TensorFlow: Plotting a graph that describes data points and decision boundary - tensorflow

Question

I would like to achieve something similar:
https://rootpy.github.io/root_numpy/_images/plot_multiclass_1.png
What would be the most elegant solution? Get the weights, bias, function and data and plot it with some other tool or does TensorFlow have support for that?

Answer 1

As far as I know, Tensorflow does not directly support plotting decision boundaries.
It is certainly not the most elegant solution, but you can create a grid. Classify each point of the grid and then plot it. For example:
#!/usr/bin/env python
"""
Solve the XOR problem with Tensorflow.
The XOR problem is a two-class classification problem. You only have four
datapoints, all of which are given during training time. Each datapoint has
two features:
x o
o x
As you can see, the classifier has to learn a non-linear transformation of
the features to find a propper decision boundary.
"""
__author__ = "Martin Thoma"
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
# The training data
XOR_X = [[0, 0], [0, 1], [1, 0], [1, 1]] # Features
XOR_Y = [[0], [1], [1], [0]] # Class labels
XOR_Y = [[1, 0], [0, 1], [0, 1], [1, 0]] # Target values
assert len(XOR_X) == len(XOR_Y) # sanity check
# The network
nb_classes = 2
input_ = tf.placeholder(tf.float32,
shape=[None, len(XOR_X[0])],
name="input")
target = tf.placeholder(tf.float32,
shape=[None, nb_classes],
name="output")
nb_hidden_nodes = 2
# enc = tf.one_hot([0, 1], 2)
w1 = tf.Variable(tf.random_uniform([2, nb_hidden_nodes], -1, 1),
name="Weights1")
w2 = tf.Variable(tf.random_uniform([nb_hidden_nodes, nb_classes], -1, 1),
name="Weights2")
b1 = tf.Variable(tf.zeros([nb_hidden_nodes]), name="Biases1")
b2 = tf.Variable(tf.zeros([nb_classes]), name="Biases2")
activation2 = tf.sigmoid(tf.matmul(input_, w1) + b1)
hypothesis = tf.nn.softmax(tf.matmul(activation2, w2) + b2)
cross_entropy = -tf.reduce_sum(target * tf.log(hypothesis))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)
# Start training
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(100000):
sess.run(train_step, feed_dict={input_: XOR_X, target: XOR_Y})
if i % 10000 == 0:
print('Epoch ', i)
print('Hypothesis ', sess.run(hypothesis,
feed_dict={input_: XOR_X,
target: XOR_Y}))
print('w1 ', sess.run(w1))
print('b1 ', sess.run(b1))
print('w2 ', sess.run(w2))
print('b2 ', sess.run(b2))
print('cost (ce)', sess.run(cross_entropy,
feed_dict={input_: XOR_X,
target: XOR_Y}))
# Visualize classification boundary
xs = np.linspace(-5, 5)
ys = np.linspace(-5, 5)
pred_classes = []
for x in xs:
for y in ys:
pred_class = sess.run(hypothesis,
feed_dict={input_: [[x, y]]})
pred_classes.append((x, y, pred_class.argmax()))
xs_p, ys_p = [], []
xs_n, ys_n = [], []
for x, y, c in pred_classes:
if c == 0:
xs_n.append(x)
ys_n.append(y)
else:
xs_p.append(x)
ys_p.append(y)
plt.plot(xs_p, ys_p, 'ro', xs_n, ys_n, 'bo')
plt.show()
which gives