TensorFlow and Deep Learning - How to run TensorFlow under Windows

The simplest way to work with TensorFlow under Windows10 is using the new Bash shell, that is integrated in Windows10!.

1. For this, you have to update your Windows10 system to the newest Windows version "Windows 10 Anniversary Update".

2. Then go to Start->"Programs and Features"->"Turn Windows features on or off" and turn on the new "Windows Subsystem for Linux (Beta)" functionality.

This will give you a native Bash shell that can run Python and thus TensorFlow!

3. Just use the Windows search functionality to search for "bash" and start the new bash shell:

Starting the bash shell for the first time, will install Ubuntu on Windows:

4. You can then use the normal pip setup procedure:

sudo apt-get install python3-pip
export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl
sudo pip3 install --upgrade $TF_BINARY_URL
		

as described in my notes here to install TensorFlow for Python3

5. For testing, whether your TensorFlow installation works, copy the following first simple TensorFlow script into a file line_fitting.py:

		
			# A first simple TensorFlow learner
			#
			# Given some random samples of 2D data points (x,y),
			# the following TF script tries to find a line
			# that best describes the data points in terms of
			# a minimal sum of squared errors (SSE)
			#
			# by Prof. Dr. Juergen Brauer, www.juergenbrauer.org

			import tensorflow as tf
			import numpy as np


			# 1.1 create 1D array with 100 random numbers drawn uniformly from [0,1)
			x_data = np.random.rand(100)
			print("\nx_data:",x_data)
			print("data type:", type(x_data))

			# 1.2 now compute the y-points
			y_data = x_data * 1.2345 + 0.6789

			# so now we have ground truth samples (x,y)
			# and the TF learner will have to estimate the line parameters
			# y=W*x+b with W=1.2345 and b=0.6789
			#
			# These parameters are called variables in TF.


			# 2. We initialize them with a random W and b=0
			W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
			b = tf.Variable(tf.zeros([1]))
			y = W * x_data + b


			# 3.1 Now define what to optimize at all:
			#     here we want to minimize the SSE.
			#     This is our "loss" of a certain line model
			loss_func = tf.reduce_mean(tf.square(y - y_data))

			# 3.2 We can use different optimizers in TF for model learning
			my_optimizer = tf.train.GradientDescentOptimizer(0.5)

			# 3.3 Tell the optimizer object to minimize the loss function
			train = my_optimizer.minimize(loss_func)


			# 4. Before starting, we have to initialize the variables
			init = tf.initialize_all_variables()


			# 5. Now distribute the graph to the computing hardware
			sess = tf.Session()


			# 6. Initialize the graph
			sess.run(init)


			# 7. Print inial value of W and b
			print("\n")
			print("initial W", sess.run(W))
			print("initial b", sess.run(b))


			# 8. For 200 steps...
			print("\n")
			for step in range(201):

				# 8.1 Do another gradient descent step to come to a better
				#     W and b
				sess.run(train)

				# 8.2 From time to time, print the current value of W and b
				if step % 10 == 0:
					print(step, sess.run(W), sess.run(b))

		
Then execute this Python3 / TensforFlow script with:
python3 line_fitting.py
In my case, I got the following output for this small TensorFlow learning script: