Let's get you up and running with TensorFlow!
But before we even get started, let's give you a sneak peek at what TensorFlow code looks like in the Python API, just so you have a sense of where we're headed.
Here's a little Python program that makes up some data in three dimensions, and then fits a plane to it.
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 = np.dot([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()
sess.run(init)
# Fit the plane.
for step in xrange(0, 201):
sess.run(train)
if step % 20 == 0:
print step, sess.run(W), sess.run(b)
# Learns best fit is W: [[0.100 0.200]], b: [0.300]To whet your appetite further, we suggest you check out what a classical machine learning problem looks like in TensorFlow. In the land of neural networks the most "classic" classical problem is the MNIST handwritten digit classification. We offer two introductions here, one for machine learning newbies, and one for pros. If you've already trained dozens of MNIST models in other software packages, please take the red pill. If you've never even heard of MNIST, definitely take the blue pill. If you're somewhere in between, we suggest skimming blue, then red.
Images licensed CC BY-SA 4.0; original by W. Carter
If you're already sure you want to learn and install TensorFlow you can skip these and charge ahead. Don't worry, you'll still get to see MNIST -- we'll also use MNIST as an example in our technical tutorial where we elaborate on TensorFlow features.