexity of the model, prevents co-adaptation of - # features. - keep_prob = tf.placeholder(tf.float32) - h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) + # Dropout - controls the complexity of the model, prevents co-adaptation of + # features. + keep_prob = tf.placeholder(tf.float32) + h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) - # Map the 1024 features to 10 classes, one for each digit - W_fc2 = weight_variable([1024, 10]) - b_fc2 = bias_variable([10]) + # Map the 1024 features to 10 classes, one for each digit + W_fc2 = weight_variable([1024, 10]) + b_fc2 = bias_variable([10]) - y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 - return y_conv, keep_prob + y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 + return y_conv, keep_prob def conv2d(x, W): - """conv2d returns a 2d convolution layer with full stride.""" - return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') + """conv2d returns a 2d convolution layer with full stride.""" + return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME") def max_pool_2x2(x): - """max_pool_2x2 downsamples a feature map by 2X.""" - return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], - strides=[1, 2, 2, 1], padding='SAME') + """max_pool_2x2 downsamples a feature map by 2X.""" + return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME") def weight_variable(shape): - """weight_variable generates a weight variable of a given shape.""" - initial = tf.truncated_normal(shape, stddev=0.1) - return tf.Variable(initial) + """weight_variable generates a weight variable of a given shape.""" + initial = tf.truncated_normal(shape, stddev=0.1) + return tf.Variable(initial) def bias_variable(shape): - """bias_variable generates a bias variable of a given shape.""" - initial = tf.constant(0.1, shape=shape) - return tf.Variable(initial) + """bias_variable generates a bias variable of a given shape.""" + initial = tf.constant(0.1, shape=shape) + return tf.Variable(initial) def main(_): - # Import data - if DATA == "MNIST": - mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) - elif DATA == "FASHION": - mnist = input_data.read_data_sets('data/fashion', - source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/', - one_hot=True) - - # Create the model - x = tf.placeholder(tf.float32, [None, 784]) - - # Define loss and optimizer - y_ = tf.placeholder(tf.float32, [None, 10]) - - # Build the graph for the deep net - y_conv, keep_prob = deepnn(x) - - cross_entropy = tf.reduce_mean( - tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) - train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) - correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - - with tf.Session() as sess: - sess.run(tf.global_variables_initializer()) - for i in range(10001): - batch = mnist.train.next_batch(50) - - ################################## MODIFIED CODE BELOW ################################## - batch_val = mnist.validation.next_batch(50) - feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} - feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} - # Writes data into run log csv file - write_data( - accuracy=accuracy, - cross_entropy=cross_entropy, - feed_dict_train=feed_dict_train, - feed_dict_val=feed_dict_val, - step=i - ) - ################################## MODIFIED CODE ABOVE ################################## - - if i % 100 == 0: - train_accuracy = accuracy.eval(feed_dict={ - x: batch[0], y_: batch[1], keep_prob: 1.0}) - print('step %d, training accuracy %g' % (i, train_accuracy)) - train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) - - print('test accuracy %g' % accuracy.eval(feed_dict={ - x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data_dir', type=str, - default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + # Import data + if DATA == "MNIST": + mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) + elif DATA == "FASHION": + mnist = input_data.read_data_sets( + "data/fashion", + source_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/", + one_hot=True, + ) + + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + + # Define loss and optimizer + y_ = tf.placeholder(tf.float32, [None, 10]) + + # Build the graph for the deep net + y_conv, keep_prob = deepnn(x) + + cross_entropy = tf.reduce_mean( + tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv) + ) + train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) + correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for i in range(10001): + batch = mnist.train.next_batch(50) + + ################################## MODIFIED CODE BELOW ################################## + batch_val = mnist.validation.next_batch(50) + feed_dict_train = {x: batch[0], y_: batch[1], keep_prob: 1.0} + feed_dict_val = {x: batch_val[0], y_: batch_val[1], keep_prob: 1.0} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i, + ) + ################################## MODIFIED CODE ABOVE ################################## + + if i % 100 == 0: + train_accuracy = accuracy.eval( + feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0} + ) + print("step %d, training accuracy %g" % (i, train_accuracy)) + train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) + + print( + "test accuracy %g" + % accuracy.eval( + feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0} + ) + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data_dir", + type=str, + default="/tmp/tensorflow/mnist/input_data", + help="Directory for storing input data", + ) + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/examples/mnist_softmax_modified.py b/apps/dash-live-model-training/examples/mnist_softmax_modified.py index 24d818509..004537664 100644 --- a/apps/dash-live-model-training/examples/mnist_softmax_modified.py +++ b/apps/dash-live-model-training/examples/mnist_softmax_modified.py @@ -32,77 +32,77 @@ FLAGS = None DATA = "MNIST" + def main(_): - # Import data - if DATA == "MNIST": - mnist = input_data.read_data_sets(FLAGS.data_dir) - elif DATA == "FASHION": - mnist = input_data.read_data_sets('data/fashion', - source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/') - # Create the model - x = tf.placeholder(tf.float32, [None, 784]) - W = tf.Variable(tf.zeros([784, 10])) - b = tf.Variable(tf.zeros([10])) - y = tf.matmul(x, W) + b - - # Define loss and optimizer - y_ = tf.placeholder(tf.int64, [None]) - - # The raw formulation of cross-entropy, - # - # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), - # reduction_indices=[1])) - # - # can be numerically unstable. - # - # So here we use tf.losses.sparse_softmax_cross_entropy on the raw - # outputs of 'y', and then average across the batch. - cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) - train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) - - ################################## MODIFIED CODE BELOW ################################## - accuracy, cross_entropy = add_eval(y, y_) - ################################## MODIFIED CODE ABOVE ################################## - - sess = tf.InteractiveSession() - tf.global_variables_initializer().run() - # Train - for i in range(10001): - batch_xs, batch_ys = mnist.train.next_batch(100) + # Import data + if DATA == "MNIST": + mnist = input_data.read_data_sets(FLAGS.data_dir) + elif DATA == "FASHION": + mnist = input_data.read_data_sets( + "data/fashion", + source_url="http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/", + ) + # Create the model + x = tf.placeholder(tf.float32, [None, 784]) + W = tf.Variable(tf.zeros([784, 10])) + b = tf.Variable(tf.zeros([10])) + y = tf.matmul(x, W) + b + + # Define loss and optimizer + y_ = tf.placeholder(tf.int64, [None]) + + # The raw formulation of cross-entropy, + # + # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), + # reduction_indices=[1])) + # + # can be numerically unstable. + # + # So here we use tf.losses.sparse_softmax_cross_entropy on the raw + # outputs of 'y', and then average across the batch. + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) + train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) ################################## MODIFIED CODE BELOW ################################## - batch = mnist.train.next_batch(100) - batch_val = mnist.validation.next_batch(100) - feed_dict_train = {x: batch[0], y_: batch[1]} - feed_dict_val = {x: batch_val[0], y_: batch_val[1]} - # Writes data into run log csv file - write_data( - accuracy=accuracy, - cross_entropy=cross_entropy, - feed_dict_train=feed_dict_train, - feed_dict_val=feed_dict_val, - step=i - ) + accuracy, cross_entropy = add_eval(y, y_) ################################## MODIFIED CODE ABOVE ################################## - sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) - - # Test trained model - correct_prediction = tf.equal(tf.argmax(y, 1), y_) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print(sess.run( - accuracy, feed_dict={ - x: mnist.test.images, - y_: mnist.test.labels - })) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument( - '--data_dir', - type=str, - default='/tmp/tensorflow/mnist/input_data', - help='Directory for storing input data') - FLAGS, unparsed = parser.parse_known_args() - tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) + sess = tf.InteractiveSession() + tf.global_variables_initializer().run() + # Train + for i in range(10001): + batch_xs, batch_ys = mnist.train.next_batch(100) + + ################################## MODIFIED CODE BELOW ################################## + batch = mnist.train.next_batch(100) + batch_val = mnist.validation.next_batch(100) + feed_dict_train = {x: batch[0], y_: batch[1]} + feed_dict_val = {x: batch_val[0], y_: batch_val[1]} + # Writes data into run log csv file + write_data( + accuracy=accuracy, + cross_entropy=cross_entropy, + feed_dict_train=feed_dict_train, + feed_dict_val=feed_dict_val, + step=i, + ) + ################################## MODIFIED CODE ABOVE ################################## + + sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) + + # Test trained model + correct_prediction = tf.equal(tf.argmax(y, 1), y_) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data_dir", + type=str, + default="/tmp/tensorflow/mnist/input_data", + help="Directory for storing input data", + ) + FLAGS, unparsed = parser.parse_known_args() + tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/apps/dash-live-model-training/requirements.txt b/apps/dash-live-model-training/requirements.txt index 1012b180d..f90db79ea 100644 --- a/apps/dash-live-model-training/requirements.txt +++ b/apps/dash-live-model-training/requirements.txt @@ -1,11 +1,5 @@ -dash==0.26.4 -dash-auth==1.1.2 -dash-core-components==0.27.1 -dash-html-components==0.11.0 -dash-renderer==0.13.2 +dash==0.42.0 gunicorn==19.9.0 -plotly==2.7.0 - -scipy==1.0.1 -numpy==1.14.3 -pandas==0.20.3 +scipy==1.2.1 +numpy==1.16.3 +pandas==0.24.2 diff --git a/apps/dash-live-model-training/tfutils.py b/apps/dash-live-model-training/tfutils.py index 4d8ff7d39..b7e51fc9b 100644 --- a/apps/dash-live-model-training/tfutils.py +++ b/apps/dash-live-model-training/tfutils.py @@ -3,8 +3,7 @@ import os -def add_eval(y, - y_): +def add_eval(y, y_): """ Add evaluation metrics. :param y: The predicted y, aka logits @@ -17,19 +16,20 @@ def add_eval(y, accuracy = tf.reduce_mean(correct_prediction) # Compute Cross Entropy - cross_entropy = tf.losses.sparse_softmax_cross_entropy( - labels=y_, logits=y) + cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y) return accuracy, cross_entropy -def write_data(accuracy, - cross_entropy, - feed_dict_train, - feed_dict_val, - step, - step_range=5, - filename='run_log.csv'): +def write_data( + accuracy, + cross_entropy, + feed_dict_train, + feed_dict_val, + step, + step_range=5, + filename="run_log.csv", +): """ Writes accuracy and cross entropy value into the log file. :param accuracy: @@ -42,7 +42,7 @@ def write_data(accuracy, :return: """ if step_range not in range(1, 1001): - raise ValueError('Invalid step range. Please choose a value between 1 and 1000') + raise ValueError("Invalid step range. Please choose a value between 1 and 1000") # At the start, we delete the log residual log file from previous training if step == 0: @@ -58,9 +58,17 @@ def write_data(accuracy, val_cross_entropy = cross_entropy.eval(feed_dict=feed_dict_val) # Write CSV - with open(filename, 'a', newline='') as file: - writer = csv.writer(file, delimiter=',') - writer.writerow([step, train_accuracy, val_accuracy, train_cross_entropy, val_cross_entropy]) + with open(filename, "a", newline="") as file: + writer = csv.writer(file, delimiter=",") + writer.writerow( + [ + step, + train_accuracy, + val_accuracy, + train_cross_entropy, + val_cross_entropy, + ] + ) return train_accuracy, val_accuracy, train_cross_entropy, val_cross_entropy From ec907edb856b508b40dfc561d4844d65f7e671a1 Mon Sep 17 00:00:00 2001 From: celinehuang Date: Wed, 22 May 2019 16:55:55 -0400 Subject: [PATCH 03/24] changed line chart colors and added containers to separate graphs --- apps/dash-live-model-training/app.py | 268 +++++++++++------- apps/dash-live-model-training/assets/base.css | 4 +- .../assets/custom_styles.css | 34 ++- 3 files changed, 199 insertions(+), 107 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 85d70201f..433f45932 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -23,111 +23,139 @@ def div_graph(name): """Generates an html Div containing graph and control options for smoothing and display, given the name""" - return html.Div(className="row", children=[ - html.Div(className="two columns", children=[ - html.Div([ - html.P( - "Smoothing:", - style={"font-weight": "bold", - "margin-bottom": "0px"}, - ), - dcc.Checklist( - options=[ - {"label": " Training", "value": "train"}, - {"label": " Validation", "value": "val"}, - ], - values=[], - id=f"checklist-smoothing-options-{name}", - ), - ], - style={"margin-top": "10px"}, + return html.Div( + className="row", + children=[ + html.Div( + className="two columns", + children=[ + html.Div( + [ + html.P( + "Smoothing:", + style={"font-weight": "bold", "margin-bottom": "0px"}, + ), + dcc.Checklist( + options=[ + {"label": " Training", "value": "train"}, + {"label": " Validation", "value": "val"}, + ], + values=[], + id=f"checklist-smoothing-options-{name}", + ), + ], + style={"margin-top": "10px"}, + ), + html.Div( + [ + dcc.Slider( + min=0, + max=1, + step=0.05, + marks={i / 5: str(i / 5) for i in range(0, 6)}, + value=0.6, + updatemode="drag", + id=f"slider-smoothing-{name}", + ) + ], + style={"margin-bottom": "40px"}, + ), + html.Div( + [ + html.P( + "Plot Display mode:", + style={"font-weight": "bold", "margin-bottom": "0px"}, + ), + dcc.RadioItems( + options=[ + {"label": " Overlapping", "value": "overlap"}, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, + ], + value="overlap", + id=f"radio-display-mode-{name}", + ), + html.Div(id=f"div-current-{name}-value"), + ] + ), + ], ), - html.Div([ - dcc.Slider( - min=0, - max=1, - step=0.05, - marks={i / 5: str(i / 5) for i in range(0, 6)}, - value=0.6, - updatemode="drag", - id=f"slider-smoothing-{name}", - ) - ], style={"margin-bottom": "40px"} ), - html.Div([ - html.P( - "Plot Display mode:", - style={"font-weight": "bold", - "margin-bottom": "0px"}, - ), - dcc.RadioItems( - options=[ - {"label": " Overlapping", "value": "overlap"}, - { - "label": " Separate (Vertical)", - "value": "separate_vertical", - }, - { - "label": " Separate (Horizontal)", - "value": "separate_horizontal", - }, - ], - value="overlap", - id=f"radio-display-mode-{name}", - ), - html.Div(id=f"div-current-{name}-value"), - ]), - ]), - html.Div(id=f"div-{name}-graph", className="ten columns"), - ]) - - -app.layout = html.Div([ - # Banner display - html.Div([ - html.H2("Live Model Training Viewer", id="title"), - html.Button("Learn More", id="learn-more-button"), - html.Img(src=app.get_asset_url("dash-by-plotly-logo.png"))], - className="banner", - ), - # Body - html.Div([ - # Extract the demo components if we are in demo mode - *demo_components(demo_mode), + html.Div(id=f"div-{name}-graph", className="ten columns"), + ], + ) + +app.layout = html.Div( + [ + # Banner display html.Div( - className="row", id="div-interval-control", + [ + html.H2("Live Model Training Viewer", id="title"), + html.Button("Learn More", id="learn-more-button"), + html.Img(src=app.get_asset_url("dash-by-plotly-logo.png")), + ], + className="banner", + ), + # Body + html.Div( + className="container", children=[ - html.Div(id="div-step-display", className="two columns", style={"float":"right"}), - dcc.Dropdown( - id="dropdown-interval-control", - options=[ - {"label": "No Updates", "value": "no"}, - {"label": "Slow Updates", "value": "slow"}, - {"label": "Regular Updates", "value": "regular"}, - {"label": "Fast Updates", "value": "fast"}, + # Extract the demo components if we are in demo mode + *demo_components(demo_mode), + html.Div( + className="row", + id="div-interval-control", + children=[ + html.Div( + id="div-step-display", + className="two columns", + style={"float": "right"}, + ), + dcc.Dropdown( + id="dropdown-interval-control", + options=[ + {"label": "No Updates", "value": "no"}, + {"label": "Slow Updates", "value": "slow"}, + {"label": "Regular Updates", "value": "regular"}, + {"label": "Fast Updates", "value": "fast"}, + ], + value="regular", + className="ten columns", + clearable=False, + searchable=False, + ), ], - value="regular", - className="ten columns", - clearable=False, - searchable=False, - )] + ), + dcc.Interval(id="interval-log-update", n_intervals=0), + # Hidden Div Storing JSON-serialized dataframe of run log + html.Div(id="run-log-storage", style={"display": "none"}), + # The html divs storing the graphs and display parameters + div_graph("accuracy"), + ], ), - - dcc.Interval(id="interval-log-update", n_intervals=0), - # Hidden Div Storing JSON-serialized dataframe of run log - html.Div(id="run-log-storage", style={"display": "none"}), - # The html divs storing the graphs and display parameters - div_graph("accuracy", style={"color":"#6b6b6b"}), - div_graph("cross-entropy", style={"color":"#6b6b6b"}), - # Explanation for the demo version of the app - demo_explanation(demo_mode), - ], - className="container", - )] + html.Div( + className="container", + children=[ + div_graph("cross-entropy"), + # Explanation for the demo version of the app + ], + ), + html.Div( + id="demo-explanation", + children=[ + # demo_explanation(demo_mode), + ], + ), + ] ) - def update_graph( graph_id, graph_title, @@ -181,10 +209,20 @@ def smooth(scalars, weight=0.6): y_val = smooth(y_val, weight=slider_smoothing) trace_train = go.Scatter( - x=step, y=y_train, mode="lines", name="Training") + x=step, + y=y_train, + mode="lines", + name="Training", + line=dict(color="rgb(54, 218, 170)"), + ) trace_val = go.Scatter( - x=step, y=y_val, mode="lines", name="Validation") + x=step, + y=y_val, + mode="lines", + name="Validation", + line=dict(color="rgb(246, 236, 145)"), + ) if display_mode == "separate_vertical": figure = tools.make_subplots( @@ -224,6 +262,31 @@ def smooth(scalars, weight=0.6): demo_callbacks(app, demo_mode) +@app.callback( + Output("demo-explanation", "children"), [Input("learn-more-button", "n_clicks")] +) +def learn_more(n_clicks): + if n_clicks == None: + n_clicks = 0 + return + else: + if (n_clicks % 2) == 1: + n_clicks += 1 + return ( + html.Div(className="container", children=[demo_explanation(demo_mode)]), + ) + + else: + n_clicks += 1 + return ( + html.Div( + children=[ + # demo_explanation(demo_mode), + ] + ), + ) + + @app.callback( Output("interval-log-update", "interval"), [Input("dropdown-interval-control", "value")], @@ -272,8 +335,7 @@ def get_run_log(_): @app.callback( - Output("div-step-display", - "children"), [Input("run-log-storage", "children")] + Output("div-step-display", "children"), [Input("run-log-storage", "children")] ) def update_div_step_display(run_log_json): if run_log_json: @@ -383,10 +445,8 @@ def update_div_current_cross_entropy_value(run_log_json): "margin-bottom": "0px", }, ), - html.Div( - f"Training: {run_log_df['train cross entropy'].iloc[-1]:.4f}"), - html.Div( - f"Validation: {run_log_df['val cross entropy'].iloc[-1]:.4f}"), + html.Div(f"Training: {run_log_df['train cross entropy'].iloc[-1]:.4f}"), + html.Div(f"Validation: {run_log_df['val cross entropy'].iloc[-1]:.4f}"), ] diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index a8b5ec3e2..56cd12640 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -62,7 +62,7 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash background-color: white; border-radius: 2px 2px 5px 5px; font-size: 1.5rem; - border: thin solid rgb(240, 240, 240); + /* border: thin solid rgb(240, 240, 240); */ margin-left: auto; margin-right: auto; color: #302f54; @@ -265,7 +265,7 @@ h5 { margin-top: 0.6rem; } h6 { - font-size: 2rem; + font-size: 1.75rem; line-height: 1.6; letter-spacing: 0; margin-bottom: 0.75rem; diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 8268b7baf..79a711b10 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -8,10 +8,42 @@ #learn-more-button { color: #e8e8e8; - margin-left: 40%; + margin-left: 50%; font-family: "Open Sans Semi Bold", sans-serif; font-weight: 200; text-transform: uppercase; letter-spacing: 1.16px; font-size: 12px; } + +.rc-slider-track { + position: absolute; + left: 0; + height: 4px; + border-radius: 6px; + background-color: #f6ec91; +} + +.rc-slider-dot-active { + border-color: #f6ec91; +} + +.rc-slider-handle { + border-color: #f6ec91; + /* border: solid 2px #f6ec91; */ +} + +#_dash-app-content > div { + background-color: #f8f9fa; +} + +/*.rc-slider-handle:hover { + border-color: white; + border: solid 2px #f6ec91; +} */ + +.char-btn input[type="radio"] { + background-color: #f6866e; + opacity: 0.7; + z-index: 100; +} From 1014f47a6234c0b2f2bbc7ce0838f6e0fbec7732 Mon Sep 17 00:00:00 2001 From: celinehuang Date: Mon, 27 May 2019 18:04:54 -0400 Subject: [PATCH 04/24] deleted unnescessary files and old links in the markdown files --- apps/dash-live-model-training/CONTRIBUTING.md | 92 ------------------ apps/dash-live-model-training/LICENSE.md | 21 ---- apps/dash-live-model-training/README.md | 4 - apps/dash-live-model-training/app.py | 56 ++++++----- apps/dash-live-model-training/assets/base.css | 29 +++--- .../assets/custom_styles.css | 19 +++- .../assets/dash-by-plotly-logo.png | Bin 5734 -> 0 bytes .../assets/dash-logo-stripe.png | Bin 3839 -> 0 bytes .../assets/dash-logo.png | Bin 0 -> 2711 bytes apps/dash-live-model-training/demo.md | 2 +- apps/dash-live-model-training/demo_utils.py | 6 +- 11 files changed, 70 insertions(+), 159 deletions(-) delete mode 100644 apps/dash-live-model-training/CONTRIBUTING.md delete mode 100644 apps/dash-live-model-training/LICENSE.md delete mode 100644 apps/dash-live-model-training/assets/dash-by-plotly-logo.png delete mode 100644 apps/dash-live-model-training/assets/dash-logo-stripe.png create mode 100644 apps/dash-live-model-training/assets/dash-logo.png diff --git a/apps/dash-live-model-training/CONTRIBUTING.md b/apps/dash-live-model-training/CONTRIBUTING.md deleted file mode 100644 index f9cb6a8b0..000000000 --- a/apps/dash-live-model-training/CONTRIBUTING.md +++ /dev/null @@ -1,92 +0,0 @@ -# Contributing - -When contributing to this repository, please first discuss the change you wish to make via issue, -email, or any other method with the owners of this repository before making a change. - -Please note we have a code of conduct, please follow it in all your interactions with the project. - -## Pull Request Process - -1. Ensure any install or build dependencies are removed before the end of the layer when doing a - build. -2. Update the README.md with details of changes to the interface, this includes new environment - variables, exposed ports, useful file locations and container parameters. -3. Increase the version numbers in any examples files and the README.md to the new version that this - Pull Request would represent. The versioning scheme we use is [SemVer](http://semver.org/). -4. You may merge the Pull Request in once you have the sign-off of two other developers, or if you - do not have permission to do that, you may request the second reviewer to merge it for you. - -## Code of Conduct - -### Our Pledge - -In the interest of fostering an open and welcoming environment, we as -contributors and maintainers pledge to making participation in our project and -our community a harassment-free experience for everyone, regardless of age, body -size, disability, ethnicity, gender identity and expression, level of experience, -nationality, personal appearance, race, religion, or sexual identity and -orientation. - -### Our Standards - -Examples of behavior that contributes to creating a positive environment -include: - -* Using welcoming and inclusive language -* Being respectful of differing viewpoints and experiences -* Gracefully accepting constructive criticism -* Focusing on what is best for the community -* Showing empathy towards other community members - -Examples of unacceptable behavior by participants include: - -* The use of sexualized language or imagery and unwelcome sexual attention or -advances -* Trolling, insulting/derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or electronic - address, without explicit permission -* Other conduct which could reasonably be considered inappropriate in a - professional setting - -### Our Responsibilities - -Project maintainers are responsible for clarifying the standards of acceptable -behavior and are expected to take appropriate and fair corrective action in -response to any instances of unacceptable behavior. - -Project maintainers have the right and responsibility to remove, edit, or -reject comments, commits, code, wiki edits, issues, and other contributions -that are not aligned to this Code of Conduct, or to ban temporarily or -permanently any contributor for other behaviors that they deem inappropriate, -threatening, offensive, or harmful. - -### Scope - -This Code of Conduct applies both within project spaces and in public spaces -when an individual is representing the project or its community. Examples of -representing a project or community include using an official project e-mail -address, posting via an official social media account, or acting as an appointed -representative at an online or offline event. Representation of a project may be -further defined and clarified by project maintainers. - -### Enforcement - -Instances of abusive, harassing, or otherwise unacceptable behavior may be -reported by contacting the project team at [INSERT EMAIL ADDRESS]. All -complaints will be reviewed and investigated and will result in a response that -is deemed necessary and appropriate to the circumstances. The project team is -obligated to maintain confidentiality with regard to the reporter of an incident. -Further details of specific enforcement policies may be posted separately. - -Project maintainers who do not follow or enforce the Code of Conduct in good -faith may face temporary or permanent repercussions as determined by other -members of the project's leadership. - -### Attribution - -This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at [http://contributor-covenant.org/version/1/4][version] - -[homepage]: http://contributor-covenant.org -[version]: http://contributor-covenant.org/version/1/4/ \ No newline at end of file diff --git a/apps/dash-live-model-training/LICENSE.md b/apps/dash-live-model-training/LICENSE.md deleted file mode 100644 index c91737e78..000000000 --- a/apps/dash-live-model-training/LICENSE.md +++ /dev/null @@ -1,21 +0,0 @@ -The MIT License (MIT) - -Copyright (c) 2018 plotly - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md index 61aeb4a7b..f1c5b8876 100644 --- a/apps/dash-live-model-training/README.md +++ b/apps/dash-live-model-training/README.md @@ -80,10 +80,6 @@ At the moment, the logging only works for iterative Tensorflow models. We are pl - [Dash](https://dash.plot.ly/) - Main server and interactive components - [Plotly Python](https://plot.ly/python/) - Used to create the interactive plots -## Contributing - -Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us. - ## Authors - **Celine Huang** [@celinehuang](https://github.com/celinehuang) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 433f45932..929fa780b 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -16,6 +16,7 @@ LOGFILE = "examples/run_log.csv" app = dash.Dash(__name__) +# app = dash.Dash(__name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}]) server = app.server demo_mode = True @@ -96,15 +97,30 @@ def div_graph(name): # Banner display html.Div( [ - html.H2("Live Model Training Viewer", id="title"), - html.Button("Learn More", id="learn-more-button"), - html.Img(src=app.get_asset_url("dash-by-plotly-logo.png")), + html.H2( + "Live Model Training Viewer", + id="title", + className="eight columns", + style={"margin-left": "3%"}, + ), + # html.Div( + # id="button", + # style={"padding-bottom": "3px"}, + # children=[html.Button("Learn More", id="learn-more-button")], + # ), + html.Button( + "Learn More", id="learn-more-button", className="two columns" + ), + html.Img( + src=app.get_asset_url("dash-logo.png"), className="two columns" + ), ], className="banner", ), # Body html.Div( className="container", + style={"padding": "35px 25px"}, children=[ # Extract the demo components if we are in demo mode *demo_components(demo_mode), @@ -112,11 +128,6 @@ def div_graph(name): className="row", id="div-interval-control", children=[ - html.Div( - id="div-step-display", - className="two columns", - style={"float": "right"}, - ), dcc.Dropdown( id="dropdown-interval-control", options=[ @@ -126,29 +137,26 @@ def div_graph(name): {"label": "Fast Updates", "value": "fast"}, ], value="regular", - className="ten columns", + className="eight columns", clearable=False, searchable=False, ), + html.Div(id="div-step-display", className="four columns"), ], ), dcc.Interval(id="interval-log-update", n_intervals=0), # Hidden Div Storing JSON-serialized dataframe of run log html.Div(id="run-log-storage", style={"display": "none"}), # The html divs storing the graphs and display parameters - div_graph("accuracy"), - ], - ), - html.Div( - className="container", - children=[ - div_graph("cross-entropy"), - # Explanation for the demo version of the app + # div_graph("accuracy"), ], ), + html.Div(className="container", children=[div_graph("accuracy")]), + html.Div(className="container", children=[div_graph("cross-entropy")]), html.Div( id="demo-explanation", children=[ + # Explanation for the demo version of the app # demo_explanation(demo_mode), ], ), @@ -208,12 +216,14 @@ def smooth(scalars, weight=0.6): if "val" in checklist_smoothing_options: y_val = smooth(y_val, weight=slider_smoothing) + # line charts trace_train = go.Scatter( x=step, y=y_train, mode="lines", name="Training", line=dict(color="rgb(54, 218, 170)"), + showlegend=False, ) trace_val = go.Scatter( @@ -222,6 +232,7 @@ def smooth(scalars, weight=0.6): mode="lines", name="Validation", line=dict(color="rgb(246, 236, 145)"), + showlegend=False, ) if display_mode == "separate_vertical": @@ -278,13 +289,7 @@ def learn_more(n_clicks): else: n_clicks += 1 - return ( - html.Div( - children=[ - # demo_explanation(demo_mode), - ] - ), - ) + return (html.Div(),) @app.callback( @@ -341,7 +346,8 @@ def update_div_step_display(run_log_json): if run_log_json: run_log_df = pd.read_json(run_log_json, orient="split") return html.H6( - f"Step: {run_log_df['step'].iloc[-1]}", style={"margin-top": "3px"} + f"Step: {run_log_df['step'].iloc[-1]}", + style={"margin-top": "3px", "float": "right"}, ) diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index 56cd12640..98fcddb1c 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -28,12 +28,14 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash height: 75px; background-color: #191e27; /* border-radius: 5px 5px 2px 2px; */ - padding-top: 0px; + /* padding-top: 0px; padding-left: 0px; - padding-right: 0px; + padding-right: 0px; */ + padding: 0px; /* width: 90%; */ - margin-left: auto; - margin-right: auto; + /* margin-left: auto; + margin-right: auto; */ + margin: auto; } .banner h2 { @@ -49,10 +51,12 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash } .banner img { - position: relative; - float: right; - right: 1.5%; - height: 75px; + /* margin: 20px 0; */ + /* width: auto !important; */ + + padding: 0px 15px; + margin-top: 20px; + margin-bottom: 5px; } /* Grid @@ -67,10 +71,11 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash margin-right: auto; color: #302f54; margin-top: 30px; - padding: 8px; + padding: 45px; width: 90%; max-width: none; box-sizing: border-box; + height: auto; } .column, @@ -214,7 +219,7 @@ body { font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */ line-height: 1.6; font-weight: 400; - font-family: "Roboto", sans-serif; + font-family: "Raleway Semi Bold", sans-serif; color: #222; } @@ -354,8 +359,8 @@ input[type="submit"].button-primary:focus, input[type="reset"].button-primary:focus, input[type="button"].button-primary:focus { color: #fff; - background-color: #1eaedb; - border-color: #1eaedb; + background-color: #db1e1e; + border-color: #db1e1e; } /* Forms diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 79a711b10..c3873dc6a 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -1,3 +1,5 @@ +/* custom styling for the new refreshed app */ + .markdown img { width: 100%; } @@ -8,12 +10,18 @@ #learn-more-button { color: #e8e8e8; - margin-left: 50%; font-family: "Open Sans Semi Bold", sans-serif; font-weight: 200; text-transform: uppercase; letter-spacing: 1.16px; font-size: 12px; + padding: 0px 15px; + margin-top: 20px; + width: auto; +} + +.dropdown-box { + padding: 15px 15px; } .rc-slider-track { @@ -37,6 +45,15 @@ background-color: #f8f9fa; } +/* #_dash-app-content > div > div:nth-child(2) > div:nth-child(2) { + width: 80.5%; +} */ + +/* +#div-interval-control { + padding: 10px 10px; +} */ + /*.rc-slider-handle:hover { border-color: white; border: solid 2px #f6ec91; diff --git a/apps/dash-live-model-training/assets/dash-by-plotly-logo.png b/apps/dash-live-model-training/assets/dash-by-plotly-logo.png deleted file mode 100644 index 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z?yn8F8-V?A8MuHOxYzC0pX07SU5dMA^bl~B{9eb4;;hjYY4Vu#&sW7cb3-PC)rt!I zGb<6JA>(f8;6pwnJXrw|b3S={WN^PKe|vn$KNGUdA4r+!i0%WvNgs070y%?CtmCXm z!EfO1!Z5Yjcg3zdqe!*;a2H{plF9Nfl6H@h?`cWDB9586g@o5}y{->x@0lULAmD6J z-q|=}Fvm!x13DueA7(4m!4iW#NSPTb- zMAE}-2rgD;#)sr;8~6>pl%z#zFI!k=jd5noC@NmBGE)Ve?L9|JDui=HosCmvs8^-R zOp5yDSDzvBUfZm}7Ch`@uc}1OS?bi7h_fMgOX$yE(DpB$(!N_d`@h2LOZC~Ek*jcU zWow+TQZ;U@h)vm3LW{^)+C-0%s_DLehqzaMn;B( zUz(dB8t Date: Tue, 28 May 2019 17:24:14 -0400 Subject: [PATCH 05/24] added multi-output call back for learn more button --- apps/dash-live-model-training/app.py | 41 +++++++++---------- apps/dash-live-model-training/assets/base.css | 6 +-- .../assets/custom_styles.css | 10 ++--- 3 files changed, 27 insertions(+), 30 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 929fa780b..f2f699ee9 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -15,8 +15,10 @@ LOGFILE = "examples/run_log.csv" -app = dash.Dash(__name__) -# app = dash.Dash(__name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}]) +# app = dash.Dash(__name__) +app = dash.Dash( + __name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}] +) server = app.server demo_mode = True @@ -103,19 +105,16 @@ def div_graph(name): className="eight columns", style={"margin-left": "3%"}, ), - # html.Div( - # id="button", - # style={"padding-bottom": "3px"}, - # children=[html.Button("Learn More", id="learn-more-button")], - # ), html.Button( - "Learn More", id="learn-more-button", className="two columns" + id="learn-more-button", + className="two columns", + children=["Learn More"], ), html.Img( src=app.get_asset_url("dash-logo.png"), className="two columns" ), ], - className="banner", + className="banner row", ), # Body html.Div( @@ -274,22 +273,23 @@ def smooth(scalars, weight=0.6): @app.callback( - Output("demo-explanation", "children"), [Input("learn-more-button", "n_clicks")] + [Output("demo-explanation", "children"), Output("learn-more-button", "children")], + [Input("learn-more-button", "n_clicks")], ) def learn_more(n_clicks): if n_clicks == None: n_clicks = 0 - return - else: - if (n_clicks % 2) == 1: - n_clicks += 1 - return ( - html.Div(className="container", children=[demo_explanation(demo_mode)]), - ) - else: - n_clicks += 1 - return (html.Div(),) + if (n_clicks % 2) == 1: + n_clicks += 1 + return ( + html.Div(className="container", children=[demo_explanation(demo_mode)]), + "Close", + ) + + else: + n_clicks += 1 + return (html.Div(), "Learn More") @app.callback( @@ -459,4 +459,3 @@ def update_div_current_cross_entropy_value(run_log_json): # Running the server if __name__ == "__main__": app.run_server(debug=True) - diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index 98fcddb1c..bc25bf0cf 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -56,7 +56,7 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash padding: 0px 15px; margin-top: 20px; - margin-bottom: 5px; + margin-bottom: 15px; } /* Grid @@ -69,7 +69,7 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash /* border: thin solid rgb(240, 240, 240); */ margin-left: auto; margin-right: auto; - color: #302f54; + color: #6b6b6b; margin-top: 30px; padding: 45px; width: 90%; @@ -86,7 +86,7 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash } /* For devices larger than 550px */ -@media (min-width: 550px) { +@media (min-width: 768px) { .column, .columns { margin-left: 0.5%; diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index c3873dc6a..b2c39a44f 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -45,6 +45,10 @@ background-color: #f8f9fa; } +#react-select-4--value-item { + color: #6b6b6b; +} + /* #_dash-app-content > div > div:nth-child(2) > div:nth-child(2) { width: 80.5%; } */ @@ -58,9 +62,3 @@ border-color: white; border: solid 2px #f6ec91; } */ - -.char-btn input[type="radio"] { - background-color: #f6866e; - opacity: 0.7; - z-index: 100; -} From 7caa98adf7052c4869f693246b20411421939043 Mon Sep 17 00:00:00 2001 From: celinehuang Date: Mon, 3 Jun 2019 14:04:32 -0400 Subject: [PATCH 06/24] removed unnecessary comments --- .DS_Store | Bin 0 -> 8196 bytes apps/.DS_Store | Bin 0 -> 8196 bytes apps/dash-live-model-training/app.py | 90 ++++++++++----- apps/dash-live-model-training/assets/base.css | 107 ++++++++++++++++-- .../assets/custom_styles.css | 77 +++++++++---- apps/dash-live-model-training/demo_utils.py | 25 ++-- 6 files changed, 230 insertions(+), 69 deletions(-) create mode 100644 .DS_Store create mode 100644 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html.Div( className="two columns", + style={"padding-bottom": "5%"}, children=[ html.Div( [ html.P( - "Smoothing:", + className="graph-checkbox-smoothing", + children=["Smoothing:"], style={"font-weight": "bold", "margin-bottom": "0px"}, ), dcc.Checklist( @@ -45,6 +47,7 @@ def div_graph(name): ], values=[], id=f"checklist-smoothing-options-{name}", + className="checklist-smoothing", ), ], style={"margin-top": "10px"}, @@ -62,28 +65,52 @@ def div_graph(name): ) ], style={"margin-bottom": "40px"}, + className="slider-smoothing", ), html.Div( [ html.P( - "Plot Display mode:", - style={"font-weight": "bold", "margin-bottom": "0px"}, - ), - dcc.RadioItems( - options=[ - {"label": " Overlapping", "value": "overlap"}, - { - "label": " Separate (Vertical)", - "value": "separate_vertical", - }, - { - "label": " Separate (Horizontal)", - "value": "separate_horizontal", - }, + className="plot-display-text", + children=[ + "Plot Display mode:", + dcc.RadioItems( + options=[ + { + "label": " Overlapping", + "value": "overlap", + }, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, + ], + value="overlap", + id=f"radio-display-mode-{name}", + className="plot-display-radio-items", + ), ], - value="overlap", - id=f"radio-display-mode-{name}", + # style={"font-weight": "bold", "margin-bottom": "0px"}, ), + # dcc.RadioItems( + # options=[ + # {"label": " Overlapping", "value": "overlap"}, + # { + # "label": " Separate (Vertical)", + # "value": "separate_vertical", + # }, + # { + # "label": " Separate (Horizontal)", + # "value": "separate_horizontal", + # }, + # ], + # value="overlap", + # id=f"radio-display-mode-{name}", + # className="plot-display-radio-items", + # ), html.Div(id=f"div-current-{name}-value"), ] ), @@ -95,7 +122,8 @@ def div_graph(name): app.layout = html.Div( - [ + style={"height": "100%"}, + children=[ # Banner display html.Div( [ @@ -111,11 +139,17 @@ def div_graph(name): children=["Learn More"], ), html.Img( - src=app.get_asset_url("dash-logo.png"), className="two columns" + src=app.get_asset_url("dash-logo.png"), + className="two columns", + id="plotly-logo", ), ], className="banner row", ), + html.Div( + # empty child function for the callback + html.Div(id="demo-explanation", children=[]) + ), # Body html.Div( className="container", @@ -147,19 +181,15 @@ def div_graph(name): # Hidden Div Storing JSON-serialized dataframe of run log html.Div(id="run-log-storage", style={"display": "none"}), # The html divs storing the graphs and display parameters - # div_graph("accuracy"), ], ), html.Div(className="container", children=[div_graph("accuracy")]), - html.Div(className="container", children=[div_graph("cross-entropy")]), html.Div( - id="demo-explanation", - children=[ - # Explanation for the demo version of the app - # demo_explanation(demo_mode), - ], + className="container", + style={"margin-bottom": "30px"}, + children=[div_graph("cross-entropy")], ), - ] + ], ) @@ -283,7 +313,11 @@ def learn_more(n_clicks): if (n_clicks % 2) == 1: n_clicks += 1 return ( - html.Div(className="container", children=[demo_explanation(demo_mode)]), + html.Div( + className="container", + style={"margin-bottom": "30px"}, + children=[demo_explanation(demo_mode)], + ), "Close", ) diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index bc25bf0cf..f153daa95 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -85,8 +85,8 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash box-sizing: border-box; } -/* For devices larger than 550px */ -@media (min-width: 768px) { +/* For devices larger than 1000px i.e. Larger than desktop */ +@media (min-width: 1000px) { .column, .columns { margin-left: 0.5%; @@ -547,21 +547,106 @@ there. */ /* Larger than mobile */ -@media (min-width: 400px) { -} - -/* Larger than phablet (also point when grid becomes active) */ -@media (min-width: 550px) { +@media (max-width: 375px) { + #title { + width: 30%; + font-size: 20px; + padding-top: 10px; + } + #learn-more-button { + margin-left: 0px; + font-size: 10px !important; + padding: 0px 10px !important; + } + #plotly-logo { + padding: 10px 10px !important; + } } /* Larger than tablet */ -@media (min-width: 750px) { +@media (max-width: 575px) { + #title { + width: 40% !important; + font-size: 15px; + padding-top: 10px; + } + #learn-more-button { + margin-left: 0px; + font-size: 10px !important; + padding: 0px 10px !important; + } + #plotly-logo { + padding: 10px 10px !important; + } } -/* Larger than desktop */ -@media (min-width: 1000px) { +/* Larger than phablet (also point when grid becomes active) */ +@media (max-width: 710px) { + #title { + width: 50% !important; + font-size: 25px; + } + + #plotly-logo { + padding: 5px 10px; + width: 10%; + } + #learn-more-button { + margin-left: 30px; + } } /* Larger than Desktop HD */ -@media (min-width: 1200px) { +@media (max-width: 1000px) { + #title { + width: 60%; + } + + #plotly-logo { + padding: 5px 10px; + width: 20%; + } + + #radio-display-mode-accuracy { + float: right; + } + + #checklist-smoothing-options-accuracy { + float: right; + } + + #slider-smoothing-accuracy > div { + float: right; + margin-bottom: 5%; + } + + #slider-smoothing-cross-entropy > div { + float: right; + margin-bottom: 5%; + } + .slider-smoothing { + width: 50%; + } + + .graph-checkbox-smoothing { + float: left; + } + + .checklist-smoothing { + float: right; + } + .plot-display-radio-items { + float: right; + } + .plot-display-text { + float: left; + } + + /* #dropdown-demo-dataset { + width: 30%; + } + + #dropdown-simulation-model { + width: 30%; + } */ } diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index b2c39a44f..b3824dde8 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -1,5 +1,22 @@ /* custom styling for the new refreshed app */ +/* .container { + box-shadow: 0 1px 3px rgba(0, 0, 0, 0.12), 0 1px 2px rgba(0, 0, 0, 0.24); + border-radius: 0.75rem; +} */ + +#div-total-step-count > h6 { + font-weight: bold; + font-size: 20px; + color: #6b6b6b; +} + +#div-step-display > h6 { + font-weight: bold; + font-size: 20px; + color: #6b6b6b; +} + .markdown img { width: 100%; } @@ -8,6 +25,10 @@ display: none; } +.two.columns { + /* padding: 0px 5px; */ +} + #learn-more-button { color: #e8e8e8; font-family: "Open Sans Semi Bold", sans-serif; @@ -24,11 +45,9 @@ padding: 15px 15px; } +/* slider colors */ + .rc-slider-track { - position: absolute; - left: 0; - height: 4px; - border-radius: 6px; background-color: #f6ec91; } @@ -38,27 +57,45 @@ .rc-slider-handle { border-color: #f6ec91; - /* border: solid 2px #f6ec91; */ } -#_dash-app-content > div { - background-color: #f8f9fa; +#slider-smoothing-accuracy > div > div.rc-slider-handle { + border-color: #f6ec91; } -#react-select-4--value-item { - color: #6b6b6b; +#slider-smoothing-accuracy + > div + > div.rc-slider-step + > div.rc-slider-dot-active { + border-color: #f6ec91; } -/* #_dash-app-content > div > div:nth-child(2) > div:nth-child(2) { - width: 80.5%; -} */ +#slider-smoothing-cross-entropy > div > div.rc-slider-track { + background-color: #36daaa; +} -/* -#div-interval-control { - padding: 10px 10px; -} */ +#slider-smoothing-cross-entropy > div > div.rc-slider-handle { + border-color: #36daaa; +} + +#slider-smoothing-cross-entropy + > div + > div.rc-slider-step + > .rc-slider-dot-active { + border-color: #36daaa; +} -/*.rc-slider-handle:hover { - border-color: white; - border: solid 2px #f6ec91; -} */ +/* overall app background color */ +#_dash-app-content > div { + background-color: #f8f9fa; +} + +/* @media (min-width: 1000px) { + #plotly-logo { + width: 10.25%; + padding: 5px; + } +} */ +/* #react-select-4--value-item { + color: #6b6b6b; +} */ diff --git a/apps/dash-live-model-training/demo_utils.py b/apps/dash-live-model-training/demo_utils.py index 9b611d7d1..e35b80988 100644 --- a/apps/dash-live-model-training/demo_utils.py +++ b/apps/dash-live-model-training/demo_utils.py @@ -3,6 +3,10 @@ import pandas as pd from dash.dependencies import Input, Output, State +# # get relative data folder +# PATH = pathlib.Path(__file__).parent +# DATA_PATH = PATH.joinpath("data").resolve() + def demo_explanation(demo_mode): if demo_mode: @@ -11,11 +15,9 @@ def demo_explanation(demo_mode): demo_md = file.read() return html.Div( - html.Div( - [dcc.Markdown(demo_md, className="markdown")], className="ten columns" - ), - className="row", - style={"margin": "20px"}, + html.Div([dcc.Markdown(demo_md, className="markdown")]), + # className="row", + style={"margin": "10px"}, ) @@ -32,13 +34,13 @@ def demo_components(demo_mode): ), html.Div( className="row", - style={"margin-bottom": "8px"}, + style={"margin": "8px 0px"}, children=[ html.Div( - className="eight columns", + className="twelve columns", children=[ html.Div( - className="six columns", + className="four columns", children=dcc.Dropdown( id="dropdown-demo-dataset", options=[ @@ -51,7 +53,7 @@ def demo_components(demo_mode): ), ), html.Div( - className="six columns", + className="four columns", children=dcc.Dropdown( id="dropdown-simulation-model", options=[ @@ -65,9 +67,12 @@ def demo_components(demo_mode): searchable=False, ), ), + html.Div( + id="div-total-step-count", className="four columns" + ), ], ), - html.Div(id="div-total-step-count", className="four columns"), + # html.Div(id="div-total-step-count", className="four columns"), ], ), ] From 6fd5f99d34681ad02df261b642458bac0a4e84a9 Mon Sep 17 00:00:00 2001 From: celinehuang Date: Tue, 4 Jun 2019 14:47:06 -0400 Subject: [PATCH 07/24] added all csv files to the data folder and cleaned up code for the mobile layout --- apps/dash-live-model-training/README.md | 11 +- apps/dash-live-model-training/app.py | 189 ++++++++++++------ apps/dash-live-model-training/assets/base.css | 10 +- .../assets/custom_styles.css | 2 +- .../cifar_cnn_run_log.csv | 0 .../cifar_softmax_run_log.csv | 0 .../fashion_cnn_run_log.csv | 0 .../fashion_softmax_run_log.csv | 0 .../mnist_cnn_run_log.csv | 0 .../mnist_softmax_run_log.csv | 0 apps/dash-live-model-training/demo_utils.py | 82 +------- 11 files changed, 143 insertions(+), 151 deletions(-) rename apps/dash-live-model-training/{demo_run_logs => data}/cifar_cnn_run_log.csv (100%) rename apps/dash-live-model-training/{demo_run_logs => data}/cifar_softmax_run_log.csv (100%) rename apps/dash-live-model-training/{demo_run_logs => data}/fashion_cnn_run_log.csv (100%) rename apps/dash-live-model-training/{demo_run_logs => data}/fashion_softmax_run_log.csv (100%) rename apps/dash-live-model-training/{demo_run_logs => data}/mnist_cnn_run_log.csv (100%) rename apps/dash-live-model-training/{demo_run_logs => data}/mnist_softmax_run_log.csv (100%) diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md index f1c5b8876..8a680f097 100644 --- a/apps/dash-live-model-training/README.md +++ b/apps/dash-live-model-training/README.md @@ -27,15 +27,14 @@ virtualenv dash-live-model-training-venv # Windows dash-live-model-training-venv\Scripts\activate # Or Linux -source venv/bin/activate +source dash-live-model-training-venv/bin/activate ``` Clone the git repo, then install the requirements with pip ``` git clone https://github.com/plotly/dash-sample-apps.git -cd apps -cd dash-live-model-training +cd dash-sample-apps/apps/dash-live-model-training pip install -r requirements.txt ``` @@ -87,12 +86,6 @@ At the moment, the logging only works for iterative Tensorflow models. We are pl See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project. -## License - -This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details - -## Acknowledgments - ## Screenshots ![screenshot1](images/screenshot1.png) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index cd2f4b29a..6c933499f 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -4,14 +4,13 @@ import dash_html_components as html import pandas as pd import plotly.graph_objs as go -from dash.dependencies import Input, Output, State +from dash.dependencies import Input, Output from plotly import tools -from demo_utils import demo_components, demo_callbacks, demo_explanation +from demo_utils import demo_callbacks, demo_explanation # get relative data folder -PATH = pathlib.Path(__file__).parent -DATA_PATH = PATH.joinpath("data").resolve() +DATA_PATH = pathlib.Path(__file__).parent.joinpath("data").resolve() LOGFILE = "examples/run_log.csv" @@ -19,13 +18,13 @@ app = dash.Dash( __name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}] ) -server = app.server +server = app.server demo_mode = True def div_graph(name): - """Generates an html Div containing graph and control options for smoothing and display, given the name""" + # Generates an html Div containing graph and control options for smoothing and display, given the name return html.Div( className="row", children=[ @@ -70,47 +69,26 @@ def div_graph(name): html.Div( [ html.P( - className="plot-display-text", - children=[ - "Plot Display mode:", - dcc.RadioItems( - options=[ - { - "label": " Overlapping", - "value": "overlap", - }, - { - "label": " Separate (Vertical)", - "value": "separate_vertical", - }, - { - "label": " Separate (Horizontal)", - "value": "separate_horizontal", - }, - ], - value="overlap", - id=f"radio-display-mode-{name}", - className="plot-display-radio-items", - ), + # className="plot-display-text", + "Plot Display mode:", + style={"font-weight": "bold", "margin-bottom": "0px"}, + ), + dcc.RadioItems( + options=[ + {"label": " Overlapping", "value": "overlap"}, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, ], - # style={"font-weight": "bold", "margin-bottom": "0px"}, + value="overlap", + id=f"radio-display-mode-{name}", + className="plot-display-radio-items", ), - # dcc.RadioItems( - # options=[ - # {"label": " Overlapping", "value": "overlap"}, - # { - # "label": " Separate (Vertical)", - # "value": "separate_vertical", - # }, - # { - # "label": " Separate (Horizontal)", - # "value": "separate_horizontal", - # }, - # ], - # value="overlap", - # id=f"radio-display-mode-{name}", - # className="plot-display-radio-items", - # ), html.Div(id=f"div-current-{name}-value"), ] ), @@ -147,7 +125,7 @@ def div_graph(name): className="banner row", ), html.Div( - # empty child function for the callback + # Empty child function for the callback html.Div(id="demo-explanation", children=[]) ), # Body @@ -155,26 +133,109 @@ def div_graph(name): className="container", style={"padding": "35px 25px"}, children=[ - # Extract the demo components if we are in demo mode - *demo_components(demo_mode), + # Hidden Div that will store the result of simulating a model run + html.Div(id="storage-simulated-run", style={"display": "none"}), + # Increment the simulation step count at a fixed time interval + dcc.Interval( + id="interval-simulated-step", + interval=125, # Updates every 100 milliseconds, i.e. every step takes 25 ms + n_intervals=0, + ), html.Div( className="row", - id="div-interval-control", + style={"margin": "8px 0px"}, children=[ - dcc.Dropdown( - id="dropdown-interval-control", - options=[ - {"label": "No Updates", "value": "no"}, - {"label": "Slow Updates", "value": "slow"}, - {"label": "Regular Updates", "value": "regular"}, - {"label": "Fast Updates", "value": "fast"}, + html.Div( + className="twelve columns", + children=[ + html.Div( + className="eight columns", + children=[ + html.Div( + dcc.Dropdown( + id="dropdown-demo-dataset", + options=[ + { + "label": "CIFAR 10", + "value": "cifar", + }, + { + "label": "MNIST", + "value": "mnist", + }, + { + "label": "Fashion MNIST", + "value": "fashion", + }, + ], + placeholder="Select a demo dataset", + searchable=False, + ), + className="six columns dropdown-box", + ), + html.Div( + dcc.Dropdown( + id="dropdown-simulation-model", + options=[ + { + "label": "1-Layer Neural Net", + "value": "softmax", + }, + { + "label": "Simple Conv Net", + "value": "cnn", + }, + ], + placeholder="Select Model to Simulate", + searchable=False, + ), + className="six columns dropdown-box", + ), + html.Div( + dcc.Dropdown( + id="dropdown-interval-control", + options=[ + { + "label": "No Updates", + "value": "no", + }, + { + "label": "Slow Updates", + "value": "slow", + }, + { + "label": "Regular Updates", + "value": "regular", + }, + { + "label": "Fast Updates", + "value": "fast", + }, + ], + value="regular", + className="twelve columns dropdown-box", + clearable=False, + searchable=False, + ) + ), + ], + ), + html.Div( + className="four columns", + id="div-interval-control", + children=[ + html.Div( + id="div-total-step-count", + className="twelve columns", + ), + html.Div( + id="div-step-display", + className="twelve columns", + ), + ], + ), ], - value="regular", - className="eight columns", - clearable=False, - searchable=False, - ), - html.Div(id="div-step-display", className="four columns"), + ) ], ), dcc.Interval(id="interval-log-update", n_intervals=0), @@ -225,7 +286,7 @@ def smooth(scalars, weight=0.6): last = smoothed_val return smoothed - if run_log_json: # exists + if run_log_json: layout = go.Layout( title=graph_title, margin=go.layout.Margin(l=50, r=50, b=50, t=50), @@ -309,7 +370,6 @@ def smooth(scalars, weight=0.6): def learn_more(n_clicks): if n_clicks == None: n_clicks = 0 - if (n_clicks % 2) == 1: n_clicks += 1 return ( @@ -320,7 +380,6 @@ def learn_more(n_clicks): ), "Close", ) - else: n_clicks += 1 return (html.Div(), "Learn More") diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index f153daa95..11ad0df0b 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -607,7 +607,7 @@ there. width: 20%; } - #radio-display-mode-accuracy { + /* #radio-display-mode-accuracy { float: right; } @@ -639,14 +639,14 @@ there. float: right; } .plot-display-text { - float: left; - } + float: left; + } - /* #dropdown-demo-dataset { + #dropdown-demo-dataset { width: 30%; } #dropdown-simulation-model { width: 30%; - } */ + }*/ } diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index b3824dde8..ab0fc71a3 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -42,7 +42,7 @@ } .dropdown-box { - padding: 15px 15px; + padding: 10px 5px; } /* slider colors */ diff --git a/apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv b/apps/dash-live-model-training/data/cifar_cnn_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/cifar_cnn_run_log.csv rename to apps/dash-live-model-training/data/cifar_cnn_run_log.csv diff --git a/apps/dash-live-model-training/demo_run_logs/cifar_softmax_run_log.csv b/apps/dash-live-model-training/data/cifar_softmax_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/cifar_softmax_run_log.csv rename to apps/dash-live-model-training/data/cifar_softmax_run_log.csv diff --git a/apps/dash-live-model-training/demo_run_logs/fashion_cnn_run_log.csv b/apps/dash-live-model-training/data/fashion_cnn_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/fashion_cnn_run_log.csv rename to apps/dash-live-model-training/data/fashion_cnn_run_log.csv diff --git a/apps/dash-live-model-training/demo_run_logs/fashion_softmax_run_log.csv b/apps/dash-live-model-training/data/fashion_softmax_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/fashion_softmax_run_log.csv rename to apps/dash-live-model-training/data/fashion_softmax_run_log.csv diff --git a/apps/dash-live-model-training/demo_run_logs/mnist_cnn_run_log.csv b/apps/dash-live-model-training/data/mnist_cnn_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/mnist_cnn_run_log.csv rename to apps/dash-live-model-training/data/mnist_cnn_run_log.csv diff --git a/apps/dash-live-model-training/demo_run_logs/mnist_softmax_run_log.csv b/apps/dash-live-model-training/data/mnist_softmax_run_log.csv similarity index 100% rename from apps/dash-live-model-training/demo_run_logs/mnist_softmax_run_log.csv rename to apps/dash-live-model-training/data/mnist_softmax_run_log.csv diff --git a/apps/dash-live-model-training/demo_utils.py b/apps/dash-live-model-training/demo_utils.py index e35b80988..21fee171d 100644 --- a/apps/dash-live-model-training/demo_utils.py +++ b/apps/dash-live-model-training/demo_utils.py @@ -2,85 +2,25 @@ import dash_html_components as html import pandas as pd from dash.dependencies import Input, Output, State +import pathlib -# # get relative data folder -# PATH = pathlib.Path(__file__).parent -# DATA_PATH = PATH.joinpath("data").resolve() +# get relative data folder +PATH = pathlib.Path(__file__).parent +DATA_PATH = PATH.joinpath("data").resolve() def demo_explanation(demo_mode): if demo_mode: # Markdown files - with open("demo.md", "r") as file: + with open(PATH.joinpath("demo.md"), "r") as file: demo_md = file.read() return html.Div( html.Div([dcc.Markdown(demo_md, className="markdown")]), - # className="row", style={"margin": "10px"}, ) -def demo_components(demo_mode): - if demo_mode: - return [ - # Hidden Div that will store the result of simulating a model run - html.Div(id="storage-simulated-run", style={"display": "none"}), - # Increment the simulation step count at a fixed time interval - dcc.Interval( - id="interval-simulated-step", - interval=125, # Updates every 100 milliseconds, i.e. every step takes 25 ms - n_intervals=0, - ), - html.Div( - className="row", - style={"margin": "8px 0px"}, - children=[ - html.Div( - className="twelve columns", - children=[ - html.Div( - className="four columns", - children=dcc.Dropdown( - id="dropdown-demo-dataset", - options=[ - {"label": "CIFAR 10", "value": "cifar"}, - {"label": "MNIST", "value": "mnist"}, - {"label": "Fashion MNIST", "value": "fashion"}, - ], - placeholder="Select a demo dataset", - searchable=False, - ), - ), - html.Div( - className="four columns", - children=dcc.Dropdown( - id="dropdown-simulation-model", - options=[ - { - "label": "1-Layer Neural Net", - "value": "softmax", - }, - {"label": "Simple Conv Net", "value": "cnn"}, - ], - placeholder="Select Model to Simulate", - searchable=False, - ), - ), - html.Div( - id="div-total-step-count", className="four columns" - ), - ], - ), - # html.Div(id="div-total-step-count", className="four columns"), - ], - ), - ] - - else: - return [] - - def demo_callbacks(app, demo_mode): if demo_mode: @@ -99,24 +39,24 @@ def load_demo_run_logs(): data_dict = { "softmax": { "cifar": pd.read_csv( - "demo_run_logs/cifar_softmax_run_log.csv", names=names + DATA_PATH.joinpath("cifar_softmax_run_log.csv"), names=names ), "mnist": pd.read_csv( - "demo_run_logs/mnist_softmax_run_log.csv", names=names + DATA_PATH.joinpath("mnist_softmax_run_log.csv"), names=names ), "fashion": pd.read_csv( - "demo_run_logs/fashion_softmax_run_log.csv", names=names + DATA_PATH.joinpath("fashion_softmax_run_log.csv"), names=names ), }, "cnn": { "cifar": pd.read_csv( - "demo_run_logs/cifar_cnn_run_log.csv", names=names + DATA_PATH.joinpath("cifar_cnn_run_log.csv"), names=names ), "mnist": pd.read_csv( - "demo_run_logs/mnist_cnn_run_log.csv", names=names + DATA_PATH.joinpath("mnist_cnn_run_log.csv"), names=names ), "fashion": pd.read_csv( - "demo_run_logs/fashion_cnn_run_log.csv", names=names + DATA_PATH.joinpath("fashion_cnn_run_log.csv"), names=names ), }, } From be304bf31cc8ba4b71ac995c6245fa2284a17265 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Tue, 25 Jun 2019 15:45:42 -0400 Subject: [PATCH 08/24] Fixed title bar, configured dropdown, centered radio items --- apps/.DS_Store | Bin 8196 -> 10244 bytes apps/dash-live-model-training/app.py | 6 +- apps/dash-live-model-training/assets/base.css | 12 ++- .../assets/custom_styles.css | 90 +++++++++++++++++- 4 files changed, 101 insertions(+), 7 deletions(-) diff --git a/apps/.DS_Store b/apps/.DS_Store index 96346ecd165cba4362884137e930b9c67a55b5cf..00be35368f0fe2a6db58e069abb24a34311a0e87 100644 GIT binary patch delta 395 zcmZp1XbF&DU|?W$DortDU{C-uIe-{M3-C-V6q~50D9R1wFar4u44Dii4Dmpy$561b za2or>2Hwr=94s8>AQjwTnM{T}WK|56o;mr+NjdpRAOnHe8i+Oig8>7>WPTA788ihe z(G^T~5a{PHvNY6DFfumXd|hA`n<)=N3PU18F+&D}E<*uBHjqpP@^cuHQO#l$M=>i; zTno)I##S^1X1bg}V?aiiFyzB5K{NC-LeIuRBX&V%ARh=6Kw;nt3bl=e-%EEp!x2Obj>23eIAitS_NHd5#3n#L{FzW}qQJ nAi)hJTtS9!Ed0(qnO`N4186kJN`}etJX1F}ih6 { + font-size: initial; + } + + #div-step-display>h6 { + font-size: initial; + } + + #checklist-smoothing-options-accuracy { + text-align: center; + margin: 10px 0; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), + #checklist-smoothing-options-accuracy>label:nth-child(2) { + text-align: center; + display: inline; + margin: 0 10px; + } + + #radio-display-mode-accuracy { + margin: 10px 0; + } + + #radio-display-mode-accuracy>label:nth-child(1), + #radio-display-mode-accuracy>label:nth-child(2), + #radio-display-mode-accuracy>label:nth-child(3) { + text-align: center; + } + + #div-current-accuracy-value>p { + margin-bottom: 10px; + } + #div-current-accuracy-value>div:nth-child(2), + #div-current-accuracy-value>div:nth-child(3) { + text-align: center; + } + + +} + /* @media (min-width: 1000px) { #plotly-logo { width: 10.25%; From e0634e109361acffdd74c3d203272e290392ec42 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Wed, 26 Jun 2019 12:02:53 -0400 Subject: [PATCH 09/24] Redesigned plot display mode and current accuracy --- .DS_Store | Bin 8196 -> 10244 bytes apps/dash-live-model-training/app.py | 38 +++--- .../assets/custom_styles.css | 118 ++++++++++++++++-- 3 files changed, 132 insertions(+), 24 deletions(-) diff --git a/.DS_Store b/.DS_Store index 9543222389c03acfac2a716d53110cc191aed631..9815863ffe35c058d16ac41c915887ea27d31b4f 100644 GIT binary patch delta 1136 zcmb`GU2oD*7{{LiqwSd5`MM=s)Og`82w|H#jfvUjFd>;bVTl(;NedNfDg|1cgpe9D zFEv3DWAxUScuDvG#+MJ_m+;CMFY$j4#n9@dC8y6h^!IZHBpHyGVnHY)}Zl1>^(X#6s4HtF+!#^`$ElW$}Z`atutpB z7crJb{K1SU21Nx5H)jR74{v`&W V12W9^>&d6XPe(pRw*E)ae*?8iAc+6~ delta 267 zcmZn(XmOBWU|?W$DortDU;r^WfEYvza8E20o2aKK$^w!H@)?rKiwlx+@{@r4lN$@C zu}^H^-OSFx!okQgSwT?T@MKa!PG)h5fx&e~CT12^Hg*mUE)FhUuGrv={PN(E#FElt zr^KRY5HBFHBqJ$ta;>0e00##r2WPy1M0K^fp@oiuiHTvYjzYD$xdD)EY*t&#$sw+4 zXzQ7fTUk|IQ(HF^XgLrt0v!Yf{7@Q3&DyLhG?{f{Njsw;Gek^+8%Vo?{Ijv}JM(0I al|T-lW{@u#Cdczk-E1q?%($_ng%JR_cQ>H` diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index b6505aa36..70d1740fe 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -73,22 +73,28 @@ def div_graph(name): "Plot Display mode:", style={"font-weight": "bold", "margin-bottom": "0px"}, ), - dcc.RadioItems( - options=[ - {"label": " Overlapping", "value": "overlap"}, - { - "label": " Separate (Vertical)", - "value": "separate_vertical", - }, - { - "label": " Separate (Horizontal)", - "value": "separate_horizontal", - }, - ], - value="overlap", - id=f"radio-display-mode-{name}", - className="plot-display-radio-items", - ), + html.Div([ + dcc.RadioItems( + options=[ + { + "label": " Overlapping", + "value": "overlap" + }, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, + ], + value="overlap", + id=f"radio-display-mode-{name}", + className="plot-display-radio-items" + ) + ], className="radio-item-div"), + html.Div(id=f"div-current-{name}-value"), ] ), diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 587eedbca..8b124aeb2 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -37,7 +37,7 @@ letter-spacing: 1.16px; font-size: 12px; padding: 0px 15px; - margin-top: 20px; + margin-top: 17px; width: auto; } @@ -94,6 +94,67 @@ background-color: #f8f9fa; } + +@media (max-width: 360px) { + #title { + width: 150px; + font-size: 1.2rem !important; + font-weight: 400; + margin-top: 30px; + margin-left: 7% !important; + } + + #learn-more-button { + width: 75px; + height: 30px; + margin-left: -5px !important; + font-size: 7.5px !important; + padding: 0px 8px !important; + margin-top: 25px; + } + + #plotly-logo { + margin-top: 30px; + margin-left: 4px; + } + + .dropdown-box-first, + .dropdown-box-second { + width: 50%; + } + + /* First page Smoothing Options */ + #checklist-smoothing-options-accuracy { + text-align: center; + margin: 10px 0; + display: inline-flex; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), + #checklist-smoothing-options-accuracy>label:nth-child(2) { + text-align: center; + display: inline; + margin: 0 20px; + + } + + /* Second page Smoothing Options */ + #checklist-smoothing-options-cross-entropy { + text-align: center; + margin: 10px 0; + display: inline-flex; + + } + + #checklist-smoothing-options-cross-entropy>label:nth-child(1), + #checklist-smoothing-options-cross-entropy>label:nth-child(2) { + text-align: center; + display: inline; + margin: 0 30px; + } + +} + @media (max-width: 550px) { #title { @@ -101,13 +162,13 @@ font-size: 1.7rem; font-weight: 400; margin-top: 30px; - margin-left: 4% !important; + margin-left: 5% !important; } #learn-more-button { width: 75px; height: 30px; - margin-left: 12px; + margin-left: 10px; font-size: 7.5px !important; padding: 0px 8px !important; margin-top: 25px; @@ -145,6 +206,7 @@ font-size: initial; } + /* First Page Smoothing Options */ #checklist-smoothing-options-accuracy { text-align: center; margin: 10px 0; @@ -154,27 +216,67 @@ #checklist-smoothing-options-accuracy>label:nth-child(2) { text-align: center; display: inline; - margin: 0 10px; + margin: 0 30px; } + /* Radio items */ #radio-display-mode-accuracy { margin: 10px 0; } - #radio-display-mode-accuracy>label:nth-child(1), - #radio-display-mode-accuracy>label:nth-child(2), - #radio-display-mode-accuracy>label:nth-child(3) { + .radio-item-div { text-align: center; } + + .plot-display-radio-items { + text-align: left; + display: inline-block; + } + /* Current Accuracy */ #div-current-accuracy-value>p { - margin-bottom: 10px; + margin-bottom: 30px; } + #div-current-accuracy-value>div:nth-child(2), #div-current-accuracy-value>div:nth-child(3) { text-align: center; + display: inline; + margin: 30px 15px; + font-size: 1.3rem; + } + + + + /* Second page smoothing options */ + #checklist-smoothing-options-cross-entropy { + text-align: center; + margin: 10px 0; } + #checklist-smoothing-options-cross-entropy>label:nth-child(1), + #checklist-smoothing-options-cross-entropy>label:nth-child(2) { + text-align: center; + display: inline; + margin: 0 30px; + } + + + + /* Current Loss */ + + #div-current-cross-entropy-value { + margin-bottom: 5px; + } + + #div-current-cross-entropy-value>div:nth-child(2), + #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 15px; + font-size: 1.3rem; + } + } From 11473e5a2b2bca8d75ce8dbb82e9c4e97a3c3620 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 27 Jun 2019 14:42:31 -0400 Subject: [PATCH 10/24] Fixed interval control div --- apps/dash-live-model-training/app.py | 2 +- .../assets/custom_styles.css | 40 +++++++++++-------- 2 files changed, 24 insertions(+), 18 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 70d1740fe..34fad7669 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -238,7 +238,7 @@ def div_graph(name): id="div-step-display", className="twelve columns", ), - ], + ] ), ], ) diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 8b124aeb2..c3beb865a 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -95,7 +95,7 @@ } -@media (max-width: 360px) { +@media (max-width: 375px) { #title { width: 150px; font-size: 1.2rem !important; @@ -123,6 +123,21 @@ width: 50%; } + + + #div-interval-control { + text-align: center; + display: flex; + } + + + #div-total-step-count>h6, + #div-step-display>h6 { + text-align: left; + display: inline-block; + font-size: 1.4rem !important; + } + /* First page Smoothing Options */ #checklist-smoothing-options-accuracy { text-align: center; @@ -184,27 +199,18 @@ } #div-interval-control { - margin-top: 10px; - } - - - #div-step-display { - width: 50%; - float: right; + text-align: center; + display: flex; } - #div-total-step-count { - width: 50%; - float: left; - } - #div-total-step-count>h6 { - font-size: initial; - } - + #div-total-step-count>h6, #div-step-display>h6 { - font-size: initial; + text-align: left; + display: inline-block; + font-size: 1.9rem; } + /* First Page Smoothing Options */ #checklist-smoothing-options-accuracy { From 155c93eea31cafdf8feb48224272e0a93d331591 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Fri, 28 Jun 2019 14:46:10 -0400 Subject: [PATCH 11/24] Fixed run-log-storage and storage-simulated-run IDs --- apps/dash-live-model-training/app.py | 21 ++++++++++++--------- apps/dash-live-model-training/demo_utils.py | 6 +++--- 2 files changed, 15 insertions(+), 12 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 34fad7669..245c60fd7 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -140,7 +140,8 @@ def div_graph(name): style={"padding": "35px 25px"}, children=[ # Hidden Div that will store the result of simulating a model run - html.Div(id="storage-simulated-run", style={"display": "none"}), + #html.Div(id="storage-simulated-run", style={"display": "none"}), + dcc.Store(id="storage-simulated-run", storage_type="memory"), # Increment the simulation step count at a fixed time interval dcc.Interval( id="interval-simulated-step", @@ -246,7 +247,8 @@ def div_graph(name): ), dcc.Interval(id="interval-log-update", n_intervals=0), # Hidden Div Storing JSON-serialized dataframe of run log - html.Div(id="run-log-storage", style={"display": "none"}), + #html.Div(id="run-log-storage", style={"display": "none"}), + dcc.Store(id="run-log-storage", storage_type='memory') # The html divs storing the graphs and display parameters ], ), @@ -413,7 +415,7 @@ def update_interval_log_update(interval_rate): if not demo_mode: @app.callback( - Output("run-log-storage", "children"), + Output("run-log-storage", "data"), [Input("interval-log-update", "n_intervals")], ) def get_run_log(_): @@ -439,7 +441,7 @@ def get_run_log(_): @app.callback( - Output("div-step-display", "children"), [Input("run-log-storage", "children")] + Output("div-step-display", "children"), [Input("run-log-storage", "data")] ) def update_div_step_display(run_log_json): if run_log_json: @@ -453,7 +455,7 @@ def update_div_step_display(run_log_json): @app.callback( Output("div-accuracy-graph", "children"), [ - Input("run-log-storage", "children"), + Input("run-log-storage", "data"), Input("radio-display-mode-accuracy", "value"), Input("checklist-smoothing-options-accuracy", "values"), Input("slider-smoothing-accuracy", "value"), @@ -480,6 +482,7 @@ def update_accuracy_graph( else: graph.figure.layout.yaxis1["range"] = [0, 1] graph.figure.layout.yaxis2["range"] = [0, 1] + graph.figure.layout() except AttributeError: pass @@ -490,7 +493,7 @@ def update_accuracy_graph( @app.callback( Output("div-cross-entropy-graph", "children"), [ - Input("run-log-storage", "children"), + Input("run-log-storage", "data"), Input("radio-display-mode-cross-entropy", "value"), Input("checklist-smoothing-options-cross-entropy", "values"), Input("slider-smoothing-cross-entropy", "value"), @@ -515,7 +518,7 @@ def update_cross_entropy_graph( @app.callback( Output("div-current-accuracy-value", "children"), - [Input("run-log-storage", "children")], + [Input("run-log-storage", "data")], ) def update_div_current_accuracy_value(run_log_json): if run_log_json: @@ -536,7 +539,7 @@ def update_div_current_accuracy_value(run_log_json): @app.callback( Output("div-current-cross-entropy-value", "children"), - [Input("run-log-storage", "children")], + [Input("run-log-storage", "data")], ) def update_div_current_cross_entropy_value(run_log_json): if run_log_json: @@ -557,4 +560,4 @@ def update_div_current_cross_entropy_value(run_log_json): # Running the server if __name__ == "__main__": - app.run_server(debug=True) + app.run_server(debug=True, port=8051) diff --git a/apps/dash-live-model-training/demo_utils.py b/apps/dash-live-model-training/demo_utils.py index 21fee171d..eae7f8aad 100644 --- a/apps/dash-live-model-training/demo_utils.py +++ b/apps/dash-live-model-training/demo_utils.py @@ -62,7 +62,7 @@ def load_demo_run_logs(): } @app.callback( - Output("storage-simulated-run", "children"), + Output("storage-simulated-run", "data"), [Input("interval-simulated-step", "n_intervals")], [ State("dropdown-demo-dataset", "value"), @@ -90,9 +90,9 @@ def reset_interval_simulated_step(*_): return 0 @app.callback( - Output("run-log-storage", "children"), + Output("run-log-storage", "data"), [Input("interval-log-update", "n_intervals")], - [State("storage-simulated-run", "children")], + [State("storage-simulated-run", "data")], ) def get_run_log(_, simulated_run): if simulate_run: From fc5b51045da29e7d680f01172bf45d702862343a Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Fri, 5 Jul 2019 11:48:19 -0700 Subject: [PATCH 12/24] run black tests --- apps/dash-live-model-training/app.py | 79 +++++++++++++++------------- 1 file changed, 41 insertions(+), 38 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 245c60fd7..b4f1e410d 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -73,28 +73,30 @@ def div_graph(name): "Plot Display mode:", style={"font-weight": "bold", "margin-bottom": "0px"}, ), - html.Div([ - dcc.RadioItems( - options=[ - { - "label": " Overlapping", - "value": "overlap" - }, - { - "label": " Separate (Vertical)", - "value": "separate_vertical", - }, - { - "label": " Separate (Horizontal)", - "value": "separate_horizontal", - }, - ], - value="overlap", - id=f"radio-display-mode-{name}", - className="plot-display-radio-items" - ) - ], className="radio-item-div"), - + html.Div( + [ + dcc.RadioItems( + options=[ + { + "label": " Overlapping", + "value": "overlap", + }, + { + "label": " Separate (Vertical)", + "value": "separate_vertical", + }, + { + "label": " Separate (Horizontal)", + "value": "separate_horizontal", + }, + ], + value="overlap", + id=f"radio-display-mode-{name}", + className="plot-display-radio-items", + ) + ], + className="radio-item-div", + ), html.Div(id=f"div-current-{name}-value"), ] ), @@ -140,7 +142,7 @@ def div_graph(name): style={"padding": "35px 25px"}, children=[ # Hidden Div that will store the result of simulating a model run - #html.Div(id="storage-simulated-run", style={"display": "none"}), + # html.Div(id="storage-simulated-run", style={"display": "none"}), dcc.Store(id="storage-simulated-run", storage_type="memory"), # Increment the simulation step count at a fixed time interval dcc.Interval( @@ -239,7 +241,7 @@ def div_graph(name): id="div-step-display", className="twelve columns", ), - ] + ], ), ], ) @@ -247,8 +249,8 @@ def div_graph(name): ), dcc.Interval(id="interval-log-update", n_intervals=0), # Hidden Div Storing JSON-serialized dataframe of run log - #html.Div(id="run-log-storage", style={"display": "none"}), - dcc.Store(id="run-log-storage", storage_type='memory') + # html.Div(id="run-log-storage", style={"display": "none"}), + dcc.Store(id="run-log-storage", storage_type="memory") # The html divs storing the graphs and display parameters ], ), @@ -334,9 +336,7 @@ def smooth(scalars, weight=0.6): ) if display_mode == "separate_vertical": - figure = tools.make_subplots( - rows=2, cols=1, print_grid=False, shared_yaxes=True - ) + figure = tools.make_subplots(rows=2, cols=1, print_grid=False) figure.append_trace(trace_train, 1, 1) figure.append_trace(trace_val, 2, 1) @@ -347,15 +347,20 @@ def smooth(scalars, weight=0.6): scene={"domain": {"x": (0.0, 0.5), "y": (0.5, 1)}}, ) + figure["layout"]["yaxis1"].update(title=yaxis_title) + figure["layout"]["yaxis2"].update(title=yaxis_title) + elif display_mode == "separate_horizontal": figure = tools.make_subplots( - rows=1, cols=2, shared_yaxes=True, print_grid=False + rows=1, cols=2, print_grid=False, shared_xaxes=True ) figure.append_trace(trace_train, 1, 1) figure.append_trace(trace_val, 1, 2) figure["layout"].update(title=layout.title, margin=layout.margin) + figure["layout"]["yaxis1"].update(title=yaxis_title) + figure["layout"]["yaxis2"].update(title=yaxis_title) elif display_mode == "overlap": figure = go.Figure(data=[trace_train, trace_val], layout=layout) @@ -415,8 +420,7 @@ def update_interval_log_update(interval_rate): if not demo_mode: @app.callback( - Output("run-log-storage", "data"), - [Input("interval-log-update", "n_intervals")], + Output("run-log-storage", "data"), [Input("interval-log-update", "n_intervals")] ) def get_run_log(_): names = [ @@ -478,11 +482,11 @@ def update_accuracy_graph( try: if display_mode in ["separate_horizontal", "overlap"]: - graph.figure.layout.yaxis["range"] = [0, 1] + graph.figure.layout.yaxis["range"] = [0, 1.3] + graph.figure.layout.yaxis2["range"] = [0, 1.3] else: - graph.figure.layout.yaxis1["range"] = [0, 1] - graph.figure.layout.yaxis2["range"] = [0, 1] - graph.figure.layout() + graph.figure.layout.yaxis1["range"] = [0, 1.3] + graph.figure.layout.yaxis2["range"] = [0, 1.3] except AttributeError: pass @@ -517,8 +521,7 @@ def update_cross_entropy_graph( @app.callback( - Output("div-current-accuracy-value", "children"), - [Input("run-log-storage", "data")], + Output("div-current-accuracy-value", "children"), [Input("run-log-storage", "data")] ) def update_div_current_accuracy_value(run_log_json): if run_log_json: From b865f51ffe596d18239eabb19e340fd37798b823 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 11 Jul 2019 10:26:49 -0400 Subject: [PATCH 13/24] apply two column format for mobile layout --- apps/dash-live-model-training/app.py | 26 ++++--- .../assets/custom_styles.css | 74 ++++++++++++++++--- 2 files changed, 78 insertions(+), 22 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index b4f1e410d..414ec07c9 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -34,10 +34,9 @@ def div_graph(name): children=[ html.Div( [ - html.P( + html.Div( className="graph-checkbox-smoothing", - children=["Smoothing:"], - style={"font-weight": "bold", "margin-bottom": "0px"}, + children=["Smoothing:"] ), dcc.Checklist( options=[ @@ -46,11 +45,12 @@ def div_graph(name): ], values=[], id=f"checklist-smoothing-options-{name}", - className="checklist-smoothing", - ), + className="checklist-smoothing" + ) ], - style={"margin-top": "10px"}, - ), + style={"margin-top": "10px"} + + ), html.Div( [ dcc.Slider( @@ -69,9 +69,10 @@ def div_graph(name): html.Div( [ html.P( - # className="plot-display-text", - "Plot Display mode:", + #className="plot-display-text", + "Plot Display Mode:", style={"font-weight": "bold", "margin-bottom": "0px"}, + className="plot-display-text" ), html.Div( [ @@ -98,8 +99,9 @@ def div_graph(name): className="radio-item-div", ), html.Div(id=f"div-current-{name}-value"), - ] - ), + ], + className="entropy-div" + ) ], ), html.Div(id=f"div-{name}-graph", className="ten columns"), @@ -563,4 +565,4 @@ def update_div_current_cross_entropy_value(run_log_json): # Running the server if __name__ == "__main__": - app.run_server(debug=True, port=8051) + app.run_server(debug=True, port=8050) diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index c3beb865a..2e57319ee 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -5,12 +5,18 @@ border-radius: 0.75rem; } */ + + #div-total-step-count > h6 { font-weight: bold; font-size: 20px; color: #6b6b6b; } +#div-total-step-count { + margin-top: 15px; +} + #div-step-display > h6 { font-weight: bold; font-size: 20px; @@ -171,6 +177,9 @@ } @media (max-width: 550px) { + .container { + padding: 20px; + } #title { width: 200px; @@ -203,6 +212,9 @@ display: flex; } + #div-total-step-count { + margin-top: 0px; + } #div-total-step-count>h6, #div-step-display>h6 { @@ -212,31 +224,65 @@ } + /* First Page Smoothing Options */ - #checklist-smoothing-options-accuracy { - text-align: center; - margin: 10px 0; + + + .graph-checkbox-smoothing { + font-weight: bold; + margin-bottom: 0px; + display: inline-block; + width: 50%; + } + + #slider-smoothing-accuracy { + width: 50%; + float: right; } + .checklist-smoothing{ + display: inline-block; + width: 50%; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), #checklist-smoothing-options-accuracy>label:nth-child(2) { - text-align: center; - display: inline; - margin: 0 30px; + display: inline-block; + font-size: 11.5px; + margin: 0; + padding: 10px; + text-align: right; + } + + .slider-smoothing { + margin-bottom: 60px !important; } + /* Radio items */ #radio-display-mode-accuracy { margin: 10px 0; + } .radio-item-div { - text-align: center; + text-align: right; + display: inline-block; + vertical-align: top; + padding-left: 25px; + } .plot-display-radio-items { text-align: left; display: inline-block; + font-size: 12px; + } + + .plot-display-text { + display: inline-block; + width: 50% } /* Current Accuracy */ @@ -255,6 +301,7 @@ /* Second page smoothing options */ + #checklist-smoothing-options-cross-entropy { text-align: center; margin: 10px 0; @@ -262,12 +309,19 @@ #checklist-smoothing-options-cross-entropy>label:nth-child(1), #checklist-smoothing-options-cross-entropy>label:nth-child(2) { - text-align: center; - display: inline; - margin: 0 30px; + display: inline-block; + font-size: 11.5px; + margin: 0; + padding: 10px; + text-align: right; } + #slider-smoothing-cross-entropy>div { + width: 50%; + float: right; + } + /* Current Loss */ From 84895b21522838cf265a1247a444b1138d54fb45 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 11 Jul 2019 15:37:23 -0400 Subject: [PATCH 14/24] correct Current Accuracy and Current Loss layouts + fix minor positional issues on smaller screens --- apps/dash-live-model-training/app.py | 4 +- .../assets/custom_styles.css | 120 +++++++++++++++--- 2 files changed, 105 insertions(+), 19 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 414ec07c9..e12dc125d 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -537,8 +537,10 @@ def update_div_current_accuracy_value(run_log_json): "margin-bottom": "0px", }, ), + html.Div(f"Training: {run_log_df['train accuracy'].iloc[-1]:.4f}"), - html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}"), + html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}") + ] diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 2e57319ee..7dd76ed21 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -104,10 +104,10 @@ @media (max-width: 375px) { #title { width: 150px; - font-size: 1.2rem !important; + font-size: 1.5rem !important; font-weight: 400; margin-top: 30px; - margin-left: 7% !important; + margin-left: 8% !important; } #learn-more-button { @@ -156,7 +156,6 @@ text-align: center; display: inline; margin: 0 20px; - } /* Second page Smoothing Options */ @@ -174,6 +173,66 @@ margin: 0 30px; } + /* Radio items */ + #radio-display-mode-accuracy { + margin: 1px 0; + + } + + .radio-item-div { + text-align: right; + display: inline-block; + vertical-align: top; + padding-left: 25px; + + } + + .plot-display-radio-items { + text-align: left; + display: inline-block; + font-size: 10px !important; + margin: 1px 0; + } + + .plot-display-text { + display: inline-block; + width: 50% + } + + /* Current Accuracy */ + #div-current-accuracy-value>p { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-accuracy-value>div:nth-child(2), + #div-current-accuracy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 5px; + font-size: 8px !important; + padding-left: 15px; + } + + + /* Current Loss */ + + #div-current-cross-entropy-value { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-cross-entropy-value>div:nth-child(2), + #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 5px; + font-size: 8px !important; + padding-left: 28px !important; + vertical-align: bottom !important; + } + + } @media (max-width: 550px) { @@ -230,7 +289,7 @@ .graph-checkbox-smoothing { font-weight: bold; - margin-bottom: 0px; + display: inline-block; width: 50%; } @@ -255,14 +314,14 @@ text-align: right; } - .slider-smoothing { + + #_dash-app-content>div>div:nth-child(4)>div>div.two.columns>div.slider-smoothing { margin-bottom: 60px !important; } - /* Radio items */ #radio-display-mode-accuracy { - margin: 10px 0; + margin: 5px 0; } @@ -278,6 +337,7 @@ text-align: left; display: inline-block; font-size: 12px; + margin: 5px 0; } .plot-display-text { @@ -285,20 +345,22 @@ width: 50% } + + /* Current Accuracy */ #div-current-accuracy-value>p { - margin-bottom: 30px; + display: inline-flex !important } #div-current-accuracy-value>div:nth-child(2), #div-current-accuracy-value>div:nth-child(3) { text-align: center; display: inline; - margin: 30px 15px; - font-size: 1.3rem; + margin: 20px 5px; + font-size: 10px; + padding-left: 15px; } - /* Second page smoothing options */ @@ -321,31 +383,53 @@ float: right; } + #_dash-app-content>div>div:nth-child(5)>div>div.two.columns>div.slider-smoothing { + margin-bottom: 60px !important; + } + /* Current Loss */ #div-current-cross-entropy-value { - margin-bottom: 5px; + display: inline-flex !important; } #div-current-cross-entropy-value>div:nth-child(2), #div-current-cross-entropy-value>div:nth-child(3) { text-align: center; display: inline; - margin: 20px 15px; - font-size: 1.3rem; + margin: 20px 5px; + font-size: 11px; + padding-left: 20px; } } -/* @media (min-width: 1000px) { + @media (max-width: 1000px) { + #title { + width: 200px; + font-size: 1.7rem; + font-weight: 400; + margin-top: 30px; + margin-left: 5% !important; + } + + #learn-more-button { + width: 75px; + height: 30px; + margin-left: 10px; + font-size: 7.5px !important; + padding: 0px 8px !important; + margin-top: 25px; + } + #plotly-logo { - width: 10.25%; - padding: 5px; + margin-top: 30px; + margin-left: 4px; } -} */ +} /* #react-select-4--value-item { color: #6b6b6b; } */ From d8f6d257dc783c591b3923c49f56ba418d686f10 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 11 Jul 2019 16:37:18 -0400 Subject: [PATCH 15/24] get rid of stylesheet for iPad, will modify later --- .../assets/custom_styles.css | 22 +++---------------- 1 file changed, 3 insertions(+), 19 deletions(-) diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 7dd76ed21..9356aa4af 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -408,27 +408,11 @@ } @media (max-width: 1000px) { - #title { - width: 200px; - font-size: 1.7rem; - font-weight: 400; - margin-top: 30px; - margin-left: 5% !important; - } + - #learn-more-button { - width: 75px; - height: 30px; - margin-left: 10px; - font-size: 7.5px !important; - padding: 0px 8px !important; - margin-top: 25px; - } - #plotly-logo { - margin-top: 30px; - margin-left: 4px; - } + + } /* #react-select-4--value-item { color: #6b6b6b; From 34524efb123fd61063ef3e23281a1bf510ad8bf0 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 11 Jul 2019 16:39:50 -0400 Subject: [PATCH 16/24] run black tests --- apps/dash-live-model-training/app.py | 23 ++++++++++------------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index e12dc125d..665c488e9 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -36,7 +36,7 @@ def div_graph(name): [ html.Div( className="graph-checkbox-smoothing", - children=["Smoothing:"] + children=["Smoothing:"], ), dcc.Checklist( options=[ @@ -45,12 +45,11 @@ def div_graph(name): ], values=[], id=f"checklist-smoothing-options-{name}", - className="checklist-smoothing" - ) + className="checklist-smoothing", + ), ], - style={"margin-top": "10px"} - - ), + style={"margin-top": "10px"}, + ), html.Div( [ dcc.Slider( @@ -69,10 +68,10 @@ def div_graph(name): html.Div( [ html.P( - #className="plot-display-text", + # className="plot-display-text", "Plot Display Mode:", style={"font-weight": "bold", "margin-bottom": "0px"}, - className="plot-display-text" + className="plot-display-text", ), html.Div( [ @@ -100,8 +99,8 @@ def div_graph(name): ), html.Div(id=f"div-current-{name}-value"), ], - className="entropy-div" - ) + className="entropy-div", + ), ], ), html.Div(id=f"div-{name}-graph", className="ten columns"), @@ -537,10 +536,8 @@ def update_div_current_accuracy_value(run_log_json): "margin-bottom": "0px", }, ), - html.Div(f"Training: {run_log_df['train accuracy'].iloc[-1]:.4f}"), - html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}") - + html.Div(f"Validation: {run_log_df['val accuracy'].iloc[-1]:.4f}"), ] From 2f120712a3ac7351c5a1ae8101f6c561a8cee314 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Fri, 12 Jul 2019 16:08:05 -0400 Subject: [PATCH 17/24] implement correct layout for all mobile viewports --- apps/.DS_Store | Bin 10244 -> 10244 bytes apps/dash-live-model-training/app.py | 26 +- apps/dash-live-model-training/assets/base.css | 42 -- .../assets/custom_styles.css | 475 +++++++++++++++--- .../dash-live-model-training/requirements.txt | 2 +- apps/dash-live-model-training/runtime.txt | 2 +- 6 files changed, 423 insertions(+), 124 deletions(-) diff --git a/apps/.DS_Store b/apps/.DS_Store index 00be35368f0fe2a6db58e069abb24a34311a0e87..9ee498105c4df41922d620b3b18b9085fbd740a0 100644 GIT binary patch delta 37 rcmZn(XbITxL7X`=IdAi4@xS~cKt>q@0|O633PU18F+&D}E<*tT2&fBl delta 37 rcmZn(XbITxL7dqyr+D*c@xS~cKt>q@0|O633PU18F+&D}E<*tT1_cXQ diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 665c488e9..8d1f74cdf 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -43,7 +43,7 @@ def div_graph(name): {"label": " Training", "value": "train"}, {"label": " Validation", "value": "val"}, ], - values=[], + value=[], id=f"checklist-smoothing-options-{name}", className="checklist-smoothing", ), @@ -68,7 +68,6 @@ def div_graph(name): html.Div( [ html.P( - # className="plot-display-text", "Plot Display Mode:", style={"font-weight": "bold", "margin-bottom": "0px"}, className="plot-display-text", @@ -134,16 +133,12 @@ def div_graph(name): className="banner row", ), html.Div( - # Empty child function for the callback html.Div(id="demo-explanation", children=[]) - ), - # Body + ), html.Div( className="container", style={"padding": "35px 25px"}, children=[ - # Hidden Div that will store the result of simulating a model run - # html.Div(id="storage-simulated-run", style={"display": "none"}), dcc.Store(id="storage-simulated-run", storage_type="memory"), # Increment the simulation step count at a fixed time interval dcc.Interval( @@ -249,10 +244,7 @@ def div_graph(name): ], ), dcc.Interval(id="interval-log-update", n_intervals=0), - # Hidden Div Storing JSON-serialized dataframe of run log - # html.Div(id="run-log-storage", style={"display": "none"}), - dcc.Store(id="run-log-storage", storage_type="memory") - # The html divs storing the graphs and display parameters + dcc.Store(id="run-log-storage", storage_type="memory") ], ), html.Div(className="container", children=[div_graph("accuracy")]), @@ -394,9 +386,9 @@ def learn_more(n_clicks): ), "Close", ) - else: - n_clicks += 1 - return (html.Div(), "Learn More") + + n_clicks += 1 + return (html.Div(), "Learn More") @app.callback( @@ -462,7 +454,7 @@ def update_div_step_display(run_log_json): [ Input("run-log-storage", "data"), Input("radio-display-mode-accuracy", "value"), - Input("checklist-smoothing-options-accuracy", "values"), + Input("checklist-smoothing-options-accuracy", "value"), Input("slider-smoothing-accuracy", "value"), ], ) @@ -500,7 +492,7 @@ def update_accuracy_graph( [ Input("run-log-storage", "data"), Input("radio-display-mode-cross-entropy", "value"), - Input("checklist-smoothing-options-cross-entropy", "values"), + Input("checklist-smoothing-options-cross-entropy", "value"), Input("slider-smoothing-cross-entropy", "value"), ], ) @@ -564,4 +556,4 @@ def update_div_current_cross_entropy_value(run_log_json): # Running the server if __name__ == "__main__": - app.run_server(debug=True, port=8050) + app.run_server(debug=True) diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index 8be347170..b58096d6f 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -613,46 +613,4 @@ there. width: 20%; } - /* #radio-display-mode-accuracy { - float: right; - } - - #checklist-smoothing-options-accuracy { - float: right; - } - - #slider-smoothing-accuracy > div { - float: right; - margin-bottom: 5%; - } - - #slider-smoothing-cross-entropy > div { - float: right; - margin-bottom: 5%; - } - .slider-smoothing { - width: 50%; - } - - .graph-checkbox-smoothing { - float: left; - } - - .checklist-smoothing { - float: right; - } - .plot-display-radio-items { - float: right; - } - .plot-display-text { - float: left; - } - - #dropdown-demo-dataset { - width: 30%; - } - - #dropdown-simulation-model { - width: 30%; - }*/ } diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 9356aa4af..6d8e6a24d 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -101,26 +101,31 @@ } -@media (max-width: 375px) { + +@media only screen and (max-width: 1000px) { + .container { + padding: 20px; + } + #title { - width: 150px; - font-size: 1.5rem !important; + width: 45%; + font-size: 2.4rem; font-weight: 400; margin-top: 30px; - margin-left: 8% !important; + margin-left: 7% !important; } #learn-more-button { width: 75px; height: 30px; - margin-left: -5px !important; + margin-left: 90px; font-size: 7.5px !important; padding: 0px 8px !important; margin-top: 25px; } #plotly-logo { - margin-top: 30px; + margin-top: 24px; margin-left: 4px; } @@ -129,53 +134,60 @@ width: 50%; } - - #div-interval-control { text-align: center; display: flex; } + #div-total-step-count { + margin-top: 0px; + } #div-total-step-count>h6, #div-step-display>h6 { text-align: left; display: inline-block; - font-size: 1.4rem !important; + font-size: 1.9rem; } - /* First page Smoothing Options */ - #checklist-smoothing-options-accuracy { - text-align: center; - margin: 10px 0; - display: inline-flex; + + /* First Page Smoothing Options */ + + .graph-checkbox-smoothing { + font-weight: bold; + display: inline-block; + width: 50%; } - #checklist-smoothing-options-accuracy>label:nth-child(1), - #checklist-smoothing-options-accuracy>label:nth-child(2) { - text-align: center; - display: inline; - margin: 0 20px; + #slider-smoothing-accuracy { + width: 50%; + float: right; } - /* Second page Smoothing Options */ - #checklist-smoothing-options-cross-entropy { + .checklist-smoothing { + display: inline-block; + width: 50%; text-align: center; - margin: 10px 0; - display: inline-flex; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), + #checklist-smoothing-options-accuracy>label:nth-child(2) { + display: inline-block; + font-size: 14.5px; + margin: 0; + padding: 10px; + text-align: right; } - #checklist-smoothing-options-cross-entropy>label:nth-child(1), - #checklist-smoothing-options-cross-entropy>label:nth-child(2) { - text-align: center; - display: inline; - margin: 0 30px; + + #_dash-app-content>div>div:nth-child(4)>div>div.two.columns>div.slider-smoothing { + margin-bottom: 60px !important; } /* Radio items */ #radio-display-mode-accuracy { - margin: 1px 0; + margin: 5px 0; } @@ -183,56 +195,82 @@ text-align: right; display: inline-block; vertical-align: top; - padding-left: 25px; - + padding-left: 20%; } .plot-display-radio-items { text-align: left; display: inline-block; - font-size: 10px !important; - margin: 1px 0; + font-size: 12px; + margin: 5px 0; } .plot-display-text { display: inline-block; - width: 50% + width: 45%; + font-size: 14px; } + + /* Current Accuracy */ #div-current-accuracy-value>p { display: inline-flex !important; - font-size: 13px !important; + width: 40%; } #div-current-accuracy-value>div:nth-child(2), #div-current-accuracy-value>div:nth-child(3) { text-align: center; display: inline; - margin: 20px 5px; - font-size: 8px !important; - padding-left: 15px; + margin: 10px 2px !important; + font-size: 13px; + padding-left: 65px; + } + + + /* Second page smoothing options */ + + #checklist-smoothing-options-cross-entropy { + text-align: center; + margin: 10px 0; } + #checklist-smoothing-options-cross-entropy>label:nth-child(1), + #checklist-smoothing-options-cross-entropy>label:nth-child(2) { + display: inline-block; + font-size: 14.5px; + margin: 0; + padding: 10px; + text-align: right; + } + + #slider-smoothing-cross-entropy>div { + width: 50%; + float: right; + } + + #_dash-app-content>div>div:nth-child(5)>div>div.two.columns>div.slider-smoothing { + margin-bottom: 60px !important; + } + + /* Current Loss */ - #div-current-cross-entropy-value { + #div-current-cross-entropy-value>p { display: inline-flex !important; - font-size: 13px !important; + width: 40%; } #div-current-cross-entropy-value>div:nth-child(2), #div-current-cross-entropy-value>div:nth-child(3) { text-align: center; display: inline; - margin: 20px 5px; - font-size: 8px !important; - padding-left: 28px !important; - vertical-align: bottom !important; + margin: 10px 10px !important; + font-size: 13px; + padding-left: 65px; } - - } @media (max-width: 550px) { @@ -241,11 +279,11 @@ } #title { - width: 200px; - font-size: 1.7rem; + width: 45%; + font-size: 1.8rem; font-weight: 400; margin-top: 30px; - margin-left: 5% !important; + margin-left: 7% !important; } #learn-more-button { @@ -282,14 +320,12 @@ font-size: 1.9rem; } - /* First Page Smoothing Options */ .graph-checkbox-smoothing { font-weight: bold; - display: inline-block; width: 50%; } @@ -302,13 +338,14 @@ .checklist-smoothing{ display: inline-block; width: 50%; + text-align: center; } #checklist-smoothing-options-accuracy>label:nth-child(1), #checklist-smoothing-options-accuracy>label:nth-child(2) { display: inline-block; - font-size: 11.5px; + font-size: 9.5px; margin: 0; padding: 10px; text-align: right; @@ -329,7 +366,7 @@ text-align: right; display: inline-block; vertical-align: top; - padding-left: 25px; + padding-left: 15%; } @@ -342,7 +379,8 @@ .plot-display-text { display: inline-block; - width: 50% + width: 45%; + font-size: 14px; } @@ -356,9 +394,9 @@ #div-current-accuracy-value>div:nth-child(3) { text-align: center; display: inline; - margin: 20px 5px; - font-size: 10px; - padding-left: 15px; + margin: 20px 2px; + font-size: 9px !important; + padding-left: 25px; } @@ -372,7 +410,7 @@ #checklist-smoothing-options-cross-entropy>label:nth-child(1), #checklist-smoothing-options-cross-entropy>label:nth-child(2) { display: inline-block; - font-size: 11.5px; + font-size: 9.5px; margin: 0; padding: 10px; text-align: right; @@ -393,27 +431,338 @@ #div-current-cross-entropy-value { display: inline-flex !important; + width: 100%; + } + + #div-current-cross-entropy-value>div:nth-child(2), + #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 10px 2px; + font-size: 9px !important; + padding-left: 10px; + padding-top: 10px; + + } + + + + +} + +@media (max-width: 375px) { + #title { + width: 150px; + font-size: 1.5rem !important; + font-weight: 400; + margin-top: 30px; + margin-left: 8% !important; + } + + #learn-more-button { + width: 75px; + height: 30px; + margin-left: -5px !important; + font-size: 7.5px !important; + padding: 0px 8px !important; + margin-top: 25px; + } + + #plotly-logo { + margin-top: 30px; + margin-left: 4px; + } + + .dropdown-box-first, + .dropdown-box-second { + width: 50%; + } + + + + #div-interval-control { + text-align: center; + display: flex; + } + + + #div-total-step-count>h6, + #div-step-display>h6 { + text-align: left; + display: inline-block; + font-size: 1.4rem !important; + } + + /* First page Smoothing Options */ + #checklist-smoothing-options-accuracy { + text-align: center; + margin: 10px 0; + display: inline-flex; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), + #checklist-smoothing-options-accuracy>label:nth-child(2) { + text-align: center; + display: inline; + + } + + /* Second page Smoothing Options */ + #checklist-smoothing-options-cross-entropy { + text-align: center; + margin: 10px 0; + display: inline-flex; + + } + + #checklist-smoothing-options-cross-entropy>label:nth-child(1), + #checklist-smoothing-options-cross-entropy>label:nth-child(2) { + text-align: center; + display: inline; + + } + + /* Radio items */ + #radio-display-mode-accuracy { + margin: 1px 0; + + } + + .radio-item-div { + text-align: right; + display: inline-block; + vertical-align: top; + padding-left: 25px; + + } + + .plot-display-radio-items { + text-align: left; + display: inline-block; + font-size: 10px !important; + margin: 1px 0; + } + + .plot-display-text { + display: inline-block; + width: 50% + } + + /* Current Accuracy */ + #div-current-accuracy-value>p { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-accuracy-value>div:nth-child(2), + #div-current-accuracy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 5px; + font-size: 8px !important; + } + + + /* Current Loss */ + + #div-current-cross-entropy-value { + display: inline-flex !important; + font-size: 13px !important; } #div-current-cross-entropy-value>div:nth-child(2), #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 2px; + font-size: 8px !important; + vertical-align: bottom !important; + padding: 0; + padding-top: 10px; + padding-left: 5px; + } + +} + +@media (max-width: 360px) { + + /* Current Accuracy */ + #div-current-accuracy-value>p { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-accuracy-value>div:nth-child(2), + #div-current-accuracy-value>div:nth-child(3) { text-align: center; display: inline; margin: 20px 5px; - font-size: 11px; - padding-left: 20px; + font-size: 7px !important; } + /* Current Loss */ + + #div-current-cross-entropy-value { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-cross-entropy-value>div:nth-child(2), + #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 2px; + font-size: 8px !important; + vertical-align: bottom !important; + padding: 0; + padding-top: 10px; + padding-left: 2px; + } - @media (max-width: 1000px) { - +@media (max-width: 320px) { + #title { + width: 150px; + font-size: 1.4rem !important; + font-weight: 400; + margin-top: 30px; + margin-left: 8% !important; + } + + #learn-more-button { + width: 60px; + height: 30px; + margin-left: -5px !important; + font-size: 5.5px !important; + padding: 0px 8px !important; + margin-top: 25px; + } + + #plotly-logo { + margin-top: 30px; + } + + .dropdown-box-first, + .dropdown-box-second { + width: 50%; + } + + + #div-interval-control { + text-align: center; + display: flex; + } + + + #div-total-step-count>h6, + #div-step-display>h6 { + text-align: left; + display: inline-block; + font-size: 1.4rem !important; + } + + /* First page Smoothing Options */ + #checklist-smoothing-options-accuracy { + text-align: center; + margin: 10px 0; + display: inline-flex; + } + + #checklist-smoothing-options-accuracy>label:nth-child(1), + #checklist-smoothing-options-accuracy>label:nth-child(2) { + text-align: center; + display: inline; + + } + + /* Second page Smoothing Options */ + #checklist-smoothing-options-cross-entropy { + text-align: center; + margin: 10px 0; + display: inline-flex; + + } + + #checklist-smoothing-options-cross-entropy>label:nth-child(1), + #checklist-smoothing-options-cross-entropy>label:nth-child(2) { + text-align: center; + display: inline; + + } + + /* Radio items */ + #radio-display-mode-accuracy { + margin: 1px 0; + + } + + .radio-item-div { + text-align: right; + display: inline-block; + vertical-align: top; + padding-left: 10px; + + } + + .plot-display-radio-items { + text-align: left; + display: inline-block; + font-size: 10px !important; + margin: 1px 0; + } + + .plot-display-text { + display: inline-block; + width: 50%; + font-size: 12px; + } + + /* Current Accuracy */ + #div-current-accuracy-value>p { + display: inline-flex !important; + font-size: 10px !important; + } + + #div-current-accuracy-value>div:nth-child(2), + #div-current-accuracy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 5px; + font-size: 8px !important; + padding-left: 5px; + } + + + /* Current Loss */ + + #div-current-cross-entropy-value { + display: inline-flex !important; + font-size: 13px !important; + } + + #div-current-cross-entropy-value>div:nth-child(2), + #div-current-cross-entropy-value>div:nth-child(3) { + text-align: center; + display: inline; + margin: 20px 2px; + font-size: 7px !important; + vertical-align: bottom !important; + padding: 0; + padding-top: 10px; + padding-left: 0px; + } + + } + + +} + -} + /* #react-select-4--value-item { color: #6b6b6b; } */ diff --git a/apps/dash-live-model-training/requirements.txt b/apps/dash-live-model-training/requirements.txt index f90db79ea..550c4d198 100644 --- a/apps/dash-live-model-training/requirements.txt +++ b/apps/dash-live-model-training/requirements.txt @@ -1,4 +1,4 @@ -dash==0.42.0 +dash==1.0.0 gunicorn==19.9.0 scipy==1.2.1 numpy==1.16.3 diff --git a/apps/dash-live-model-training/runtime.txt b/apps/dash-live-model-training/runtime.txt index 3530c033c..c85dc0b5f 100644 --- a/apps/dash-live-model-training/runtime.txt +++ b/apps/dash-live-model-training/runtime.txt @@ -1 +1 @@ -python-3.6.6 \ No newline at end of file +python-3.6.7 \ No newline at end of file From ccb4eecdb65d0cdb0bed4ec6cfe6b40a016e25fe Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Fri, 12 Jul 2019 16:09:05 -0400 Subject: [PATCH 18/24] run Black tests again --- apps/dash-live-model-training/app.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index 8d1f74cdf..f8f608d38 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -132,9 +132,7 @@ def div_graph(name): ], className="banner row", ), - html.Div( - html.Div(id="demo-explanation", children=[]) - ), + html.Div(html.Div(id="demo-explanation", children=[])), html.Div( className="container", style={"padding": "35px 25px"}, @@ -244,7 +242,7 @@ def div_graph(name): ], ), dcc.Interval(id="interval-log-update", n_intervals=0), - dcc.Store(id="run-log-storage", storage_type="memory") + dcc.Store(id="run-log-storage", storage_type="memory"), ], ), html.Div(className="container", children=[div_graph("accuracy")]), @@ -386,7 +384,7 @@ def learn_more(n_clicks): ), "Close", ) - + n_clicks += 1 return (html.Div(), "Learn More") From 5ef771fb391c6e059455ba9b14a6070a33993e88 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Tue, 16 Jul 2019 14:44:48 -0400 Subject: [PATCH 19/24] correct label beside radio buttoms + reposition steps --- apps/dash-live-model-training/app.py | 1 + apps/dash-live-model-training/assets/base.css | 47 +------------- .../assets/custom_styles.css | 64 ++++++++++++++++--- 3 files changed, 57 insertions(+), 55 deletions(-) diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index f8f608d38..caeacfd2c 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -91,6 +91,7 @@ def div_graph(name): ], value="overlap", id=f"radio-display-mode-{name}", + labelStyle={"verticalAlign": "middle"}, className="plot-display-radio-items", ) ], diff --git a/apps/dash-live-model-training/assets/base.css b/apps/dash-live-model-training/assets/base.css index b58096d6f..1be444f16 100644 --- a/apps/dash-live-model-training/assets/base.css +++ b/apps/dash-live-model-training/assets/base.css @@ -27,33 +27,19 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash .banner { height: 75px; background-color: #191e27; - /* border-radius: 5px 5px 2px 2px; */ - /* padding-top: 0px; - padding-left: 0px; - padding-right: 0px; */ padding: 0px; - /* width: 90%; */ - /* margin-left: auto; - margin-right: auto; */ margin: auto; } .banner h2 { - /* color: white; */ color: #e6ecf4; margin-left: 1.5%; display: inline-block; - /* font-family: "Raleway"; */ font-family: "Raleway Medium", sans-serif; - /* font-size: 4rem; */ - /* line-height: 1; */ font-size: 30px; } .banner img { - /* margin: 20px 0; */ - /* width: auto !important; */ - padding: 0px 15px; margin-top: 20px; margin-bottom: 15px; @@ -66,7 +52,6 @@ Development: https://rawgit.com/xhlulu/0acba79000a3fd1e6f552ed82edb8a64/raw/dash background-color: white; border-radius: 2px 2px 5px 5px; font-size: 1.5rem; - /* border: thin solid rgb(240, 240, 240); */ margin-left: auto; margin-right: auto; color: #6b6b6b; @@ -548,41 +533,11 @@ there. /* Larger than mobile */ @media (max-width: 375px) { - /* - #title { - width: 30%; - font-size: 20px; - padding-top: 10px; - } - #learn-more-button { - margin-left: 0px; - font-size: 55px !important; - padding: 0px 10px !important; - } - #plotly-logo { - padding: 100px 500px !important; - } - */ + } /* Larger than tablet */ @media (max-width: 575px) { - /* - #title { - width: 40% !important; - font-size: 15px; - padding-top: 10px; - } - #learn-more-button { - margin-left: 0px; - font-size: 7.5px !important; - padding: 0px 10px !important; - } - #plotly-logo { - padding: 10px 10px !important; - } - - */ } diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index 6d8e6a24d..a3548b96d 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -150,6 +150,16 @@ font-size: 1.9rem; } + #div-total-step-count>h6 { + float: right; + margin-right: 10%; + } + + #div-step-display>h6 { + float: left !important; + margin-left: 25%; + } + /* First Page Smoothing Options */ @@ -211,6 +221,12 @@ font-size: 14px; } + #radio-display-mode-accuracy>label:nth-child(1)>input[type=radio], + #radio-display-mode-accuracy>label:nth-child(2)>input[type=radio], + #radio-display-mode-accuracy>label:nth-child(3)>input[type=radio] { + vertical-align: middle !important; + } + /* Current Accuracy */ @@ -315,11 +331,19 @@ #div-total-step-count>h6, #div-step-display>h6 { - text-align: left; - display: inline-block; - font-size: 1.9rem; + display: block; + font-size: 1.4rem; } + #div-total-step-count>h6 { + float: right; + margin-right: 10%; + } + + #div-step-display>h6 { + float: left !important; + margin-left: 25%; + } /* First Page Smoothing Options */ @@ -367,7 +391,6 @@ display: inline-block; vertical-align: top; padding-left: 15%; - } .plot-display-radio-items { @@ -383,8 +406,7 @@ font-size: 14px; } - - + /* Current Accuracy */ #div-current-accuracy-value>p { display: inline-flex !important @@ -493,6 +515,15 @@ font-size: 1.4rem !important; } + #div-total-step-count>h6 { + float: right !important; + margin-right: 5%; + } + + #div-step-display>h6 { + float: left !important; + } + /* First page Smoothing Options */ #checklist-smoothing-options-accuracy { text-align: center; @@ -540,14 +571,19 @@ text-align: left; display: inline-block; font-size: 10px !important; - margin: 1px 0; + margin: 10px 0 !important; + + } .plot-display-text { display: inline-block; - width: 50% + width: 50%; + margin-bottom: 10px; } + + /* Current Accuracy */ #div-current-accuracy-value>p { display: inline-flex !important; @@ -661,7 +697,17 @@ #div-step-display>h6 { text-align: left; display: inline-block; - font-size: 1.4rem !important; + font-size: 1.2rem !important; + } + + #div-total-step-count>h6 { + float: right; + margin-right: 10%; + } + + #div-step-display>h6 { + float: left; + margin-left: 10%; } /* First page Smoothing Options */ From 7fe92f24eb7489a318114b28769e599e37ce167f Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Wed, 17 Jul 2019 10:16:05 -0400 Subject: [PATCH 20/24] update README dead link --- apps/dash-live-model-training/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md index 8a680f097..f5484e850 100644 --- a/apps/dash-live-model-training/README.md +++ b/apps/dash-live-model-training/README.md @@ -7,7 +7,7 @@ This is a demo of the Dash interactive Python framework developed by [Plotly](ht Dash abstracts away all of the technologies and protocols required to build an interactive web-based application and is a simple and effective way to bind a user interface around your Python code. To learn more check out our [documentation](https://plot.ly/dash). -Try out the [demo app here](https://dash-live-model-training.plot.ly/). +Try out the [demo app here](https://dash-gallery.plotly.host/dash-live-model-training/). ![animated1](images/animated1.gif) From 9db3d43440f97e65c960b7c1f896f86d6354fbdd Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Wed, 17 Jul 2019 15:46:04 -0400 Subject: [PATCH 21/24] fix positioning of radio buttons on second page --- apps/dash-live-model-training/assets/custom_styles.css | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/apps/dash-live-model-training/assets/custom_styles.css b/apps/dash-live-model-training/assets/custom_styles.css index a3548b96d..e90b7a324 100644 --- a/apps/dash-live-model-training/assets/custom_styles.css +++ b/apps/dash-live-model-training/assets/custom_styles.css @@ -226,8 +226,12 @@ #radio-display-mode-accuracy>label:nth-child(3)>input[type=radio] { vertical-align: middle !important; } - + #radio-display-mode-cross-entropy>label:nth-child(1)>input[type=radio], + #radio-display-mode-cross-entropy>label:nth-child(2)>input[type=radio], + #radio-display-mode-cross-entropy>label:nth-child(3)>input[type=radio] { + vertical-align: middle !important; + } /* Current Accuracy */ #div-current-accuracy-value>p { From be62d100763b01145e02dc538b3f33defe08f641 Mon Sep 17 00:00:00 2001 From: Chris Jeon Date: Thu, 18 Jul 2019 14:42:00 -0400 Subject: [PATCH 22/24] fix issues brought up from Ronnie's/Sham's code review --- apps/dash-live-model-training/README.md | 2 +- apps/dash-live-model-training/app.py | 2 + apps/dash-live-model-training/assets/font.css | 113 +----------------- .../assets/live-model-training.css | 4 - apps/dash-live-model-training/demo.md | 2 +- .../images/animated.gif | Bin 0 -> 13283130 bytes .../images/animated1.gif | Bin 1629390 -> 0 bytes 7 files changed, 5 insertions(+), 118 deletions(-) create mode 100644 apps/dash-live-model-training/images/animated.gif delete mode 100644 apps/dash-live-model-training/images/animated1.gif diff --git a/apps/dash-live-model-training/README.md b/apps/dash-live-model-training/README.md index f5484e850..c3dcbee4b 100644 --- a/apps/dash-live-model-training/README.md +++ b/apps/dash-live-model-training/README.md @@ -9,7 +9,7 @@ Dash abstracts away all of the technologies and protocols required to build an i Try out the [demo app here](https://dash-gallery.plotly.host/dash-live-model-training/). -![animated1](images/animated1.gif) +![animated1](images/animated.gif) ## Getting Started diff --git a/apps/dash-live-model-training/app.py b/apps/dash-live-model-training/app.py index caeacfd2c..bed635c2a 100644 --- a/apps/dash-live-model-training/app.py +++ b/apps/dash-live-model-training/app.py @@ -172,6 +172,7 @@ def div_graph(name): "value": "fashion", }, ], + value='mnist', placeholder="Select a demo dataset", searchable=False, ), @@ -190,6 +191,7 @@ def div_graph(name): "value": "cnn", }, ], + value="cnn", placeholder="Select Model to Simulate", searchable=False, ), diff --git a/apps/dash-live-model-training/assets/font.css b/apps/dash-live-model-training/assets/font.css index cab8e83e4..0535e2ad8 100644 --- a/apps/dash-live-model-training/assets/font.css +++ b/apps/dash-live-model-training/assets/font.css @@ -1,112 +1 @@ -/* cyrillic-ext */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWJ0bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; -} -/* cyrillic */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFUZ0bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; -} -/* greek-ext */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWZ0bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+1F00-1FFF; -} -/* greek */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFVp0bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+0370-03FF; -} -/* vietnamese */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFWp0bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB; -} -/* latin-ext */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFW50bf8pkAp6a.woff2) format('woff2'); - unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; -} -/* latin */ -@font-face { - font-family: 'Open Sans'; - font-style: normal; - font-weight: 400; - src: local('Open Sans Regular'), local('OpenSans-Regular'), url(https://fonts.gstatic.com/s/opensans/v16/mem8YaGs126MiZpBA-UFVZ0bf8pkAg.woff2) format('woff2'); - unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; -} -/* cyrillic-ext */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu72xKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; -} -/* cyrillic */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu5mxKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; -} -/* greek-ext */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7mxKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+1F00-1FFF; -} -/* greek */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu4WxKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+0370-03FF; -} -/* vietnamese */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7WxKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB; -} -/* latin-ext */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu7GxKKTU1Kvnz.woff2) format('woff2'); - unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; -} -/* latin */ -@font-face { - font-family: 'Roboto'; - font-style: normal; - font-weight: 400; - src: local('Roboto'), local('Roboto-Regular'), url(https://fonts.gstatic.com/s/roboto/v19/KFOmCnqEu92Fr1Mu4mxKKTU1Kg.woff2) format('woff2'); - unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; -} \ No newline at end of file +@import url('https://fonts.googleapis.com/css?family=Open+Sans|Roboto&display=swap'); \ No newline at end of file diff --git a/apps/dash-live-model-training/assets/live-model-training.css b/apps/dash-live-model-training/assets/live-model-training.css index dfcf0545a..454576da3 100644 --- a/apps/dash-live-model-training/assets/live-model-training.css +++ b/apps/dash-live-model-training/assets/live-model-training.css @@ -1,7 +1,3 @@ .markdown img { width: 100%; } - -._dash-undo-redo { - display: none; -} \ No newline at end of file diff --git a/apps/dash-live-model-training/demo.md b/apps/dash-live-model-training/demo.md index e178b110f..3a3cd188f 100644 --- a/apps/dash-live-model-training/demo.md +++ b/apps/dash-live-model-training/demo.md @@ -1,4 +1,4 @@ -## Getting Started with the Demo +## Getting Started With the Demo The purpose of this demo is to show what can be done with the Viewer. Therefore, the trainings shown above are displayed using pre-generated data. 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