Getting Started with TensorFlow by Zaccone Giancarlo

Author:Zaccone, Giancarlo [Zaccone, Giancarlo]
Language: eng
Format: azw3
Publisher: Packt Publishing
Published: 2016-07-29T04:00:00+00:00

Building the training set

Import all the necessary libraries to our simulation:

import matplotlib.pyplot as plt import numpy as np import tensorflow as tf import pandas as pd

Note

Pandas is an open source, easy-to-use data structure, and data analysis tool for the Python programming language. To install it, type the following command:

sudo pip install pandas

We must define the parameters of our problem. The total number of points that we want to cluster is 1000 points:

num_vectors = 1000

The number of partitions you want to achieve by all initial:

num_clusters = 4

We set the number of computational steps of the k-means algorithm:

num_steps = 100

We initialize the initial input data structures:

x_values = [] y_values = [] vector_values = []

The training set creates a random set of points, which is why we use the random.normal NumPy function, allowing us to build the x_values and y_values vectors:

for i in xrange(num_vectors): if np.random.random() > 0.5: x_values.append(np.random.normal(0.4, 0.7)) y_values.append(np.random.normal(0.2, 0.8)) else: x_values.append(np.random.normal(0.6, 0.4)) y_values.append(np.random.normal(0.8, 0.5))

We use the Python zip function to obtain the complete list of vector_values:

vector_values = zip(x_values,y_values)

Then vector_values is converted into a constant, usable by TensorFlow:

vectors = tf.constant(vector_values)

We can see our training set for the clustering algorithm with the following commands:

plt.plot(x_values,y_values, 'o', label='Input Data') plt.legend() plt.show()

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