Data-Visualization
Data Visualization is a presentation of the data in a pictorial format or graphical format.
Visualizations using
1.Matplotlib
2.Pandas - Data Visualization
3.Seaborn
4.TensorBoard
Matplotlib
- Excellent Data Visualization Library
- Multi-Platform Support
- It supports several plots like line, bar, scatter, histogram etc.
Pandas - Data Visualization
Pandas uses the standard convention for referencing the matplotlib API.
Seaborn
- Seaborn is a Python data visualization library based on matplotlib.
- It provides a high-level interface for drawing attractive and informative statistical graphics.
- Seaborn will provide a variety of visualization patterns than matplotlib.
TensorBoard
TensorBoard provides the visualization and tooling needed for machine learning experimentation:
- Tracking and visualizing metrics such as loss and accuracy
- Visualizing the model graph (ops and layers)
- Viewing histograms of weights, biases, or other tensors as they change over time
- Projecting embeddings to a lower dimensional space
- Displaying images, text, and audio data
- Profiling TensorFlow programs
TensorBoard - Installation Steps
pip install tensorboard
(upgrade the torch,tensorboard,tensorflow)
pip install --upgrade torchvision
pip install --upgrade torch
pip install --upgrade tensorboard
pip install --upgrade tensorflow
Note
supported versions
pytorch version - greater than 1.1.0
torchvision version - greater than 0.3.0
tensorboard version - greater than 1.15
TensorBoard - Command for Run
Run the tensorboard --logdir=runs in command prompt.
Open http://localhost:6006/ to see the results.
To quit , press CRTL+C.
Alternative Method
Open from Jupyter-Notebook using magic comments
%load_ext tensorboard
%tensorboard --logdir logs
Tensorboard dashboards
Scalar Dashboard
TensorBoard's Scalar Dashboard visualizes scalar statistics that vary over time; for example, you might want to track the model's loss or learning rate. you can compare multiple runs, and the data is organized by tag. The line charts have the following interactions:
Clicking on the small blue icon in the lower-left corner of each chart will expand the chart
Dragging a rectangular region on the chart will zoom in
Double clicking on the chart will zoom out
Mousing over the chart will produce crosshairs, with data values recorded in the run-selector on the left.
Additionally, you can create new folders to organize tags by writing regular expressions in the box in the top-left of the dashboard.
Histogram Dashboard
The HistogramDashboard displays how the statistical distribution of a Tensor has varied over time. It visualizes data recorded via tf.summary.histogram. Each chart shows temporal "slices" of data, where each slice is a histogram of the tensor at a given step. It's organized with the oldest timestep in the back, and the most recent timestep in front. By changing the Histogram Mode from "offset" to "overlay", the perspective will rotate so that every histogram slice is rendered as a line and overlaid with one another.
Distribution Dashboard
The Distribution Dashboard is another way of visualizing histogram data from tf.summary.histogram. It shows some high-level statistics on a distribution. Each line on the chart represents a percentile in the distribution over the data: for example, the bottom line shows how the minimum value has changed over time, and the line in the middle shows how the median has changed. Reading from top to bottom, the lines have the following meaning: [maximum, 93%, 84%, 69%, 50%, 31%, 16%, 7%, minimum]. These percentiles can also be viewed as standard deviation boundaries on a normal distribution: [maximum, μ+1.5σ, μ+σ, μ+0.5σ, μ, μ-0.5σ, μ-σ, μ-1.5σ, minimum] so that the colored regions, read from inside to outside, have widths [σ, 2σ, 3σ] respectively.
Image Dashboard
The Image Dashboard can display pngs that were saved via a tf.summary.image. The dashboard is set up so that each row corresponds to a different tag, and each column corresponds to a run. Since the image dashboard supports arbitrary pngs, you can use this to embed custom visualizations (e.g. matplotlib scatterplots) into TensorBoard. This dashboard always shows you the latest image for each tag.
Audio Dashboard
The Audio Dashboard can embed playable audio widgets for audio saved via a tf.summary.audio. The dashboard is set up so that each row corresponds to a different tag, and each column corresponds to a run. This dashboard always embeds the latest audio for each tag.
Graph Explorer
The Graph Explorer can visualize a TensorBoard graph, enabling inspection of the TensorFlow model. To get best use of the graph visualizer, you should use name scopes to hierarchically group the ops in your graph - otherwise, the graph may be difficult to decipher. For more information, including examples, see the graph visualizer tutorial.
Embedding Projector
The Embedding Projector allows you to visualize high-dimensional data; for example, you may view your input data after it has been embedded in a high- dimensional space by your model. The embedding projector reads data from your model checkpoint file, and may be configured with additional metadata, like a vocabulary file or sprite images.
Text Dashboard
The Text Dashboard displays text snippets saved via tf.summary.text. Markdown features including hyperlinks, lists, and tables are all supported.
References :
https://matplotlib.org/3.1.1/tutorials/introductory/pyplot.html
https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html
https://seaborn.pydata.org/
https://www.tensorflow.org/tensorboard
https://pytorch.org/docs/stable/tensorboard.html