tf-matplotlib renders your everyday matplotlib figures tinside TensorFlow's Tensorboard visualization interface. The library
- takes care of evaluating input tensors prior to plotting,
- avoids matplotlib threading issues,
- support multiple figures and,
- provides blitting for runtime critical plotting.
The following TensorFlow summary is generated by sgd.py. It plots the progress of gradient descent optimizers on a test surface. To avoid redrawing the test surface, it makes use of blitting. See usage below for a more introductory example.
pip install tfmpl
Requirements
- Python 3.5/3.6
- TensorFlow 1.x
- matplotlib 2.2.0
| Branch | Linux | Windows |
|---|---|---|
| master |  |
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| develop | |
Below are the relevant snippets to render a simple scatter plot. See scatter.py for the complete self-contained example.
import tensorflow as tf
import numpy as np
import tfmpl
@tfmpl.figure_tensor
def draw_scatter(scaled, colors):
'''Draw scatter plots. One for each color.'''
figs = tfmpl.create_figures(len(colors), figsize=(4,4))
for idx, f in enumerate(figs):
ax = f.add_subplot(111)
ax.axis('off')
ax.scatter(scaled[:, 0], scaled[:, 1], c=colors[idx])
f.tight_layout()
return figs
with tf.Session(graph=tf.Graph()) as sess:
# A point cloud that can be scaled by the user
points = tf.constant(
np.random.normal(loc=0.0, scale=1.0, size=(100, 2)).astype(np.float32)
)
scale = tf.placeholder(tf.float32)
scaled = points*scale
# Note, `scaled` above is a tensor. Its being passed `draw_scatter` below.
# However, when `draw_scatter` is invoked, the tensor will be evaluated and a
# numpy array representing its content is provided.
image_tensor = draw_scatter(scaled, ['r', 'g'])
image_summary = tf.summary.image('scatter', image_tensor)
all_summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter('log', sess.graph)
summary = sess.run(all_summaries, feed_dict={scale: 2.})
writer.add_summary(summary, global_step=0)
When doing classification, a common task is to generate a confusion matrix. tf-matplotlib provides tfmpl.draw.confusion_matrix to quickly generate such a plot from labels and predictions. The following plot shows classification training progress on the MNIST classification task. Full sample code is provided in mnist.py.
MIT License
Copyright (c) 2018 Christoph Heindl
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