<jupyter_start><jupyter_text>IntroductionLet's get you up and running with TensorFlow!But before we even get started, let's peek at what TensorFlow code looks like in the Python API, so you have a sense of where we're headed.Here's a little Python program that makes up some data in two dimensions, and then fits a line to it.<jupyter_code>import tensorflow as tf
import numpy as np
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables.  We will 'run' this first.
init = tf.initialize_all_variables()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))
# Learns best fit is W: [0.1], b: [0.3]<jupyter_output><empty_output>
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