How to write ‘hello world’ in TensorFlow

The following program finds out the cost of chocolate per gram. It takes as input some example chocolate bars: each one with a weight and a price.

import tensorflow as tf
import numpy as np

weight_grams = np.linspace(10, 70, 10)
price_dollars = TRUE_DOLLARS_PER_GRAM*weight_grams + np.random.randn(*weight_grams.shape)*0.33

input_grams_tensor = tf.placeholder(tf.float32)
true_dollars_tensor = tf.placeholder(tf.float32)
dollars_per_gram_tensor = tf.Variable(0.0)  # train to find the best value here
predicted_dollars_tensor = tf.multiply(input_grams_tensor, dollars_per_gram_tensor)  # to improve this prediction
error_in_dollars_tensor = tf.square(true_dollars_tensor - predicted_dollars_tensor)  # i.e. to minimize this error

train_op = tf.train.GradientDescentOptimizer(0.0001).minimize(error_in_dollars_tensor)

with tf.Session() as sess:
	for _ in range(1000):
		for (example_weight_grams, example_price_dollars) in zip(weight_grams, price_dollars):, feed_dict={
				input_grams_tensor: example_weight_grams,
				true_dollars_tensor: example_price_dollars
	print("Final cost per gram: $%s" %

I say the program “finds the cost of chocolate per gram”, rather than “learns to predict the price of chocolate bars based on their weight”. The program is only able to consider linear “models” of price, and this linear model is decided by you, the programmer.

There are other ways to calculate this cost per gram. You could divide the total cost by the total weight. This program is much simpler:

import numpy as np
weight_grams = np.linspace(0, 50, 10)
price_dollars = TRUE_DOLLARS_PER_GRAM*weight_grams + np.random.randn(*weight_grams.shape)*0.33
print("Final cost per gram: $%s" % (np.sum(price_dollars) / np.sum(weight_grams)))

The “machine learning” approach becomes useful with more complex, non-linear models. Chocolate bars aren’t actually priced linearly by weight: they get comparatively cheaper as they get bigger. The price is not just influenced by weight: it’s also influenced by the type of chocolate, the brand, the location it’s sold in. Solving for a “cost per gram” doesn’t work in such models.

Tagged #machine-learning.

Similar posts

More by Jim

Want to build a fantastic product using LLMs? I work at Granola where we're building the future IDE for knowledge work. Come and work with us! Read more or get in touch!

This page copyright James Fisher 2017. Content is not associated with my employer. Found an error? Edit this page.