Machine Learning and TensorFlow


TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use.

Environment Versions

Operating System : Ubuntu 17.04 (Zesty Zapus) 64-bit Server Edition
Python           : 2.7.13

Install TensorFlow on Linux (CPU-only)

$ sudo apt-get install python-pip python-dev
$ pip install tensorflow
Installing collected packages: wheel, six, appdirs, pyparsing, packaging, setuptools, protobuf, funcsigs, pbr, mock, numpy, werkzeug, tensorflow
Successfully installed appdirs-1.4.3 funcsigs-1.0.2 mock-2.0.0 numpy-1.12.1 packaging-16.8 pbr-3.0.1 protobuf-3.3.0 pyparsing-2.2.0 setuptools-35.0.2 six-1.10.0 tensorflow-1.1.0 werkzeug-0.12.2 wheel-0.29.0

Fit the plane example:

import tensorflow as tf
import numpy as np

# Make 100 phony data points in NumPy.
x_data = np.float32(np.random.rand(2, 100)) # Random input
y_data =[0.100, 0.200], x_data) + 0.300

# Construct a linear model.
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b

# Minimize the squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# For initializing the variables.
init = tf.initialize_all_variables()

# Launch the graph
sess = tf.Session()

# Fit the plane.
for step in xrange(0, 201):
    if step % 20 == 0:
        print step,,

# Learns best fit is W: [[0.100  0.200]], b: [0.300]

This is an essay introduction of TensorFlow, I will keep updating this post.

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