Data Generation for Neural Network Playground of Deep Insider
Project description
playground-data
Data Generation for Neural Network Playground of Deep Insider.
This project/package that exists as an aid to the Nerural Network Playground - Deep Insider which was forked from tensorflow/playground: Deep playground.
Official pages
- The python package "playground-data" on PyPI for this project is available here.
- The source for this package is available here.
Requirements
- Python 3.4+
- numpy
- matplotlib
Install this package using pip
pip install playground-data
Usage
from __future__ import print_function print('Import plygdata package as pg') import plygdata as pg # Or, you can 'import' classes and functions directly like this: # from plygdata.datahelper import DatasetType # from plygdata.dataset import generate
print('Imported "playground-data" package version is ...') print(pg.__version__)
print('Code for plotting sample graph') import pprint pprint.pprint(dir(pg)) # How to find class members pprint.pprint(dir(pg.DatasetType)) #['ClassifyCircleData', # 'ClassifySpiralData', # 'ClassifyTwoGaussData', # 'ClassifyXORData', # 'RegressGaussian', # 'RegressPlane', # ...] fig, ax = pg.plot_sample(pg.DatasetType.ClassifyCircleData) # # uncomment if a graph is not shown # import matplotlib.pyplot as plt # plt.show()
print('Basic code for generating and graphing data') data_noise=0.0 validation_data_ratio = 0.5 # Generate data data_array = pg.generate_data(pg.DatasetType.ClassifyCircleData, data_noise) #data_array = pg.generate_data(pg.DatasetType.ClassifyXORData, data_noise) #data_array = pg.generate_data(pg.DatasetType.ClassifyTwoGaussData, data_noise) #data_array = pg.generate_data(pg.DatasetType.ClassifySpiralData, data_noise) #data_array = pg.generate_data(pg.DatasetType.RegressPlane, data_noise) #data_array = pg.generate_data(pg.DatasetType.RegressGaussian, data_noise) # Divide the data for training and validating at a specified ratio (further, separate each data into Coordinate point data part and teacher label part) X_train, y_train, X_valid, y_valid = pg.split_data(data_array, validation_size=validation_data_ratio) # You can use training_size instead of validation_size. training_size takes precedence over validation_size. # Plot the data on the standard graph for Playground fig, ax = pg.plot_points_with_playground_style(X_train, y_train, X_valid, y_valid, figsize = (6, 6), dpi = 100) # # get figure + axes of matplotlib graph and plot the data points # fig = pg.get_playground_figure(enable_colorbar=True) # ax = pg.get_playground_axes(fig) # pg.plot_points(ax, X_train, y_train, X_valid, y_valid) # # These 3 lines equal to `plot_points_with_playground_style` function # draw the decision boundary of X1 input (feature) pg.draw_decision_boundary(fig, ax, node_id=pg.InputType.X1, discretize=False) # # uncomment if a graph is not shown # import matplotlib.pyplot as plt # plt.show()
print('Signature of main @staticmethod') import sys if sys.version_info[0] < 3: # inspect.signature was introduced at version Python 3.3 !pip install funcsigs try: from inspect import signature except ImportError: from funcsigs import signature print('pg.plot_sample', str(signature(pg.plot_sample))) # pg.plot_sample (data_type, noise=0.0, validation_size=0.5, visualize_validation_data=False, figsize=(5, 5), dpi=100, node_id=None, discretize=False) print('pg.generate', str(signature(pg.generate))) # pg.generate (data_type, noise=0.0) print('pg.split_data', str(signature(pg.data))) # pg.split_data (data, validation_size=0.5, label_num=1) print('pg.plot_points_with_playground_style', str(signature(pg.plot_points_with_playground_style))) # pg.plot_points_with_playground_style (X_train, y_train, X_valid=None, y_valid=None, figsize=(5, 5), dpi=100) print('pg.get_playground_figure', str(signature(pg.get_playground_figure))) # pg.get_playground_figure (enable_colorbar=False) print('pg.get_playground_axes', str(signature(pg.get_playground_axes))) # pg.get_playground_axes (fig) print('pg.plot_points', str(signature(pg.plot_points))) # pg.plot_points (ax, X_train, y_train, X_valid=None, y_valid=None) print('pg.draw_decision_boundary', str(signature(pg.draw_decision_boundary))) # pg.draw_decision_boundary (fig, ax, node_id='x', discretize=False, enable_colorbar=True)
Sample Web app
License
Copyright 2018-2019 Digital Advantage Co., Ltd. All Rights Reserved. Licensed under the Apache License, Version 2.0.
The licenses of using open-source code
This project uses the JavaScript-to-Python-translation of the following open-source code:
tensorflow / playground (Deep playground) / dataset.ts, heatmap.ts, playground.ts, state.ts
Copyright 2016 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0.
d3 / d3-scale / linear.js
Copyright 2010-2015 Mike Bostock. All rights reserved.
Licensed under the BSD 3-Clause "New" or "Revised" License.
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